Neural Network Libraries<a class="headerlink" href="#neural-network-libraries" title="Permalink to this headline">¶

rewire_on(self, var)¶

Rewire a successor graph of this variable on top of var.

Parameters: var (nnabla.Variable) – The array elements and the parent function of var is copied to self as references. Note that the parent function of var is removed.

Example

# A. Create a graph A.
xa = nn.Variable((2, 8), need_grad=True)
ya = F.tanh(PF.affine(xa, 10, name='a'))

# B. Create a graph B.
xb = nn.Variable((2, 16), need_grad=True)
yb = F.tanh(PF.affine(
    F.tanh(PF.affine(xb, 8, name='b1')),
    8, name='b2'))

# C. Rewire the graph A on top of B such that
#    `xb->B->(yb->)xa->A->ya`. Note `yb` is gone.
xa.rewire_on(yb)

# D. Execute the rewired graph.
xb.d = 1
ya.forward()
ya.backward()

shape¶

Gets the shape of the variable.

Returns: tuple of int

size¶

Gets the size of the variable.

Returns: int

size_from_axis(self, axis=- 1)¶

Gets the size followed by the provided axis.

Example

a = nnabla.Variable([10,9])
a.size_from_axis()
# ==> 90
a.size_from_axis(0)
# ==> 90
a.size_from_axis(1)
# ==> 9
a.size_from_axis(2)
# ==> 1

Parameters: axis (int, optional) – -1 as default
Returns: int

unlinked(self, need_grad=None)¶: This function is deprecated, use get_unlinked_variable instead.

visit(self, f)¶

Visit functions recursively in forward order.

Parameters: f (function) – Function object which takes nnabla._function.Function object as an argument.
Returns: None

Example

import nnabla as nn
import nnabla.functions as F
import nnabla.parametric_functions as PF

# Define a simple network-graph
def network_graph(x, maps=16, test=False):
    h = x
    h = PF.convolution(h, maps, kernel=(3, 3), pad=(1, 1), name="first-conv", with_bias=False)
    h = F.average_pooling(h, h.shape[2:])
    pred = PF.affine(h, 10, name="pred")
    return pred

# You can modify this PrintFunc to get the other information like inputs(nnabla_func.inputs), outputs and arguments(nnabla_func.info.args) of nnabla functions.
class PrintFunc(object):
    def __call__(self, nnabla_func):
        print(nnabla_func.info.type_name)

x = nn.Variable([1, 3, 16, 16])
output = network_graph(x)
output.visit(PrintFunc())

Output :

Convolution
AveragePooling
Affine

visit_check(self, f)¶

Visit functions recursively in forward order.

Note

If any of evaluation of the function object returns True, the visit propagation will stop immediately, and will return True.

Parameters: f (function) – Function object which takes nnabla._function.Function object as an argument.
Returns: bool Returns True if any of the function object call returns True.

Example

Define a simple network-graph where AveragePooling function can be added explicitly as below:

def network_graph(x, add_avg_pool=False, maps=16, test=False):
    h = x
    h = PF.convolution(h, maps, kernel=(3, 3), pad=(1, 1), name="first-conv", with_bias=False)
    if add_avg_pool :
        h = F.average_pooling(h, h.shape[2:])
    else :
        h = F.relu(h)
    pred = PF.affine(h, 10, name="pred")
    return pred

# Define 'PrintFunc()' to check whether "AveragePooling" function exists in the network-graph
class PrintFunc(object):
    def __call__(self, nnabla_func):
        if nnabla_func.info.type_name =="AveragePooling" :
            print("{} exists in the graph".format(nnabla_func.info.type_name))
            return True
        else :
            return False

Create a network-graph which has AveragePooling function and call visit_check() method :

x = nn.Variable([1, 3, 16, 16])
output = network_graph(x, add_avg_pool=True)  #Adding AveragePooling function to the graph
print("The return value of visit_check() method is : {}".format(output.visit_check(PrintFunc())))

Output :

AveragePooling exists in the graph
The return value of visit_check() method is : True

Create a network-graph which doesn’t have AveragePooling function and call visit_check() method :

nn.clear_parameters()                         # call this in case you want to run the following code again
output = network_graph(x, add_avg_pool=False) # Exclusion of AveragePooling function in the graph
print("The return value of visit_check() method is : {}".format(output.visit_check(PrintFunc())))

Output :

The return value of visit_check() method is : False

Computation Graph¶

nnabla.forward_all(variables, bool clear_buffer=False, bool clear_no_need_grad=False, function_pre_hook=None, function_post_hook=None)¶

Performs a forward propagation up to variables specified as the 1st argument. See also forward.

Parameters

clear_buffer (bool) –
Clear the no longer referenced variables during forward propagation to save memory. This is usually set as True in an inference or a validation phase. Default is False. Note that starting variable and destination variable of the input graph will not be cleared, regardless of their persistent flag. All intermediate variables will be cleared unless set explicitly as persistent=True. For example,
```
forward_all([h_i, y], clear_buffer=True)
```
will clear all intermediate variables between h_i and y unless set explicitly as persistent=True, but h_i and y will not be cleared regardless of their persistent flag.
clear_no_need_grad (bool) – Clear the unreferenced variables with need_grad=False during forward propagation. True is usually used when calling this during training. This is ignored when clear_buffer=True.
function_pre_hook (callable) – This callable object is called immediately before each function is executed. It must take Function as an input. The default is None.
function_post_hook (callable) – This callable object is called immediately after each function is executed. It must take Function as an input. The default is None.

Example

import numpy as np
import nnabla as nn
import nnabla.parametric_functions as PF

# Create a graph which has two outputs
x = nn.Variable.from_numpy_array(np.array([[1, 2], [3, 4]]))
y = PF.affine(x, 4, name="y")
z = PF.affine(x, 8, name="z")

# Execute a forward propagation recursively up to y and z
nn.forward_all([y, z], clear_buffer)

nnabla.no_grad(no_grad_=True)[source]¶

No gradients for the whole network.

No gradients are required when creating a network, such that when the forward pass is executed, all intermediate buffers except for the leafs in the network are gone at the same time, resulting in memory optimization.

This is useful for example when an output of a pre-trained network is used for an input to another network, where the first pre-trained network does not need to be fine-tuned, but the other network is optimized.

Parameters: no_grad (bool) – No gradient flag. Default is True.

Example:

with nn.no_grad():
    output0 = <Network0>(<input0>)

output1 = <Network1>(<input1>, output0)
loss = <Loss>(output1, <ground_truth>)
loss.forward(clear_no_need_grad=True)

This context also works in the dynamic mode.

with nn.auto_forward(), nn.no_grad():
    output0 = <Network0>(<input0>)

Note

When working with the static network, the need_grad property of the input (e.g., input image) must be False and do not forget to add <root>.forward(clear_no_need_grad=True); otherwise, all intermediate buffers are not gone as expected.

Functions¶

All NNabla functions are derived from the nnabla.function.Function class.

Function¶

class nnabla.function.Function¶

Function interface class.

Instances of nnabla.function.Function are not directly created by users. It is indirectly created by the functions available in nnabla.functions. These functions return nnabla.Variable (s) holding the created function instance as the parent property.

args¶

Experimental

Get args of the function.

backward(self, inputs, outputs, accum=None)¶

forward(self, inputs, outputs)¶

grad_depends_output_data(self, int i, int o)¶

info¶

object

Type: info

inplace_data(self, int i)¶

inplace_data_with(self, int i)¶

min_outputs(self)¶

need_setup_recompute(self, int o)¶

recompute(self, inputs, outputs)¶

setup(self, inputs, outputs)¶

setup_recompute(self, inputs, outputs)¶

tags¶

Experimental

Get tags of the function.

class nnabla.function.PythonFunction(ctx=None)¶

Creates a user-defined custom function in the subclsass.

To implement the naive multiplicaiton function of two variables using PythonFunction,

import nnabla as nn
import nnabla.functions as F
from nnabla.function import PythonFunction

class Mul2(PythonFunction):

    def __init__(self, ctx):
        super(Mul2, self).__init__(ctx)

    @property
    def name(self):
        return self.__class__.__name__

    def min_outputs(self):
        return 1

    def setup_impl(self, inputs, outputs):
        i0 = inputs[0]
        i1 = inputs[1]
        assert i0.shape == i1.shape, "Shapes of inputs are different."
        o0 = outputs[0]
        o0.reset_shape(i0.shape, True)

    def forward_impl(self, inputs, outputs):
        x0 = inputs[0].data
        x1 = inputs[1].data
        y = outputs[0].data

        # We can also write like, y.copy_from(x0 * x1)
        y.copy_from(F.mul2(x0, x1))

    def backward_impl(self, inputs, outputs, propagate_down, accum):
        # Data of inputs and outputs
        x0 = inputs[0].data
        x1 = inputs[1].data
        y = outputs[0].data
        # Grads of inputs and outputs
        dx0 = inputs[0].grad
        dx1 = inputs[1].grad
        dy = outputs[0].grad

        # backward w.r.t. x0
        if propagate_down[0]:
            if accum[0]:
                dx0 += F.mul2(dy, x1)
            else:
                dx0.copy_from(F.mul2(dy, x1))

        # backward w.r.t. x1
        if propagate_down[1]:
            if accum[1]:
                dx1 += F.mul2(dy, x0)
            else:
                dx1.copy_from(F.mul2(dy, x0))

    def grad_depends_output_data(self, i, o):
        return False

    def grad_depends_input_data(self, i, j):
        return True

def mul2(x, y, ctx=None):
    func = Mul2(ctx)
    return func(x, y)

__init__(self, ctx=None)¶

Parameters: ctx (nnabla.Context) – Context used for the forward and backward pass. If not specified, the current context is used.

backward_impl(self, inputs, outputs, propagate_down, accum)¶

Backward method.

Parameters

inputs – (list of nnabla.Variable): Inputs to the function.
outputs – (list of nnabla.Variable): Outputs from the function.

property ctx¶: Context Return the context if the context is set in the constructor; otherwise return the global context

forward_impl(self, inputs, outputs)¶

Forward method.

Parameters

inputs – (list of nnabla.Variable): Inputs to the function.
outputs – (list of nnabla.Variable): Outputs from the function.

grad_depends_input_data(self, i, j)¶

Checking if i-th input’ gradient computation requires j-th input’s data or not.

Parameters

i – (list of nnabla.Variable): Input variable index.
i – (list of nnabla.Variable): Input variable index.

grad_depends_output_data(self, i, o)¶

Checking if i-th input’ gradient computation requires o-th output’s data or not.

Parameters

i – (list of nnabla.Variable): Input variable index.
o – (list of nnabla.Variable): Output variable index.

min_outputs(self)¶: Minimum number of outputs of the function.

property name¶: Name of the function.

setup_impl(self, inputs, outputs)¶

Setup method.

Parameters

inputs – (list of nnabla.Variable): Inputs to the function.
outputs – (list of nnabla.Variable): Outputs from the function.

List of Functions¶

The nnabla.functions module provides various types of functions listed below. These functions takes input nnabla.Variable (s) as its leading argument(s), followed by options specific to each function.

Note

The functions can also take NdArray (s) as inputs instead of Variable (s). It will execute the function operation immediately, and returns NdArray (s) as output(s) holding output values of the operation. We call this “Imperative Mode” (NdArray + Functions).

Neural Network Layers¶

nnabla.functions.affine(x, weight, bias=None, base_axis=1, n_outputs=- 1, outputs=None)[source]¶

Affine layer, also called as the fully connected layer. It calculates:

\[{\mathbf y} = {\mathbf A} {\mathbf x} + {\mathbf b}.\]

where ${\mathbf x}$ is the input and ${\mathbf y}$ is the output.

Parameters

x (Variable) – Input N-D array with shape ($M_0 \times ... \times M_{B-1} \times D_B \times ... \times D_N$). Dimensions before and after base_axis are flattened as if it is a matrix.
weight (Variable) – Weight matrix with shape ($(D_B \times ... \times D_N) \times L_{0} \times \ldots \times L_{I}$) [parameter]
bias (Variable) – Bias vector ($L_{0} \times \ldots \times L_{I}$) [optional][parameter]
base_axis (int) – Base axis of Affine operation. Dimensions up to base_axis is treated as sample dimension. [default= 1 ]

Returns

$(B + 1)$-D array. ($M_0 \times ... \times M_{B-1} \times L_{0} \times \ldots \times L_{I}$)

Return type

Note

All nnabla functions in nnabla.functions are decorated with the nnabla.function_bases.function_api decorator, which queries the current context and passes it into the first argument of the original function. The original function always takes a context as the first argument.

nnabla.functions.convolution(x, weight, bias=None, base_axis=1, pad=None, stride=None, dilation=None, group=1, channel_last=False, n_outputs=- 1, outputs=None)[source]¶

N-D Convolution with bias.

See references for dilated convolution (a.k.a. atrous convolution).

References

Note

Convolution is a computationally intensive operation that should preferrably be run with the cudnn backend. NNabla then uses CuDNN library functions to determine and cache the fastest algorithm for the given set of convolution parameters, which results in additional memory consumption which may pose a problem for GPUs with insufficient memory size. In that case, the NNABLA_CUDNN_WORKSPACE_LIMIT environment variable can be used to restrict the choice of algorithms to those that fit the given workspace memory limit, expressed in bytes. In some cases it may also be desired to restrict the automatic search to algorithms that produce deterministic (reproducable) results. This can be requested by setting the the environment variable NNABLA_CUDNN_DETERMINISTIC to a non-zero value.

Parameters

x (Variable) – $(B + 1 + N)$-D array ($M_1 \times ... \times M_B \times C \times L_1 \times ... \times L_N$).
weight (Variable) – $(2 + N)$-D array ($C' \times C \times K_1 \times ... \times K_N$). [parameter]
bias (Variable) – Bias vector ($C'$). [optional][parameter]
base_axis (int) – base axis $B$. [default= 1 ]
pad (tuple of int) – Padding sizes for dimensions. [default= (0,) * (len(x.shape) - (base_axis+1)) ]
stride (tuple of int) – Stride sizes for dimensions. [default= (1,) * (len(x.shape) - (base_axis+1)) ]
dilation (tuple of int) – Dilation sizes for dimensions. [default= (1,) * (len(x.shape) - (base_axis+1)) ]
group (int) – Number of groups of channels. This makes the connection across channels sparser, by grouping connections along the mapping direction. [default= 1 ]
channel_last (bool) – If True, the last dimension is considered as channel dimension, a.k.a NHWC order. [default= False ]

Returns

$(B + 1 + N)$-D array ($M_1 \times ... \times M_B \times C' \times L'_1 \times ... \times L'_N$).

A spatial size of the output is calculated as

\[L'_i = \frac{L_i + 2 p_i - d_i (k_i - 1) - 1}{s_i} + 1,\]

where $L_i$ is the spatial size, $p_i$ is the padding, $d_i$ is the dilation, $k_i$ is the kernel size, and $s_i$ is the stride for $i$-th spatial dimension. The same calculation can also be applied to the other spatial dimensions.

Return type

Note

All nnabla functions in nnabla.functions are decorated with the nnabla.function_bases.function_api decorator, which queries the current context and passes it into the first argument of the original function. The original function always takes a context as the first argument.

nnabla.functions.depthwise_convolution(x, weight, bias=None, base_axis=1, pad=None, stride=None, dilation=None, multiplier=1, n_outputs=- 1, outputs=None)[source]¶

N-D Depthwise Convolution with bias.

References

F. Chollet. Xception: Deep Learning with Depthwise Separable Convolutions.

Parameters

x (Variable) – $(B + 1 + N)$-D array ($M_1 \times ... \times M_B \times C \times L_1 \times ... \times L_N$).
weight (Variable) – $(1 + N)$-D array ($C \times K_1 \times ... \times K_N$). [parameter]
bias (Variable) – Bias vector ($C'$). [optional][parameter]
base_axis (int) – base axis $B$. [default= 1 ]
pad (tuple of int) – Padding sizes for dimensions. [default= (0,) * (len(x.shape) - (base_axis+1)) ]
stride (tuple of int) – Stride sizes for dimensions. [default= (1,) * (len(x.shape) - (base_axis+1)) ]
dilation (tuple of int) – Dilation sizes for dimensions. [default= (1,) * (len(x.shape) - (base_axis+1)) ]
multiplier (int) – Number of output feature maps per input feature map. [default= 1 ]

Returns

$(B + 1 + N)$-D array ($M_1 \times ... \times M_B \times C' \times L'_1 \times ... \times L'_N$).

The output map size $C'$ is $C$ multiplied by $m$

\[C' = m \times C,\]

where $m$ is the multiplier.

A spatial size of the output is calculated as

\[L'_i = \frac{L_i + 2 p_i - d_i (k_i - 1) - 1}{s_i} + 1,\]

where $L_i$ is the spatial size, $p_i$ is the padding, $d_i$ is the dilation, $k_i$ is the kernel size, and $s_i$ is the stride for $i$-th spatial dimension. The same calculation can also be applied to the other spatial dimensions.

Return type

Note

All nnabla functions in nnabla.functions are decorated with the nnabla.function_bases.function_api decorator, which queries the current context and passes it into the first argument of the original function. The original function always takes a context as the first argument.

nnabla.functions.deconvolution(x, weight, bias=None, base_axis=1, pad=None, stride=None, dilation=None, group=1, channel_last=False, output_padding=None, n_outputs=- 1, outputs=None)[source]¶

N-D deconvolution, also known as transposed convolution, with bias operates backward convolution (derivative of the output w.r.t. the input) plus channel-wise learned bias.

The weights are specified in the same manner as convolution() , as if it was an ordinary convolution function. The forward operation of deconvolution() will then be operationally equivalent to the backward pass of convolution() . Therefore, the number of input channels (can be seen as output channels of forward convolution) is specified in the first dimension, and the number of the output channels divided by the number of groups is specified in the second dimension.

For stride > 1, a parameter-wise identical deconvolution on the output of a convolution may not produce the same output shape as the input to the convolution if, due to striding, the convolution did not fully cover the input spatial dimension. The output_padding parameter can then be used to appropriately increase the calculated output shape. Note that this is used to find the output shape for the deconvolution operation, but not to add zero-padding to the output.

Parameters

x (Variable) – $(B + 1 + N)$-D array ($M_1 \times ... \times M_B \times C \times L_1 \times ... \times L_N$).
weight (Variable) – $(2 + N)$-D array ($C \times C' \times K_1 \times ... \times K_N$). [parameter]
bias (Variable) – Bias vector ($C'$). [optional][parameter]
base_axis (int) – base axis $B$. [default= 1 ]
pad (tuple of int) – Padding sizes for dimensions. [default= (0,) * (len(x.shape) - (base_axis+1)) ]
stride (tuple of int) – Stride sizes for dimensions. [default= (1,) * (len(x.shape) - (base_axis+1)) ]
dilation (tuple of int) – Dilation sizes for dimensions. [default= (1,) * (len(x.shape) - (base_axis+1)) ]
group (int) – Number of groups of channels. This makes the connection across channels sparser, by grouping connections along the mapping direction. [default= 1 ]
channel_last (bool) – If True, the last dimension is considered as channel dimension, a.k.a NHWC order. [default= False ]
output_padding (tuple of int) – Additional size added to the output shape. [default= (0,) * (len(x.shape) - (base_axis+1)) ]

Returns

$(B + 1 + N)$-D array ($M_1 \times ... \times M_B \times C' \times L'_1 \times ... \times L'_N$).

A spatial size of the output is calculated as

\[L'_i =s_i (L_i - 1) - 2 p_i + d_i (k_i - 1) + 1,\]

where $s_i$ is the stride, $L_i$ is the spatial size, $p_i$ is the padding, $d_i$ is the dilation, and $k_i$ is the kernel size for $i$-th spatial dimension. The same calculation can also be applied to the other spatial dimensions.

Return type

Note

All nnabla functions in nnabla.functions are decorated with the nnabla.function_bases.function_api decorator, which queries the current context and passes it into the first argument of the original function. The original function always takes a context as the first argument.

nnabla.functions.depthwise_deconvolution(x, weight, bias=None, base_axis=1, pad=None, stride=None, dilation=None, divisor=1, n_outputs=- 1, outputs=None)[source]¶

Depthwise deconvolution computes the transposed depthwise convolution with bias for one-dimensional and two-dimensional input data.

Parameters

x (Variable) – $(B + 1 + N)$-D array ($M_1 \times ... \times M_B \times C \times L_1 \times ... \times L_N$).
weight (Variable) – $(1 + N)$-D array ($C \times K_1 \times ... \times K_N$). [parameter]
bias (Variable) – Bias vector ($C'$). [optional][parameter]
base_axis (int) – base axis $B$. [default= 1 ]
pad (tuple of int) – Padding sizes for dimensions. [default= (0,) * (len(x.shape) - (base_axis+1)) ]
stride (tuple of int) – Stride sizes for dimensions. [default= (1,) * (len(x.shape) - (base_axis+1)) ]
dilation (tuple of int) – Dilation sizes for dimensions. [default= (1,) * (len(x.shape) - (base_axis+1)) ]
divisor (int) – Number of input feature maps per output feature map. [default= 1 ]

Returns

$(B + 1 + N)$-D array ($M_1 \times ... \times M_B \times C' \times L'_1 \times ... \times L'_N$).

The output map size $C'$ is $C$ multiplied by $m$

\[C' = \frac{C}{d},\]

where $d$ is the divisor.

A spatial size of the output is calculated as

\[L'_i =s_i (L_i - 1) - 2 p_i + d_i (k_i - 1) + 1,\]

where $s_i$ is the stride, $L_i$ is the spatial size, $p_i$ is the padding, $d_i$ is the dilation, and $k_i$ is the kernel size for $i$-th spatial dimension. The same calculation can also be applied to the other spatial dimensions.

Return type

Note

All nnabla functions in nnabla.functions are decorated with the nnabla.function_bases.function_api decorator, which queries the current context and passes it into the first argument of the original function. The original function always takes a context as the first argument.

nnabla.functions.deformable_convolution(x, weight, offset, mask=None, bias=None, base_axis=1, pad=None, stride=None, dilation=None, group=1, deformable_group=1, channel_last=False, n_outputs=- 1, outputs=None)[source]¶

2-D Deformable Convolution with bias. Another convolution with fixed output channels must be passed externally to calculate the offsets and mask. Mask should be normalized to $[0,1]$ interval.

\[\begin{eqnarray} y(p) = \sum_{k=1}^{K} w_k \cdot x(p + p_k + \Delta p_k) \cdot \Delta m_k, \end{eqnarray}\]

where $x$ and $y$ are input and output, $w_k$ is the weight, $p$ is the pixel location of interest, $p_k$ is the fixed displacement e.g., $p_k \in \{(-1, -1), (-1, 0), \ldots (1, 1)\}$ for the 2D 3x3 receptive field, $\Delta p_k$ is the learnable displacement, and $\Delta m_k$ is the learnable scale normalized in $[0, 1]$ by a function like the sigmoid. Note that $\Delta p_k$ and $\Delta m_k$ are sample-dependent, location-dependent, and feature-independent.

References

Parameters

x (Variable) – $(B + 1 + N)$-D array ($M_1 \times ... \times M_B \times C \times L_1 \times ... \times L_N$).
weight (Variable) – $(2 + N)$-D array ($C' \times C \times K_1 \times ... \times K_N$). [parameter]
offset (Variable) – Offsets for deformable convolutions. Shape is fixed to $(N, deformable_group \times 2 \times Kh \times Kw, H, W)$. Offsets must be calculated externally through a separate convolution layer.
mask (Variable) – Normalized mask for deformable convolutions v2. Shape is fixed to $(N, deformable_group \times 2 \times Kh \times Kw, H, W)$. Masks must be calculated externally together with the offsets through a separate convolution layer. [optional]
bias (Variable) – Bias vector ($C'$). [optional][parameter]
base_axis (int) – base axis $B$. [default= 1 ]
pad (tuple of int) – Padding sizes for dimensions. [default= (0,) * (len(x.shape) - (base_axis+1)) ]
stride (tuple of int) – Stride sizes for dimensions. [default= (1,) * (len(x.shape) - (base_axis+1)) ]
dilation (tuple of int) – Dilation sizes for dimensions. [default= (1,) * (len(x.shape) - (base_axis+1)) ]
group (int) – Number of groups of channels. This makes the connection across channels sparser, by grouping connections along the mapping direction. [default= 1 ]
deformable_group (int) – Number of deformable groups of channels. [default= 1 ]
channel_last (bool) – If True, the last dimension is considered as channel dimension, a.k.a NHWC order. [default= False ]

Returns

$(B + 1 + N)$-D array ($M_1 \times ... \times M_B \times C' \times L'_1 \times ... \times L'_N$).

A spatial size of the output is calculated as

\[L'_i = \frac{L_i + 2 p_i - d_i (k_i - 1) - 1}{s_i} + 1,\]

where $L_i$ is the spatial size, $p_i$ is the padding, $d_i$ is the dilation, $k_i$ is the kernel size, and $s_i$ is the stride for $i$-th spatial dimension. The same calculation can also be applied to the other spatial dimensions.

Return type

Note

All nnabla functions in nnabla.functions are decorated with the nnabla.function_bases.function_api decorator, which queries the current context and passes it into the first argument of the original function. The original function always takes a context as the first argument.

nnabla.functions.adaptive_separable_convolution(x, vertical_kernel, horizontal_kernel, n_outputs=- 1, outputs=None)[source]¶

2-D Adaptive Separable Convolution for NCHW (the channel-first tensor). Sample and pixel dependent vertical and horizontal kernels are dynamically generated ones, which are used for approximating a feature-independent 2-D kernel in this function. Thus, the kernel used in this function is dependent on samples and pixels but independent on features.

If the padding is needed, use the pad function to the input $x$ before this function.

Adaptive separable convolution is formulated as

\[\tilde{I}(c, h, w) = \sum_{j, i} K_v(j, h, w) \times K_h(i, h, w) \times I(c, h + j, w + i),\]

where $I(c, h, w)$ and $\tilde{I}(c, h, w)$ are the input and output images at $c$-th channel, $h$-th height, $w$-th width. $K_V(:, h, w)$ and $K_h(:, h, w)$ are vertical and horizontal 1-D kernels at $h$-th height and $w$-th width.

References

Parameters

x (Variable) – $4-D$ array ($B \times C \times H \times W$)
vertical_kernel (Variable) – $4-D$ array ($B \times K_v \times H \times W$)
horizontal_kernel (Variable) – $4-D$ array ($B \times K_h \times H \times W$)

Returns

$4-D$ array ($B \times C \times H - K_v + 1 \times W - K_h + 1$)

Return type

Note

All nnabla functions in nnabla.functions are decorated with the nnabla.function_bases.function_api decorator, which queries the current context and passes it into the first argument of the original function. The original function always takes a context as the first argument.

nnabla.functions.max_pooling(x, kernel, stride=None, ignore_border=True, pad=None, channel_last=False, n_outputs=- 1, outputs=None)[source]¶

Max pooling. It pools the maximum values inside the scanning kernel:

\[y_{i_1, i_2} = \max_{k_1, k_2 \in K} (x_{i_1 + k_1, i_2 + k_2})\]

where $x_{i_1 + k_1, i_2 + k_2}$ is the input and $y_{i_1, i_2}$ is the output.

Parameters

x (Variable) – Input variable.
kernel (tuple of int) – Kernel sizes for each spatial axis.
stride (tuple of int) – Subsampling factors for each spatial axis. [default= kernel ]
ignore_border (bool) – If false, kernels covering borders are also considered for the output. [default= True ]
pad (tuple of int) – Border padding values for each spatial axis. Padding will be added both sides of the dimension. [default= (0,) * len(kernel) ]
channel_last (bool) – If True, the last dimension is considered as channel dimension, a.k.a NHWC order. [default= False ]

Returns

Maximum values variable

Return type

Note

All nnabla functions in nnabla.functions are decorated with the nnabla.function_bases.function_api decorator, which queries the current context and passes it into the first argument of the original function. The original function always takes a context as the first argument.

nnabla.functions.average_pooling(x, kernel, stride=None, ignore_border=True, pad=None, channel_last=False, including_pad=True, n_outputs=- 1, outputs=None)[source]¶

Average pooling. It pools the averaged values inside the scanning kernel:

\[y_{i_1, i_2} = \frac{1}{K_1 K_2} \sum_{k1} \sum_{k2} x_{i_1 + k_1, i_2 + k_2}\]

where $x_{i_1 + k_1, i_2 + k_2}$ is the input and $y_{i_1, i_2}$ is the output.

Parameters

x (Variable) – Input variable.
kernel (tuple of int) – Kernel sizes for each spatial axis.
stride (tuple of int) – Subsampling factors for each spatial axis. [default= kernel ]
ignore_border (bool) – If false, kernels covering borders are also considered for the output. [default= True ]
pad (tuple of int) – Border padding values for each spatial axis. Padding will be added both sides of the dimension. [default= (0,) * len(kernel) ]
channel_last (bool) – If True, the last dimension is considered as channel dimension, a.k.a NHWC order. [default= False ]
including_pad (bool) – If true, border padding values are considered for the output. [default= True ]

Returns

Average values variable

Return type

Note

All nnabla functions in nnabla.functions are decorated with the nnabla.function_bases.function_api decorator, which queries the current context and passes it into the first argument of the original function. The original function always takes a context as the first argument.

nnabla.functions.global_average_pooling(x, n_outputs=- 1, outputs=None)[source]¶

Warning

This function is experimental support, so please do not actively use it.

Global average pooling. It pools an averaged value from the whole image

Parameters: x (Variable) – Input variable.
Returns: Average values variable
Return type: Variable

Note

All nnabla functions in nnabla.functions are decorated with the nnabla.function_bases.function_api decorator, which queries the current context and passes it into the first argument of the original function. The original function always takes a context as the first argument.

nnabla.functions.sum_pooling(x, kernel, stride=None, ignore_border=True, pad=None, channel_last=False, n_outputs=- 1, outputs=None)[source]¶

Sum pooling. It pools the summed values inside the scanning kernel:

\[y_{i_1, i_2} = \sum_{k1} \sum_{k2} x_{i_1 + k_1, i_2 + k_2}\]

where $x_{i_1 + k_1, i_2 + k_2}$ is the input and $y_{i_1, i_2}$ is the output.

Parameters

x (Variable) – Input variable.
kernel (tuple of int) – Kernel sizes for each spatial axis.
stride (tuple of int) – Subsampling factors for each spatial axis. [default= kernel ]
ignore_border (bool) – If false, kernels covering borders are also considered for the output. [default= True ]
pad (tuple of int) – Border padding values for each spatial axis. Padding will be added both sides of the dimension. [default= (0,) * len(kernel) ]
channel_last (bool) – If True, the last dimension is considered as channel dimension, a.k.a NHWC order. [default= False ]

Returns

Summed values variable

Return type

Note

All nnabla functions in nnabla.functions are decorated with the nnabla.function_bases.function_api decorator, which queries the current context and passes it into the first argument of the original function. The original function always takes a context as the first argument.

nnabla.functions.unpooling(x, kernel, channel_last=False, n_outputs=- 1, outputs=None)[source]¶

Inverse operation of pooling. It spreads the input values:

\[y_{k_1 i_1 + j_1, k_2 i_2 + j_2} = x_{i_1, i_2}\]

where $_{i_1, i_2}$ is the input and $y_{k_1 i_1 + j_1, k_2 i_2 + j_2}$ is the output.

Parameters

x (Variable) – Input variable.
kernel (tuple of int) – Kernel sizes for each spatial axis.
channel_last (bool) – If True, the last dimension is considered as channel dimension, a.k.a NHWC order. [default= False ]

Returns

Spread values variable

Return type

Note

All nnabla functions in nnabla.functions are decorated with the nnabla.function_bases.function_api decorator, which queries the current context and passes it into the first argument of the original function. The original function always takes a context as the first argument.

nnabla.functions.embed(x0, w, n_outputs=- 1, outputs=None)[source]¶

Embed slices of a matrix/tensor with indexing array/tensor.

Parameters

x0 (Variable) – Indices with shape $(I_0, ..., I_N)$
w (Variable) – Weights with shape $(W_0, ..., W_M)$ [parameter]

Returns

Output with shape $(I_0, ..., I_N, W_1, ..., W_M)$

Return type

Note

All nnabla functions in nnabla.functions are decorated with the nnabla.function_bases.function_api decorator, which queries the current context and passes it into the first argument of the original function. The original function always takes a context as the first argument.

nnabla.functions.rnn(x, h, weight_l0, weight=None, bias=None, num_layers=1, nonlinearity='tanh', dropout=None, bidirectional=False, training=True, n_outputs=- 1, outputs=None)[source]¶

RNN function implements Elman RNN with nonlineraity to input sequence. RNN function is defined as following:

\[{\mathbf h_t} = {\mathbf \tanh}( {\mathbf w_{ih}} *{\mathbf x_t} + {\mathbf b_{ih}} + {\mathbf w_{hh}}* {\mathbf h_{(t-1)}} + {\mathbf b_{hh}}).\]

We use the following notations to describe the inputs and outputs below. $T$: sequcne length, $B$: batch size, $I$: input size, $L$: number of layers, $D$: number of directions, can be either 1 or 2, $H$: hidden size.

References

Jeffrey Elman, Finding Structure in Time.

Parameters

x (Variable) – Input N-D array with shape $(T, B, I)$.
h (Variable) – Input N-D array with shape $(L, D, B, H)$.
weight_l0 (Variable) – Input N-D array with shape $(D, H, I + H)$. [parameter]
weight (Variable) – Input N-D array with shape $(L-1, D, H, D * H + H)$. [optional][parameter]
bias (Variable) – Input N-D array with shape $(L, D, H)$. [optional][parameter]
num_layers (int) – Number of layers in the network. If set to 1, only the weights for the first layer will be invoked. Default is 1. [default= 1 ]
nonlinearity (string) – Type of nonlinearity applied to input sequcne. Must be either tanh or relu. Default is tanh. [default= 'tanh' ]
dropout (float) – Dropout ratio applied to parameters. Default is 0.0. [default= 0.0 ]
bidirectional (bool) – If True, bidirectional computation will be performed in each layer. Default is False. [default= False ]
training (bool) – Backpropagation will be performed only when it is true. Default is True. [default= True ]

Returns

Output $y$ with shape $(T, B, D * H)$ ~nnabla.Variable: Output $h_n$ with shape $(L, D, B, H)$

Return type

Note

All nnabla functions in nnabla.functions are decorated with the nnabla.function_bases.function_api decorator, which queries the current context and passes it into the first argument of the original function. The original function always takes a context as the first argument.

nnabla.functions.lstm(x, h, c, weight_l0, weight=None, bias=None, num_layers=1, dropout=None, bidirectional=False, training=True, n_outputs=- 1, outputs=None)[source]¶

N-Step LSTM layer.

\[\begin{split}{\mathbf f_t} &=& {\mathbf \sigma}( {\mathbf W_f} *{\mathbf x_t} + {\mathbf U_f}* {\mathbf h_{(t-1)}} + {\mathbf b_f})\\ {\mathbf i_t} &=& {\mathbf \sigma}( {\mathbf W_i} *{\mathbf x_t} + {\mathbf U_i}* {\mathbf h_{(t-1)}} + {\mathbf b_i})\\ {\mathbf o_t} &=& {\mathbf \sigma}( {\mathbf W_o} *{\mathbf x_t} + {\mathbf U_o}* {\mathbf h_{(t-1)}} + {\mathbf b_o})\\ {\mathbf c_t} &=& {\mathbf f_t}\odot {\mathbf c_{(t-1)}} + {\mathbf i_t}\odot {\mathbf \tanh}({\mathbf W_c}*{\mathbf x_t} + {\mathbf U_c} *{\mathbf h_{(t-1)}} + {\mathbf b_c})\\ {\mathbf h_t} &=& {\mathbf o_t} \odot {\mathbf \tanh}({\mathbf c_t}).\end{split}\]

We use the following notations to describe the inputs and outputs below. $T$: sequcne length, $B$: batch size, $I$: input size, $L$: number of layers, $D$: number of directions, can be either 1 or 2, $H$: hidden size.

References

S. Hochreiter and J. Schmidhuber, Long Short-Term Memory.

Parameters

x (Variable) – Input N-D array with shape $(T, B, I)$.
h (Variable) – Input N-D array with shape $(L, D, B, H)$.
c (Variable) – Input N-D array with shape $(L, D, B, H)$.
weight_l0 (Variable) – weight parameters for the first layer. Shape is $(D, 4, H, I + H)$. [parameter]
weight (Variable) – weight parameters for the second layer and above. Shape is $(L-1, D, 4, H, D * H + H)$. [optional][parameter]
bias (Variable) – Bias vector ($L$). Shape is $(L, D, 4, H)$. [optional][parameter]
num_layers (int) – Number of layers in the network. If set to 1, only the weights for the first layer will be invoked. Default is 1. [default= 1 ]
dropout (float) – Dropout ratio applied to parameters. Default is 0.0. [default= 0.0 ]
bidirectional (bool) – If True, bidirecitonal computation will be performed in each layer. Default is False. [default= False ]
training (bool) – Backpropagation will be performed only when it is True. Default is True. [default= True ]

Returns

Output $y$ with shape $(T, B, D * H)$. Its memory layout can be reshaped as $(T, B, D, H)$. ~nnabla.Variable: Output $h_n$ with shape $(L, D, B, H)$ ~nnabla.Variable: Output $c_n$ with shape $(L, D, B, H)$

Return type

Note

All nnabla functions in nnabla.functions are decorated with the nnabla.function_bases.function_api decorator, which queries the current context and passes it into the first argument of the original function. The original function always takes a context as the first argument.

nnabla.functions.gru(x, h, weight_l0, weight=None, bias=None, num_layers=1, dropout=None, bidirectional=False, training=True, n_outputs=- 1, outputs=None)[source]¶

N-Step GRU layer.

\[\begin{split}{\mathbf r_t} &=& {\mathbf \sigma}( {\mathbf W_r} *{\mathbf x_t} + {\mathbf U_r}* {\mathbf h_{(t-1)}} + {\mathbf b_r})\\ {\mathbf z_t} &=& {\mathbf \sigma}( {\mathbf W_z} *{\mathbf x_t} + {\mathbf U_z}* {\mathbf h_{(t-1)}} + {\mathbf b_z})\\ {\mathbf n_t} &=& {\mathbf \tanh}( {\mathbf W_n}{\mathbf x_t}+ {\mathbf b_{in}}+ {\mathbf r_n}\odot( {\mathbf U_n}{\mathbf h_{t-1}}+ {\mathbf b_{hn}})) \\ {\mathbf h_t} &=& (1- {\mathbf z_t})\odot {\mathbf n_t} + {\mathbf z_t}\odot {\mathbf h_{t-1}}.\end{split}\]

We use the following notations to describe the inputs and outputs below. $T$: sequcne length, $B$: batch size, $I$: input size, $L$: number of layers, $D$: number of directions, can be either 1 or 2, $H$: hidden size.

References

K. cho et al., Learning Phrases Representations using RNN Encoder-Decoder for Statistical Machine Translation.

Parameters

x (Variable) – Input N-D array with shape $(T, B, I)$.
h (Variable) – Input N-D array with shape $(L, D, B, H)$.
weight_l0 (Variable) – weight parameters for the first layer. Shape is $(D, 3, H, I + H)$. [parameter]
weight (Variable) – weight parameters for the second layer and above. Shape is $(L-1, D, 3, H, D * H + H)$. [optional][parameter]
bias (Variable) – Bias vector ($L$). Shape is $(L, D, 4, H)$. [optional][parameter]
num_layers (int) – Number of layers in the network. If set to 1, only the weights for the first layer will be invoked. Default is 1. [default= 1 ]
dropout (float) – Dropout ratio applied to parameters. Default is 0.0. [default= 0.0 ]
bidirectional (bool) – If True, bidirecitonal computation will be performed in each layer. Default is False. [default= False ]
training (bool) – Backpropagation will be performed only when it is True. Default is True. [default= True ]

Returns

Output $y$ with shape $(T, B, D * H)$. Its memory layout can be reshaped as $(T, B, D, H)$. ~nnabla.Variable: Output $h_n$ with shape $(L, D, B, H)$

Return type

Note

All nnabla functions in nnabla.functions are decorated with the nnabla.function_bases.function_api decorator, which queries the current context and passes it into the first argument of the original function. The original function always takes a context as the first argument.

nnabla.functions.multi_head_attention(query, key, value, num_heads, q_weight, k_weight, v_weight, out_weight, q_bias=None, k_bias=None, v_bias=None, out_bias=None, attn_bias_k=None, attn_bias_v=None, dropout=0.0, additive_mask=None, key_padding_mask=None)[source]¶

MultiHeadAttention.

Computes multi-headed attention with query, key, and value. We use the following notations to describe the inputs and outputs below. $L_T$: target sequence length, $L_S$: source sequence length, $B$: batch size, $D$: input dimension, $E$: embedding dimension, $H$: number of attention heads.

References

A. Vaswani et al. “Attention is All You Need.” NIPS. 2017. <https://papers.nips.cc/paper/7181-attention-is-all-you-need.pdf>

Parameters

query (Variable) – Input N-D array with shape $(L_T, B, D_q)$.
key (Variable) – Input N-D array with shape $(L_S, B, D_k)$.
value (Variable) – Input N-D array with shape $(L_S, B, D_v)$.
num_heads (int) – Number of attention heads. Note that embedding dimensoin E must be divisible by the number of heads. Default is 12 which is conventional.
q_weight (Variable) – Input N-D array with shape $(D_q, E)$.
k_weight (Variable) – Input N-D array with shape $(D_k, E)$.
v_weight (Variable) – Input N-D array with shape $(D_v, E_v)$.
out_weight (Variable) – Input N-D array with shape $(D_v, E_{out})$.
q_bias (Variable, optional) – Input N-D array with shape $(E, )$.
k_bias (Variable, optional) – Input N-D array with shape $(E, )$.
v_bias (Variable, optional) – Input N-D array with shape $(E_v, )$.
out_bias (Variable, optional) – Input N-D array with shape $(E_{out}, )$.
attn_bias_k (Variable, optional) – Input N-D array with shape $(E, )$.
attn_bias_v (Variable, optional) – Input N-D array with shape $(E_v, )$.
dropout (float, optional) – Dropout ratio applied to parameters. Default is 0.
additive_mask (Variable, optional) – Input N-D array with shape $(L_T, L_S)$. Values will be added to the attention layer to prevent attention to certain positions.
key_padding_mask (Variable, optional) – Input N-D array with shape $(B, L_S)$. Specified padding elements will be ignored by the attention layer. Values must be either 1 or 0.

Returns

Output $y$ with shape $(L_T, B, E_{out})$ ~nnabla.Variable: Output $h_n$ with shape $(B, L_T, L_S)$

Return type

nnabla.functions.patch_correlation(x1, x2, patch=(1, 1), shift=(0, 0), patch_step=(1, 1), shift_step=(1, 1), padding=(0, 0, 0, 0), channel_last=False)[source]¶

Multiplicative patch-wise comparison between inputs x1 and x2, which must both be 4-dimensional NCHW (with channel_last=False) or NHWC (with channel_last=True) arrays (where N is the number of samples, H and W are the sample height and width and C is the number of channels). The function returns a 5-D array with shape $(N, C_y, C_x, H_o, W_o)$ where $H_o, W_o$ are determined by the possible patch locations within the, optionally padded, input image sizeand $C_y, C_x$ are determined by the optionally shifted patch positions.

Mathematically, the patch correlation is formulated as

\[O(s_y, s_x, h_0, w_0) = \sum_{c} \sum_{k_h} \sum_{k_w} I_1(c, h + k_h, w + k_w) \times I_2(c, h + k_h + s_h, w + k_w + s_w),\]

where $I_1(c, h, w)$ and $I_2(c, h, w)$ are the inputs at $c$-th channel, $h$-th height, and $w$-th width, $k_h, k_w$ indices for the patch size and $s_h, s_w$ indices for the shifts.

A single correlation value (per sample) is produced if the patch extends to the image dimensions and all other parameters use the default values.

>>> import numpy as np, nnabla as nn, nnabla.functions as F
>>> N, C, H, W = (1, 2, 3, 4)
>>> x = nn.Variable.from_numpy_array(np.ones([N, C, H, W]))
>>> F.patch_correlation(x, x, patch=(H, W)).d
array([[[[[24.]]]]], dtype=float32)

A patch that is smaller than the image size moves horizontal and vertical producing a value per position. The patch_step argument may be used to control the position increments.

>>> F.patch_correlation(x, x, patch=(H-1, W-1)).d
array([[[[[12., 12.],
          [12., 12.]]]]], dtype=float32)
>>> F.patch_correlation(x, x, patch=(H-1, W-1), patch_step=(2, 1)).d
array([[[[[12., 12.]]]]], dtype=float32)

Multiple correlations may be performed at each position between the patch from x1 and patches from x2 at relative offsets striding the maximum vertical and horizontal distance given by the shift values at increments of shift_step. The shifted correlation values can be obtained for the from the second and third output dimension for the vertical and horizontal shifts.

>>> F.patch_correlation(x, x, (H, 1), shift=(0, 1)).shape
(1, 1, 3, 1, 4)
>>> F.patch_correlation(x, x, (H, 1), shift=(0, 1)).d
array([[[[[0., 6., 6., 6.]],
         [[6., 6., 6., 6.]],
         [[6., 6., 6., 0.]]]]], dtype=float32)
>>> F.patch_correlation(x, x, (H, 1), shift=(0, 1), shift_step=(1, 2)).d
array([[[[[0., 6., 6., 6.]],
         [[6., 6., 6., 0.]]]]], dtype=float32)

Padding with zero values may be applied individually to the top, bottom, left and right side of the input image.

>>> F.patch_correlation(x, x, patch=(H, W), padding=(0, 1, W, W)).d
array([[[[[ 0.,  6., 12., 18., 24., 18., 12.,  6.,  0.],
          [ 0.,  4.,  8., 12., 16., 12.,  8.,  4.,  0.]]]]], dtype=float32)

This function may be used to implement the FlowNetC correlation layer.

>>> N, C, H, W = (1, 256, 44, 60)
>>> x1, x2 = nn.Variable((N, C, H, W)), nn.Variable((N, C, H, W))
>>> F.patch_correlation(x1, x2, shift=20, shift_step=2).shape
(1, 21, 21, 44, 60)

References

Fischer et al., FlowNet: Learning Optical Flow with Convolutional Networks.

Parameters

x1 (Variable) – Input N-D array with shape $(N, C, H, W)$ or $(N, H, W, C)$.
x2 (Variable) – Input N-D array with shape $(N, C, H, W)$ or $(N, H, W, C)$.
patch – A tuple with height and width of the correlation patch. A single integer expands to identical height and width.
shift – A tuple of maximum vertical and horizontal displacement of patches from x2 that are correlated with a single patch from x1. A single integer expands to identical vertical and horizontal displacement.
patch_step – A tuple of vertical and horizontal increments for advancing the position of the correlation patch within the input image shape. A single integer expands to identical vertical and horizontal increments.
shift_step – A tuple of vertical and horizontal increments for advancing the relative offset position within the shift range. A single integer expands to identical vertical and horizontal increments.
padding – A tuple of top, bottom, left and right padding extent. A tuple of two values yields identical top/bottom and left/right padding from the first and second tuple value. A single integer expands to indential padding extent for all sides.
channel_last – Last dimension is the channel (NHWC order) if True.

Returns

N-D array with shape $(N, C_y, C_x, H_o, W_o)$ or $(N, H, W, C_y, C_x)$ if channel_last=True.

A spatial size of the output is calculated as

\[H_o = \frac{H + (top\_pad + bottom\_pad) - patch_v}{patch\_step_v} + 1.\]

A channel size of the ouptut is calculated as

\[C_y = \frac{2 \times shift_v}{shift\_step_v} + 1.\]

$W_o$ and $C_x$ are the same calculation with differenct components.

Return type

nnabla.functions.roi_align(input, boxes, output_size, spatial_scale=(1.0, 1.0), sampling_ratio=None, channel_last=None, n_outputs=- 1, outputs=None)[source]¶

Map Regions of Interest (RoI) defined by bounding boxes to features of output_size height and width using bilinear interpolation with sampling_ratio points in the interpolation grid.

>>> import numpy as np, nnabla as nn, nnabla.functions as F
>>> nn.set_auto_forward(True)
>>> input = F.pad(F.constant(1, (1, 1, 2, 2)) * 2, (1, 1, 1, 1), "constant", 1)
>>> print(input.d)
[[[[1. 1. 1. 1.]
   [1. 2. 2. 1.]
   [1. 2. 2. 1.]
   [1. 1. 1. 1.]]]]
>>> boxes = nn.Variable.from_numpy_array([[0, 0, 0, 4, 4], [0, 1, 1, 3, 3]])
>>> output = F.roi_align(input, boxes, (2, 2))
>>> print(output.d[0])
[[[[1.25 1.25]
   [1.25 1.25]]]
>>> print(output.d[1])
[[[2.   2.  ]
  [2.   2.  ]]]]

The spatial_scale argument tuple may be used to appropriately scale the box coordinates, for example, to scale normalized box coordinate to the input height and width dimensions.

>>> input = F.reshape(F.arange(1, 13), (1, 1, 3, 4))
>>> print(input.d)
>>> boxes = nn.Variable.from_numpy_array([[0, 1/4, 1/3, 3/4, 2/30]])
>>> output = F.roi_align(input, boxes, (1, 2), spatial_scale=(3, 4))
>>> print(input.d)
[[[[6. 7.]]]]

References:

He et al., Mask R-CNN.

Parameters

input (Variable) – N-D array with shape $(N, H, W, C)$ or $(N, C, H, W)$.
boxes (Variable) – N-D array with shape $(K, 5)$ containing box coordinates in (b, x1, y1, x2, y2) format where b is the batch index. Note that an invalid (out-of-range) batch index will generate an error only when running on CPU; when using a GPU context the batch index values are clipped to the range of input samples.
output_size (tuple of int) – the height and width of the output feature maps.
spatial_scale (repeated float) – Scaling factor from box to input coordinates, as (x, y). [default= (1.0, 1.0) ]
sampling_ratio (int) – The number of sampling points used for interpolation. Computed as ceil((y2 - y1) / output_size[0]) for height and likewise for width if sampling_ratio <= 0. [default= -1 ]
channel_last (bool) – If True, the last dimension is considered as channel dimension, a.k.a NHWC order. [default= False ]

Returns

N-D array with shape $(K, C, output\_size[0], output\_size[1])$ or $(K, output\_size[0], output\_size[1], C)$.

Return type

Note

All nnabla functions in nnabla.functions are decorated with the nnabla.function_bases.function_api decorator, which queries the current context and passes it into the first argument of the original function. The original function always takes a context as the first argument.

Neural Network Activation¶

nnabla.functions.sigmoid(x, n_outputs=- 1, outputs=None)[source]¶

Element-wise sigmoid function.

\[f(x) = \frac{1}{1 + \exp(-x)},\]

Parameters: x (Variable) – Input
Returns: Output
Return type: Variable

Note

All nnabla functions in nnabla.functions are decorated with the nnabla.function_bases.function_api decorator, which queries the current context and passes it into the first argument of the original function. The original function always takes a context as the first argument.

nnabla.functions.swish(x, n_outputs=- 1, outputs=None)[source]¶

Element-wise swish function, by Ramachandran et al. (2017).

\[y_i = \frac{x_i}{1 + \exp(-x_i)},\]

References

Prajit Ramachandran, Barret Zoph, and Quoc V. Le, Swish: a Self-Gated Activation Function, arXiv:1710.05941 [cs.NE]

Parameters: x (Variable) – Input
Returns: Output
Return type: Variable

Note

All nnabla functions in nnabla.functions are decorated with the nnabla.function_bases.function_api decorator, which queries the current context and passes it into the first argument of the original function. The original function always takes a context as the first argument.

nnabla.functions.tanh(x, n_outputs=- 1, outputs=None)[source]¶

Element-wise hyperbolic tangent (tanh) function.

\[y_i = \tanh (x_i)\]

Parameters: x (Variable) – N-D array
Returns: N-D array with the same shape as x
Return type: Variable

Note

All nnabla functions in nnabla.functions are decorated with the nnabla.function_bases.function_api decorator, which queries the current context and passes it into the first argument of the original function. The original function always takes a context as the first argument.

nnabla.functions.relu(x, inplace=False, n_outputs=- 1, outputs=None)[source]¶

Element-wise Rectified Linear Unit (ReLU) function.

\[y_i = \max (0, x_i)\]

Parameters

x (Variable) – N-D array
inplace (bool) – This option is obsolete and ignored. Output is never in-placed with input. [default= False ]

Returns

N-D array with the same shape as x

Return type

Note

All nnabla functions in nnabla.functions are decorated with the nnabla.function_bases.function_api decorator, which queries the current context and passes it into the first argument of the original function. The original function always takes a context as the first argument.

nnabla.functions.softmax(x, axis=None, n_outputs=- 1, outputs=None)[source]¶

Softmax normalization. Calculates

\[y_i = \frac{\exp(x_i)}{\sum_j \exp(x_j)}\]

along the dimension specified by axis, where $x_i$ is the input and $y_i$ is the output.

Parameters

x (Variable) – N-D array. Typically indicates a score.
axis (int) – Axis normalization is taken. [default= len(x.shape) - 1 ]

Returns

N-D array with the same shape as x

Return type

Note

All nnabla functions in nnabla.functions are decorated with the nnabla.function_bases.function_api decorator, which queries the current context and passes it into the first argument of the original function. The original function always takes a context as the first argument.

nnabla.functions.log_softmax(x, axis=None, n_outputs=- 1, outputs=None)[source]¶

Fused operation of Softmax normalization followed by log, which is defined as

\[y_i = \log \frac{\exp(x_i)}{\sum_j \exp(x_j)},\]

where $y_i$ is the input and $x_i$ is the output at i-th channel. An advantage of this fusion is reducing the numerical instability due to the log application.

The original definition can be rewritten as

\[y_i = x_i - \max_j(x_j) - \log\left(\sum_j \exp(x_j - \max_k(x_k))\right).\]

It is more stable as a log is always applied to a value $\ge e$, while a log can be evaluated for 0 in the non-fused operation.

Also, backward gradient computation is more stable than the original one as it doesn’t perform division by x due to a gradient of log. The definition is as following.

\[dx_i = dy_i - y_i * \sum_j dy_j\]

where $dx_i$ and $dy_i$ denote gradients of loss wrt $x_i$ and $y_i$ respectively.

Parameters

x (Variable) – N-D array. Typically indicates a score.
axis (int) – Axis normalization is taken. [default= len(x.shape) - 1 ]

Returns

N-D array with the same shape as x

Return type

Note

All nnabla functions in nnabla.functions are decorated with the nnabla.function_bases.function_api decorator, which queries the current context and passes it into the first argument of the original function. The original function always takes a context as the first argument.

nnabla.functions.elu(x, alpha=1.0, n_outputs=- 1, outputs=None)[source]¶

Element-wise Exponential Linear Unit (ELU) function.

\[\begin{split}y_i= \left\{ \begin{array}{ll} x_i & (x > 0)\\ \alpha (\exp(x_i) - 1) & (x \leq 0) \end{array} \right..\end{split}\]

References

Clevart et al., Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs).

Parameters

x (Variable) – N-D array
alpha (float) – Coefficient for negative outputs. $\alpha$ in definition [default= 1.0 ]

Returns

N-D array with the same shape as x

Return type

Note

All nnabla functions in nnabla.functions are decorated with the nnabla.function_bases.function_api decorator, which queries the current context and passes it into the first argument of the original function. The original function always takes a context as the first argument.

nnabla.functions.selu(x, scale=1.05070098735548, alpha=1.673263242354377, n_outputs=- 1, outputs=None)[source]¶

Element-wise Scaled Exponential Linear Unit (SELU) function by Klambauer et al. (2017).

\[\begin{split}y_i= \lambda \left\{ \begin{array}{ll} x_i & (x > 0)\\ \alpha (\exp(x_i) - 1) & (x \leq 0) \end{array} \right..\end{split}\]

The coefficients $\lambda$ and $\alpha$ default to the following values $\lambda_{01}$ and $\alpha_{01}$, respectively, provided by Klambauer et al. (2017):

\[\begin{split}\begin{array}{lll} \lambda_{01} &=& \left( 1 - \operatorname{erfc}\left( \frac{1}{\sqrt{2}} \right) \sqrt{e} \right) \sqrt{2 \pi} \\ && \left( 2 \operatorname{erfc} \left( \sqrt{2} \right) e^2 + \pi \operatorname{erfc}\left( \frac{1}{\sqrt{2}} \right)^2 e \right. \\ && \left. - 2(2 + \pi) \operatorname{erfc} \left( \frac{1}{\sqrt{2}} \right) \sqrt{e} + \pi + 2 \right)^{-1/2} \\ &\approx& 1.0507 \\ \alpha_{01} &=& - \frac {\sqrt {\frac {2}{\pi}}} {\operatorname{erfc} \left( \frac{1}{\sqrt{2}} \right) \exp \left(\frac {1} {2} \right) - 1} \\ &\approx& 1.67326 \end{array}\end{split}\]

References

Klambauer, G., Unterthiner, T., Mayr, A., & Hochreiter, S. (2017). Self-Normalizing Neural Networks. In Advances in Neural Information Processing Systems (NIPS).

Parameters

x (Variable) – N-D array
scale (float) – The coefficient $\lambda$ in the definition. [default= 1.05070098735548 ]
alpha (float) – The coefficient $\alpha$ in the definition. [default= 1.673263242354377 ]

Returns

N-D array with the same shape as x

Return type

Note

All nnabla functions in nnabla.functions are decorated with the nnabla.function_bases.function_api decorator, which queries the current context and passes it into the first argument of the original function. The original function always takes a context as the first argument.

nnabla.functions.crelu(x, axis=1, n_outputs=- 1, outputs=None)[source]¶

Element-wise Concatenated Rectified Linear Unit (CReLU) function. This function calculates the ReLU of $x$ and $-x$ , then concatenates the results together at a specified axis, and returns the resulting array.

References

Wenling Shang, Kihyuk Sohn, Diogo Almeida, Honglak Lee. Understanding and Improving Convolutional Neural Networks via Concatenated Rectified Linear Units.

Parameters

x (Variable) – N-D array.
axis (int) – The ReLU activations of positive inputs and negative inputs are concatenated at axis. [default= 1 ]

Returns

N-D array where axis dimension is doubled by concatenating.

Return type

Note

All nnabla functions in nnabla.functions are decorated with the nnabla.function_bases.function_api decorator, which queries the current context and passes it into the first argument of the original function. The original function always takes a context as the first argument.

nnabla.functions.celu(x, alpha=1.0, axis=1, n_outputs=- 1, outputs=None)[source]¶

Element-wise Concatenated Exponential Linear Unit (CELU) function. Concatenates ELU outputs of positive and negative inputs together at specified axis.

Parameters

x (Variable) – N-D array.
alpha (float) – Coefficient for negative outputs. $\alpha$ in definition. [default= 1.0 ]
axis (int) – The ELU activations of positive inputs and negative inputs are concatenated at axis. [default= 1 ]

Returns

N-D array where axis dimension is doubled by concatenating.

Return type

Note

All nnabla functions in nnabla.functions are decorated with the nnabla.function_bases.function_api decorator, which queries the current context and passes it into the first argument of the original function. The original function always takes a context as the first argument.

nnabla.functions.gelu(x, n_outputs=- 1, outputs=None)[source]¶

Gaussian Error Unit (GELU) function.

\[GELU(x) = xP(X \leq x) = x \Phi (x)\]

which is approximated by

\[GELU(x) = 0.5x (1 + \tanh ( \sqrt(2/\pi)(x + 0.044715x^3) ))\]

References

Dan Hendrycks and Kevin Gimpel. Gaussian Error Linera Units (GELUs).

Parameters: x (Variable) – N-D array
Returns: N-D array with the same shape as x
Return type: Variable

Note

All nnabla functions in nnabla.functions are decorated with the nnabla.function_bases.function_api decorator, which queries the current context and passes it into the first argument of the original function. The original function always takes a context as the first argument.

nnabla.functions.mish(x, n_outputs=- 1, outputs=None)[source]¶

Mish activation function.

\[Mish(x) = x \tanh(\log(1+\exp(x_i)))\]

References

Diganta Misra. Mish A Self Regularized Non-Monotonic Neural Activation Function.

Parameters: x (Variable) – N-D array
Returns: N-D array with the same shape as x
Return type: Variable

Note

All nnabla functions in nnabla.functions are decorated with the nnabla.function_bases.function_api decorator, which queries the current context and passes it into the first argument of the original function. The original function always takes a context as the first argument.

nnabla.functions.prelu(x0, x1, base_axis=1, n_outputs=- 1, outputs=None)[source]¶

Element-wise Parametrized Rectified Linear Unit function. Calculates:

\[y_i = \max(0, x_i) + w_i \min(0, x_i)\]

where negative slope $w$ is learned and can vary across channels (an axis specified with base_axis).

Parameters

x0 (Variable) – (N-D array) Input
x1 (Variable) – (N-D array) Weights
base_axis (int) – Dimensions up to base_axis is treated as sample dimension. [default= 1 ]

Returns

N-D array.

Return type

Note

All nnabla functions in nnabla.functions are decorated with the nnabla.function_bases.function_api decorator, which queries the current context and passes it into the first argument of the original function. The original function always takes a context as the first argument.

nnabla.functions.leaky_relu(x, alpha=0.1, inplace=False, n_outputs=- 1, outputs=None)[source]¶

Element-wise Leaky Rectified Linear Unit (ReLU) function.

It is defined as:

\[y_i = \alpha * \min(0, x_i) + \max (0, x_i)\]

Parameters

x (Variable) – N-D array
alpha (float) – The slope value multiplied to negative numbers. $\alpha$ in the definition. [default= 0.1 ]
inplace (bool) – This option is obsolete and ignored. Output is never in-placed with input. [default= False ]

Returns

N-D array with the same shape as x

Return type

Note

All nnabla functions in nnabla.functions are decorated with the nnabla.function_bases.function_api decorator, which queries the current context and passes it into the first argument of the original function. The original function always takes a context as the first argument.

nnabla.functions.relu6(x, n_outputs=- 1, outputs=None)[source]¶

Element-wise ReLU6 function. Capping ReLU activation to 6 is often observed to learn sparse features earlier.

\[ReLU6(x) = \min(\max(0,x,),6)\]

Parameters: x (Variable) – N-D array
Returns: N-D array with the same shape as x
Return type: Variable

Note

All nnabla functions in nnabla.functions are decorated with the nnabla.function_bases.function_api decorator, which queries the current context and passes it into the first argument of the original function. The original function always takes a context as the first argument.

nnabla.functions.hard_sigmoid(x, n_outputs=- 1, outputs=None)[source]¶

Segment-wise linear approximation of sigmoid. Preferable when speed of computation is more important than precision. Returns $0$ if $x < -2.5$. Returns $1$ if $x> 2.5$. Returns $0.2x + 0.5$ if $-2.5 <= x <= 2.5$.

Parameters: x (Variable) – N-D array
Returns: N-D array with the same shape as x
Return type: Variable

Note

All nnabla functions in nnabla.functions are decorated with the nnabla.function_bases.function_api decorator, which queries the current context and passes it into the first argument of the original function. The original function always takes a context as the first argument.

nnabla.functions.hard_tanh(x, n_outputs=- 1, outputs=None)[source]¶

Element-wise HardTanh function. Computationally cheaper than Tanh function. Returns $1$ if $x > 1$. Returns $-1$ if $x < -1$. Returns $x$ otherwise.

Parameters: x (Variable) – N-D array
Returns: N-D array with the same shape as x
Return type: Variable

Note

All nnabla functions in nnabla.functions are decorated with the nnabla.function_bases.function_api decorator, which queries the current context and passes it into the first argument of the original function. The original function always takes a context as the first argument.

nnabla.functions.log_sigmoid(x, n_outputs=- 1, outputs=None)[source]¶

Element-wise LogSigmoid function.

\[LogSigmoid(x) = \log(1/(1+\exp(-x_i)))\]

Parameters: x (Variable) – N-D array
Returns: N-D array with the same shape as x
Return type: Variable

Note

All nnabla functions in nnabla.functions are decorated with the nnabla.function_bases.function_api decorator, which queries the current context and passes it into the first argument of the original function. The original function always takes a context as the first argument.

nnabla.functions.softplus(x, beta=1.0, n_outputs=- 1, outputs=None)[source]¶

Element-wise SoftPlus function. Unlike Sigmoid and Tanh that have upper and lower bound, SoftPlus is only lower-bounded by 0.

\[SoftPlus(x) = \frac{1}{\beta} * \log(1+\exp(\beta * x_i))\]

Parameters

x (Variable) – N-D array
beta (float) – the beta value for SoftPlus formulation [default= 1.0 ]

Returns

N-D array with the same shape as x

Return type

Note

All nnabla functions in nnabla.functions are decorated with the nnabla.function_bases.function_api decorator, which queries the current context and passes it into the first argument of the original function. The original function always takes a context as the first argument.

nnabla.functions.softsign(x, n_outputs=- 1, outputs=None)[source]¶

Element-wise SoftSign. Can be used in place of Tanh function. While Tanh converges exponentially, SoftSign converges polynomially.

\[SoftSign(x) = x/(1+|x|)\]

Parameters: x (Variable) – N-D array
Returns: N-D array with the same shape as x
Return type: Variable

Note

All nnabla functions in nnabla.functions are decorated with the nnabla.function_bases.function_api decorator, which queries the current context and passes it into the first argument of the original function. The original function always takes a context as the first argument.

nnabla.functions.tanh_shrink(x, n_outputs=- 1, outputs=None)[source]¶

Element-wies TanhShrink function.

\[TanhShrink(x) = x - \tanh(x)\]

Parameters: x (Variable) – N-D array
Returns: N-D array with the same shape as x
Return type: Variable

Note

All nnabla functions in nnabla.functions are decorated with the nnabla.function_bases.function_api decorator, which queries the current context and passes it into the first argument of the original function. The original function always takes a context as the first argument.

nnabla.functions.sinc(x, n_outputs=- 1, outputs=None)[source]¶

Element-wise Sinc function. Unlike other popular activation functions, it has rises and falls. returns $1$ if $x = 0$. returns $\sin(x)/x$ otherwise.

Parameters: x (Variable) – N-D array
Returns: N-D array with the same shape as x
Return type: Variable

Note

All nnabla functions in nnabla.functions are decorated with the nnabla.function_bases.function_api decorator, which queries the current context and passes it into the first argument of the original function. The original function always takes a context as the first argument.

Normalization¶

nnabla.functions.batch_normalization(x, beta, gamma, mean, variance, axes=[1], decay_rate=0.9, eps=1e-05, batch_stat=True, output_stat=False, n_outputs=None)[source]¶

Batch normalization.

\[\begin{split}\begin{eqnarray} \mu &=& \frac{1}{M} \sum x_i \\ \sigma^2 &=& \frac{1}{M} \sum \left(x_i - \mu\right)^2 \\ \hat{x}_i &=& \frac{x_i - \mu}{\sqrt{\sigma^2 + \epsilon}} \\ y_i &=& \hat{x}_i \gamma + \beta. \end{eqnarray}\end{split}\]

At testing time, the mean and variance values used are those that were computed during training by moving average.

References

Ioffe and Szegedy, Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift.

Parameters

x (Variable) – N-D array of input.
beta (Variable or None) – N-D array of beta which is learned. If None, the bias term is omitted.
gamma (Variable or None) – N-D array of gamma which is learned. If None, the scale term is omitted.
mean (Variable or None) – N-D array of running mean (modified during forward execution). If None, dummy variable is created and running mean is not updated. mean=None with batch_stat=False is prohibited.
variance (Variable or None) – N-D array of running variance (modified during forward execution). If None, dummy variable is created and running variance is not updated. variance=None with batch_stat=False is prohibited.
axes (list of int or int) – Mean and variance are calculated along these axes.
decay_rate (float) – Decay rate of running mean and variance.
eps (float) – Tiny value to avoid zero division by std.
batch_stat (bool) – Use mini-batch statistics rather than running ones. If False, mean and variance must be ~nnabla.Variable. (None is prohibited.)
output_stat (bool) – It true, the batch statistics of mean and variance, will be returned as Variables. They are also differentiable.

Returns

Returns batch normalization output as Variable. If output_stat=True, it also returns the mean and variance of the mini-batch

Variable: Output of the batch normalization
Variable: Mean (if ``output_stat=True`)
Variable: Variance (if ``output_stat=True`)

See also

nnabla.function_bases.batch_normalization.

nnabla.functions.fused_batch_normalization(x, beta, gamma, mean, variance, z=None, axes=[1], decay_rate=0.9, eps=1e-05, batch_stat=True, nonlinearity='relu', output_stat=False, n_outputs=None)[source]¶

Batch normalization fused with an add operation and an activation.

References

Ioffe and Szegedy, Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift.

Parameters

x (Variable) – N-D array of input.
beta (Variable or None) – N-D array of beta which is learned. If None, the bias term is omitted.
gamma (Variable or None) – N-D array of gamma which is learned. If None, the scale term is omitted.
mean (Variable or None) – N-D array of running mean (modified during forward execution). If None, dummy variable is created and running mean is never updated. mean=None with batch_stat=False is prohibited.
variance (Variable) – N-D array of running variance (modified during forward execution). If None, dummy variable is created and running variance is not updated. variance=None with batch_stat=False is prohibited.
z (Variable, optional) – N-D array
axes (list of int or int) – Mean and variance are calculated along these axes.
decay_rate (float) – Decay rate of running mean and variance.
eps (float) – Tiny value to avoid zero division by std.
batch_stat (bool) – Use mini-batch statistics rather than running ones. If False, mean and variance must be ~nnabla.Variable. (None is prohibited.)
nonlinearity (str) – Nonlinearity chosen from relu. Default is relu.
output_stat (bool) – It true, the batch statistics of mean and variance, will be returned as Variables. They are also differentiable.

Returns

Returns batch normalization output as Variable. If output_stat=True, it also returns the mean and variance of the mini-batch

Variable: Output of the batch normalization
Variable: Mean (if ``output_stat=True`)
Variable: Variance (if ``output_stat=True`)

See also

nnabla.function_bases.batch_normalization.

nnabla.functions.sync_batch_normalization(x, beta, gamma, mean, variance, comm, group='world', axes=[1], decay_rate=0.9, eps=1e-05, batch_stat=True, output_stat=False, n_outputs=None)[source]¶

Synchronized batch normalization.

