class nbla::PReLU

template<typename T>
class PReLU : public nbla::BaseFunction<int>

Parametrized Rectified Linear Unit function defined as.

\[ y_i = \max(0, x_i) + \alpha \min(0, x_i) \]
where negative slope \( \alpha \) is learned and can vary across channels (an axis specified with base_axis).

Inputs:

  • N-D array

  • Scalar or a vector with size of channel axis (base_axis).

Outputs:

  • N-D array.

See also

He et.al, Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. https://arxiv.org/abs/1502.01852

Template Parameters:

T – Data type for computation.

Param base_axis:

Valid if negative slope is a vector. The negative slope vector size and input shape of base_axis must match.

Public Functions

inline virtual shared_ptr<Function> copy() const

Copy another instance of Function with the same context.

inline virtual vector<dtypes> in_types()

Get input dtypes.

Last in_type will be used repeatedly if size of in_types is smaller than size of inputs

inline virtual vector<dtypes> out_types()

Get output dtypes.

Last out_type will be used repeatedly if size of out_types is smaller than size of outputs

inline virtual int min_inputs()

Get minimum number of inputs.

This is meant to be used in setup function with in_types which is used to get maximum number of inputs.

inline virtual int min_outputs()

Get minimum number of outputs.

This is meant to be used in setup function with out_types which is used to get max number of outputs.

inline virtual string name()

Get function name in string.

inline virtual vector<string> allowed_array_classes()

Get array classes that are allowed to be specified by Context.

inline virtual bool grad_depends_output_data(int i, int o) const

Dependency flag for checking if in-grad depends on out-data.

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

Note

If any of inputs requires an output variable data when computing its gradient, this function must be overridden to return appropriate boolean value. Otherwise, backward computation will be incorrect.

Parameters:

  • i – [in] Input variable index.

  • o – [in] Output variable index.