For some tasks (e.g., semantic segmentation), batch size will be too small and BatchNormalization layer might not work well. SyncBatchNorlization layer solves these problems by synchronizing batch stats (mean and var) between multiple processes.

\[\begin{split}\begin{eqnarray} \mu &=& \frac{1}{M} \sum x_i \\ \sigma^2 &=& \frac{1}{M} \left(\sum x_i - \mu\right)^2 \\ \hat{x}_i &=& \frac{x_i - \mu}{\sqrt{\sigma^2 + \epsilon}} \\ y_i &=& \hat{x}_i \gamma + \beta. \end{eqnarray}\end{split}\]

References

Implementing Synchronized Multi-GPU Batch Normalization https://hangzhang.org/PyTorch-Encoding/notes/syncbn.html

Parameters

x (Variable) – N-D array of input.
beta (Variable or None) – N-D array of beta which is learned. If None, the bias term is omitted.
gamma (Variable or None) – N-D array of gamma which is learned. If None, the scale term is omitted.
mean (Variable or None) – N-D array of running mean (modified during forward execution). If None, dummy variable is created and running mean is never updated. mean=None with batch_stat=False is prohibited.
variance (Variable or None) – N-D array of running variance (modified during forward execution). If None, dummy variable is created and running variance is never updated. variance=None with batch_stat=False is prohibited.
comm (Communicator) – The communicator
group (string) – The name of the communicator group
axes (list of int or int) – Mean and variance are calculated along these axes.
decay_rate (float) – Decay rate of running mean and variance.
eps (float) – Tiny value to avoid zero division by std.
batch_stat (bool) – Use mini-batch statistics rather than running ones. If False, mean and variance must be ~nnabla.Variable. (None is prohibited.)
output_stat (bool) – It true, the batch statistics of mean and variance, will be returned as Variables. They are also differentiable.

Returns

Returns batch normalization output as Variable. If output_stat=True, it also returns the mean and variance of the mini-batch

Variable: Output of the batch normalization
Variable: Mean (if ``output_stat=True`)
Variable: Variance (if ``output_stat=True`)

See also

nnabla.function_bases.batch_normalization.

nnabla.functions.mean_subtraction(x, mean, t, base_axis=1, update_running_mean=True)[source]¶

It subtracts the mean of the elements of the input array, and normalizes it to $0$. Preprocessing arrays with this function has the effect of improving accuracy in various tasks such as image classification.

At training time, this function is defined as

\[\begin{split}\begin{eqnarray} \mu &=& \frac{1}{M} \sum x_i \\ y_i &=& x_i - \mu \end{eqnarray}\end{split}\]

At testing time, the mean values used are those that were computed during training by moving average.

Note

The backward performs an approximated differentiation that takes into account only the latest mini-batch.

Parameters

x (Variable) – N-D array of input.
mean (Variable) – N-D array of running mean (modified during forward execution).
t (Variable) – Scalar of num of iteration of running mean (modified during forward execution).
base_axis (int) – Base axis of Mean Subtraction operation. Dimensions up to base_axis is treated as sample dimension. [default=``1``]
update_running_mean (bool) – Update running mean during forward execution. [default=``True``]

Returns

N-D array.

Return type

See also

nnabla.function_bases.mean_subtraction.

nnabla.functions.norm_normalization(x, p=None, axes=None, eps=1e-12)[source]¶

Norm normalization.

\[y = \frac{x_i}{\|x\|_p}\]

Parameters

x (Variable) – N-D array.
p (float) – Order of the norm. [default= 2 ]
axes (repeated int64) – Axes to be reduced. If empty list is given, all dimensions are reduced. [default= range(x.ndim) ]
eps (float) – Epsilon for the normalization. This eps is added before taking the p-th root in the norm computation. [default= 1e-12 ]

Returns

N-D array

Return type

nnabla.functions.clip_by_value(x, min, max)[source]¶

Clip inputs by values.

\[\begin{split}y = \begin{cases} max & (x > max) \\ x & (otherwise) \\ min & (x < min) \end{cases}.\end{split}\]

Parameters

x (Variable) – An input variable.
min (Variable or float) – A min variable or float value by which x is clipped. Note that if Variable is given, its shape must be the same as x’s.
max (Variable or float) – A max variable or float value by which x is clipped. Note that if Variable is given, its shape must be the same as x’s

Returns

N-D array.

Return type

nnabla.functions.clip_grad_by_value(x, min, max, n_outputs=- 1, outputs=None)[source]¶

In forward pass, the function behaves as the identity.

In backward pass,

\[\begin{split}g_x = \begin{cases} max & (g_y > max) \\ g_y & (otherwise) \\ min & (g_y < min) \end{cases}.\end{split}\]

A typical case for use is to prevent the gradient explosion through a whole computational graph. For example, if you want to clip gradient values for each feature map,

x = nn.Variable([16, 3, 32, 32])
min = F.broadcast(nn.Variable.from_numpy_array(np.asarray([-1.0]).reshape((1, 1, 1, 1))), (16, 3, 32, 32))
max = F.broadcast(nn.Variable.from_numpy_array(np.asarray([1.0]).reshape((1, 1, 1, 1))), (16, 3, 32, 32))
c = F.clip_grad_by_value(x, min=min, max=max)
h = PF.convolution(c, 64, (3, 3), pad=(1, 1))

Parameters

x (Variable) – N-D array of input.
min (Variable) – N-D array of minimum input value by which the gradients of the y are clipped. Note that the shape of min must be the same as x’s and the backward to min is not performed.
max (Variable) – N-D array of maximum input value by which the gradients of the y are clipped. Note that the shape of max must be the same as x’s and the backward to max is not performed.

Returns

N-D array.

Return type

Note

All nnabla functions in nnabla.functions are decorated with the nnabla.function_bases.function_api decorator, which queries the current context and passes it into the first argument of the original function. The original function always takes a context as the first argument.

nnabla.functions.clip_by_norm(x, clip_norm, axis=None)[source]¶

Clip inputs by its L2 norm when the L2 norm is larger than the threshold value (defined by clip_norm). If it is less than the threshold, inputs are not modified. If it is applied, the operation is represented as

\[y = N \times \frac{x}{\|x\|_2}.\]

where $x$ is the input, $y$ is the output, and $N$ is clip_norm. this is the case that axes is not set. When axes is set, the norm is computed over axes.

Parameters

x (Variable) – An input variable.
clip_norm (Variable or float) – An input scalar variable or float value. Must be positive.
axis (None, int or tuple of ints) – Axis or axes along which the reduction is performed. Passing the default value None will reduce all dimensions.

Returns

N-D array.

Return type

nnabla.functions.clip_grad_by_norm(x, clip_norm=None, axes=None, n_outputs=- 1, outputs=None)[source]¶

In the forward pass, the function behaves like the identity.

In the backward pass,

\[g_x = N \times \frac{g_y}{\|g_y\|_2}.\]

where $g_x$ is the gradient w.r.t the input, $g_y$ is the gradient w.r.t. the output, and $N$ is clip_norm where the norm of $g_y$ becomes. this is the case that axes is not set. When axes is set, the norm is computed over axes.

A typical case for use is to prevent the gradient explosion through a whole computational graph. For example, if you want to normalize gradient values over feature axis,

x = nn.Variable([16, 3, 32, 32])
c = F.clip_grad_by_norm(x, axes=(1, ))
h = PF.convolution(c, 64, (3, 3), pad=(1, 1))

Parameters

x (Variable) – N-D array of input.
clip_norm (float) – Clip to the norm of input to clip_norm in the backward pass. [default= 1.0 ]
axes (repeated int64) – Axes to be reduced. If empty list is given, all dimensions are reduced to scalar. This is used in the forward pass. [default= range(x.ndim) ]

Returns

N-D array.

Return type

Note

All nnabla functions in nnabla.functions are decorated with the nnabla.function_bases.function_api decorator, which queries the current context and passes it into the first argument of the original function. The original function always takes a context as the first argument.

nnabla.functions.layer_normalization(x, beta, gamma, batch_axis=0, eps=1e-05, output_stat=False)[source]¶

Applies Layer Normalization over an input tensor, which is defined as:

\[\begin{split}\begin{eqnarray} \mu^l &=& \frac{1}{H} \sum_{i=1}^{H} x_i^l \\ \sigma^l &=& \sqrt{\frac{1}{H} \sum_{i=1}^{H} \left(x_i^l - \mu^l\right)^2 + \epsilon} \\ y &=& \frac{x - \mu^l}{\sigma^l} \gamma + \beta \end{eqnarray}\end{split}\]

where $x$ and $y$ are input and output variable, $\mu^l$ and $\sigma^l$ are the mean and std of each layer which is separately calculated for each batch, and $\beta$ and $\gamma$ are adaptive biases and gains.

If the input shape is [B, C, H, W] (= batch_axis=0), the shape of calculated mean and std are [B, 1, 1, 1]

References

Jimmy Lei Ba, Jamie Ryan Kiros, Geoffrey E. Hinton, Layer Normalization.

Parameters

x (Variable) – An input variable.
beta (Variable or None) – An Adaptive biases. If None, the bias term is omitted.
gamma (Variable or None) – An Adaptive gains. If None, the scale term is omitted.
batch_axis (int or repeated int) – Axes mean and variance are taken.
eps (float) – Tiny value to avoid zero division by std.
output_stat (bool) – If true, calculated mean and variance are also returned.

Returns

output variable which is normalized its statics and rescaled by alpha and beta. * Variable: Mean (if ``output_stat=True`). * Variable: Std (if ``output_stat=True`)

Return type

Variable

nnabla.functions.instance_normalization(x, beta, gamma, channel_axis=1, batch_axis=0, eps=1e-05, output_stat=False)[source]¶

Applies Instance Normalization over an input tensor, which is defined as:

\[\begin{split}\begin{eqnarray} \mu^i &=& \frac{1}{H} \sum_{i=1}^{H} x_i^i \\ \sigma^i &=& \sqrt{\frac{1}{H} \sum_{i=1}^{H} \left(x_i^i - \mu^i\right)^2 + \epsilon} \\ y &=& \frac{x - \mu^i}{\sigma^i} \gamma + \beta \end{eqnarray}\end{split}\]

where $x$ and $y$ are input and output variable, $\mu^i$ and $\sigma^i$ are the mean and std of each instance which is separately calculated for each batch and channel, and $\gamma$ and $\beta$ are adaptive gains and biases.

If the input shape is [B, C, H, W] (= channel_axis=1, batch_axis=0), the shape of calculated mean and std are [B, C, 1, 1]

References

Dmitry Ulyanov, Andrea Vedaldi, Victor Lempitsky, Instance Normalization: The Missing Ingredient for Fast Stylization.

Parameters

x (Variable) – An input variable.
beta (Variable or None) – An Adaptive biases. If None, the bias term is omitted.
gamma (Variable or None) – An Adaptive gains. If None, the scale term is omitted.
channel_axis (int) – Channel axis.
batch_axis (int or repeated int) – Batch axes.
eps (float) – Tiny value to avoid zero division by std.
output_stat (bool) – If true, the batch statistics of mean and variance.

Returns

Normalized output variable. * Variable: Mean (if ``output_stat=True`) * Variable: Std (if ``output_stat=True`)

Return type

Variable

nnabla.functions.group_normalization(x, beta, gamma, num_groups, channel_axis=1, batch_axis=0, eps=1e-05, output_stat=False)[source]¶

Applies Group Normalization over an input tensor, which is defined as:

\[\begin{split}\begin{eqnarray} \mu^g &=& \frac{1}{H} \sum_{i=1}^{H} x_i^g \\ \sigma^g &=& \sqrt{\frac{1}{H} \sum_{i=1}^{H} \left(x_i^g - \mu^g\right)^2 + \epsilon} \\ y &=& \frac{x - \mu^g}{\sigma^g} \gamma + \beta \end{eqnarray}\end{split}\]

where $x$ and $y$ are input and output variable, $\mu^g$ and $\sigma^g$ are the mean and std of each group which contains num_channels / num_groups channels, and $\gamma$ and $\beta$ are adaptive gains and biases.

The input channels, specified by channel_axis, are separated into num_groups groups, and the mean and std are calculated over the each group. For example, if the input shape is [B, C, H, W] (= channel_axis=1, batch_axis=0), an input variable is once reshaped to [B, num_groups, C / num_groups, H, W] and standardize by its mean and std whose shapes are [B, num_groups, 1, 1, 1]. Finally, an output variable is reshaped again to the original input shape (= [B, C, H, W] in the case above).

References

Yuxin Wu, Kaiming He, Group Normalization.

Parameters

x (Variable) – An input variable.
beta (Variable or None) – An Adaptive biases. If None, the bias term is omitted.
gamma (Variable or None) – An Adaptive gains. If None, the scale term is omitted.
num_groups (int) – A number of groups. The channel dim of ‘x’ must be integer multiple of num_groups.
channel_axis (int) – Channel axis.
batch_axis (int or repeated int) – Batch axes.
eps (float) – Tiny value to avoid zero division by std.
output_stat (bool) – If true, the batch statistics of mean and variance.

Returns

Normalized output variable. * Variable: Mean (if ``output_stat=True`) * Variable: Std (if ``output_stat=True`)

Return type

Variable

nnabla.functions.weight_standardization(w, channel_axis=0, eps=1e-05, output_stat=False)[source]¶

Applies Weight Standardization over an input weight, which is defined as:

\[\begin{split}\begin{eqnarray} \mu_{W_i} &=& \frac{1}{I} \sum_{j=1}^{I} W_{ij} \\ \sigma_{W_i} &=& \sqrt{\frac{1}{I} \sum_{i=1}^{I} \left(W_{ij} - \mu_{W_{i}}\right)^2 + \epsilon} \\ \hat{W_{ij}} &=& \frac{W_{ij} - \mu_{W_i}}{\sigma_{W_i}} \\ y &=& \hat{W} \ast x \end{eqnarray}\end{split}\]

Example

import numpy as np
import nnabla as nn
import nnabla.functions as F
import nnabla.parametric_functions as PF

rng = np.random.RandomState(313)
x = nn.Variable.from_numpy_array(rng.randn(*(32, 16, 3, 3)))

# For convolution:

def ws_callback_conv(w):
    return F.weight_standardization(w, channel_axis=0)

y = PF.convolution(x, 10, (2, 2), apply_w=ws_callback_conv)

# For affine:

def ws_callback_affine(w):
    return F.weight_standardization(w, channel_axis=1)

y = PF.affine(x, 10, apply_w=ws_callback_affine)

References

Siyuan Qiao, Huiyu Wang, Chenxi Liu, Wei Shen, Alan Yuille, Weight Standardization

Parameters

w (Variable) – A weight variable.
channel_axis (int) – An axis for output channel. Default value is 0 which assumes the weights of convolution.
eps (float) – Tiny value to avoid zero division by std.
output_stat (bool) – If true, the batch statistics of mean and variance.

Returns

Standardized output weight. * Variable: Mean (if ``output_stat=True`) * Variable: Std (if ``output_stat=True`)

Return type

Variable

nnabla.functions.weight_normalization(w, g, dim=0, eps=1e-12, n_outputs=- 1, outputs=None)[source]¶

Weight normalization.

\[\mathbf{w}_{WN} = g \dfrac{\mathbf{w}}{\|\mathbf{w}\|}\]

where $\mathbf{w}$ is the input weights to be normalized. and $g$ is learnable multiplication factors each of which is applied to each data at dim.

References

Tim Salimans, Diederik P. Kingma, Weight Normalization: A Simple Reparameterization to Accelerate Training of Deep Neural Networks.

Parameters

w (Variable) – N-D array of learnable weights.
g (Variable) – 1-D array of learnable scales.
dim (int) – Output dimension. For the other dimensions, the norms are computed. [default= 0 ]
eps (float) – Epsilon for the normalization. This eps is added before taking the sqrt in the norm computation. [default= 1e-12 ]

Returns

N-D array

Return type

Note

All nnabla functions in nnabla.functions are decorated with the nnabla.function_bases.function_api decorator, which queries the current context and passes it into the first argument of the original function. The original function always takes a context as the first argument.

nnabla.functions.spectral_norm(w, u, dim=0, itr=1, eps=1e-12, test=False, output_u=False)[source]¶

Spectral Normalization.

\[\begin{split}W_{sn} = \\frac{W}{\\sigma(W)}.\end{split}\]

where $W$ is the input matrix, and the $\\sigma(W)$ is the spectral norm of $W$. The spectral norm is approximately computed by the power iteration.

References

Takeru Miyato, Toshiki Kataoka, Masanori Koyama, Yuichi Yoshida, “Spectral Normalization for Generative Adversarial Networks”, International Conference on Learning Representations. 2018.

Parameters

w (Variable) – N-D array of learnable weights. This is normally network parameter.
u (Variable) – 1-D array of singular vector. When test == False, the data region of u will be updated during forward calculation.
dim (int) – Output dimension. Default is 0. If the dimension is not 0, then the specified dimension becomes the most-left dimension by transposing. [default= 0 ]
itr (int) – Number of power iterations. Default is 1. [default= 1 ]
eps (float) – Epsilon for the normalization. This eps is added before taking the sqrt in the norm computation. [default= 1e-12 ]
test (bool) – When in True, u will not be updated. Default is False. [default= False ]
output_u (bool) – Output original u or not. u is updated when test == True but you can get original u as output with this option. Default is False. [default= False ]

Returns

Spectrally normalized $W_{sn}$ with the same shape as $W$.

Return type

Reduction¶

nnabla.functions.sum(x, axis=None, keepdims=False)[source]¶

Reduction along axes with sum operation.

Parameters

x (Variable) – An input variable.
axis (None, int or tuple of ints) – Axis or axes along which the sum is calculated. Passing the default value None will reduce all dimensions.
keepdims (bool) – Flag whether the reduced axes are kept as a dimension with 1 element.

Returns

N-D array.

Return type

nnabla.functions.mean(x, axis=None, keepdims=False)[source]¶

Reduction along axes with mean operation.

Parameters

x (Variable) – An input variable.
axis (None, int or tuple of ints) – Axis or axes along which mean is calculated. Passing the default value None will reduce all dimensions.
keepdims (bool) – Flag whether the reduced axes are kept as a dimension with 1 element.

Returns

N-D array.

Return type

nnabla.functions.max(x, axis=None, keepdims=False, with_index=False, only_index=False)[source]¶

Reduce the input N-D array x along the given axis using the max operation. The axis argument may be a single integer to reduce over one axis, a tuple of integers to reduce over multiple axes, or None to reduce over all axes. If keepdims is True, the output will keep all reduced dimensions with size 1. If with_index is True, result is a tuple (sorted, indices) or only indices if only_index is True. Setting only_index to True implies that with_index is also True.

import numpy as np
import nnabla as nn
import nnabla.functions as F

nn.set_auto_forward(True)
x = nn.Variable.from_numpy_array(np.random.rand(2, 3, 4))

maxval = F.max(x, axis=1)
assert np.allclose(maxval.d, np.max(x.d, axis=1))

maxval, indices = F.max(x, axis=1, with_index=True)
assert np.allclose(maxval.d, np.max(x.d, axis=1))
assert np.all(indices.d == np.argmax(x.d, axis=1))

indices = F.max(x, axis=1, only_index=True)
assert np.all(indices.d == np.argmax(x.d, axis=1))

Parameters

x (Variable) – An input variable.
axis (None, int or tuple of ints) – Axis or axes along which max is calculated. The default value None will reduce all dimensions.
keepdims (bool) – Keep reduced axes as dimension with 1 element.
with_index (bool) – Return tuple of max values and index.
only_index (bool) – Return only the index of max values.

Returns

N-D array.

Return type

nnabla.functions.min(x, axis=None, keepdims=False, with_index=False, only_index=False)[source]¶

Reduce the input N-D array x along the given axis using the min operation. The axis argument may be a single integer to reduce over one axis, a tuple of integers to reduce over multiple axes, or None to reduce over all axes. If keepdims is True, the output will keep all reduced dimensions with size 1. If with_index is True, result is a tuple (sorted, indices) or only indices if only_index is True. Setting only_index to True implies that with_index is also True.

import numpy as np
import nnabla as nn
import nnabla.functions as F

nn.set_auto_forward(True)
x = nn.Variable.from_numpy_array(np.random.rand(2, 3, 4))

minval = F.min(x, axis=1)
assert np.allclose(minval.d, np.min(x.d, axis=1))

minval, indices = F.min(x, axis=1, with_index=True)
assert np.allclose(minval.d, np.min(x.d, axis=1))
assert np.all(indices.d == np.argmin(x.d, axis=1))

indices = F.min(x, axis=1, only_index=True)
assert np.all(indices.d == np.argmin(x.d, axis=1))

Parameters

x (Variable) – An input variable.
axis (None, int or tuple of ints) – Axis or axes along which min is calculated. The default value None will reduce all dimensions.
keepdims (bool) – Keep reduced axes as dimension with 1 element.
with_index (bool) – Return tuple of min values and index.
only_index (bool) – Return only the index of min values.

Returns

N-D array.

Return type

nnabla.functions.norm(x, p=None, axis=None, keepdims=False)[source]¶

Reduction along axes with norm operation.

\[y = \|x\|_p = \left( \sum_i |x_i|^p \right)^{\frac{1}{p}}\]

Parameters

x (Variable) – An input variable.
p (float) – Order of the norm.
axis (None, int or tuple of ints) – Axis or axes along which product is calculated. Passing the default value None will reduce all dimensions.
keepdims (bool) – Flag whether the reduced axes are kept as a dimension with 1 element.

Returns

N-D array.

Return type

nnabla.functions.prod(x, axis=None, keepdims=False)[source]¶

Reduction along axes with product operation.

Parameters

x (Variable) – An input variable.
axis (None, int or tuple of ints) – Axis or axes along which product is calculated. Passing the default value None will reduce all dimensions.
keepdims (bool) – Flag whether the reduced axes are kept as a dimension with 1 element.

Returns

N-D array.

Return type

Note

Backward computation is not accurate in a zero value input.

nnabla.functions.reduce_sum(x, n_outputs=- 1, outputs=None)[source]¶

Reduction along an axis with sum operation.

Note

This is deprecated. Use sum instead.

Parameters: x (Variable) – N-D array.
Returns: N-D array
Return type: Variable

Note

All nnabla functions in nnabla.functions are decorated with the nnabla.function_bases.function_api decorator, which queries the current context and passes it into the first argument of the original function. The original function always takes a context as the first argument.

nnabla.functions.reduce_mean(x, n_outputs=- 1, outputs=None)[source]¶

Reduction by mean along an axis.

Note

This is deprecated. Use mean instead.

Parameters: x (Variable) – N-D array
Returns: N-D array
Return type: Variable

Note

All nnabla functions in nnabla.functions are decorated with the nnabla.function_bases.function_api decorator, which queries the current context and passes it into the first argument of the original function. The original function always takes a context as the first argument.

Arithmetic¶

nnabla.functions.add2(x0, x1, inplace=False, n_outputs=- 1, outputs=None)[source]¶

Element-wise addition.

\[y_i = x^{(0)}_i + x^{(1)}_i\]

Parameters

x0 (Variable) – N-D array
x1 (Variable) – N-D array
inplace (bool) – This option is obsolete and ignored. Output is never in-placed with input. [default= False ]

Returns

N-D array

Return type

Note

All nnabla functions in nnabla.functions are decorated with the nnabla.function_bases.function_api decorator, which queries the current context and passes it into the first argument of the original function. The original function always takes a context as the first argument.

nnabla.functions.add_n(*x, **kw)[source]¶

Element-wise addition.

\[y_i = x^{(0)}_i + . . . + x^{(n-1)}_i\]

Parameters: *x (Variable) – N-D arrays [variadic]
Returns: N-D array
Return type: Variable

Note

All nnabla functions in nnabla.functions are decorated with the nnabla.function_bases.function_api decorator, which queries the current context and passes it into the first argument of the original function. The original function always takes a context as the first argument.

nnabla.functions.sub2(x0, x1, inplace=False, n_outputs=- 1, outputs=None)[source]¶

Element-wise subtraction.

\[y_i = x^{(0)}_i - x^{(1)}_i\]

Parameters

x0 (Variable) – N-D array
x1 (Variable) – N-D array
inplace (bool) – This option is obsolete and ignored. Output is never in-placed with input. [default= False ]

Returns

N-D array

Return type

Note

All nnabla functions in nnabla.functions are decorated with the nnabla.function_bases.function_api decorator, which queries the current context and passes it into the first argument of the original function. The original function always takes a context as the first argument.

nnabla.functions.mul2(x0, x1, inplace=False, n_outputs=- 1, outputs=None)[source]¶

Element-wise multiplication.

\[y_i = x^{(0)}_i x^{(1)}_i\]

Parameters

x0 (Variable) – N-D array
x1 (Variable) – N-D array
inplace (bool) – This option is obsolete and ignored. Output is never in-placed with input. [default= False ]

Returns

N-D array

Return type

Note

All nnabla functions in nnabla.functions are decorated with the nnabla.function_bases.function_api decorator, which queries the current context and passes it into the first argument of the original function. The original function always takes a context as the first argument.

nnabla.functions.mul_n(*x, **kw)[source]¶

Element-wise multiplication.

\[y_i = x^{(0)}_i . . . x^{(n-1)}_i\]

Parameters: *x (Variable) – N-D arrays [variadic]
Returns: N-D array
Return type: Variable

Note

All nnabla functions in nnabla.functions are decorated with the nnabla.function_bases.function_api decorator, which queries the current context and passes it into the first argument of the original function. The original function always takes a context as the first argument.

nnabla.functions.div2(x0, x1, inplace=False, n_outputs=- 1, outputs=None)[source]¶

Element-wise division.

\[y_i = \frac{x^{(0)}_i} {x^{(1)}_i}\]

Parameters

x0 (Variable) – N-D array
x1 (Variable) – N-D array
inplace (bool) – This option is obsolete and ignored. Output is never in-placed with input. [default= False ]

Returns

N-D array

Return type

Note

All nnabla functions in nnabla.functions are decorated with the nnabla.function_bases.function_api decorator, which queries the current context and passes it into the first argument of the original function. The original function always takes a context as the first argument.

nnabla.functions.pow2(x0, x1, inplace=False, n_outputs=- 1, outputs=None)[source]¶

Element-wise power function.

\[y_i = {(x^{(0)}_i)} ^ {x^{(1)}_i}\]

Parameters

x0 (Variable) – N-D array
x1 (Variable) – N-D array
inplace (bool) – This option is obsolete and ignored. Output is never in-placed with input. [default= False ]

Returns

N-D array

Return type

Note

All nnabla functions in nnabla.functions are decorated with the nnabla.function_bases.function_api decorator, which queries the current context and passes it into the first argument of the original function. The original function always takes a context as the first argument.

nnabla.functions.add_scalar(x, val=1, inplace=False, n_outputs=- 1, outputs=None)[source]¶

Element-wise scalar addition.

\[y_i = x_i + v\]

Parameters

x (Variable) – Input variable
val (float) – Value of the scalar [default= 1 ]
inplace (bool) – This option is obsolete and ignored. Output is never in-placed with input. [default= False ]

Returns

N-D array with the same shape as x

Return type

Note

All nnabla functions in nnabla.functions are decorated with the nnabla.function_bases.function_api decorator, which queries the current context and passes it into the first argument of the original function. The original function always takes a context as the first argument.

nnabla.functions.mul_scalar(x, val=1, inplace=False, n_outputs=- 1, outputs=None)[source]¶

Element-wise scalar multiplication.

\[y_i = v x_i\]

Parameters

x (Variable) – Input variable
val (float) – Value of the scalar [default= 1 ]
inplace (bool) – This option is obsolete and ignored. Output is never in-placed with input. [default= False ]

Returns

N-D array with the same shape as x

Return type

Note

All nnabla functions in nnabla.functions are decorated with the nnabla.function_bases.function_api decorator, which queries the current context and passes it into the first argument of the original function. The original function always takes a context as the first argument.

nnabla.functions.pow_scalar(x, val=1, inplace=False, n_outputs=- 1, outputs=None)[source]¶

Element-wise scalar power function.

\[y_i = (x_i) ^ v\]

Parameters

x (Variable) – Input variable
val (float) – Value of the scalar [default= 1 ]
inplace (bool) – This option is obsolete and ignored. Output is never in-placed with input. [default= False ]

Returns

N-D array with the same shape as x

Return type

Note

All nnabla functions in nnabla.functions are decorated with the nnabla.function_bases.function_api decorator, which queries the current context and passes it into the first argument of the original function. The original function always takes a context as the first argument.

nnabla.functions.r_sub_scalar(x, val=1, n_outputs=- 1, outputs=None)[source]¶

Element-wise scalar subtraction.

\[y_i = v - x_i\]

Parameters

x (Variable) – Input variable
val (float) – Value of the scalar [default= 1 ]

Returns

N-D array with the same shape as x

Return type

Note

All nnabla functions in nnabla.functions are decorated with the nnabla.function_bases.function_api decorator, which queries the current context and passes it into the first argument of the original function. The original function always takes a context as the first argument.

nnabla.functions.r_div_scalar(x, val=1, n_outputs=- 1, outputs=None)[source]¶

Element-wise scalar division.

\[y_i = \frac{v}{x_i}\]

Parameters

x (Variable) – Input variable
val (float) – Value of the scalar [default= 1 ]

Returns

N-D array with the same shape as x

Return type

Note

All nnabla functions in nnabla.functions are decorated with the nnabla.function_bases.function_api decorator, which queries the current context and passes it into the first argument of the original function. The original function always takes a context as the first argument.

nnabla.functions.r_pow_scalar(x, val=1, n_outputs=- 1, outputs=None)[source]¶

Element-wise scalar power function.

\[y_i = v ^ {x_i}\]

Parameters

x (Variable) – Input variable
val (float) – Value of the scalar [default= 1 ]

Returns

N-D array with the same shape as x

Return type

Note

All nnabla functions in nnabla.functions are decorated with the nnabla.function_bases.function_api decorator, which queries the current context and passes it into the first argument of the original function. The original function always takes a context as the first argument.

Logical¶

nnabla.functions.equal(x0, x1, n_outputs=- 1, outputs=None)[source]¶

Element wise ‘equal’

\[\begin{split}f(x^{(0)}_i,x^{(1)}_i) = \begin{cases} 1 & (x^{(0)}_i = x^{(1)}_i) \\ 0 & otherwise \end{cases}.\end{split}\]

Parameters

x0 (Variable) – N-D array
x1 (Variable) – N-D array

Returns

No Description

Return type

Note

All nnabla functions in nnabla.functions are decorated with the nnabla.function_bases.function_api decorator, which queries the current context and passes it into the first argument of the original function. The original function always takes a context as the first argument.

nnabla.functions.equal_scalar(x0, val=1, n_outputs=- 1, outputs=None)[source]¶

Element wise ‘equal’ with a scalar

\[\begin{split}f(x_i,v) = \begin{cases} 1 & (x_i = v) \\ 0 & otherwise \end{cases}.\end{split}\]

Parameters

x0 (Variable) – Input variable
val (float) – Value of the scalar [default= 1 ]

Returns

N-D array with the same shape as x

Return type

Note

All nnabla functions in nnabla.functions are decorated with the nnabla.function_bases.function_api decorator, which queries the current context and passes it into the first argument of the original function. The original function always takes a context as the first argument.

nnabla.functions.greater(x0, x1, n_outputs=- 1, outputs=None)[source]¶

Element wise comparison. The $i^{th}$ element of the output is:

\[\begin{split}f(x^{(0)}_i,x^{(1)}_i) = \begin{cases} 1 & (x^{(0)}_i > x^{(1)}_i) \\ 0 & (x^{(0)}_i \leq x^{(1)}_i) \end{cases}.\end{split}\]

Parameters

x0 (Variable) – N-D array
x1 (Variable) – N-D array

Returns

No Description

Return type

Note

All nnabla functions in nnabla.functions are decorated with the nnabla.function_bases.function_api decorator, which queries the current context and passes it into the first argument of the original function. The original function always takes a context as the first argument.

nnabla.functions.greater_equal(x0, x1, n_outputs=- 1, outputs=None)[source]¶

Element wise comparison. The $i^{th}$ element of the output is:

\[\begin{split}f(x^{(0)}_i,x^{(1)}_i) = \begin{cases} 1 & (x^{(0)}_i \geq x^{(1)}_i) \\ 0 & (x^{(0)}_i < x^{(1)}_i) \end{cases}.\end{split}\]

Parameters

x0 (Variable) – N-D array
x1 (Variable) – N-D array

Returns

No Description

Return type

Note

All nnabla functions in nnabla.functions are decorated with the nnabla.function_bases.function_api decorator, which queries the current context and passes it into the first argument of the original function. The original function always takes a context as the first argument.

nnabla.functions.greater_equal_scalar(x0, val=1, n_outputs=- 1, outputs=None)[source]¶

Element wise comparison with a scalar. The $i^{th}$ element of the output is:

\[\begin{split}f(x^{(0)}_i,v) = \begin{cases} 1 & (x^{(0)}_i \geq v \\ 0 & (x^{(0)}_i < v \end{cases}.\end{split}\]

Parameters

x0 (Variable) – Input variable
val (float) – Value of the scalar [default= 1 ]

Returns

N-D array with the same shape as x

Return type

Note

All nnabla functions in nnabla.functions are decorated with the nnabla.function_bases.function_api decorator, which queries the current context and passes it into the first argument of the original function. The original function always takes a context as the first argument.

nnabla.functions.greater_scalar(x0, val=1, n_outputs=- 1, outputs=None)[source]¶

Element wise comparison with a scalar. The $i^{th}$ element of the output is:

\[\begin{split}f(x^{(0)}_i,v) = \begin{cases} 1 & (x^{(0)}_i > v \\ 0 & (x^{(0)}_i \leq v \end{cases}.\end{split}\]

Parameters

x0 (Variable) – Input variable
val (float) – Value of the scalar [default= 1 ]

Returns

N-D array with the same shape as x

Return type

Note

All nnabla functions in nnabla.functions are decorated with the nnabla.function_bases.function_api decorator, which queries the current context and passes it into the first argument of the original function. The original function always takes a context as the first argument.

nnabla.functions.less(x0, x1, n_outputs=- 1, outputs=None)[source]¶

Element wise comparison. The $i^{th}$ element of the output is:

\[\begin{split}f(x^{(0)}_i,x^{(1)}_i) = \begin{cases} 1 & (x^{(0)}_i < x^{(1)}_i) \\ 0 & (x^{(0)}_i \geq x^{(1)}_i) \end{cases}.\end{split}\]

Parameters

x0 (Variable) – N-D array
x1 (Variable) – N-D array

Returns

No Description

Return type

Note

All nnabla functions in nnabla.functions are decorated with the nnabla.function_bases.function_api decorator, which queries the current context and passes it into the first argument of the original function. The original function always takes a context as the first argument.

nnabla.functions.less_equal(x0, x1, n_outputs=- 1, outputs=None)[source]¶

Element wise comparison. The $i^{th}$ element of the output is:

\[\begin{split}f(x^{(0)}_i,x^{(1)}_i) = \begin{cases} 1 & (x^{(0)}_i \leq x^{(1)}_i) \\ 0 & (x^{(0)}_i > x^{(1)}_i) \end{cases}.\end{split}\]

Parameters

x0 (Variable) – N-D array
x1 (Variable) – N-D array

Returns

No Description

Return type

Note

All nnabla functions in nnabla.functions are decorated with the nnabla.function_bases.function_api decorator, which queries the current context and passes it into the first argument of the original function. The original function always takes a context as the first argument.

nnabla.functions.less_equal_scalar(x0, val=1, n_outputs=- 1, outputs=None)[source]¶

Element wise comparison with a scalar. The $i^{th}$ element of the output is:

\[\begin{split}f(x^{(0)}_i,v) = \begin{cases} 1 & (x^{(0)}_i \leq v) \\ 0 & (x^{(0)}_i > v) \end{cases}.\end{split}\]

Parameters

x0 (Variable) – Input variable
val (float) – Value of the scalar [default= 1 ]

Returns

N-D array with the same shape as x

Return type

Note

All nnabla functions in nnabla.functions are decorated with the nnabla.function_bases.function_api decorator, which queries the current context and passes it into the first argument of the original function. The original function always takes a context as the first argument.

nnabla.functions.less_scalar(x0, val=1, n_outputs=- 1, outputs=None)[source]¶

Element wise comparison with a scalar. The $i^{th}$ element of the output is:

\[\begin{split}f(x^{(0)}_i,v) = \begin{cases} 1 & (x^{(0)}_i < v) \\ 0 & (x^{(0)}_i \geq v) \end{cases}.\end{split}\]

Parameters

x0 (Variable) – Input variable
val (float) – Value of the scalar [default= 1 ]

Returns

N-D array with the same shape as x

Return type

Note

All nnabla functions in nnabla.functions are decorated with the nnabla.function_bases.function_api decorator, which queries the current context and passes it into the first argument of the original function. The original function always takes a context as the first argument.

nnabla.functions.logical_and(x0, x1, n_outputs=- 1, outputs=None)[source]¶

Elementwise logical AND.

\[\begin{split}f(x^{(0)}_i,x^{(1)}_i) = \begin{cases} 1 & (x^{(0)}_i \neq 0 \;\&\; x^{(1)}_i \neq 0) \\ 0 & otherwise \end{cases}.\end{split}\]

Parameters

x0 (Variable) – N-D array
x1 (Variable) – N-D array

Returns

No Description

Return type

Note

All nnabla functions in nnabla.functions are decorated with the nnabla.function_bases.function_api decorator, which queries the current context and passes it into the first argument of the original function. The original function always takes a context as the first argument.

nnabla.functions.logical_and_scalar(x0, val, n_outputs=- 1, outputs=None)[source]¶

Elementwise logical AND with scalar.

\[\begin{split}f(x_i,v) = \begin{cases} 1 & (x_i \neq 0 \;\&\; v \neq 0) \\ 0 & otherwise \end{cases}.\end{split}\]

Parameters

x0 (Variable) – Input variable
val (bool) – No Description

Returns

N-D array with the same shape as x

Return type

Note

All nnabla functions in nnabla.functions are decorated with the nnabla.function_bases.function_api decorator, which queries the current context and passes it into the first argument of the original function. The original function always takes a context as the first argument.

nnabla.functions.logical_not(x0, n_outputs=- 1, outputs=None)[source]¶

Element-wise logical NOT operation

\[\begin{split}f(x_i) = \begin{cases} 1 & (x_i = 0) \\ 0 & otherwise \end{cases}.\end{split}\]

Parameters: x0 (Variable) – Input variable
Returns: N-D array with the same shape as x
Return type: Variable

Note

All nnabla functions in nnabla.functions are decorated with the nnabla.function_bases.function_api decorator, which queries the current context and passes it into the first argument of the original function. The original function always takes a context as the first argument.

nnabla.functions.logical_or(x0, x1, n_outputs=- 1, outputs=None)[source]¶

Elementwise logical OR.

\[\begin{split}f(x^{(0)}_i,x^{(1)}_i) = \begin{cases} 0 & (x^{(0)}_i = 0 \;\&\; x^{(1)}_i = 0) \\ 1 & otherwise \end{cases}.\end{split}\]

Parameters

x0 (Variable) – N-D array
x1 (Variable) – N-D array

Returns

No Description

Return type

Note

All nnabla functions in nnabla.functions are decorated with the nnabla.function_bases.function_api decorator, which queries the current context and passes it into the first argument of the original function. The original function always takes a context as the first argument.

nnabla.functions.logical_or_scalar(x0, val, n_outputs=- 1, outputs=None)[source]¶

Elementwise logical OR with scalar.

\[\begin{split}f(x_i,v) = \begin{cases} 0 & (x_i = 0 \;\&\; v = 0) \\ 1 & otherwise \end{cases}.\end{split}\]

Parameters

x0 (Variable) – Input variable
val (bool) – No Description

Returns

N-D array with the same shape as x

Return type

Note

All nnabla functions in nnabla.functions are decorated with the nnabla.function_bases.function_api decorator, which queries the current context and passes it into the first argument of the original function. The original function always takes a context as the first argument.

nnabla.functions.logical_xor(x0, x1, n_outputs=- 1, outputs=None)[source]¶

Elementwise logical XOR.

\[\begin{split}f(x^{(0)}_i,x^{(1)}_i) = \begin{cases} 1 & (x^{(0)}_i = 0 \;\&\; x^{(1)}_i = 0) \\ 1 & (x^{(0)}_i \neq 0 \;\&\; x^{(1)}_i \neq 0) \\ 0 & otherwise \end{cases}.\end{split}\]

Parameters

x0 (Variable) – N-D array
x1 (Variable) – N-D array

Returns

No Description

Return type

Note

All nnabla functions in nnabla.functions are decorated with the nnabla.function_bases.function_api decorator, which queries the current context and passes it into the first argument of the original function. The original function always takes a context as the first argument.

nnabla.functions.logical_xor_scalar(x0, val, n_outputs=- 1, outputs=None)[source]¶

Elementwise logical XOR with scalar.

\[\begin{split}f(x_i,v) = \begin{cases} 1 & (x_i = 0 \;\&\; v = 0) \\ 1 & (x_i \neq 0 \;\&\; v \neq 0) \\ 0 & otherwise \end{cases}.\end{split}\]

Parameters

x0 (Variable) – Input variable
val (bool) – No Description

Returns

N-D array with the same shape as x

Return type

Note

All nnabla functions in nnabla.functions are decorated with the nnabla.function_bases.function_api decorator, which queries the current context and passes it into the first argument of the original function. The original function always takes a context as the first argument.

nnabla.functions.not_equal(x0, x1, n_outputs=- 1, outputs=None)[source]¶

Element wise ‘not equal’

\[\begin{split}f(x^{(0)}_i,x^{(1)}_i) = \begin{cases} 0 & (x^{(0)}_i = x^{(1)}_i) \\ 1 & otherwise \end{cases}.\end{split}\]

Parameters

x0 (Variable) – N-D array
x1 (Variable) – N-D array

Returns

No Description

Return type

Note

All nnabla functions in nnabla.functions are decorated with the nnabla.function_bases.function_api decorator, which queries the current context and passes it into the first argument of the original function. The original function always takes a context as the first argument.

nnabla.functions.not_equal_scalar(x0, val=1, n_outputs=- 1, outputs=None)[source]¶

Element wise ‘not equal’ with a scalar

\[\begin{split}f(x_i,v) = \begin{cases} 0 & (x_i = v) \\ 1 & otherwise \end{cases}.\end{split}\]

Parameters

x0 (Variable) – Input variable
val (float) – Value of the scalar [default= 1 ]

Returns

N-D array with the same shape as x

Return type

Note

All nnabla functions in nnabla.functions are decorated with the nnabla.function_bases.function_api decorator, which queries the current context and passes it into the first argument of the original function. The original function always takes a context as the first argument.

nnabla.functions.sign(x, alpha=1.0, n_outputs=- 1, outputs=None)[source]¶

Element-wise sign function.

In the forward pass, it is defined as

\[\begin{split}f(x) = \begin{cases} 1 & (x > 0) \\ -1 & (x < 0) \\ \alpha & (x = 0) \end{cases}.\end{split}\]

In the backward pass, it is defined as

\[\frac{\partial f(x)}{\partial x} = 1,\]

or in other words, it behaves as the identity function for the gradient in the backward pass.

Parameters

x (Variable) – Input
alpha (float) – Value in case of $x = 0$. [default= 1.0 ]

Returns

N-D array with the same shape as x

Return type

Note

All nnabla functions in nnabla.functions are decorated with the nnabla.function_bases.function_api decorator, which queries the current context and passes it into the first argument of the original function. The original function always takes a context as the first argument.

nnabla.functions.minimum2(x0, x1, n_outputs=- 1, outputs=None)[source]¶

Element-wise minimum.

\[y_i = \min(x^{(0)}_i, x^{(1)}_i)\]

Parameters

x0 (Variable) – N-D array
x1 (Variable) – N-D array

Returns

N-D array of min value

Return type

Note

All nnabla functions in nnabla.functions are decorated with the nnabla.function_bases.function_api decorator, which queries the current context and passes it into the first argument of the original function. The original function always takes a context as the first argument.

nnabla.functions.maximum2(x0, x1, n_outputs=- 1, outputs=None)[source]¶

Element-wise maximum.

\[y_i = \max(x^{(0)}_i, x^{(1)}_i)\]

Parameters

x0 (Variable) – N-D array
x1 (Variable) – N-D array

Returns

N-D array of max value

Return type

Note

All nnabla functions in nnabla.functions are decorated with the nnabla.function_bases.function_api decorator, which queries the current context and passes it into the first argument of the original function. The original function always takes a context as the first argument.

nnabla.functions.minimum_scalar(x, val=1.0, n_outputs=- 1, outputs=None)[source]¶

Element-wise scalar minimum.

\[y_i = \min(x_i, v)\]

Parameters

x (Variable) – Input variable
val (float) – Value of the scalar [default= 1.0 ]

Returns

N-D array with the same shape as x

Return type

Note

All nnabla functions in nnabla.functions are decorated with the nnabla.function_bases.function_api decorator, which queries the current context and passes it into the first argument of the original function. The original function always takes a context as the first argument.

nnabla.functions.maximum_scalar(x, val=1.0, n_outputs=- 1, outputs=None)[source]¶

Element-wise scalar maximum.

\[y_i = \max (x_i, v)\]

Parameters

x (Variable) – Input variable
val (float) – Value of the scalar [default= 1.0 ]

Returns

N-D array with the same shape as x

Return type

Note

All nnabla functions in nnabla.functions are decorated with the nnabla.function_bases.function_api decorator, which queries the current context and passes it into the first argument of the original function. The original function always takes a context as the first argument.

nnabla.functions.isnan(x0, n_outputs=- 1, outputs=None)[source]¶

Test element-wise for NaN and return a 0/1 array.

Parameters: x0 (Variable) – Input variable
Returns: N-D array with the same shape as x
Return type: Variable

Note

All nnabla functions in nnabla.functions are decorated with the nnabla.function_bases.function_api decorator, which queries the current context and passes it into the first argument of the original function. The original function always takes a context as the first argument.

nnabla.functions.isinf(x0, n_outputs=- 1, outputs=None)[source]¶

Test element-wise for inf/-inf and return a 0/1 array.

Parameters: x0 (Variable) – Input variable
Returns: N-D array with the same shape as x
Return type: Variable

Note

All nnabla functions in nnabla.functions are decorated with the nnabla.function_bases.function_api decorator, which queries the current context and passes it into the first argument of the original function. The original function always takes a context as the first argument.

nnabla.functions.reset_nan(x0, val=0, n_outputs=- 1, outputs=None)[source]¶

Replace NaNs with a scalar value specified by val.

Parameters

x0 (Variable) – Input variable
val (float) – Value of the scalar [default= 0 ]

Returns

N-D array with the same shape as x

Return type

Note

All nnabla functions in nnabla.functions are decorated with the nnabla.function_bases.function_api decorator, which queries the current context and passes it into the first argument of the original function. The original function always takes a context as the first argument.

nnabla.functions.reset_inf(x0, val=0, n_outputs=- 1, outputs=None)[source]¶

Replace -inf/inf with a scalar value specified by val.

Parameters

x0 (Variable) – Input variable
val (float) – Value of the scalar [default= 0 ]

Returns

N-D array with the same shape as x

Return type

Note

All nnabla functions in nnabla.functions are decorated with the nnabla.function_bases.function_api decorator, which queries the current context and passes it into the first argument of the original function. The original function always takes a context as the first argument.

nnabla.functions.where(condition, x_true, x_false, n_outputs=- 1, outputs=None)[source]¶

Return elements, either from x_true or x_false, depending on condition.

If rank of condition is higher than those of x_true and x_false, the first dimensions of x_true and x_false must match the dimensions of condition.

Example:

import numpy as np
import nnabla as nn
import nnabla.functions as F

a = nn.Variable.from_numpy_array(np.random.rand(2, 3))
x = nn.Variable.from_numpy_array(np.random.rand(2, 3, 4))
y = nn.Variable.from_numpy_array(np.random.rand(2, 3, 4))
z = F.where(F.greater_scalar(a, 0.5), x, y)
z.forward()

# Numpy equivalent
z_numpy = np.where(a.d > 0.5, x.d, y.d)
assert np.allclose(z_numpy, z.d)

Parameters

condition (Variable) – N-d array. For all i, when condition[i] == true, yield x_true[i], otherwise x_false[i].
x_true (Variable) – N-d array with higher or equal rank to condition.
x_false (Variable) – N-d array with higher or equal rank to condition.

Returns

N-D array with the same shape as condition

Return type

Note

All nnabla functions in nnabla.functions are decorated with the nnabla.function_bases.function_api decorator, which queries the current context and passes it into the first argument of the original function. The original function always takes a context as the first argument.

Math¶

nnabla.functions.constant(val=0, shape=[], n_outputs=- 1, outputs=None)[source]¶

Generate a constant-valued array.

Parameters

val (float) – Constant value. [default= 0 ]
shape (tuple of int) – Shape of the output array. [default= [] ]

Returns

N-D array where all values are the specified constant.

Return type

Note

All nnabla functions in nnabla.functions are decorated with the nnabla.function_bases.function_api decorator, which queries the current context and passes it into the first argument of the original function. The original function always takes a context as the first argument.

nnabla.functions.arange(start, stop, step=1, n_outputs=- 1, outputs=None)[source]¶

Generate a range of values within the half-open interval [start, stop) (the interval including start but excluding stop) with step increments.

Parameters

start (float) – Start value.
stop (float) – End value.
step (float) – Step value. [default= 1 ]

Returns

1-D array with the generated values.

Return type

Note

All nnabla functions in nnabla.functions are decorated with the nnabla.function_bases.function_api decorator, which queries the current context and passes it into the first argument of the original function. The original function always takes a context as the first argument.

nnabla.functions.abs(x, n_outputs=- 1, outputs=None)[source]¶

Element-wise absolute value function.

\[y_i = |x_i|\]

Parameters: x (Variable) – Input variable
Returns: Element-wise absolute variable
Return type: Variable

Note

All nnabla functions in nnabla.functions are decorated with the nnabla.function_bases.function_api decorator, which queries the current context and passes it into the first argument of the original function. The original function always takes a context as the first argument.

nnabla.functions.exp(x, n_outputs=- 1, outputs=None)[source]¶

Element-wise natural exponential function.

\[y_i = \exp(x_i).\]

Parameters: x (Variable) – Input variable
Returns: Element-wise exp variable
Return type: Variable

Note

All nnabla functions in nnabla.functions are decorated with the nnabla.function_bases.function_api decorator, which queries the current context and passes it into the first argument of the original function. The original function always takes a context as the first argument.

nnabla.functions.log(x, n_outputs=- 1, outputs=None)[source]¶

Element-wise natural logarithm function.

\[y_i = \ln(x_i).\]

Parameters: x (Variable) – Input variable
Returns: Element-wise log variable
Return type: Variable

Note

All nnabla functions in nnabla.functions are decorated with the nnabla.function_bases.function_api decorator, which queries the current context and passes it into the first argument of the original function. The original function always takes a context as the first argument.

nnabla.functions.round(x, n_outputs=- 1, outputs=None)[source]¶

Element-wise round function.

In the forward pass, this function simply computes round to the nearest integer value.

\[y_i = round(x_i).\]

In the backward pass, the simple Straight-Through Estimator (STE) is applied,

\[\frac{\partial y_i}{\partial x_i} = 1.\]

Parameters: x (Variable) – Input variable
Returns: N-D array with the same shape as x
Return type: Variable

Note

All nnabla functions in nnabla.functions are decorated with the nnabla.function_bases.function_api decorator, which queries the current context and passes it into the first argument of the original function. The original function always takes a context as the first argument.

nnabla.functions.ceil(x, n_outputs=- 1, outputs=None)[source]¶

Element-wise ceil function.

In the forward pass, this function simply returns the smallest integer which is not less than the input.

\[y_i = ceil(x_i).\]

In the backward pass, the simple Straight-Through Estimator (STE) is applied,

\[\frac{\partial y_i}{\partial x_i} = 1.\]

Parameters: x (Variable) – Input variable
Returns: N-D array with the same shape as x
Return type: Variable

Note

All nnabla functions in nnabla.functions are decorated with the nnabla.function_bases.function_api decorator, which queries the current context and passes it into the first argument of the original function. The original function always takes a context as the first argument.

nnabla.functions.floor(x, n_outputs=- 1, outputs=None)[source]¶

Element-wise floor function.

In the forward pass, this function simply returns the largest integer which is not greater than the input.

\[y_i = floor(x_i).\]

In the backward pass, the simple Straight-Through Estimator (STE) is applied,

\[\frac{\partial y_i}{\partial x_i} = 1.\]

Parameters: x (Variable) – Input variable
Returns: N-D array with the same shape as x
Return type: Variable

Note

All nnabla functions in nnabla.functions are decorated with the nnabla.function_bases.function_api decorator, which queries the current context and passes it into the first argument of the original function. The original function always takes a context as the first argument.

nnabla.functions.identity(x, n_outputs=- 1, outputs=None)[source]¶

Identity function.

\[y = x\]

Parameters: x (Variable) – N-D array.
Returns: N-D array
Return type: Variable

Note

All nnabla functions in nnabla.functions are decorated with the nnabla.function_bases.function_api decorator, which queries the current context and passes it into the first argument of the original function. The original function always takes a context as the first argument.

nnabla.functions.matrix_diag(x, n_outputs=- 1, outputs=None)[source]¶

Returns an array where the last two dimensions consist of the diagonal matrix.

Parameters: x (Variable) – N-D array with shape ($M_0 \times \ldots \times M_N$).
Returns: N-D array with shape ($M_0 \times \ldots \times M_N \times M_N$).
Return type: Variable

Note

All nnabla functions in nnabla.functions are decorated with the nnabla.function_bases.function_api decorator, which queries the current context and passes it into the first argument of the original function. The original function always takes a context as the first argument.

nnabla.functions.matrix_diag_part(x, n_outputs=- 1, outputs=None)[source]¶

Returns an array in which the values of the last dimension consist of the diagonal elements of the last two dimensions of an input array.

Parameters: x (Variable) – N-D array with shape ($M_0 \times \ldots \times M_N \times M_N$).
Returns: N-D array with shape ($M_0 \times \ldots \times M_N$).
Return type: Variable

Note

All nnabla functions in nnabla.functions are decorated with the nnabla.function_bases.function_api decorator, which queries the current context and passes it into the first argument of the original function. The original function always takes a context as the first argument.

nnabla.functions.batch_matmul(a, b, transpose_a=False, transpose_b=False, n_outputs=- 1, outputs=None)[source]¶

Batch matrix multiplication.

Two of batchs of matrices are multiplied for each sample in a batch. A batch of matrices is composed as […, P, Q] where the last two dimensions compose matrix dimensions, and the first dimensions up to the third last dimension are considered as batch samples. These batch dimensions are internally broadcasted when the size of a dimension is 1.

Example:

import nnabla as nn
import nnabla.functions as F
import numpy as np

nn.set_auto_forward(True)

# Same batch size
a = nn.Variable.from_numpy_array(np.random.rand(2, 2, 3, 4))
b = nn.Variable.from_numpy_array(np.random.rand(2, 2, 4, 3))
c = F.batch_matmul(a, b)

# Different batch size with the broadcast
a = nn.Variable.from_numpy_array(np.random.rand(2, 1, 3, 4))
b = nn.Variable.from_numpy_array(np.random.rand(1, 3, 4, 3))
c = F.batch_matmul(a, b)

Warning

Since the version 1.13, the behavior of the batch dimensions changed, it supported the internal broadcast when the size of a dimension is 1. Accordingly, this function does not supports different batch dimensions between two inputs even if the total sample size for each input is same.

Parameters

a (Variable) – N-D array with >= 2-dim. The last two dimensions will be treated as a matrix.
b (Variable) – N-D array with >= 2-dim. The last two dimensions will be treated as a matrix. The product of the size of 0-th dimension through the size of the third last dimension must be same as that of the input a.
transpose_a (bool) – Transpose the last two axes of a in matrix multiplication. [default= False ]
transpose_b (bool) – Transpose the last two axes of b in matrix multiplication. [default= False ]

Returns

Output of sample-wise matrix multiplication in a batch. When a is of a shape of [N, P, Q], b is of a shape of [N, Q, R], and transpose options are all False, the output will be a shape of [N, P, R].

Return type

Note

All nnabla functions in nnabla.functions are decorated with the nnabla.function_bases.function_api decorator, which queries the current context and passes it into the first argument of the original function. The original function always takes a context as the first argument.

nnabla.functions.sin(x, n_outputs=- 1, outputs=None)[source]¶

Element-wise sine (sin) function.

\[y_i = \sin (x_i)\]

Parameters: x (Variable) – N-D array
Returns: N-D array with the same shape as x
Return type: Variable

Note

All nnabla functions in nnabla.functions are decorated with the nnabla.function_bases.function_api decorator, which queries the current context and passes it into the first argument of the original function. The original function always takes a context as the first argument.

nnabla.functions.cos(x, n_outputs=- 1, outputs=None)[source]¶

Element-wise cosine (cos) function.

\[y_i = \cos (x_i)\]

Parameters: x (Variable) – N-D array
Returns: N-D array with the same shape as x
Return type: Variable

Note

All nnabla functions in nnabla.functions are decorated with the nnabla.function_bases.function_api decorator, which queries the current context and passes it into the first argument of the original function. The original function always takes a context as the first argument.

nnabla.functions.tan(x, n_outputs=- 1, outputs=None)[source]¶

Element-wise tangent (tan) function.

\[y_i = \tan (x_i)\]

Parameters: x (Variable) – N-D array
Returns: N-D array with the same shape as x
Return type: Variable

Note

All nnabla functions in nnabla.functions are decorated with the nnabla.function_bases.function_api decorator, which queries the current context and passes it into the first argument of the original function. The original function always takes a context as the first argument.

nnabla.functions.sinh(x, n_outputs=- 1, outputs=None)[source]¶

Element-wise hyperbolic sine (sinh) function.

\[y_i = \sinh (x_i)\]

Parameters: x (Variable) – N-D array
Returns: N-D array with the same shape as x
Return type: Variable

Note

All nnabla functions in nnabla.functions are decorated with the nnabla.function_bases.function_api decorator, which queries the current context and passes it into the first argument of the original function. The original function always takes a context as the first argument.

nnabla.functions.cosh(x, n_outputs=- 1, outputs=None)[source]¶

Element-wise hyperbolic cosine (cosh) function.

\[y_i = \cosh (x_i)\]

Parameters: x (Variable) – N-D array
Returns: N-D array with the same shape as x
Return type: Variable

Note

All nnabla functions in nnabla.functions are decorated with the nnabla.function_bases.function_api decorator, which queries the current context and passes it into the first argument of the original function. The original function always takes a context as the first argument.

nnabla.functions.tanh(x, n_outputs=- 1, outputs=None)[source]¶

Element-wise hyperbolic tangent (tanh) function.

\[y_i = \tanh (x_i)\]

Parameters: x (Variable) – N-D array
Returns: N-D array with the same shape as x
Return type: Variable

Note

All nnabla functions in nnabla.functions are decorated with the nnabla.function_bases.function_api decorator, which queries the current context and passes it into the first argument of the original function. The original function always takes a context as the first argument.

nnabla.functions.asin(x, n_outputs=- 1, outputs=None)[source]¶

Element-wise arcsine (asin) function.

\[y_i = \arcsin (x_i)\]

Parameters: x (Variable) – N-D array
Returns: N-D array with the same shape as x
Return type: Variable

Note

All nnabla functions in nnabla.functions are decorated with the nnabla.function_bases.function_api decorator, which queries the current context and passes it into the first argument of the original function. The original function always takes a context as the first argument.

nnabla.functions.acos(x, n_outputs=- 1, outputs=None)[source]¶

Element-wise arccosine (acos) function.

\[y_i = \arccos (x_i)\]

Parameters: x (Variable) – N-D array
Returns: N-D array with the same shape as x
Return type: Variable

Note

All nnabla functions in nnabla.functions are decorated with the nnabla.function_bases.function_api decorator, which queries the current context and passes it into the first argument of the original function. The original function always takes a context as the first argument.

nnabla.functions.atan(x, n_outputs=- 1, outputs=None)[source]¶

Element-wise arctangent (atan) function.

\[y_i = \arctan (x_i)\]

Parameters: x (Variable) – N-D array
Returns: N-D array with the same shape as x
Return type: Variable

Note

All nnabla functions in nnabla.functions are decorated with the nnabla.function_bases.function_api decorator, which queries the current context and passes it into the first argument of the original function. The original function always takes a context as the first argument.

nnabla.functions.atan2(x0, x1, n_outputs=- 1, outputs=None)[source]¶

Element-wise arctangent (atan) function with 2 input variables.

\[y_i = \arctan2 (x_{i1}, x_{i2})\]

Parameters

x0 (Variable) – N-D array
x1 (Variable) – N-D array

Returns

N-D array with the same shape as input variables

Return type

Note

All nnabla functions in nnabla.functions are decorated with the nnabla.function_bases.function_api decorator, which queries the current context and passes it into the first argument of the original function. The original function always takes a context as the first argument.

nnabla.functions.asinh(x, n_outputs=- 1, outputs=None)[source]¶

Element-wise hyperbolic arcsine (asinh) function.

\[y_i = \text{arcsinh} (x_i)\]

Parameters: x (Variable) – N-D array
Returns: N-D array with the same shape as x
Return type: Variable

Note

All nnabla functions in nnabla.functions are decorated with the nnabla.function_bases.function_api decorator, which queries the current context and passes it into the first argument of the original function. The original function always takes a context as the first argument.

nnabla.functions.acosh(x, n_outputs=- 1, outputs=None)[source]¶

Element-wise hyperbolic arccosine (acosh) function.

\[y_i = \text{arccosh} (x_i)\]

Parameters: x (Variable) – N-D array
Returns: N-D array with the same shape as x
Return type: Variable

Note

All nnabla functions in nnabla.functions are decorated with the nnabla.function_bases.function_api decorator, which queries the current context and passes it into the first argument of the original function. The original function always takes a context as the first argument.

nnabla.functions.atanh(x, n_outputs=- 1, outputs=None)[source]¶

Element-wise hyperbolic arctangent (atanh) function.

\[y_i = \text{arctanh} (x_i)\]

Parameters: x (Variable) – N-D array
Returns: N-D array with the same shape as x
Return type: Variable

Note

All nnabla functions in nnabla.functions are decorated with the nnabla.function_bases.function_api decorator, which queries the current context and passes it into the first argument of the original function. The original function always takes a context as the first argument.

nnabla.functions.cumsum(x, axis=None, exclusive=False, reverse=False, n_outputs=- 1, outputs=None)[source]¶

Cumulative sum along a given axis.

Parameters

x (Variable) – N-D array.
axis (int) – Axis along which cumulative sum is to be calculated [default= 0 ]
exclusive (bool) – If True, perform exclusive cumsum [default= False ]
reverse (bool) – If True, perform cumsum in reverse direction [default= False ]

Returns

N-D array

Return type

Note

All nnabla functions in nnabla.functions are decorated with the nnabla.function_bases.function_api decorator, which queries the current context and passes it into the first argument of the original function. The original function always takes a context as the first argument.

nnabla.functions.cumprod(x, axis=None, exclusive=False, reverse=False, n_outputs=- 1, outputs=None)[source]¶

Cumulative product along a given axis.

Note

Backward computation is not accurate in a zero value input.

Parameters

x (Variable) – N-D array.
axis (int) – Axis along which cumulative product is to be calculated [default= 0 ]
exclusive (bool) – If True, perform exclusive cumprod [default= False ]
reverse (bool) – If True, perform cumprod in reverse direction [default= False ]

Returns

N-D array

Return type

Note

All nnabla functions in nnabla.functions are decorated with the nnabla.function_bases.function_api decorator, which queries the current context and passes it into the first argument of the original function. The original function always takes a context as the first argument.

nnabla.functions.batch_inv(x, n_outputs=- 1, outputs=None)[source]¶

Returns an array of inverted matrix

Parameters: x (Variable) – batched N-D array
Returns: batched N-D array of inverted matrix
Return type: Variable

Note

All nnabla functions in nnabla.functions are decorated with the nnabla.function_bases.function_api decorator, which queries the current context and passes it into the first argument of the original function. The original function always takes a context as the first argument.

nnabla.functions.batch_det(x, n_outputs=- 1, outputs=None)[source]¶

Batch-wise determinant function.

\[Y_b = \det(X_b),\]

where $X_b$ and $Y_b$ are the $b$-th input and output, respectively.

Parameters: x (Variable) – batched N-D array
Returns: batched N-D array of determinant
Return type: Variable

Note

All nnabla functions in nnabla.functions are decorated with the nnabla.function_bases.function_api decorator, which queries the current context and passes it into the first argument of the original function. The original function always takes a context as the first argument.

nnabla.functions.batch_logdet(x, n_outputs=- 1, outputs=None)[source]¶

Batch-wise log absolute determinant function.

\[Y_b = \log(|\det(X_b)|),\]

where $X_b$ and $Y_b$ are the $b$-th input and output, respectively.

Parameters: x (Variable) – batched N-D array
Returns: batched N-D array of log absolute determinant
Return type: Variable

Note

All nnabla functions in nnabla.functions are decorated with the nnabla.function_bases.function_api decorator, which queries the current context and passes it into the first argument of the original function. The original function always takes a context as the first argument.

Array Manipulation¶

nnabla.functions.concatenate(*x, **kw)[source]¶

Concatenate a variable number of input arrays along the specified axis.

Parameters

*x (Variable) – N-D arrays. [variadic]
axis (int) – Axis [default= len(x[0].shape) - 1 ]

Returns

Concatenate variable

Return type

Note

All nnabla functions in nnabla.functions are decorated with the nnabla.function_bases.function_api decorator, which queries the current context and passes it into the first argument of the original function. The original function always takes a context as the first argument.

nnabla.functions.split(x, axis=0)[source]¶

Split arrays at the specified axis.

It returns a number corresponding the size of the given axis (i.e x.shape[axis]) of Variable s.

Parameters

x (Variable) – N-D array
axis (int) – Axis

Returns: A tuple of Variable s

See also

nnabla.function_bases.split().

nnabla.functions.stack(*x, **kw)[source]¶

Joins two or more arrays on a new axis.

Note

Unlike nnabla.functions.concatenate() , which joins arrays on an existing axis, Stack joins arrays on a new axis.

Parameters

*x (Variable) – N-D arrays. The sizes of all the arrays to be stacked must be the same. [variadic]
axis (int) – The axis on which to concatenate arrays. Axis indices take on values 0, 1, 2, and so on from the left. For example, to stack four (3,28,28) inputs on the second axis, specify 1. In this case, the output size will be (3,4,28,28). [default= 0 ]

Returns

Output

Return type

Note

All nnabla functions in nnabla.functions are decorated with the nnabla.function_bases.function_api decorator, which queries the current context and passes it into the first argument of the original function. The original function always takes a context as the first argument.

nnabla.functions.slice(x, start=None, stop=None, step=None, n_outputs=- 1, outputs=None)[source]¶

Slice arrays along specified axis. This function complies with python slice wherre slice(None, None, -1) and slice(-1, None, -1) are the special case, which flips the input array and results in the output array from the end to the beginning of the input array along the corresponding dimension.

Parameters

x (Variable) – N-D array
start (repeated int64) – Start indices for each axis [default=``(0,) * len(x.shape)``]
stop (repeated int64) – Stop indices for each axis [default=``tuple(x.shape)``]
step (repeated int64) – Step indices for each axis [default=``(1,) * len(x.shape)``]

Returns

Sliced N-D array

Return type

Note

All nnabla functions in nnabla.functions are decorated with the nnabla.function_bases.function_api decorator, which queries the current context and passes it into the first argument of the original function. The original function always takes a context as the first argument.

nnabla.functions.gather(x, Indices, axis=None, batch_dims=None, n_outputs=- 1, outputs=None)[source]¶

Gather from the input data according to the index.

Given the input data $X$ of $(D_{0}, \ldots, D_{N-1})$ shape and the indices $IDX$ of $(I_{0}, \ldots, I_{M-1})$ shape, in case of batch_dims = 0, the gather outputs

\[\begin{split}&& Y[d_{0}, \ldots, d_{axis - 1}, i_{0}, \ldots, i_{M-1}, d_{axis + 1}, \ldots, d_{N-1}] = \\ && X[d_{0}, \ldots, d_{axis - 1}, IDX[i_{0}, \ldots, i_{M-1}], d_{axis + 1}, \ldots, d_{N-1}].\end{split}\]

Generally, the gather ouptuts

\[\begin{split}&& Y[d_{0}, \ldots, d_{axis - 1}, i_{B}, \ldots, i_{M-1}, d_{axis + 1}, \ldots, d_{N-1}] = \\ && X[d_{0}, \ldots, d_{axis - 1}, IDX[i_{0}, \ldots, i_{B - 1}, i_{B} \ldots, i_{M-1}], d_{axis + 1}, \ldots d_{N-1}].\end{split}\]

where $B$ = batch_dims.

x.shape[:batch_dims] must be equal to indices.shape[:batch_dims].

Output shape is x.shape[:axis] + indices.shape[batch_dims:] + x.shape[axis + 1].

Parameters

x (Variable) – Data from which to gather.
Indices (Variable) – Index with which to gather.
axis (int) – Axis in x to gather from. axis must be greater than or equal to batch_dims. [default= 0 ]
batch_dims (int) – The number of batch dimensions. [default= 0 ]

Returns

Gathered output.

Return type

Note

All nnabla functions in nnabla.functions are decorated with the nnabla.function_bases.function_api decorator, which queries the current context and passes it into the first argument of the original function. The original function always takes a context as the first argument.

nnabla.functions.gather_nd(data, indices)[source]¶

Gather elements or slices from data according to indices, which must be at least two-dimensional with the first dimension $M$ being less or equal to the $N$ dimensions of data. Given data with shape $(X_0, X_1, ..., X_{N-1})$ and indices with shape $(M, Y_0, ..., Y_{K-1})$ output has shape $(Y_0, ..., Y_{K-1}, X_M, ..., X_{N-1})$. If $M == N$, output shape is simply $(Y_0, ..., Y_{K-1})$.

The forward of gather_nd() is equivalent to:

def gather_nd(data, index):
    import numpy as np
    tmp_index = index.reshape(index.shape[0], -1)
    tmp_index = (idx + (Ellipsis,) for idx in zip(*new_index))
    out_shape = index.shape[1:] + data.shape[index.shape[0]:]
    return np.vstack(data[idx] for idx in tmp_index).reshape(*out_shape)

Examples:

>>> import numpy as np, nnabla as nn, nnabla.functions as F
>>> nn.set_auto_forward(True)
>>> data = F.arange(1, 11).reshape([2, 5])
>>> print(data.d)
[[ 1.  2.  3.  4.  5.]
 [ 6.  7.  8.  9. 10.]]
>>> F.gather_nd(data, [[1, 1, 0]]).shape
(3, 5)
>>> F.gather_nd(data, [[1, 1, 0], [0, 1, 0]]).shape
(3,)
>>> print(F.gather_nd(data, [[1, 1, 0], [0, 1, 0]]).d)
[6. 7. 1.]
>>> print(F.gather_nd(data, [[1, 1, 0]]).d)
[[ 6.  7.  8.  9. 10.]
 [ 6.  7.  8.  9. 10.]
 [ 1.  2.  3.  4.  5.]]

When indices is provided as a Variable it will be possible to change the actual index values after function creation. It is important to note that out-of-bound indices raise errors when running on CPU but are ignored when using an accelerated computation context.

>>> indices = nn.Variable((2, 1))
>>> indices.d = [[0], [0]]
>>> y = F.gather_nd(data, indices)
>>> print(y.d)
[1.]
>>> indices.d = [[1], [4]]
>>> y.forward()
>>> print(y.d)
[10.]

Parameters

data (Variable, NdArray) – input data
indices (list, numpy.ndarray, Variable, NdArray) – gather indices

Returns: ~nnabla.Variable or ~nnabla.NdArray of gathered elements.

nnabla.functions.scatter_nd(data, indices, shape=None, out=None, add=False)[source]¶

Scatter data according to indices into a new array of given shape or an existing array provided as out. Exactly one of the shape or out argument must be given. Given output shape, or shape of out array, $(X_0,X_1,\ldots,X_{N-1})$ and indices shape $(M,Y_0,\ldots,Y_{K-1})$ the input data shape is $(Y_0,\ldots,Y_{K-1},X_M,\ldots,X_{N-1})$, where $M<=N$. If $M==N$ the data shape is simply $(Y_0,\ldots,Y_{K-1})$. Note that indices are treated as integers and potentially converted.

The forward of scatter_nd() is equivalent to:

def scatter_nd(data, indices, shape=None, out=None):
    assert (shape and not out) or (out and not shape)
    if isinstance(indices, numpy.ndarray)
        indices = indices.tolist()
    result = out if out else numpy.zeros(shape)
    result[indices] = data
    return result

Examples:

>>> import numpy as np, nnabla as nn, nnabla.functions as F
>>> nn.set_auto_forward(True)
>>> data = nn.Variable.from_numpy_array(np.array([9, 10, 11, 12]))
>>> indices = nn.Variable.from_numpy_array(np.array([[4, 3, 1, 7]]))
>>> scattered = F.scatter_nd(data, indices, shape=(8,))
>>> print(scatterd.d)
[ 0. 11.  0. 10.  9.  0.  0. 12.]
>>> print(F.gather_nd(scattered, indices).d)
[ 9. 10. 11. 12.]

Parameters

data (Variable, NdArray) – input data
indices (list, numpy.ndarray, Variable, NdArray) – scatter indices
shape (tuple, list) – shape of new output array
out (Variable, NdArray) – existing output array
add (tool) – Add the input data to the same destination specified by the indices.

Returns: ~nnabla.Variable or ~nnabla.NdArray of given shape.

nnabla.functions.scatter_add(x0, indices, x1, axis=None)[source]¶

Add all values from x1 into the x0 according to index specified by indices. This function adds x1 into the copy of x0 and outputs the copy. The original x0 will not be changed. x0, indices and x1 must have same number of dimensions.

The forward of scatter_add() is equivalent to:

def scatter_add(x0, indices, x1, axis):
    # Assuming each input is 3 dimensional
    import numpy as np
    output = np.copy(x0)
    for i in range(indices.shape[0]):
        for j in range(indices.shape[1]):
            for k in range(indices.shape[2]):
                if axis == 0:
                    output[indices[i][j][k]][j][k] += x1[i][j][k]
                elif axis == 1:
                    output[i][indices[i][j][k]][k] += x1[i][j][k]
                elif axis == 2:
                    output[i][j][indices[i][j][k]] += x1[i][j][k]
    return output

Parameters

x0 (Variable) – N-D array which the data is added to its copy.
indices (Variable) – N-D array scatter indices. The size of each dimension must be equal or smaller than that of x0 except for the specified axis. The value of indices must be smaller than the size of specified axis’ dimension of x0. The size of each dimension must be equal or smaller than that of x1. Indices must not be negative.
x1 (Variable) – N-D array which is scattered and added to x0.
axis (int) – Axis along which to index. The axis must not exceed the inputs’ dimension. [default= 0 ]

Returns

N-D array which contains the result of scatter addition. The shape is same as x0.

Return type

nnabla.functions.pad(x, pad_width, mode='constant', constant_value=0, n_outputs=- 1, outputs=None)[source]¶

Pad the input N-D array x over the number of dimensions given by half the length of the pad_width iterable, where every two values in pad_width determine the before and after pad size of an axis. The pad_width iterable must hold an even number of positive values which may cover all or fewer dimensions of the input variable x. If pad_width covers fewer dimensions then it applies to the innermost dimensions of x.

x = nn.Variable.from_numpy_array(np.ones((2, 3, 4)))
assert F.pad(x, (1, 1, 2, 2)).shape == (2, 5, 8)

Padding is performed according to the requested mode:

constant

Pads with a value given by the keyword argument constant_value.

x = nn.Variable.from_numpy_array(np.array([1, 2, 3, 4], dtype=np.int))
y = F.pad(x, (3, 3), 'constant', constant_value = -1)
y.forward()
assert np.all(y.d == np.array([-1, -1, -1, 1, 2, 3, 4, -1, -1, -1]))

reflect

Pads with the reflection of the vector mirrored on the first and last values of the vector along each axis.

x = nn.Variable.from_numpy_array(np.array([1, 2, 3, 4], dtype=np.int))
y = F.pad(x, (3, 3), 'reflect')
y.forward()
assert np.all(y.d == np.array([4, 3, 2, 1, 2, 3, 4, 3, 2, 1]))

repeat

Pads with the edge value of the vector along each axis.

x = nn.Variable.from_numpy_array(np.array([1, 2, 3, 4], dtype=np.int))
y = F.pad(x, (3, 3), 'repeat')
y.forward()
assert np.all(y.d == np.array([1, 1, 1, 1, 2, 3, 4, 4, 4, 4]))

Parameters

x (Variable) – N-D array
pad_width (repeated int64) – Iterable of before and after pad values.
mode (string) – Padding mode string. [default= 'constant' ]
constant_value (float) – Fill value if mode is constant. [default= 0 ]

Returns

Padded N-D array with the same number of dimensions as the input.

x = nn.Variable((3, 3, 4, 2))  # a shape like (B, C, H, W)
# 1-D padding: last dim by 1 left and 2 on the right side
assert F.pad(x, (1, 2)).shape == (3, 3, 4, 5)
# 2-D padding: last dim by (1, 1) and 2nd to last by (2, 2)
assert F.pad(x, (2, 2, 1, 1)).shape == (3, 3, 8, 4)
# 3-D padding: dims C by (0, 1), H by (2, 1), and W by (3, 3)
assert F.pad(x, (0, 1, 2, 1, 3, 3)).shape == (3, 4, 7, 8)

Return type

Note

All nnabla functions in nnabla.functions are decorated with the nnabla.function_bases.function_api decorator, which queries the current context and passes it into the first argument of the original function. The original function always takes a context as the first argument.

nnabla.functions.transpose(x, axes, n_outputs=- 1, outputs=None)[source]¶

Transposes tensor dimensions.

Parameters

x (Variable) – N-D array
axes (repeated int64) – Source axis indices for each axis.

Returns

Transposed N-D array.

Return type

Note

All nnabla functions in nnabla.functions are decorated with the nnabla.function_bases.function_api decorator, which queries the current context and passes it into the first argument of the original function. The original function always takes a context as the first argument.

nnabla.functions.broadcast(x, shape, n_outputs=- 1, outputs=None)[source]¶

Broadcasting ND-array to the specified shape.

Parameters

x (Variable) – N-D array
shape (tuple of int) – Shape broadcasted to. The size must be the same in axis where x’s shape is not 1.

Returns

Broadcasted N-D array

Return type

Note

All nnabla functions in nnabla.functions are decorated with the nnabla.function_bases.function_api decorator, which queries the current context and passes it into the first argument of the original function. The original function always takes a context as the first argument.

nnabla.functions.broadcast_to(x, y, axis=None, n_outputs=- 1, outputs=None)[source]¶

Warning

This function is experimental support, so please do not actively use it.

Broadcasting ND-array to the specified buffer.

Parameters

x (Variable) – N-D array
y (Variable) – N-D array
axis (int) – Target axis to start broadcasting. If this is not set, broadcast will try to fit y to x starting from the last dimension [default= -1 ]

Returns

Broadcasted N-D array

Return type

Note

All nnabla functions in nnabla.functions are decorated with the nnabla.function_bases.function_api decorator, which queries the current context and passes it into the first argument of the original function. The original function always takes a context as the first argument.

nnabla.functions.tile(x, reps)[source]¶

Forward x repeated the number of times given by reps. If reps is a sequence, the output has dimension of d = max(len(reps), x.ndim) and either x is promoted to be d-dimensional by prepending new axes or reps is promoted to x.ndim by prepending 1’s.

Parameters

x (Variable) – Input N-D array.
reps (int or sequence of int) – Repetitions of x along each axis.

Returns

N-D array.

Return type

>>> import numpy as np, nnabla as nn, nnabla.functions as F
>>> F.tile(nn.Variable([2, 3]), 3).shape    # reps is promoted to [1, 3]
(2, 9)
>>> F.tile(nn.Variable([3]), [2, 3]).shape  # x is promoted to shape (1, 3)
(2, 9)
>>> nn.set_auto_forward(True)
>>> x = nn.Variable.from_numpy_array(np.array([1, 2, 3]))
>>> print(F.tile(x, 3).d)
[1. 2. 3. 1. 2. 3. 1. 2. 3.]
>>> print(F.tile(x, [2, 3]).d)
[[1. 2. 3. 1. 2. 3. 1. 2. 3.]
 [1. 2. 3. 1. 2. 3. 1. 2. 3.]]
>>> x = nn.Variable.from_numpy_array(np.array([[1, 3], [2, 4]]))
>>> print(F.tile(x, 3).d)
[[1. 3. 1. 3. 1. 3.]
 [2. 4. 2. 4. 2. 4.]]
>>> print(F.tile(x, [2, 3]).d)
[[1. 3. 1. 3. 1. 3.]
 [2. 4. 2. 4. 2. 4.]
 [1. 3. 1. 3. 1. 3.]
 [2. 4. 2. 4. 2. 4.]]

nnabla.functions.meshgrid(*x, ij_indexing=False)[source]¶

nnabla.functions.flip(x, axes=None, n_outputs=- 1, outputs=None)[source]¶

Reverses the order of elements of the specified dimension of an array.

Parameters

x (Variable) – N-D array
axes (repeated int64) – The index of the dimension to reverse the order of the elements. Axis indices take on values 0, 1, 2, and so on from the left. For example, to flip a 32 (W) by 24 (H) 100 RGB image (100,3,24,32) vertically and horizontally, specify (2,3). [default= [len(x.shape) - 1] ]

Returns

N-D array

Return type

Note

All nnabla functions in nnabla.functions are decorated with the nnabla.function_bases.function_api decorator, which queries the current context and passes it into the first argument of the original function. The original function always takes a context as the first argument.

nnabla.functions.shift(x, shifts=None, border_mode='nearest', n_outputs=- 1, outputs=None)[source]¶

Shifts the array elements by the specified amount.

Parameters

x (Variable) – N-D array.
shifts (repeated int64) – The amount to shift elements. For example, to shift image data to the right by 2 pixels and up 3 pixels, specify (-3,2). [default= (0,) * len(x.shape) ]
border_mode (string) – Specify how to process the ends of arrays whose values will be undetermined as a result of shifting. nearest: The data at the ends of the original array is copied and used. reflect: Original data reflected at the ends of the original array is used. [default= 'nearest' ]

Returns

N-D array.

Return type

Note

All nnabla functions in nnabla.functions are decorated with the nnabla.function_bases.function_api decorator, which queries the current context and passes it into the first argument of the original function. The original function always takes a context as the first argument.

nnabla.functions.sort(x, axis=- 1, reverse=False, with_index=False, only_index=False)[source]¶

Sorts the elements of x along a given axis in ascending order by value. A negative axis counts from the last dimension of x, so the default of -1 sorts along the last dimension. If reverse is True, then the elements are soreted in descending order.

If with_index is True, result is a tuple (sorted, indices) or only indices if only_index is True. Setting only_index to True implies that with_index is also True.

import numpy as np
import nnabla as nn
import nnabla.functions as F

nn.set_auto_forward(True)
x = nn.Variable.from_numpy_array(np.random.rand(2, 3, 4))

sorted = F.sort(x)
assert np.allclose(sorted.d, np.sort(x.d))

sorted, indices = F.sort(x, with_index=True)
assert np.allclose(sorted.d, np.sort(x.d))
assert np.all(indices.d == np.argsort(x.d))

indices = F.sort(x, only_index=True)
assert np.all(indices.d == np.argsort(x.d))

Parameters

x (Variable) – N-D array
axis (int) – Axis along which to sort.
reverse (bool) – Sort in descending order.
with_index (bool) – Return sorted values and index.
only_index (bool) – Return only the sort index.

Returns: ~nnabla.Variable sorted or ~nnabla.Variable indices or (~nnabla.Variable sorted, ~nnabla.Variable indices)

nnabla.functions.reshape(x, shape, inplace=True, n_outputs=- 1, outputs=None)[source]¶

Reshapes the input variable in-place. It does not create a copy of the variable. The output variable (y) has a new shape but points to the same data as the input variable (x). This means that if the data in the output variable (y) is modified, the data in the input variable (x) also gets modified since the reshape was done in-place.

Note

This function has the same behavior as the nnabla.Variable.reshape() method.

Parameters

x (Variable) – N-D array.
shape (tuple of int) – Dimensions for each axis. -1 can be specified only in one shape dimension. The value is calculated from the size of the array and remaining dimensions.
inplace (bool) – The output array is shared with the input array if True. [default= True ]

Returns

Reshaped N-D array

Return type

Note

All nnabla functions in nnabla.functions are decorated with the nnabla.function_bases.function_api decorator, which queries the current context and passes it into the first argument of the original function. The original function always takes a context as the first argument.

nnabla.functions.one_hot(x, shape, n_outputs=- 1, outputs=None)[source]¶

This function creates one-hot vector based on input indices.

Example:

import nnabla as nn
import nnabla.functions as F
import numpy as np

labels = nn.Variable.from_numpy_array(np.array([[9], [4], [5], [1], [0]]))
print(labels.shape)  # (5, 1)

num_class = 10

y_train = F.one_hot(labels, shape=(num_class, ))
y_train.forward()

print(y_train.shape)  # (5, 10)
print(y_train.d)

# [[0. 0. 0. 0. 0. 0. 0. 0. 0. 1.]
#  [0. 0. 0. 0. 1. 0. 0. 0. 0. 0.]
#  [0. 0. 0. 0. 0. 1. 0. 0. 0. 0.]
#  [0. 1. 0. 0. 0. 0. 0. 0. 0. 0.]
#  [1. 0. 0. 0. 0. 0. 0. 0. 0. 0.]]

# Can also be used for ndarray.

labels = nn.Variable.from_numpy_array(np.array([[1, 7], [4, 7], [8, 6], [5, 0], [2, 6]]))
print(labels.shape)  # (5, 2)

num_class_1, num_class_2  = 10, 8

y_train = F.one_hot(labels, shape=(num_class_1, num_class_2))
y_train.forward()

print(y_train.shape)  # (5, 10, 8)
print(y_train.d)

# [[[0. 0. 0. 0. 0. 0. 0. 0.]          [[0. 0. 0. 0. 0. 0. 0. 0.]
#   [0. 0. 0. 0. 0. 0. 0. 1.]           [0. 0. 0. 0. 0. 0. 0. 0.]
#   [0. 0. 0. 0. 0. 0. 0. 0.]           [0. 0. 0. 0. 0. 0. 1. 0.]
#   [0. 0. 0. 0. 0. 0. 0. 0.]           [0. 0. 0. 0. 0. 0. 0. 0.]
#   [0. 0. 0. 0. 0. 0. 0. 0.]           [0. 0. 0. 0. 0. 0. 0. 0.]
#   [0. 0. 0. 0. 0. 0. 0. 0.]    ...    [0. 0. 0. 0. 0. 0. 0. 0.]
#   [0. 0. 0. 0. 0. 0. 0. 0.]           [0. 0. 0. 0. 0. 0. 0. 0.]
#   [0. 0. 0. 0. 0. 0. 0. 0.]           [0. 0. 0. 0. 0. 0. 0. 0.]
#   [0. 0. 0. 0. 0. 0. 0. 0.]           [0. 0. 0. 0. 0. 0. 0. 0.]
#   [0. 0. 0. 0. 0. 0. 0. 0.]],         [0. 0. 0. 0. 0. 0. 0. 0.]]]

Parameters

x (Variable) – N-D array representing label’s indice.
shape (tuple of int) – Number of classes. Note that it must be exactly the same as the number of classes included in label data. Passing incorrect numbers might cause an unexpected error and currently this function doesn’t check if the input is valid or not. Also, when nd-labels are given, dimensions must match. See the example above.

Returns

N-D array one-hot vector/tensor.

Return type

Note

All nnabla functions in nnabla.functions are decorated with the nnabla.function_bases.function_api decorator, which queries the current context and passes it into the first argument of the original function. The original function always takes a context as the first argument.

nnabla.functions.assign(dst, src, n_outputs=- 1, outputs=None)[source]¶

Assign source array to destination array just like tf.assign. This is useful to synchronize or manually update parameters.

dst = nn.Variable((2, 3, 4))
src = nn.Variable((2, 3, 4))
assign = F.assign(dst, src)

assign.forward()
assert np.allclose(dst.d, src.d) # dst and src have identical values.
assert np.allclose(assign.d dst.d) # returned Variable is also identical to dst.

Unlike TensorFlow, the returned Variable has a backward path to dst:

\[g_{dst} = g_{y}\]

Parameters

dst (Variable) – A destination N-D array
src (Variable) – A source N-D array

Returns

An assigned array

Return type

Note

All nnabla functions in nnabla.functions are decorated with the nnabla.function_bases.function_api decorator, which queries the current context and passes it into the first argument of the original function. The original function always takes a context as the first argument.

nnabla.functions.top_k_data(x, k, abs=False, reduce=True, base_axis=1, n_outputs=- 1, outputs=None)[source]¶

Select the k largest values from each sample in x to propagate unmodified and set all other values to 0. If abs is True, the k largest values are selected by magnitude. If reduce is True (the default), all feature dimensions are reduced to a single dimension of size k that propagates only the k largest values. Otherwise, if reduce is False, input and output dimensions are identical. Dimensions before base_axis are treated as number of sample dimensions and k values get selected from all elements of a sample (dimensions from base_axis) regardless of shape.

>>> import nnabla as nn, nnabla.functions as F
>>> x = nn.Variable((4, 5, 6))
>>> F.top_k_data(x, 3, reduce=False).shape
(4, 5, 6)
>>> F.top_k_data(x, 3, reduce=True).shape
(4, 3)
>>> F.top_k_data(x, 3, reduce=True, base_axis=2).shape
(4, 5, 3)

Parameters

x (Variable) – N-D array
k (int) – Number of largest data values to propagate.
abs (bool) – Determine largest data values by magnitude. [default= False ]
reduce (bool) – Reduce feature size to one dimension of size k. [default= True ]
base_axis (int) – First dimension of the sample shape. [default= 1 ]

Returns

N-D array.

Return type

Note

All nnabla functions in nnabla.functions are decorated with the nnabla.function_bases.function_api decorator, which queries the current context and passes it into the first argument of the original function. The original function always takes a context as the first argument.

nnabla.functions.top_k_grad(x, k, abs=False, base_axis=1, n_outputs=- 1, outputs=None)[source]¶

Select the k largest gradients for each sample in x to back-propagate unmodified and set all other gradients to 0. If abs is True, the k largest gradients are selected by magnitude. Dimensions before base_axis are treated as number of sample dimensions and k gradients get selected from all gradients of a sample (dimensions from base_axis) regardless of shape.

Parameters

x (Variable) – N-D array
k (int) – Number of largest gradients to propagate.
abs (bool) – Determine largest gradients by magnitude. [default= False ]
base_axis (int) – First dimension of the sample shape. [default= 1 ]

Returns

N-D array with same shape and data as x.

Return type

Note

All nnabla functions in nnabla.functions are decorated with the nnabla.function_bases.function_api decorator, which queries the current context and passes it into the first argument of the original function. The original function always takes a context as the first argument.

nnabla.functions.pack_padded_sequence(padded_sequence, lengths, batch_first=False, n_outputs=- 1, outputs=None)[source]¶

Pack a padded variable-length sequences.

This method packs a padded variable-length sequences.

$T_i$ is the length of the $i$-th Variable in the sequences. $B$ is the batch size equal to the length of the sequences. $T$ is the max of $T_i$ for all $i$. $*$ is the remaining dimensions including none.

Note

This function assumes the length-sorted padded sequence in the decreasing order and must be used by pack_padded_sequence() in the dynamic computation mode. See :

Parameters

padded_sequence (Variable) – Padded sequence of ($T \times B \times *$) or ($B \times T \times *$) shape.
lengths (Variable) – Sequence length for each batch and always resides in CPU.
batch_first (bool) –
padded_sequence is of ($T$, $B$, $*$) shape if False, otherwise ($B$, $T$, $*$).

[default= False ]

Returns

Packed sequence of ($N$, $*$) shape. ~nnabla.Variable: Batch size for each time and always resides in CPU.

Return type

Note

All nnabla functions in nnabla.functions are decorated with the nnabla.function_bases.function_api decorator, which queries the current context and passes it into the first argument of the original function. The original function always takes a context as the first argument.

nnabla.functions.pad_packed_sequence(packed_sequence, batch_sizes, batch_first=False, padding_value=None, total_length=None, n_outputs=- 1, outputs=None)[source]¶

Pad packed sequence.

This method unpacks the packed sequqnce and pad it, the inverse operation of pack_padded_sequence().

$T_i$ is the length of the $i$-th Variable in the sequences. $B$ is the batch size equal to the length of the sequences. $T$ is the max of $T_i$ for all $i$. $*$ is the remaining dimensions including none.

Note

This function assumes the output of the length-sorted padded sequence in the decreasing order and must be used by pad_packed_sequence() in the dynamic computation mode.

Parameters

packed_sequence (Variable) – Packed sequence of ($N$, $*$) shape.
batch_sizes (Variable) – Batch size for each time and always resides in CPU.
batch_first (bool) –
padded_sequence is of ($T$, $B$, $*$) shape if False, otherwise ($B$, $T$, $*$).

[default= False ]
padding_value (float) – Padding value. [default= 0.0 ]
total_length (int) –
If not None, the outputs are padded up to the total_length. If the total_length is less than the max length in the sequences, the error is thrown.

[default= -1 ]

Returns

Padded sequence of ($T \times B \times *$) or ($B \times T \times *$) shape. ~nnabla.Variable: Sequence length for each batch and always resides in CPU.

Return type

Note

All nnabla functions in nnabla.functions are decorated with the nnabla.function_bases.function_api decorator, which queries the current context and passes it into the first argument of the original function. The original function always takes a context as the first argument.

nnabla.functions.searchsorted(sorted_sequence, values, right=None, n_outputs=- 1, outputs=None)[source]¶

Finds indices in the innermost dimension of a sorted sequance where values must be inserted in order to maintain value

Parameters

sorted_sequence (Variable) – N-D array of sorted sequence where search is to be performed. Note that this must be a sorted array
values (Variable) – N-D array of Search values
right (bool) – :If True, given a value v, the function returns index i such that sorted_sequence[i-1] <= v < sorted_sequence[i] (index of closest upper bound of v). By default, this is false so the function returns index i such that a[i-1] < v <= a[i] (index of closest lower bound of v) [default= False ]

Returns

N-D array containing the required indices

Return type

Note

All nnabla functions in nnabla.functions are decorated with the nnabla.function_bases.function_api decorator, which queries the current context and passes it into the first argument of the original function. The original function always takes a context as the first argument.

nnabla.functions.bool_gather(input, mask, n_outputs=- 1, outputs=None)[source]¶

Gather from the input data according to the mask.

Given an input of $(B_1, \ldots, B_N, D_1, \ldots, D_M)$ shape and mask of $(B_1, \ldots, B_N)$ shape, the function returns an output of $(nnz, D_1, \ldots, D_M)$ shape and $nnz$ is the number of non-zero elements in mask.

import numpy as np
import nnabla as nn
import nnabla.functions as F

nn.set_auto_forward(True)

input = nn.Variable.from_numpy_array([[1, 2], [3, 4], [5, 6]])
mask = nn.Variable.from_numpy_array([1, 0, 1])
output = F.bool_gather(input, mask)

print(output.d) # [[1, 2], [5, 6]]

Note that this function is normally used with the dynamic graph since this function outputs a variable-length output. If used with the static graph, a network has to be constructed all time in iteration.

Parameters

input (Variable) – Data from which to gather.
mask (Variable) – Mask with which to gather. Non-zero/zero elements are supposed to be a binary mask as 1/0. No gradients are computed with respect to mask.

Returns

Gathered output.

Return type

Note

All nnabla functions in nnabla.functions are decorated with the nnabla.function_bases.function_api decorator, which queries the current context and passes it into the first argument of the original function. The original function always takes a context as the first argument.

nnabla.functions.bool_scatter(input, mask, output=None, n_outputs=- 1, outputs=None)[source]¶

Scatter the input according to the mask.

Given an input of $(nnz, D_1, \ldots, D_M)$ shape and mask of $(B_1, \ldots, B_N)$ shape, the function returns an output $(B_1, \ldots, B_N, D_1, \ldots, D_M)$ and $nnz$ is the number of non-zero elements in the mask.

import numpy as np
import nnabla as nn
import nnabla.functions as F

nn.set_auto_forward(True)

input0 = nn.Variable.from_numpy_array([[1, 2], [3, 4], [5, 6]])
mask = nn.Variable.from_numpy_array([1, 0, 1])
output0 = F.bool_gather(input0, mask)

input1 = output0 + 10
output1 = F.bool_scatter(input1, mask)

print(output1.d)  # [[11, 12], [0, 0], [15, 16]]

Note that the higher-order gradients of this function relies on F.gather, thus the higher-order gradients of this function is normally used with the dynamic graph.

Parameters

input (Variable) – Data to be scattered.
mask (Variable) – Mask with which to scatter. Non-zero/zero elements are supposed to be a binary mask as 1/0. No gradients are computed with respect to mask.
output (Variable) – Destination of output. If specified, data are inplaced. [optional]

Returns

Scattered output.

Return type

Note

All nnabla functions in nnabla.functions are decorated with the nnabla.function_bases.function_api decorator, which queries the current context and passes it into the first argument of the original function. The original function always takes a context as the first argument.

nnabla.functions.bool_fill(data, mask, value=0, n_outputs=- 1, outputs=None)[source]¶

Fill the data with the value to according to the mask.

import numpy as np
import nnabla as nn
import nnabla.functions as F

nn.set_auto_forward(True)

input = nn.Variable.from_numpy_array([[np.inf, 2], [3, np.nan]])
mask = nn.Variable.from_numpy_array([[1, 0], [0, 1]])
output = F.bool_fill(input, mask, -1)

print(output.d)  # [[-1, 2], [3, -1]]

Parameters

data (Variable) – Data to be filled.
mask (Variable) – Mask with which to fill. Non-zero/zero elements are supposed to be a binary mask as 1/0. No gradients are computed with respect to mask.
value (float) – Value to fill. [default= 0 ]

Returns

Filled output.

Return type

Note

All nnabla functions in nnabla.functions are decorated with the nnabla.function_bases.function_api decorator, which queries the current context and passes it into the first argument of the original function. The original function always takes a context as the first argument.

nnabla.functions.dot(a, b, out=None)[source]¶

A compatible operation with numpy.dot.

Note

Any operation between nnabla’s Variable/NdArray and numpy array is not supported.

If both arguments are 1-D, it is inner product of vectors. If both arguments are 2-D, it is matrix multiplication. If either a or b is 0-D(scalar), it is equivalent to multiply. If b is a 1-D array, it is a sum product over the last axis of a and b. If b is an M-D array (M>=2), it is a sum product over the last axis of a and the second-to-last axis of b.

Parameters

a (Variable, NdArray or scalar) – Left input array.
b (Variable, NdArray or scalar) – Right input array.
out – Output argument. This must have the same shape, dtype, and type as the result that would be returned for F.dot(a,b).

Returns

~nnabla.Variable or ~nnabla.NdArray

Examples:

import numpy as np
import nnabla as nn
import nnabla.functions as F

# 2-D matrix * 2-D matrix
arr1 = np.arange(5*6).reshape(5, 6)
arr2 = np.arange(6*8).reshape(6, 8)
nd1 = nn.NdArray.from_numpy_array(arr1)
nd2 = nn.NdArray.from_numpy_array(arr2)
ans1 = F.dot(nd1, nd2)
print(ans1.shape)
#(5, 8)

var1 = nn.Variable.from_numpy_array(arr1)
var2 = nn.Variable.from_numpy_array(arr2)
ans2 = F.dot(var1, var2)
ans2.forward()
print(ans2.shape)
#(5, 8)

out1 = nn.NdArray((5, 8))
out1.cast(np.float32)
F.dot(nd1, nd2, out1)
print(out1.shape)
#(5, 8)

out2 = nn.Variable((5, 8))
out2.data.cast(np.float32)
F.dot(var1, var2, out2)
out2.forward()
print(out2.shape)
#(5, 8)

# N-D matrix * M-D matrix (M>=2)
arr1 = np.arange(5*6*7*8).reshape(5, 6, 7, 8)
arr2 = np.arange(2*3*8*6).reshape(2, 3, 8, 6)
nd1 = nn.NdArray.from_numpy_array(arr1)
nd2 = nn.NdArray.from_numpy_array(arr2)
ans1 = F.dot(nd1, nd2)
print(ans1.shape)
#(5, 6, 7, 2, 3, 6)

var1 = nn.Variable.from_numpy_array(arr1)
var2 = nn.Variable.from_numpy_array(arr2)
ans2 = F.dot(var1, var2)
ans2.forward()
print(ans2.shape)
#(5, 6, 7, 2, 3, 6)

out1 = nn.NdArray((5, 6, 7, 2, 3, 6))
out1.cast(np.float32)
F.dot(nd1, nd2, out1)
print(out1.shape)
#(5, 6, 7, 2, 3, 6)

out2 = nn.Variable((5, 6, 7, 2, 3, 6))
out2.data.cast(np.float32)
F.dot(var1, var2, out2)
out2.forward()
print(out2.shape)
#(5, 6, 7, 2, 3, 6)

Stochasticity¶

nnabla.functions.rand(low=0, high=1, shape=[], seed=- 1, n_outputs=- 1, outputs=None)[source]¶

Samples numbers from a uniform distribution $x \sim U(low, high)$ given lowest value $low$, upper bound $high$, and shape of the returned Variable.

Parameters

low (float) – $low$ in definition. [default= 0 ]
high (float) – $high$ in definition. [default= 1 ]
shape (tuple of int) – Shape of returned variable. [default= [] ]
seed (int) – Random seed. When -1, seed is sampled from global random number generator. [default= -1 ]

Returns

Variable with the shape specified in the argument.

Return type

Note

All nnabla functions in nnabla.functions are decorated with the nnabla.function_bases.function_api decorator, which queries the current context and passes it into the first argument of the original function. The original function always takes a context as the first argument.

nnabla.functions.randint(low=0, high=1, shape=[], seed=- 1, n_outputs=- 1, outputs=None)[source]¶

Samples integer numbers from a uniform distribution $x \sim U(low, high)$ given lowest value $low$, upper bound $high$, and the shape of the returned Variable. The lowest value $low$ is included in the range, while the upper bound $high$ is excluded, corresponding to the half-open interval $[low, high)$.

Parameters

low (int) – $low$ in definition. [default= 0 ]
high (int) – $high$ in definition. [default= 1 ]
shape (tuple of int) – Shape of returned variable. [default= [] ]
seed (int) – Random seed. When -1, seed is sampled from global random number generator. [default= -1 ]

Returns

Variable with the shape specified in the argument. The dtype is int32.

Return type

Note

All nnabla functions in nnabla.functions are decorated with the nnabla.function_bases.function_api decorator, which queries the current context and passes it into the first argument of the original function. The original function always takes a context as the first argument.

nnabla.functions.randn(mu=0, sigma=1, shape=[], seed=- 1, n_outputs=- 1, outputs=None)[source]¶

Samples numbers from a normal distribution $x \sim N(\mu, \sigma)$ given mean $\mu$, standard deviation $\sigma$, and shape of the returned Variable.

Parameters

mu (float) – $\mu$ in definition. [default= 0 ]
sigma (float) – $\sigma$ in definition. [default= 1 ]
shape (tuple of int) – Shape of returned variable. [default= [] ]
seed (int) – Random seed. When -1, seed is sampled from global random number generator. [default= -1 ]

Returns

Variable with the shape specified in the argument.

Return type

Note

All nnabla functions in nnabla.functions are decorated with the nnabla.function_bases.function_api decorator, which queries the current context and passes it into the first argument of the original function. The original function always takes a context as the first argument.

nnabla.functions.rand_binomial(n=1, p=0.5, shape=[], seed=- 1, n_outputs=- 1, outputs=None)[source]¶

Samples numbers from a binomial distribution $x \sim B(n, p)$ given the numbers of trials $n$, probability $p$, and shape of the returned Variable. When $n = 1$, this behaves like the Bernoulli distriburion.

Parameters

n (int) – $n$ in definition, the number of trials. [default= 1 ]
p (float) – $p$ in definition, probability of success. [default= 0.5 ]
shape (tuple of int) – Shape of returned variable. [default= [] ]
seed (int) – Random seed. When -1, seed is sampled from global random number generator. [default= -1 ]

Returns

Variable with the shape specified in the argument.

Return type

Note

All nnabla functions in nnabla.functions are decorated with the nnabla.function_bases.function_api decorator, which queries the current context and passes it into the first argument of the original function. The original function always takes a context as the first argument.

nnabla.functions.rand_beta(alpha=0.5, beta=0.5, shape=[], seed=- 1, n_outputs=- 1, outputs=None)[source]¶

Samples numbers from a beta distribution $x \sim \beta(\alpha, \beta)$.

Parameters

alpha (float) – $\alpha$, scale parameter. [default= 0.5 ]
beta (float) – $\beta$, scale parameter. [default= 0.5 ]
shape (tuple of int) – Shape of returned variable. [default= [] ]
seed (int) – Random seed. When -1, seed is sampled from global random number generator. [default= -1 ]

Returns

Variable with the shape specified in the argument.

Return type

Note

All nnabla functions in nnabla.functions are decorated with the nnabla.function_bases.function_api decorator, which queries the current context and passes it into the first argument of the original function. The original function always takes a context as the first argument.

nnabla.functions.rand_gamma(k=0.5, theta=1, shape=[], seed=- 1, n_outputs=- 1, outputs=None)[source]¶

Samples numbers from a gamma distribution $x \sim \frac {\gamma(k, \frac {x}{\theta})}{\Gamma(k)}$.

Parameters

k (float) – k, scale parameter. [default= 0.5 ]
theta (float) – $\theta$, scale parameter. [default= 1 ]
shape (tuple of int) – Shape of returned variable. [default= [] ]
seed (int) – Random seed. When -1, seed is sampled from global random number generator. [default= -1 ]

Returns

Variable with the shape specified in the argument.

Return type

Note

All nnabla functions in nnabla.functions are decorated with the nnabla.function_bases.function_api decorator, which queries the current context and passes it into the first argument of the original function. The original function always takes a context as the first argument.

nnabla.functions.dropout(x, p=0.5, seed=- 1, output_mask=False)[source]¶

Dropout. Samples a number $u$ from a uniform distribution in $[0, 1]$, and ignores the input if $u \leq p$.

\[\begin{split}\begin{equation} y = \left\{ \begin{array}{ll} \frac{x}{1 - p} & (u > p) \\ 0 & ({\rm otherwise}) \end{array} \right. \end{equation}\end{split}\]

Parameters

x (Variable) – An input variable.
p (float) – math:p in definition. [default= 0.5 ]
seed (int) – Random seed. When -1, seed is sampled from global random number generator. [default= -1 ]
output_mask (bool) – Whether or not to output mask. [default= False ]

Returns

N-D array.

Return type

Note

Usually dropout only applied during training as below (except `MC dropout`_). If you want to use dropout as an MC dropout, remove if train:.

h = PF.affine(x, num_hidden)
if train:
    h = F.dropout(h, 0.5)

reference: https://arxiv.org/abs/1506.02142

Note

If you use nn.grad to a graph having dropout, you must set output_mask=True for all dropouts. Otherwise, backward function of dropout raises ValueError when you call nn.grad.

h = PF.affine(x, num_hidden)
h, mask = F.dropout(h, p=0.1, output_mask=True)
y = PF.affine(h, num_hidden)

grad = nn.grad([y], nn.get_parameters().values())

nnabla.functions.random_choice(x, w, shape=[], replace=True, seed=- 1, n_outputs=- 1, outputs=None)[source]¶

Generate random samples from population x with selection probabilities determined by the relative weights w. The number of samples to draw is given by the product of shapes dimensions, and the samples are returned with the given shape. By default, samples are drawn with replacement, i.e. selection of a specific population member is solely determined by its associated weight. Sampling without replacement, where any population member may be drawn only once, is used if replace is set to False.

For both x and w the innermost dimension corresponds to the individual populations and their weights from which samples are returned with the requested shape following all outermost dimensions of the input.

import nnabla as nn
import nnabla.functions as F
import numpy as np
nn.set_auto_forward(True)

# x holds two populations
x = nn.Variable.from_numpy_array(np.array([[11, 22, 33], [110, 220, 330]]))
# w holds the weights for each population
w = nn.Variable.from_numpy_array(np.array([[10, 20, 70], [70, 20, 10]]))

# draw one sample from each population
y = F.random_choice(x, w)  # y.shape => (2, 1)

# draw 12 samples with shape (3, 4) from each population
y = F.random_choice(x, w, shape=(3, 4))  # y.shape => (2, 3, 4)

Note that weights must not be less than zero and for each population the sum of weights must be greater than zero. Additionally, sampling without replacement requires that the number of non-zero weights is not less than the number of samples to be drawn. These conditions are verified in “cpu” computation context but not when using “cuda” or “cudnn” acceleration (this would require additional device synchronization steps penalizing performance).

Random sampling from an implicit array of index values (like categorical or multinomial) can be realized with input x constructed as indices.

w = nn.Variable.from_numpy_array(np.array([1, 2, 3, 2, 1]))
y = F.random_choice(F.arange(0, 5), w)

Parameters

x (Variable) – N-D array from which a random sample is generated.
w (Variable) – N-D array of associated weights of elements in x.
shape (tuple of int) – Number and shape of generated samples. [default= [] ]
replace (bool) – Whether sampling is with or without replacement. [default= True ]
seed (int) – Random seed. [default= -1 ]

Returns

N-D array

Return type

Note

All nnabla functions in nnabla.functions are decorated with the nnabla.function_bases.function_api decorator, which queries the current context and passes it into the first argument of the original function. The original function always takes a context as the first argument.

nnabla.functions.random_crop(x, shape=None, base_axis=1, seed=- 1, n_outputs=- 1, outputs=None)[source]¶

RandomCrop randomly extracts a portion of an array.

Parameters

x (Variable) – N-D array
shape (tuple of int) – The data size to extract. For example, to randomly extract a portion of the image (3,48,48) from a 3,64,64 image, specify (3,48,48). [default= x.shape ]
base_axis (int) – No Description [default= 1 ]
seed (int) – Random seed. When -1, seed is sampled from global random number generator. [default= -1 ]

Returns

N-D array

Return type

Note

All nnabla functions in nnabla.functions are decorated with the nnabla.function_bases.function_api decorator, which queries the current context and passes it into the first argument of the original function. The original function always takes a context as the first argument.

nnabla.functions.random_erase(x, prob=0.5, area_ratios=(0.02, 0.4), aspect_ratios=(0.3, 3.3333), replacements=(0.0, 255.0), n=None, share=True, inplace=False, base_axis=1, seed=- 1, channel_last=False, ste_fine_grained=True, n_outputs=- 1, outputs=None)[source]¶

Randomly erase patches of the inputs and replace with random values.

Erasing is applied for each sample and for each n with the given probability, the randomly selected area ratio and aspect ratio if share is True; otherwise (share`=`False), for each feature additionally.

Random patch are selected by random coordinates as the following,

\[\begin{split}S_e &&= Uniform(s_l, s_h) \times S \\ r_e &&= Uniform(r_l, r_h) \\ H_e &&= \sqrt{S_e \times r_e} \\ W_e &&= \sqrt{S_e / r_e} \\ y_e &&= Uniform(0, H - H_e) \\ x_e &&= Uniform(0, W - W_e),\end{split}\]

where $S$ is the area, $s_l$ and $s_h$ are the low and high values of the area ratio range, $r_l$ and $r_h$ are the low and high values of the aspect ratio range, $H_e$ and $W_e$ are height and width of a patch, and $y_e$ and $x_e$ are the start coordinates of a patch. If a pixel of the inputs falls in this patch, the value of that pixel is replaced with a random value in replacements range.

Backward is implemented as passing gradients if ste_fine_grained is False; otherwise, the backward only occurs in regions not erased.

References

Zhun Zhong, Liang Zheng, Guoliang Kang, Shaozi Li, Yi Yang, Random Erasing Data Augmentation,

Parameters

x (Variable) – N-D array.
prob (float) – Probability to erase. [default= 0.5 ]
area_ratios (repeated float) – Low and high of the area ratio range. [default= (0.02, 0.4) ]
aspect_ratios (repeated float) – Low and high of the aspect ratios range. [default= (0.3, 3.3333) ]
replacements (repeated float) – Low and high of the replacement value range. [default= (0.0, 255.0) ]
n (int) – Max number of patches to be erased. [default= 1 ]
share (bool) – Use a same bounding box randomly picked over the feature dimension when being True. Default is False. [default= True ]
inplace (bool) – This option is obsolete and ignored. Output is never in-placed with input. [default= False ]
base_axis (int) – Dimensions up to base_axis is treated as sample dimension. [default= 1 ]
seed (int) – Random seed. When -1, seed is sampled from global random number generator. [default= -1 ]
channel_last (bool) – If True, the last dimension is considered as channel dimension, a.k.a NHWC order. [default= False ]
ste_fine_grained (bool) – Straight Through Estimator is fine-grained or not. Default is True. [default= True ]

Returns

N-D array.

Return type

Note

All nnabla functions in nnabla.functions are decorated with the nnabla.function_bases.function_api decorator, which queries the current context and passes it into the first argument of the original function. The original function always takes a context as the first argument.

nnabla.functions.random_flip(x, axes=None, base_axis=1, seed=- 1, n_outputs=- 1, outputs=None)[source]¶

Reverses the order of elements of the specified dimension of an array at 50% probability.

Parameters

x (Variable) – N-D array
axes (repeated int64) – The index of the axis to reverse the order of the elements. Axis indices take on values 0, 1, 2, and so on from the left. For example, to flip a 32 (W) by 24 (H) 100 RGB images (100, 3,24,32) vertically and horizontally at random, specify (2,3). [default= [len(x.shape) - 1] ]
base_axis (int) – No Description [default= 1 ]
seed (int) – Random seed. When -1, seed is sampled from global random number generator. [default= -1 ]

Returns

N-D array

Return type

Note

All nnabla functions in nnabla.functions are decorated with the nnabla.function_bases.function_api decorator, which queries the current context and passes it into the first argument of the original function. The original function always takes a context as the first argument.

nnabla.functions.random_shift(x, shifts=None, border_mode='nearest', constant_value=0, base_axis=1, seed=- 1, n_outputs=- 1, outputs=None)[source]¶

Randomly shifts the array elements within the specified range.

Parameters

x (Variable) – N-D array.
shifts (repeated int64) – Max absolute amount to shift elements. For example, to shift image data horizontally by $\pm 2$ pixels and vertically by $\pm 3$ pixels, specify (3,2). [default= (0,) * len(x.shape) ]
border_mode (string) – Specify how to process the ends of arrays whose values will be undetermined as a result of shifting. nearest: The data at the ends of the original array is copied and used. reflect: Original data reflected at the ends of the original array is used. constant: Constant value is used. [default= 'nearest' ]
constant_value (float) – Value used for outside of the original array if border_mode=’constant’. [default= 0 ]
base_axis (int) – No Description [default= 1 ]
seed (int) – Random seed. When -1, seed is sampled from global random number generator. [default= -1 ]

Returns

N-D array.

Return type

Note

All nnabla functions in nnabla.functions are decorated with the nnabla.function_bases.function_api decorator, which queries the current context and passes it into the first argument of the original function. The original function always takes a context as the first argument.

nnabla.functions.image_augmentation(x, shape=None, pad=(0, 0), min_scale=1.0, max_scale=1.0, angle=0.0, aspect_ratio=1.0, distortion=0.0, flip_lr=False, flip_ud=False, brightness=0.0, brightness_each=False, contrast=1.0, contrast_center=0.0, contrast_each=False, noise=0.0, seed=- 1, n_outputs=- 1, outputs=None)[source]¶

ImageAugmentation randomly alters the input image.

Parameters

x (Variable) – N-D array.
shape (tuple of int) – The output image data size. [default= x.shape ]
pad (tuple of int) – Border padding values for each spatial axis. Padding will be added both sides of the dimension. [default= (0, 0) ]
min_scale (float) – The minimum scale ratio when randomly scaling the image. For example, to scale down to 0.8 times the size of the original image, specify “0.8”. To not apply random scaling, set both min_scale and max_scale to “1.0”. [default= 1.0 ]
max_scale (float) – The maximum scale ratio when randomly scaling the image. For example, to scale down to 2 times the size of the original image, specify “2.0”. [default= 1.0 ]
angle (float) – The rotation angle range in radians when randomly rotating the image. The image is randomly rotated in the -Angle to +Angle range. For example, to rotate in a +-15 degree range, specify “0.26” (15 degrees/360 degrees * 2PI). To not apply random rotation, specify “0.0”. [default= 0.0 ]
aspect_ratio (float) – The aspect ratio range when randomly deforming the image. For example, to deform aspect ratio of image from 1:1.3 to 1.3:1, specify “1.3”. To not apply random deforming, specify “1.0”. [default= 1.0 ]
distortion (float) – The distortion range when randomly distorting the image. To not apply distortion, specify “0.0”. [default= 0.0 ]
flip_lr (bool) – Whether to randomly flip the image horizontally at 50% probability. [default= False ]
flip_ud (bool) – Whether to randomly flip the image vertically at 50% probability. [default= False ]
brightness (float) – The absolute range of values to randomly add to the brightness. A random value in the -Brightness to +Brightness range is added to the brightness. For example, to vary the brightness in the -0.05 to +0.05 range, specify “0.05”. To not apply random addition to brightness, specify “0.0”. [default= 0.0 ]
brightness_each (bool) – Whether to apply the random addition to brightness (as specified by brightness) to each color channel. True: brightness is added based on a different random number for each channel. False: brightness is added based on a random number common to all channels. [default= False ]
contrast (float) – The range in which to randomly vary the image contrast. The contrast is varied in the 1/Contrast times to Contrast times range. The output brightness is equal to (input - contrast_center) * contrast + contrast_center. For example, to vary the contrast in the 0.91 times to 1.1 times range, specify “1.1”. To not apply random contrast variation, specify “1.0”. [default= 1.0 ]
contrast_center (float) – Intensity center used for applying contrast. [default= 0.0 ]
contrast_each (bool) – Whether to apply the random contrast variation (as specified by contrast) to each color channel. True: contrast is varied based on a different random number for each channel. False: contrast is varied based on a random number common to all channels. [default= False ]
noise (float) – Sigma of normal random number to be added. [default= 0.0 ]
seed (int) – Random seed. When -1, seed is sampled from global random number generator. [default= -1 ]

Returns

N-D array.

Return type

Note

All nnabla functions in nnabla.functions are decorated with the nnabla.function_bases.function_api decorator, which queries the current context and passes it into the first argument of the original function. The original function always takes a context as the first argument.

Loss Functions¶

nnabla.functions.sigmoid_cross_entropy(x, target, n_outputs=- 1, outputs=None)[source]¶

Element-wise cross entropy between x and the target variables, passed to a sigmoid function.

\[y_i = - \left(x^{(1)}_i \ln \left(\sigma \left(x^{(0)}_i \right)\right) + \ \left(1 - x^{(1)}_i\right) \ln \left(1 - \sigma \left(x^{(0)}_i \ \right)\right)\right)\]

where $\sigma(s)=\frac{1}{1+\exp(-s)}$.

Note

SigmoidCrossEntropy is equivalent to Sigmoid+BinaryCrossEntropy, but computing them at once has the effect of reducing computational error.

Parameters

x (Variable) – N-D array. Typically indicates a score. The value lies in $[-\infty, \infty]$ [parameter]
target (Variable) – N-D array of labels. Only 0 or 1 value is allowed. [parameter]

Returns

N-D array of element-wise losses.

Return type

Note

All nnabla functions in nnabla.functions are decorated with the nnabla.function_bases.function_api decorator, which queries the current context and passes it into the first argument of the original function. The original function always takes a context as the first argument.

nnabla.functions.binary_cross_entropy(x, target, n_outputs=- 1, outputs=None)[source]¶

Element-wise cross entropy between x and the target variables.

\[y_i = - \left(x^{(1)}_i * \ln \left(x^{(0)}_i\right) + \left(1 - \ x^{(1)}_i\right) * \ln \left(1 - x^{(0)}_i\right)\right).\]

Parameters

x (Variable) – Probabilities N-D array. $-\infty$ to $\infty$.
target (Variable) – N-D array of labels. Usually set as 0 or 1, but, unlike SigmoidCrossEntropy, it allows probability (0 to 1) as inputs and backpropagation can be done.

Returns

N-D array of element-wise losses.

Return type

Note

All nnabla functions in nnabla.functions are decorated with the nnabla.function_bases.function_api decorator, which queries the current context and passes it into the first argument of the original function. The original function always takes a context as the first argument.

nnabla.functions.softmax_cross_entropy(x, target, axis=None, n_outputs=- 1, outputs=None)[source]¶

Element-wise cross entropy between the variables and the variables of a label given by a category index with Softmax normalization.

\[y_{j} = -\ln \left(\frac{\exp(x_{j,t_j})}{\sum_{i'} \exp(x_{j,i'})}\right)\]

along dimension specified by axis ($i$ is the axis where normalization is performed on).

Note

SoftmaxCrossEntropy is equivalent to Softmax+CategoricalCrossEntropy, but computing them at once has the effect of reducing computational error.

Parameters

x (Variable) – N-D array. Typically indicates a score. $(D_1 \times ... \times D_i \times ... \times D_N)$ [parameter]
target (Variable) – N-D array of labels. $(D_1 \times ... \times 1 \times ... \times D_N)$ , each label should be the index from 0 to n-class, -1 if not belongs any class. [parameter]
axis (int) – Axis normalization is taken. [default= len(x.shape) - 1 ]

Returns

N-D array of element-wise losses. $(D_1 \times ... \times 1 \times ... \times D_N)$

Return type

Note

All nnabla functions in nnabla.functions are decorated with the nnabla.function_bases.function_api decorator, which queries the current context and passes it into the first argument of the original function. The original function always takes a context as the first argument.

nnabla.functions.categorical_cross_entropy(x, target, axis=None, n_outputs=- 1, outputs=None)[source]¶

Element-wise cross entropy between x and the target t where targets are given by a category index.

\[y_{j} = -\ln \left( x_{j, t_j} \right)\]

along dimension specified by axis ($i$ is the axis where normalization is performed on).

Parameters

x (Variable) – N-D array. Typically indicates a score. $(D_1 \times ... \times D_i \times ... \times D_N)$ [parameter]
target (Variable) – N-D array of labels. $(D_1 \times ... \times 1 \times ... \times D_N)$, each label should be the index from 0 to n-class, -1 if not belongs any class. [parameter]
axis (int) – Axis normalization is taken. [default= len(x.shape) - 1 ]

Returns

N-D array of element-wise losses. $(D_1 \times ... \times 1 \times ... \times D_N)$

Return type

Note

All nnabla functions in nnabla.functions are decorated with the nnabla.function_bases.function_api decorator, which queries the current context and passes it into the first argument of the original function. The original function always takes a context as the first argument.

nnabla.functions.squared_error(x0, x1, n_outputs=- 1, outputs=None)[source]¶

Element-wise squared error

\[y_i = \left(x^{(0)}_i - x^{(1)}_i\right)^2.\]

Parameters

x0 (Variable) – N-D array.
x1 (Variable) – N-D array.

Returns

N-D array.

Return type

Note

All nnabla functions in nnabla.functions are decorated with the nnabla.function_bases.function_api decorator, which queries the current context and passes it into the first argument of the original function. The original function always takes a context as the first argument.

nnabla.functions.absolute_error(x0, x1, n_outputs=- 1, outputs=None)[source]¶

Element-wise absolute error

\[y_i = | x^{(0)}_i - x^{(1)}_i |.\]

Parameters

x0 (Variable) – N-D array.
x1 (Variable) – N-D array.

Returns

N-D array.

Return type

Note

All nnabla functions in nnabla.functions are decorated with the nnabla.function_bases.function_api decorator, which queries the current context and passes it into the first argument of the original function. The original function always takes a context as the first argument.

nnabla.functions.huber_loss(x0, x1, delta=1.0, n_outputs=- 1, outputs=None)[source]¶

Element-wise Huber loss

\[\begin{split}y_i= \left\{ \begin{array}{ll} d^2 & (|d| < \delta)\\ \delta (2 |d| - \delta) & ({\rm otherwise}) \end{array} \right.\end{split}\]

where $d = x^{(0)}_i - x^{(1)}_i$

Parameters

x0 (Variable) – N-D array.
x1 (Variable) – N-D array.
delta (float) – Delta [default= 1.0 ]

Returns

N-D array of element-wise losses.

Return type

Note

All nnabla functions in nnabla.functions are decorated with the nnabla.function_bases.function_api decorator, which queries the current context and passes it into the first argument of the original function. The original function always takes a context as the first argument.

nnabla.functions.epsilon_insensitive_loss(x0, x1, epsilon, n_outputs=- 1, outputs=None)[source]¶

Element-wise Epsilon Insensitive Loss

\[\begin{split}y_i= \left\{ \begin{array}{ll} | x^{(0)}_i - x^{(1)}_i | - \epsilon & if \ \ | x^{(0)}_i - x^{(1)}_i | > \epsilon \\ 0 & otherwise \end{array} \right.\end{split}\]

Parameters

x0 (Variable) – N-D array.
x1 (Variable) – N-D array.
epsilon (float) – Insensitive parameter.

Returns

N-D array of element-wise losses.

Return type

Note

All nnabla functions in nnabla.functions are decorated with the nnabla.function_bases.function_api decorator, which queries the current context and passes it into the first argument of the original function. The original function always takes a context as the first argument.

nnabla.functions.kl_multinomial(p, q, base_axis=1, n_outputs=- 1, outputs=None)[source]¶

The Kullback Leibler Divergence for multinomial distributions.

\[D = \sum_i p_i \log \left( \frac{p_i}{q_i} \right)\]

Parameters

p (Variable) – N-D array of the source categorical probabilities
q (Variable) – N-D array of the target categorical probabilities
base_axis (int) – Dimensions up to base_axis is treated as sample dimension. [default= 1 ]

Returns

Kullback Leibler divergence $KL(p \parallel q)$.

Return type

Note

All nnabla functions in nnabla.functions are decorated with the nnabla.function_bases.function_api decorator, which queries the current context and passes it into the first argument of the original function. The original function always takes a context as the first argument.

Signal Processing¶

nnabla.functions.interpolate(x, scale=None, output_size=None, mode='linear', align_corners=False, half_pixel=False, half_pixel_for_nn=False, channel_last=False)[source]¶

Resize an ND array with interpolation.

Scaling factors for spatial dimensions are determined by either scale or output_size.

nd = len(scale) or nd = len(output_size) determines the number of spatial dimensions, and the last nd dimensions of the input x are considered as the spatial dimensions to be resized.

If scale is given, the output_size is calculated by

output_size[i] = floor(scale[i] * x.shape[i - len(scale)]).

Calculation of the coordinate transformation are as follows.

The input coordinate i_input is computed by the output coordinate i_output, the input size size_input, and the output size size_output as

align_corners	half_pixel	i_input
True	True	Not supported.
True	False	i_output * (size_input - 1) / (size_output - 1)
False	True	(i_output + 0.5) * size_input / size_output - 0.5
False	False	i_output * size_input / size_output

In the case of the nearest mode and half_pixel_for_nn is True, the input coordinate i_input is computed by the output coordinate i_output as

i_input = (i_output + 0.5) * size_input / size_output.

Example:

import numpy as np
import nnabla as nn
import nnabla.functions as F

x_data = np.random.rand(64, 3, 224, 224)
x = nn.Variable.from_numpy_array(x_data)

# Resize by scales
y = F.interpolate(x, scale=(2, 2), mode='linear')
print(y.shape)  # (64, 3, 448, 448)
y.forward()
print(y.d)  # Print output

# Resize to a size
y2 = F.interpolate(x, output_size=(320, 257), mode='linear')
print(y2.shape)  # (64, 3, 320, 257)
y2.forward()
print(y2.d)  # Print output

Parameters

x (Variable) – N-D array with an arbitrary number of dimensions.
scale (tuple of ints) – Scale factors along axes. The default is None, and if this is omitted, output_size must be specified.
output_size (tuple of ints) – The output sizes for axes. If this is given, the scale factors are determined by the output sizes and the input sizes. The default is None, and if this is omitted, scale must be specified.
mode (str) – Interpolation mode chosen from (‘linear’|’nearest’). The default is ‘linear’.
align_corners (bool) – If true, the corner pixels of input and output arrays are aligned, such that the output corner pixels have the same values with the input corner pixels. Default is False.
half_pixel – If true, in the coordinate transformation, 0.5 is added to the output coordinate and 0.5 is subtracted from the input coordinate after scaling. Default is False.
half_pixel_for_nn – This is a special argument to support the backward-compatibility of the nearest neighbor interpolation. Default is False. When in True, the implementation of nearest neighbor interpolation is the old one.
channel_last – Last dimension is the channel (NHWC order) if True.

Returns

N-D array.

Return type

Warning

Up to the version 1.8.0, the default of align_corners was None, and it becomes True if mode is linear, otherwise False.

Warning

Up to the version 1.8.0, the nearest mode interpolation corresponds to the nearest mode and half_pixel_for_nn = True after the version 1.8.0.

nnabla.functions.fft(x, signal_ndim, normalized=False, n_outputs=- 1, outputs=None)[source]¶

Complex-to-complex Discrete Fourier Transform,

\[X_{k_1, \ldots, k_d} = \sum_{n_1=0}^{N_1-1} \dots \sum_{n_d=0}^{N_d-1} x_{n_1, \ldots, n_d} \exp\left(-2 \pi j \left( \sum_{i=0}^{d} \frac{k_i n_i}{N_i} \right) \right),\]

where

\[k_i = 0, \ldots, N_i - 1.\]

This function now supports 1-D, 2-D, and 3-D DFT with or without the leading batch dimension(s).

The input is expected to be complex-valued with at least signal_ndim + 1 dimensions. The last dimension has a shape of two where x[…, 0] is the real part and x[…, 1] the imaginary part.

Example:

import numpy as np
import nnabla as nn
import nnabla.functions as F
from nnabla.ext_utils import get_extension_context

ctx = get_extension_context("cudnn")
nn.set_default_context(ctx)

# Example for a batched 2D-FFT and 2D-IFFT (batch-size: 2, data-size: 4x3)
x_data = np.random.rand(2, 4, 3) + 1j * np.random.rand(2, 4, 3)
x = nn.Variable.from_numpy_array(np.stack([np.real(x_data), np.imag(x_data)], axis=3))
y = F.fft(x, signal_ndim=2, normalized=True)
z = F.ifft(y, signal_ndim=2, normalized=True)
z.forward()

np.allclose(z.d[..., 0] + 1j*z.d[...,1], x_data)

Parameters

x (Variable) – Input.
signal_ndim (int) – The number of dimensions for each signal. It must be 1, 2, or 3.
normalized (bool) – Use unitary normalization. If True, the normalization constant $\sqrt{\frac{1}{\prod_{i=1}^{d} N_i}}$ is multiplied. [default= False ]

Returns

FFT transformed signal.

Return type

Note

All nnabla functions in nnabla.functions are decorated with the nnabla.function_bases.function_api decorator, which queries the current context and passes it into the first argument of the original function. The original function always takes a context as the first argument.

nnabla.functions.ifft(x, signal_ndim, normalized=False, n_outputs=- 1, outputs=None)[source]¶

Complex-to-complex inverse Discrete Fourier Transform,

\[X_{k_1, \ldots, k_d} = \frac{1}{\prod_{i=1}^{d} N_i} \sum_{n_1=0}^{N_1-1} \dots \sum_{n_d=0}^{N_d-1} x_{n_1, \ldots, n_d} \exp\left(2 \pi j \left( \sum_{i=0}^{d} \frac{k_i n_i}{N_i} \right) \right),\]

where

\[k_i = 0, \ldots, N_i - 1.\]

This function now supports 1-D, 2-D, and 3-D DFT with or without the leading batch dimension(s).

The input is expected to be complex-valued with at least signal_ndim + 1 dimensions. The last dimension has a shape of two where x[…, 0] is the real part and x[…, 1] the imaginary part.

Parameters

x (Variable) – Input.
signal_ndim (int) – The number of dimensions for each signal. It must be 1, 2, or 3.
normalized (bool) – Use unitary normalization. If True, the normalization constant $\frac{1}{\prod_{i=1}^{d} N_i}$ becomes $\sqrt{\frac{1}{\prod_{i=1}^{d} N_i}}$. [default= False ]

Returns

IFFT transformed signal.

Return type

Note

All nnabla functions in nnabla.functions are decorated with the nnabla.function_bases.function_api decorator, which queries the current context and passes it into the first argument of the original function. The original function always takes a context as the first argument.

nnabla.functions.stft(x, window_size, stride, fft_size, window_type='hanning', center=True, pad_mode='reflect')[source]¶

Computes the short-time Fourier transform

Parameters

x (Variable) – Time domain sequence of size batch_size x sample_size.
window_size (int) – Size of STFT analysis window.
stride (int) – Number of samples that we shift the window, also called hop size.
fft_size (int) – Size of the FFT, the output will have fft_size // 2+ 1 frequency bins.
window_type (str) – Analysis window, can be either hanning, hamming or rectangular. For convenience, also window_type=None is supported which is equivalent to window_type='rectangular'.
center (bool) – If True, then the signal x is padded by half the FFT size using reflection padding.
pad_mode (str) – Padding mode, which can be 'constant' or 'reflect'. 'constant' pads with 0.

Returns

Returns real and imaginary parts of STFT result.

Variable: Real part of STFT of size batch_size x fft_size//2 + 1 x frame_size.
Variable: Imaginary part of STFT of size batch x fft_size//2 + 1 x frame_size.

nnabla.functions.istft(y_r, y_i, window_size, stride, fft_size, window_type='hanning', center=True)[source]¶

Computes the inverse shoft-time Fourier transform

Note: We use a constant square inverse window for the reconstruction of the time-domain signal, therefore, the first and last window_size - stride are not perfectly reconstructed.

Parameters

y_r (Variable) – Real part of STFT of size batch_size x fft_size//2 + 1 x frame_size.
y_i (Variable) – Imaginary part of STFT of size batch_size x fft_size//2 + 1 x frame_size.
window_size (int) – Size of STFT analysis window.
stride (int) – Number of samples that we shift the window, also called hop size.
fft_size (int) – Size of the FFT, (STFT has fft_size // 2 + 1 frequency bins).
window_type (str) – Analysis window, can be either hanning, hamming or rectangular. For convenience, also window_type=None is supported which is equivalent to window_type='rectangular'.
center (bool) – If True, then it is assumed that the time-domain signal has centered frames.

Returns

Time domain sequence of size batch_size x sample_size.

Return type

Geometric Neural Network Layers¶

nnabla.functions.affine_grid(theta, size, align_corners=False, n_outputs=- 1, outputs=None)[source]¶

Generate the source grid based on the normalized target grid with size. The target grid is first normalized in [-1, 1], then tranformed by the affine transformation $\theta$ to generate the source grid. 2D and 3D grid are supported now.

This function is normally used with the warp_by_grid function for constructing the spatial transformer.

Parameters

theta (Variable) – N-D array with the shape ($B \times 2 \times 3$), the sample-wise affine transformation matrix.
size (repeated int64) – The grid size of ($H \times W$) for 2D and ($D \times H \times W$) for 3D.
align_corners (bool) – If True, the top-left and bottom-right pixels correspond to (-1, -1) and (1, 1) respectively since a pixel is located on the corner of a grid, and the target grid is normalized in [-1, 1]. If False, the normalized target grid in [-1, 1] is scaled by size - 1 / size according to the respective spatial size (e.g., $H$ and $W$) before the transformation since a pixel is located on a center of a cell in a grid. [default= False ]

Returns

N-D array with the shape ($B \times H \times W \times 2$) for 2D and ($B \times D \times H \times W \times 3$) for 3D. The last dimension of 2 is for (x, y) and of 3 for (x, y, z). The gird is used as the source grid for the warping.

Return type

Note

All nnabla functions in nnabla.functions are decorated with the nnabla.function_bases.function_api decorator, which queries the current context and passes it into the first argument of the original function. The original function always takes a context as the first argument.

nnabla.functions.warp_by_grid(x, grid, mode='linear', padding_mode='zero', align_corners=False, channel_last=False, n_outputs=- 1, outputs=None)[source]¶

Warp the input data by the grid. This function is normally used with the generated normalized grid by the affine_grid function for constructing the spatial transformer.

Parameters

x (Variable) – Input data to be warped with the shape ($B \times C \times H_{in} \times W_{in}$) for 2D and ($B \times C \times D_{in} \times H_{in} \times W_{in}$) for 3D.
grid (Variable) – Grid warping the input data with the shape ($B \times H_{out} \times W_{out} \times 2$) for 2D and ($B \times D_{out} \times H_{out} \times W_{out} \times 3$) for 3D. The last dimension of 2 is for (x, y) or 3 for (x, y, z).
mode (string) – Interpolation mode, linear or nearest. [default= 'linear' ]
padding_mode (string) – Padding mode when the grid value is outside [-1, 1]. If this is “zero”, 0 is used for padding. “reflect” uses the values reflected at the ends of the original input data like the mirror. “repeat” used the values at the ends of the original input data. [default= 'zero' ]
align_corners (bool) – The target grid normalized in [-1, 1] is scaled by size - 1 / size according to the respective spatial size (e.g., $H$ and $W$) before the transformation if this is False. If this is True, the top-left and bottom-right pixels correspond to (-1, -1) and (1, 1) respectively. [default= False ]
channel_last (bool) – If True, the last dimension is considered as channel dimension, a.k.a NHWC order. [default= False ]

Returns

Output data warped by the grid.

Return type

Note

All nnabla functions in nnabla.functions are decorated with the nnabla.function_bases.function_api decorator, which queries the current context and passes it into the first argument of the original function. The original function always takes a context as the first argument.

nnabla.functions.warp_by_flow(data, flow, n_outputs=- 1, outputs=None)[source]¶

Transform the image(s) data by flow field(s) of offset vectors such that each output pixel corresponds to the input image pixel at the relative offset location given by horizontal and vertical flow values (in other words, the flow field describes the coordinate displacements for each output pixel to the corresponding input pixel). Both data and flow are 4-D variables (in “NCHW” layout) with identical shape except the flow channel dimension (which is always 2).

\[output_{n,c,y,x} = data_{n,c,y',x'},\]

where

\[\begin{split}y' &=& y + flow_{n,1,y,x}, \\ x' &=& x + flow_{n,0,y,x}.\end{split}\]

The output pixel values at $y'$ and $x'$ locations are obtained by bilinear interpolating between the 4 closest pixels of the input image. Pixel values outside of the input image are implicitly padded with the value of the closest boundary pixel.

Parameters

data (Variable) – Input image data with shape (N, Channels, Height, Width).
flow (Variable) – Flow field vectors with shape (N, 2, Height, Width).

Returns

Transformed image data with shape (N, Channels, Height, Width).

Return type

Note

All nnabla functions in nnabla.functions are decorated with the nnabla.function_bases.function_api decorator, which queries the current context and passes it into the first argument of the original function. The original function always takes a context as the first argument.

Quantized Neural Network Layers¶

nnabla.functions.binary_sigmoid(x, n_outputs=- 1, outputs=None)[source]¶

Element-wise binary sigmoid function. In the forward pass, it computes

\[\begin{split}f(x) = \begin{cases} 1 & (x > 0) \\ 0 & ({\rm otherwise})\end{cases},\end{split}\]

but in the backward pass, a straight-through approximation of the gradient is used, i.e.,

\[\begin{split}\frac{\partial f(x)}{\partial x} = \begin{cases} 0 & (|x| \geq 1) \\ \frac{1}{2} & ({\rm otherwise}) \end{cases}.\end{split}\]

References

Courbariaux, Matthieu, and Yoshua Bengio. Binarynet: Training deep neural networks with weights and activations constrained to+ 1 or-1.

Parameters: x (Variable) – Input .
Returns: Output.
Return type: Variable

Note

All nnabla functions in nnabla.functions are decorated with the nnabla.function_bases.function_api decorator, which queries the current context and passes it into the first argument of the original function. The original function always takes a context as the first argument.

nnabla.functions.binary_tanh(x, n_outputs=- 1, outputs=None)[source]¶

Element-wise binary tanh function. In the forward pass, it computes

\[\begin{split}f(x) = \begin{cases} 1 & (x > 0) \\ -1 & ({\rm otherwise}) \end{cases},\end{split}\]

but in the backward pass, a straight-through approximation of the gradient is used, i.e.,

\[\begin{split}\frac{\partial f(x)}{\partial x} = \begin{cases} 0 & (|x| \geq 1) \\ 1 & ({\rm otherwise}) \end{cases}.\end{split}\]

References

Courbariaux, Matthieu, and Yoshua Bengio. Binarynet: Training deep neural networks with weights and activations constrained to+ 1 or-1.

Parameters: x (Variable) – Input .
Returns: Output.
Return type: Variable

Note

All nnabla functions in nnabla.functions are decorated with the nnabla.function_bases.function_api decorator, which queries the current context and passes it into the first argument of the original function. The original function always takes a context as the first argument.

nnabla.functions.binary_connect_affine(x, weight, binary_weight, bias=None, base_axis=1, quantize_zero_to=1.0, n_outputs=- 1, outputs=None)[source]¶

This function provides a BinaryConnect affine layer. It computes in the forward pass

\[y_j = \sum_{i} sign(w_{j,i}) x_i,\]

i.e., the weights $w_{j,i}$ are binarized to $sign(w_{j,i})$ and, hence, each weight is in $\{-1,\,1\}$. By this weight binarization, the inner product computations do not require any multiplications anymore as they turn into additions/subtractions.

This function should be used together with batch_normalization().

Note

1) If you would like to share the binary weights between other layers, please use the standard, floating value weights (weight) and not the binary weights (binary_weight).

2) The weights and the binary weights become in sync only after a call to forward(), and not after a call to backward(). If you wish to store the parameters of the network, remember to call forward(), once before doing so, otherwise the weights and the binary weights will not be in sync.

3) CPU and GPU implementations now use floating values for binary_weight, since this function is for simulation purposes.

References

M. Courbariaux, Y. Bengio, and J.-P. David. BinaryConnect: Training Deep Neural Networks with binary weights during propagations.

Parameters

x (Variable) – Input .
weight (Variable) – Weight . [parameter]
binary_weight (Variable) – Binarized weight . [parameter]
bias (Variable) – Bias. [optional][parameter]
base_axis (int) – Dimensions up to base_axis is treated as sample dimension. [default= 1 ]
quantize_zero_to (float) – Input value at zero is quantized to this value. [default= 1.0 ]

Returns

Output.

Return type

Note

All nnabla functions in nnabla.functions are decorated with the nnabla.function_bases.function_api decorator, which queries the current context and passes it into the first argument of the original function. The original function always takes a context as the first argument.

nnabla.functions.binary_connect_convolution(x, weight, binary_weight, bias=None, base_axis=1, pad=None, stride=None, dilation=None, group=1, quantize_zero_to=1.0, n_outputs=- 1, outputs=None)[source]¶

This function provides a BinaryConnect convolution layer. It computes in the forward pass

\[y_{n, a, b} = \sum_{m} \sum_{i} \sum_{j} sign(w_{n, m, i, j}) x_{m, a + i, b + j},\]

i.e., the weights $w_{n, m, i, j}$ are binarized to $sign(w_{n, m, i, j})$ and, hence, each weight is in $\{-1,\,1\}$. By this weight binarization, the inner product computations do not require any multiplications anymore as they turn into additions/subtractions.

This function should be used together with batch_normalization().

Reference

M. Courbariaux, Y. Bengio, and J.-P. David. BinaryConnect: Training Deep Neural Networks with binary weights during propagations.

Note

1) If you would like to share the binary weights between other layers, please use the standard, floating value weights (weight) and not the binary weights (binary_weight).

2) The weights and the binary weights become in sync only after a call to forward(), and not after a call to backward(). If you wish to store the parameters of the network, remember to call forward(), once before doing so, otherwise the weights and the binary weights will not be in sync.

3) CPU and GPU implementations now use floating values for binary_weight, since this function is for simulation purposes.

Parameters

x (Variable) – Input.
weight (Variable) – Weight. [parameter]
binary_weight (Variable) – Binarized weight. [parameter]
bias (Variable) – Bias. [optional][parameter]
base_axis (int) – Dimensions up to base_axis is treated as sample dimension. [default= 1 ]
pad (tuple of int) – Padding sizes for dimensions. [default= (0,) * (len(x.shape) - (base_axis+1)) ]
stride (tuple of int) – Stride sizes for dimensions. [default= (1,) * (len(x.shape) - (base_axis+1)) ]
dilation (tuple of int) – Dilation sizes for dimensions. [default= (1,) * (len(x.shape) - (base_axis+1)) ]
group (int) – Number of groups of channels. This makes the connection across channels sparser, by grouping connections along the mapping direction. [default= 1 ]
quantize_zero_to (float) – Input value at zero is quantized to this value. [default= 1.0 ]

Returns

Output

Return type

Note

All nnabla functions in nnabla.functions are decorated with the nnabla.function_bases.function_api decorator, which queries the current context and passes it into the first argument of the original function. The original function always takes a context as the first argument.

nnabla.functions.binary_weight_affine(x, weight, binary_weight, alpha, bias=None, base_axis=1, quantize_zero_to=1.0, n_outputs=- 1, outputs=None)[source]¶

This function provides a Binary Weight Network affine layer. It computes in the forward pass

\[y_j = \frac{1}{\|\mathbf{w}_j\|_{\ell_1}} \sum_{i} sign(w_{j,i}) x_i\]

i.e., the weights $w_{j,i}$ are binarized to $sign(w_{j,i})$ and, hence, each weight is in $\{-1,\,1\}$. By this weight binarization, the inner product computations turn into additions/subtractions which are followed by multiplication with the scaling factor $\alpha_j = \frac{1}{\|\mathbf{w}_j\|_{\ell_1}}$.

Reference

Rastegari, Mohammad, et al. XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networks.

Note

1) If you would like to share the binary weights with other layers, please use the standard, floating value weights (weight) and not the binary weights (binary_weight).

2) The weights and the binary weights become in sync only after a call to forward(), and not after a call to backward(). If you wish to store the parameters of the network, remember to call forward(), once before doing so, otherwise the weights and the binary weights will not be in sync.

3) CPU and GPU implementations now use floating values for binary_weight, since this function is for simulation purposes.

Parameters

x (Variable) – Input .
weight (Variable) – Weight. [parameter]
binary_weight (Variable) – Binarized weight. [parameter]
alpha (Variable) – Alpha. [parameter]
bias (Variable) – Bias. [optional][parameter]
base_axis (int) – Dimensions up to base_axis is treated as sample dimension. [default= 1 ]
quantize_zero_to (float) – Input value at zero is quantized to this value. [default= 1.0 ]

Returns

Output.

Return type

Note

All nnabla functions in nnabla.functions are decorated with the nnabla.function_bases.function_api decorator, which queries the current context and passes it into the first argument of the original function. The original function always takes a context as the first argument.

nnabla.functions.binary_weight_convolution(x, weight, binary_weight, alpha, bias=None, base_axis=1, pad=None, stride=None, dilation=None, group=1, quantize_zero_to=1.0, n_outputs=- 1, outputs=None)[source]¶

This function provides a Binary Weight Network convolution layer. It computes in the forward pass

\[y_{n, a, b} = \frac{1}{\|\mathbf{w}_n\|_{\ell_1}} \sum_{m} \sum_{i} \sum_{j} sign(w_{n, m, i, j}) x_{m, a + i, b + j}.\]

i.e., the weights $w_{n, m, i, j}$ are binarized to $sign(w_{n, m, i, j})$ and, hence, each weight is in $\{-1,\,1\}$. By this weight binarization, the inner product computations turn into additions/subtractions which are followed by multiplication with the scaling factor $\alpha_n = \frac{1}{\|\mathbf{w}_n\|_{\ell_1}}$.

Reference

Rastegari, Mohammad, et al. XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networks.

Note

1) If you would like to share the binary weights between other standard layers, please use the standard, floating value weights (weight) and not the binary weights (binary_weight).

2) The weights and the binary weights become in sync only after a call to forward(), and not after a call to backward(). If you wish to store the parameters of the network, remember to call forward(), once before doing so, otherwise the weights and the binary weights will not be in sync.

3) CPU and GPU implementations now use floating values for binary_weight, since this function is for simulation purposes.

Parameters

x (Variable) – Input.
weight (Variable) – Weight. [parameter]
binary_weight (Variable) – Binarized weight. [parameter]
alpha (Variable) – Alpha. [parameter]
bias (Variable) – Bias. [optional][parameter]
base_axis (int) – Dimensions up to base_axis is treated as sample dimension. [default= 1 ]
pad (tuple of int) – Padding sizes for dimensions. [default= (0,) * (len(x.shape) - (base_axis+1)) ]
stride (tuple of int) – Stride sizes for dimensions. [default= (1,) * (len(x.shape) - (base_axis+1)) ]
dilation (tuple of int) – Dilation sizes for dimensions. [default= (1,) * (len(x.shape) - (base_axis+1)) ]
group (int) – Number of groups of channels. This makes the connection across channels sparser, by grouping connections along the mapping direction. [default= 1 ]
quantize_zero_to (float) – Input value at zero is quantized to this value. [default= 1.0 ]

Returns

Output

Return type

Note

All nnabla functions in nnabla.functions are decorated with the nnabla.function_bases.function_api decorator, which queries the current context and passes it into the first argument of the original function. The original function always takes a context as the first argument.

nnabla.functions.fixed_point_quantize(x, sign=True, n=8, delta=0.0625, quantize=True, ste_fine_grained=True, outputs=None)[source]¶

Fixed Point Quantize.

This function simulates to uniformly quantize values in fixed-point number representation.

Parameters

x (Variable) – An input variable.
sign (bool) – Indicate the signed number or the unsigned number. Default is true.
n (int) – Bit width used. Note that sign consumes one bit. $n-1$ is used for number representation in signed case.
delta (float) – Step size.
quantize (bool) – If true, quantize input, otherwise not.
ste_fine_grained (bool) – If true, STE is not 1.

Returns

N-D array.

Return type

See also

nnabla.function_bases.fixed_point_quantize.

In the forward pass,

\[\begin{split}\begin{equation} q_i= \left\{ \begin{array}{ll} max & if \ \ \ x_i > max \\ sign(x_i) \times floor(|x_i| \delta^{-1} + 2^{-1}) \times \delta & if \ \ min \le x_i \le max \\ min & if \ \ x_i < min \\ \end{array} \right., \end{equation}\end{split}\]

where $\delta$ is the step size, $(min, max) :=(- (2^{n-1} - 1)\delta, (2^{n-1} - 1)\delta)$ if $sign$ is true, $(min, max) := (0, (2^n - 1) \delta)$ otherwise, and $n$ is the total bit-width used.

In the backward pass when using ste_fine_grained as false,

\[\begin{equation} \frac{\partial q_i}{\partial x_i} = 1. \end{equation}\]

In the backward pass when using ste_fine_grained as true,

\[\begin{split}\begin{equation} \frac{\partial q_i}{\partial x_i}= \left\{ \begin{array}{ll} 0 & if \ \ \ x_i > max \\ 1 & if \ \ min \le x_i \le max \\ 0 & if \ \ x_i < min \\ \end{array} \right.. \end{equation}\end{split}\]

Note

Quantized values are stored as floating point number, since this function is for simulation purposes.

nnabla.functions.min_max_quantize(x, qr_min, qr_max, ql_min, ql_max, decay=0.999, x_min_max=False, ema=False, ste_fine_grained=True, eps=0.01, quantize=True, outputs=None)[source]¶

Min-max quantization.

This function simulates to uniformly quantize values in fixed-point number representation.

Min-max quantization is defined as the following equation

\[y = round \left(\frac{\min(\max(x, m), M) - m}{scale} \right) \times scale + m,\]

where the $scale$ is defined as

\[scale = \frac{M - m}{M_q - m_q},\]

and

\[\begin{split}m_q = ql_{min}, \\ M_q = ql_{max}, \\ m = qr_{min}, \\ M = qr_{max}.\end{split}\]

In the backward pass when using ste_fine_grained as false,

\[\frac{\partial q_i}{\partial x_i} = 1.\]

In the backward pass when using ste_fine_grained as true,

\[\begin{split} \frac{\partial q_i}{\partial x_i}= \left\{ \begin{array}{ll} 0 & if \ \ \ x_i > M \\ 1 & if \ \ m \le x_i \le M \\ 0 & if \ \ x_i < m \\ \end{array} \right..\end{split}\]

$qr_{min}$ and $qr_{max}$ are treaded as follows.

x_min_max is True and ema is True: Exponential moving average are computed for each $min(x)$ and $max(x)$ then stored in $qr_{min}$ and $qr_{max}$.

x_min_max is True and ema is False: $min(x)$ and $max(x)$ are computed then stored in $qr_{min}$ and $qr_{max}$.

x_min_max is False and ema is True: Exponential moving average stored in $qr_{min}$ and $qr_{max}$ are used.

x_min_max is False and ema is False Gradients of $qr_{min}$ and $qr_{max}$ are computed in the backward pass.

More precisely, in inference of the min-max quantization, one has to consider zero-point (zp) which corresponds to the real value 0, and its data type is an integer. zero-point is defined as

\[\begin{split} && zp_f = ql_{min} -\frac{qr_{min}}{scale}, \\ && zp = \left\{ \begin{array}{ll} ql_{max} & if \ \ \ zp_f >= ql_{max} \\ round(zp_f) & if \ \ otherwise \\ ql_{min} & if \ \ zp_f <= ql_{min} \\ \end{array} \right..\end{split}\]

Accordingly, in order to simulate quantization effect of zero-point, during both forward and backward pass, $qr_{min}$ and $qr_{max}$ are adjusted as follows,

\[\begin{split}qr_{min}^{adj} = ql_{min} - zp * scale, \\ qr_{max}^{adj} = ql_{max} - zp * scale.\end{split}\]

These operations are often called nudge.

Finally, in the formulas of the min-max quantization, $m$ and $M$ are replaced by $qr_{min}^{adj}$ and $qr_{max}^{adj}$ respectively.

Parameters

x (Variable) – Input N-D array.
qr_min (Variable) – Minimum quantization range (modified during forward execution).
qr_max (Variable) – Maximum quantization range (modified during forward execution).
ql_min (Variable) – Minimum quantization level, typically 0.
ql_max (Variable) – Maximum quantization level, typically 255.
decay (float) – The decay rate for the exponential moving average.
x_min_max (bool) – Use the min and max of x to compute quantization ranges. Default is False.
ema (bool) – Use the exponential moving average for the min and max quantization ranges. Default is False.
ste_fine_grained (bool) – If True, STE is not 1, the {0, 1}-mask computed from the min-max is applied to the gradient in the backward; otherwise, STE is 1.
eps (float) – Epsilon, or small value to ensure $qr_{max} - qr_{min}$ must be greater than the epsilon.
quantize (bool) – Apply quantization or not.

References

Benoit Jacob, Skirmantas Kligys, Bo Chen, Menglong Zhu, Matthew Tang, Andrew Howard, Hartwig Adam, and Dmitry Kalenichenko, “Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference”, https://arxiv.org/abs/1712.05877

nnabla.functions.pow2_quantize(x, sign=True, with_zero=True, n=8, m=1, quantize=True, ste_fine_grained=True, outputs=None)[source]¶

Pow2 Quantize.

This function simulates to uniformly quantize values in fixed-point number representation.

Parameters

x (Variable) – An input variable.
sign (bool) – Indicate the signed number or the unsigned number. Default is true.
with_zero (bool) – Indicate using zero as a quantized value. Default is true. Note that zero consumes one bit.
n (int) – Bit width used. Note that sign consumes one bit. $n-1$ is used for number representation in signed case. Default is 8.
m (int) – $2^m$ is the upper bound of the dynamic range and $-2^m$ is the lower bound, $m \in \mathcal{Z}$. Default is 1.
quantize (bool) – If true, quantize input, otherwise not.
ste_fine_grained (bool) – If true, STE is not 1.

Returns

N-D array.

Return type

See also

nnabla.function_bases.pow2_quantize.

In the forward pass of signed case,

\[\begin{split}q_i= \left\{ \begin{array}{ll} max_{+} & if \ \ \overline{q_i} > max_{+} \\ \overline{q_i} & if \ \ min_{+} \le \overline{q_i} \le max_{+} \\ min_{+} & if \ \ 0 \le \overline{q_i} < min_{+} \\ min_{-} & if \ \ min_{-} < \overline{q_i} < 0 \\ \overline{q_i} & if \ \ max_{-} \le \overline{q_i} \le min_{-}\\ max_{-} & if \ \ \overline{q_i} < max_{-} \\ \end{array} \right.,\end{split}\]

where

\[\begin{split}&& max_{+} = 2^{m}, min_{+} = 2^{m - (2^{n-1} - 1)},\\ && max_{-} = -2^{m}, min_{-} = -2^{m - (2^{n-1} - 1)},\\ && \overline{q_i} = sign(x_i) \times 2^{round(\log_2 |x_i|)}.\end{split}\]

This quantization uses the geometric mean between two power-of-two numbers as quantization threshold.

In the forward pass of unsigned case,

\[\begin{split}q_i= \left\{ \begin{array}{ll} max & if \ \ \overline{q_i} > max \\ \overline{q_i} & if \ \ min \le \overline{q_i} \le max \\ min & if \ \ 0 < \overline{q_i} < min \\ \end{array} \right.,\end{split}\]

where

\[\begin{split}&& max = 2^{m}, min = 2^{m - (2^{n} - 1)},\\ && \overline{q_i} = 2^{int(\log_2 |x_i|)}.\end{split}\]

When using with_zero as true, a pruning threshold is used to round an input to 0 or $min$. The pruning threshold is defined in this function as the following,

\[pruning\ threshold = min \times 2^{-\frac{1}{2}}.\]

If an absolute value of the input is lesser than this value, the input is rounded to 0, otherwise $min$.

In the backward pass when using ste_fine_grained as false,

\[\frac{\partial q_i}{\partial x_i} = 1.\]

In the backward pass when using ste_fine_grained as true,

\[\begin{split}\frac{\partial q_i}{\partial x_i}= \left\{ \begin{array}{ll} 0 & if \ \ \overline{q_i} > max_{+} \\ 1 & if \ \ otherwise \\ 0 & if \ \ \overline{q_i} < max_{-} \\ \end{array} \right..\end{split}\]

nnabla.functions.prune(x, rate=0.9, n_outputs=- 1, outputs=None)[source]¶

Prune the input as the following equation,

\[\begin{split}q_i = \left \{ \begin{array}{ll} 0 & abs(x_i) < threshold \\ x_i & otherwise \end{array} \right.\end{split}\]

where $threshold$ is determined by threshold = np.sort(np.abs(x))[int((x.size - 1) * rate)].

Parameters

x (Variable) – N-D array
rate (float) – Sparse rate, or pruning rate. [default= 0.9 ]

Returns

N-D array with the same shape as x

Return type

Note

All nnabla functions in nnabla.functions are decorated with the nnabla.function_bases.function_api decorator, which queries the current context and passes it into the first argument of the original function. The original function always takes a context as the first argument.

nnabla.functions.inq_affine(x, weight, indicator_fixedweights, bias=None, base_axis=1, num_bits=4, inq_iterations=(), selection_algorithm='largest_abs', seed=- 1, n_outputs=- 1, outputs=None)[source]¶

This function provides a INQ affine layer. It computes in the forward pass

\[y_j = \sum_{i} w_{j,i} x_i,\]

where the weights $w_{j,i}$ are quantized sequentially during training to power-of-two numbers. In the backward pass, only the non-fixed (i.e., learnable) weights are updated.

References

Zhou A, Yao A, Guo Y, Xu L, Chen Y. Incremental network quantization: Towards lossless CNNs with low-precision weights.

Parameters

x (Variable) – Input .
weight (Variable) – Weight . [parameter]
indicator_fixedweights (Variable) – Indicates which weights are already fixed (0 = not fixed, 1 = fixed) . [parameter]
bias (Variable) – Bias. [optional][parameter]
base_axis (int) – Dimensions up to base_axis is treated as sample dimension. [default= 1 ]
num_bits (int) – Number of bits per weight. Needs to be >= 2 as two bits are used to code zero and sign of weight. [default= 4 ]
inq_iterations (repeated int64) – List which specifies after how many forward passes we fix 50% of the learnable weights. If we have done as many iterations as specified in the last element of inq_iterations, then all weights are fixed. [default= () ]
selection_algorithm (string) – Chooses algorithm that we use for selecting the weights to fix (“largest_abs” … fix weights with largest absolute value, “random” … fix weights randomly) [default= 'largest_abs' ]
seed (int) – Random seed. When -1, seed is sampled from global random number generator. [default= -1 ]

Returns

Output.

Return type

Note

All nnabla functions in nnabla.functions are decorated with the nnabla.function_bases.function_api decorator, which queries the current context and passes it into the first argument of the original function. The original function always takes a context as the first argument.

nnabla.functions.inq_convolution(x, weight, indicator_fixedweights, bias=None, base_axis=1, pad=None, stride=None, dilation=None, group=1, num_bits=4, inq_iterations=(), selection_algorithm='largest_abs', seed=- 1, n_outputs=- 1, outputs=None)[source]¶

This function provides a INQ convolution layer. It computes in the forward pass

\[y_{n, a, b} = \sum_{m} \sum_{i} \sum_{j} w_{n, m, i, j} x_{m, a + i, b + j},\]

where the weights $w_{j,i}$ are quantized sequentially during training to power-of-two numbers. In the backward pass, only the non-fixed (i.e., learnable) weights are updated.

Reference

Zhou A, Yao A, Guo Y, Xu L, Chen Y. Incremental network quantization: Towards lossless CNNs with low-precision weights.

Parameters

x (Variable) – Input.
weight (Variable) – Weight. [parameter]
indicator_fixedweights (Variable) – Indicates which weights are already fixed (0 = not fixed, 1 = fixed) . [parameter]
bias (Variable) – Bias. [optional][parameter]
base_axis (int) – Dimensions up to base_axis is treated as sample dimension. [default= 1 ]
pad (tuple of int) – Padding sizes for dimensions. [default= (0,) * (len(x.shape) - (base_axis+1)) ]
stride (tuple of int) – Stride sizes for dimensions. [default= (1,) * (len(x.shape) - (base_axis+1)) ]
dilation (tuple of int) – Dilation sizes for dimensions. [default= (1,) * (len(x.shape) - (base_axis+1)) ]
group (int) – Number of groups of channels. This makes the connection across channels sparser, by grouping connections along the mapping direction. [default= 1 ]
num_bits (int) – Number of bits per weight. Needs to be >= 2 as two bits are used to code zero and sign of weight. [default= 4 ]
inq_iterations (repeated int64) – List which specifies after how many forward passes we fix 50% of the learnable weights. If we have done as many iterations as specified in the last element of inq_iterations, then all weights are fixed. [default= () ]
selection_algorithm (string) – Chooses algorithm that we use for selecting the weights to fix (“largest_abs” … fix weights with largest absolute value, “random” … fix weights randomly) [default= 'largest_abs' ]
seed (int) – Random seed. When -1, seed is sampled from global random number generator. [default= -1 ]

Returns

Output

Return type

Note

All nnabla functions in nnabla.functions are decorated with the nnabla.function_bases.function_api decorator, which queries the current context and passes it into the first argument of the original function. The original function always takes a context as the first argument.

Unsupported, Special Use¶

nnabla.functions.vat_noise(x, w, base_axis=1, eps=1.0, n_outputs=- 1, outputs=None)[source]¶

Noise for virtual adversarial training.

This layer is a special layer for GUI network designing, specialized for getting the noise of virtual adversarial training.

In the backward process, the weight parameter will be replaced with the gradient.

Forward

\[y_i = \frac{\epsilon x_i}{\sqrt{\sum_k x_k^2 + c}}\]

Backward

\[\delta x_i = 0\]

\[w_i = \epsilon \delta y_i\]

Note

This layer is a special layer for GUI network designing.

References

Miyato et.al, Distributional Smoothing with Virtual Adversarial Training.

Parameters

x (Variable) – N-D array of noise input. Noise is standard Gaussian noise initially, but the next step, fed back gradient variable.
w (Variable) – N-D array for keep gradient values.
base_axis (int) – Dimensions up to base_axis is treated as sample dimension. [default= 1 ]
eps (float) – Noise norm (l2) factor. [default= 1.0 ]

Returns

N-D array

Return type

Note

All nnabla functions in nnabla.functions are decorated with the nnabla.function_bases.function_api decorator, which queries the current context and passes it into the first argument of the original function. The original function always takes a context as the first argument.

nnabla.functions.unlink(x, n_outputs=- 1, outputs=None)[source]¶

This function behaves as an identity function on the forward pass, and deletes the gradient for the background pass.

This layer is a special layer for GUI network designing, used for getting zero backward operation by adding this layer.

Forward

\[y_i = x_i\]

Backward

\[\delta x_i = 0\]

Note

This layer is a special layer for GUI network designing.

Parameters: x (Variable) – N-D array.
Returns: N-D array.
Return type: Variable

Note

All nnabla functions in nnabla.functions are decorated with the nnabla.function_bases.function_api decorator, which queries the current context and passes it into the first argument of the original function. The original function always takes a context as the first argument.

nnabla.functions.sink(*x, **kw)[source]¶

Creates a dummy variable used to call forward or backward function of multiple variables at one place.

This takes any numbers of input variables with any shape, and creates a single 0-shape outputs. The forward pass does nothing. The backward pass set ones to the input grads if one_input_grad is set as true.

Note

sink can only be called at the very end of the graph, and grad of input variables are cleared

when y.backward(clear_buffer=True) is called.

Parameters

*x (Variable) – Any number of inputs with any shape. [variadic]
one_input_grad (bool) – Set grads of inputs as one during backward. It is useful to set false if you want to set external gradients to the input variables. [default= True ]

Returns

Dummy variable.

Return type

Note

All nnabla functions in nnabla.functions are decorated with the nnabla.function_bases.function_api decorator, which queries the current context and passes it into the first argument of the original function. The original function always takes a context as the first argument.

nnabla.functions.confusion_matrix(x, target, axis=None, n_outputs=- 1, outputs=None)[source]¶

Confusion matrix. The return value is already summed over samples.

Parameters

x (Variable) – Probabilities N-D array. ($D_1 \times ... \times D_i \times ... \times D_N$)
target (Variable) – Labels N-D array. ($D_1 \times ... \times 1 \times ... \times D_N$)
axis (int) – Axis on which the confusion matrix is calculated. [default= len(x.shape) - 1 ]

Returns

Confusion matrix 2-D array. Col index is estimated class. Row index is label class.

Return type

Note

All nnabla functions in nnabla.functions are decorated with the nnabla.function_bases.function_api decorator, which queries the current context and passes it into the first argument of the original function. The original function always takes a context as the first argument.

Image Object Detection¶

nnabla.functions.nms_detection2d(x, thresh=None, nms=None, nms_per_class=None, n_outputs=- 1, outputs=None)[source]¶

Non-Maximum Suppression (NMS) to 2D Object detector output. The input is a 3-dimensional tensor with shape of (B, N, 5 + C) where B denotes batch size, N denotes the number of detection box candidates, and C denotes the number of classes of object detection. 5 + C consists of the box coordinates x, y, w, h in normalized coordinates (size of each x and y are 1.0), objectness (learned to predict IoU value to ground truth box), and the class probabilities of C classes. It outputs a tensor with the same dimensions as the input, where all values are copied from the input to the output, except the class probabilities are multiplied by objectness, and possibly suppressed to 0 by NMS. During NMS, all of combination of pairs of bounding boxes is compared. For each pair, the bounding box with a lower detection score (described below) is suppressed if the overlap ratio (the IoU) is greater than the value of nms.

There are two suppression modes for NMS.

1. Suppress by class probability (nms_per_class is True): For each bounding box, the detection score is calculated by objectness * probability[class_id] for each class. The suppression is done for each class independently.

2. Suppress by objectness (nms_per_class is False): The suppression is done for each bounding box using objectness as a detection score. All class probabilities becomes 0 for every suppressed boxes.

References

Joseph Redmon, Ali Farhadi, YOLO9000: Better, Faster, Stronger.

Parameters

x (Variable) – A 3-dimensional array.
thresh (float) – Detection score threshold. [default= 0.5 ]
nms (float) – IoU threshold for Non-maximum suppression (NMS). [default= 0.45 ]
nms_per_class (bool) – If true, NMS is applied for each class. [default= True ]

Returns

A 3-dim array with the same dimensions with the input.

Return type

Note

All nnabla functions in nnabla.functions are decorated with the nnabla.function_bases.function_api decorator, which queries the current context and passes it into the first argument of the original function. The original function always takes a context as the first argument.

Validation¶

nnabla.functions.top_n_error(x, target, axis=None, n=1, n_outputs=- 1, outputs=None)[source]¶

Top N error along the dimension specified by the axis, the element of outputs is

\[\begin{split}y_i = \left \{ \begin{array}{l} 1 \ (x_i \ is \ not \ within \ N-th \ place) \\ 0 \ (x_i \ is \ within \ N-th \ place) \end{array} \right.\end{split}\]

Parameters

x (Variable) – Probabilities N-D array. $D_1 \times ... \times D_i \times ... \times D_N$
target (Variable) – N-D array of labels. $D_1 \times ... \times 1 \times ... \times D_N$
axis (int) – Axis on which the top N error is calculated. [default= len(x.shape) - 1 ]
n (int) – top N [default= 1 ]

Returns

Element-wise error N-D array. ($D_1 \times ... \times 1 \times ... \times D_N$)

Return type

Note

All nnabla functions in nnabla.functions are decorated with the nnabla.function_bases.function_api decorator, which queries the current context and passes it into the first argument of the original function. The original function always takes a context as the first argument.

nnabla.functions.binary_error(x, target, n_outputs=- 1, outputs=None)[source]¶

Elementwise binary error.

\[\begin{split}y_i = \left \{ \begin{array}{l} 0 ((x^{(0)} \geq 0.5) = (x^{(1)} \geq 0.5)) \\ 1 ((x^{(0)} \geq 0.5) \neq (x^{(1)} \geq 0.5)) \end{array} \right.\end{split}\]

Parameters

x (Variable) – Probabilities N-D array. $-\infty$ to $\infty$.
target (Variable) – Labels N-D array. Usually set as 0 or 1, but, it allows probability (0 to 1) as inputs.

Returns

Element-wise errors N-D array.

Return type

Note

All nnabla functions in nnabla.functions are decorated with the nnabla.function_bases.function_api decorator, which queries the current context and passes it into the first argument of the original function. The original function always takes a context as the first argument.

Parametric Functions¶

In NNabla, trainable models are created by composing functions that have optimizable parameters. These functions are called parametric functions. Parametric functions are provided by nnabla.parametric_functions.

See also:: Python API Tutorial.

Parameter Management API¶

The parameters registered by List of Parametric Functions can be managed using APIs listed in this section.

nnabla.parameter.parameter_scope(name, scope=None)[source]¶

Grouping parameters registered by parametric functions listed in nnabla.parametric_functions.

Parameters

name (str) – Parameter scope name.
scope (OrderedDict, optional) – Specify current parameter scope as a local dictionary. The default value is None. In this case, the current parameter scope maintained in global is used.

Example:

import nnabla as nn
import nnabla.parametric_functions as PF
import nnabla.functions as F

with nn.parameter_scope('conv1'):
    conv_out1 = PF.convolution(x, 32, (5, 5))
    bn_out1 = PF.batch_normalization(conv_out1)
    act_out1 = F.relu(bn_out1)
with nn.parameter_scope('conv2'):
    conv_out2 = PF.convolution(act_out1, 64, (3, 3))
    bn_out2 = PF.batch_normalization(conv_out2)
    act_out2 = F.relu(bn_out2)

Nesting The with statement blocks allows you to nest parameter scopes. This can also be done by using “/” inside the parameter names.

Example:

with nn.parameter_scope('network1'):
    with nn.parameter_scope('conv1'):
        conv_out1 = PF.convolution(x, 32, (5, 5))
        bn_out1 = PF.batch_normalization(conv_out1)
        act_out1 = F.relu(bn_out1)
    with nn.parameter_scope('conv2'):
        conv_out2 = PF.convolution(act_out1, 64, (3, 3))
        bn_out2 = PF.batch_normalization(conv_out2)
        act_out2 = F.relu(bn_out2)

is equivalent to

with nn.parameter_scope('network1/conv1'):
    conv_out1 = PF.convolution(x, 32, (5, 5))
    bn_out1 = PF.batch_normalization(conv_out1)
    act_out1 = F.relu(bn_out1)
with nn.parameter_scope('network1/conv2'):
    conv_out2 = PF.convolution(act_out1, 64, (3, 3))
    bn_out2 = PF.batch_normalization(conv_out2)
    act_out2 = F.relu(bn_out2)

nnabla.parameter.get_current_parameter_scope()[source]¶: Returns current parameter scope.

nnabla.parameter.get_parameters(params=None, path='', grad_only=True)[source]¶

Get parameter Variables under the current parameter scope.

Parameters

params (dict) – Internal use. User doesn’t set it manually.
path (str) – Internal use. User doesn’t set it manually.
grad_only (bool) – Retrieve all parameters under the current scope if False, while only parameters with need_grad=True are retrieved if True.

Returns

{str : Variable}

Return type

dict

nnabla.parameter.clear_parameters()[source]¶: Clear all parameters in the current scope.

nnabla.parameter.save_parameters(path, params=None, extension=None)[source]¶

Save all parameters into a file with the specified format.

Currently hdf5 and protobuf formats are supported.

Parameters

path – path or file object
params (dict, optional) – Parameters to be saved. Dictionary is of a parameter name (str) to Variable.

nnabla.parameter.load_parameters(path, proto=None, needs_proto=False, extension='.nntxt')[source]¶

Load parameters from a file with the specified format.

Parameters: path – path or file object

nnabla.parameter.get_parameter_or_create(name, shape=None, initializer=None, need_grad=True, as_need_grad=None)[source]¶

Returns an existing parameter variable in current parameter scope with the provided name.

If a variable with the provided name does not exist, a new variable is created and registered to the current parameter scope with the name, then returned.

Parameters

name (str) – The name under the current scope. If it already exists, the name is queried from the parameter manager.
shape (tuple of int) – Shape of created parameter. The shape of the specified parameter must match with this shape. The default is None which is only valid if initializer is given as an numpy.ndarray.
initializer (nnabla.initializer.BaseInitializer or numpy.ndarray) – An initialization function to be applied to the parameter. numpy.ndarray can also be given to initialize parameters from numpy array data.
need_grad (bool) – Register the parameter with the specified need_grad flag. The default is True. If the flag is different from the previously specified one, the flag will be overwritten, but the values will be kept.
as_need_grad (bool) – Get a parameter variable with the specified need_grad flag. Note that this doesn’t overwrite the flag of the registered parameter variable with the provided name. Instead, if the given flag mismatches with the previously registered need_grad flag, it returns a new variable referring to the same array contents but with need_grad=as_need_grad.

Note

It returns a Variable which is unlinked from the registered one in the current parmeter scope (using nnabla.Variable.get_unlinked_variable()). That means changing a need_grad attribute doesn’t affect the variable existing in the current parameter scope.

List of Parametric Functions¶

Parametric functions are provided by nnabla.parametric_functions , as listed below. Like functions listed in Functions, they take Variable (s) as first argument(s) followed by options specific to a parametric function. In addition, they register parameter Variable (s) into the parameter scope.

The parameter variables are registered with need_grad properties specific to a parametric function. The variables with need_grad=False flag will not be updated by gradient descent. Hence, backward computation is not executed for those variables. False is usually specified when the parameters are updated during foward pass and/or backward pass, e.g., batch normalization.

All parametric functions take an optional argument fix_parameters=False. By giving True, the associated parameter variables are connected to a computation graph with a property need_grad=False regardless properties of the registered variables, then backward gradient computation is not executed for those variables. This is useful when you create a computation graph for evaluation purpose, fixing parameters partially in a graph, and so on.

All parametric functions listed below are decorated with the following decorator.

nnabla.parametric_functions.parametric_function_api(scope_name=None, param_desc=None)[source]¶

Decorator for parametric functions.

The decorated function is always called under a parameter scope scope_name. Also, the decorator adds an additional argument name (str, default is None) at the end. If name is specified, the scope scope_name comes under a scope name. This feature could reduce vertical space usage of the source code. Any parametric function should be decorated by this.

Parameters

scope_name (str, optional) – The original function will be called under a parameter scope named by scope_name.
param_desc (list, optional) – Descriptions of parameters will be automatically included into docstring. This must be a list of tuples with 4 elements composed of (name (str), description (str), shape info (str), need_grad (bool)).

Returns

A decorated parametric function.

Return type

function

See Parameter Management API to know how to query and manipulate registered variables.

Here is the list of parametric functions.

nnabla.parametric_functions.affine(inp, n_outmaps, base_axis=1, w_init=None, b_init=None, fix_parameters=False, rng=None, with_bias=True, apply_w=None, apply_b=None, name=None)[source]¶

The affine layer, also known as the fully connected layer. Computes

\[{\mathbf y} = {\mathbf A} {\mathbf x} + {\mathbf b}.\]

where ${\mathbf x}, {\mathbf y}$ are the inputs and outputs respectively, and ${\mathbf A}, {\mathbf b}$ are constants.

Parameters

inp (Variable) – Input N-D array with shape ($M_0 \times \ldots \times M_{B-1} \times D_B \times \ldots \times D_N$). Dimensions before and after base_axis are flattened as if it is a matrix.
n_outmaps (int or tuple of int) – Number of output neurons per data.
base_axis (int) – Dimensions up to base_axis are treated as the sample dimensions.
w_init (nnabla.initializer.BaseInitializer or numpy.ndarray) – Initializer for weight. By default, it is initialized with nnabla.initializer.UniformInitializer within the range determined by nnabla.initializer.calc_uniform_lim_glorot.
b_init (nnabla.initializer.BaseInitializer or numpy.ndarray) – Initializer for bias. By default, it is initialized with zeros if with_bias is True.
fix_parameters (bool) – When set to True, the weights and biases will not be updated.
rng (numpy.random.RandomState) – Random generator for Initializer.
with_bias (bool) – Specify whether to include the bias term.
apply_w (function) – Lambda, function, or callable object applied to the weights.
apply_b (function) – Lambda, function, or callable object applied to the bias.

Returns

$(B + 1)$-D array. ($M_0 \times \ldots \times M_{B-1} \times L$)

Return type

Parameters to be registered

The following variables are registered in a parameter scope "affine";

W (need_grad=True) : Weight matrix. (shape: (inmaps, outmaps))
b (need_grad=True) : bias vector. (shape: (outputs,))

Note

If the name option is passed, the parameters become wrapped inside the parameter scope with the specified name, yielding the same results as the following code. This can be used to simplify the code.

with parameter_scope(name):
    output = affine(<args>)

nnabla.parametric_functions.convolution(inp, outmaps, kernel, pad=None, stride=None, dilation=None, group=1, channel_last=False, w_init=None, b_init=None, base_axis=1, fix_parameters=False, rng=None, with_bias=True, apply_w=None, apply_b=None, name=None)[source]¶

N-D Convolution with a bias term.

For Dilated Convolution (a.k.a. Atrous Convolution), refer to:

Chen et al., DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs. https://arxiv.org/abs/1606.00915
Yu et al., Multi-Scale Context Aggregation by Dilated Convolutions. https://arxiv.org/abs/1511.07122

Note

Convolution is a computationally intensive operation that should preferably be run with the cudnn backend. NNabla then uses CuDNN library functions to determine and cache the fastest algorithm for the given set of convolution parameters, which results in additional memory consumption which may pose a problem for GPUs with insufficient memory size. In that case, the NNABLA_CUDNN_WORKSPACE_LIMIT environment variable can be used to restrict the choice of algorithms to those that fit the given workspace memory limit, expressed in bytes. In some cases it may also be desired to restrict the automatic search to algorithms that produce deterministic (reproducible) results. This can be requested by setting the the environment variable NNABLA_CUDNN_DETERMINISTIC to a non-zero value.

Parameters

inp (Variable) – N-D array.
outmaps (int) – Number of convolution kernels (which is equal to the number of output channels). For example, to apply convolution on an input with 16 types of filters, specify 16.
kernel (tuple of int) – Convolution kernel size. For example, to apply convolution on an image with a 3 (height) by 5 (width) two-dimensional kernel, specify (3,5).
pad (tuple of int) – Padding sizes for dimensions.
stride (tuple of int) – Stride sizes for dimensions.
dilation (tuple of int) – Dilation sizes for dimensions.
group (int) – Number of groups of channels. This makes connections across channels more sparse by grouping connections along map direction.
channel_last (bool) – If True, the last dimension is considered as channel dimension, a.k.a. NHWC order.
w_init (nnabla.initializer.BaseInitializer or numpy.ndarray) – Initializer for weight. By default, it is initialized with nnabla.initializer.UniformInitializer within the range determined by nnabla.initializer.calc_uniform_lim_glorot.
b_init (nnabla.initializer.BaseInitializer or numpy.ndarray) – Initializer for bias. By default, it is initialized with zeros if with_bias is True.
base_axis (int) – Dimensions up to base_axis are treated as the sample dimensions.
fix_parameters (bool) – When set to True, the weights and biases will not be updated.
rng (numpy.random.RandomState) – Random generator for Initializer.
with_bias (bool) – Specify whether to include the bias term.
apply_w (function) – Lambda, function, or callable object applied to the weights.
apply_b (function) – Lambda, function, or callable object applied to the bias.

Returns

N-D array. See convolution for the output shape.

Return type

Parameters to be registered

The following variables are registered in a parameter scope "conv";

W (need_grad=True) : Filter weights. (shape: (outmaps, inmaps // group, *kernel))
b (need_grad=True) : Bias vector. (shape: (outmaps,))

Note

If the name option is passed, the parameters become wrapped inside the parameter scope with the specified name, yielding the same results as the following code. This can be used to simplify the code.

with parameter_scope(name):
    output = convolution(<args>)

nnabla.parametric_functions.depthwise_convolution(inp, kernel, pad=None, stride=None, dilation=None, multiplier=1, w_init=None, b_init=None, base_axis=1, fix_parameters=False, rng=None, with_bias=True, name=None)[source]¶

N-D Depthwise Convolution with a bias term.

Reference:

1. Chollet: Chollet, Francois. “Xception: Deep Learning with Depthwise Separable Convolutions. https://arxiv.org/abs/1610.02357

Parameters

inp (Variable) – N-D array.
kernel (tuple of int) – Convolution kernel size. For example, to apply convolution on an image with a 3 (height) by 5 (width) two-dimensional kernel, specify (3,5).
pad (tuple of int) – Padding sizes for dimensions.
stride (tuple of int) – Stride sizes for dimensions.
dilation (tuple of int) – Dilation sizes for dimensions.
multiplier (int) – Number of output feature maps per input feature map.
w_init (nnabla.initializer.BaseInitializer or numpy.ndarray) – Initializer for weight. By default, it is initialized with nnabla.initializer.UniformInitializer within the range determined by nnabla.initializer.calc_uniform_lim_glorot.
b_init (nnabla.initializer.BaseInitializer or numpy.ndarray) – Initializer for bias. By default, it is initialized with zeros if with_bias is True.
base_axis (int) – Dimensions up to base_axis are treated as the sample dimensions.
fix_parameters (bool) – When set to True, the weights and biases will not be updated.
rng (numpy.random.RandomState) – Random generator for Initializer.
with_bias (bool) – Specify whether to include the bias term.

Returns

N-D array. See depthwise_convolution for the output shape.

Return type

Parameters to be registered

The following variables are registered in a parameter scope "depthwise_conv";

W (need_grad=True) : Filter weights. (shape: (inmaps * multiplier, *kernel))
b (need_grad=True) : Bias vector. (shape: (inmaps * multiplier,))

Note

If the name option is passed, the parameters become wrapped inside the parameter scope with the specified name, yielding the same results as the following code. This can be used to simplify the code.

with parameter_scope(name):
    output = depthwise_convolution(<args>)

nnabla.parametric_functions.deconvolution(inp, outmaps, kernel, pad=None, stride=None, dilation=None, group=1, channel_last=False, output_padding=None, w_init=None, b_init=None, base_axis=1, fix_parameters=False, rng=None, with_bias=True, apply_w=None, apply_b=None, name=None)[source]¶

Deconvolution layer.

Parameters

inp (Variable) – N-D array.
outmaps (int) – Number of deconvolution kernels (which is equal to the number of output channels). For example, to apply deconvolution on an input with 16 types of filters, specify 16.
kernel (tuple of int) – Convolution kernel size. For example, to apply deconvolution on an image with a 3 (height) by 5 (width) two-dimensional kernel, specify (3,5).
pad (tuple of int) – Padding sizes for dimensions.
stride (tuple of int) – Stride sizes for dimensions.
dilation (tuple of int) – Dilation sizes for dimensions.
group (int) – Number of groups of channels. This makes connections across channels sparser by grouping connections along map direction.
w_init (nnabla.initializer.BaseInitializer or numpy.ndarray) – Initializer for weight. By default, it is initialized with nnabla.initializer.UniformInitializer within the range determined by nnabla.initializer.calc_uniform_lim_glorot.
b_init (nnabla.initializer.BaseInitializer or numpy.ndarray) – Initializer for bias. By default, it is initialized with zeros if with_bias is True.
base_axis (int) – Dimensions up to base_axis are treated as the sample dimensions.
fix_parameters (bool) – When set to True, the weights and biases will not be updated.
rng (numpy.random.RandomState) – Random generator for Initializer.
with_bias (bool) – Specify whether to include the bias term.
apply_w (function) – Lambda, function, or callable object applied to the weights.
apply_b (function) – Lambda, function, or callable object applied to the bias.

Returns

N-D array. See deconvolution for the output shape.

Return type

Parameters to be registered

The following variables are registered in a parameter scope "deconv";

W (need_grad=True) : Filter weights. (shape: (inmaps, outmaps // group, *kernel))
b (need_grad=True) : Bias vector. (shape: (outmaps,))

Note

If the name option is passed, the parameters become wrapped inside the parameter scope with the specified name, yielding the same results as the following code. This can be used to simplify the code.

with parameter_scope(name):
    output = deconvolution(<args>)

nnabla.parametric_functions.depthwise_deconvolution(inp, kernel, pad=None, stride=None, dilation=None, divisor=1, w_init=None, b_init=None, base_axis=1, fix_parameters=False, rng=None, with_bias=True, name=None)[source]¶

Depthwise deconvolution computes the transposed depthwise convolution for one-dimensional and two-dimensional input data.

Parameters

inp (Variable) – N-D array.
kernel (tuple of int) – Convolution kernel size. For example, to apply convolution on an image with a 3 (height) by 5 (width) two-dimensional kernel, specify (3,5).
pad (tuple of int) – Padding sizes for dimensions.
stride (tuple of int) – Stride sizes for dimensions.
dilation (tuple of int) – Dilation sizes for dimensions.
divisor (int) – Number of input feature maps per output feature map.
w_init (nnabla.initializer.BaseInitializer or numpy.ndarray) – Initializer for weight. By default, it is initialized with nnabla.initializer.UniformInitializer within the range determined by nnabla.initializer.calc_uniform_lim_glorot.
b_init (nnabla.initializer.BaseInitializer or numpy.ndarray) – Initializer for bias. By default, it is initialized with zeros if with_bias is True.
base_axis (int) – Dimensions up to base_axis are treated as the sample dimensions.
fix_parameters (bool) – When set to True, the weights and biases will not be updated.
rng (numpy.random.RandomState) – Random generator for Initializer.
with_bias (bool) – Specify whether to include the bias term.

Returns

N-D array. See depthwise_deconvolution for the output shape.

Return type

Parameters to be registered

The following variables are registered in a parameter scope "depthwise_deconv";

W (need_grad=True) : Filter weights. (shape: (inmaps,) + kernel)
b (need_grad=True) : Bias vector. (shape: (inmaps / divisor,))

Note

If the name option is passed, the parameters become wrapped inside the parameter scope with the specified name, yielding the same results as the following code. This can be used to simplify the code.

with parameter_scope(name):
    output = depthwise_deconvolution(<args>)

nnabla.parametric_functions.deformable_convolution(inp, outmaps, kernel, offset, mask=None, pad=None, stride=None, dilation=None, group=1, deformable_group=1, channel_last=False, w_init=None, b_init=None, base_axis=1, fix_parameters=False, rng=None, with_bias=True, apply_w=None, apply_b=None, name=None)[source]¶

2D Deformable Convolution with a bias term. If use mask, this function is Deformable Convolution v2.

Dai et al., Deformable Convolutional Networks. https://arxiv.org/abs/1703.06211
Zhu et al., Deformable ConvNets v2: More Deformable, Better Results. https://arxiv.org/abs/1811.11168

Parameters

inp (Variable) – N-D array.
outmaps (int) – Number of convolution kernels (which is equal to the number of output channels). For example, to apply convolution on an input with 16 types of filters, specify 16.
kernel (tuple of int) – Convolution kernel size. For example, to apply convolution on an image with a 3 (height) by 5 (width) two-dimensional kernel, specify (3,5).
offset (Variable) – Offsets for deformable convolutions. Shape is fixed to $(N, deformable_group imes 2 imes Kh imes Kw, H, W)$. Offsets must be calculated externally through a separate convolution layer.
mask (Variable) – Normalized mask for deformable convolutions v2. Shape is fixed to $(N, deformable_group imes 1 imes Kh imes Kw, H, W)$. Masks must be calculated externally together with the offsets through a separate convolution layer.
pad (tuple of int) – Padding sizes for dimensions.
stride (tuple of int) – Stride sizes for dimensions.
dilation (tuple of int) – Dilation sizes for dimensions.
group (int) – Number of groups of channels. This makes connections across channels more sparse by grouping connections along map direction.
deformable_group (int) – Number of deformable groups of channels. This makes connections across channels more sparse by grouping connections along map direction.
channel_last (bool) – If True, the last dimension is considered as channel dimension, a.k.a. NHWC order.
w_init (nnabla.initializer.BaseInitializer or numpy.ndarray) – Initializer for weight. By default, it is initialized with nnabla.initializer.UniformInitializer within the range determined by nnabla.initializer.calc_uniform_lim_glorot.
b_init (nnabla.initializer.BaseInitializer or numpy.ndarray) – Initializer for bias. By default, it is initialized with zeros if with_bias is True.
base_axis (int) – Dimensions up to base_axis are treated as the sample dimensions.
fix_parameters (bool) – When set to True, the weights and biases will not be updated.
rng (numpy.random.RandomState) – Random generator for Initializer.
with_bias (bool) – Specify whether to include the bias term.
apply_w (function) – Lambda, function, or callable object applied to the weights.
apply_b (function) – Lambda, function, or callable object applied to the bias.

Returns

N-D array. See convolution for the output shape.

Return type

Parameters to be registered

The following variables are registered in a parameter scope "deformable_conv";

W (need_grad=True) : Filter weights. (shape: (outmaps, inmaps // group, *kernel))
b (need_grad=True) : Bias vector. (shape: (outmaps,))

Note

If the name option is passed, the parameters become wrapped inside the parameter scope with the specified name, yielding the same results as the following code. This can be used to simplify the code.

with parameter_scope(name):
    output = deformable_convolution(<args>)

nnabla.parametric_functions.batch_normalization(inp, axes=[1], decay_rate=0.9, eps=1e-05, batch_stat=True, output_stat=False, fix_parameters=False, param_init=None, no_scale=False, no_bias=False, name=None)[source]¶

Batch normalization layer.

\[\begin{split}\begin{array}{lcl} \mu &=& \frac{1}{M} \sum x_i\\ \sigma^2 &=& \frac{1}{M} \sum \left(x_i - \mu\right)^2\\ \hat{x}_i &=& \frac{x_i - \mu}{\sqrt{\sigma^2 + \epsilon }}\\ y_i &= & \hat{x}_i \gamma + \beta. \end{array}\end{split}\]

where $x_i, y_i$ are the inputs. In testing, the mean and variance computed by moving average calculated during training are used.

Parameters

inp (Variable) – N-D array of input.
axes (tuple of int) – Mean and variance for each element in axes are calculated using elements on the rest axes. For example, if an input is 4 dimensions, and axes is [1], batch mean is calculated as np.mean(inp.d, axis=(0, 2, 3), keepdims=True) (using numpy expression as an example).
decay_rate (float) – Decay rate of running mean and variance.
eps (float) – Tiny value to avoid zero division by std.
batch_stat (bool) – Use mini-batch statistics rather than running ones.
output_stat (bool) – Output batch mean and variance.
fix_parameters (bool) – When set to True, the beta and gamma will not be updated.
param_init (dict) – Parameter initializers can be set with a dict. A key of the dict must be 'beta', 'gamma', 'mean' or 'var'. A value of the dict must be an Initializer or a numpy.ndarray. E.g. {'beta': ConstantInitializer(0), 'gamma': np.ones(gamma_shape) * 2}.
no_scale (bool) – If True, the scale term is omitted.
no_bias (bool) – If True, the bias term is omitted.

Returns

N-D array.

Return type

References

Ioffe and Szegedy, Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. https://arxiv.org/abs/1502.03167

The shape of parameters has the same number of dimensions with the input data, and the shapes in axes has the same dimensions with the input, while the rest has 1. If an input is 4-dim and axes=[1], the parameter shape will be param_shape = np.mean(inp.d, axis=(0, 2, 3), keepdims=True).shape (using numpy expression as an example).

Parameters to be registered

The following variables are registered in a parameter scope "bn";

beta (need_grad=True) : Trainable bias $\beta$. (shape: <see above>)
gamma (need_grad=True) : Trainable scaling factor $\gamma$. (shape: <see above>)
mean (need_grad=False) : Moving average of batch mean. (shape: <see above>)
var (need_grad=False) : Moving average of batch variance. (shape: <see above>)

Note

If the name option is passed, the parameters become wrapped inside the parameter scope with the specified name, yielding the same results as the following code. This can be used to simplify the code.

with parameter_scope(name):
    output = batch_normalization(<args>)

nnabla.parametric_functions.fused_batch_normalization(inp, z=None, axes=[1], decay_rate=0.9, eps=1e-05, batch_stat=True, nonlinearity='relu', output_stat=False, fix_parameters=False, param_init=None, no_scale=False, no_bias=False, name=None)[source]¶

Batch normalization layer fused with the following add2 operation of a residual input and an nonlinear activation.

Parameters

inp (Variable) – N-D array of input.
z (Variable, optional) – A residual input. By specifying None, the activation function will follow immediately after BN operation.
axes (tuple of int) – Mean and variance for each element in axes are calculated using elements on the rest axes. For example, if an input is 4 dimensions, and axes is [1], batch mean is calculated as np.mean(inp.d, axis=(0, 2, 3), keepdims=True) (using numpy expression as an example).
decay_rate (float) – Decay rate of running mean and variance.
eps (float) – Tiny value to avoid zero division by std.
batch_stat (bool) – Use mini-batch statistics rather than running ones.
nonlinearity (string) – Activation function. The default is ‘relu’.
output_stat (bool) – Output batch mean and variance.
fix_parameters (bool) – When set to True, the beta and gamma will not be updated.
no_scale (bool) – If True, the scale term is omitted.
no_bias (bool) – If True, the bias term is omitted.

Returns

N-D array.

Return type

Parameters to be registered

The following variables are registered in a parameter scope "bn";

beta (need_grad=True) : Trainable bias $\beta$. (shape: <see above>)
gamma (need_grad=True) : Trainable scaling factor $\gamma$. (shape: <see above>)
mean (need_grad=False) : Moving average of batch mean. (shape: <see above>)
var (need_grad=False) : Moving average of batch variance. (shape: <see above>)

Note

If the name option is passed, the parameters become wrapped inside the parameter scope with the specified name, yielding the same results as the following code. This can be used to simplify the code.

with parameter_scope(name):
    output = fused_batch_normalization(<args>)

nnabla.parametric_functions.sync_batch_normalization(inp, comm, group='world', axes=[1], decay_rate=0.9, eps=1e-05, batch_stat=True, output_stat=False, fix_parameters=False, param_init=None, no_scale=False, no_bias=False, name=None)[source]¶

Synchronized batch normalization layer.

For some tasks (e.g., semantic segmentation), batch size will be too small and BatchNormalization layer might not work well. SyncBatchNorlization layer solves these problems by synchronizing batch stats (mean and var) between multiple processes.

\[\begin{split}\begin{array}{lcl} \mu &=& \frac{1}{M} \sum x_i\\ \sigma^2 &=& \frac{1}{M} \left(\sum x_i - \mu\right)^2\\ \hat{x}_i &=& \frac{x_i - \mu}{\sqrt{\sigma^2 + \epsilon }}\\ y_i &= & \hat{x}_i \gamma + \beta. \end{array}\end{split}\]

where $x_i, y_i$ are the inputs.

Parameters

inp (Variable) – N-D array of input.
comm (Communicator) – The communicator
group (string) – The name of the communicator group
axes (tuple of int) – Mean and variance for each element in axes are calculated using elements on the rest axes. For example, if an input is 4 dimensions, and axes is [1], batch mean is calculated as np.mean(inp.d, axis=(0, 2, 3), keepdims=True) (using numpy expression as an example).
decay_rate (float) – Decay rate of running mean and variance.
eps (float) – Tiny value to avoid zero division by std.
batch_stat (bool) – Use mini-batch statistics rather than running ones.
output_stat (bool) – Output batch mean and variance.
fix_parameters (bool) – When set to True, the beta and gamma will not be updated.
param_init (dict) – Parameter initializers can be set with a dict. A key of the dict must be 'beta', 'gamma', 'mean' or 'var'. A value of the dict must be an Initializer or a numpy.ndarray. E.g. {'beta': ConstantInitializer(0), 'gamma': np.ones(gamma_shape) * 2}.
no_scale (bool) – If True, the scale term is omitted.
no_bias (bool) – If True, the bias term is omitted.

Returns

N-D array.

Return type

References

Ioffe and Szegedy, Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift, https://arxiv.org/abs/1502.03167
Hang Zhang, Kristin Dana, Jianping Shi, Zhongyue Zhang, Xiaogang Wang, Ambrish Tyagi, Amit Agrawal, Context Encoding for Semantic Segmentation, https://arxiv.org/abs/1803.08904
Implementing Synchronized Multi-GPU Batch Normalization https://hangzhang.org/PyTorch-Encoding/notes/syncbn.html

The shape of parameters has the same number of dimensions with the input data, and the shapes in axes has the same dimensions with the input, while the rest has 1. If an input is 4-dim and axes=[1], the parameter shape will be param_shape = np.mean(inp.d, axis=(0, 2, 3), keepdims=True).shape (using numpy expression as an example).

Parameters to be registered

The following variables are registered in a parameter scope "bn";

beta (need_grad=True) : Trainable bias $\beta$. (shape: <see above>)
gamma (need_grad=True) : Trainable scaling factor $\gamma$. (shape: <see above>)
mean (need_grad=False) : Moving average of batch mean. (shape: <see above>)
var (need_grad=False) : Moving average of batch variance. (shape: <see above>)

Note

If the name option is passed, the parameters become wrapped inside the parameter scope with the specified name, yielding the same results as the following code. This can be used to simplify the code.

with parameter_scope(name):
    output = sync_batch_normalization(<args>)

nnabla.parametric_functions.mean_subtraction(inp, base_axis=1, update_running_mean=True, fix_parameters=False, name=None)[source]¶

Mean subtraction layer.

It subtracts the mean of the elements of the input array, and normalizes it to $0$. Preprocessing arrays with this function has the effect of improving accuracy in various tasks such as image classification.

At training time, this function is defined as

\[\begin{split}\begin{array}{lcl} \mu &=& \frac{1}{M} \sum x_i \\ y_i &=& x_i - \mu \end{array}\end{split}\]

At testing time, the mean values used are those that were computed during training by moving average.

Note

The backward performs an approximated differentiation that takes into account only the latest mini-batch.

Parameters

inp (Variable) – N-D array of input.
base_axis (int) – Base axis of Mean Subtraction operation. Dimensions up to base_axis is treated as sample dimension.
update_running_mean (bool) – When set to True, the running mean will not be updated.
fix_parameters (bool) – dummy parameter. This argument dose not affect anything.

Returns

N-D array.

Return type

Parameters to be registered

The following variables are registered in a parameter scope "mean_subtraction";

mean (need_grad=False) : Moving average. (shape: inp.shape[base_axis:])
t (need_grad=False) : Minibatch counter used in forward pass. (shape: (1,))

Note

If the name option is passed, the parameters become wrapped inside the parameter scope with the specified name, yielding the same results as the following code. This can be used to simplify the code.

with parameter_scope(name):
    output = mean_subtraction(<args>)

nnabla.parametric_functions.layer_normalization(inp, batch_axis=0, eps=1e-05, output_stat=False, fix_parameters=False, param_init=None, no_scale=False, no_bias=False, name=None)[source]¶

Applies Layer Normalization over an input variable, which is defined as:

\[\begin{split}\begin{eqnarray} \mu^l &=& \frac{1}{H} \sum_{i=1}^{H} x_i^l \\ \sigma^l &=& \sqrt{\frac{1}{H} \sum_{i=1}^{H} \left(x_i^l - \mu^l\right)^2} \\ y &=& \frac{x - \mu^l}{\sigma^l + \epsilon} \gamma + \beta \end{eqnarray}\end{split}\]

where $x$ and $y$ are input and output variable, $\mu^l$ and $\sigma^l$ are the mean and std of each layer along batch axis, and $\alpha$ and $\beta$ are trainable parameter.

Note

Unlike other normalizations, which applies scalar scale and bias for each entire channel/plane, Layer Normalization applies per-element scale and bias.

References

Jimmy Lei Ba, Jamie Ryan Kiros, Geoffrey E. Hinton, Layer Normalization.

Parameters

inp (Variable) – An input variable.
batch_axis (int or repeated int) – Axes mean and variance are taken.
eps (float) – Tiny value to avoid zero division by std.
output_stat (bool) – It True, calculated mean and variance are also returned.
fix_parameters (bool) – When set to True, the beta and gamma will not be updated.
param_init (dict) – Parameter initializers can be set with a dict. A key of the dict must be 'gamma', 'beta'. A value of the dict must be an Initializer or a numpy.ndarray. E.g. {'gamma': np.ones(...) * 2, 'beta': ConstantInitializer(0)}.
no_scale (bool) – If True, the scale term is omitted.
no_bias (bool) – If True, the bias term is omitted.

Returns

Normalized output variable. * Variable: Mean (if ``output_stat=True`). * Variable: Std (if ``output_stat=True`)

Return type

Variable

Parameters to be registered

The following variables are registered in a parameter scope "layer_normalization";

beta (need_grad=True) : Trainable bias $\beta$. (shape: <see above>)
gamma (need_grad=True) : Trainable scaling factor $\gamma$. (shape: <see above>)

Note

If the name option is passed, the parameters become wrapped inside the parameter scope with the specified name, yielding the same results as the following code. This can be used to simplify the code.

with parameter_scope(name):
    output = layer_normalization(<args>)

nnabla.parametric_functions.instance_normalization(inp, channel_axis=1, batch_axis=0, eps=1e-05, output_stat=False, fix_parameters=False, param_init=None, no_scale=False, no_bias=False, name=None)[source]¶

Applies Instance Normalization over an input variable, which is defined as:

\[\begin{split}\begin{eqnarray} \mu^i &=& \frac{1}{H} \sum_{i=1}^{H} x_i^i \\ \sigma^i &=& \sqrt{\frac{1}{H} \sum_{i=1}^{H} \left(x_i^i - \mu^i\right)^2} \\ y &=& \frac{x - \mu^i}{\sigma^ + \epsilon} \gamma + \beta \end{eqnarray}\end{split}\]

where $x$ and $y$ are input and output variable, $\mu^i$ and $\sigma^i$ are the mean and std of each instance which is separately calculated for each batch and channel, and $\gamma$ and $\beta$ are adaptive gains and biases.

If the input shape is [B, C, H, W] (= channel_axis=1, batch_axis=0), the shape of calculated mean and std are [B, C, 1, 1]

References

Dmitry Ulyanov, Andrea Vedaldi, Victor Lempitsky, Instance Normalization: The Missing Ingredient for Fast Stylization.

Parameters

inp (Variable) – An input variable.
channel_axis (int or repeated int) – Channel axes.
batch_axis (int or repeated int) – Batch axes.
eps (float) – Tiny value to avoid zero division by std.
output_stat (bool) – It True, the batch statistics of mean and variance.
fix_parameters (bool) – If True, the beta and gamma will not be updated.
param_init (dict) – Parameter initializers can be set with a dict. A key of the dict must be 'gamma', 'beta'. A value of the dict must be an Initializer or a numpy.ndarray. E.g. {'gamma': np.ones(...) * 2, 'beta': ConstantInitializer(0)}.
no_scale (bool) – If True, the scale term is omitted.
no_bias (bool) – If True, the bias term is omitted.
Returns –
- Variable: Normalized output variable.
- Variable: Mean (if ``output_stat=True`)
- Variable: Std (if ``output_stat=True`)

Parameters to be registered

The following variables are registered in a parameter scope "instance_normalization";

beta (need_grad=True) : Trainable bias $\beta$. (shape: <see above>)
gamma (need_grad=True) : Trainable scaling factor $\gamma$. (shape: <see above>)

Note

If the name option is passed, the parameters become wrapped inside the parameter scope with the specified name, yielding the same results as the following code. This can be used to simplify the code.

with parameter_scope(name):
    output = instance_normalization(<args>)

nnabla.parametric_functions.group_normalization(inp, num_groups, channel_axis=1, batch_axis=0, eps=1e-05, output_stat=False, fix_parameters=False, param_init=None, no_scale=False, no_bias=False, name=None)[source]¶

Applies Group Normalization over an input tensor, which is defined as:

\[\begin{split}\begin{eqnarray} \mu^g &=& \frac{1}{H} \sum_{i=1}^{H} x_i^g \\ \sigma^g &=& \sqrt{\frac{1}{H} \sum_{i=1}^{H} \left(x_i^g - \mu^g\right)^2} \\ y &=& \frac{x - \mu^g}{\sigma^g + \epsilon} \gamma + \beta \end{eqnarray}\end{split}\]

where $x$ and $y$ are input and output variable, $\mu^g$ and $\sigma^g$ are the mean and std of each group which contains num_channels / num_groups channels, and $\gamma$ and $\beta$ are adaptive gains and biases.

The input channels, specified by channel_axis, are separeted into num_groups groups, and the mean and std are calculated over the each group. For example, if the input shape is [B, C, H, W] (= channel_axis=1, batch_axis=0), an input variable is once reshaped to [B, num_groups, C / num_groups, H, W] and standardize by its mean and std whose shapes are [B, num_groups, C / num_groups, 1, 1]. Before returning, an output variable is reshaped again to the original input shape (= [B, C, H, W] in the case above).

References

Yuxin Wu, Kaiming He, Group Normalization.

Parameters

inp (Variable) – An input variable.
num_groups (int) – A number of groups. The channel dim of ‘x’ must be integer multiple of num_groups.
channel_axis (int) – Channel axis.
batch_axis (int or repeated int) – Axes mean and variance are taken.
eps (float) – Tiny value to avoid zero division by std.
output_stat (bool) – It true, the batch statistics of mean and variance.
fix_parameters (bool) – When set to True, the beta and gamma will not be updated.
param_init (dict) – Parameter initializers can be set with a dict. A key of the dict must be 'gamma', 'beta'. A value of the dict must be an Initializer or a numpy.ndarray. E.g. {'gamma': np.ones(...) * 2, 'beta': ConstantInitializer(0)}.
no_scale (bool) – If True, the scale term is omitted.
no_bias (bool) – If True, the bias term is omitted.

Returns

Normalized output variable. * Variable: Mean (if ``output_stat=True`) * Variable: Std (if ``output_stat=True`)

Return type

Variable

Parameters to be registered

The following variables are registered in a parameter scope "group_normalization";

beta (need_grad=True) : Trainable bias $\beta$. (shape: <see above>)
gamma (need_grad=True) : Trainable scaling factor $\gamma$. (shape: <see above>)

Note

If the name option is passed, the parameters become wrapped inside the parameter scope with the specified name, yielding the same results as the following code. This can be used to simplify the code.

with parameter_scope(name):
    output = group_normalization(<args>)

nnabla.parametric_functions.rnn(x, h, w0_init=None, w_init=None, b_init=None, num_layers=1, nonlinearity='tanh', dropout=0.0, bidirectional=False, training=True, rng=None, with_bias=True, fix_parameters=False, name=None)[source]¶

N-Step RNN (recurrent neural networks).

N-Step RNN function implements Elman RNN with nonlineraity to input sequence. N-Step RNN function is defined as following:

\[h_t = \tanh(w_{ih}x_t+b_{ih}+w_{hh}h_{(t-1)}).\]

We use the following notations to describe the inputs and outputs below. $T$: sequcne length, $B$: batch size, $I$: input size, $L$: number of layers, $D$: number of directions, can be either 1 or 2, $H$: hidden size.

References

Jeffrey L. Elman. “Finding Structure in Time.” Cognitive Science. 1990.

Parameters

x (Variable) – Input N-D array with shape $(T, B, I)$.
h (Variable) – Input N-D array with shape $(L, D, B, H)$.
w0_init (nnabla.initializer.BaseInitializer or numpy.ndarray, optional) – Initializer for weight at the first layer. Shape is $(D, H, I + H)$.
w_init (nnabla.initializer.BaseInitializer or numpy.ndarray, optional) – Initializer for weights at the second layer and up. Shape is $(L-1, D, H, D*H + H)$.
b_init (nnabla.initializer.BaseInitializer or numpy.ndarray, optional) – Initializer for bias. Shape is $(L, D, H)$.
num_layers (int, optional) – Number of layers in the network. If set to 1, only the weights for the first layer will be invoked. Default is 1.
nonlinearity (str, optional) – Type of nonlinearity applied to input sequcne. Must be either tanh or relu. Default is tanh.
dropout (float, optional) – Dropout ratio applied to parameters. Default is 0.0.
bidirectional (bool, optional) – If True, bidirectional computation will be performed in each layer. Default is False.
training (bool, optional) – Backpropagation will be performed only when it is true. Default is True.
with_bias (bool, optional) – Specify whether to include the bias term.

Returns

Output $y$ with shape $(T, B, D * H)$ ~nnabla.Variable: Output $h_n$ with shape $(L, D, B, H)$

Return type

Example

x = nn.Variable((seq_len, batch_size, input_size))
h = nn.Variable((num_layers, num_directions, batch_size, hidden_size))
y, hn = PF.rnn(x, h)

Parameters to be registered

The following variables are registered in a parameter scope "rnn";

weight_l0 (need_grad=True) : Filter weights at 0-th layer. (shape: (D, H, I + H))
weight (need_grad=True) : Filter weights at 1-st layer and above. (shape: (L-1, D, H, DH + H))
bias (need_grad=True) : Biases. (shape: (L, D, H))

Note

If the name option is passed, the parameters become wrapped inside the parameter scope with the specified name, yielding the same results as the following code. This can be used to simplify the code.

with parameter_scope(name):
    output = rnn(<args>)

nnabla.parametric_functions.lstm(x, h, c, w0_init=None, w_init=None, b_init=None, num_layers=1, dropout=0.0, bidirectional=False, training=True, rng=None, with_bias=True, fix_parameters=False, name=None)[source]¶

LSTM (long short-term memory).

Long Short-Term Memory, or LSTM, is a building block for recurrent neural networks (RNN) layers. LSTM unit consists of a cell and input, output, forget gates whose functions are defined as following:

\[\begin{split}f_t&&=\sigma(W_fx_t+U_fh_{t-1}+b_f) \\ i_t&&=\sigma(W_ix_t+U_ih_{t-1}+b_i) \\ o_t&&=\sigma(W_ox_t+U_oh_{t-1}+b_o) \\ c_t&&=f_t\odot c_{t-1}+i_t\odot\tanh(W_cx_t+U_ch_{t-1}+b_c) \\ h_t&&=o_t\odot\tanh(c_t).\end{split}\]

We use the following notations to describe the inputs and outputs below. $T$: sequcne length, $B$: batch size, $I$: input size, $L$: number of layers, $D$: number of directions, can be either 1 or 2, $H$: hidden size.

References

S. Hochreiter, and J. Schmidhuber. “Long Short-Term Memory.” Neural Computation. 1997.

Parameters

x (Variable) – Input N-D array with shape $(T, B, I)$.
h (Variable) – Input N-D array with shape $(L, D, B, H)$.
c (Variable) – Input N-D array with shape $(L, D, B, H)$ .
w0_init (nnabla.initializer.BaseInitializer or numpy.ndarray, optional) – Initializer for weight at the first layer. Shape is $(D, 4, H, I + H)$.
w_init (nnabla.initializer.BaseInitializer or numpy.ndarray, optional) – Initializer for weights at the second layer and up. Shape is $(L-1, D, 4, H, D * H + H)$.
b_init (nnabla.initializer.BaseInitializer or numpy.ndarray, optional) – Initializer for bias. Shape is $(L, D, 4, H)$.
num_layers (int, optional) – Number of layers in the network. If set to 1, only the weights for the first layer will be invoked. Default is 1.
dropout (float, optional) – Dropout ratio applied to parameters. Default is 0.0.
bidirectional (bool, optional) – If True, bidirectional computation will be performed in each layer. Default is False.
training (bool, optional) – Backpropagation will be performed only when it is true. Default is True.
with_bias (bool, optional) – Specify whether to include the bias term.
fix_parameters (bool) – When set to True, the weights and biases will not be updated.

Returns

Output $y$ with shape $(T, B, D * H)$ ~nnabla.Variable: Output $h_n$ with shape $(L, D, B, H)$ ~nnabla.Variable: Output $c_n$ with shape $(L, D, B, H)$

Return type

Example

x = nn.Variable((seq_len, batch_size, input_size))
h = nn.Variable((num_layers, num_directions, batch_size, hidden_size))
c = nn.Variable((num_layers, num_directions, batch_size, hidden_size))
y, hn, cn = PF.lstm(x, h, c)

Parameters to be registered

The following variables are registered in a parameter scope "lstm";

weight_l0 (need_grad=True) : Filter weights at 0-th layer. (shape: (D, 4, H, I + H))
weight (need_grad=True) : Filter weights at 1-st layer and above. (shape: (L-1, D, 4, H, DH + H))
bias (need_grad=True) : Biases. (shape: (L, D, 4, H))

Note

If the name option is passed, the parameters become wrapped inside the parameter scope with the specified name, yielding the same results as the following code. This can be used to simplify the code.

with parameter_scope(name):
    output = lstm(<args>)

nnabla.parametric_functions.gru(x, h, w0_init=None, w_init=None, b_init=None, num_layers=1, dropout=0.0, bidirectional=False, training=True, rng=None, with_bias=True, fix_parameters=False, name=None)[source]¶

GRU (gated recurrent units).

GRU is defined as following:

\[\begin{split}r_t&&=\sigma(W_rx_t+U_rh_{t-1}+b_r) \\ z_t&&=\sigma(W_zx_t+U_zh_{t-1}+b_z) \\ n_t&&=\tanh(W_nx_t+b_{in}+r_n \odot (U_nh_{t-1}+b_{hn})) \\ h_t&&=(1-z_t) \odot n_t+z_t \odot h_{t-1}.\end{split}\]

We use the following notations to describe the inputs and outputs below. $T$: sequcne length, $B$: batch size, $I$: input size, $L$: number of layers, $D$: number of directions, can be either 1 or 2, $H$: hidden size.

References

K. Cho et al. “Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation.” Empirical Methods in Natural Language Processing. 2014.

Parameters

x (Variable) – Input N-D array with shape $(T, B, I)$.
h (Variable) – Input N-D array with shape $(L, D, B, H)$.
w0_init (nnabla.initializer.BaseInitializer or numpy.ndarray, optional) – Initializer for weight at the first layer. Shape is $(D, 3, H, I + H)$.
w_init (nnabla.initializer.BaseInitializer or numpy.ndarray, optional) – Initializer for weights at the second layer and up. Shape is $(L-1, D, 3, H, D * H + H)$.
b_init (nnabla.initializer.BaseInitializer or numpy.ndarray, optional) – Initializer for bias. Shape is $(L, D, 4, H)$.
num_layers (int, optional) – Number of layers in the network. If set to 1, only the weights for the first layer will be invoked. Default is 1.
dropout (float, optional) – Dropout ratio applied to parameters. Default is 0.0.
bidirectional (bool, optional) – If True, bidirectional computation will be performed in each layer. Default is False.
training (bool, optional) – Backpropagation will be performed only when it is true. Default is True.
with_bias (bool, optional) – Specify whether to include the bias term.

Returns

Output $y$ with shape $(T, B, D * H)$ ~nnabla.Variable: Output $h_n$ with shape $(L, D, B, H)$

Return type

Example

x = nn.Variable((seq_len, batch_size, input_size))
h = nn.Variable((num_layers, num_directions, batch_size, hidden_size))
y, hn = PF.gru(x, h)

Parameters to be registered

The following variables are registered in a parameter scope "gru";

weight_l0 (need_grad=True) : Filter weights at 0-th layer. (shape: (D, 3, H, I + H))
weight (need_grad=True) : Filter weights at 1-st layer and above. (shape: (L-1, D, 3, H, DH + H))
bias (need_grad=True) : Biases. (shape: (L, D, 4, H))

Note

If the name option is passed, the parameters become wrapped inside the parameter scope with the specified name, yielding the same results as the following code. This can be used to simplify the code.

with parameter_scope(name):
    output = gru(<args>)

nnabla.parametric_functions.embed(inp, n_inputs, n_features, initializer=None, fix_parameters=False, apply_w=None, name=None)[source]¶

Embed.

Embed slices a matrix/tensor with indexing array/tensor. Weights are initialized with nnabla.initializer.UniformInitializer within the range of $-\sqrt{3}$ and $\sqrt{3}$.

Parameters

x (Variable) – [Integer] Indices with shape $(I_0, ..., I_N)$
n_inputs – number of possible inputs, words or vocabraries
n_features – number of embedding features
fix_parameters (bool) – When set to True, the embedding weight matrix will not be updated.
apply_w (function) – Lambda, function, or callable object applied to the weights.

Returns

Output with shape $(I_0, ..., I_N, W_1, ..., W_M)$

Return type

Parameters to be registered

The following variables are registered in a parameter scope "embed";

W (need_grad=True) : Embedding matrix. (shape: (n_inputs, n_features))

Note

If the name option is passed, the parameters become wrapped inside the parameter scope with the specified name, yielding the same results as the following code. This can be used to simplify the code.

with parameter_scope(name):
    output = embed(<args>)

nnabla.parametric_functions.prelu(inp, base_axis=1, shared=True, fix_parameters=False, slope_init=None, name=None)[source]¶

Parametrized Rectified Linear Unit function defined as

\[y_i = \max(0, x_i) + w_i \min(0, x_i)\]

where negative slope $w$ is learned and can vary across channels (an axis specified with base_axis). Weights are initialized with $-1$.

Parameters

x (Variable) – N-D array as input
base_axis (int) – Dimensions up to base_axis is treated as sample dimension.
shared (bool) – Use shared weight value or not
fix_parameters (bool) – When set to True, the negative slope values will not be updated.
slope_init (nnabla.initializer.BaseInitializer or numpy.ndarray) – Initializer of negative slopes. By default, they are initialized with 0.25.

Returns

N-D array.

Return type

Parameters to be registered

The following variables are registered in a parameter scope "prelu";

slope (need_grad=True) : Negative slope. (shape: tuple() if shared else (inp.shape[base_axis],))

Note

If the name option is passed, the parameters become wrapped inside the parameter scope with the specified name, yielding the same results as the following code. This can be used to simplify the code.

with parameter_scope(name):
    output = prelu(<args>)

nnabla.parametric_functions.svd_affine(inp, n_outmaps, r, base_axis=1, uv_init=None, b_init=None, fix_parameters=False, rng=None, with_bias=True, name=None)[source]¶

SVD affine is a low rank approximation of the affine layer. It can be seen as two consecutive affine layers with a bottleneck. It computes:

\[{\mathbf y} = {\mathbf U} {\mathbf V} {\mathbf x} + {\mathbf b}.\]

where ${\mathbf x}, {\mathbf y}$ are the inputs and outputs respectively, and ${\mathbf U}, {\mathbf V}, {\mathbf b}$ are constants.

The weights ${\mathbf U}$ and ${\mathbf V}$ are approximated with singular value decomposition (SVD) of the original weight matrix ${\mathbf W}$ and by selecting the ${R}$ dominant singular values and the corresponding singular vectors. Therefore the low rank ${R}$ is the size of the bottleneck.

If uv_init is a numpy array, ${\mathbf U}$ and ${\mathbf V}$ are computed such that uv_init is approximated by ${\mathbf{UV}}$. If uv_init is None or an initializer, the product of ${\mathbf U}$ and ${\mathbf V}$ approximates the random initialization.

If ${\mathbf U}$ and ${\mathbf V}$ exist in the context, they take precedence over uv_init.

Suppose the weight of the affine is of ${I \times O}$ and the compression rate you want to specify is ${CR}$, then you set ${R}$ as

\[R = \left\lfloor \frac{(1 - CR)OI}{O + I} \right\rfloor.\]

Parameters

inp (Variable) – Input N-D array with shape ($M_0 \times \ldots \times M_{B-1} \times D_B \times \ldots \times D_N$). Dimensions before and after base_axis are flattened as if it is a matrix.
n_outmaps (int or tuple) – Number of output neurons per data.
r (int) – rank of the factorized layer (size of the bottleneck)
base_axis (int) – Dimensions up to base_axis are treated as the sample dimensions.
uv_init (nnabla.initializer.BaseInitializer or numpy.ndarray) – Initializer for weight. By default, it is initialized with nnabla.initializer.UniformInitializer within the range determined by nnabla.initializer.calc_uniform_lim_glorot.
b_init (nnabla.initializer.BaseInitializer or numpy.ndarray) – Initializer for bias. By default, it is initialized with zeros if with_bias is True.
fix_parameters (bool) – When set to True, the weights and biases will not be updated.
rng (numpy.random.RandomState) – Random generator for Initializer.
with_bias (bool) – Specify whether to include the bias term.

Returns

$(B + 1)$-D array. ($M_0 \times \ldots \times M_{B-1} \times L$)

Return type

Parameters to be registered

The following variables are registered in a parameter scope "svd_affine";

U (need_grad=True) : ${\mathbf U}$. (shape: (inmaps, r))
V (need_grad=True) : ${\mathbf V}$. (shape: (r, outmaps))
b (need_grad=True) : Bias vector. (shape: (outmaps,))

Note

If the name option is passed, the parameters become wrapped inside the parameter scope with the specified name, yielding the same results as the following code. This can be used to simplify the code.

with parameter_scope(name):
    output = svd_affine(<args>)

nnabla.parametric_functions.svd_convolution(inp, outmaps, kernel, r, pad=None, stride=None, dilation=None, uv_init=None, b_init=None, base_axis=1, fix_parameters=False, rng=None, with_bias=True, name=None)[source]¶

SVD convolution is a low rank approximation of the convolution layer. It can be seen as a depth wise convolution followed by a 1x1 convolution.

The flattened kernels for the i-th input map are expressed by their low rank approximation. The kernels for the i-th input ${\mathbf W_i}$ are approximated with the singular value decomposition (SVD) and by selecting the ${R}$ dominant singular values and the corresponding singular vectors.

\[{\mathbf W_{:,i,:}} ~ {\mathbf U_i} {\mathbf V_i}.\]

${\mathbf U}$ contains the weights of the depthwise convolution with multiplier ${R}$ and ${\mathbf V}$ contains the weights of the 1x1 convolution.

If uv_init is a numpy array, ${\mathbf U}$ and ${\mathbf V}$ are computed such that uv_init is approximated by ${\mathbf{UV}}$. If uv_init is None or an initializer, the product of ${\mathbf U}$ and ${\mathbf V}$ approximates the random initialization.

If ${\mathbf U}$ and ${\mathbf V}$ exist in the context, they take precedence over uv_init.

Suppose the kernel tensor of the convolution is of ${O \times I \times K \times K}$ and the compression rate you want to specify is ${CR}$, then you set ${R}$ as

\[R = \left\lfloor \frac{(1 - CR)OIK^2}{I(O + K^2)} \right\rfloor.\]

Parameters

inp (Variable) – N-D array.
outmaps (int) – Number of convolution kernels (which is equal to the number of output channels). For example, to apply convolution on an input with 16 types of filters, specify 16.
kernel (tuple) – Convolution kernel size. For example, to apply convolution on an image with a 3 (height) by 5 (width) two-dimensional kernel, specify (3, 5).
r (int) – Rank of the factorized layer.
pad (tuple) – Padding sizes (int) for dimensions.
stride (tuple) – Stride sizes (int) for dimensions.
dilation (tuple) – Dilation sizes (int) for dimensions.
uv_init (nnabla.initializer.BaseInitializer or numpy.ndarray) – Initializer for weight. By default, it is initialized with nnabla.initializer.UniformInitializer within the range determined by nnabla.initializer.calc_uniform_lim_glorot.
b_init (nnabla.initializer.BaseInitializer or numpy.ndarray) – Initializer for bias. By default, it is initialized with zeros if with_bias is True.
base_axis (int) – Dimensions up to base_axis are treated as the sample dimensions.
fix_parameters (bool) – When set to True, the weights and biases will not be updated.
rng (numpy.random.RandomState) – Random generator for Initializer.
with_bias (bool) – Specify whether to include the bias term.

Returns

$(B + 1)$-D array. ($M_0 \times \ldots \times M_{B-1} \times L$)

Return type

Parameters to be registered

The following variables are registered in a parameter scope "svd_conv";

U (need_grad=True) : Decomposed filter weights ${\mathbf U}$. (shape: (inmaps * r, *kernel))
V (need_grad=True) : Decomposed filter weights ${\mathbf V}$. (shape: (outmaps, inmaps * r, 1, ...))
b (need_grad=True) : Bias vector. (shape: (outmaps,))

Note

If the name option is passed, the parameters become wrapped inside the parameter scope with the specified name, yielding the same results as the following code. This can be used to simplify the code.

with parameter_scope(name):
    output = svd_convolution(<args>)

nnabla.parametric_functions.cpd3_convolution(inp, outmaps, kernel, r, pad=None, stride=None, dilation=None, oik_init=None, b_init=None, base_axis=1, fix_parameters=False, rng=None, with_bias=True, max_iter=500, stopping_criterion=1e-05, lambda_reg=0.0, name=None)[source]¶

CP convolution is a low rank approximation of a convolution layer. A 3D tensor containing the parameter is built by collapsing the N-D kernels into 1D, then the tensor is decomposed into three matrices. The decomposed layer can be seen as linear combinations of the input feature maps to ${R}$ feature maps followed by a depthwise convolution and followed by linear combinations of the feature maps to compute the output feature maps.

The CP decomposition allows to approximate the kernel tensor by ${R}$ rank-1 tensors of the form:

\[\sum_{r=1}^{R} \lambda_r {\mathbf{o}^{(r)} \otimes \mathbf{i}^{(r)} \otimes \mathbf{k}^{(r)}},\]

where ${\lambda}_r$ is the normalization coefficient and ${\otimes}$ is the outer product.

If oik_init is a numpy array, U and V are computed so that uv_init can be approximates from UV If oik_init is None or an initializer, the product of U and V approximate the randomly initialized array

If O, I and K exist in context, they are used to initialize the layer and oik_init is not used.

Suppose the kernel tensor of the affine is of ${I \times O}$ and the compression rate you want to specify is ${CR}$, then you set ${R}$ as

\[R = \left\lfloor \frac{(1 - CR)OIK^2}{O + I + K^2} \right\rfloor.\]

References

Lebedev, Vadim, Yaroslav Ganin, Maksim Rakhuba, Ivan Oseledets, and Victor Lempitsky, “Speeding-up convolutional neural networks using fine-tuned cp-decomposition.”, arXiv preprint arXiv:1412.6553 (2014).
Marcella Astrid, Seung-Ik Lee, “CP-decomposition with Tensor Power Method for Convolutional Neural Networks Compression”, BigComp 2017.

Parameters

inp (Variable) – N-D array.
outmaps (int) – Number of convolution kernels (which is equal to the number of output channels). For example, to apply convolution on an input with 16 types of filters, specify 16.
kernel (tuple of int) – Convolution kernel size. For example, to apply convolution on an image with a 3 (height) by 5 (width) two-dimensional kernel, specify (3,5).
r (int) – rank of the factorized layer
pad (tuple of int) – Padding sizes for dimensions.
stride (tuple of int) – Stride sizes for dimensions.
dilation (tuple of int) – Dilation sizes for dimensions.
oik_init (numpy array or nnabla.initializer.BaseInitializer) – Initializer for weight. Initializer for weight. By default, it is initialized with nnabla.initializer.UniformInitializer within the range determined by nnabla.initializer.calc_uniform_lim_glorot.
b_init (nnabla.initializer.BaseInitializer or numpy.ndarray) – Initializer for bias. It is initialized with zeros if with_bias is True.
base_axis (int) – Dimensions up to base_axis are treated as the sample dimensions.
fix_parameters (bool) – When set to True, the weights and biases will not be updated.
rng (numpy.random.RandomState) – Random generator for Initializer.
with_bias (bool) – Specify whether to include the bias term.
max_iter (int) – Max iteration of the ALS.
stopping_criterion (float) – Threshold for stopping the ALS. If the value is negative, the convergence check is ignored; in other words, it may reduce the computation time.
lambda_reg (float) – regularization parameter for the ALS. Larger lambda_reg means larger regularization.

Returns

$(B + 1)$-D array. ($M_0 \times \ldots \times M_{B-1} \times L$)

Return type

Parameters to be registered

The following variables are registered in a parameter scope "cpd3_conv";

I (need_grad=True) : Decomposed filter weights ${\mathbf I}$. (shape: (r, inmaps, 1, ...))
K (need_grad=True) : Decomposed filter weights ${\mathbf K}$. (shape: (r, *kernel))
O (need_grad=True) : Decomposed filter weights ${\mathbf O}$. (shape: (outmaps, r, 1, ...))
b (need_grad=True) : Bias vector. (shape: (outmaps,))

Note

If the name option is passed, the parameters become wrapped inside the parameter scope with the specified name, yielding the same results as the following code. This can be used to simplify the code.

with parameter_scope(name):
    output = cpd3_convolution(<args>)

nnabla.parametric_functions.binary_connect_affine(inp, n_outmaps, base_axis=1, quantize_zero_to=1.0, w_init=None, wb_init=None, b_init=None, fix_parameters=False, rng=None, with_bias=True, name=None)[source]¶

Binary Connect Affine, multiplier-less inner-product.

Binary Connect Affine is an affine function, except the definition of the inner product is modified. The input-output relation of this function is as follows:

\[y_i = \sum_{i} sign(w_i) x_i.\]

Therefore $sign(w_i)$ is either $1$ or $-1$ and the inner product simplifies to addition.

This function should be used together with Batch Normalization.

References

M. Courbariaux, Y. Bengio, and J.-P. David. “BinaryConnect: Training Deep Neural Networks with binary weights during propagations.” Advances in Neural Information Processing Systems. 2015.

Note

1) if you would like to share weights between some layers, please make sure to share the standard, floating value weights (weight) and not the binarized weights (binary_weight)

2) The weights and the binary weights become synced only after forward() is called, and not after a call to backward(). To access the parameters of the network, remember to call forward() once before doing so, otherwise the float weights and the binary weights will not be in sync.

3) Quantized values are stored as floating point number for binary_weight, since this function is only for simulation purposes.

Parameters

inp (Variable) – Input N-D array with shape ($M_0 \times \ldots \times M_{B-1} \times D_B \times \ldots \times D_N$). Dimensions before and after base_axis are flattened as if it is a matrix.
n_outmaps (int or tuple of int) – Number of output neurons per data.
base_axis (int) – Dimensions up to base_axis are treated as the sample dimensions.
quantize_zero_to (float) – Input value at zero is quantized to this value.
w_init (nnabla.initializer.BaseInitializer or numpy.ndarray) – Initializer for weight. By default, it is initialized with nnabla.initializer.UniformInitializer within the range determined by nnabla.initializer.calc_uniform_lim_glorot.
wb_init (nnabla.initializer.BaseInitializer or numpy.ndarray) – Initializer for binary weight. By default, it is initialized with nnabla.initializer.UniformInitializer within the range determined by nnabla.initializer.calc_uniform_lim_glorot.
b_init (nnabla.initializer.BaseInitializer or numpy.ndarray) – Initializer for bias. By default, it is initialized with zeros if with_bias is True.
fix_parameters (bool) – When set to True, the weights and biases will not be updated.
rng (numpy.random.RandomState) – Random generator for Initializer.

Returns

Parameters to be registered

The following variables are registered in a parameter scope "bicon_affine";

W (need_grad=True) : Weight matrix in floating type. (shape: (inmaps, outmaps))
Wb (need_grad=False) : Binarized weights. (shape: (inmaps, outmaps))
b (need_grad=True) : Bias vector. (shape: (outmaps,))

Note

If the name option is passed, the parameters become wrapped inside the parameter scope with the specified name, yielding the same results as the following code. This can be used to simplify the code.

with parameter_scope(name):
    output = binary_connect_affine(<args>)

nnabla.parametric_functions.binary_connect_convolution(inp, outmaps, kernel, pad=None, stride=None, dilation=None, group=1, quantize_zero_to=1.0, w_init=None, wb_init=None, b_init=None, base_axis=1, fix_parameters=False, rng=None, with_bias=True, name=None)[source]¶

Binary Connect Convolution, multiplier-less inner-product.

Binary Connect Convolution is the convolution function, except the definition of the inner product is modified. The input-output relation of this function is as follows:

\[y_{n, a, b} = \sum_{m} \sum_{i} \sum_{j} sign(w_{n, m, i, j}) x_{m, a + i, b + j}.\]

Therefore $sign(w_i)$ is either $1$ or $-1$ and the inner product simplifies to addition.

This function should be used together with BatchNormalization.

References

M. Courbariaux, Y. Bengio, and J.-P. David. “BinaryConnect: Training Deep Neural Networks with binary weights during propagations.” Advances in Neural Information Processing Systems. 2015.

Note

1) if you would like to share weights between some layers, please make sure to share the standard, floating value weights (weight) and not the binarized weights (binary_weight)

2) The weights and the binary weights become synced only after forward() is called, and not after a call to backward(). To access the parameters of the network, remember to call forward() once before doing so, otherwise the float weights and the binary weights will not be in sync.

3) Quantized values are stored as floating point number for binary_weight, since this function is only for simulation purposes.

Parameters

inp (Variable) – N-D array.
outmaps (int) – Number of convolution kernels (which is equal to the number of output channels). For example, to apply convolution on an input with 16 types of filters, specify 16.
kernel (tuple of int) – Convolution kernel size. For example, to apply convolution on an image with a 3 (height) by 5 (width) two-dimensional kernel, specify (3,5).
pad (tuple of int) – Padding sizes for dimensions.
stride (tuple of int) – Stride sizes for dimensions.
dilation (tuple of int) – Dilation sizes for dimensions.
group (int) – Number of groups of channels. This makes connections across channels sparser by grouping connections along map direction.
quantize_zero_to (float) – Input value at zero is quantized to this value.
w_init (nnabla.initializer.BaseInitializer or numpy.ndarray) – Initializer for weight. By default, it is initialized with nnabla.initializer.UniformInitializer within the range determined by nnabla.initializer.calc_uniform_lim_glorot.
wb_init (nnabla.initializer.BaseInitializer or numpy.ndarray) – Initializer for binary weight. By default, it is initialized with nnabla.initializer.UniformInitializer within the range determined by nnabla.initializer.calc_uniform_lim_glorot.
b_init (nnabla.initializer.BaseInitializer or numpy.ndarray) – Initializer for bias. By default, it is initialized with zeros if with_bias is True.
base_axis (int) – Dimensions up to base_axis are treated as the sample dimensions.
fix_parameters (bool) – When set to True, the weights and biases will not be updated.
rng (numpy.random.RandomState) – Random generator for Initializer.
with_bias (bool) – Specify whether to include the bias term.

Returns

Parameters to be registered

The following variables are registered in a parameter scope "bicon_conv";

W (need_grad=True) : Filter weights in float. (shape: (outmaps, inmaps, *kernel))
Wb (need_grad=False) : Binarized filter weights. (shape: (outmaps, inmaps, *kernel))
b (need_grad=True) : Bias vector. (shape: (outmaps,))

Note

If the name option is passed, the parameters become wrapped inside the parameter scope with the specified name, yielding the same results as the following code. This can be used to simplify the code.

with parameter_scope(name):
    output = binary_connect_convolution(<args>)

nnabla.parametric_functions.binary_weight_affine(inp, n_outmaps, base_axis=1, quantize_zero_to=1.0, w_init=None, wb_init=None, b_init=None, fix_parameters=False, rng=None, with_bias=True, name=None)[source]¶

Binary Weight Affine, multiplier-less inner-product with a scale factor.

Binary Weight Affine is the affine function, but the inner product in this function is the following,

\[y_j = \frac{1}{\|\mathbf{w}_j\|_{\ell_1}} \sum_{i} sign(w_{ji}) x_i\]

Therefore $sign(w_{ji})$ is either $1$ or $-1$ and the inner product simplifies to addition followed by scaling factor $\alpha = \frac{1}{\|\mathbf{w}_j\|_{\ell_1}}$. The number of :$\alpha$ is the outmaps of the affine function.

References

Rastegari, Mohammad, et al. “XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networks.” arXiv preprint arXiv:1603.05279 (2016).

Note

1) if you would like to share weights between some layers, please make sure to share the standard, floating value weights (weight) and not the binarized weights (binary_weight)

2) The weights and the binary weights become synced only after forward() is called, and not after a call to backward(). To access the parameters of the network, remember to call forward() once before doing so, otherwise the float weights and the binary weights will not be in sync.

3) Quantized values are stored as floating point number for binary_weight, since this function is only for simulation purposes.

Parameters

inp (Variable) – Input N-D array with shape ($M_0 \times \ldots \times M_{B-1} \times D_B \times \ldots \times D_N$). Dimensions before and after base_axis are flattened as if it was a matrix.
n_outmaps (int or tuple of int) – Number of output neurons per data.
base_axis (int) – Dimensions up to base_axis are treated as the sample dimensions.
quantize_zero_to (float) – Input value at zero is quantized to this value.
w_init (nnabla.initializer.BaseInitializer or numpy.ndarray) – Initializer for the weight. By default, it is initialized with nnabla.initializer.UniformInitializer within the range determined by nnabla.initializer.calc_uniform_lim_glorot.
wb_init (nnabla.initializer.BaseInitializer or numpy.ndarray) – Initializer for the binary weight. By default, it is initialized with nnabla.initializer.UniformInitializer within the range determined by nnabla.initializer.calc_uniform_lim_glorot.
b_init (nnabla.initializer.BaseInitializer or numpy.ndarray) – Initializer for the bias. By defalut, it is initialized with zeros if with_bias is True.
fix_parameters (bool) – When set to True, the weight and bias will not be updated.
rng (numpy.random.RandomState) – Random generator for Initializer.
with_bias (bool) – Specify whether to include the bias term.

Returns

Parameters to be registered

The following variables are registered in a parameter scope "bwn_affine";

W (need_grad=True) : Weight matrix in floating type. (shape: (inmaps, outmaps))
Wb (need_grad=False) : Binarized weights. (shape: (inmaps, outmaps))
alpha (need_grad=False) : Scaling factor $\alpha$. (shape: (outmaps,))
b (need_grad=True) : Bias vector. (shape: (outmaps,))

Note

If the name option is passed, the parameters become wrapped inside the parameter scope with the specified name, yielding the same results as the following code. This can be used to simplify the code.

with parameter_scope(name):
    output = binary_weight_affine(<args>)

nnabla.parametric_functions.binary_weight_convolution(inp, outmaps, kernel, pad=None, stride=None, dilation=None, group=1, quantize_zero_to=1.0, w_init=None, wb_init=None, b_init=None, base_axis=1, fix_parameters=False, rng=None, with_bias=True, name=None)[source]¶

Binary Weight Convolution, multiplier-less inner-product with a scale factor.

Binary Weight Convolution is the convolution function, but the inner product in this function is the following,

\[y_{n, a, b} = \frac{1}{\|\mathbf{w}_n\|_{\ell_1}} \sum_{m} \sum_{i} \sum_{j} sign(w_{n, m, i, j}) x_{m, a + i, b + j}.\]

Therefore $sign(w_{n, m, i, j})$ is either $1$ or $-1$ and the inner product simplifies to addition followed by scaling factor $\alpha = \frac{1}{\|\mathbf{w}_n\|_{\ell_1}}$. The number of $n$ is the number of outmaps of the convolution function.

References

Rastegari, Mohammad, et al. “XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networks.” arXiv preprint arXiv:1603.05279 (2016).

Note

1) if you would like to share weights between some layers, please make sure to share the standard, floating value weights (weight) and not the binarized weights (binary_weight)

2) The weights and the binary weights become synced only after forward() is called, and not after a call to backward(). To access the parameters of the network, remember to call forward() once before doing so, otherwise the float weights and the binary weights will not be in sync.

3) Quantized values are stored as floating point number for binary_weight, since this function is only for simulation purposes.

Parameters

inp (Variable) – N-D array.
outmaps (int) – Number of convolution kernels (which is equal to the number of output channels). For example, to apply convolution on an input with 16 types of filters, specify 16.
kernel (tuple of int) – Convolution kernel size. For example, to apply convolution on an image with a 3 (height) by 5 (width) two-dimensional kernel, specify (3,5).
pad (tuple of int) – Padding sizes for dimensions.
stride (tuple of int) – Stride sizes for dimensions.
dilation (tuple of int) – Dilation sizes for dimensions.
group (int) – Number of groups of channels. This makes connections across channels sparser by grouping connections along map direction.
quantize_zero_to (float) – Input value at zero is quantized to this value.
w_init (nnabla.initializer.BaseInitializer or numpy.ndarray) – Initializer for weight. By default, it is initialized with nnabla.initializer.UniformInitializer within the range determined by nnabla.initializer.calc_uniform_lim_glorot.
wb_init (nnabla.initializer.BaseInitializer or numpy.ndarray) – Initializer for binary weight. By default, it is initialized with nnabla.initializer.UniformInitializer within the range determined by nnabla.initializer.calc_uniform_lim_glorot.
b_init (nnabla.initializer.BaseInitializer or numpy.ndarray) – Initializer for bias. By default, it is initialized with zeros if with_bias is True.
base_axis (int) – Dimensions up to base_axis are treated as the sample dimensions.
fix_parameters (bool) – When set to True, the weights and biases will not be updated.
rng (numpy.random.RandomState) – Random generator for Initializer.
with_bias (bool) – Specify whether to include the bias term.

Returns

Parameters to be registered

The following variables are registered in a parameter scope "bwn_conv";

W (need_grad=True) : Filter weights in float. (shape: (outmaps, inmaps, *kernel))
Wb (need_grad=False) : Binarized filter weights. (shape: (outmaps, inmaps, *kernel))
alpha (need_grad=False) : Scaling factor $\alpha$. (shape: (outmaps,))
b (need_grad=True) : Bias vector. (shape: (outmaps,))

Note

If the name option is passed, the parameters become wrapped inside the parameter scope with the specified name, yielding the same results as the following code. This can be used to simplify the code.

with parameter_scope(name):
    output = binary_weight_convolution(<args>)

nnabla.parametric_functions.inq_affine(inp, n_outmaps, base_axis=1, num_bits=4, inq_iterations=(), selection_algorithm='random', seed=- 1, w_init=None, i_init=None, b_init=None, fix_parameters=False, rng=None, with_bias=True, name=None)[source]¶

Incremental Network Quantization Affine Layer

During training, the weights are sequentially quantized to power-of-two values, which allows the training of a multiplierless network.

Using inq_iterations, one can specify after how many forward passes half of the learnable weights are fixed and quantized to powers-of-two. After reaching the last value in inq_iterations, all weights are fixed.

For more details, please refer to the reference.

Reference: Zhou A, Yao A, Guo Y, Xu L, Chen Y. Incremental network quantization: Towards lossless CNNs with low-precision weights. <https://arxiv.org/abs/1702.03044>

Parameters

inp (Variable) – Input N-D array with shape ($M_0 \times \ldots \times M_{B-1} \times D_B \times \ldots \times D_N$). Dimensions before and after base_axis are flattened as if it was a matrix.
n_outmaps (int or tuple of int) – Number of output neurons per data.
base_axis (int) – Dimensions up to base_axis are treated as the sample dimensions.
quantize_zero_to (float) – Input value at zero is quantized to this value.
num_bits (int) – Number of bits per weight. Value has to be larger than 1 as one bit is already used to code the value “0”
inq_iterations (tuple of int) – Tuple of iteration numbers at which we fix half of the weights.
selection_algorithm (str) – Chooses algorithm that is used to decide which weights are fixed. (“largest_abs” … fix weights with largest absolute value, “random” … fix weights randomly)
seed (int) – Random seed for INQ algorithm
w_init (nnabla.initializer.BaseInitializer or numpy.ndarray) – Initializer for weight. By default, it is initialized with nnabla.initializer.UniformInitializer within the range determined by nnabla.initializer.calc_uniform_lim_glorot.
i_init (nnabla.initializer.BaseInitializer or numpy.ndarray) – Initializer for indicators (0 … learnable, 1 … fixed). By default, it is initialized with zeros.
b_init (nnabla.initializer.BaseInitializer or numpy.ndarray) – Initializer for bias. By default, it is initialized with zeros if with_bias is True.
fix_parameters (bool) – When set to True, the weight and bias will not be updated.
rng (numpy.random.RandomState) – Random generator for Initializer.
with_bias (bool) – Specify whether to include the bias term.

Returns

Parameters to be registered

The following variables are registered in a parameter scope "inq_affine";

W (need_grad=True) : Weight matrix in floating type. (shape: (inmaps, outmaps))
I (need_grad=False) : Binary indicator matrix of fixed weights. (shape: (inmaps, outmaps))
b (need_grad=True) : Bias vector. (shape: (outmaps,))

Note

If the name option is passed, the parameters become wrapped inside the parameter scope with the specified name, yielding the same results as the following code. This can be used to simplify the code.

with parameter_scope(name):
    output = inq_affine(<args>)

nnabla.parametric_functions.inq_convolution(inp, outmaps, kernel, pad=None, stride=None, dilation=None, group=1, num_bits=4, inq_iterations=(), selection_algorithm='random', seed=- 1, w_init=None, i_init=None, b_init=None, base_axis=1, fix_parameters=False, rng=None, with_bias=True, name=None)[source]¶

Incremental Network Quantization Convolution Layer

During training, the weights are sequentially quantized to power-of-two values, which allows the training of a multiplierless network.

Using inq_iterations, one can specify after how many forward passes half of the learnable weights are fixed and quantized to powers-of-two. After reaching the last value in inq_iterations, all weights are fixed.

For more details, please refer to the reference.

Reference: Zhou A, Yao A, Guo Y, Xu L, Chen Y. Incremental network quantization: Towards lossless CNNs with low-precision weights. <https://arxiv.org/abs/1702.03044>

Parameters

inp (Variable) – Input N-D array with shape ($M_0 \times \ldots \times M_{B-1} \times D_B \times \ldots \times D_N$). Dimensions before and after base_axis are flattened as if it was a matrix.
n_outmaps (int or tuple of int) – Number of output neurons per data.
base_axis (int) – Dimensions up to base_axis are treated as the sample dimensions.
num_bits (int) – Number of bits per weight. Value has to be larger than 1 as one bit is already used to code the value “0”
inq_iterations (tuple of int) – Tuple of iteration numbers at which we fix half of the weights.
selection_algorithm (str) – Chooses algorithm that is used to decide which weights are fixed. (“largest_abs” … fix weights with largest absolute value, “random” … fix weights randomly)
seed (int) – Random seed for INQ algorithm
w_init (nnabla.initializer.BaseInitializer or numpy.ndarray) – Initializer for the weight. By default, it is initialized with nnabla.initializer.UniformInitializer within the range determined by nnabla.initializer.calc_uniform_lim_glorot.
i_init (nnabla.initializer.BaseInitializer or numpy.ndarray) – Initializer for the indicators (0 … learnable, 1 … fixed). By default, it is initialized with zeros.
b_init (nnabla.initializer.BaseInitializer or numpy.ndarray) – Initializer for the bias. By default, it is initialized with zeros if with_bias is True.
fix_parameters (bool) – When set to True, the weight and bias will not be updated.
rng (numpy.random.RandomState) – Random generator for Initializer.
with_bias (bool) – Specify whether to include the bias term.

Returns

Parameters to be registered

The following variables are registered in a parameter scope "inq_conv";

W (need_grad=True) : Filter weights in float. (shape: (outmaps, inmaps, *kernel))
I (need_grad=False) : Binary indicator matrix of fixed weights. (shape: (outmaps, inmaps, *kernel))
b (need_grad=True) : Bias vector. (shape: (outmaps,))

Note

If the name option is passed, the parameters become wrapped inside the parameter scope with the specified name, yielding the same results as the following code. This can be used to simplify the code.

with parameter_scope(name):
    output = inq_convolution(<args>)

nnabla.parametric_functions.fixed_point_quantized_affine(inp, n_outmaps, base_axis=1, w_init=None, b_init=None, fix_parameters=False, rng=None, with_bias=True, quantize_w=True, sign_w=True, n_w=8, delta_w=0.0625, ste_fine_grained_w=True, quantize_b=True, sign_b=True, n_b=8, delta_b=0.0625, ste_fine_grained_b=True, name=None)[source]¶

Fixed-Point Quantized Affine.

Fixed-Point Quantized Affine is the affine function, except the definition of the inner product is modified. The input-output relation of this function is as follows:

\[y_j = \sum_{i} Q(w_{ji}) x_i,\]

where $Q(w_{ji})$ is the fixed-point quantization function.

Note

1) if you would like to share weights between some layers, please make sure to share the standard, floating value weights (weight) and not the quantized weights (quantized weight)

2) The weights and the quantized weights become synced only after forward() is called, and not after a call to backward(). To access the parameters of the network, remember to call forward() once before doing so, otherwise the float weights and the quantized weights will not be in sync.

3) CPU and GPU implementations now use float value for quantized weight, since this function is only for simulation purposes.

Parameters

inp (Variable) – Input N-D array with shape ($M_0 \times \ldots \times M_{B-1} \times D_B \times \ldots \times D_N$). Dimensions before and after base_axis are flattened as if it is a matrix.
n_outmaps (int or tuple of int) – Number of output neurons per data.
base_axis (int) – Dimensions up to base_axis are treated as the sample dimensions.
w_init (nnabla.initializer.BaseInitializer or numpy.ndarray) – Initializer for weight. By default, it is initialized with nnabla.initializer.UniformInitializer within the range determined by nnabla.initializer.calc_uniform_lim_glorot.
b_init (nnabla.initializer.BaseInitializer or numpy.ndarray) – Initializer for bias. By default, it is initialized with zeros if with_bias is True.
fix_parameters (bool) – When set to True, the weights and biases will not be updated.
rng (numpy.random.RandomState) – Random generator for Initializer.
with_bias (bool) – Specify whether to include the bias term.
quantize_w (bool) – Quantize weights if True.
sign_w (bool) – Use signed quantization if True.
n_w (int) – Bit width used for weight.
delta_w (float) – Step size for weight.
ste_fine_grained_w (bool) – STE is fine-grained if True.
quantize_b (bool) – Quantize bias if True.
n_b (int) – Bit width used for bias.
delta_w – Step size for bias.
ste_fine_grained_b (bool) – STE is fine-grained if True.

Returns

$(B + 1)$-D array. ($M_0 \times \ldots \times M_{B-1} \times L$)

Return type

Parameters to be registered

The following variables are registered in a parameter scope "fp_quantized_affine";

W (need_grad=True) : Weight matrix in float. (shape: (inmaps, outmaps))
b (need_grad=True) : Bias vector in float. (shape: (outmaps,))
W_q (need_grad=False) : Quantized weights. (shape: (inmaps, outmaps))
b_q (need_grad=False) : Quantized biases. (shape: (outmaps,))

Note

If the name option is passed, the parameters become wrapped inside the parameter scope with the specified name, yielding the same results as the following code. This can be used to simplify the code.

with parameter_scope(name):
    output = fixed_point_quantized_affine(<args>)

nnabla.parametric_functions.fixed_point_quantized_convolution(inp, outmaps, kernel, pad=None, stride=None, dilation=None, group=1, channel_last=False, w_init=None, b_init=None, base_axis=1, fix_parameters=False, rng=None, with_bias=True, quantize_w=True, sign_w=True, n_w=8, delta_w=0.0625, ste_fine_grained_w=True, quantize_b=True, sign_b=True, n_b=8, delta_b=0.0625, ste_fine_grained_b=True, name=None)[source]¶

Fixed-Point Quantized Convolution.

Fixed-Point Quantized Convolution is the convolution function, except the definition of the inner product is modified. The input-output relation of this function is as follows:

\[y_{n, a, b} = \sum_{m} \sum_{i} \sum_{j} Q(w_{n, m, i, j}) x_{m, a + i, b + j},\]

where $Q(w_{n, m, i, j})$ is the fixed-point quantization function.

Note

1) if you would like to share weights between some layers, please make sure to share the standard, floating value weights (weight) and not the quantized weights (quantized weight)

2) The weights and the quantized weights become synced only after forward() is called, and not after a call to backward(). To access the parameters of the network, remember to call forward() once before doing so, otherwise the float weights and the quantized weights will not be in sync.

3) CPU and GPU implementations now use float value for quantized weight, since this function is only for simulation purposes.

Parameters

inp (Variable) – N-D array.
outmaps (int) – Number of convolution kernels (which is equal to the number of output channels). For example, to apply convolution on an input with 16 types of filters, specify 16.
kernel (tuple of int) – Convolution kernel size. For example, to apply convolution on an image with a 3 (height) by 5 (width) two-dimensional kernel, specify (3,5).
pad (tuple of int) – Padding sizes for dimensions.
stride (tuple of int) – Stride sizes for dimensions.
dilation (tuple of int) – Dilation sizes for dimensions.
group (int) – Number of groups of channels. This makes connections across channels more sparse by grouping connections along map direction.
w_init (nnabla.initializer.BaseInitializer or numpy.ndarray) – Initializer for weight. By default, it is initialized with nnabla.initializer.UniformInitializer within the range determined by nnabla.initializer.calc_uniform_lim_glorot.
b_init (nnabla.initializer.BaseInitializer or numpy.ndarray) – Initializer for bias. By default, it is initialized with zeros if with_bias is True.
base_axis (int) – Dimensions up to base_axis are treated as the sample dimensions.
fix_parameters (bool) – When set to True, the weights and biases will not be updated.
rng (numpy.random.RandomState) – Random generator for Initializer.
with_bias (bool) – Specify whether to include the bias term.
quantize_w (bool) – Quantize weights if True.
quantize_bias (bool) – Quantize bias if True.
sign_w (bool) – Use signed quantization if True.
n_w (int) – Bit width used for weight.
delta_w (float) – Step size for weight.
ste_fine_grained_w (bool) – STE is fine-grained if True.
quantize_b (bool) – Quantize bias if True.
n_b (int) – Bit width used for bias.
delta_w – Step size for bias.
ste_fine_grained_b (bool) – STE is fine-grained if True.

Returns

N-D array.

Return type

Parameters to be registered

The following variables are registered in a parameter scope "fp_quantized_conv";

W (need_grad=True) : Filter weights in float. (shape: (outmaps, inmaps // group, *kernel))
b (need_grad=True) : Bias vector in float. (shape: (outmaps,))
W_q (need_grad=False) : Quantized weights. (shape: (outmaps, inmaps // group, *kernel))
b_q (need_grad=False) : Quantized biases. (shape: (outmaps,))

Note

If the name option is passed, the parameters become wrapped inside the parameter scope with the specified name, yielding the same results as the following code. This can be used to simplify the code.

with parameter_scope(name):
    output = fixed_point_quantized_convolution(<args>)

nnabla.parametric_functions.min_max_quantized_affine(inp, n_outmaps, base_axis=1, w_init=None, b_init=None, fix_parameters=False, rng=None, with_bias=True, quantize_w=True, ql_min_w=0, ql_max_w=255, w_min_max=False, qr_min_w_init=None, qr_max_w_init=None, ste_fine_grained_w=True, quantize_b=True, ql_min_b=0, ql_max_b=255, b_min_max=False, qr_min_b_init=None, qr_max_b_init=None, ste_fine_grained_b=True, eps=0.01, name=None)[source]¶

Min-max Quantized Affine.

Min-max Quantized Affine is the affine function, except the definition of the inner product is modified. The input-output relation of this function is as follows:

\[y_j = \sum_{i} Q(w_{ji}) x_i,\]

where $Q(w_{ji})$ is the min-max quantization function.

In the min_max_quantized affine, the exponential moving average is not used. the min and max quantization ranges are either the min-max of weights and bias or trained.

Notice that the min and max values of inputs are always used instead of the exponential moving average.

Note

1) if you would like to share weights between some layers, please make sure to share the standard, floating value weights (weight) and not the quantized weights (quantized weight)

2) The weights and the quantized weights become synced only after forward() is called, and not after a call to backward(). To access the parameters of the network, remember to call forward() once before doing so, otherwise the float weights and the quantized weights will not be in sync.

3) CPU and GPU implementations now use float value for quantized weight, since this function is only for simulation purposes.

Parameters

inp (Variable) – Input N-D array with shape ($M_0 \times \ldots \times M_{B-1} \times D_B \times \ldots \times D_N$). Dimensions before and after base_axis are flattened as if it is a matrix.
n_outmaps (int or tuple of int) – Number of output neurons per data.
base_axis (int) – Dimensions up to base_axis are treated as the sample dimensions.
w_init (nnabla.initializer.BaseInitializer or numpy.ndarray) – Initializer for weight. By default, it is initialized with nnabla.initializer.UniformInitializer within the range determined by nnabla.initializer.calc_uniform_lim_glorot.
b_init (nnabla.initializer.BaseInitializer or numpy.ndarray) – Initializer for bias. By default, it is initialized with zeros if with_bias is True.
fix_parameters (bool) – When set to True, the weights and biases will not be updated.
rng (numpy.random.RandomState) – Random generator for Initializer.
with_bias (bool) – Specify whether to include the bias term.
quantize_w (bool) – Quantize weights if True.
ql_min_w (int, float, or Variable) – Minimum quantization level for weights. Default is 0.
ql_max_w (int, float, or Variable) – Maximum quantization level for weights. Default is 255.
w_min_max (bool) – Use the min and max of weights to compute quantization ranges. Default is False.
qr_min_w_init (nnabla.initializer.BaseInitializer or numpy.ndarray) – Initializer for the minimum quantization range, qr_min. Default is nnabla.initializer.ConstantInitializer (-2.0).
qr_max_w_init (nnabla.initializer.BaseInitializer or numpy.ndarray) – Initializer for the maximum quantization range, qr_max. Default is nnabla.initializer.ConstantInitializer (2.0).
ste_fine_grained_w (bool) – If true, STE is not 1, the {0, 1}-mask computed from the min-max is applied to the gradient in the backward; otherwise, STE is 1.
quantize_b (bool) – Quantize bias if True.
ql_min_b (int, float, or Variable) – Minimum quantization level for bias. Default is 0.
ql_max_b (int, float, or Variable) – Maximum quantization level for bias. Default is 255.
b_min_max (bool) – Use the min and max of bias to compute quantization ranges. Default is False.
qr_min_b_init (nnabla.initializer.BaseInitializer or numpy.ndarray) – Initializer for the minimum quantization range, qr_min. Default is nnabla.initializer.ConstantInitializer (-6.0).
qr_max_b_init (nnabla.initializer.BaseInitializer or numpy.ndarray) – Initializer for the maximum quantization range, qr_max. Default is nnabla.initializer.ConstantInitializer (6.0).
ste_fine_grained_b (bool) – If true, STE is not 1, the {0, 1}-mask computed from the min-max is applied to the gradient in the backward; otherwise, STE is 1.
eps (float) – Epsilon, or small value to ensure $qr_{max} - qr_{min}$ must be greater than the epsilon for both weights and bias.

Returns

$(B + 1)$-D array. ($M_0 \times \ldots \times M_{B-1} \times L$)

Return type

Parameters to be registered

The following variables are registered in a parameter scope "min_max_quantized_affine";

W (need_grad=True) : Weight matrix in float. (shape: (inmaps, outmaps))
b (need_grad=True) : Bias vector in float. (shape: (outmaps,))
W_q (need_grad=False) : Quantized weights. (shape: (inmaps, outmaps))
b_q (need_grad=False) : Quantized biases. (shape: (outmaps,))
qr_min (need_grad=False) : Minimum quantization range. Minimum values of inputs or trainable range.. (shape: ql_min.shape)
qr_max (need_grad=False) : Maximum quantization range. Maximum values of inputs or trainable range.. (shape: ql_max.shape)

Note

If the name option is passed, the parameters become wrapped inside the parameter scope with the specified name, yielding the same results as the following code. This can be used to simplify the code.

with parameter_scope(name):
    output = min_max_quantized_affine(<args>)

nnabla.parametric_functions.min_max_quantized_convolution(inp, outmaps, kernel, pad=None, stride=None, dilation=None, group=1, channel_last=False, w_init=None, b_init=None, base_axis=1, fix_parameters=False, rng=None, with_bias=True, quantize_w=True, ql_min_w=0, ql_max_w=255, w_min_max=False, qr_min_w_init=None, qr_max_w_init=None, ste_fine_grained_w=True, quantize_b=True, ql_min_b=0, ql_max_b=255, b_min_max=False, qr_min_b_init=None, qr_max_b_init=None, ste_fine_grained_b=True, eps=0.01, name=None)[source]¶

Min-max Quantized Convolution.

Min-max Quantized Convolution is the convolution function, except the definition of the inner product is modified. The input-output relation of this function is as follows:

\[y_{n, a, b} = \sum_{m} \sum_{i} \sum_{j} Q(w_{n, m, i, j}) x_{m, a + i, b + j},\]

where $Q(w_{n, m, i, j})$ is the min-max quantization function.

In the min_max_quantized convolution, the exponential moving average is not used. the min and max quantization ranges are either the min-max of weights and bias or trained.

Notice that the min and max values of inputs are always used instead of the exponential moving average.

Note

1) if you would like to share weights between some layers, please make sure to share the standard, floating value weights (weight) and not the quantized weights (quantized weight)

2) The weights and the quantized weights become synced only after forward() is called, and not after a call to backward(). To access the parameters of the network, remember to call forward() once before doing so, otherwise the float weights and the quantized weights will not be in sync.

3) CPU and GPU implementations now use float value for quantized weight, since this function is only for simulation purposes.

Parameters

inp (Variable) – N-D array.
outmaps (int) – Number of convolution kernels (which is equal to the number of output channels). For example, to apply convolution on an input with 16 types of filters, specify 16.
kernel (tuple of int) – Convolution kernel size. For example, to apply convolution on an image with a 3 (height) by 5 (width) two-dimensional kernel, specify (3,5).
pad (tuple of int) – Padding sizes for dimensions.
stride (tuple of int) – Stride sizes for dimensions.
dilation (tuple of int) – Dilation sizes for dimensions.
group (int) – Number of groups of channels. This makes connections across channels more sparse by grouping connections along map direction.
channel_last (bool) – If True, the last dimension is considered as channel dimension, a.k.a. NHWC order.
w_init (nnabla.initializer.BaseInitializer or numpy.ndarray) – Initializer for weight. By default, it is initialized with nnabla.initializer.UniformInitializer within the range determined by nnabla.initializer.calc_uniform_lim_glorot.
b_init (nnabla.initializer.BaseInitializer or numpy.ndarray) – Initializer for bias. By default, it is initialized with zeros if with_bias is True.
base_axis (int) – Dimensions up to base_axis are treated as the sample dimensions.
fix_parameters (bool) – When set to True, the weights and biases will not be updated.
rng (numpy.random.RandomState) – Random generator for Initializer.
with_bias (bool) – Specify whether to include the bias term.
quantize_w (bool) – Quantize weights if True.
ql_min_w (int, float, or Variable) – Minimum quantization level for weights. Default is 0.
ql_max_w (int, float, or Variable) – Maximum quantization level for weights. Default is 255.
w_min_max (bool) – Use the min and max of weights to compute quantization ranges. Default is False.
qr_min_w_init (nnabla.initializer.BaseInitializer or numpy.ndarray) – Initializer for the minimum quantization range, qr_min. Default is nnabla.initializer.ConstantInitializer (-2.0).
qr_max_w_init (nnabla.initializer.BaseInitializer or numpy.ndarray) – Initializer for the maximum quantization range, qr_max Default is nnabla.initializer.ConstantInitializer (2.0).
ste_fine_grained_w (bool) – If true, STE is not 1, the {0, 1}-mask computed from the min-max is applied to the gradient in the backward; otherwise, STE is 1.
quantize_b (bool) – Quantize bias if True.
ql_min_b (int, float, or Variable) – Minimum quantization level for bias. Default is 0.
ql_max_b (int, float, or Variable) – Maximum quantization level for bias. Default is 255.
b_min_max (bool) – Use the min and max of bias to compute quantization ranges. Default is False.
qr_min_b_init (nnabla.initializer.BaseInitializer or numpy.ndarray) – Initializer for the minimum quantization range, qr_min. Default is nnabla.initializer.ConstantInitializer (-6.0).
qr_max_b_init (nnabla.initializer.BaseInitializer or numpy.ndarray) – Initializer for the maximum quantization range, qr_max Default is nnabla.initializer.ConstantInitializer (6.0).
ste_fine_grained_b (bool) – If true, STE is not 1, the {0, 1}-mask computed from the min-max is applied to the gradient in the backward; otherwise, STE is 1.
eps (float) – Epsilon, or small value to ensure $qr_{max} - qr_{min}$ must be greater than the epsilon for both weights and bias.

Returns

N-D array.

Return type

Parameters to be registered

The following variables are registered in a parameter scope "min_max_quantized_conv";

W (need_grad=True) : Filter weights in float. (shape: (outmaps, inmaps // group, *kernel))
b (need_grad=True) : Bias vector in float. (shape: (outmaps,))
W_q (need_grad=False) : Quantized weights. (shape: (outmaps, inmaps // group, *kernel))
b_q (need_grad=False) : Quantized biases. (shape: (outmaps,))
qr_min (need_grad=False) : Minimum quantization range. Minimum values of inputs or trainable range.. (shape: ql_min.shape)
qr_max (need_grad=False) : Maximum quantization range. Maximum values of inputs or trainable range.. (shape: ql_max.shape)

Note

If the name option is passed, the parameters become wrapped inside the parameter scope with the specified name, yielding the same results as the following code. This can be used to simplify the code.

with parameter_scope(name):
    output = min_max_quantized_convolution(<args>)

nnabla.parametric_functions.pow2_quantized_affine(inp, n_outmaps, base_axis=1, w_init=None, b_init=None, fix_parameters=False, rng=None, with_bias=True, quantize_w=True, sign_w=True, with_zero_w=False, n_w=8, m_w=2, ste_fine_grained_w=True, quantize_b=True, sign_b=True, with_zero_b=False, n_b=8, m_b=2, ste_fine_grained_b=True, name=None)[source]¶

Pow2 Quantized Affine.

Pow2 Quantized Affine is the affine function, except the definition of the inner product is modified. The input-output relation of this function is as follows:

\[y_j = \sum_{i} Q(w_{ji}) x_i,\]

where $Q(w_{ji})$ is the power-of-2 quantization function.

Note

1) if you would like to share weights between some layers, please make sure to share the standard, floating value weights (weight) and not the quantized weights (quantized weight)

2) The weights and the quantized weights become synced only after forward() is called, and not after a call to backward(). To access the parameters of the network, remember to call forward() once before doing so, otherwise the float weights and the quantized weights will not be in sync.

3) Quantized values are stored as floating point number for quantized weight, since this function is only for simulation purposes.

Parameters

inp (Variable) – Input N-D array with shape ($M_0 \times \ldots \times M_{B-1} \times D_B \times \ldots \times D_N$). Dimensions before and after base_axis are flattened as if it is a matrix.
n_outmaps (int or tuple of int) – Number of output neurons per data.
base_axis (int) – Dimensions up to base_axis are treated as the sample dimensions.
w_init (nnabla.initializer.BaseInitializer or numpy.ndarray) – Initializer for weight. By default, it is initialized with nnabla.initializer.UniformInitializer within the range determined by nnabla.initializer.calc_uniform_lim_glorot.
b_init (nnabla.initializer.BaseInitializer or numpy.ndarray) – Initializer for bias. By default, it is initialized with zeros if with_bias is True.
fix_parameters (bool) – When set to True, the weights and biases will not be updated.
rng (numpy.random.RandomState) – Random generator for Initializer.
with_bias (bool) – Specify whether to include the bias term.
quantize_w (bool) – Quantize weights if True.
sign_w (bool) – Use signed quantization if True.
with_zero_w (bool) – Indicate using zero as a quantized value. Default is false.
n_w (int) – Bit width used for weight.
m_w (int) – $2^m$ is upper bound and $-2^m$ is lower bound for weights. Default is 2.
ste_fine_grained_w (bool) – STE is fine-grained if True.
quantize_b (bool) – Quantize bias if True.
with_zero_b (bool) – Indicate using zero as a quantized value. Default is false.
n_b (int) – Bit width used for bias.
m_b (int) – $2^m$ is upper bound and $-2^m$ is lower bound for bias. Default is 2.
ste_fine_grained_b (bool) – STE is fine-grained if True.

Returns

$(B + 1)$-D array. ($M_0 \times \ldots \times M_{B-1} \times L$)

Return type

Parameters to be registered

The following variables are registered in a parameter scope "pow2_quantized_affine";

W (need_grad=True) : Weight matrix in float. (shape: (inmaps, outmaps))
b (need_grad=True) : Bias vector in float. (shape: (outmaps,))
W_q (need_grad=False) : Quantized weights. (shape: (inmaps, outmaps))
b_q (need_grad=False) : Quantized biases. (shape: (outmaps,))

Note

If the name option is passed, the parameters become wrapped inside the parameter scope with the specified name, yielding the same results as the following code. This can be used to simplify the code.

with parameter_scope(name):
    output = pow2_quantized_affine(<args>)

nnabla.parametric_functions.pow2_quantized_convolution(inp, outmaps, kernel, pad=None, stride=None, dilation=None, group=1, w_init=None, b_init=None, base_axis=1, fix_parameters=False, rng=None, with_bias=True, quantize_w=True, with_zero_w=False, sign_w=True, n_w=8, m_w=2, ste_fine_grained_w=True, quantize_b=True, with_zero_b=False, sign_b=True, n_b=8, m_b=2, ste_fine_grained_b=True, name=None)[source]¶

Pow2 Quantized Convolution.

Pow2 Quantized Convolution is the convolution function, except the definition of the inner product is modified. The input-output relation of this function is as follows:

\[y_{n, a, b} = \sum_{m} \sum_{i} \sum_{j} Q(w_{n, m, i, j}) x_{m, a + i, b + j},\]

where $Q(w_{n, m, i, j})$ is the power-of-2 quantization function.

Note

1) if you would like to share weights between some layers, please make sure to share the standard, floating value weights (weight) and not the quantized weights (quantized weight)

2) The weights and the quantized weights become synced only after forward() is called, and not after a call to backward(). To access the parameters of the network, remember to call forward() once before doing so, otherwise the float weights and the quantized weights will not be in sync.

3) Quantized values are stored as floating point number for quantized weight, since this function is only for simulation purposes.

Parameters

inp (Variable) – N-D array.
outmaps (int) – Number of convolution kernels (which is equal to the number of output channels). For example, to apply convolution on an input with 16 types of filters, specify 16.
kernel (tuple of int) – Convolution kernel size. For example, to apply convolution on an image with a 3 (height) by 5 (width) two-dimensional kernel, specify (3,5).
pad (tuple of int) – Padding sizes for dimensions.
stride (tuple of int) – Stride sizes for dimensions.
dilation (tuple of int) – Dilation sizes for dimensions.
group (int) – Number of groups of channels. This makes connections across channels more sparse by grouping connections along map direction.
w_init (nnabla.initializer.BaseInitializer or numpy.ndarray) – Initializer for weight. By default, it is initialized with nnabla.initializer.UniformInitializer within the range determined by nnabla.initializer.calc_uniform_lim_glorot.
b_init (nnabla.initializer.BaseInitializer or numpy.ndarray) – Initializer for bias. By default, it is initialized with zeros if with_bias is True.
base_axis (int) – Dimensions up to base_axis are treated as the sample dimensions.
fix_parameters (bool) – When set to True, the weights and biases will not be updated.
rng (numpy.random.RandomState) – Random generator for Initializer.
with_bias (bool) – Specify whether to include the bias term.
quantize_w (bool) – Quantize weights if True.
sign_w (bool) – Use signed quantization if True.
n_w (int) – Bit width used for weight.
m_w (int) – $2^m$ is upper bound and $-2^m$ is lower bound for weights. Default is 2.
ste_fine_grained_w (bool) – STE is fine-grained if True.
quantize_b (bool) – Quantize bias if True.
sign_b (bool) – Use signed quantization if True.
n_b (int) – Bit width used for bias.
m_b (int) – $2^m$ is upper bound and $-2^m$ is lower bound for bias. Default is 2.
ste_fine_grained_b (bool) – STE is fine-grained if True.

Returns

N-D array.

Return type

Parameters to be registered

The following variables are registered in a parameter scope "pow2_quantized_conv";

W (need_grad=True) : Filter weights in float. (shape: (outmaps, inmaps // group, *kernel))
b (need_grad=True) : Bias vector in float. (shape: (outmaps,))
W_q (need_grad=False) : Quantized weights. (shape: (outmaps, inmaps // group, *kernel))
b_q (need_grad=False) : Quantized biases. (shape: (outmaps,))

Note

If the name option is passed, the parameters become wrapped inside the parameter scope with the specified name, yielding the same results as the following code. This can be used to simplify the code.

with parameter_scope(name):
    output = pow2_quantized_convolution(<args>)

nnabla.parametric_functions.pruned_affine(inp, n_outmaps, base_axis=1, w_init=None, b_init=None, fix_parameters=False, rng=None, with_bias=True, prune_w=True, rate_w=0.9, prune_b=True, rate_b=0.9, name=None)[source]¶

Pruned Affine.

Pruned Affine is the affine function, except the definition of the inner product is modified. The input-output relation of this function is as follows:

\[y_j = \sum_{i} Q(w_{ji}) x_i,\]

where $Q(w_{ji})$ is the pruning function, i.e., F.prune.

Note

1) if you would like to share weights between some layers, please make sure to share the standard, floating value weights (weight) and not the quantized weights (quantized weight)

2) The weights and the quantized weights become synced only after forward() is called, and not after a call to backward(). To access the parameters of the network, remember to call forward() once before doing so, otherwise the float weights and the quantized weights will not be in sync.

3) CPU and GPU implementations now use float value for quantized weight, since this function is only for simulation purposes.

Parameters

inp (Variable) – Input N-D array with shape ($M_0 \times \ldots \times M_{B-1} \times D_B \times \ldots \times D_N$). Dimensions before and after base_axis are flattened as if it is a matrix.
n_outmaps (int or tuple of int) – Number of output neurons per data.
base_axis (int) – Dimensions up to base_axis are treated as the sample dimensions.
w_init (nnabla.initializer.BaseInitializer or numpy.ndarray) – Initializer for weight.
b_init (nnabla.initializer.BaseInitializer or numpy.ndarray) – Initializer for bias.
fix_parameters (bool) – When set to True, the weights and biases will not be updated.
rng (numpy.random.RandomState) – Random generator for Initializer.
with_bias (bool) – Specify whether to include the bias term.
prune_w (bool) – Quantize weights if True.
rate_w (float) – Pruning rate for weights.
prune_b (bool) – Quantize bias if True.
rate_b (float) – Pruning rate for bias.

Returns

$(B + 1)$-D array. ($M_0 \times \ldots \times M_{B-1} \times L$)

Return type

Parameters to be registered

The following variables are registered in a parameter scope "pruned_affine";

W (need_grad=True) : Weight matrix in float. (shape: (inmaps, outmaps))
b (need_grad=True) : Bias vector in float. (shape: (outmaps,))
W_q (need_grad=False) : Qunatized weights. (shape: (inmaps, outmaps))
b_q (need_grad=False) : Quantized biases. (shape: (outmaps,))

Note

If the name option is passed, the parameters become wrapped inside the parameter scope with the specified name, yielding the same results as the following code. This can be used to simplify the code.

with parameter_scope(name):
    output = pruned_affine(<args>)

nnabla.parametric_functions.pruned_convolution(inp, outmaps, kernel, pad=None, stride=None, dilation=None, group=1, channel_last=False, w_init=None, b_init=None, base_axis=1, fix_parameters=False, rng=None, with_bias=True, prune_w=True, rate_w=0.9, prune_b=True, rate_b=0.9, name=None)[source]¶

Pruned Convolution.

Pruned Convolution is the convolution function, except the definition of the inner product is modified. The input-output relation of this function is as follows:

\[y_{n, a, b} = \sum_{m} \sum_{i} \sum_{j} Q(w_{n, m, i, j}) x_{m, a + i, b + j},\]

where $Q(w_{ji})$ is the pruning function, i.e., F.prune.

Note

1) if you would like to share weights between some layers, please make sure to share the standard, floating value weights (weight) and not the quantized weights (quantized weight)

2) The weights and the quantized weights become synced only after forward() is called, and not after a call to backward(). To access the parameters of the network, remember to call forward() once before doing so, otherwise the float weights and the quantized weights will not be in sync.

3) CPU and GPU implementations now use float value for quantized weight, since this function is only for simulation purposes.

Parameters

inp (Variable) – N-D array.
outmaps (int) – Number of convolution kernels (which is equal to the number of output channels). For example, to apply convolution on an input with 16 types of filters, specify 16.
kernel (tuple of int) – Convolution kernel size. For example, to apply convolution on an image with a 3 (height) by 5 (width) two-dimensional kernel, specify (3,5).
pad (tuple of int) – Padding sizes for dimensions.
stride (tuple of int) – Stride sizes for dimensions.
dilation (tuple of int) – Dilation sizes for dimensions.
group (int) – Number of groups of channels. This makes connections across channels more sparse by grouping connections along map direction.
w_init (nnabla.initializer.BaseInitializer or numpy.ndarray) – Initializer for weight.
b_init (nnabla.initializer.BaseInitializer or numpy.ndarray) – Initializer for bias.
base_axis (int) – Dimensions up to base_axis are treated as the sample dimensions.
fix_parameters (bool) – When set to True, the weights and biases will not be updated.
rng (numpy.random.RandomState) – Random generator for Initializer.
with_bias (bool) – Specify whether to include the bias term.
prune_w (bool) – Quantize weights if True.
rate_w (float) – Pruning rate for weights.
prune_b (bool) – Quantize bias if True.
rate_b (float) – Pruning rate for bias.

Returns

N-D array.

Return type

Parameters to be registered

The following variables are registered in a parameter scope "pruned_conv";

W (need_grad=True) : Filter weights in float. (shape: (outmaps, inmaps // group, *kernel))
b (need_grad=True) : Bias vector in float. (shape: (outmaps,))
W_q (need_grad=False) : Qunatized weights. (shape: (outmaps, inmaps // group, *kernel))
b_q (need_grad=False) : Quantized biases. (shape: (outmaps,))

Note

If the name option is passed, the parameters become wrapped inside the parameter scope with the specified name, yielding the same results as the following code. This can be used to simplify the code.

with parameter_scope(name):
    output = pruned_convolution(<args>)

nnabla.parametric_functions.min_max_quantize(x, ql_min=0, ql_max=255, decay=0.999, x_min_max=False, ema=False, ste_fine_grained=True, eps=0.01, qr_min_init=None, qr_max_init=None, fix_parameters=False, outputs=None, name=None)[source]¶

Min-max quantization.

This function uniformly quantizes values in the range of min and max quantization levels.

Min-max quantization is defined as the following equation

\[y = round \left(\frac{\min(\max(x, m), M) - m}{scale} \right) \times scale + m,\]

where the $scale$ is defined as

\[scale = \frac{M - m}{M_q - m_q},\]

and

\[\begin{split}m_q = ql_{min}, \\ M_q = ql_{max}, \\ m = qr_{min}, \\ M = qr_{max}.\end{split}\]

In the backward pass when using ste_fine_grained as false,

\[\frac{\partial q_i}{\partial x_i} = 1.\]

In the backward pass when using ste_fine_grained as true,

\[\begin{split} \frac{\partial q_i}{\partial x_i}= \left\{ \begin{array}{ll} 0 & if \ \ \ x_i > M \\ 1 & if \ \ m \le x_i \le M \\ 0 & if \ \ x_i < m \\ \end{array} \right..\end{split}\]

$qr_{min}$ and $qr_{max}$ are treaded as follows.

x_min_max is True and ema is True: Exponential moving average are computed for each $min(x)$ and $max(x)$ then stored in $qr_{min}$ and $qr_{max}$.

x_min_max is True and ema is False: $min(x)$ and $max(x)$ are computed then stored in $qr_{min}$ and $qr_{max}$.

x_min_max is False and ema is True: Exponential moving average stored in $qr_{min}$ and $qr_{max}$ are used.

x_min_max is False and ema is False Gradients of $qr_{min}$ and $qr_{max}$ are computed in the backward pass.

More precisely, in inference of the min-max quantization, one has to consider zero-point (zp) which corresponds to the real value 0, and its data type is an integer. zero-point is defined as

\[\begin{split} && zp_f = ql_{min} -\frac{qr_{min}}{scale}, \\ && zp = \left\{ \begin{array}{ll} ql_{max} & if \ \ \ zp_f >= ql_{max} \\ round(zp_f) & if \ \ otherwise \\ ql_{min} & if \ \ zp_f <= ql_{min} \\ \end{array} \right..\end{split}\]

Accordingly, in order to simulate quantization effect of zero-point, during both forward and backward pass, $qr_{min}$ and $qr_{max}$ are adjusted as follows,

\[\begin{split}qr_{min}^{adj} = ql_{min} - zp * scale, \\ qr_{max}^{adj} = ql_{max} - zp * scale.\end{split}\]

These operations are often called nudge.

Finally, in the formulas of the min-max quantization, $m$ and $M$ are replaced by $qr_{min}^{adj}$ and $qr_{max}^{adj}$ respectively.

Parameters

x (Variable) – Input N-D array.
ql_min (int, float, or Variable) – Minimum quantization level. Default is 0.
ql_max (int, float, or Variable) – Maximum quantization level. Default is 255.
decay (float) – The decay rate for the exponential moving average.
x_min_max (bool) – Use the min and max of x to compute quantization ranges. Default is False.
ema (bool) – Use the exponential moving average for the min and max quantization ranges. Default is False.
ste_fine_grained (bool) – If true, STE is not 1, the {0, 1}-mask computed from the min-max is applied to the gradient in the backward; otherwise, STE is 1.
eps (float) – Epsilon, or small value to ensure $qr_{max} - qr_{min}$ must be greater than the epsilon for both weights and bias.
qr_min_init (nnabla.initializer.BaseInitializer or numpy.ndarray) – Initializer for the minimum quantization range, qr_min. Default is nnabla.initializer.ConstantInitializer (-6.0).
qr_max_init (nnabla.initializer.BaseInitializer or numpy.ndarray) – Initializer for the maximum quantization range, qr_max Default is nnabla.initializer.ConstantInitializer (6.0).
fix_parameters (bool) – When set to True, the weights and biases will not be updated.

References

Benoit Jacob, Skirmantas Kligys, Bo Chen, Menglong Zhu, Matthew Tang, Andrew Howard, Hartwig Adam, and Dmitry Kalenichenko, “Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference”, https://arxiv.org/abs/1712.05877

Parameters to be registered

The following variables are registered in a parameter scope "min_max_quantize";

qr_min (need_grad=False) : Minimum quantization range, the exponential movining average of min values of inputs initialized with -6.0 if ema is True. (shape: ql_min.shape)
qr_max (need_grad=False) : Maximum quantization range, the exponential movining average of max values of inputs initialized with 6.0 if ema is True. (shape: ql_max.shape)

Note

If the name option is passed, the parameters become wrapped inside the parameter scope with the specified name, yielding the same results as the following code. This can be used to simplify the code.

with parameter_scope(name):
    output = min_max_quantize(<args>)

nnabla.parametric_functions.lstm_cell(x, h, c, state_size, w_init=None, b_init=None, fix_parameters=False, name=None)[source]¶

Long Short-Term Memory.

Long Short-Term Memory, or LSTM, is a building block for recurrent neural networks (RNN) layers. LSTM unit consists of a cell and input, output, forget gates whose functions are defined as following:

\[\begin{split}f_t&&=\sigma(W_fx_t+U_fh_{t-1}+b_f) \\ i_t&&=\sigma(W_ix_t+U_ih_{t-1}+b_i) \\ o_t&&=\sigma(W_ox_t+U_oh_{t-1}+b_o) \\ c_t&&=f_t\odot c_{t-1}+i_t\odot\tanh(W_cx_t+U_ch_{t-1}+b_c) \\ h_t&&=o_t\odot\tanh(c_t).\end{split}\]

References

S. Hochreiter, and J. Schmidhuber. “Long Short-Term Memory.” Neural Computation. 1997.

Parameters

x (Variable) – Input N-D array with shape (batch_size, input_size).
h (Variable) – Input N-D array with shape (batch_size, state_size).
c (Variable) – Input N-D array with shape (batch_size, state_size).
state_size (int) – Internal state size is set to state_size.
w_init (nnabla.initializer.BaseInitializer or numpy.ndarray, optional) – Initializer for weight. By default, it is initialized with nnabla.initializer.UniformInitializer within the range determined by nnabla.initializer.calc_uniform_lim_glorot.
b_init (nnabla.initializer.BaseInitializer or numpy.ndarray, optional) – Initializer for bias. By default, it is initialized with zeros if with_bias is True.
fix_parameters (bool) – When set to True, the weights and biases will not be updated.

Returns

Parameters to be registered

The following variables are registered in a parameter scope "lstm";

affine/W (need_grad=True) : Stacked weight matrixes of LSTM block. (shape: (inmaps, 4, state_size))
affine/b (need_grad=True) : Stacked bias vectors of LSTM block. (shape: (4, state_size,))

Note

If the name option is passed, the parameters become wrapped inside the parameter scope with the specified name, yielding the same results as the following code. This can be used to simplify the code.

with parameter_scope(name):
    output = lstm_cell(<args>)

class nnabla.parametric_functions.LSTMCell(batch_size, state_size, h=None, c=None, name=None)[source]¶

__call__(x, w_init, b_init, fix_parameters)[source]¶

Updates h and c by calling lstm function.

Parameters

x (Variable) – Input N-D array with shape (batch_size, input_size).
w_init (nnabla.initializer.BaseInitializer or numpy.ndarray, optional) – Initializer for weight. By default, it is initialized with nnabla.initializer.UniformInitializer within the range determined by nnabla.initializer.calc_uniform_lim_glorot.
b_init (nnabla.initializer.BaseInitializer or numpy.ndarray, optional) – Initializer for bias. By default, it is initialized with zeros if with_bias is True.
fix_parameters (bool) – When set to True, the weights and biases will not be updated.

nnabla.parametric_functions.spectral_norm(w, dim=0, itr=1, eps=1e-12, test=False, u_init=None, fix_parameters=True, name=None)[source]¶

Spectral Normalization.

\[W_{sn} = \frac{W}{\sigma(W)}.\]

where $W$ is the input matrix, and the $\sigma(W)$ is the spectral norm of $W$. The spectral norm is approximately computed by the power iteration.

References

Takeru Miyato, Toshiki Kataoka, Masanori Koyama, Yuichi Yoshida, “Spectral Normalization for Generative Adversarial Networks”, International Conference on Learning Representations. 2018.

Parameters

W (Variable) – Input N-D array with shape. This is normally network parameter.
dim (int) – Output dimension. Default is 0. If the dimension is not 0, then the specified dimension becomes the most-left dimension by transposing.
itr (int) – Number of iterations. Default is 1.
eps (float) – Epsilon for the normalization. Default is 1e-12.
test (bool) – Use test mode. Default is False.

Returns

Spectrally normalized $W_{sn}$ with the same shape as $W$.

Return type

Example

import nnabla as nn
import nnabla.parametric_functions as PF

b, c, h, w = 4, 64, 32, 32

# Spectrally normalized convolution
apply_w = lambda w: PF.spectral_norm(w, dim=0)
h = nn.Variable.from_numpy_array(np.random.randn(b, c, h, w))
h = PF.convolution(h, with_bias=False, apply_w=apply_w)

# Spectrally normalized affine
apply_w = lambda w: PF.spectral_norm(w, dim=1)
h = nn.Variable.from_numpy_array(np.random.randn(b, c))
h = PF.affine(h, with_bias=False, apply_w=apply_w)

# Spectrally normalized embed
apply_w = lambda w: PF.spectral_norm(w, dim=1)
h = nn.Variable.from_numpy_array(np.random.randn(b, c))
h = PF.embed(h, c, apply_w=apply_w)

Parameters to be registered

The following variables are registered in a parameter scope "spectral-norm";

u (need_grad=False) : singular vector. (shape: (w.shape[dim], ))

Note

If the name option is passed, the parameters become wrapped inside the parameter scope with the specified name, yielding the same results as the following code. This can be used to simplify the code.

with parameter_scope(name):
    output = spectral_norm(<args>)

nnabla.parametric_functions.weight_normalization(w, dim=0, eps=1e-12, g_init=None, fix_parameters=False, name=None)[source]¶

Weight Normalization.

\[\mathbf{w}_{WN} = g \dfrac{\mathbf{w}}{\|\mathbf{w}\|}\]

where $\mathbf{w}$ is the input weights to be normalized, and $g$ is learnable multiplication factors each of which is applied to each input weights at dim. This function is in general used as callback passed to apply_w for PF.convolution, PF.affine and so on. According to the author`s original implementation, $v$ should be initialized by $N(0, 0.05)$. To meet this condition, initializer should be passed to convolution which Weight Normalization is applied, like an example below.

References

Tim Salimans, Diederik P. Kingma, Weight Normalization: A Simple Reparameterization to Accelerate Training of Deep Neural Networks.

Parameters

W (Variable) – Input N-D array with shape. This is normally network parameter.
dim (int) – Output dimension. Default is 0. If the dimension is not 0, then the specified dimension becomes the most-left dimension by transposing.
eps (float) – Epsilon for the normalization. Default is 1e-12.
g_init (nnabla.initializer.BaseInitializer or numpy.ndarray) – Initializer for the scale. By default, L2-norm of weights corresponding to dim are used.

Returns

$W$ with the same shape as $v$.

Return type

Example

import nnabla as nn
import nnabla.parametric_functions as PF
import nnabla.initializer as I

# h is nn.Variable.

# convolution
# according to the original implementation, w should be initialized by N(0, 0.05).
h = PF.convolution(h, ..., apply_w=PF.weight_normalization, w_init=I.NormalInitializer(0.05))

# affine
h = PF.affine(h, ..., apply_w=lambda w: PF.weight_normalization(w, dim=1), w_init=I.NormalInitializer(0.05))

Warning

Up to the version 1.10.0, this had been implemented as the composite functions.

Parameters to be registered

The following variables are registered in a parameter scope "wn";

g (need_grad=True) : Weight Normalization adaptive scale scalar.. (shape: w.shape[dim])

Note

If the name option is passed, the parameters become wrapped inside the parameter scope with the specified name, yielding the same results as the following code. This can be used to simplify the code.

with parameter_scope(name):
    output = weight_normalization(<args>)

nnabla.parametric_functions.multi_head_attention(query, key, value, num_heads=12, dropout=0.0, k_embed_dim=None, v_embed_dim=None, out_dim=None, rng=None, with_bias=True, add_attn_bias=False, additive_mask=None, key_padding_mask=None, fix_parameters=False, param_init=None, name=None)[source]¶

MultiHeadAttention.

Computes multi-headed attention with query, key, and value. We use the following notations to describe the inputs and outputs below. $L_T$: target sequence length, $L_S$: source sequence length, $B$: batch size, $D$: input dimension, $E$: embedding dimension.

References

A. Vaswani et al. “Attention is All You Need.” NIPS. 2017. <https://papers.nips.cc/paper/7181-attention-is-all-you-need.pdf>

Example:

q = nn.Variable((tgt_len, batch_size, q_input_dim))
k = nn.Variable((src_len, batch_size, k_input_dim))
v = nn.Variable((src_len, batch_size, v_input_dim))

out, w = PF.multi_head_attention(q, k, v)
out.forward()

Parameters

query (Variable) – Input N-D array with shape $(L_T, B, D_q)$.
key (Variable) – Input N-D array with shape $(L_S, B, D_k)$.
value (Variable) – Input N-D array with shape $(L_S, B, D_v)$.
num_heads (int, optional) – Number of attention heads. Note that embedding dimensoin E must be divisible by the number of heads. Default is 12 which is conventional.
dropout (float, optional) – Dropout ratio applied to parameters. Default is 0.
k_embed_dim (int, optional) – Embedding dimension for key. If specified, embedding dimensions for both query and key are set as that value. Otherwise, k_embed_dim is set as the same alue as embedding dimension for query.
v_embed_dim (int, optional) – Embedding dimension for value. If not specified, it is defaulted as the same value as embedding dimension for query.
out_dim (int, optional) – Embedding dimension for output weight. If not spefied, it is defaulted as the same value as embedding dimension for value.
rng (numpy.random.RandomState, optional) – Random generator for Initializer. Default is None.
with_bias (bool, optional) – Specify whether to include the bias parameters. Default is True.
add_attn_bias (bool, optional) – Specify whether to add attention bias parameters for key and value. Default is False.
additive_mask (Variable, optional) – Input N-D array with shape $(L_T, L_S)$. Values will be added to the attention layer to prevent attention to certain positions.
key_padding_mask (Variable, optional) – Input N-D array with shape $(B, L_S)$. Specified padding elements will be ignored by the attention layer. Values must be either 1 or 0.
fix_parameters (bool, optional) – When set to True, the weights and biases will not be updated. Default is False.
param_init (dict, optional) – Parameter initializers can be set with a dict. Possible keys of the dict include q_weight, k_weight, v_weight, q_bias, k_bias, v_bias, out_weight, out_bias, attn_bias_k, attn_bias_v. A value of the dict must be an Initializer or a numpy.ndarray. E.g. {'q_bias': ConstantInitializer(0)}.

Returns

Output $y$ with shape $(L_T, B, E)$ ~nnabla.Variable: Output $h_n$ with shape $(B, L_T, L_S)$

Return type

Parameters to be registered

The following variables are registered in a parameter scope "multi_head_attention";

q_weight (need_grad=True) : weights for query. (shape: (E, E))
k_weight (need_grad=True) : weights for key. (shape: (E_k, E))
v_weight (need_grad=True) : weights for value. (shape: (E_v, E))
out_weight (need_grad=True) : weigths for out projection. (shape: (E, E))
q_bias (need_grad=True) : bias for query. (shape: (E, ))
k_bias (need_grad=True) : bias for key. (shape: (E, ))
v_bias (need_grad=True) : bais for value. (shape: (E, ))
out_bias (need_grad=True) : bias for out projection. (shape: (E, ))
attn_bias_k (need_grad=True) : attnetion bias for k. (shape: (E, 1))
attn_bias_v (need_grad=True) : attnetion bias for v. (shape: (E, 1))

Note

If the name option is passed, the parameters become wrapped inside the parameter scope with the specified name, yielding the same results as the following code. This can be used to simplify the code.

with parameter_scope(name):
    output = multi_head_attention(<args>)

nnabla.parametric_functions.transformer(src, tgt, embed_dim=512, num_heads=8, num_encoder_layers=6, num_decoder_layers=6, dim_feedforward=2048, dropout=0.1, activation=None, src_additive_mask=None, tgt_additive_mask=None, memory_additive_mask=None, src_key_padding_mask=None, tgt_key_padding_mask=None, memory_key_padding_mask=None, rng=None, add_attn_bias=False, fix_parameters=False, name=None)[source]¶

Transformer.

We use the following notations to describe the inputs and outputs below. $L_T$: target sequence length, $L_S$: source sequence length, $B$: batch size, $E$: embedding dimension.

References

A. Vaswani et al. “Attention is All You Need.” NIPS. 2017. <https://papers.nips.cc/paper/7181-attention-is-all-you-need.pdf>

Examples:

src = nn.Variable((src_len, batch_size, embed_dim),need_grad=True)
tgt = nn.Variable((tgt_len, batch_size, embed_dim),need_grad=True)
out = PF.transformer(src, tgt, num_heads=16, num_encoder_layers=12)
out.forward()

Parameters

src (Variable) – Input source sequence to the encoder with shape:math:(L_S, B, E).
tgt (Variable) – Input target sequence to the decoder with shape $(L_T, B, E)$.
embed_dim (int, optional) – Embedding dimension to be used. Default is 512.
num_heads (int, optional) – Number of attention heads. Default is 12.
num_encoder_layers (int, optional) – Number of encoder layers to stack. Default is 6.
num_decoder_layers (int, optional) – Number of decoder layers to stack. Default is 6.
dim_feedforward (int, optional) – Dimension of the feedforward network model. Default is 2048.
dropout (float, optional) – Dropout ratio applied. Default is 0.1.
activation (function, optional) – Non-linear activation function to be used. Default is None, which is set as F.relu in the code.
src_additive_mask (Variable, optional) – Additive mask for the src sequence (optional). $(L_S, L_S)$.
tgt_additive_mask (Variable, optional) – Additive mask for the tgt sequence (optional). $(L_T, L_T)$.
memory_additive_mask (Variable, optional) – Additive mask for the encoder output (optional). $(L_T, L_S)$.
src_key_padding_mask (Variable, optional) – Key padding mask for src keys per batch (optional). $(B, L_S)$. Specified padding elements will be ignored by the attention layer. Values must be either 1 or 0.
tgt_key_padding_mask (Variable, optional) – Key padding mask for tgt keys per batch (optional). $(B, L_T)$. Specified padding elements will be ignored by the attention layer. Values must be either 1 or 0.
memory_key_padding_mask (Variable, optional) – Key padding mask for memory keys per batch (optional). $(B, L_S)$. Specified padding elements will be ignored by the attention layer. Values must be either 1 or 0.
rng (numpy.random.RandomState, optional) – Random generator for Initializer. Default is None.
add_attn_bias (bool, optional) – Specify whether to add attention bias parameters for key and value. Default is False.
fix_parameters (bool, optional) – When set to True, the weights and biases will not be updated. Default is False.

Returns

Output $y$ with shape $(L_T, B, E)$

Return type

Parameters to be registered

The following variables are registered in a parameter scope "transformer";

encoder{layer#} (need_grad=True) : parameters for the n’th encoder layer. (shape: Refer to transformer_encode for details)
decoder{layer#} (need_grad=True) : parameters for the n’th decoder layer. (shape: Refer to transformer_decode for details)

Note

If the name option is passed, the parameters become wrapped inside the parameter scope with the specified name, yielding the same results as the following code. This can be used to simplify the code.

with parameter_scope(name):
    output = transformer(<args>)

nnabla.parametric_functions.transformer_encode(src, embed_dim, num_heads, dim_feedforward=2048, dropout=0.1, activation=None, src_additive_mask=None, src_key_padding_mask=None, rng=None, add_attn_bias=False, fix_parameters=False, name=None)[source]¶

Transformer Encoder.

Parameters

src (Variable) – Input sequnce to the encoder layer with shape $(L_S, B, E)$.
embed_dim (int) – Number of embedding dimension.
num_heads (int) – Number of attention heads.
dim_feedforward (int, optional) – Dimension of the feedforward network model. Default is 2048.
dropout (float, optional) – Dropout ratio. Default is 0.1.
activation (function, optional) – Non-linear activation function to be used. Default is None, which is set as F.relu in the code.
src_additive_mask (Variable, optional) – Additive mask for the source sequence with shape $(L_S, L_S)$
src_key_padding_mask (Variable, optional) – Padding mask for the source sequence with shape $(B, L_S)$. Specified padding elements will be ignored by the attention layer. Values must be either 1 or 0.
rng (numpy.random.RandomState, optional) – Random generator for Initializer. Defalut is None.
add_attn_bias (bool, optional) – Specify whether to add attention bias parameters for key and value. Default is False.
fix_parameters (bool, optional) – When set to True, the weights and biases will not be updated. Default is False.

Returns

Output $y$ with shape $(L_S, B, E)$

Return type

Parameters to be registered

The following variables are registered in a parameter scope "transformer_encode";

src_self_attn (need_grad=True) : self-attention parameters for source sequence. (shape: Refer to multi_head_attention for details)
enc_affine1 (need_grad=True) : first affine used in encoder. (shape: Refer to affine for details)
enc_affine2 (need_grad=True) : second affine used in encoder. (shape: Refer to affine for details)
enc_layer_norm1 (need_grad=True) : fist layer normalization used in encoder. (shape: Refer to layer_normalization for details)
enc_layer_norm2 (need_grad=True) : second layer normalization used in encoder. (shape: Refer to layer_normalization for details)

Note

If the name option is passed, the parameters become wrapped inside the parameter scope with the specified name, yielding the same results as the following code. This can be used to simplify the code.

with parameter_scope(name):
    output = transformer_encode(<args>)

nnabla.parametric_functions.transformer_decode(tgt, memory, embed_dim, num_heads, dim_feedforward=2048, dropout=0.1, activation=None, tgt_additive_mask=None, memory_additive_mask=None, tgt_key_padding_mask=None, memory_key_padding_mask=None, rng=None, add_attn_bias=False, fix_parameters=False, name=None)[source]¶

Transformer Decoder.

Parameters

tgt (Variable) – Input sequnce to the decoder layer with shape $(L_T, B, E)$.
memory (Variable) – Output sequnce from the last layer of the encoder with shape $(L_T, B, E)$.
embed_dim (int) – Number of embedding dimension.
num_heads (int) – Number of attention heads.
dim_feedforward (int, optional) – Dimension of the feedforward network model. Default is 2048.
dropout (float, optional) – Dropout ratio. Default is 0.1.
activation (function, optional) – Non-linear activation function to be used. Default is None, which is set as F.relu in the code.
tgt_additive_mask (Variable, optional) – Additive mask for the target sequence with shape $(L_T, L_T)$.
memory_additive_mask (Variable, optional) – Additive mask for the memory sequcne with shape $(L_T, L_S)$.
tgt_key_padding_mask (Variable, optional) – Padding mask for the target sequence with shape $(B, L_T)$. Specified padding elements will be ignored by the attention layer. Values must be either 1 or 0.
memory_key_padding_mask (Variable, optional) – Padding mask for the mask sequence with shape $(B, L_S)$. Specified padding elements will be ignored by the attention layer. Values must be either 1 or 0.
rng (numpy.random.RandomState) – Random generator for Initializer. Default is None.
add_attn_bias (bool, optional) – Specify whether to add attention bias parameters for key and value. Default is False.
fix_parameters (bool) – When set to True, the weights and biases will not be updated. Default is False.

Returns

Output $y$ with shape $(L_T, B, E)$

Return type

Parameters to be registered

The following variables are registered in a parameter scope "transformer_decode";

tgt_self_attn (need_grad=True) : self-attention parameters for target sequence. (shape: Refer to multi_head_attention for details)
tgt_memory_attn (need_grad=True) : attention parameters for target sequence with output from encoder as key. (shape: Refer to multi_head_attention for details)
dec_affine1 (need_grad=True) : first affine used in decoder. (shape: Refer to affine for details)
dec_affine2 (need_grad=True) : second affine used in decoder. (shape: Refer to affine for details)
dec_layer_norm1 (need_grad=True) : fist layer normalization used in decoder. (shape: Refer to layer_normalization for details)
dec_layer_norm2 (need_grad=True) : second layer normalization used in decoder. (shape: Refer to layer_normalization for details)
dec_layer_norm3 (need_grad=True) : third layer normalization used in decoder. (shape: Refer to layer_normalization for details)

Note

If the name option is passed, the parameters become wrapped inside the parameter scope with the specified name, yielding the same results as the following code. This can be used to simplify the code.

with parameter_scope(name):
    output = transformer_decode(<args>)

Parameter Initializer¶

Some of the parametric functions optionally takes parameter initializer listed below.

class nnabla.initializer.BaseInitializer[source]¶

Base class of the parameter initializer.

__call__(shape)[source]¶

Generates an array with an initializer.

Parameters: shape (tuple of int) – numpy.ndarray with the shape created.
Returns: Array.
Return type: numpy.ndarray

Note

Subclasses of BaseInitializer must override this method.

class nnabla.initializer.ConstantInitializer(value=0)[source]¶

Generates a constant valued array.

Parameters: value (float) – A constant value.

Example:

import nnabla as nn
import nnabla.parametric_functions as PF
import nnabla.initializer as I

x = nn.Variable([60,1,28,28])
w = I.ConstantInitializer(0.1)
b = I.ConstantInitializer() # this generates constant valued array of default value 0
h = PF.convolution(x, 64, [3, 3], w_init=w, b_init=b, pad=[1, 1], name='conv'

class nnabla.initializer.NormalInitializer(sigma=1.0, rng=None)[source]¶

Generates a random array from a specified normal distribution.

\[\mathbf x \sim {\cal N} (\mathbf 0 | \sigma^2 \mathbf I)\]

Parameters

sigma (float) – $\sigma$.
rng (numpy.random.RandomState) – Random number generator.

Example:

import nnabla as nn
import nnabla.parametric_functions as PF
import nnabla.initializer as I

x = nn.Variable([60,1,28,28])
w = I.NormalInitializer(5e-5)
b = I.NormalInitializer(0.0)
h = PF.convolution(x, 64, [3, 3], w_init=w, b_init=b, pad=[1, 1], name='conv')

class nnabla.initializer.UniformInitializer(lim=(- 1, 1), rng=None)[source]¶

Generates a random array from a specified uniform distribution.

\[\mathbf x \sim {\cal U} (a, b)\]

Parameters

lim (tuple of float) – A tuple of two floats, $(a, b)$.
rng (numpy.random.RandomState) – Random number generator.

Example:

import nnabla as nn
import nnabla.parametric_functions as PF
import nnabla.initializer as I

x = nn.Variable([60,1,28,28])
w = I.UniformInitializer() # this generates uniform distribution within the default range of (-1,1)
b = I.UniformInitializer((-0.5,0.5))
h = PF.convolution(x, 64, [3, 3], w_init=w, b_init=b, pad=[1, 1], name='conv')

class nnabla.initializer.UniformIntInitializer(lim=(0, 10), rng=None)[source]¶

Generates a random array from a specified integer uniform distribution.

\[\mathbf x \sim {\cal U} ([a, b))\]

Parameters

lim (tuple of int) – A tuple of two ints, $(a, b)$.
rng (numpy.random.RandomState) – Random number generator.

Example:

import nnabla as nn
import nnabla.parametric_functions as PF
import nnabla.initializer as I

x = nn.Variable([60,1,28,28])
w = I.UniformIntInitializer() # this generates uniform integer distribution within the default range of (0,10)
b = I.UniformIntInitializer((-1,1))
h = PF.convolution(x, 64, [3, 3], w_init=w, b_init=b, pad=[1, 1], name='conv')

class nnabla.initializer.RangeInitializer(start=0, step=1)[source]¶

Generates an array with sequence of numbers.

\[\mathbf x[i] = start + step * i\]

Parameters

start (int) – A start value.
step (int) – A step value.

Example:

import nnabla as nn
import nnabla.initializer as I

x = nn.Variable([100])
x.d = I.RangeInitializer(0, 1)(x.shape)

class nnabla.initializer.OrthogonalInitializer(gain=1.0, rng=None)[source]¶

Generates an orthogonal matrix weights proposed by Saxe et al.

Parameters

gain (float) – scaling factor which should be decided depending on a type of units.
rng (numpy.random.RandomState) – Random number generator.

Example:

import numpy as np
import nnabla as nn
import nnabla.parametric_functions as PF
import nnabla.initializer as I

x = nn.Variable([60,1,28,28])
w = I.OrthogonalInitializer(np.sqrt(2.0))
b = I.ConstantInitializer(0.0)
h = PF.convolution(x, 64, [3, 3], w_init=w, b_init=b, pad=[1, 1], name='conv')

References

Saxe, et al. Exact solutions to the nonlinear dynamics of learning in deep linear neural networks.

class nnabla.initializer.WeightNormalizationScaleInitializer(w, dim=0, eps=1e-12)[source]¶

Compute the L2-norm for each weight kernel.

This initializer is specific to the weight normalization scale to keep the same magnitude of the originally initialized weights even after the applicaiton of the weight normalization at only initialization.

Parameters

w (Variable) – Weight the weight normalization is applied.
dim (int) – Output dimension of the weight normalization.
eps (float) – Eplision of the weight normalization.

nnabla.initializer.calc_normal_std_he_forward(inmaps, outmaps, kernel=(1, 1))[source]¶

Calculates the standard deviation proposed by He et al.

\[\sigma = \sqrt{\frac{2}{NK}}\]

Parameters

inmaps (int) – Map size of an input Variable, $N$.
outmaps (int) – Map size of an output Variable, $M$.
kernel (tuple of int) – Convolution kernel spatial shape. In above definition, $K$ is the product of shape dimensions. In Affine, the default value should be used.

Example:

import nnabla as nn
import nnabla.parametric_functions as PF
import nnabla.initializer as I

x = nn.Variable([60,1,28,28])
s = I.calc_normal_std_he_forward(x.shape[1],64)
w = I.NormalInitializer(s)
b = I.ConstantInitializer(0)
h = PF.convolution(x, 64, [3, 3], w_init=w, b_init=b, pad=[1, 1], name='conv')

References

He, et al. Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification.

nnabla.initializer.calc_normal_std_he_backward(inmaps, outmaps, kernel=(1, 1))[source]¶

Calculates the standard deviation of He et al. (backward case).

\[\sigma = \sqrt{\frac{2}{MK}}\]

Parameters

inmaps (int) – Map size of an input Variable, $N$.
outmaps (int) – Map size of an output Variable, $M$.
kernel (tuple of int) – Convolution kernel spatial shape. In above definition, $K$ is the product of shape dimensions. In Affine, the default value should be used.

Example:

import nnabla as nn
import nnabla.parametric_functions as PF
import nnabla.initializer as I

x = nn.Variable([60,1,28,28])
s = I.calc_normal_std_he_backward(x.shape[1],64)
w = I.NormalInitializer(s)
b = I.ConstantInitializer(0)
h = PF.convolution(x, 64, [3, 3], w_init=w, b_init=b, pad=[1, 1], name='conv')

References

He, et al. Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification.

nnabla.initializer.calc_normal_std_glorot(inmaps, outmaps, kernel=(1, 1))[source]¶

Calculates the standard deviation proposed by Glorot et al.

Note

We have updated the definition as following from v.1.2. It may affect the behavior of existing scripts that rely on the default initialization.

\[\sigma = \sqrt{\frac{2}{K(N + M)}}\]

Parameters

inmaps (int) – Map size of an input Variable, $N$.
outmaps (int) – Map size of an output Variable, $M$.
kernel (tuple of int) – Convolution kernel spatial shape. In above definition, $K$ is the product of shape dimensions. In Affine, the default value should be used.

Example:

import nnabla as nn
import nnabla.parametric_functions as PF
import nnabla.initializer as I

x = nn.Variable([60,1,28,28])
s = I.calc_normal_std_glorot(x.shape[1],64)
w = I.NormalInitializer(s)
b = I.ConstantInitializer(0)
h = PF.convolution(x, 64, [3, 3], w_init=w, b_init=b, pad=[1, 1], name='conv')

References

Glorot and Bengio. Understanding the difficulty of training deep feedforward neural networks

nnabla.initializer.calc_uniform_lim_glorot(inmaps, outmaps, kernel=(1, 1))[source]¶

Calculates the lower bound and the upper bound of the uniform distribution proposed by Glorot et al.

Note

We have updated the definition as following from v.1.3. It may affect the behavior of existing scripts that rely on the default initialization.

\[\begin{split}b &= \sqrt{\frac{6}{K(N + M)}}\\ a &= -b\end{split}\]

Parameters

inmaps (int) – Map size of an input Variable, $N$.
outmaps (int) – Map size of an output Variable, $M$.
kernel (tuple of int) – Convolution kernel spatial shape. In above definition, $K$ is the product of shape dimensions. In Affine, the default value should be used.

Example:

import nnabla as nn
import nnabla.parametric_functions as PF
import nnabla.initializer as I

x = nn.Variable([60,1,28,28])
lb,ub= I.calc_uniform_lim_glorot(x.shape[1],64)
w = I.UniformInitializer((lb,ub))
b = I.ConstantInitializer(0)
h = PF.convolution(x, 64, [3, 3], w_init=w, b_init=b, pad=[1, 1], name='conv')

References

Glorot and Bengio. Understanding the difficulty of training deep feedforward neural networks

Grad¶

nnabla.grad.grad(outputs, inputs, grad_outputs=None, persistent_outputs=[], bind_grad_output=False)[source]¶

Gradient function for the outputs with respect to the inputs.

The grad function computes the sum of gradients of the outputs w.r.t. the inputs.

\[g_i = \sum_{j} {\frac{\partial y_j}{\partial x_i}},\]

$y_j$ is each output, $x_i$ is each input, and $g_i$ is the sum of the gradient of $y_j$ w.r.t. $x_i$ over all $j$.

Parameters

outputs (list of Variable or Variable) – Outputs of the differentiable function.
inputs (list of Variable or Variable) – Inputs w.r.t. which the gradients of outputs are computed.
grad_outputs (None, scalar, numpy.ndarray, nnabla.NdArray, or list of scalar, numpy.ndarray, or nnabla.NdArray,) – Gradient outputs corresponding to outputs. This is same as the grad argument of backward(). Default is None, so 1 is used as the in-coming gradient at the very beginning of the Variable in the gradient graph.
persistent_outputs (list of bool) – Outputs become persistent accordingly. If not specified, all outputs become persistent.
bind_grad_output (bool) – Bind data to grad of input variable. This is useful for the case where one wants to use the gradient graph for training a neural network using the first-order gradients only. Default is False.

Returns

List of Variable.

If the backpropagation does not reach input(s), the corresponding returned value(s) are zero (i.e., the gradients w.r.t. inputs are zero) and not connected as a part of the gradient graph.

Example (Gradient Penalty):

import nnabla as nn
import nnabla.functions as F
import nnabla.parametric_functions as PF
import numpy as np
from nnabla.ext_utils import get_extension_context

# Context
extension_module = "cudnn"
ctx = get_extension_context(extension_module)
nn.set_default_context(ctx)

# Input and label
x = nn.Variable.from_numpy_array(np.random.randn(4, 3, 32, 32))
y = nn.Variable.from_numpy_array(np.random.randint(0, 10, 4).reshape(4, 1))

# Network
h = PF.convolution(x, 8, (3, 3), (1, 1), name="conv1")
h = F.relu(h)
h = F.max_pooling(h, (2, 2))
h = PF.convolution(h, 16, (3, 3), (1, 1), name="conv2")
h = F.relu(h)
h = F.max_pooling(h, (2, 2))
p = PF.affine(h, 10, name="pred")
loss = F.mean(F.softmax_cross_entropy(p, y))

# Grad
outputs = [loss]
inputs = nn.get_parameters().values()
grads = nn.grad(outputs, inputs)  # gradients of the parameters

# Backward of the outputs w.r.t. the parameters by constraining the gradient norms.
t = 0 # or 1
gp = sum([(F.sum(g ** 2) ** 0.5 - t) ** 2 for g in grads])
loss += gp
loss.forward()
loss.backward()

Example (Higer-order Gradients):

import nnabla as nn
import nnabla.functions as F
import numpy as np

x = nn.Variable.from_numpy_array(np.random.randn(2, 2)).apply(need_grad=True)
x.grad.zero()
y = F.sin(x)
def grad(y, x, n=1):
    dx = [y]
    for _ in range(n):
        dx = nn.grad([dx[0]], [x])
    return dx[0]
dnx = grad(y, x, n=10)
dnx.forward()
print(np.allclose(-np.sin(x.d), dnx.d))
dnx.backward()
print(np.allclose(-np.cos(x.d), x.g))

# Show the supported status for each function
from nnabla.backward_functions import show_registry
show_registry()

nnabla.backward_functions.register(func_name, func)[source]¶

Register the backward function to a function.

Parameters

func_name (str) – The function class name, for example, Affine.
func (function) – The function to be called as the backward function to the function func_name.. Arguments of the func must be (ctx: nn.Context, inputs: list of nn.Variable, **kwargs).. The inputs are the ones to the function of the func_name. The kwargs are the arguments of the function. For example, if the func_name is Affine, func is affine_backward, the inputs are data, weights, and bias if necessary, and kwargs = dict(base_axis=base_axis).

nnabla.backward_functions.show_registry()[source]¶: Show all backward fuctions registry

Solvers¶

The nnabla.solvers.Solver class represents a stochastic gradient descent based optimizer for optimizing the parameters in the computation graph. NNabla provides various solvers listed below.

Solver¶

class nnabla.solvers.Solver¶

Solver interface class.

The same API provided in this class can be used to implement various types of solvers.

Example:

# Network building comes above
import nnabla.solvers as S
solver = S.Sgd(lr=1e-3)
solver.set_parameters(nn.get_parameters())

for itr in range(num_itr):
    x.d = ... # set data
    t.d = ... # set label
    loss.forward()
    solver.zero_grad()  # All gradient buffer being 0
    loss.backward()
    solver.weight_decay(decay_rate)  # Apply weight decay
    solver.clip_grad_by_norm(clip_norm)  # Apply clip grad by norm
    solver.update()  # updating parameters

Note

All solvers provided by NNabla belong to an inherited class of Solver . A solver is never instantiated by this class itself.

check_inf_grad(self, pre_hook=None, post_hook=None)¶: Check if there is any inf on the gradients which were setup.

check_inf_or_nan_grad(self, pre_hook=None, post_hook=None)¶: Check if there is any inf or nan on the gradients which were setup.

check_nan_grad(self, pre_hook=None, post_hook=None)¶: Check if there is any nan on the gradients which were setup.

clear_parameters(self)¶: Clear all registered parameters and states.

clip_grad_by_norm(self, float clip_norm, pre_hook=None, post_hook=None)¶

Clip gradients by norm. When called, the gradient will be clipped by the given norm.

Parameters: clip_norm (float) – The value of clipping norm.

get_parameters(self)¶: Get all registered parameters

get_states(self)¶: Get all states

info¶

object

Type: info

learning_rate(self)¶: Get the learning rate.

load_states(self, path)¶

Load solver states.

Parameters: path – path to the state file to be loaded.

name¶: Get the name of the solver.

remove_parameters(self, vector[string] keys)¶: Remove previously registered parameters, specified by a vector of its keys.

save_states(self, path)¶

Save solver states.

Parameters: path – path or file object

scale_grad(self, scale, pre_hook=None, post_hook=None)¶: Rescale gradient

set_learning_rate(self, learning_rate)¶: Set the learning rate.

set_parameters(self, param_dict, bool reset=True, bool retain_state=False)¶

Set parameters by dictionary of keys and parameter Variables.

Parameters

param_dict (dict) – key:string, value: Variable.
reset (bool) – If true, clear all parameters before setting parameters. If false, parameters are overwritten or added (if it’s new).
retain_state (bool) – The value is only considered if reset is false. If true and a key already exists (overwriting), a state (such as momentum) associated with the key will be kept if the shape of the parameter and that of the new param match.

set_states(self, states)¶: Set states. Call set_parameters to initialize states of a solver first, otherwise this method raise an value error.

set_states_from_protobuf(self, optimizer_proto)¶

Set states to the solver from the protobuf file.

Internally used helper method.

set_states_to_protobuf(self, optimizer)¶

Set states to the protobuf file from the solver.

Internally used helper method.

setup(self, params)¶: Deprecated. Call set_parameters with param_dict .

update(self, update_pre_hook=None, update_post_hook=None)¶

When this function is called, parameter values are updated using the gradients accumulated in backpropagation, stored in the grad field of the parameter Variable s. Update rules are implemented in the C++ core, in derived classes of Solver. The updated parameter values will be stored into the data field of the parameter Variable s.

Parameters

update_pre_hook (callable) – This callable object is called immediately before each update of parameters. The default is None.
update_post_hook (callable) – This callable object is called immediately after each update of parameters. The default is None.

weight_decay(self, float decay_rate, pre_hook=None, post_hook=None)¶

Apply weight decay to gradients. When called, the gradient weight will be decayed by a rate of the current parameter value.

Parameters: decay_rate (float) – The coefficient of weight decay.

zero_grad(self)¶: Initialize gradients of all registered parameter by zero.

List of solvers¶

nnabla.solvers.Sgd(lr=0.001)¶

Stochastic gradient descent (SGD) optimizer.

\[w_{t+1} \leftarrow w_t - \eta \Delta w_t\]

Parameters

lr (float) – Learning rate ($\eta$).

Returns

An instance of Solver class.: See Solver API guide for details.

Return type

Note

You can instantiate a preferred target implementation (ex. CUDA) of a Solver given a Context. A Context can be set by nnabla.set_default_context(ctx) or nnabla.context_scope(ctx). See API docs.

nnabla.solvers.Momentum(lr=0.001, momentum=0.9)¶

SGD with Momentum.

\[\begin{split}v_t &\leftarrow \gamma v_{t-1} + \eta \Delta w_t\\ w_{t+1} &\leftarrow w_t - v_t\end{split}\]

Parameters

lr (float) – Learning rate ($\eta$).
momentum (float) – Decay rate of momentum.

Returns

An instance of Solver class.: See Solver API guide for details.

Return type

Note

You can instantiate a preferred target implementation (ex. CUDA) of a Solver given a Context. A Context can be set by nnabla.set_default_context(ctx) or nnabla.context_scope(ctx). See API docs.

References

Ning Qian : On the Momentum Term in Gradient Descent Learning Algorithms.

nnabla.solvers.Lars(lr=0.001, momentum=0.9, coefficient=0.001, eps=1e-06)¶

LARS with Momentum.

\[\begin{split}\lambda &\leftarrow \eta \frac{\| w_t \|}{\| \Delta w_t + \beta w_t \|} \\ v_{t+1} &\leftarrow m v_t + \gamma \lambda (\Delta w_t + \beta w_t) \\ w_{t+1} &\leftarrow w_t - v_{t+1}\end{split}\]

Parameters

lr (float) – Learning rate ($\eta$).
momentum (float) – Decay rate of momentum.
coefficient (float) – Trust coefficient
eps (float) – Small value for avoiding zero devision($\epsilon$).

Returns

An instance of Solver class.: See Solver API guide for details.

Return type

Note

You can instantiate a preferred target implementation (ex. CUDA) of a Solver given a Context. A Context can be set by nnabla.set_default_context(ctx) or nnabla.context_scope(ctx). See API docs.

References

Yang You, Igor Gitman, Boris Ginsburg Large Batch Training of Convolutional Networks

nnabla.solvers.Nesterov(lr=0.001, momentum=0.9)¶

Nesterov Accelerated Gradient optimizer.

\[\begin{split}v_t &\leftarrow \gamma v_{t-1} - \eta \Delta w_t\\ w_{t+1} &\leftarrow w_t - \gamma v_{t-1} + \left(1 + \gamma \right) v_t\end{split}\]

Parameters

lr (float) – Learning rate ($\eta$).
momentum (float) – Decay rate of momentum.

Returns

An instance of Solver class.: See Solver API guide for details.

Return type

Note

You can instantiate a preferred target implementation (ex. CUDA) of a Solver given a Context. A Context can be set by nnabla.set_default_context(ctx) or nnabla.context_scope(ctx). See API docs.

References

Yurii Nesterov. A method for unconstrained convex minimization problem with the rate of convergence $o(1/k2)$.

nnabla.solvers.Adadelta(lr=1.0, decay=0.95, eps=1e-06)¶

AdaDelta optimizer.

\[\begin{split}g_t &\leftarrow \Delta w_t\\ v_t &\leftarrow - \frac{RMS \left[ v_t \right]_{t-1}} {RMS \left[ g \right]_t}g_t\\ w_{t+1} &\leftarrow w_t + \eta v_t\end{split}\]

Parameters

lr (float) – Learning rate ($\eta$).
decay (float) – Decay rate ($\gamma$).
eps (float) – Small value for avoiding zero division($\epsilon$).

Returns

An instance of Solver class.: See Solver API guide for details.

Return type

Note

You can instantiate a preferred target implementation (ex. CUDA) of a Solver given a Context. A Context can be set by nnabla.set_default_context(ctx) or nnabla.context_scope(ctx). See API docs.

References

Matthew D. Zeiler. ADADELTA: An Adaptive Learning Rate Method.

nnabla.solvers.Adagrad(lr=0.01, eps=1e-08)¶

ADAGrad optimizer.

\[\begin{split}g_t &\leftarrow \Delta w_t\\ G_t &\leftarrow G_{t-1} + g_t^2\\ w_{t+1} &\leftarrow w_t - \frac{\eta}{\sqrt{G_t} + \epsilon} g_t\end{split}\]

Parameters

lr (float) – Learning rate ($\eta$).
eps (float) – Small value for avoiding zero division($\epsilon$).

Returns

An instance of Solver class.: See Solver API guide for details.

Return type

Note

You can instantiate a preferred target implementation (ex. CUDA) of a Solver given a Context. A Context can be set by nnabla.set_default_context(ctx) or nnabla.context_scope(ctx). See API docs.

References

John Duchi, Elad Hazan and Yoram Singer (2011). Adaptive Subgradient Methods for Online Learning and Stochastic Optimization.

nnabla.solvers.AdaBelief(alpha=0.001, beta1=0.9, beta2=0.999, eps=1e-08, wd=0.0, amsgrad=False, weight_decouple=False, fixed_decay=False, rectify=False)¶

AdaBelief optimizer.

\[\begin{split}m_t &\leftarrow \beta_1 m_{t-1} + (1 - \beta_1) g_t\\ s_t &\leftarrow \beta_2 s_{t-1} + (1 - \beta_2) (g_t - m_t)^2\\ w_{t+1} &\leftarrow w_t - \alpha \frac{\sqrt{1 - \beta_2^t}}{1 - \beta_1^t} \frac{m_t}{\sqrt{s_t + \epsilon} + \epsilon}\end{split}\]

Parameters

alpha (float) – Step size ($\alpha$).
beta1 (float) – Decay rate of first-order momentum ($\beta_1$).
beta2 (float) – Decay rate of second-order momentum ($\beta_2$).
eps (float) – Small value for avoiding zero division($\epsilon$).
wd (float) – Weight decay rate. This option only takes effect when weight_decouple option is enabled.
amsgrad (bool) – Perform AMSGrad variant of AdaBelief.
weight_decouple (bool) – Perform decoupled weight decay as in AdamW.
fixed_decay (bool) – If True, the weight decay ratio will be kept fixed. Note that this option only takes effect when weight_decouple option is enabled.
rectify (bool) – Perform RAdam variant of AdaBelief.

Returns

An instance of Solver class.: See Solver API guide for details.

Return type

Note

You can instantiate a preferred target implementation (ex. CUDA) of a Solver given a Context. A Context can be set by nnabla.set_default_context(ctx) or nnabla.context_scope(ctx). See API docs.

References

Juntang Zhuang, et al. (2020). AdaBelief Optimizer: Adapting Stepsizes by the Belief in Observed Gradients.

nnabla.solvers.RMSprop(lr=0.001, decay=0.9, eps=1e-08)¶

RMSprop optimizer (Geoffery Hinton).

\[\begin{split}g_t &\leftarrow \Delta w_t\\ v_t &\leftarrow \gamma v_{t-1} + \left(1 - \gamma \right) g_t^2\\ w_{t+1} &\leftarrow w_t - \eta \frac{g_t}{\sqrt{v_t} + \epsilon}\end{split}\]

Parameters

lr (float) – Learning rate ($\eta$).
decay (float) – Decay rate ($\gamma$).
eps (float) – Small value for avoiding zero division($\epsilon$).

Returns

An instance of Solver class.: See Solver API guide for details.

Return type

Note

You can instantiate a preferred target implementation (ex. CUDA) of a Solver given a Context. A Context can be set by nnabla.set_default_context(ctx) or nnabla.context_scope(ctx). See API docs.

References

Geoff Hinton. Lecture 6a : Overview of mini-batch gradient descent.

nnabla.solvers.RMSpropGraves(lr=0.0001, decay=0.95, momentum=0.9, eps=0.0001)¶

RMSpropGraves optimizer (Alex Graves).

\[\begin{split}n_t &\leftarrow \rho n_{t-1} + \left(1 - \rho \right) {e_t}^2\\ g_t &\leftarrow \rho g_{t-1} + \left(1 - \rho \right) e_t\\ d_t &\leftarrow \beta d_{t-1} - \eta \frac{e_t}{\sqrt{n_t - {g_t}^2 + \epsilon}}\\ w_{t+1} &\leftarrow w_t + d_t\end{split}\]

where $e_t$ denotes the gradient.

Parameters

lr (float) – Learning rate ($\eta$).
decay (float) – Decay rate ($\rho$).
momentum (float) – Momentum ($\beta$)
eps (float) – Small value for avoiding zero division($\epsilon$).

Returns

An instance of Solver class.: See Solver API guide for details.

Return type

Note

You can instantiate a preferred target implementation (ex. CUDA) of a Solver given a Context. A Context can be set by nnabla.set_default_context(ctx) or nnabla.context_scope(ctx). See API docs.

References

Alex Graves. Generating Sequences With Recurrent Neural Networks.

nnabla.solvers.Adam(alpha=0.001, beta1=0.9, beta2=0.999, eps=1e-08)¶

ADAM optimizer.

\[\begin{split}m_t &\leftarrow \beta_1 m_{t-1} + (1 - \beta_1) g_t\\ v_t &\leftarrow \beta_2 v_{t-1} + (1 - \beta_2) g_t^2\\ w_{t+1} &\leftarrow w_t - \alpha \frac{\sqrt{1 - \beta_2^t}}{1 - \beta_1^t} \frac{m_t}{\sqrt{v_t} + \epsilon}\end{split}\]

where $g_t$ denotes a gradient, and let $m_0 \leftarrow 0$ and $v_0 \leftarrow 0$.

Parameters

alpha (float) – Step size ($\alpha$).
beta1 (float) – Decay rate of first-order momentum ($\beta_1$).
beta2 (float) – Decay rate of second-order momentum ($\beta_2$).
eps (float) – Small value for avoiding zero division($\epsilon$).

Returns

An instance of Solver class.: See Solver API guide for details.

Return type

Note

You can instantiate a preferred target implementation (ex. CUDA) of a Solver given a Context. A Context can be set by nnabla.set_default_context(ctx) or nnabla.context_scope(ctx). See API docs.

References

Kingma and Ba, Adam: A Method for Stochastic Optimization.

nnabla.solvers.AdaBound(alpha=0.001, beta1=0.9, beta2=0.999, eps=1e-08, final_lr=0.1, gamma=0.001)¶

AdaBound optimizer applies dynamic bounds on learning rates to Adam.

\[\begin{split}w_{t+1} &\leftarrow w_t - \eta_t*m_t\\ \eta_t = clip( \alpha\frac{\sqrt{1 - \beta_2^t}}{(1 - \beta_1^t)(\sqrt{v_t} + \epsilon)}, \eta_l(t), \eta_u(t))\\ \eta_l(t) = (1 - (1/((1-\gamma)t+1)))\alpha^*\\ \eta_u(t) = (1 + (1/((1-\gamma)t)))\alpha^*\end{split}\]

where $\alpha^*$ (final_lr) is scaled by a factor defined as the current value of $\alpha$ (set by set_learning_rate(lr)) over initial value of $\alpha$, so that learnign rate scheduling is properly applied to both $\alpha$ and $\alpha^*$.

Parameters

alpha (float) – Step size ($\alpha$).
beta1 (float) – Decay rate of first-order momentum ($\beta_1$).
beta2 (float) – Decay rate of second-order momentum ($\beta_2$).
eps (float) – Small value for avoiding zero division($\epsilon$).
final_lr (float) – Final (SGD) learning rate.
gamma (float) – Convergence speed of the bound functions.

Returns

An instance of Solver class.: See Solver API guide for details.

Return type

Note

You can instantiate a preferred target implementation (ex. CUDA) of a Solver given a Context. A Context can be set by nnabla.set_default_context(ctx) or nnabla.context_scope(ctx). See API docs.

References

L. Luo, Y. Xiong, Y. Liu and X. Sun. Adaptive Gradient Methods with Dynamic Bound of Learning Rate.

nnabla.solvers.Adamax(alpha=0.002, beta1=0.9, beta2=0.999, eps=1e-08)¶

ADAMAX Optimizer.

\[\begin{split}m_t &\leftarrow \beta_1 m_{t-1} + (1 - \beta_1) g_t\\ v_t &\leftarrow \max\left(\beta_2 v_{t-1}, |g_t|\right)\\ w_{t+1} &\leftarrow w_t - \alpha \frac{\sqrt{1 - \beta_2^t}}{1 - \beta_1^t} \frac{m_t}{v_t + \epsilon}\end{split}\]

where $g_t$ denotes a gradient, and let $m_0 \leftarrow 0$ and $v_0 \leftarrow 0$, $v_t$ is an exponentially weighted infinity norm of a sequence of gradients $t=0,...,t$.

Parameters

alpha (float) – Step size ($\alpha$).
beta1 (float) – Decay rate of first-order momentum ($\beta_1$).
beta2 (float) – Decay rate of inf-order momentum ($\beta_2$).
eps (float) – Small value for avoiding zero division($\epsilon$).

Returns

An instance of Solver class.: See Solver API guide for details.

Return type

Note

You can instantiate a preferred target implementation (ex. CUDA) of a Solver given a Context. A Context can be set by nnabla.set_default_context(ctx) or nnabla.context_scope(ctx). See API docs.

References

Kingma and Ba, Adam: A Method for Stochastic Optimization.

nnabla.solvers.AMSGRAD(alpha=0.001, beta1=0.9, beta2=0.999, eps=1e-08, bias_correction=False)¶

AMSGRAD optimizer.

\[\begin{split}m_t &\leftarrow \beta_1 m_{t-1} + (1 - \beta_1) g_t\\ v_t &\leftarrow \beta_2 v_{t-1} + (1 - \beta_2) g_t^2\\ \hat{v_t} = \max(\hat{v_{t-1}}, v_t)\\ w_{t+1} &\leftarrow w_t - \alpha \frac{m_t}{\sqrt{\hat{v_t}} + \epsilon}\end{split}\]

where $g_t$ denotes a gradient, and let $m_0 \leftarrow 0$ and $v_0 \leftarrow 0$.

Parameters

alpha (float) – Step size ($\alpha$).
beta1 (float) – Decay rate of first-order momentum ($\beta_1$).
beta2 (float) – Decay rate of second-order momentum ($\beta_2$).
eps (float) – Small value for avoiding zero division($\epsilon$). Note this does not appear in the paper.
bias_correction (bool) – Apply bias correction to moving averages defined in ADAM. Note this does not appear in the paper.

Returns

An instance of Solver class.: See Solver API guide for details.

Return type

Note

You can instantiate a preferred target implementation (ex. CUDA) of a Solver given a Context. A Context can be set by nnabla.set_default_context(ctx) or nnabla.context_scope(ctx). See API docs.

References

Reddi et al. On the convergence of ADAM and beyond.

nnabla.solvers.AMSBound(alpha=0.001, beta1=0.9, beta2=0.999, eps=1e-08, final_lr=0.1, gamma=0.001, bias_correction=False)¶

AMSBound optimizer applies dynamic bounds on learning rates to AMSGrad.

\[\begin{split}w_{t+1} &\leftarrow w_t - \eta_t*m_t\\ \eta_t = clip( \alpha\frac{\sqrt{1 - \beta_2^t}}{(1 - \beta_1^t)(\sqrt{\hat{v_t}} + \epsilon)}, \eta_l(t), \eta_u(t))\\ \hat{v_t} = \max(\hat{v_{t-1}}, v_t)\\ \eta_l(t) = (1 - (1/((1-\gamma)t+1)))\alpha^*\\ \eta_u(t) = (1 + (1/((1-\gamma)t)))\alpha^*\end{split}\]

where $\alpha^*$ (final_lr) is scaled by a factor defined as the current value of $\alpha$ (set by set_learning_rate(lr)) over initial value of $\alpha$, so that learnign rate scheduling is properly applied to both $\alpha$ and $\alpha^*$.

Parameters

alpha (float) – Step size ($\alpha$).
beta1 (float) – Decay rate of first-order momentum ($\beta_1$).
beta2 (float) – Decay rate of second-order momentum ($\beta_2$).
eps (float) – Small value for avoiding zero division($\epsilon$). Note this does not appear in the paper.
final_lr (float) – Final (SGD) learning rtae
gamma (float) – Convergence speed of the bound functions
bias_correction (bool) – Apply bias correction to moving averages defined in ADAM. Note this does not appear in the paper.

Returns

An instance of Solver class.: See Solver API guide for details.

Return type

Note

You can instantiate a preferred target implementation (ex. CUDA) of a Solver given a Context. A Context can be set by nnabla.set_default_context(ctx) or nnabla.context_scope(ctx). See API docs.

References

L. Luo, Y. Xiong, Y. Liu and X. Sun. Adaptive Gradient Methods with Dynamic Bound of Learning Rate.

nnabla.solvers.AdamW(alpha=0.001, beta1=0.9, beta2=0.999, eps=1e-08, wd=0.0001)¶

ADAM optimizer with decoupled weight decay.

\[\begin{split}m_t &\leftarrow \beta_1 m_{t-1} + (1 - \beta_1) g_t\\ v_t &\leftarrow \beta_2 v_{t-1} + (1 - \beta_2) g_t^2\\ w_{t+1} &\leftarrow w_t - \alpha \frac{\sqrt{1 - \beta_2^t}}{1 - \beta_1^t} \frac{m_t}{\sqrt{v_t} + \epsilon} - \eta_t\lambda w_t\end{split}\]

where $g_t$ denotes a gradient, $\lambda$ is the decoupled weight decay rate, and $m_0 \leftarrow 0$ and $v_0 \leftarrow 0$.

Parameters

alpha (float) – Step size ($\alpha$).
beta1 (float) – Decay rate of first-order momentum ($\beta_1$).
beta2 (float) – Decay rate of second-order momentum ($\beta_2$).
eps (float) – Small value for avoiding zero division($\epsilon$).
wd (float) – Weight decay rate.

Returns

An instance of Solver class.: See Solver API guide for details.

Return type

Note

You can instantiate a preferred target implementation (ex. CUDA) of a Solver given a Context. A Context can be set by nnabla.set_default_context(ctx) or nnabla.context_scope(ctx). See API docs.

References

Loshchilov and Hutter, Decoupled Weight Decay Regularization.

nnabla.solvers.SgdW(lr=0.001, momentum=0.9, wd=0.0001)¶

Stochastic gradient descent (SGD) optimizer with decoupled weight decay.

\[\begin{split}v_t \leftarrow \gamma v_{t-1} + \eta g_t - (\eta / \eta_0)\lambda v_{t-1}\\ w_{t+1} \leftarrow w_t - v_t\end{split}\]

where $\lambda$ is the decoupled weight decay rate.

Parameters

lr (float) – Learning rate ($\eta$).
momentum (float) – Decay rate of momentum.
wd (float) – Weight decay rate.

Returns

An instance of Solver class.: See Solver API guide for details.

Return type

Note

You can instantiate a preferred target implementation (ex. CUDA) of a Solver given a Context. A Context can be set by nnabla.set_default_context(ctx) or nnabla.context_scope(ctx). See API docs.

References

Loshchilov and Hutter, Decoupled Weight Decay Regularization.

Communicator¶

Communicator transfers parameters over the compute graphs.

This is an alias to communicator.py.

Communicator interface¶

class nnabla.communicators.Communicator¶

Communicator interface class.

Communicator exchanges data (e.g., gradient) using MPI-like collectives. This class is used for the distributed training.

abort(self)¶: Terminates MPI execution environment

add_context_and_parameters(self, ctx_param_dict)¶

Add context and parameters.

Parameters: ctx_param_dict (tuple of Context, dict) – Key of the dictionary is string and value of the dictionary is Variable.

all_gather(self, ndarray, ndarray_list, string group='world')¶

All gather over data in different device.

Parameters

ndarray (NdArray) – Data to be gathered.
ndarray_list (NdArray) – Data to be saved.
group (string) – Name of a group. This groups is used when the collective is called.

Example:

# Run like `mpirun -n 2 python <code_snippet.py>`
# note: the order of the output to stdout are stochastic because of multiprocesses.

# Communicator and Context
import numpy as np
import nnabla as nn
import nnabla.communicators as C
from nnabla.ext_utils import get_extension_context

extension_module = "cudnn"
ctx = get_extension_context(extension_module)
comm = C.MultiProcessCommunicator(ctx)
comm.init()

# Data
x = nn.Variable([2, 2])
x.d = np.random.rand(*x.shape)
y_list = [nn.Variable([2, 2]), nn.Variable([2, 2])]
print("Before the collective ({}-th)".format(comm.rank))
print(x.d)

# AllGather
comm.all_gather(x.data, [y.data for y in y_list])

# Check
print("After the collective ({}-th)".format(comm.rank))
for y in y_list:
    print(y.d)

all_reduce(self, data, bool division=False, bool inplace=False, string group='world')¶

All reduce over data in different device.

Parameters

data (NdArray or list of NdArray) –
division (bool) – Flag to divide the reduce data by the number of contexts added, or the number of devices.
inplace (bool) – Flag to use a packed array. Default is false. When true, it is memory-efficient but slow. When false, it is not memory efficient but fast. In both case, one can get the result in the same memory region.
group (string) – Name of a group. This groups is used when the collective is called.

Example:

# Run like `mpirun -n 2 python <code_snippet.py>`
# note: the order of the output to stdout are stochastic because of multiprocesses.

# Communicator and Context
import numpy as np
import nnabla as nn
import nnabla.communicators as C
from nnabla.ext_utils import get_extension_context

extension_module = "cudnn"
ctx = get_extension_context(extension_module)
comm = C.MultiProcessCommunicator(ctx)
comm.init()

# Data
x_list = [nn.Variable([2, 2]), nn.Variable([2, 2])]
print("Before the collective ({}-th)".format(comm.rank))
for x in x_list:
    x.d = np.random.rand(*x.shape)
    print(x.d)

# AllReduce
comm.all_reduce([x.data for x in x_list], inplace=True)

# Check
print("After the collective ({}-th)".format(comm.rank))
for x in x_list:
    print(x.d)

all_reduce_callback(self, data, size_t pack_size, bool division=False, string group='world')¶

All reduce over data in different device.

Note

This function does not support shared parameters (such as RNNs) currently.

Parameters

data (NdArray or list of NdArray) –
pack_size (int) – The number of values contained in the packed data.
division (bool) – Flag to divide the reduce data by the number of contexts added, or the number of devices.
group (string) – Name of a group. This groups is used when the collective is called.

Example:

In case of the multi-process data parallel distributed training,

# Run like `mpirun -n 2 python <code_snippet.py>`

# Communicator and Context
import numpy as np
import nnabla as nn
import nnabla.communicators as C
from nnabla.ext_utils import get_extension_context

extension_module = "cudnn"
ctx = get_extension_context(extension_module)
comm = C.MultiProcessCommunicator(ctx)
comm.init()

n_class = 2
b, c, h, w = 4, 1, 32, 32

# Data
x = nn.Variable([b, c, h, w])
y = nn.Variable([b, 1])

# Network setting
h = PF.convolution(x, 1, (3, 3), (1, 1), (1, 1))
pred = PF.affine(h, 2)
loss = F.mean(F.softmax_cross_entropy(pred, y))

loss.forward()
# AllReduce during backward
loss.backward(communicator_callbacks = comm.all_reduce_callback([v.grad for v in nn.get_parameters().values()], 1024 * 1024 * 2))

allreduce(self, bool division=False, bool inplace=False)¶

Deprecated. See all_reduce, instead.

Allreduce over parameters added. Currently, allreduce is applied to gradient regions.

Parameters

division (bool) – Flag to divide the reduce data by the number of contexts added, or the number of devices.
inplace (bool) – Flag to use a packed array. Default is false. When true, it is memory-efficient but slow. When false, it is not memory efficient but fast. In both case, one can get the result in the same memory region.

barrier(self)¶: Blocks until all processes in the communicator have reached this routine.

bcast(self, data, int src, bool inplace=False, string group='world')¶

Broadcast data to different devices.

Parameters

data (NdArray or list of NdArray) –
src (int) – Source rank where the data is broadcasted.
inplace (bool) – Flag to use a packed array. Default is false. When true, it is memory-efficient but slow. When false, it is not memory efficient but fast. In both case, one can get the result in the same memory region.
group (string) – Name of a group. This groups is used when the collective is called.

Example:

# Run like `mpirun -n 2 python <code_snippet.py>`
# note: the order of the output to stdout are stochastic because of multiprocesses.

# Communicator and Context
import numpy as np
import nnabla as nn
import nnabla.communicators as C
from nnabla.ext_utils import get_extension_context

extension_module = "cudnn"
ctx = get_extension_context(extension_module)
comm = C.MultiProcessCommunicator(ctx)
comm.init()

# Data
x_list = [nn.Variable([2, 2]), nn.Variable([2, 2])]
print("Before the collective ({}-th)".format(comm.rank))
for x in x_list:
    x.d = np.random.rand(*x.shape)
    print(x.d)

# Bcast
comm.bcast([x.data for x in x_list], src=0, inplace=True)

# Check
print("After the collective ({}-th)".format(comm.rank))
for x in x_list:
    print(x.d)

clear_context_parameters(self)¶: Clear all registered contexts and parameters.

find_group(self, group)¶

Return the list of ranks in the group. If the group does not exist, the empty list is returned.

Parameters: group (str) – Name of the group.
Returns: List of ranks (int).
Return type: ranks (list)

init(self)¶

Initialize a communicator.

Initall or initrank, depending multi-threads or multi-processes. This function MUST be called after all parameters communicated are added by add_context_and_parameters.

list_groups(self)¶

Returns: Groups (str) of name to ranks (list).
Return type: groups (dict)

local_rank¶: Get local rank of communicator.

name¶: Get communicator name.

new_group(self, name_ranks)¶

Parameters: name_ranks (tuple) – Tuple of name (str) and ranks (list).
Returns: group name (str)

Example:

# Communicator and Context
extension_module = "cudnn"
ctx = get_extension_context(extension_module)
comm = C.MultiProcessCommunicator(ctx)
comm.init()

# New group
group = comm.new_group("node0", [0, 1, 2, 3])

rank¶: Get rank of communicator.

reduce(self, data, int dst, bool division=False, bool inplace=False, string group='world')¶

Reduce over data in different device.

Parameters

data (NdArray or list of NdArray) –
dst (int) – Destination rank where the result is saved.
division (bool) – Flag to divide the reduce data by the number of contexts added, or the number of devices.
inplace (bool) – Flag to use a packed array. Default is false. When true, it is memory-efficient but slow. When false, it is not memory efficient but fast. In both case, one can get the result in the same memory region.
group (string) – Name of a group. This groups is used when the collective is called.

Example:

# Run like `mpirun -n 2 python <code_snippet.py>`
# note: the order of the output to stdout are stochastic because of multiprocesses.

# Communicator and Context
import numpy as np
import nnabla as nn
import nnabla.communicators as C
from nnabla.ext_utils import get_extension_context

extension_module = "cudnn"
ctx = get_extension_context(extension_module)
comm = C.MultiProcessCommunicator(ctx)
comm.init()

# Data
x_list = [nn.Variable([2, 2]), nn.Variable([2, 2])]
print("Before the collective ({}-th)".format(comm.rank))
for x in x_list:
    x.d = np.random.rand(*x.shape)
    print(x.d)

# Reduce
comm.reduce([x.data for x in x_list], dst=0, inplace=True)

# Check
print("After the collective ({}-th)".format(comm.rank))
for x in x_list:
    print(x.d)

reduce_scatter(self, ndarray_list, ndarray, bool division=False, string group='world')¶

Reduce scatter over data in different device.

Parameters

ndarray_list (NdArray) – List of data to be reduced over different devices.
ndarray (NdArray) – Data to be saved.
group (string) – Name of a group. This groups is used when the collective is called.

Example:

# Run like `mpirun -n 2 python <code_snippet.py>`
# note: the order of the output to stdout are stochastic because of multiprocesses.

# Communicator and Context
import numpy as np
import nnabla as nn
import nnabla.communicators as C
from nnabla.ext_utils import get_extension_context

extension_module = "cudnn"
ctx = get_extension_context(extension_module)
comm = C.MultiProcessCommunicator(ctx)
comm.init()

# Data
x_list = [nn.Variable([2, 2]), nn.Variable([2, 2])]
y = nn.Variable([2, 2])
print("Before the collective ({}-th)".format(comm.rank))
for x in x_list:
    x.d = np.random.rand(*x.shape)
    print(x.d)

# ReduceScatter
comm.reduce_scatter([x.data for x in x_list], y.data)

# Check
print("After the collective ({}-th)".format(comm.rank))
print(y.d)

size¶: Get size of communicator.

List of communicators¶

nnabla.communicators.MultiProcessDataParalellCommunicator()¶

MultiProcessDataParallelCommunicator(CContext ctx)

Multi Process Data Parallel Communicator for Distributed Training.

Parameters: context (Context) – context used in this communicator.

Example:

In case of the multi-process data parallel distributed training,

# Communicator and Context
extension_module = "cudnn"
ctx = get_extension_context(extension_module)
comm = C.MultiProcessCommunicator(ctx)
comm.init()
n_devices = comm.size
mpi_rank = comm.rank
device_id = comm.local_rank
ctx.device_id = str(device_id)
nn.set_default_context(ctx)

# Network and Solver created here

...


# Training loop
for itr in range(num_itr):
    # Forward, zerograd, backward
    loss.forward()
    solver.zero_grad()
    loss.backward()

    # Allreduce
    comm.all_reduce([v.grad for v in nn.get_parameters().values()])

    # Update
    solver.update()

Monitors¶

The Monitor API provides helpers for logging the progress of neural network training.

class nnabla.monitor.Monitor(save_path)[source]¶: This class is created to setup the output directory of the monitoring logs. The created nnabla.monitor.Monitor instance is passed to classes in the following Monitors.

List of Monitors¶

class nnabla.monitor.MonitorSeries(name, monitor=None, interval=1, verbose=True)[source]¶

Logs a series of values.

The values are displayed and/or output to the file <name>-series.txt.

Example:

mons = MonitorSeries('mon', interval=2)
for i in range(10):
    mons.add(i, i * 2)

Parameters

name (str) – Name of the monitor. Used in the log.
monitor (Monitor) – Monitor class instance.
interval (int) – Interval of flush the outputs. The values added by .add() are averaged during interval.
verbose (bool) – Output to screen.

add(index, value)[source]¶

Add a value to the series.

Parameters

index (int) – Index.
value (float) – Value.

class nnabla.monitor.MonitorTimeElapsed(name, monitor=None, interval=100, verbose=True)[source]¶

Logs the elapsed time.

The values are displayed and/or output to the file <name>-timer.txt.

Example:

import time
mont = MonitorTimeElapsed("time", interval=2)
for i in range(10):
    time.sleep(1)
    mont.add(i)

Parameters

name (str) – Name of the monitor. Used in the log.
monitor (Monitor) – Monitor class instance.
interval (int) – Interval of flush the outputs. The elapsed time is calculated within the interval.
verbose (bool) – Output to screen.

add(index)[source]¶

Calculate time elapsed from the point previously called this method or this object is created to this is called.

Parameters: index (int) – Index to be displayed, and be used to take intervals.

class nnabla.monitor.MonitorImage(name, monitor, interval=1000, verbose=True, num_images=16, normalize_method=None)[source]¶

Saves a series of images.

The .add() method takes a (N,..., C, H, W) array as an input, and num_images of [H, W, :min(3, C)] are saved into the monitor folder for each interval.

The values are displayed and/or output to the file <name>/{iter}-{image index}.png.

Example:

import numpy as np
m = Monitor('tmp.monitor')
mi = MonitorImage('noise', m, interval=2, num_images=2)
x = np.random.randn(10, 3, 8, 8)
for i in range(10):
    mi.add(i, x)

Parameters

name (str) – Name of the monitor. Used in the log.
monitor (Monitor) – Monitor class instance.
interval (int) – Interval of flush the outputs.
num_images (int) – Number of images to be saved in each iteration.
normalize_method (function) – A function that takes a NCHW format image minibatch as numpy.ndarray. The function should define a normalizer which map any inputs to a range of [0, 1]. The default normalizer normalizes the images into min-max normalization.

add(index, var)[source]¶

Add a minibatch of images to the monitor.

Parameters

index (int) – Index.
var (Variable, NdArray, or ndarray) – A minibatch of images with (N, ..., C, H, W) format. If C == 2, blue channel is appended with ones. If C > 3, the array will be sliced to remove C > 3 sub-array.

class nnabla.monitor.MonitorImageTile(name, monitor, interval=1000, verbose=True, num_images=16, normalize_method=None)[source]¶

Saving a series of images.

The .add() method takes a (N,..., C, H, W) array as an input, and num_images tiled (H, W, :min(3, C)) images are saved into the monitor folder for each interval.

The values are displayed and/or output to the file <name>/{iter}-{image index}.png.

Example:

import numpy as np
m = Monitor('tmp.monitor')
mi = MonitorImageTile('noise_noise', m, interval=2, num_images=4)
x = np.random.randn(10, 3, 8, 8)
for i in range(10):
    mi.add(i, x)

Parameters

name (str) – Name of the monitor. Used in the log.
monitor (Monitor) – Monitor class instance.
interval (int) – Interval of flush the outputs.
num_images (int) – Number of images tiled to be saved into a single image in each iteration.
normalize_method (function) – A function that takes a NCHW format image minibatch as numpy.ndarray. The function should define a normalizer which map any inputs to a range of [0, 1]. The default normalizer normalizes the images into min-max normalization.

add(index, var)[source]¶

Add a minibatch of images to the monitor.

Parameters

index (int) – Index.
var (Variable, NdArray, or ndarray) – A minibatch of images with (N, ..., C, H, W) format. If C == 2, blue channel is appended with ones. If C > 3, the array will be sliced to remove C > 3 sub-array.

Utility functions¶

nnabla.monitor.tile_images(data, padsize=1, padval=0)[source]¶

Convert an array with shape of (B, C, H, W) into a tiled image.

Parameters

data (ndarray) – An array with shape of (B, C, H, W).
padsize (int) – Each tile has padding with this size.
padval (float) – Padding pixels are filled with this value.

Returns

A tile image.

Return type

tile_image (ndarray)

nnabla.monitor.plot_series(filename, plot_kwargs=None)[source]¶

Plot series data from MonitorSeries output text file.

Parameters

filename (str) – Path to *.series.txt file produced by MonitorSeries class.
plot_kwags (dict, optional) – Keyward arguments passed to :function:`matplotlib.pyplot.plot`.

Note

matplotlib package is required.

nnabla.monitor.plot_time_elapsed(filename, elapsed=False, unit='s', plot_kwargs=None)[source]¶

Plot series data from MonitorTimeElapsed output text file.

Parameters

filename (str) – Path to *.series.txt file produced by MonitorSeries class.
elapsed (bool) – If True, it plots the total elapsed time.
unit (str) – Time unit chosen from 's', 'm', 'h', or 'd'.
plot_kwags (dict, optional) – Keyward arguments passed to :function:`matplotlib.pyplot.plot`.

Note

matplotlib package is required.

Utils¶

NNP save and load utilities¶

IMPORTANT NOTICE: To handle NNP file from Neural Network Console, if the network you want to save/load contains LoopControl functions RepeatStart, RepeatEnd, RecurrentInput, RecurrentOutput or Delay, you must expand network with File format converter.

nnabla.utils.save.save(filename, contents, include_params=False, variable_batch_size=True, extension='.nnp', parameters=None)[source]¶

Save network definition, inference/training execution configurations etc.

Parameters

filename (str or file object) –
Filename to store information. The file extension is used to determine the saving file format. .nnp: (Recommended) Creating a zip archive with nntxt (network definition etc.) and h5 (parameters). .nntxt: Protobuf in text format. .protobuf: Protobuf in binary format (unsafe in terms of

backward compatibility).
contents (dict) – Information to store.
include_params (bool) – Includes parameter into single file. This is ignored when the extension of filename is nnp.
variable_batch_size (bool) – By True, the first dimension of all variables is considered as batch size, and left as a placeholder (more specifically -1). The placeholder dimension will be filled during/after loading.
extension – if files is file-like object, extension is one of “.nntxt”, “.prototxt”, “.protobuf”, “.h5”, “.nnp”.

Example

The following example creates a two inputs and two outputs MLP, and save the network structure and the initialized parameters.

import nnabla as nn
import nnabla.functions as F
import nnabla.parametric_functions as PF
from nnabla.utils.save import save

batch_size = 16
x0 = nn.Variable([batch_size, 100])
x1 = nn.Variable([batch_size, 100])
h1_0 = PF.affine(x0, 100, name='affine1_0')
h1_1 = PF.affine(x1, 100, name='affine1_0')
h1 = F.tanh(h1_0 + h1_1)
h2 = F.tanh(PF.affine(h1, 50, name='affine2'))
y0 = PF.affine(h2, 10, name='affiney_0')
y1 = PF.affine(h2, 10, name='affiney_1')

contents = {
    'networks': [
        {'name': 'net1',
         'batch_size': batch_size,
         'outputs': {'y0': y0, 'y1': y1},
         'names': {'x0': x0, 'x1': x1}}],
    'executors': [
        {'name': 'runtime',
         'network': 'net1',
         'data': ['x0', 'x1'],
         'output': ['y0', 'y1']}]}
save('net.nnp', contents)

To get a trainable model, use following code instead.

contents = {
'global_config': {'default_context': ctx},
'training_config':
    {'max_epoch': args.max_epoch,
     'iter_per_epoch': args_added.iter_per_epoch,
     'save_best': True},
'networks': [
    {'name': 'training',
     'batch_size': args.batch_size,
     'outputs': {'loss': loss_t},
     'names': {'x': x, 'y': t, 'loss': loss_t}},
    {'name': 'validation',
     'batch_size': args.batch_size,
     'outputs': {'loss': loss_v},
     'names': {'x': x, 'y': t, 'loss': loss_v}}],
'optimizers': [
    {'name': 'optimizer',
     'solver': solver,
     'network': 'training',
     'dataset': 'mnist_training',
     'weight_decay': 0,
     'lr_decay': 1,
     'lr_decay_interval': 1,
     'update_interval': 1}],
'datasets': [
    {'name': 'mnist_training',
     'uri': 'MNIST_TRAINING',
     'cache_dir': args.cache_dir + '/mnist_training.cache/',
     'variables': {'x': x, 'y': t},
     'shuffle': True,
     'batch_size': args.batch_size,
     'no_image_normalization': True},
    {'name': 'mnist_validation',
     'uri': 'MNIST_VALIDATION',
     'cache_dir': args.cache_dir + '/mnist_test.cache/',
     'variables': {'x': x, 'y': t},
     'shuffle': False,
     'batch_size': args.batch_size,
     'no_image_normalization': True
     }],
'monitors': [
    {'name': 'training_loss',
     'network': 'validation',
     'dataset': 'mnist_training'},
    {'name': 'validation_loss',
     'network': 'validation',
     'dataset': 'mnist_validation'}],
}

class nnabla.utils.nnp_graph.NnpLoader(filepath, scope=None, extension='.nntxt')[source]¶

An NNP file loader.

Parameters

filepath – file-like object or filepath.
extension – if filepath is file-like object, extension is one of “.nnp”, “.nntxt”, “.prototxt”.

Example

from nnabla.utils.nnp_graph import NnpLoader

# Read a .nnp file.
nnp = NnpLoader('/path/to/nnp.nnp')
# Assume a graph `graph_a` is in the nnp file.
net = nnp.get_network(network_name, batch_size=1)
# `x` is an input of the graph.
x = net.inputs['x']
# 'y' is an outputs of the graph.
y = net.outputs['y']
# Set random data as input and perform forward prop.
x.d = np.random.randn(*x.shape)
y.forward(clear_buffer=True)
print('output:', y.d)

get_network(name, batch_size=None, callback=None)[source]¶

Create a variable graph given network by name

Returns: NnpNetwork

get_network_names()[source]¶: Returns network names available.

class nnabla.utils.nnp_graph.NnpNetwork(proto_network, batch_size, callback)[source]¶

A graph object which is read from nnp file.

An instance of NnpNetwork is usually created by an NnpLoader instance. See an example usage described in NnpLoader.

variables¶

A dict of all variables in a created graph with a variable name as a key, and a nnabla.Variable as a value.

Type: dict

inputs¶

All input variables.

Type: dict

outputs¶

All output variables.

Type: dict

Image Utils¶

This module provides read, write and resize functions for images. The backends of these functions are automatically changed, depending on the user`s environment. The priority of the backends is as below (upper is higher priority):

OpenCV (cv2)

scikit-image (skimage)

pillow (PIL) (need to be installed)

At least one of these modules needs to be installed to use this module.

nnabla.utils.image_utils.imread(path, grayscale=False, size=None, interpolate='bilinear', channel_first=False, as_uint16=False, num_channels=- 1, **kwargs)[source]¶

Read image from path. If you specify the size, the output array is resized. Default output shape is (height, width, channel) for RGB image and (height, width) for gray-scale image.

Parameters

path (String or File Object) – Input image path.
grayscale (bool) – If True, the img is rescaled to gray-scale. Default is False.
size (tuple of int) – Output shape. The order is (width, height). If None, the image is not resized. Default is None.
interpolate (str) –
Interpolation method. This argument is depend on the backend. If you want to specify this argument, you should pay much attention to which backend you use now. What you can select is below:
- pil backend: [“nearest”, “box”, “bilinear”, “hamming”, “bicubic”, “lanczos”].
- cv2 backend: [“nearest”, “bilinear”, “bicubic”, “lanczos”].
Default is “bilinear” for both backends.
channel_first (bool) – If True, the shape of the output array is (channel, height, width) for RGB image. Default is False.
as_uint16 (bool) – If True, this function tries to read img as np.uint16. Default is False.
num_channels (int) – channel size of output array. Default is -1 which preserves raw image shape.
return_palette_indices (bool) – This argument can be used only by pil backend. On pil backend, if this flag is True and PIL.Image has the mode “P”, then this function returns 2-D array containing the indices into palette. Otherwise, 3-D array of “RGB” or “RGBA” (it depends on an image info) will be returned. Default value is False.

Returns

if as_uint16=True output dtype is np.uint16, else np.uint8 (default).

Return type

numpy.ndarray

nnabla.utils.image_utils.imsave(path, img, channel_first=False, as_uint16=False, auto_scale=True, **kwargs)[source]¶

Save img to the file specified by path. As default, the shape of img has to be (height, width, channel).

Parameters

path (str) – Output path.
img (numpy.ndarray) – Input image. All pixel values must be positive and in the range [0, 255] of int for uint8, [0, 65535] of int for uint16 or [0, 1] for float. When you pass float image, you must set auto_scale as True (If not, exception will be raised). If img with negative values is passed as input, exception will be raised.
channel_first (bool) – If True, you can input the image whose shape is (channel, height, width). Default is False.
as_uint16 (bool) – If True, cast image to uint16 before save. Default is False.
auto_scale (bool) – Whether the range of pixel values are scaled up or not. The range of upscaled pixel values depends on output dtype, which is [0, 255] as uint8 and [0, 65535] as uint16.

nnabla.utils.image_utils.imresize(img, size, interpolate='bilinear', channel_first=False, **kwargs)[source]¶

Resize img to size. As default, the shape of input image has to be (height, width, channel).

Parameters

img (numpy.ndarray) – Input image.
size (tuple of int) – Output shape. The order is (width, height).
interpolate (str) –
Interpolation method. This argument is depend on the backend. If you want to specify this argument, you should pay much attention to which backend you use now. What you can select is below:
- pil backend: [“nearest”, “box”, “bilinear”, “hamming”, “bicubic”, “lanczos”].
- cv2 backend: [“nearest”, “bilinear”, “bicubic”, “lanczos”].
Default is “bilinear” for both backends.
channel_first (bool) – If True, the shape of the output array is (channel, height, width) for RGB image. Default is False.

Returns

numpy.ndarray

Data Iterators¶

NNabla provides various utilities for using data for training.

DataSource¶

class nnabla.utils.data_source.DataSource(shuffle=False, rng=None)[source]¶

Bases: object

This class contains various properties and methods for the data source, which are utilized by py:class:DataIterator.

Parameters

shuffle (bool) – Indicates whether the dataset is shuffled or not.
rng (None or numpy.random.RandomState) – Numpy random number generator.

property position¶

Data position in current epoch.

Returns: Data position
Return type: int

property shuffle¶

Whether dataset is shuffled or not.

Returns: whether dataset is shuffled.
Return type: bool

property variables¶

Variable names of the data.

Returns: tuple of Variable names
Return type: tuple

class nnabla.utils.data_source.DataSourceWithFileCache(data_source, cache_dir=None, cache_file_name_prefix='cache', shuffle=False, rng=None)[source]¶

Bases: nnabla.utils.data_source.DataSource

This class contains properties and methods for data source that can be read from cache files, which are utilized by data iterator.

Parameters

data_source (DataSource) – Instance of DataSource class which provides data.
cache_dir (str) – Location of file_cache. If this value is None, data_source.DataSourceWithFileCache creates file caches implicitly on temporary directory and erases them all when data_iterator is finished. Otherwise, data_source.DataSourceWithFileCache keeps created cache. Default is None.
cache_file_name_prefix (str) – Beginning of the filenames of cache files. Default is ‘cache’.
shuffle (bool) – Indicates whether the dataset is shuffled or not.
rng (None or numpy.random.RandomState) – Numpy random number generator.

property position¶

Data position in current epoch.

Returns: Data position
Return type: int

property shuffle¶

Whether dataset is shuffled or not.

Returns: whether dataset is shuffled.
Return type: bool

property variables¶

Variable names of the data.

Returns: tuple of Variable names
Return type: tuple

class nnabla.utils.data_source.DataSourceWithMemoryCache(data_source, shuffle=False, rng=None)[source]¶

Bases: nnabla.utils.data_source.DataSource

This class contains properties and methods for data source that can be read from memory cache, which is utilized by data iterator.

Parameters

data_source (DataSource) – Instance of DataSource class which provides data.
shuffle (bool) – Indicates whether the dataset is shuffled or not.
rng (None or numpy.random.RandomState) – Numpy random number generator.

property position¶

Data position in current epoch.

Returns: Data position
Return type: int

property shuffle¶

Whether dataset is shuffled or not.

Returns: whether dataset is shuffled.
Return type: bool

property variables¶

Variable names of the data.

Returns: tuple of Variable names
Return type: tuple

DataIterator¶

class nnabla.utils.data_iterator.DataIterator(data_source, batch_size, rng=None, use_thread=True, epoch_begin_callbacks=[], epoch_end_callbacks=[], stop_exhausted=False)[source]¶

Bases: object

Collect data from data_source and yields bunch of data.

Parameters

data_source (DataSource) – Instance of DataSource class witch provides data for this class.
batch_size (int) – Size of data unit.
rng (None or numpy.random.RandomState) – Numpy random number generator.
use_thread (bool) – If use_thread is set to True, iterator will use another thread to fetch data. If use_thread is set to False, iterator will use current thread to fetch data.
epoch_begin_callbacks (list of functions) – An item is a function which takes an epoch index as a argument. These are called at the beginning of an epoch.
epoch_end_callbacks (list of functions) – An item is a function which takes an epoch index as a argument. These are called at the end of an epoch.
stop_exhausted (bool) – If stop_exhausted is set to False, iterator will be reset so that iteration can be continued. If stop_exhausted is set to True, iterator will raise StopIteration to stop the loop.

property batch_size¶

Number of training samples that next() returns.

Returns: Number of training samples.
Return type: int

property epoch¶

The number of times position() returns to zero.

Returns: epoch
Return type: int

next()[source]¶

It generates tuple of data.

For example, if self._variables == ('x', 'y')() This method returns :py:meth:` ( [[X] * batch_size], [[Y] * batch_size] )`

Returns: tuple of data for mini-batch in numpy.ndarray.
Return type: tuple

property position¶

Data position in current epoch.

Returns: Data position
Return type: int

register_epoch_begin_callback(callback)[source]¶

Register epoch begin callback.

Parameters: callback (function) – A function takes an epoch index as an argument.

register_epoch_end_callback(callback)[source]¶

Register epoch end callback.

Parameters: callback (function) – A function takes an epoch index as an argument.

property size¶

Data size that DataIterator will generate. This is the largest integer multiple of batch_size not exceeding self._data_source.size().

Returns: Data size
Return type: int

slice(rng, num_of_slices=None, slice_pos=None, slice_start=None, slice_end=None, cache_dir=None, use_cache=False)[source]¶

Slices the data iterator so that newly generated data iterator has access to limited portion of the original data.

Parameters

rng (numpy.random.RandomState) – Random generator for Initializer.
num_of_slices (int) – Total number of slices to be made. Muts be used together with slice_pos.
slice_pos (int) – Position of the slice to be assigned to the new data iterator. Must be used together with num_of_slices.
slice_start (int) – Starting position of the range to be sliced into new data iterator. Must be used together with slice_end.
slice_end (int) – End position of the range to be sliced into new data iterator. Must be used together with slice_start.
cache_dir (str) – Directory to save cache files. if cache_dir is None and use_cache is True, will used memory cache.
use_cache (bool) – Whether use cache for data_source.

Example:

from nnabla.utils.data_iterator import data_iterator_simple
import numpy as np

def load_func1(index):
    d = np.ones((2, 2)) * index
    return d

di = data_iterator_simple(load_func1, 1000, batch_size=3)

di_s1 = di.slice(None, num_of_slices=10, slice_pos=0)
di_s2 = di.slice(None, num_of_slices=10, slice_pos=1)

di_s3 = di.slice(None, slice_start=100, slice_end=200)
di_s4 = di.slice(None, slice_start=300, slice_end=400)

property variables¶

Variable names of the data.

Returns: tuple of Variable names
Return type: tuple

Utilities¶

nnabla.utils.data_iterator.data_iterator(data_source, batch_size, rng=None, use_thread=True, with_memory_cache=True, with_file_cache=False, cache_dir=None, epoch_begin_callbacks=[], epoch_end_callbacks=[], stop_exhausted=False)[source]¶

Helper method to use DataSource.

You can use DataIterator with your own DataSource for easy implementation of data sources.

For example,

ds = YourOwnImplementationOfDataSource()
batch = data_iterator(ds, batch_size)

Parameters

data_source (DataSource) – Instance of DataSource class which provides data.
batch_size (int) – Batch size.
rng (None or numpy.random.RandomState) – Numpy random number generator.
use_thread (bool) – If use_thread is set to True, iterator will use another thread to fetch data. If use_thread is set to False, iterator will use current thread to fetch data.
with_memory_cache (bool) – If True, use data_source.DataSourceWithMemoryCache to wrap data_source. It is a good idea to set this as true unless data_source provides on-memory data. Default value is True.
with_file_cache (bool) – If True, use data_source.DataSourceWithFileCache to wrap data_source. If data_source is slow, enabling this option a is good idea. Default value is False.
cache_dir (str) – Location of file_cache. If this value is None, data_source.DataSourceWithFileCache creates file caches implicitly on temporary directory and erases them all when data_iterator is finished. Otherwise, data_source.DataSourceWithFileCache keeps created cache. Default is None.
epoch_begin_callbacks (list of functions) – An item is a function which takes an epoch index as an argument. These are called at the beginning of an epoch.
epoch_end_callbacks (list of functions) – An item is a function which takes an epoch index as an argument. These are called at the end of an epoch.
stop_exhausted (bool) – If stop_exhausted is set to False, iterator will be reset so that iteration can be continued. If stop_exhausted is set to True, iterator will raise StopIteration to stop the loop.

Returns

Instance of DataIterator.

Return type

nnabla.utils.data_iterator.data_iterator_simple(load_func, num_examples, batch_size, shuffle=False, rng=None, use_thread=True, with_memory_cache=False, with_file_cache=False, cache_dir=None, epoch_begin_callbacks=[], epoch_end_callbacks=[], stop_exhausted=False)[source]¶

A generator that yield s minibatch data as a tuple, as defined in load_func . It can unlimitedly yield minibatches at your request, queried from the provided data.

Parameters

load_func (function) – Takes a single argument i, an index of an example in your dataset to be loaded, and returns a tuple of data. Every call by any index i must return a tuple of arrays with the same shape.
num_examples (int) – Number of examples in your dataset. Random sequence of indexes is generated according to this number.
batch_size (int) – Size of data unit.
shuffle (bool) – Indicates whether the dataset is shuffled or not. Default value is False.
rng (None or numpy.random.RandomState) – Numpy random number generator.
use_thread (bool) – If use_thread is set to True, iterator will use another thread to fetch data. If use_thread is set to False, iterator will use current thread to fetch data.
with_memory_cache (bool) – If True, use data_source.DataSourceWithMemoryCache to wrap data_source. It is a good idea to set this as true unless data_source provides on-memory data. Default value is False.
with_file_cache (bool) – If True, use data_source.DataSourceWithFileCache to wrap data_source. If data_source is slow, enabling this option a is good idea. Default value is False.
cache_dir (str) – Location of file_cache. If this value is None, data_source.DataSourceWithFileCache creates file caches implicitly on temporary directory and erases them all when data_iterator is finished. Otherwise, data_source.DataSourceWithFileCache keeps created cache. Default is None.
epoch_begin_callbacks (list of functions) – An item is a function which takes an epoch index as an argument. These are called at the beginning of an epoch.
epoch_end_callbacks (list of functions) – An item is a function which takes an epoch index as an argument. These are called at the end of an epoch.
stop_exhausted (bool) – If stop_exhausted is set to False, iterator will be reset so that iteration can be continued. If stop_exhausted is set to True, iterator will raise StopIteration to stop the loop.

Returns

Instance of DataIterator.

Return type

Here is an example of load_func which returns an image and a label of a classification dataset.

import numpy as np
from nnabla.utils.image_utils import imread
image_paths = load_image_paths()
labels = load_labels()
def my_load_func(i):
    '''
    Returns:
        image: c x h x w array
        label: 0-shape array
    '''
    img = imread(image_paths[i]).astype('float32')
    return np.rollaxis(img, 2), np.array(labels[i])

nnabla.utils.data_iterator.data_iterator_csv_dataset(uri, batch_size, shuffle=False, rng=None, use_thread=True, normalize=True, with_memory_cache=True, with_file_cache=True, cache_dir=None, epoch_begin_callbacks=[], epoch_end_callbacks=[], stop_exhausted=False)[source]¶

Get data directly from a dataset provided as a CSV file.

You can read files located on the local file system, http(s) servers or Amazon AWS S3 storage.

For example,

batch = data_iterator_csv_dataset('CSV_FILE.csv', batch_size, shuffle=True)

Parameters

uri (str) – Location of dataset CSV file.
batch_size (int) – Size of data unit.
shuffle (bool) – Indicates whether the dataset is shuffled or not. Default value is False.
rng (None or numpy.random.RandomState) – Numpy random number generator.
use_thread (bool) – If use_thread is set to True, iterator will use another thread to fetch data. If use_thread is set to False, iterator will use current thread to fetch data.
normalize (bool) – If True, each sample in the data gets normalized by a factor of 255. Default is True.
with_memory_cache (bool) – If True, use data_source.DataSourceWithMemoryCache to wrap data_source. It is a good idea to set this as true unless data_source provides on-memory data. Default value is True.
with_file_cache (bool) – If True, use data_source.DataSourceWithFileCache to wrap data_source. If data_source is slow, enabling this option a is good idea. Default value is False.
cache_dir (str) – Location of file_cache. If this value is None, data_source.DataSourceWithFileCache creates file caches implicitly on temporary directory and erases them all when data_iterator is finished. Otherwise, data_source.DataSourceWithFileCache keeps created cache. Default is None.
epoch_begin_callbacks (list of functions) – An item is a function which takes an epoch index as an argument. These are called at the beginning of an epoch.
epoch_end_callbacks (list of functions) – An item is a function which takes an epoch index as an argument. These are called at the end of an epoch.
stop_exhausted (bool) – If stop_exhausted is set to False, iterator will be reset so that iteration can be continued. If stop_exhausted is set to True, iterator will raise StopIteration to stop the loop.

Returns

Instance of DataIterator

Return type

nnabla.utils.data_iterator.data_iterator_cache(uri, batch_size, shuffle=False, rng=None, use_thread=True, normalize=True, with_memory_cache=True, epoch_begin_callbacks=[], epoch_end_callbacks=[], stop_exhausted=False)[source]¶

Get data from the cache directory.

Cache files are read from the local file system.

For example,

batch = data_iterator_cache('CACHE_DIR', batch_size, shuffle=True)

Parameters

uri (str) – Location of directory with cache files.
batch_size (int) – Size of data unit.
shuffle (bool) – Indicates whether the dataset is shuffled or not. Default value is False.
rng (None or numpy.random.RandomState) – Numpy random number generator.
use_thread (bool) – If use_thread is set to True, iterator will use another thread to fetch data. If use_thread is set to False, iterator will use current thread to fetch data.
normalize (bool) – If True, each sample in the data gets normalized by a factor of 255. Default is True.
with_memory_cache (bool) – If True, use data_source.DataSourceWithMemoryCache to wrap data_source. It is a good idea to set this as true unless data_source provides on-memory data. Default value is True.
epoch_begin_callbacks (list of functions) – An item is a function which takes an epoch index as an argument. These are called at the beginning of an epoch.
epoch_end_callbacks (list of functions) – An item is a function which takes an epoch index as an argument. These are called at the end of an epoch.
stop_exhausted (bool) – If stop_exhausted is set to False, iterator will be reset so that iteration can be continued. If stop_exhausted is set to True, iterator will raise StopIteration to stop the loop.

Returns

Instance of DataIterator

Return type

nnabla.utils.data_iterator.data_iterator_concat_datasets(data_source_list, batch_size, shuffle=False, rng=None, use_thread=True, with_memory_cache=True, with_file_cache=False, cache_dir=None, epoch_begin_callbacks=[], epoch_end_callbacks=[], stop_exhausted=False)[source]¶

Get data from multiple datasets.

For example,

batch = data_iterator_concat_datasets([DataSource0, DataSource1, ...], batch_size)

Parameters

data_source_list (list of DataSource) – list of datasets.
batch_size (int) – Size of data unit.
shuffle (bool) – Indicates whether the dataset is shuffled or not. Default value is False.
rng (None or numpy.random.RandomState) – Numpy random number generator.
use_thread (bool) – If use_thread is set to True, iterator will use another thread to fetch data. If use_thread is set to False, iterator will use current thread to fetch data.
with_memory_cache (bool) – If True, use data_source.DataSourceWithMemoryCache to wrap data_source. It is a good idea to set this as true unless data_source provides on-memory data. Default value is True.
with_file_cache (bool) – If True, use data_source.DataSourceWithFileCache to wrap data_source. If data_source is slow, enabling this option a is good idea. Default value is False.
cache_dir (str) – Location of file_cache. If this value is None, data_source.DataSourceWithFileCache creates file caches implicitly on temporary directory and erases them all when data_iterator is finished. Otherwise, data_source.DataSourceWithFileCache keeps created cache. Default is None.
epoch_begin_callbacks (list of functions) – An item is a function which takes an epoch index as an argument. These are called at the beginning of an epoch.
epoch_end_callbacks (list of functions) – An item is a function which takes an epoch index as an argument. These are called at the end of an epoch.
stop_exhausted (bool) – If stop_exhausted is set to False, iterator will be reset so that iteration can be continued. If stop_exhausted is set to True, iterator will raise StopIteration to stop the loop.

Returns

Instance of DataIterator

Return type

Debug Utils¶

Graph Profiler¶

class nnabla.utils.profiler.GraphProfiler(graph, device_id, ext_name, solver=None, n_run=100, max_measure_execution_time=1, time_scale='m', backward_accum=False)[source]¶

Class for measuring calculation time of each functions which compose nnabla computation graph.

You can check some performances of your nnabla network. This can measure the calculation times of :

function-wise forward
function-wise backward
whole graph forward
whole graph backward
training (forward + backward + update) (if solver is not None)

Example:

import nnabla as nn
import nnabla.functions as F
import nnabla.solvers as S
from nnabla.utils.profiler import GraphProfiler

# Set up nnabla context
device = "cpu"  # you can also use GPU ("cudnn")
ctx = get_extension_context(device)
nn.set_default_context(ctx)

# Network building
x = nn.Variable(shape=...)
t = nn.Variable(shape=...)
y = CNN(x) # you can build not only CNN but any networks
loss = F.mean(F.softmax_cross_entropy(y, t)) # any loss functions or variables can be used

# solver setting
solver = S.Sgd()
solver.set_parameters(nn.get_parameters())

# SOME CODE (data loading or so on)

B = GraphProfiler(loss, solver=solver, device_id=0, ext_name=device, n_run=1000)
B.run()

Parameters

graph (nnabla.Variable) – Instance of nnabla.Variable class. GraphProfiler find all functions which compose network graph from root nnabla.Variable to this nnabla.Variable.
device_id (str) – gpu device id.
ext_name (str) – Extension name. e.g. ‘cpu’, ‘cuda’, ‘cudnn’ etc.
solver (nnabla.solvers.Solver) – Instance of nnabla.solvers.Solver for optimizing the parameters of the computation graph. if None, the training process is ignored. Default value is None.
n_run (int) – This argument specifies how many times the each functions` execution time are measured. Default value is 100.
max_measure_execution_time (float) – Maximum time of executing measurement for each functions. This argument has higher priority than n_run. When the measurement time for each functions get bigger than this argument, this class stops measuring and goes to next function, unless the total times of measurement are less than n_run. Default value is 1 [sec].
time_scale (str) – Time scale to display. [‘m’, ‘u’, ‘n’] (which stands for ‘mili’, ‘micro’ and ‘nano’)
backward_accum (bool) – Accumulation flag passed to the each backward function. The flag will fulfill the all accumulation flags with the same value of backward_accum. This flag is only valid for the time measurement of each function. For whole graph comutation, the NNabla graph engine set the appropriate accumulation flags to functions. Pay attention to inplace flag for your graph because accumulation and inplace flags cannot be set at the same time. If even one inplace flag is true in your graph, this backward_accum must be false. Default value is False.

run()[source]¶

Execute profiling.

This executes the 5 types of measurement:

function-wise forward
function-wise backward
whole graph forward
whole graph backward
training (forward + backward + update) (if solver is not None.)

class nnabla.utils.profiler.GraphProfilerCsvWriter(gb, file=<_io.TextIOWrapper name='<stdout>' mode='w' encoding='UTF-8'>)[source]¶

csv writer for GraphProfiler class.

Example:

from nnabla.utils.profiler import GraphProfiler, GraphProfilerCsvWriter

# Network building comes above

B = GraphProfiler(variable, solver=solver, device_id=0, ext_name=device, n_run=1000)
B.run()

with open("./profile.csv", "w") as f:
    writer = GraphProfilerCsvWriter(B, file=f)
    writer.write()

Parameters

gb (GraphProfiler) – Instance of GraphProfiler class which is main executor of profiling.
file (Python file object) – Output file object. Profile results will be written to the file which is specified by this argument.

write()[source]¶: Write result to the file. The output file is specified by file.

Time Profiler¶

class nnabla.utils.inspection.profile.TimeProfiler(ext_name, device_id)[source]¶

An utility API to create function_hook callbacks to profile the execution time of each function. Passing ext_name and device_id, you can define which device time you want to profile. If ext_name = “cuda” or “cudnn”, then cudaEvent will be used to measure the execution time. For more information about cudaEvent, see the CUDA document. If `ext_name`=”cpu” , then wall-clock-time on host will be used.

Example:

ext_name = "cpu"
device_id = "0"

from nnabla.ext_utils import get_extension_context
ctx = get_extension_context(ext_name, device_id=device_id)
nn.set_default_context(ctx)

y = model(...)

from nnabla.utils.inspection import TimeProfiler
tp = TimeProfiler(ext_name=ext_name, device_id=device_id)

for i in range(max_iter):
    # All results of executions under "forward" scope are registered as "forward" execution.
    with tp.scope("forward"):
        y.forward(function_pre_hook=tp.pre_hook, function_post_hook=tp.post_hook)

    # All results of executions under "backward" scope are registered as "backward" execution.
    with tp.scope("backward") as tp:
        y.backward(function_pre_hook=tp.pre_hook, function_post_hook=tp.post_hook)

    # All results are evaluated by passing scopes to .calc_elapsed_time().
    # Be sure to call calc_elapsed_time at each iteration, otherwise nothing is measured.
    tp.calc_elapsed_time(["forward", "backward", "summary"])

# To output results on stdout, call instance as a function.
tp()

# To write out as csv file, call .to_csv().
tp.to_csv(output_file_name)

calc_elapsed_time(names=None)[source]¶

Evaluate all elapsed times. Note that elapsed time is not recorded until calc_elapsed_time is called.

Parameters: names (str or list of str) – Scope name(s) to evaluate elapsed time.

property post_hook¶

Get a callback for function_post_hook. This function can be used like the example below:

tp = TimeProfiler(..)
with tp.scope("forward"):
    v.forward(function_post_hook=tp.post_hook())

with tp.scope("backward"):
    v.backward(function_post_hook=tp.post_hook())

property pre_hook¶

Get a callback for function_pre_hook. This function can be used like the example below:

tp = TimeProfiler(..)
with tp.scope("forward"):
    v.forward(function_pre_hook=tp.pre_hook())

with tp.scope("backward"):
    v.backward(function_pre_hook=tp.pre_hook())

scope(scope_name)[source]¶

Change a scope to aggregate results. This function is used as context (The with statement statement),

and all results under the context are labeled by scope_name.

In adttion to the execution time of each function, the elapsed times between entering and exiting the each context are also recorded

and they are aggregated as “summary” scope.

Parameters: scope_name (str) – Scope name.

to_csv(out_dir='./', ignore_init=True)[source]¶

Writes out to csv file. Output directory can be specified by out_dir. As default, the elapsed times of first iteration will be omitted. If you evaluate the first iteration as well, pass True to ignore_init.

Parameters

out_dir (str) – Output directory.
ignore_init (bool) – Ignore the result of the first iteration or not.

Nan/Inf Tracer¶

class nnabla.utils.inspection.value_trace.NanInfTracer(trace_nan=True, trace_inf=True, need_details=True)[source]¶

An utility API to create function_hook callbacks to check whether the outputs of all layers have NaN or inf as their values. During forward and backward execution, passed as function_hook, this API reports ValueError if at least one of all layer outputs has Nan or inf as its values. Otherwise, all tensors passed to next layer or function as is.

Example:

pred = model(...)

from nnabla.utils.inspection import NanInfTracer
nit = NanInfTracer(trace_inf=True, trace_nan=True, need_details=True)

with nit.trace():
    pred.forward(function_post_hook=nit.forward_post_hook)
    pred.backward(function_post_hook=nit.backward_post_hook)

property backward_post_hook¶: Create callback function object which can be used as a function_post_hook argument of backward().

check()[source]¶: Checks nan/inf existence at all outputs of all layers and raises ValueError only if exist.

property forward_post_hook¶: Create callback function object which can be used as a function_post_hook argument of forward().

trace()[source]¶

Create context manager to check nan/inf existence by using with statement. Using this context manager, checking nan/inf is performed automatically just before exiting with scope. Unless you use this context manager, be sure to call .check() explicitly to check nan/inf.

Example:

nit = NanInfTracer()
with nit.trace():
    pred.forward(function_post_hook=nit.forward_post_hook)
    pred.backward(function_post_hook=nit.backward_post_hook)

Pretty Printer¶

class nnabla.utils.inspection.pretty_print.PrettyPrinter(summary=False, hidden=False)[source]¶

Pretty printer to print the graph structure used with the visit method of a Variable.

functions¶

List of functions of which element is the dictionary. The (key, value) pair is the (name, function name), (inputs, list of input variables), and (outputs, list of output variables) of a function.

Type: list of dict

nnabla.utils.inspection.pretty_print.pprint(v, forward=False, backward=False, summary=False, hidden=False, printer=False)[source]¶

Pretty print information of a graph from a root variable v.

Note that in order to print the summary statistics, this function stores, i.e., does not reuse the intermediate buffers of a computation graph, increasing the memory usage if either the forward or backward is True.

Parameters

v (nnabla.Variable) – Root variable.
forward (bool) – Call the forward method of a variable v.
backward (bool) – Call the backward method of a variable v.
summary (bool) – Print statictis of a intermediate variable.
hidden (bool) – Store the intermediate input and output variables if True.
printer (bool) – Return the printer object if True.

Example:

pred = Model(...)

from nnabla.utils.inspection import pprint

pprint(pred, summary=True, forward=True, backward=True)

DLPack¶

Via a DLPack capsule, you can borrow a tensor from external software as a nnabla.NdArray and can share a NdArray to external software.

nnabla.utils.dlpack.from_dlpack(dlp, arr=None)¶

Decode a DLPack to NdArray.

Example:

# Create a tensor of an external tool, and encode as an DLPack.
import torch
from torch.utils.dlpack import to_dlpack
t = torch.ones((5, 5), dtype=torch.float32,
               device=torch.device('cuda'))
dlp = to_dlpack(t)

# Borrow the DLPack tensor as nnabla.NdArray
from nnabla.utils.dlpack import from_dlpack
arr = from_dlpack(dlp)

If you want to move an ownership of DLPack to an exiting NdArray;

from nnabla import NdArray
arr = NdArray()
from_dlpack(dlp, arr=arr)

Parameters

dlp (PyCapsule) – A PyCapsule object of a DLManagedTensor (as "dltensor") which internal memory is borrowed by a tensor of an external package. The ownership of the DLManagedTensor is moved to an NdArray object, and the PyCapsule object is marked as "used_dltensor" to inform that the ownership has been moved.
arr (NdArray) – If specified, a given DLPack is decoded to it. Otherwise, it creates a new NdArray object and decodes the DLPack to it.

Returns

an NdArray object borrowing the DLPack tensor.

Return type

NdArray

nnabla.utils.dlpack.to_dlpack(a, dtype=None, ctx=None)¶

Returns a DLPack which owns an internal array object borrowed by a specified NdArray.

Example:

# Create a nnabla.NdArray in CUDA.
import nnabla as nn
from nnabla.ext_utils import get_extension_context
ctx = get_extension_context('cudnn')
nn.set_default_context(ctx)

a = nn.NdArray.from_numpy_array(np.ones((5, 5), dtype=np.float32))
a.cast(np.float32, ctx)

# Expose as a DLPack.
from nnabla.utils.dlpack import to_dlpack
dlp = to_dlpack(a)

# Use the DLPack in PyTorch.
import torch
from torch.utils.dlpack import from_dlpack
t = from_dlpack(dlp)

# Changing the values in Torch will also be affected in nnabla
# because they share memory.
t.add_(1)
print(a.data)  # All values become 2.

Parameters

a (NdArray) – An NdArray object. An internal array which is recently modified or created will be encoded into a DLPack.
dtype (numpy.dtype) – If specified, in-place cast operation may be performed before encoding it to a DLPack.
ctx (Context) – If specified, in-place device transfer operation may be performed before encoding it into a DLPack.

Returns

A PyCapsule object of a DLManagedTensor (as "dltensor") which internal memory is borrowed by the specified NdArray.

Return type

PyCapsule

RNN Utils¶

class nnabla.utils.rnn.PackedSequence[source]¶

Parameters

data (nnabla.Variable) – Packed sequence.
batch_sizes (nnabla.Variable) – Batch size for each time step and always resides in CPU.
sorted_indices (nnabla.Variable) – Sorted indices to reconstruct the original sequences.
unsorted_indices (nnabla.Variable) – Unsorted indices to reconstruct the original sequences.

nnabla.utils.rnn.pad_sequence(sequences, batch_first=False, padding_value=0.0)[source]¶

Pad a list of variable-length Variables.

This method stacks a list of variable-length nnabla.Variable s with the padding_value.

$T_i$ is the length of the $i$-th Variable in the sequences. $B$ is the batch size equal to the length of the sequences. $T$ is the max of $T_i$ for all $i$. $*$ is the remaining dimensions including none.

Note

This function must be used the dynamic computation mode.

Example:

import numpy as np
import nnabla as nn
import nnabla.functions as F
import nnabla.utils.rnn as rnn_utils

nn.set_auto_forward(True)

l2v = lambda ldata: nn.Variable.from_numpy_array(np.asarray(ldata))
a = l2v([1, 1, 1, 1])
b = l2v([2, 2, 2])
c = l2v([2, 2, 2])
d = l2v([3, 3])
e = l2v([3, 3])
sequences = [a, b, c, d, e]

padded_sequence = rnn_utils.pad_sequence(sequences)
print(padded_sequence.d)

Parameters

sequences (list of nnabla.Variable) – Sequence of the variable of ($T_i$, $*$) shape.
batch_first (bool) – If False, output is of ($T$, $B$, $*$) shape, otherwise ($B$, $T$, $*$).
padding_value (float) – Padding value.

Returns

nnabla.Variable of ($T$, $B$, $*$) or ($B$, $T$, $*$) shape

nnabla.utils.rnn.pack_padded_sequence(padded_sequence, lengths, batch_first=False, enforce_sorted=True)[source]¶

Pack a padded variable-length sequences.

This method packs a padded variable-length sequences.

$T$ is the max length over the lengths of sequences. $B$ is the batch size equal to the length of the sequences. $*$ is the remaining dimensions including none.

Note

This function must be used the dynamic computation mode.

Example:

import numpy as np
import nnabla as nn
import nnabla.functions as F
import nnabla.utils.rnn as rnn_utils

nn.set_auto_forward(True)

l2v = lambda ldata: nn.Variable.from_numpy_array(np.asarray(ldata))
a = l2v([1, 1, 1, 1])
b = l2v([2, 2, 2])
c = l2v([2, 2, 2])
d = l2v([3, 3])
e = l2v([3, 3])
sequences = [a, b, c, d, e]
lengths = l2v([seq.shape[0] for seq in sequences])

padded_sequence = rnn_utils.pad_sequence(sequences)
print(padded_sequence.d)

packed_sequence = rnn_utils.pack_padded_sequence(padded_sequence, lengths)
print(packed_sequence.data.d)
print(packed_sequence.batch_sizes.d)

Parameters

padded_sequence (nnabla.Variable) – Padded sequence of ($T \times B \times *$) or ($B \times T \times *$) shape.
lengths (nnabla.Variable) – Sequence length for each batch and always resides in CPU.
batch_first (bool) – padded_sequence is of ($T$, $B$, $*$) shape if False, otherwise ($B$, $T$, $*$).
enforce_sorted (bool) – Sequences are sorted by the length in a decreasing order if True. Default is True.

Returns

PackedSequence

nnabla.utils.rnn.pack_sequence(sequences, batch_first=False, enforce_sorted=True)[source]¶

Pack a list of variable-length Variables.

This method packs a list of variable-length Variables.

$T_i$ is the length of the $i$-th Variable in the sequences. $T$ is the max of $T_i$ for all $i$. $B$ is the batch size equal to the length of the sequences. $*$ is the remaining dimensions including none.

Note

This function must be used the dynamic computation mode.

Example:

import numpy as np
import nnabla as nn
import nnabla.functions as F
import nnabla.utils.rnn as rnn_utils

nn.set_auto_forward(True)

l2v = lambda ldata: nn.Variable.from_numpy_array(np.asarray(ldata))
a = l2v([3, 3])
b = l2v([2, 2, 2])
c = l2v([2, 2, 2])
d = l2v([1, 1, 1, 1])
e = l2v([3, 3])
sequences = [a, b, c, d, e]

packed_sequence = rnn_utils.pack_sequence(sequences, enforce_sorted=False)
print(packed_sequence.data.d)
print(packed_sequence.batch_sizes.d)

Parameters

sequences (list of nnabla.Variable) – List of nnabla.Variable of ($T_i$, $*$) shape.
enforce_sorted (bool) – Sequences are sorted by the length in a decreasing order if True. Default is True.

Returns

packed_sequence

Return type

PackedSequence

nnabla.utils.rnn.pad_packed_sequence(sequence, batch_first=False, padding_value=0.0, total_length=None)[source]¶

Pad packed sequence.

This method unpacks the packed sequqnce and pad it, the inverse operation of pack_padded_sequence().

$T_i$ is the length of the $i$-th Variable in the sequences. $B$ is the batch size equal to the length of the sequences. $T$ is the max of $T_i$ for all $i$. $*$ is the remaining dimensions including none.

Note

This function must be used the dynamic computation mode.

Example:

import numpy as np
import nnabla as nn
import nnabla.functions as F
import nnabla.utils.rnn as rnn_utils

nn.set_auto_forward(True)

l2v = lambda ldata: nn.Variable.from_numpy_array(np.asarray(ldata))
a = l2v([3, 3])
b = l2v([2, 2, 2])
c = l2v([2, 2, 2])
d = l2v([1, 1, 1, 1])
e = l2v([3, 3])
sequences = [a, b, c, d, e]

packed_sequence = rnn_utils.pack_sequence(sequences, enforce_sorted=False)
print(packed_sequence.data.d)
print(packed_sequence.batch_sizes.d)

padded_sequence, lengths = rnn_utils.pad_packed_sequence(packed_sequence)
print(padded_sequence.d)
print(lengths.d)

Parameters

sequence (PackedSequence) – PackedSequence.
batch_first (bool) – If False, output is of ($T$, $B$, $*$) shape, otherwise ($B$, $T$, $*$).
padding_value (float) – Padding value.
total_length (int) – If not None, the outputs are padded up to the total_length. If the total_length is less than the max length in the sequences, the error is thrown. This is normally used in the distributed training to align with the longest sequence in a distributed system.

Returns

nnabla.Variable of ($T$, $B$, $*$) or ($B$, $T$, $*$) shape

Misc¶

Python function profiler utilities¶

nnabla.utils.function_profile.profile(fn=None, condition=None, profile_class=<class 'cProfile.Profile'>, print_freq=0, sort_keys=None, print_restrictions=None)[source]¶

Decorating a function that is profiled with a Python profiler such as cProfile.Profile.

Note: function doesn’t refer to Function. A Python function.

Parameters

fn (function) – A function that is profiled. If None is specified (default), it returns a new decorator function. It is used when you want to specify optional arguments of this decorator function.
condition (function) – A function object which takes the same inputs with the decorated function, and returns a boolean value. The decorated function is profiled only when the condition function returns True. By default, it returns always True, hence profiling is performed everytime the decorated function is called.
profile_class (class) – A profiler class such as cProfile.Profile and Profile.Profile. The default value is cProfile.Profile.
print_freq (int) – The profiling result is printed at function calls with an interval specified by print_freq. If 0 is specified (default), the profiling result is only printed at the end of the Python process unless decorated_func.profiler.print_stats() is called manually.
sort_keys (iterable) – A list or tuple of string, which is passed to pstats.Stats.sort_stats() as arguments. The default is ('cumulative', 'time', 'calls').
print_restriction (iterable) – A list or tuple which is passed to pstats.Stats.print_stats() as arguments. The default value is (40,), which results in only 40 functions inside the decorated function are printed in the profiling result.

Returns: function

A decorated function. If fn is None, a new decorator function with optional arguments specified.

Example

By decorating a function as following, the profling result is printed at the end of the Python process.

from nnabla.utils import function_profile

@function_profile.profile
def foo(a, b, c=None, d=None):
    ...

If you want to manually print the profiling result so far, use FunctionProfile.print_stats() of the FunctionProfile object attached to the decorated function as profiler attribute.

foo.profiler.print_stats()

If you want to profile the function only when a specific argument is passed to, use the condition argument as following.

def profile_only_if_c_is_not_none(a, b, c=None, d=None):
    return c is not None

@function_profile.profile(condition=profile_only_if_c_is_not_none)
def foo(a, b, c=None, d=None):
    ...

class nnabla.utils.function_profile.FunctionProfile(fn, condition=None, profile_class=<class 'cProfile.Profile'>, print_freq=0, sort_keys=None, print_restrictions=None)[source]¶

Function profiler object.

This is usually not directly used by users. It’s created via profile(), and attached to a decorated function object as an attribute profiler. See profile function for details.

print_stats(reset=True)[source]¶

Manually print profiling result.

Parameters: reset (bool) – If False is specified, the profiling statistics so far is maintained. If True (default), reset_stats is called to reset the profiling statistics.

reset_stats()[source]¶: Manually reset the profiling statistics collected so far.

Extensions¶

NNabla offers easy extensibility for developers to add new device extensions. The NNabla Python package officially supports the cpu, cuda and cudnn extension, cuda and cudnn extension can dramatically accelerate computation by leveraging NVIDIA CUDA GPUs with cuDNN computation primitives.

You can manually import extensions by:

import nnabla_ext.cudnn

See :ref:`python-package-installation` to install the CUDA extension.

Utilities for extension¶

Utilities for NNabla extensions.

nnabla.ext_utils.list_extensions()[source]¶

List up available extensions.

Note

It may not work on some platforms/environments since it depends on the directory structure of the namespace packages.

Returns: list of str: Names of available extensions.

nnabla.ext_utils.import_extension_module(ext_name)[source]¶

Import an extension module by name.

The extension modules are installed under the nnabla_ext package as namespace packages. All extension modules provide a unified set of APIs.

Parameters: ext_name (str) – Extension name. e.g. ‘cpu’, ‘cuda’, ‘cudnn’ etc.

Returns: module: An Python module of a particular NNabla extension.

Example

ext = import_extension_module('cudnn')
available_devices = ext.get_devices()
print(available_devices)
ext.device_synchronize(available_devices[0])
ext.clear_memory_cache()

nnabla.ext_utils.get_extension_context(ext_name, **kw)[source]¶

Get the context of the specified extension.

All extension’s module must provide context(**kw) function.

Parameters

ext_name (str) – Module path relative to nnabla_ext.
kw (dict) – Additional keyword arguments for context function in a extension module.

Returns

The current extension context.

Return type

nnabla.Context

Example

ctx = get_extension_context('cudnn', device_id='0', type_config='half')
nn.set_default_context(ctx)

APIs of extension modules¶

All extension modules must have the following functions.

nnabla.ext_utils.context(*kw)¶: Returns a default context descriptor of the extension module. This method takes optional arguments depending on the extension. For example, in the cudnn extension, it takes the device_id as an int to specify the GPU where computation runs on.

nnabla.ext_utils.device_synchronize(*kw)¶: This method is used to synchronize the device execution stream with respect to the host thread. For example, in CUDA, the kernel execution is enqueued into a stream, and is executed asynchronously w.r.t. the host thread. This function is only valid in devices that use such features. In the CPU implementation, this method is implemented as dummy function, and therefore calls to this function are ignored. The function in the cudnn extension takes the device_id as an optional argument, which specifies the device you want to synchronize with.

Pretrained Models¶

The nnabla.models package provides APIs that allow users to execute state-of-the-art pre-trained models for inference and training in few lines of code.

ImageNet Models¶

This subpackage provides a variety of pre-trained state-of-the-art models which is trained on ImageNet dataset.

The pre-trained models can be used for both inference and training as following:

# Create ResNet-50 for inference
import nnabla as nn
import nnabla.functions as F
import nnabla.parametric_functions as PF
import numpy as np
from nnabla.models.imagenet import ResNet50
model = ResNet50()
batch_size = 1
# model.input_shape returns (3, 224, 224) when ResNet-50
x = nn.Variable((batch_size,) + model.input_shape)
y = model(x, training=False)

# Execute inference
# Load input image as uint8 array with shape of (3, 224, 224)
from nnabla.utils.image_utils import imread
img = imread('example.jpg', size=model.input_shape[1:], channel_first=True)
x.d[0] = img
y.forward()
predicted_label = np.argmax(y.d[0])
print('Predicted label:', model.category_names[predicted_label])


# Create ResNet-50 for fine-tuning
batch_size=32
x = nn.Variable((batch_size,) + model.input_shape)
# * By training=True, it sets batch normalization mode for training
#   and gives trainable attributes to parameters.
# * By use_up_to='pool', it creats a network up to the output of
#   the final global average pooling.
pool = model(x, training=True, use_up_to='pool')

# Add a classification layer for another 10 category dataset
# and loss function
num_classes = 10
y = PF.affine(pool, num_classes, name='classifier10')
t = nn.Variable((batch_size, 1))
loss = F.sum(F.softmax_cross_entropy(y, t))

# Training...

Available models are summarized in the following table. Error rates are calculated using single center crop.

Available ImageNet models¶
Name	Class	Top-1 error	Top-5 error	Trained by/with
ResNet-18	ResNet18	30.28	10.90	Neural Network Console
ResNet-34	ResNet34	26.72	8.89	Neural Network Console
ResNet-50	ResNet50	24.59	7.48	Neural Network Console
ResNet-101	ResNet101	23.81	7.01	Neural Network Console
ResNet-152	ResNet152	23.48	7.09	Neural Network Console
MobileNet	MobileNet	29.51	10.34	Neural Network Console
MobileNetV2	MobileNetV2	29.94	10.82	Neural Network Console
SENet-154	SENet	22.04	6.29	Neural Network Console
SqueezeNet v1.0	SqueezeNetV10	42.71	20.12	Neural Network Console
SqueezeNet v1.1	SqueezeNetV11	41.23	19.18	Neural Network Console
VGG-11	VGG11	30.85	11.38	Neural Network Console
VGG-13	VGG13	29.51	10.46	Neural Network Console
VGG-16	VGG16	29.03	10.07	Neural Network Console
NIN	NIN	42.91	20.66	Neural Network Console
DenseNet-161	DenseNet	23.82	7.02	Neural Network Console
InceptionV3	InceptionV3	21.82	5.88	Neural Network Console
Xception	Xception	23.59	6.91	Neural Network Console
GoogLeNet	GoogLeNet	31.22	11.34	Neural Network Console
ResNeXt-50	ResNeXt50	22.95	6.73	Neural Network Console
ResNeXt-101	ResNeXt101	22.80	6.74	Neural Network Console
ShuffleNet	ShuffleNet10	34.15	13.85	Neural Network Console
ShuffleNet-0.5x	ShuffleNet05	41.99	19.64	Neural Network Console
ShuffleNet-2.0x	ShuffleNet20	30.34	11.12	Neural Network Console

Common interfaces¶

class nnabla.models.imagenet.base.ImageNetBase[source]¶

Most of ImageNet pretrained models are inherited from this class so that it provides some common interfaces.

__call__(input_var=None, use_from=None, use_up_to='classifier', training=False, force_global_pooling=False, check_global_pooling=True, returns_net=False, verbose=0)[source]¶

Create a network (computation graph) from a loaded model.

Parameters

input_var (Variable, optional) – If given, input variable is replaced with the given variable and a network is constructed on top of the variable. Otherwise, a variable with batch size as 1 and a default shape from self.input_shape.
use_up_to (str) – Network is constructed up to a variable specified by a string. A list of string-variable correspondences in a model is described in documentation for each model class.
training (bool) – This option enables additional training (fine-tuning, transfer learning etc.) for the constructed network. If True, the batch_stat option in batch normalization is turned True, and need_grad attribute in trainable variables (conv weights and gamma and beta of bn etc.) is turned True. The default is False.
force_global_pooling (bool) – Regardless the input image size, the final average pooling before classification layer will be automatically transformed to a global average pooling. The default is False.
check_global_pooling (bool) – If True, and if the stride configuration of the final average pooling is not for global pooling, it raises an exception. The default is True. Use False when user want to do the pooling with the trained stride (7, 7) regardless the input spatial size.
returns_net (bool) – When True, it returns a NnpNetwork object. Otherwise, It only returns the last variable of the constructed network. The default is False.
verbose (bool, or int) – Verbose level. With 0, it says nothing during network construction.

property category_names¶: Returns category names of 1000 ImageNet classes.

property input_shape¶: Should returns default image size (channel, height, width) as a tuple.

List of models¶

class nnabla.models.imagenet.ResNet18[source]¶: An alias of ResNet (18).

class nnabla.models.imagenet.ResNet34[source]¶: An alias of ResNet (34).

class nnabla.models.imagenet.ResNet50[source]¶: An alias of ResNet (50).

class nnabla.models.imagenet.ResNet101[source]¶: An alias of ResNet (101).

class nnabla.models.imagenet.ResNet152[source]¶: An alias of ResNet (152).

class nnabla.models.imagenet.ResNet(num_layers=18)[source]¶

ResNet architectures for 18, 34, 50, 101, and 152 of number of layers.

Parameters: num_layers (int) – Number of layers chosen from 18, 34, 50, 101, and 152.

The following is a list of string that can be specified to use_up_to option in __call__ method;

'classifier' (default): The output of the final affine layer for classification.
'pool': The output of the final global average pooling.
'lastconv': The input of the final global average pooling without ReLU activation.
'lastconv+relu': Network up to 'lastconv' followed by ReLU activation.

References

He et al, Deep Residual Learning for Image Recognition.

class nnabla.models.imagenet.MobileNet[source]¶

MobileNet architecture.

The following is a list of string that can be specified to use_up_to option in __call__ method;

'classifier' (default): The output of the final affine layer for classification.
'pool': The output of the final global average pooling.
'lastconv': The input of the final global average pooling without ReLU activation.
'lastconv+relu': Network up to 'lastconv' followed by ReLU activation.

References

Howard et al., MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications.

class nnabla.models.imagenet.MobileNetV2[source]¶

MobileNetV2 architecture.

The following is a list of string that can be specified to use_up_to option in __call__ method;

'classifier' (default): The output of the final affine layer for classification.
'pool': The output of the final global average pooling.
'lastconv': The input of the final global average pooling without ReLU activation.
'lastconv+relu': Network up to 'lastconv' followed by ReLU activation.

References

Sandelr et al., MobileNetV2: Inverted Residuals and Linear Bottlenecks.

class nnabla.models.imagenet.SENet[source]¶

SENet-154 model which integrates SE blocks with a modified ResNeXt architecture.

The following is a list of string that can be specified to use_up_to option in __call__ method;

'classifier' (default): The output of the final affine layer for classification.
'pool': The output of the final global average pooling.
'lastconv': The input of the final global average pooling without ReLU activation.
'lastconv+relu': Network up to 'lastconv' followed by ReLU activation.

References

Hu et al., Squeeze-and-Excitation Networks.

class nnabla.models.imagenet.SqueezeNetV10[source]¶: SquezeNetV10 An alias of SqueezeNet ('v1.0').

class nnabla.models.imagenet.SqueezeNetV11[source]¶: SquezeNetV11 An alias of SqueezeNet ('v1.1').

class nnabla.models.imagenet.SqueezeNet(version='v1.1')[source]¶

SqueezeNet model for architecture-v1.0 and v1.1 .

Parameters: version (str) – Version chosen from ‘v1.0’ and ‘v1.1’.

The following is a list of string that can be specified to use_up_to option in __call__ method;

'classifier' (default): The output of the final affine layer for classification.
'pool': The output of the final global average pooling.
'lastconv': The input of the final global average pooling without ReLU activation.
'lastconv+relu': Network up to 'lastconv' followed by ReLU activation.

References

class nnabla.models.imagenet.VGG11[source]¶: An alias of VGG (11).

class nnabla.models.imagenet.VGG13[source]¶: An alias of VGG (13).

class nnabla.models.imagenet.VGG16[source]¶: An alias of VGG (16).

class nnabla.models.imagenet.VGG(num_layers=11)[source]¶

VGG architectures for 11, 13, 16 layers.

Parameters: num_layers (int) – Number of layers chosen from 11, 13, 16.

The following is a list of string that can be specified to use_up_to option in __call__ method;

'classifier' (default): The output of the final affine layer for classification.
'pool': The output of the final global average pooling.
'lastconv': The input of the final global average pooling without ReLU activation.
'lastconv+relu': Network up to 'lastconv' followed by ReLU activation.
'lastfeature': Network up to one layer before 'classifier', but without activation.

References

Simonyan and Zisserman, Very Deep Convolutional Networks for Large-Scale Image Recognition.

class nnabla.models.imagenet.NIN[source]¶

NIN(Network In Network) architecture.

The following is a list of string that can be specified to use_up_to option in __call__ method;

'classifier' (default): The output of the final affine layer for classification.
'pool': The output of the final global average pooling.
'lastconv': The input of the final global average pooling without ReLU activation.
'lastconv+relu': Network up to 'lastconv' followed by ReLU activation.

References

Lin et al., Network In Network.

class nnabla.models.imagenet.DenseNet[source]¶

The following is a list of string that can be specified to use_up_to option in __call__ method;

'classifier' (default): The output of the final affine layer for classification.
'pool': The output of the final global average pooling.
'lastconv': The output from last denseblock.
'lastconv+relu': Network up to 'lastconv' followed by ReLU activation.

References

Huang et al., Densely Connected Convolutional Networks.

class nnabla.models.imagenet.InceptionV3[source]¶

InceptionV3 architecture.

The following is a list of string that can be specified to use_up_to option in __call__ method;

'classifier' (default): The output of the final affine layer for classification.
'pool': The output of the final global average pooling.
'prepool': The input of the final global average pooling, i.e. the output of the final inception block.

References

Szegedy et al., Rethinking the Inception Architecture for Computer Vision.

class nnabla.models.imagenet.Xception[source]¶

Xception model.

The following is a list of string that can be specified to use_up_to option in __call__ method;

'classifier' (default): The output of the final affine layer for classification.
'pool': The output of the final global average pooling.
'lastconv': The input of the final global average pooling without ReLU activation.
'lastconv+relu': Network up to 'lastconv' followed by ReLU activation.

References

Francois Chollet, Xception: Deep Learning with Depthwise Separable Convolutions.

class nnabla.models.imagenet.GoogLeNet[source]¶

GoogLeNet model.

The following is a list of string that can be specified to use_up_to option in __call__ method;

'classifier' (default): The output of the final affine layer for classification.
'pool': The output of the final global average pooling.
'prepool': The input of the final global average pooling, i.e. the output of the final inception block.

References

Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, Andrew Rabinovich: Going Deeper with Convolutions.

class nnabla.models.imagenet.ResNeXt50[source]¶: An alias of ResNeXt (50).

class nnabla.models.imagenet.ResNeXt101[source]¶: An alias of ResNeXt (101).

class nnabla.models.imagenet.ResNeXt(num_layers=50)[source]¶

ResNeXt architectures for 50 and 101 of number of layers.

Parameters: num_layers (int) – Number of layers chosen from 50 and 101.

The following is a list of string that can be specified to use_up_to option in __call__ method;

'classifier' (default): The output of the final affine layer for classification.
'pool': The output of the final global average pooling.
'lastconv': The input of the final global average pooling without ReLU activation.
'lastconv+relu': Network up to 'lastconv' followed by ReLU activation.

References

class nnabla.models.imagenet.ShuffleNet10[source]¶: An alias of ShuffleNet (10).

class nnabla.models.imagenet.ShuffleNet05[source]¶: An alias of ShuffleNet (5).

class nnabla.models.imagenet.ShuffleNet20[source]¶: An alias of ShuffleNet (20).

class nnabla.models.imagenet.ShuffleNet(scaling_factor=10)[source]¶

Model for architecture ShuffleNet, ShuffleNet-0.5x and ShufffleNet-2.0x.

Parameters: Factor (Scaling) – To customize the network to a desired complexity, we can simply apply a scale factor on the number of channnels. This can be chosen from ‘10’, ‘5’ and ‘20’.

The following is a list of string that can be specified to use_up_to option in __call__ method;

'classifier' (default): The output of the final affine layer for classification.
'pool': The output of the final global average pooling.
'lastconv': The input of the final global average pooling without ReLU activation.
'lastconv+relu': Network up to 'lastconv' followed by ReLU activation.

References

Object Detection Models¶

This subpackage provides a pre-trained state-of-the-art models for the purpose of object detection which is trained on ImageNet dataset and fine-tuned on Pascal VOC and MS COCO dataset.

The pre-trained models can be used for both inference and training as following:

# Import required modules
import nnabla as nn
from nnabla.models.object_detection import YoloV2
from nnabla.models.object_detection.utils import (
    LetterBoxTransform,
    draw_bounding_boxes)
from nnabla.utils.image_utils import imread, imsave
import numpy as np

# Set device
from nnabla.ext_utils import get_extension_context
nn.set_default_context(get_extension_context('cudnn', device_id='0'))

# Load and create a detection model
h, w = 608, 608
yolov2 = YoloV2('coco')
x = nn.Variable((1, 3, h, w))
y = yolov2(x)

# Load an image and scale it to fit inside the (h, w) frame
img_orig = imread('dog.jpg')
lbt = LetterBoxTransform(img_orig, h, w)

# Execute detection
x.d = lbt.image.transpose(2, 0, 1)[None]
y.forward(clear_buffer=True)

# Draw bounding boxes to the original image
bboxes = lbt.inverse_coordinate_transform(y.d[0])
img_draw = draw_bounding_boxes(
    img_orig, bboxes, yolov2.get_category_names())
imsave("detected.jpg", img_draw)

Available models trained on COCO dataset¶
Name	Class	mAP	Training framework	Notes
YOLO v2	YoloV2	44.12	Darknet	Weights converted from author’s model

Available models trained on VOC dataset¶
Name	Class	mAP	Training framework	Notes
YOLO v2	YoloV2	76.00	Darknet	Weights converted from author’s model

Common interfaces¶

class nnabla.models.object_detection.base.ObjectDetection[source]¶

__call__(input_var=None, use_from=None, use_up_to='detection', training=False, returns_net=False, verbose=0)[source]¶

Create a network (computation graph) from a loaded model.

Parameters

input_var (Variable, optional) – If given, input variable is replaced with the given variable and a network is constructed on top of the variable. Otherwise, a variable with batch size as 1 and a default shape from self.input_shape.
use_up_to (str) – Network is constructed up to a variable specified by a string. A list of string-variable correspondences in a model is described in documentation for each model class.
training (bool) – This option enables additional training (fine-tuning, transfer learning etc.) for the constructed network. If True, the batch_stat option in batch normalization is turned True, and need_grad attribute in trainable variables (conv weights and gamma and beta of bn etc.) is turned True. The default is False.
returns_net (bool) – When True, it returns a NnpNetwork object. Otherwise, It only returns the last variable of the constructed network. The default is False.
verbose (bool, or int) – Verbose level. With 0, it says nothing during network construction.

property input_shape¶: Should returns default image size (channel, height, width) as a tuple.

class nnabla.models.object_detection.utils.LetterBoxTransform(image, height, width)[source]¶

Create an object holding a new letterboxed image as image attribute.

Letterboxing is defined as scaling the input image to fit inside the desired output image frame (letterbox) while preserving the aspect ratio of the original image. The pixels that are not filled with the original image pixels become 127.

The created object also provides a functionality to convert bounding box coordinates back to the original image frame.

Parameters

image (numpy.ndarray) – An uint8 3-channel image
height (int) – Letterbox height
width (int) – Letterbox width

inverse_coordinate_transform(coords)[source]¶

Convert the bounding boxes back to the original image frame.

Parameters: coords (numpy.ndarray) – N x M array where M >= 4 and first 4 elements of M are x, y (center coordinates of bounding box), w and h (bouding box width and height).

nnabla.models.object_detection.utils.draw_bounding_boxes(img, bboxes, names, colors=None, thresh=0.5)[source]¶

The transformed cordinates are further used to draw bounding boxes for the detected objects.

Parameters

img (numpy.ndarray) – Input image
bboxes (numpy.ndarray) – Transformed bounding box coorinates from the model.
names (list of str) – Name of categories in the dataset
colors (list of tuple of 3 ints) – Colors for bunding boxes
thresh (float) – Threshold of bounding boxes.

List of models¶

class nnabla.models.object_detection.YoloV2(dataset='voc')[source]¶

The following is a list of string that can be specified to use_up_to option in __call__ method;

'detection' (default): The output from the last convolution (detection layer) after post-processing.
'convdetect': The output of last convolution without post-processing.
'lastconv': Network till the convolution layer+relu which comes before detection convolution layer.

References

Joseph Redmon et al., YOLO9000: Better, Faster, Stronger.

Semantic Segmentation Models¶

This subpackage provides a pre-trained state-of-the-art model for the purpose of semantic segmentation (DeepLabv3+, Xception-65 as backbone) which is trained on ImageNet dataset and fine-tuned on Pascal VOC and MS COCO dataset.

The pre-trained models can be used for inference as following:

#Import reauired modules
import numpy as np
import nnabla as nn
from nnabla.utils.image_utils import imread
from nnabla.models.semantic_segmentation import DeepLabV3plus
from nnabla.models.semantic_segmentation.utils import ProcessImage

target_h = 513
target_w = 513
# Get context
from nnabla.ext_utils import get_extension_context
nn.set_default_context(get_extension_context('cudnn', device_id='0'))

# Build a Deeplab v3+ network
image = imread("./test.jpg")
x = nn.Variable((1, 3, target_h, target_w), need_grad=False)
deeplabv3 = DeepLabV3plus('voc-coco',output_stride=8)
y = deeplabv3(x)

# preprocess image
processed_image = ProcessImage(image, target_h, target_w)
input_array = processed_image.pre_process()

# Compute inference
x.d = input_array
y.forward(clear_buffer=True)
print ("done")
output = np.argmax(y.d, axis=1)

# Apply post processing
post_processed = processed_image.post_process(output[0])

#Display predicted class names
predicted_classes = np.unique(post_processed).astype(int)
for i in range(predicted_classes.shape[0]):
    print('Classes Segmented: ', deeplabv3.category_names[predicted_classes[i]])

# save inference result
processed_image.save_segmentation_image("./output.png")

Available models trained on voc dataset¶
Name	Class	Output stride	mIOU	Training framework	Notes
DeepLabv3+	DeepLabv3+	8	81.48	Nnabla	Backbone (Xception-65) weights converted from author’s model and used for finetuning
DeepLabv3+	DeepLabv3+	16	82.20	Nnabla	Backbone (Xception-65) weights converted from author’s model and used for finetuning

Available models trained on Voc and coco dataset¶
Name	Class	Output stride	mIOU	Training framework	Notes
DeepLabv3+	DeepLabv3+	8	82.20	Tensorflow	Weights converted from author’s model
DeepLabv3+	DeepLabv3+	16	83.58	Tensorflow	Weights converted from author’s model

Common interfaces¶

class nnabla.models.semantic_segmentation.base.SemanticSegmentation[source]¶

Semantic Segmentation pretrained models are inherited from this class so that it provides some common interfaces.

__call__(input_var=None, use_from=None, use_up_to='segmentation', training=False, returns_net=False, verbose=0)[source]¶

Create a network (computation graph) from a loaded model.

Parameters

input_var (Variable, optional) – If given, input variable is replaced with the given variable and a network is constructed on top of the variable. Otherwise, a variable with batch size as 1 and a default shape from self.input_shape.
use_up_to (str) – Network is constructed up to a variable specified by a string. A list of string-variable correspondences in a model is described in documentation for each model class.
training (bool) – This option enables additional training (fine-tuning, transfer learning etc.) for the constructed network. If True, the batch_stat option in batch normalization is turned True, and need_grad attribute in trainable variables (conv weights and gamma and beta of bn etc.) is turned True. The default is False.
returns_net (bool) – When True, it returns a NnpNetwork object. Otherwise, It only returns the last variable of the constructed network. The default is False.
verbose (bool, or int) – Verbose level. With 0, it says nothing during network construction.

property input_shape¶: Should return default image size (channel, height, width) as a tuple.

List of models¶

class nnabla.models.semantic_segmentation.DeepLabV3plus(dataset='voc', output_stride=16)[source]¶

DeepLabV3+.

Parameters

dataset (str) – Specify a training dataset name from ‘voc’ or ‘coco’.
output_stride (int) – DeepLabV3 uses atrous (a.k.a. dilated) convolutions. The atrous rate depends on the output stride. the output stride has to be selected from 8 or 16. Default is 8. If the output_stride is 8 the atrous rate will be [12,24,36] and if the output_stride is 16 the atrous rate will be [6,12,18].

The following is a list of string that can be specified to use_up_to option in __call__ method;

'segmentation' (default): The output of the final layer.
'lastconv': The output from last Convolution.
'lastconv+relu': Network up to 'lastconv' followed by ReLU activation.

References

Chen et al., Rethinking Atrous Convolution for Semantic Image Segmentation.

Out-of-core execution¶

The nnabla.lms package provides APIs that allow users to execute large-scale networks than allotted GPU memory by utilizing out-of-core algorithm. Out-of-core algorithm, or external memory algorithm, is an algorithm that enables processing data that are too large to fit into a main memory at once.

SwapInOutScheduler¶

class nnabla.lms.SwapInOutScheduler¶

Interface class for out-of-core execution / training.

This API enables training neural networks whose size is larger than alloted GPU memory. See https://arxiv.org/abs/2010.14109 for more detail of shcheduling strategy.

Note

cuda_init.prefer_cuda_virtual_array() used in following example can be used under cuda >= 10.2 and cudnn >= 8. We utilize virtual memory management supported from cuda 10.2. Additionally, when we tested virtual memory management with cuda >= 10.2 and cudnn < 8, we found the computation results of some cuddn functions are inaccurate. So, when your environment has cuda < 10.2 or cudnn < 8, the virtual memory allocator in nnabla will not be built and you can’t use it. If you would like to use SwapInOutScheduler to the fullest extent, please install cuda >= 10.2 and cudnn >= 8 and reinstall the corresponding nnabla-ext-cuda package.

Example:

from nnabla.lms import SwapInOutScheduler

# Change memory allocator which is preferable for SwapInOutScheduler.
from nnabla_ext.cuda.init as cuda_init
cuda_init.prefer_cpu_pinned_array()  # To accelerate memory transfer, using pinned memory for cpu memory will be preferable.

# Only for cuda >= 10.2 and cudnn >= 8. This setting is the best for SwapInOutScheduler.
cuda_init.prefer_cuda_virtual_array()  # To reduce memory fragmentation due to cpu-gpu memory transfers, using virtual allocator for gpu memory will be preferable.

# create context for both host and device
from nnabla.ext_utils import get_extension_context
host_ctx = get_extension_context("cpu", device_id="", type_config="float") # device_id is dummy
device_ctx = get_extension_context("cudnn", device_id="0", type_config="float")

scheduler = SwapInOutScheduler(host_ctx, device_ctx, size=max_gpu_memory_size)

# Make sure to call `nn.set_default_context` after calling prefer_xxx_array() to activate a change of memory preference.
nn.set_default_context(device_ctx)

x = nn.Variable(...)
loss = build_network(x)

solver = S.Sgd(nn.get_parameters())

for i in range(iteration):
    # scheduling memory transfers for all tensors appearing under the context of scheduler.
    with scheduler:
        x.d = next_data()

        loss.forward(clear_no_need_grad=True)

        solver.zero_grad()
        loss.backward(clear_buffer=True)

        solver.update()

When you get Out-of-Memory (OOM) error under the SwapInOutScheduler, possibly there are 2 options to avoid this OOM.

Set small budget of GPU memory for scheduling.
Set small size for a physical memory chunk allocated by virtual memory allocator.

These are examplified as follows:

Example:

# 1. Set small budget of GPU memory for scheduling
# You can reduce the below ratio until you can execute your network.
memsize_for_scheduler = max_gpu_memory_size * 0.8
scheduler = SwapInOutScheduler(..., size=memsize_for_scheduler)

# 2. Set small size for a physical memory chunk allocated by virtual memory allocator
# In default, the chunk size is set as 20MB (20 << 20).
from nnabla_ext.cuda.init import set_cuda_virtual_memory_chunk_size
set_cuda_virtual_memory_chunk_size(2 << 20)  # Set 2MB, for example.

end_scheduling(self)¶

An interface to specify the end point for scheduling. A range between start_scheduling() and end_scheduling() is a target for a single scheduling.

Note that, using with statement of SwapInOutScheduler, end_scheduling() will be automatically called when exiting with statement. In general, avoid to use start_scheduling() and end_scheduling() and use with statement insted (with scheduler:, see an example above).

function_post_hook(self, func)¶

A callback executed as function_post_hook in forward and backward.

For all forward and backward wrapped by with statement of SwapInOutScheduler, this callback is automatically set. In general, avoid to set this manually and use with statement of SwapInOutScheduler.

function_pre_hook(self, func)¶

A callback executed as function_pre_hook in forward and backward.

For all forward and backward wrapped by with statement of SwapInOutScheduler, this callback is automatically set. In general, avoid to set this manually and use with statement of SwapInOutScheduler.

start_scheduling(self)¶

An interface to specify the starting point for scheduling. A range between start_scheduling() and end_scheduling() is a target for a single scheduling.

Note that, using with statement of SwapInOutScheduler, start_scheduling() will be automatically called when entering with statement. In general, avoid to use start_scheduling() and end_scheduling() and use with statement insted (with scheduler:, see an example above).

update_post_hook(self)¶

A callback executed as post_hook in all solver functions, e.g. solver.update, solver.weight_decay, solver.clip_grad_by_norm, and so on.

For all solver functions wrapped by with statement of SwapInOutScheduler, this callback is automatically set. In general, avoid to set this manually and use with statement of SwapInOutScheduler.

update_pre_hook(self)¶

A callback executed as pre_hook in all solver functions, e.g. solver.update, solver.weight_decay, solver.clip_grad_by_norm, and so on.

For all solver functions wrapped by with statement of SwapInOutScheduler, this callback is automatically set. In general, avoid to set this manually and use with statement of SwapInOutScheduler.

Modules¶

The nnabla.core.module.Module class represents a construction block of neural network.

Module¶

class nnabla.core.module.Module[source]¶

Module is a construction block of a computation model. Modules normally are constructed by lower level operators or other Modules, thus, nesting them in a tree-like structure may construct a more complex computation model.

Example

User may construct his model by derived from this class. Like:

import nnabla as nn
import nnabla.parametric_functions as PF
import nnabla.functions as F

class ConvBn(nn.Module):
    def __init__(self, outmaps, kernel=1, stride=1, act=None):
        self.outmaps = outmaps
        self.kernel = kernel
        self.stride = stride
        self.act = act

    def call(self, x, training=True):
        kernel = (self.kernel, self.kernel)
        pad = (self.kernel // 2, self.kernel // 2)
        stride = (self.stride, self.stride)
        h = PF.convolution(x, self.outmaps, kernel,
                           pad, stride, with_bias=False)
        h = PF.batch_normalization(h, batch_stat=training)
        if self.act is None:
            return h
        return self.act(h)


class ResUnit(nn.Module):
    def __init__(self, channels, stride=1, skip_by_conv=True):
        self.conv1 = ConvBn(channels // 4, 1, 1,
                            act=lambda x: F.relu(x, inplace=True))
        self.conv2 = ConvBn(channels // 4, 3, stride,
                            act=lambda x: F.relu(x, inplace=True))
        self.conv3 = ConvBn(channels, 1)
        self.skip_by_conv = skip_by_conv
        self.skip = ConvBn(channels, 1, stride)

    def call(self, x, training=True):
        h = self.conv1(x)
        h = self.conv2(h)
        h = self.conv3(h)

        s = x
        if self.skip_by_conv:
            s = self.skip(s)
        h = F.relu(F.add2(h, s, inplace=True), inplace=True)
        return h

To use this model, user may do like the following code:

res_unit = ResUnit(1024)
x = nn.Variable((64, 3, 32, 32))
x.d = np.random.random(x.shape)
y = res_unit(x)
y.forward(clear_buffer=True)

For working with dynamic network, user may do like the following:

res_unit = ResUnit(1024)
with nn.auto_forward():
    x = nn.Variable.from_numpy_array(np.random.random((1, 3, 32, 32)))
    y = res_unit(x)
    print(y.d)

For training, please set the parameters in module scope to optimizer. For example,

import nnabla.solvers as S

resnet = ResNet(18)
loss = resnet(x, y_)

solver = S.Sgd(lr=1e-3)
solver.set_parameters(resnet.get_parameters())

for _ in range(max_iter):
    x.d, y_.d = data.next()
    loss.forward()
    solver.zero_grad()
    loss.backward()
    solver.weight_decay(1e-5)
    solver.update()

In this example, we supposed ResNet is a derived class of Module, x, y_ is Variable, data is an instance of DataIterator, supposed it has already been attached to a DataSet.

Note:: From this example, we knew that model parameters are owned by model. Here it is variable resnet. These parameters will be referred when network is forward or backward or solve.update(). Hence, it is necessary to keep this module instance from being unexpectedly released, to ensure forward() or backward() can refer to these variables.

call(*args, **kwargs)[source]¶

User needs implement this function to construct their neural network. In the implementation, user may instantiate existing predefined Modules as its members, then use it. For example:

class AModule(nn.Module):
   def __init__(...):
      ...
      self.cnb = ConvBN(128) # A submodule is instantiated here.

   def call(...):
      h = self.cnb(x) # Using beforehand instantiated submodule.

or directly use parametric functions or functions:

class AModule(nn.Module):
    ...
    def call(...):
        ...
        h = PF.convolution(x, self.outmaps, ...)
        return h

Note

The following usage is currently not supported, it might be supported in future version:

class AModule(nn.Module):
   def __init__(...):
      ...
      self.cnb = [ConvBN(k) for k in [8, 16, 32]] # using an array to hold module instances.
      self.cnb = {f'name_{k}': ConvBN(k) for k in [8, 16, 32]} # using a dict to hold module instances.

Note

The following method to temporarily instantiate a module is also not allowed:

class AModule(nn.Module):
   def call(...):
      ...
      cnb = ConvBN(k) # Instantiate a temporary instance of Module is not allowed
      y = cnb(x)
      return y

Because when leave this scope, the parameters registered to cnb module will be released, which cause unexpected result.

get_parameters(recursive=True, grad_only=False, memo=None)[source]¶

Obtain an OrderedDict object of all parameters in current Module.

For example,

x = nn.Variable.from_numpy_array((np.random.random((8, 32, 256, 256))))
conv_bn = ConvBn(2)
y = conv_bn(x)

params = conv_bn.get_parameters()
for parameter_name, parameter_value in params.items():
    print("{}:{}".format(parameter_name, parameter_value.shape))

The output looks like:

conv/W:(2, 32, 1, 1)
bn/beta:(1, 2, 1, 1)
bn/gamma:(1, 2, 1, 1)
bn/mean:(1, 2, 1, 1)
bn/var:(1, 2, 1, 1)

Notice that the parameter name looks like a filepath, with splash separated nested scope name. In addition, module name default is used with a prefix @.

Parameters

recursive (bool, optional, default=True) – Whether obtain the parameters of current module’s submodules. Default is True.
grad_only (bool, optional, default=False) – Whether only obtain the grad. Default is False.

Returns

Flattened parameter’s name-value pairs of current Module.

Return type

OrderedDict

load_parameters(path, extension='.h5')[source]¶

Load parameters from a file into this module.

Parameters: path – str or file-like object

property parameter_scope¶

A module has its owned parameter_scope, which can avoid to pollute global parameter name space. User may obtain the parameter_scope of a module by this property.

Returns: The parameter scope of current Module.
Return type: OrderedDict

save_parameters(path, extension='.h5')[source]¶

Save parameters of this module to a file.

Parameters: path – str or file-like object

property training¶

Return a bool value which indicates whether current Module is in training state or not. A module may be set to training state or not, so that the computation graph created from this module can be changed according to this state. For example,

class ConvBN(Module):
    ...
    def call(self, x):
        h = self.conv1(x)
        if self.training:
            h = self.drop_out(h)
        h = F.relu(h, inplace=True)
        return h

conv_bn = ConvBN()
conv_bn.training = True
train_y = conv_bn(x)

conv_bn.training = False
eval_y = conv_bn(x)

Returns: which indicates whether current Module is in training state.
Return type: bool

zero_grad()[source]¶: Clear the gradient of the parameters in this module to 0.

Graph Definition¶

In NNabla, Graph Definition represents a kind of representation of a computation graph which is special designed for storage optimization and format converter.

A computation graph can be defined by the call of NNabla functions. Such computation graph has instantiated the input and output variables of the functions, inherent topology has been established for forward or backward computation. But for persistence of such graph, another abstract representation, so-called protobuf graph(or network), as abbreviation - proto graph is used normally. In this graph, only the information being necessary for persistence are kept, the information only used for computation will be dropped.

Graph Definition provides a group of functions and classes, tends to facilitate user creates protobuf network from their computation graph, and saving and restoring their neural network from a persistent protobuf network representation.

ProtoGraph¶

class nnabla.graph_def.ProtoGraph(networks=None, parameter_scope=None)¶

This class represents a group of proto networks. It normally corresponds to a .nnp file. In a .nnp file, there might be one or multiple networks, for example, there might be a network for doing directly inferring, another network with similar network structure, sharing same parameters, using for training. This class works as a container of proto networks, providing a group of functions for accessing proto network by its name. Especially, when there is only one network in it, also some short-cut functions are provided for directly operating with this network. For example,

import nnabla as nn

g = nn.graph_def.load("my_model.nnp") # Suppose there is only one network in this file.
x1 = nn.Variable(input_shape)
x1.d = ... # load data here.
y1 = g.networks['executor_net'](x1)  #<== (1)
y1.forward()
print(y1.d)

x2 = nn.Variable(input_shape)
x2.d = ... # load data here.
y2 = g(x2) #<== (2)
# y2 = g.default_graph()(x2) #<== (3)
y2.forward()
print(y2.d)

The computation graph y1 and y2 are exactly same. (2) and (3) are equal. If there are multiple networks in a graph, the first network being loaded acted as its default network. Please not use default_graph() when there are multiple networks in graph, since the default heavily depends on concrete implementation.

If you know the name of each network, you may access proto network in this graph by its member name. For example,

g = nn.graph_def.load("my_model.nnp") # Suppose there is only one network in this file.
x = nn.Variable(input_shape)
x.d = ... # load data here.
y = g.executor_net(x) # here, we knew there is a network named "executor_net" existed.
y.forward()
print(y.d)

as_proto(include_parameter=False, only_parameter=False, networks=None, variable_batch_size=True)¶

This function exports a protobuf data structure, which can be manipulated by google protobuf APIs.

Parameters

include_parameter (bool, optional, default=False) – Whether exports the parameters to protobuf data structure.
only_parameter (bool, optional, default=False) – Whether only exports the parameters to protobuf data structure.
networks (array of proto networks, optional, default=None) – User may provides their networks to export a protobuf data structure.
variable_batch_size (bool, optional, default=True) – Replace batch size of current network with an abstract placeholder, so that batch size can be replaced with other value in use time.

property current_context¶: Current backend context of this proto network.

default_graph()¶: This function returns default proto network in this graph. Which network is default graph depends on its loading sequence. Hence, it is safe to be used when there is only one network.

expand_loop_control()¶: This function expands loop control statements for all networks in this graph.

static from_proto(proto, batch_size=None, param_scope=None, rng=None)¶

This function create a proto graph object from a protobuf data structure.

Parameters

proto (protobuf object) – A protobuf data structure.
batch_size (int, optional, default=None) – The batch size will be applied to this graph. If it is None, it is pending to apply a the batch size value.
param_scope (OrderedDict, optional, default=None) – User may provide an owned parameter scope.
rng (np.random.RandomState, optional, default=None) – A random seed, which is used in parameter initialization.

get_parameters(grad_only=False)¶: Get parameters in current module name scope.

ProtoNetwork¶

class nnabla.graph_def.ProtoNetwork(owner, name=None, batch_size=None)¶

This class represents a protobuf network, which comes from a corresponding computation graph or restored from a saved protobuf network(e.g. .nnp file).

This class describes a neural network by the following members:

functions: An OrderedDict of name-value pairs, the value is ProtoFunction object.

variables: An OrderedDict of name-value pairs, the value is ProtoVariable object.

parameters: An OrderedDict of name-value pairs, the value is ProtoVariable object.

inputs: A string list, which contains the name of input variables of this network.

outputs: A string list, which contains the name of output variables of this network.

variables represents activations in networks, parameters mainly includes weights and all learnable parameters. functions represents functions in networks, the sequence of functions might not equal forward sequence. Please use forward_sequence to obtain exactly forward function sequence.

__call__(*args, **kwargs)¶

Generate a computation graph of this protonetwork.

Parameters

args (tuple of nn.Variables or None) –

The inputs of network, which can be different from the inputs of original computation graph as long as the network allows.

For example,
import nnabla as nn
import numpy as np

resnet = nn.graph_def.load("resnet.nnp")
x.d = np.random.random(input_shape)
y = resnet(x)
The variable y corresponding to a computation graph, user may perform forward like:
y.forward()
If user does not provide inputs for this function, because proto network has the memory of network inputs, this function will create corresponding nn.Variable objects as the inputs of this network. This input variables actually are placeholder of input, hence, users need to find these input variables and fill actual value to these placeholders, so that this computation graph is ready for forward or backward.

For example,
g = nn.graph_def.load("resnet.nnp")
y = g() # Not provide input variables
To feed training or evaluation data to this network, user needs to locate input variable, for example:
input = g.networks[network_name].variables[input_name].variable_instance
input.d = np.random.random(input_shape)

batch_size (int, optional, default=None):

If provided, batch_size will be applied for newly created computation graph. For example,
g = nn.graph_def.load("my_model.nnp")
y = g(batch_size=32)

In this sample, batch_size will be used to create a computation graph with specified batch size. Supposed x is the input of network, its original shape is (1, 3, 32, 32), then the actual computation graph will be (32, 3, 32, 32).

as_proto(**kwargs)¶

This function returns a protobuf data structure, which can be directly accessed by the functions in nnabla.utils.nnabla_pb2. Thus, it allows user further manipulates this protobuf representation, for example, performing format converting, or network structure optimization.

Parameters: variable_batch_size (bool, optional) – If true, the batch size of the network will be replaced with an abstract representation, so that it can be replaced with other value in restoring computation graph.
Returns: A protobuf object.
Return type: protobuf

execute_on_proto(execute)¶

This function performs a virtual forward, following the sequence from inputs to output. This function does not use recursive call to perform graph-travel, instead, a non-recursive algorithm is used to graph-travel. For each function, execute is called when meet a function, a ProtoFunction object is passed in for further operation with this function.

Parameters

execute (callable) –

A callback function (or callable object), which is called when each ProtoFunction is met in travelling graph.

execute should look like:

def execute(pf: ProtoFunction):
    # Do what you want to do with pf
    pass

Or:

class MyCallback:
    def __call__(pf: ProtoFunction):
        # Do what you want to do with pf
        pass

expand_loop_control()¶

This function expand loop control statement and generate a new proto network object without loop control statement. loop control statement cannot be created by python code, it can be only created by interactive neural network design tool. The following briefly introduce its specification:

As for variable,

In nntxt, if the variable includes a field repeat_id, it means that this variable is in surround with a loop control structure. A renaming rule is applied if expanding this network. The variable name will be added a postfix, like:

For old style, e.g.:

BatchNormalization_6/bn/mean --> BatchNormalization_6/bn/mean_RepeatStart[0]
                                                                 ^        ^  repeat_time
                                                              repeat_id[index]

original_name --> original_name + << _%repeat_id%[%repeat_time%],  for each in repeat_id >>

For new style, e.g.:

BatchNormalization_6{RepeatStart}/bn/mean --> BatchNormalization_6[0]/bn/mean_RepeatStart
                                                                   ^
                                                              repeat_time
original_name --> original_name + << [%repeat_time%],  for each in repeat_id >>

As for RepeatStart, RepeatEnd
The functions or variables nodes between these 2 layers will be repeated. Expanding will create times of functions and variables, and connected them each other.
As for RecurrentInput,
Axis of RecurrentParam points out which axis will be split-ed. And each branch will duplicated the functions and variables with this repeat_id. This layer works like a split function.
As for RecurrentOutput,
RecurrentOutput merge multiple branches into one output, looks like a stack function.
As for Delay
First time, the output is its input[1], after that, the output is its input[0]

forward_sequence()¶

This function creates an iteratable for iterating functions in network with the sequence of actually forward.

For example,

for pf in proto_network.forward_sequence():
    print(pf.name)

promote(callback)¶

User may manipulate a proto network by a callback, like NnpNetworkPass.

Parameters: callback (NnpNetworkPass,) – Currently, only NnpNetworkPass object is supported as a network promotion callback.

Developers may manipulate a proto network by a modifier, acts as a callback. nnabla.utils.nnp_graph.NnpNetworkPass is a kind of modifier. The following gives a simple example to illustrate this usage:

Example

from nnabla as nn
from nnabla.utils import nnp_graph

verbose = 1
callback = nnp_graph.NnpNetworkPass(verbose)

@callback.on_generate_function_by_name('Convolution')
def change_convolution_param(f):
    print('{}'.format(f.proto.convolution_param.pad.dim[:]))
    f.proto.convolution_param.pad.dim[:] = [1, 1]
    return f

g = nn.graph_def.load("my_model.nnp")
n = g.default_graph().promote(callback)
x = nn.Variable(input_shape)
y = n(x)
y.forward()

In this example, a callback is defined to change pad of a Convolution function, locating this target function by the name of function, here, only the function with the name 'Convolution' is located and operated.

save(filename, include_parameter=False, variable_batch_size=True)¶

This function saves current proto network to a file, which is specified by filename, normally, e.g. a .nnp file.

Parameters

filename (str) – string filename, its extension name is used to determine the file format. The extension name normally is .nnp.
include_parameter (bool, optional, default=False) – Whether saving parameters to protobuf tree.
variable_batch_size (bool, optional, default=True) – Whether replace current network’s batch size dimension with an abstract representation. If it is true, it is possible to use another batch size when this network is reused.

ProtoVariable¶

class nnabla.graph_def.ProtoVariable(shape, name=None, need_grad=False, var_type='Buffer')¶: This class represents a variable, so-called proto variable. Passing this variable to network definition, a proto network will be generated in a proto graph scope. If this procedure is done under a with statement as g, a proto network will be generated in g. Otherwise, a global graph scope is used, a proto network will be generated in global graph scope.

ProtoFunction¶

class nnabla.graph_def.ProtoFunction(func, f_type, args, name=None, owner=None)¶

This class represent a function that is used to define a proto network.

There are the following properties to describe a proto function:

name: The name of this function.
type: The type of this function, e.g. ReLU.
inputs: An array of string name, which represents the proto variables of inputs.
outputs: An array of string name, which represents the proto variables of outputs.

graph_call(**kwargs)¶: This function create function instance for generating computation graph.

load¶

nnabla.graph_def.load(filename, batch_size=None, exclude_parameter=False, parameter_only=False, extension='.nntxt', parameter_scope=None, rng=None)¶

Load network from files

Parameters

filename (str or list or file-like object) – Filename string ,list of filenames or file-like object.
batch_size (int) – The batch size expected to be set.
exclude_parameter (bool) – If True, only load model, not load parameters of this model. Default is False.
parameter_only (bool) – If True, only load model parameters. Default is False.
extension (str) – This parameter is needed when filename is a file-like object. Default is .nntxt.
parameter_scope (OrderedDict) – User may provide a user owned parameter scope. If this parameter is not provided, loaded parameters will be created in created proto_graph’s parameter_scope. This parameter_scope is default initialized with empty dictionary.
rng (random state) – User may specify random state, which cause parameters are initialized by determined random seed.

Returns

A ProtoGraph object, in which, there are one or multiple ProtoNetwork objects.

Return type

ProtoGraph

Example

The following example loads a model and generate the output variable through this model:

import nnabla as nn
import nnabla.functions as F
import nnabla.parametric_functions as PF

def fusion_net(x):
    def unit(i, prefix):
        c1 = PF.convolution(i, 4, (3, 3), pad=(1, 1), name=prefix + '-c1')
        c2 = PF.convolution(F.relu(c1), 4,
                            (3, 3), pad=(1, 1), name=prefix + '-c2')
        c = F.add2(c2, c1, inplace=True)
        return c
    c = unit(x, 'c1')
    c2 = unit(c, 'c2')
    y = PF.affine(c2, 5, name='fc')
    return y

x = nn.ProtoVariable((64, 3, 32, 32))
y = fusion_net(x)
g = nn.graph_def.get_default_graph()  # Get generated graph_def
g.save("fusion_net.nnp")
...
g = nn.graph_def.load("fusion_net.nnp")
x = nn.Variable((64, 3, 32, 32))
x.d = ... # user provided input data for this graph
y = g(x) # create computation graph by passing in nn.Variable()
y.forward() # calculate output by this graph
...

# You may use your special context(e.g. cuda context)
with context_scope(ctx):
   y = g(x) # create computation graph representation with specified backend context.
   y.forward() # forward using specified backend

save¶

nnabla.graph_def.save(filename, content, include_parameters=False, variable_batch_size=True, extension='.nnp')¶

Save network

Parameters

filename (str or file object) –
Filename to store information. The file extension is used to determine the saving file format. .nnp: (Recommended) Creating a zip archive with nntxt (network definition etc.) and h5 (parameters). .nntxt: Protobuf in text format. .protobuf: Protobuf in binary format (unsafe in terms of

backward compatibility).
content (list) – Currently only ProtoGraph or PhotoNetwork objects are supported.
include_parameters (bool) – Includes parameter into single file. This is ignored when the extension of filename is nnp.
variable_batch_size (bool) – Whether or not convert batch size of computation graph to a special value, so that user may use any other batch_size value when using it.

Example

The following example creates a two inputs and two outputs MLP, and save the network structure and the initialized parameters.

import nnabla as nn
import nnabla.functions as F
import nnabla.parametric_functions as PF

def mlp_module(x0, x1):
    h1_0 = PF.affine(x0, 100, name='affine1_0')
    h1_1 = PF.affine(x1, 100, name='affine1_0')
    h1 = F.tanh(h1_0 + h1_1)
    h2 = F.tanh(PF.affine(h1, 50, name='affine2'))
    y0 = PF.affine(h2, 10, name='affiney_0')
    y1 = PF.affine(h2, 10, name='affiney_1')
    return y0, y1

with nn.graph_def.graph() as g:
    x0 = nn.ProtoVariable((64, 100))
    x1 = nn.ProtoVariable((64, 100))
    y0, y1 = mlp_module(x0, x1)

nn.graph_def.save("mlp_net.nnp", [g])

Create Protobuf Representation from Computation Graph¶

create_graph_from_variable¶

nnabla.graph_def.create_graph_from_variable(name, variables, names=None, parameter_scope=None)¶

Create a Proto Graph from one or multiple outputs.

If developers have a computation graph, it means that they have a nn.Variable() object, it might be loss of a network or an output variable of an executor network, this variable inherently corresponds to a computation network. From these variables, we can create corresponding proto network by this function.

Parameters

name (str) – The name of generated proto_network.
variables (nn.Variables) – One or multiple variables, if multiple variables, this function adds a sink function to reduce these multiple outputs to one.
names (dict, optional, default=None) – A name to nn.Variable mapping table. This function default names all activation variables and parameters with internal naming rule. But sometimes, developers expects specially name some of variables so that these variable can be accessed conveniently. In generating proto network, when the variable occurs in this mapping table, the corresponding name of that variable will be used to name the variable in proto network.
parameter_scope (OrderedDict, optional, default=None) – Developers may provide a parameter scope, thus, when create proto networks, the name will be replaced if corresponding variable is found in specified parameter_scope, which make the name of weights or some parameters meaningful.

Example

import nnabla as nn

x = nn.Variable((1, 3, 32, 32))
y = my_model(x)
g = nn.graph_def.create_graph_from_variable("proto_network_name", y)
g.save("my_model.nnp")

get_default_graph¶

nnabla.graph_def.get_default_graph(*args, **kwargs)¶

This function obtain current default graph_def.

If user does not create their proto network in a with statement scope, proto network will default be created in a global scope. User may retrieve this proto graph by this function.

Example

import nnabla as nn
from nnabla.core.modules import ResUnit

resunit = ResUnit(16)
input = nn.ProtoVariable((64, 3, 32, 32))
y = resunit(input)
graph_def = nn.graph_def.get_graph_graph()

Note

If user does not ensure whether there is any previous existing proto graph remained in global graph scope. It is better to call reset_default_graph(). If user uses with statement like with nn.graph_def.graph() as g, this point is no need to care about.

Returns: A proto graph is returned
Return type: ProtoGraph

get_default_graph_by_variable¶

nnabla.graph_def.get_default_graph_by_variable(proto_variable)¶

This function obtain a specify network by its outputs.

User may retrieve one of the networks in default proto graph scope, if this network has the specified outputs. Let us image that there is a global proto graph, when user passed a ProtoVariable to model, during the procedure that create output variables, proto network is generated in this global proto graph. By this function, user may retrieve this generated proto network, saving it or do any other operations.

Note

This proto network will become invalid after reset_default_graph(). For example,

proto_variable_inputs = [nn.ProtoVariable(v.d.shape) for v in inputs]
outputs = module(*proto_variable_inputs)
net = nn.graph_def.get_default_graph_by_variable(outputs[0])
...
nn.graph_def.reset_default_graph()
y = net(x) # Cannot access net anymore, become invalid at this point

graph¶

nnabla.graph_def.graph(**kwargs)¶

This function is only used in with statement.

Parameters

name (str, optional, default=None) – User may specify a name for the generated proto network. This name is useful for saving to .nnp.
parameter_scope (OrderedDict, optional, default=None) – User may specify a parameter scope, thus, the parameters are created during creating model will be placed into this parameter scope.

For example,

import nnabla as nn

proto_variable_inputs = [nn.ProtoVariable(v.d.shape) for v in inputs]
with nn.graph_def.graph() as g:
    outputs = module(*proto_variable_inputs)

g.save("my_model.nnp")
Here, inputs is an array of input nn.Variables. Modules is a module object instantiated from a Module definition.

reset_default_graph¶

nnabla.graph_def.reset_default_graph()¶: This function clear all information in global graph scope.

Sequential¶

The nnabla.core.sequential.Sequential class represents a construction block of neural network.

Sequential¶

class nnabla.core.sequential.Sequential(*args, **kwargs)[source]¶

A sequential block. User may construct their network by a sequential block. Importantly, the component within sequential block must be an instance of nn.Module.

For intuitive understanding, some small examples as follows:

import nnabla as nn
import nnabla.parametric_functions as PF
import nnabla.functions as F

class ConvLayer(nn.Module):
    def __init__(self, outmaps, kernel, stride=1, pad=0):
        self.outmaps = outmaps
        self.kernel = (kernel, kernel)
        self.pad = (pad, pad)
        self.stride = (stride, stride)

    def call(self, x):
        x = PF.convolution(x, outmaps=self.outmaps, kernel=self.kernel, pad=self.pad, stride=self.stride)
        x = F.relu(x)
        return x

# Example of using Sequentional
layer = nn.Sequential(
    ConvLayer(48, kernel=1),
    ConvLayer(64, kernel=3, pad=1)
)

# Example of using Sequentional with a specify name for each layer
layer = nn.Sequential(
    ('conv1', ConvLayer(48, kernel=1)),
    ('conv2', ConvLayer(64, kernel=3, pad=1))
)

Experimental¶

Viewers¶

SimpleGraph¶

class nnabla.experimental.viewers.SimpleGraph(format='png', verbose=False, fname_color_map=None, vname_color_map=None)[source]¶

Simple Graph with GraphViz.

Example:

import nnabla as nn
import nnabla.functions as F
import nnabla.parametric_functions as PF

import nnabla.experimental.viewers as V

# Model definition
def network(image, test=False):
    h = image
    h /= 255.0
    h = PF.convolution(h, 16, kernel=(3, 3), pad=(1, 1), name="conv")
    h = PF.batch_normalization(h, name="bn", batch_stat=not test)
    h = F.relu(h)
    pred = PF.affine(h, 10, name='fc')
    return pred

# Model
image = nn.Variable([4, 3, 32, 32])
pred = network(image, test=False)

# Graph Viewer
graph = V.SimpleGraph(verbose=False)
graph.view(pred)
graph.save(pred, "sample_grpah")

If the parameters are module-scoped, for example, the pred comes from a module output, parameters should be obtained beforehand then passed to view():

Example:

import nnabla as nn
import nnabla.functions as F
from nnabla.core.modules import ConvBn

import nnabla.experimental.viewers as V

class TSTNetNormal(nn.Module):
    def __init__(self):
        self.conv_bn_1 = ConvBn(1)
        self.conv_bn_2 = ConvBn(1)

    def call(self, x1, x2):
        y1 = self.conv_bn_1(x1)
        y2 = self.conv_bn_2(x2)
        y = F.concatenate(y1, y2, axis=1)
        return y

tnd = TSTNetNormal()

v1 = nn.Variable((4, 3, 32, 32))
v2 = nn.Variable((4, 3, 32, 32))

ya = tnd(v1, v2)

graph = V.SimpleGraph(verbose=False)
graph.view(ya, params=tnd.get_parameters(grad_only=False))

create_graphviz_digraph(vleaf, params=None, format=None)[source]¶

Create a graphviz.Digraph object given the leaf variable of a computation graph.

One of nice things of getting Digraph directly is that the drawn graph can be displayed inline in a Jupyter notebook as described in Graphviz documentation.

Parameters

vleaf (nnabla.Variable) – End variable. All variables and functions which can be traversed from this variable are shown in the reuslt.
params (dict) – The parameters dictionary, it can be obtained by nn.get_parameters().
format (str) – Force overwrite format ('pdf', 'png', ...)) configuration.

Returns: graphviz.Digraph

save(vleaf, fpath, cleanup=False, format=None)[source]¶

Save the graph to a given file path.

Parameters

vleaf (nnabla.Variable) – End variable. All variables and functions which can be traversed from this variable are shown in the reuslt.
fpath (str) – The file path used to save.
cleanup (bool) – Clean up the source file after rendering. Default is False.
format (str) – Force overwrite format ('pdf', 'png', ...)) configuration.

view(vleaf, fpath=None, cleanup=True, format=None, params=None)[source]¶

View the graph.

Parameters

vleaf (nnabla.Variable) – End variable. All variables and functions which can be traversed from this variable are shown in the reuslt.
fpath (str) – The file path used to save.
cleanup (bool) – Clean up the source file after rendering. Default is True.
format (str) – Force overwrite format ('pdf', 'png', ...)) configuration.
params (dict) – Parameter dictionary, which can be obtained by get_parameters() function. Default is None. If params is None, global parameters are obtained.

Show Graph by Tensorboard¶

TBGraphWriter¶

Graph Converters¶

class nnabla.experimental.graph_converters.GraphConverter(modifiers=[])[source]¶

Convert a graph with the modifiers by traversing from output variables.

convert(o)[source]¶

Parameters: o (list of nnabla.Variable) – Output variables.

class nnabla.experimental.graph_converters.FunctionModifier[source]¶

Base class of modifiers.

The modify method is called for a function with inputs in a graph topological order when you call the GraphConverter(<modifiers>).convert(<root variable>) method.

finish_up()[source]¶

Finish the very time function modification.

Clean up the internal modifier states.

Parameters: None –
Returns: None

get_parameter_scope(v)[source]¶

Get the parameter name corresponding to v

Parameters: v (nnabla.Variable) – NNabla Variable Object.
Returns: Scope name
Return type: str

modify(f, inputs)[source]¶

Modify the function.

Implement this method in a sub class to modify a function.

Examples:

class ReLUToLeakyReLUModifier(FunctionModifier):

  def __init__(self):
    super(ReLUToLeakyReLUModifier, self).__init__()

  def modify(self, f, inputs):
    if f.info.type_name == 'ReLU':
      x = inputs[0]
      return F.leaky_relu(x)

This examples is a simple case since the network topological order does not change. In GraphConverter, we expect the modify method is called along the original network tolopogical order not the modified order. In such a complex case, see themodify method of BatchNormalizationFoldingModifierInner as a reference.

Parameters

f (nnabla.function.Function) – NNabla function object.
inputs (list of Variable) – New inputs to f. This may be modified one or the same as f.inputs.

Returns

Variable or list of Variable.

Function Modifiers¶

class nnabla.experimental.graph_converters.BatchNormalizationFoldingModifier(opposite=False, channel_last=False)[source]¶

Single Convolution -> BatchNormalization pass is folded into one Convolution.

If there is a Convolution -> BatchNormalization pass, fold the batch normalization parameters to the kernel and bias (if it exists) of the preceding convolution, then skip the batch normalization following the convolution.

Supported folding functions: Convolution, Deconvolution, Affine.

Examples:

pred = Model(...)

import nnabla.experimental.graph_converters as GC

modifiers = [GC.BatchNormalizationFoldingModifier()]
gc = GC.GraphConverter(modifiers)
pred = gc.convert(pred)

class nnabla.experimental.graph_converters.AddBiasModifier[source]¶

Add bias to Convolution in BatchNormalization folding case if it doesn’t have bias.

Supported folding functions: Convolution, Deconvolution, Affine.

Examples:

pred = Model(...)

import nnabla.experimental.graph_converters as GC

modifiers = [GC.AddBiasModifier()]
gc = GC.GraphConverter(modifiers)
pred = gc.convert(pred)

class nnabla.experimental.graph_converters.BatchNormalizationFoldingModifierInner(channel_last=False)[source]¶

Single Convolution -> BatchNormalization pass is folded into one Convolution.

If there is a Convolution -> BatchNormalization pass, fold the batch normalization parameters to the kernel and bias (if it exists) of the preceding convolution, then skip the batch normalization following the convolution.

Supported folding functions: Convolution, Deconvolution, Affine.

class nnabla.experimental.graph_converters.BatchNormalizationFoldingOppositeModifierInner(channel_last=False)[source]¶

Single BatchNormalization -> Convolution pass is folded into one Convolution.

If there is a BatchNormalization -> Convolution pass, fold the batch normalization parameters to the kernel and bias (if it exists) of the preceding convolution, then skip the batch normalization following the convolution.

Supported folding functions: Convolution, Deconvolution, Affine.

class nnabla.experimental.graph_converters.BatchNormalizationSelfFoldingModifier(name='bn-self-folding')[source]¶

The parameters of the batch normalization replaced simple scale and bias.

Parameters: name (str) – Prefix of the parameter scope.

Examples:

pred = Model(...)

import nnabla.experimental.graph_converters as GC

modifiers = [GC.BatchNormalizationSelfFoldingModifier()]
gc = GC.GraphConverter(modifiers)
pred = gc.convert(pred)

class nnabla.experimental.graph_converters.FusedBatchNormalizationModifier[source]¶

Block BatchNormalization -> Add2 -> Non-Linear pass is fused into one FusedBatchNormalization.

If there is a block BatchNormalization -> Add2 -> Non-Linear pass, remove all the block functions and replace the whole block to FusedBatchNormalization.

Examples:

pred = Model(...)

import nnabla.experimental.graph_converters as GC

modifiers = [GC.FusedBatchNormalizationModifier()]
gc = GC.GraphConverter(modifiers)
pred = gc.convert(pred)

class nnabla.experimental.graph_converters.UnfusedBatchNormalizationModifier[source]¶

Unfuse FusedBatchNormalization to BatchNormalization -> Add2 -> Non-Linear block.

If there is a FusedBatchNormalization pass, remove the fused batch normalization and replace it with the block BatchNormalization -> Add2 -> Non-Linear.

Supported Non-Linear functions: relu

Examples:

pred = Model(...)

import nnabla.experimental.graph_converters as GC

modifiers = [GC.UnfusedBatchNormalizationModifier()]
gc = GC.GraphConverter(modifiers)
pred = gc.convert(pred)

class nnabla.experimental.graph_converters.ChannelLastModifier(inputs, inputs_cl=None)[source]¶

Convert graph shape from Channel first (NCHW) to Channel last (NHWC) format.

Supported functions: Convolution, Deconvolution, BatchNormalization, MaxPooling, AveragePooling, SumPooling, Unpooling, Concatenate

Parameters

inputs (list of nn.Variable) – Original very begining inputs (NCHW) of a network.
inputs_cl (list of nn.Variable) – Channel last version of very begining inputs (NHWC) of a network. If this is not given, inputs_cl are generated internally and holded.

Examples:

pred = Model(...)

import nnabla.experimental.graph_converters as GC

modifiers = [GC.ChannelLastModifier(<inputs of pred>)]
gc = GC.GraphConverter(modifiers)
pred = gc.convert(pred)

class nnabla.experimental.graph_converters.ChannelFirstModifier(inputs, inputs_cf=None)[source]¶

Convert graph shape from Channel last (NHWC) to Channel first (NCHW) format.

Supported functions: Convolution, Deconvolution, BatchNormalization, MaxPooling, AveragePooling, SumPooling, Unpooling, Concatenate

Parameters

inputs (list of nn.Variable) – Original channel last version of very begining inputs (NHWC) of a network.
inputs_cf (list of nn.Variable) – Channel first version of very begining inputs (NCHW) of a network. If this is not given, inputs_cf are generated internally and holded.

Examples:

pred = Model(...)

import nnabla.experimental.graph_converters as GC

modifiers = [GC.ChannelFirstModifier(<inputs of pred>)]
gc = GC.GraphConverter(modifiers)
pred = gc.convert(pred)

class nnabla.experimental.graph_converters.RemoveFunctionModifier(rm_funcs=[])[source]¶

Remove specified function layer(s) from a graph.

A convenient converter when one or more functions in an existing graph needs to be removed. This converter remove specified function(s) without recreating a new graph from scratch.

Parameters: rm_funcs (list of str) – list of function name

Examples:

pred = Model(...)

import nnabla.experimental.graph_converters as GC

modifiers = [GC.RemoveFunctionModifier(rm_funcs=['BatchNormalization', 'MulScalar'])]
gc = GC.GraphConverter(modifiers)
pred = gc.convert(pred)

class nnabla.experimental.graph_converters.BatchNormBatchStatModifier[source]¶

Change batch_stat to False. Supported functions: BatchNormalization, FusedBatchNormalization, SyncBatchNormalization.

Examples:

pred = Model(...)

import nnabla.experimental.graph_converters as GC

modifiers = [GC.BatchNormBatchStatModifier()]
gc = GC.GraphConverter(modifiers)
pred = gc.convert(pred)

class nnabla.experimental.graph_converters.TestModeModifier(rm_funcs=[])[source]¶

This converter combines BatNormBatchStateModifier and RemoveFunctionModifer. It changes batch_stat to False. Supported functions: BatchNormalization, FusedBatchNormalization, SyncBatchNormalization.

Functions that specified rm_funcs will be removed from a graph.

Parameters: rm_funcs (list of str) – list of function name

Examples:

pred = Model(...)

import nnabla.experimental.graph_converters as GC

modifiers = [GC.TestModeModifier(rm_funcs=['MulScalar'])]
gc = GC.GraphConverter(modifiers)
pred = gc.convert(pred)

class nnabla.experimental.graph_converters.IdentityModifier(inputs={}, copy_value=False)[source]¶

All functions are replaced to the same new function.

Parameters: inputs (dict) – Input variable mapping from the original input to another input. Default is the empty dictionary, so the new graph shares the original inputs.

Examples:

pred = Model(...)
x = nn.Variable(...)

import nnabla.experimental.graph_converters as GC

modifiers = [GC.IdentityModifier({x0: x1})]
gc = GC.GraphConverter(modifiers)
pred = gc.convert(pred)

class nnabla.experimental.graph_converters.NoGradModifier[source]¶

All functions are replaced to the same new function.

Parameters: inputs (dict) – Input variable mapping from the original input to another input. Default is the empty dictionary, so the new graph shares the original inputs.

Examples:

pred = Model(...)
x = nn.Variable(...)

import nnabla.experimental.graph_converters as GC

modifiers = [GC.NoGradModifier()]
gc = GC.GraphConverter(modifiers)
pred = gc.convert(pred)

Trainers¶

class nnabla.experimental.trainers.Trainer(updater=None, evaluator=None, model_save_path=None, max_epoch=1, iter_per_epoch=None, callback_on_start=<function Trainer.<lambda>>, callback_on_finish=<function Trainer.<lambda>>, update_callback_on_start=<function Trainer.<lambda>>, update_callback_on_finish=<function Trainer.<lambda>>)[source]¶

Trainer API

Trainer class is the very basic class for training neural network. You can composite this class to your own trainer class and delegate the train method of this class to your class.

Parameters

updater (Updater or list of Updater) – Updater object.
evaluator (Evaluator or list of Evaluator) – Evaluator object.
model_save_path (str) – Model save path.
max_epoch (int) – Max epoch to train.
iter_per_epoch (int, optional) – Iterations per one epoch.
callback_on_start (callable object, function, lambda, or list of these, optional) – Callback called before the trainer.train.
callback_on_finish (callable object, function, lambda, or list of these, optional) – Callback called after the trainer.train.
update_callback_on_start (callable object, function, lambda, or list of these, optional) – Callback called before the updater.update.
update_callback_on_finish (callable object, function, lambda, or list of these, optional) – Callback called after the updater.update.

The following example is a complete snippet to use this base trainer.

Example

import nnabla as nn
import nnabla.functions as F
import nnabla.parametric_functions as PF
import nnabla.solvers as S

from nnabla.monitor import Monitor, MonitorSeries, MonitorTimeElapsed

import numpy as np

from nnabla.experimental.trainers import Trainer, Updater, Evaluator

# Batch, channel, height, width
b, c, h, w = 32, 1, 128, 128

# Train Input
tinput = nn.Variable([b, c, h, w])
tlabel = nn.Variable([b, c, h, w])

# Train Model and Loss
tpred = <training model>.apply(persistent=True)
tloss = F.mean(F.softmax_cross_entropy(tpred, tlabel))

# Test Input
vinput = nn.Variable([b, c, h, w])
vlabel = nn.Variable([b, c, h, w])

# Test Model and Error
vpred = <evaluation model>.apply(persistent=True)
vloss = F.mean(F.softmax_cross_entropy(vpred, vlabel))
verror = F.mean(F.top_n_error(vpred.get_unlinked_variable(), vlabel))

# Solver
solver = S.Adam()
solver.set_parameters(nn.get_parameters())

# DataIterator
tdata = <training_data_iterator>
vdata = <validation_data_iterator>

# Monitor
monitor = Monitor(<monitor_path>)
monitor_loss = MonitorSeries("Training loss", monitor, interval=10)
monitor_err = MonitorSeries("Training error", monitor, interval=10)
monitor_time = MonitorTimeElapsed("Training time", monitor, interval=100)
monitor_verr = MonitorSeries("Valid error", monitor, interval=10)

# Updater
def tdata_feeder():
    tinput.d, tlabel.d = tdata.next()
def update_callback_on_finish(i):
    monitor_loss.add(i, tloss.d)
    monitor_time.add(i)
updater = Updater(solver, tloss,
                  data_feeder=tdata_feeder,
                  update_callback_on_finish=update_callback_on_finish)

# Evaluator
def vdata_feeder():
    vinput.d, vlabel.d = vdata.next()
def eval_callback_on_finish(i, ve):
    monitor_verr.add(i, ve)
evaluator = Evaluator(verror,
                      data_feeder=vdata_feeder,
                      val_iter=vdata.size // b,
                      callback_on_finish=eval_callback_on_finish)

# Trainer
trainer = Trainer(updater, evaluator, <model_save_path>,
                  max_epoch=<max_epoch>, iter_per_epoch=tdata.size // b)
trainer.train()

class nnabla.experimental.trainers.NaiveClassificationTrainer(solver, tinput=None, tlabel=None, tpred=None, tdata=None, vinput=None, vlabel=None, vpred=None, vdata=None, monitor_path=None, model_save_path=None, max_epoch=1, iter_per_epoch=None, val_iter=None)[source]¶

Naive Classification Trainer

Parameters

solver (Solver) – Solver object.
tinput (Variable) – Input variable for input feature in training.
tlabel (Variable) – Label variable for lable in training.
tpred (Variable) – Root variable for prediction in the training graph.
tdata (nnabla.utils.data_iterator.DataIterator) – DataIterator for training.
vinput (Variable) – Input variable for input feature in evaluation.
vlabel (Variable) – Label variable for label in evaluation.
vpred (Variable) – Root variable for prediction in the evaluation graph.
vdata (DataIterator) – DataIterator for evaluation.
monitor_path (str) – Monitor path.
model_save_path (str) – Model save path.
max_epoch (int) – Max epoch to train.
iter_per_epoch (int, optional) – Iterations per one epoch. If not set, this value are determined by tdata.size // tdata.batch_size.
val_iter (int, optional) – Iterations for evaluation. If not set, this value are determined by vdata.size // vdata.batch_size.

The following example is a complete snippet to use this base trainer.

Example

import nnabla as nn
import nnabla.functions as F
import nnabla.parametric_functions as PF
import nnabla.solvers as S

import numpy as np

from nnabla.experimental.trainers import NaiveClassificationTrainer

# Batch, channel, height, width
b, c, h, w = 32, 1, 128, 128

# Train Input
tinput = nn.Variable([b, c, h, w])
tlabel = nn.Variable([b, c, h, w])

# Train Model and Loss
tpred = <training model>

# Test Input
vinput = nn.Variable([b, c, h, w])

# Test Model
vpred = <evaluation model>

# Solver
solver = S.Adam()
solver.set_parameters(nn.get_parameters())

# DataIterator
tdata = <training_data_iterator>
vdata = <validation_data_iterator>

# Trainer
trainer = NaiveClassificationTrainer(solver,
                                     tinput, tlabel, tpred, tdata,
                                     vinput, vlabel, vpred, vdata,
                                     <monitor_path>,
                                     <model_save_path>,
                                     max_epoch=<max_epoch>)
trainer.train()

class nnabla.experimental.trainers.NaiveRegressionTrainer(solver, tinput=None, tlabel=None, tpred=None, tdata=None, vinput=None, vlabel=None, vpred=None, vdata=None, monitor_path=None, model_save_path=None, max_epoch=1, iter_per_epoch=None, val_iter=None)[source]¶

Naive Regression Trainer

Parameters

solver (Solver) – Solver object.
tinput (Variable) – Input variable for input feature in training.
tlabel (Variable) – Label variable for lable in training.
tpred (Variable) – Root variable for prediction in the training graph.
tdata (nnabla.utils.data_iterator.DataIterator) – DataIterator for training.
vinput (Variable) – Input variable for input feature in evaluation.
vlabel (Variable) – Label variable for label in evaluation.
vpred (Variable) – Root variable for prediction in the evaluation graph.
vdata (DataIterator) – DataIterator for evaluation.
monitor_path (str) – Monitor path.
model_save_path (str) – Model save path.
max_epoch (int) – Max epoch to train.
iter_per_epoch (int, optional) – Iterations per one epoch. If not set, this value are determined by tdata.size // tdata.batch_size.
val_iter (int, optional) – Iterations for evaluation. If not set, this value are determined by vdata.size // vdata.batch_size.

Example

import nnabla as nn
import nnabla.functions as F
import nnabla.parametric_functions as PF
import nnabla.solvers as S

import numpy as np

from nnabla.experimental.trainers import NaiveRegressionTrainer

# Batch, channel, height, width
b, c, h, w = 32, 1, 128, 128

# Train Input
tinput = nn.Variable([b, c, h, w])
tlabel = nn.Variable([b, c, h, w])

# Train Model and Loss
tpred = <training model>

# Test Input
vinput = nn.Variable([b, c, h, w])
vlabel = nn.Variable([b, c, h, w])

# Test Model
vpred = <evaluation model>

# Solver
solver = S.Adam()
solver.set_parameters(nn.get_parameters())

# DataIterator
tdata = <training_data_iterator>
vdata = <validation_data_iterator>

# Trainer
trainer = NaiveRegressionTrainer(solver,
                                 tinput, tlabel, tpred, tdata,
                                 vinput, vlabel, vpred, vdata,
                                 <monitor_path>,
                                 <model_save_path>,
                                 max_epoch=<max_epoch>)
trainer.train()

class nnabla.experimental.trainers.Updater(solver=None, loss=None, data_feeder=<function Updater.<lambda>>, forward_callback_on_start=<function Updater.<lambda>>, forward_callback_on_finish=<function Updater.<lambda>>, backward_callback_on_start=<function Updater.<lambda>>, backward_callback_on_finish=<function Updater.<lambda>>, comm_callback_on_start=<function Updater.<lambda>>, comm_callback_on_finish=<function Updater.<lambda>>, update_callback_on_start=<function Updater.<lambda>>, update_callback_on_finish=<function Updater.<lambda>>, clear_buffer=True, accum_grad=1, comm=None, grads=[])[source]¶

Parameters

solver (nnabla.solvers.Solver) – Solver object. E.g., Momentum or Adam.
loss (nnabla.Variable) – Loss variable from which the forward and the backward is called.
data_feeder (callable object, function, or lambda) – Data feeder.
forward_callback_on_start (callable object, function, lambda, or list of these, optional) – Callback called before forward function.
forward_callback_on_finish (callable object, function, lambda, or list of these, optional) – Callback called after forward function.
backward_callback_on_start (callable object, function, lambda, or list of these, optional) – Callback called before backward function.
backward_callback_on_finish (callable object, function, lambda, or list of these, optional) – Callback called after backward function.
comm_callback_on_start (callable object, function, lambda, or list of these, optional) – Callback called before comm.all_reduce.
comm_callback_on_finish (callable object, function, lambda, or list of these, optional) – Callback called after comm.all_reduce.
update_callback_on_start (callable object, function, lambda, or list of these, optional) – Callback called before update function.
update_callback_on_finish (callable object, function, lambda, or list of these, optional) – Callback called after update function.
clear_buffer (bool, optional) – Clears the no longer referenced variables during backpropagation to save memory.
accum_grad (int, optional) – Number of accumulation of gradients. Update method of the Solver is called after the accum_grad number of the forward and backward is called. Default is 1.
comm (nnabla.communicators.Communicator, optional) – Communicator when to do distributed training. Default is None.
grads (list of nnabla.NdArray, optional) – The list of gradients to be exchanged when to do distributed training. Default is the empty list.

Example

from nnabla.experimental.trainers import Updater

solver = <Solver>
loss = <Loss Variable of Network>

def tdata_feeder():
    ...
def update_callback_on_finish(i):
    ...
updater = Updater(solver, loss, tdata_feeder, updater_callback_on_finish)

# Training iteration
for itr in range(<max_iter>):
    updater.update()

update(i)[source]¶

Monolithic update method.

This method calls the following methods with the dynamic loss scaling.

solver.zerograd
feed data
loss.forward
loss.backward
comm.all_reduce (if it is specified)
solver.update

class nnabla.experimental.trainers.Evaluator(vroot=None, data_feeder=None, val_iter=None, callback_on_start=<function Evaluator.<lambda>>, callback_on_finish=<function Evaluator.<lambda>>, clear_buffer=True, comm=None)[source]¶

Parameters

vroot (Variable) – Root varible of the evaluation graph.
data_feeder (callable object, function, or lambda) – Data feeder.
val_iter (int, optional) – Iterations for evaluation.
callback_on_start (callable object, function, lambda, or list of these, optional) – Callback called before the evaluator.evalute.
callback_on_finish (callable object, function, lambda, or list of these, optional) – Callback called after the evaluator.evalute.
clear_buffer (bool, optional) – Clears the no longer referenced variables during backpropagation to save memory.
comm (nnabla.communicators.Communicator, optional) – Communicator when to do distributed training. Default is None.

Example

from nnabla.experimental.trainers import Evaluator

# Evaluator
def vdata_feeder():
    ...
def eval_callback_on_finish(i, ve):
    ...
evaluator = Evaluator(verror,
                      data_feeder=vdata_feeder,
                      val_iter=<val_iter>,
                      callback_on_finish=eval_callback_on_finish)

Mixed Precision Trainings¶

DynamicLossScalingUpdater¶

class nnabla.experimental.mixed_precision_training.DynamicLossScalingUpdater(solver, loss, data_feeder=<function DynamicLossScalingUpdater.<lambda>>, scale=8.0, scaling_factor=2.0, N=2000, clear_buffer=True, accum_grad=1, weight_decay=None, comm=None, grads=[])[source]¶

Dynamic Loss Scaling Updater for the mixed precision training.

Parameters

solver (nnabla.solvers.Solver) – Solver object. E.g., Momentum or Adam.
loss (nnabla.Variable) – Loss variable from which the forward and the backward is called.
data_feeder (callable object, function, or lambda) – Data feeder
scale (float) – Loss scale constant. This is dynamically changing during training.
scaling_factor (float) – Scaling factor for the dynamic loss scaling.
N (int) – Interval, the number of iterations in training for increasing loss scale by scaling_factor.
clear_buffer (bool) – Clears the no longer referenced variables during backpropagation to save memory.
accum_grad (int) – Number of accumulation of gradients. Update method of the Solver is called after the accum_grad number of the forward and backward is called.
weight_decay (float) – Decay constant. Default is None, not applying the weight decay.
comm (nnabla.communicators.Communicator) – Communicator when to do distributed training. Default is None.
grads (list of nnabla.NdArray) – The list of gradients to be exchanged when to do distributed training. Default is the empty list.

solver¶

Solver object. E.g., Momentum or Adam.

Type: nnabla.solvers.Solver

loss¶

Loss variable from which the forward and the backward is called.

Type: nnabla.Variable

data_feeder¶

Data feeder

Type: callable object, function, lambda

scale¶

Loss scale constant. This is dynamically changing during training.

Type: float

scaling_factor¶

Scaling factor for the dynamic loss scaling.

Type: float

N¶

Interval, the number of iterations in training for increasing loss scale by scaling_factor.

Type: int

clear_buffer¶

Clears the no longer referenced variables during backpropagation to save memory.

Type: bool

accum_grad¶

Number of accumulation of gradients. Update method of the Solver is called after the accum_grad number of the forward and backward is called.

Type: int

weight_decay¶

Decay constant. Default is None, not applying the weight decay.

Type: float

comm¶

Communicator when to do distributed training.

Type: nnabla.communicators.Communicator

grads¶

The list of gradients to be exchanged when to do distributed training.

Type: list of nnabla.NdArray

Example

Reference:

https://docs.nvidia.com/deeplearning/sdk/mixed-precision-training/index.html#scalefactor

update()[source]¶

Monolithic update method.

This method calls the following methods with the dynamic loss scaling.

solver.zerograd
feed data
loss.forward
loss.backward
comm.all_reduce (if it is specified)
solver.update

Parametric Function Classes¶

class nnabla.experimental.parametric_function_class.affine.Affine(n_inmaps, n_outmaps, base_axis=1, w_init=None, b_init=None, fix_parameters=False, rng=None, with_bias=True)[source]¶

The affine layer, also known as the fully connected layer. Computes

\[{\mathbf y} = {\mathbf A} {\mathbf x} + {\mathbf b}.\]

where ${\mathbf x}, {\mathbf y}$ are the inputs and outputs respectively, and ${\mathbf A}, {\mathbf b}$ are constants.

Parameters

inp (Variable) – Input N-D array with shape ($M_0 \times \ldots \times M_{B-1} \times D_B \times \ldots \times D_N$). Dimensions before and after base_axis are flattened as if it is a matrix.
n_outmaps (int or tuple of int) – Number of output neurons per data.
base_axis (int) – Dimensions up to base_axis are treated as the sample dimensions.
w_init (nnabla.initializer.BaseInitializer or numpy.ndarray) – Initializer for weight. By default, it is initialized with nnabla.initializer.UniformInitializer within the range determined by nnabla.initializer.calc_uniform_lim_glorot.
b_init (nnabla.initializer.BaseInitializer or numpy.ndarray) – Initializer for bias. By default, it is initialized with zeros if with_bias is True.
fix_parameters (bool) – When set to True, the weights and biases will not be updated.
rng (numpy.random.RandomState) – Random generator for Initializer.
with_bias (bool) – Specify whether to include the bias term.

Returns

$(B + 1)$-D array. ($M_0 \times \ldots \times M_{B-1} \times L$)f

Return type

nnabla.experimental.parametric_function_class.affine.Linear¶: alias of nnabla.experimental.parametric_function_class.affine.Affine

class nnabla.experimental.parametric_function_class.convolution.Convolution(inmaps, outmaps, kernel, pad=None, stride=None, dilation=None, group=1, w_init=None, b_init=None, base_axis=1, fix_parameters=False, rng=None, with_bias=True)[source]¶

N-D Convolution with a bias term.

For Dilated Convolution (a.k.a. Atrous Convolution), refer to:

Chen et al., DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs. https://arxiv.org/abs/1606.00915
Yu et al., Multi-Scale Context Aggregation by Dilated Convolutions. https://arxiv.org/abs/1511.07122

Note

Convolution is a computationally intensive operation that should preferably be run with the cudnn backend. NNabla then uses CuDNN library functions to determine and cache the fastest algorithm for the given set of convolution parameters, which results in additional memory consumption which may pose a problem for GPUs with insufficient memory size. In that case, the NNABLA_CUDNN_WORKSPACE_LIMIT environment variable can be used to restrict the choice of algorithms to those that fit the given workspace memory limit, expressed in bytes. In some cases it may also be desired to restrict the automatic search to algorithms that produce deterministic (reproducable) results. This can be requested by setting the the environment variable NNABLA_CUDNN_DETERMINISTIC to a non-zero value.

Parameters

inp (Variable) – N-D array.
outmaps (int) – Number of convolution kernels (which is equal to the number of output channels). For example, to apply convolution on an input with 16 types of filters, specify 16.
kernel (tuple of int) – Convolution kernel size. For example, to apply convolution on an image with a 3 (height) by 5 (width) two-dimensional kernel, specify (3,5).
pad (tuple of int) – Padding sizes for dimensions.
stride (tuple of int) – Stride sizes for dimensions.
dilation (tuple of int) – Dilation sizes for dimensions.
group (int) – Number of groups of channels. This makes connections across channels more sparse by grouping connections along map direction.
w_init (nnabla.initializer.BaseInitializer or numpy.ndarray) – Initializer for weight. By default, it is initialized with nnabla.initializer.UniformInitializer within the range determined by nnabla.initializer.calc_uniform_lim_glorot.
b_init (nnabla.initializer.BaseInitializer or numpy.ndarray) – Initializer for bias. By default, it is initialized with zeros if with_bias is True.
base_axis (int) – Dimensions up to base_axis are treated as the sample dimensions.
fix_parameters (bool) – When set to True, the weights and biases will not be updated.
rng (numpy.random.RandomState) – Random generator for Initializer.
with_bias (bool) – Specify whether to include the bias term.

Returns

N-D array. See convolution for the output shape.

Return type

nnabla.experimental.parametric_function_class.convolution.Conv1d¶: alias of nnabla.experimental.parametric_function_class.convolution.Convolution

nnabla.experimental.parametric_function_class.convolution.Conv2d¶: alias of nnabla.experimental.parametric_function_class.convolution.Convolution

nnabla.experimental.parametric_function_class.convolution.Conv3d¶: alias of nnabla.experimental.parametric_function_class.convolution.Convolution

nnabla.experimental.parametric_function_class.convolution.ConvNd¶: alias of nnabla.experimental.parametric_function_class.convolution.Convolution

class nnabla.experimental.parametric_function_class.deconvolution.Deconvolution(inmaps, outmaps, kernel, pad=None, stride=None, dilation=None, group=1, w_init=None, b_init=None, base_axis=1, fix_parameters=False, rng=None, with_bias=True)[source]¶

Deconvolution layer.

Parameters

inp (Variable) – N-D array.
outmaps (int) – Number of deconvolution kernels (which is equal to the number of output channels). For example, to apply deconvolution on an input with 16 types of filters, specify 16.
kernel (tuple of int) – Convolution kernel size. For example, to apply deconvolution on an image with a 3 (height) by 5 (width) two-dimensional kernel, specify (3,5).
pad (tuple of int) – Padding sizes for dimensions.
stride (tuple of int) – Stride sizes for dimensions.
dilation (tuple of int) – Dilation sizes for dimensions.
group (int) – Number of groups of channels. This makes connections across channels sparser by grouping connections along map direction.
w_init (nnabla.initializer.BaseInitializer or numpy.ndarray) – Initializer for weight. By default, it is initialized with nnabla.initializer.UniformInitializer within the range determined by nnabla.initializer.calc_uniform_lim_glorot.
b_init (nnabla.initializer.BaseInitializer or numpy.ndarray) – Initializer for bias. By default, it is initialized with zeros if with_bias is True.
base_axis (int) – Dimensions up to base_axis are treated as the sample dimensions.
fix_parameters (bool) – When set to True, the weights and biases will not be updated.
rng (numpy.random.RandomState) – Random generator for Initializer.
with_bias (bool) – Specify whether to include the bias term.

Returns

N-D array. See deconvolution for the output shape.

Return type

nnabla.experimental.parametric_function_class.deconvolution.Deconv1d¶: alias of nnabla.experimental.parametric_function_class.deconvolution.Deconvolution

nnabla.experimental.parametric_function_class.deconvolution.Deconv2d¶: alias of nnabla.experimental.parametric_function_class.deconvolution.Deconvolution

nnabla.experimental.parametric_function_class.deconvolution.Deconv3d¶: alias of nnabla.experimental.parametric_function_class.deconvolution.Deconvolution

nnabla.experimental.parametric_function_class.deconvolution.DeconvNd¶: alias of nnabla.experimental.parametric_function_class.deconvolution.Deconvolution

class nnabla.experimental.parametric_function_class.batch_normalization.BatchNormalization(n_features, n_dims, axes=[1], decay_rate=0.9, eps=1e-05, batch_stat=True, output_stat=False, fix_parameters=False, param_init=None)[source]¶

Batch normalization layer.

\[\begin{split}\begin{array}{lcl} \mu &=& \frac{1}{M} \sum x_i\\ \sigma^2 &=& \frac{1}{M} \left(\sum x_i - \mu\right)^2\\ \hat{x}_i &=& \frac{x_i - \mu}{\sqrt{\sigma^2 + \epsilon }}\\ y_i &= & \hat{x}_i \gamma + \beta. \end{array}\end{split}\]

where $x_i, y_i$ are the inputs. In testing, the mean and variance computed by moving average calculated during training are used.

Parameters

inp (Variable) – N-D array of input.
axes (tuple of int) – Mean and variance for each element in axes are calculated using elements on the rest axes. For example, if an input is 4 dimensions, and axes is [1], batch mean is calculated as np.mean(inp.d, axis=(0, 2, 3), keepdims=True) (using numpy expression as an example).
decay_rate (float) – Decay rate of running mean and variance.
eps (float) – Tiny value to avoid zero division by std.
batch_stat (bool) – Use mini-batch statistics rather than running ones.
output_stat (bool) – Output batch mean and variance.
fix_parameters (bool) – When set to True, the beta and gamma will not be updated.
param_init (dict) – Parameter initializers can be set with a dict. A key of the dict must be 'beta', 'gamma', 'mean' or 'var'. A value of the dict must be an Initializer or a numpy.ndarray. E.g. {'beta': ConstantInitializer(0), 'gamma': np.ones(gamma_shape) * 2}.

Returns

N-D array.

Return type

References

Ioffe and Szegedy, Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. https://arxiv.org/abs/1502.03167

The shape of parameters has the same number of dimensions with the input data, and the shapes in axes has the same dimensions with the input, while the rest has 1. If an input is 4-dim and axes=[1], the parameter shape will be param_shape = np.mean(inp.d, axis=(0, 2, 3), keepdims=True).shape (using numpy expression as an example).

class nnabla.experimental.parametric_function_class.batch_normalization.BatchNorm1d(n_features, axes=[1], decay_rate=0.9, eps=1e-05, batch_stat=True, output_stat=False, fix_parameters=False, param_init=None)[source]¶

Batch normalization layer for 3d-Array or 3d-Variable. This is typically used together with Conv1d.

\[\begin{split}\begin{array}{lcl} \mu &=& \frac{1}{M} \sum x_i\\ \sigma^2 &=& \frac{1}{M} \left(\sum x_i - \mu\right)^2\\ \hat{x}_i &=& \frac{x_i - \mu}{\sqrt{\sigma^2 + \epsilon }}\\ y_i &= & \hat{x}_i \gamma + \beta. \end{array}\end{split}\]

where $x_i, y_i$ are the inputs. In testing, the mean and variance computed by moving average calculated during training are used.

Parameters

inp (Variable) – N-D array of input.
axes (tuple of int) – Mean and variance for each element in axes are calculated using elements on the rest axes. For example, if an input is 4 dimensions, and axes is [1], batch mean is calculated as np.mean(inp.d, axis=(0, 2, 3), keepdims=True) (using numpy expression as an example).
decay_rate (float) – Decay rate of running mean and variance.
eps (float) – Tiny value to avoid zero division by std.
batch_stat (bool) – Use mini-batch statistics rather than running ones.
output_stat (bool) – Output batch mean and variance.
fix_parameters (bool) – When set to True, the beta and gamma will not be updated.
param_init (dict) – Parameter initializers can be set with a dict. A key of the dict must be 'beta', 'gamma', 'mean' or 'var'. A value of the dict must be an Initializer or a numpy.ndarray. E.g. {'beta': ConstantInitializer(0), 'gamma': np.ones(gamma_shape) * 2}.

Returns

N-D array.

Return type

References

Ioffe and Szegedy, Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. https://arxiv.org/abs/1502.03167

The shape of parameters has the same number of dimensions with the input data, and the shapes in axes has the same dimensions with the input, while the rest has 1. If an input is 4-dim and axes=[1], the parameter shape will be param_shape = np.mean(inp.d, axis=(0, 2, 3), keepdims=True).shape (using numpy expression as an example).

class nnabla.experimental.parametric_function_class.batch_normalization.BatchNorm2d(n_features, axes=[1], decay_rate=0.9, eps=1e-05, batch_stat=True, output_stat=False, fix_parameters=False, param_init=None)[source]¶

Batch normalization layer for 4d-Array or 4d-Variable. This is typically used together with Conv2d.

\[\begin{split}\begin{array}{lcl} \mu &=& \frac{1}{M} \sum x_i\\ \sigma^2 &=& \frac{1}{M} \left(\sum x_i - \mu\right)^2\\ \hat{x}_i &=& \frac{x_i - \mu}{\sqrt{\sigma^2 + \epsilon }}\\ y_i &= & \hat{x}_i \gamma + \beta. \end{array}\end{split}\]

where $x_i, y_i$ are the inputs. In testing, the mean and variance computed by moving average calculated during training are used.

Parameters

inp (Variable) – N-D array of input.
axes (tuple of int) – Mean and variance for each element in axes are calculated using elements on the rest axes. For example, if an input is 4 dimensions, and axes is [1], batch mean is calculated as np.mean(inp.d, axis=(0, 2, 3), keepdims=True) (using numpy expression as an example).
decay_rate (float) – Decay rate of running mean and variance.
eps (float) – Tiny value to avoid zero division by std.
batch_stat (bool) – Use mini-batch statistics rather than running ones.
output_stat (bool) – Output batch mean and variance.
fix_parameters (bool) – When set to True, the beta and gamma will not be updated.
param_init (dict) – Parameter initializers can be set with a dict. A key of the dict must be 'beta', 'gamma', 'mean' or 'var'. A value of the dict must be an Initializer or a numpy.ndarray. E.g. {'beta': ConstantInitializer(0), 'gamma': np.ones(gamma_shape) * 2}.

Returns

N-D array.

Return type

References

Ioffe and Szegedy, Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. https://arxiv.org/abs/1502.03167

The shape of parameters has the same number of dimensions with the input data, and the shapes in axes has the same dimensions with the input, while the rest has 1. If an input is 4-dim and axes=[1], the parameter shape will be param_shape = np.mean(inp.d, axis=(0, 2, 3), keepdims=True).shape (using numpy expression as an example).

class nnabla.experimental.parametric_function_class.batch_normalization.BatchNorm3d(n_features, axes=[1], decay_rate=0.9, eps=1e-05, batch_stat=True, output_stat=False, fix_parameters=False, param_init=None)[source]¶

Batch normalization layer for 5d-Array or 5d-Variable. This is typically used together with Conv3d.

\[\begin{split}\begin{array}{lcl} \mu &=& \frac{1}{M} \sum x_i\\ \sigma^2 &=& \frac{1}{M} \left(\sum x_i - \mu\right)^2\\ \hat{x}_i &=& \frac{x_i - \mu}{\sqrt{\sigma^2 + \epsilon }}\\ y_i &= & \hat{x}_i \gamma + \beta. \end{array}\end{split}\]

where $x_i, y_i$ are the inputs. In testing, the mean and variance computed by moving average calculated during training are used.

Parameters

inp (Variable) – N-D array of input.
axes (tuple of int) – Mean and variance for each element in axes are calculated using elements on the rest axes. For example, if an input is 4 dimensions, and axes is [1], batch mean is calculated as np.mean(inp.d, axis=(0, 2, 3), keepdims=True) (using numpy expression as an example).
decay_rate (float) – Decay rate of running mean and variance.
eps (float) – Tiny value to avoid zero division by std.
batch_stat (bool) – Use mini-batch statistics rather than running ones.
output_stat (bool) – Output batch mean and variance.
fix_parameters (bool) – When set to True, the beta and gamma will not be updated.
param_init (dict) – Parameter initializers can be set with a dict. A key of the dict must be 'beta', 'gamma', 'mean' or 'var'. A value of the dict must be an Initializer or a numpy.ndarray. E.g. {'beta': ConstantInitializer(0), 'gamma': np.ones(gamma_shape) * 2}.

Returns

N-D array.

Return type

References

Ioffe and Szegedy, Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. https://arxiv.org/abs/1502.03167

The shape of parameters has the same number of dimensions with the input data, and the shapes in axes has the same dimensions with the input, while the rest has 1. If an input is 4-dim and axes=[1], the parameter shape will be param_shape = np.mean(inp.d, axis=(0, 2, 3), keepdims=True).shape (using numpy expression as an example).

class nnabla.experimental.parametric_function_class.embed.Embed(n_inputs, n_features, w_init=None, fix_parameters=False)[source]¶

Embed.

Embed slices a matrix/tensor with indexing array/tensor. Weights are initialized with nnabla.initializer.UniformInitializer within the range of $-\sqrt{3}$ and $\sqrt{3}$.

Parameters

x (Variable) – [Integer] Indices with shape $(I_0, ..., I_N)$
n_inputs – number of possible inputs, words or vocabraries
n_features – number of embedding features
fix_parameters (bool) – When set to True, the embedding weight matrix will not be updated.

Returns

Output with shape $(I_0, ..., I_N, W_1, ..., W_M)$

Return type