MLask/SoftMax_8hpp_source.html

#pragma once

#include "Layer.hpp"


namespace mlask{


class SoftMax: Layer{

    vectorIn input_;

    vectorOut forwardOutput_;

public:

    SoftMax(std::size_t in = 0);


    vectorOut forward(vectorIn)override;

    vectorIn backward(vectorOut)override;

    void fit(float_t learning_rate)override{}


    bool tryConvertToONNX(onnx::GraphProto* graph, std::string input, std::string output) const override;

    std::string str() const override{ return "SoftMax"; }

};


}

Layer.hpp

mlask::Layer
Base class for all layers in the neural network.
Definition Layer.hpp:10

mlask::SoftMax
class reprsenting Soft Max activation function, since the definition is to complex for activation fun...
Definition SoftMax.hpp:7

mlask::SoftMax::SoftMax
SoftMax(std::size_t in=0)
Construct a new SoftMax object.

mlask::SoftMax::str
std::string str() const override
Returns a string representation of the layer.
Definition SoftMax.hpp:28

mlask::SoftMax::fit
void fit(float_t learning_rate) override
This does nothing, since softmax doesnt change.
Definition SoftMax.hpp:20

mlask::SoftMax::tryConvertToONNX
bool tryConvertToONNX(onnx::GraphProto *graph, std::string input, std::string output) const override
Try to convert the layer to ONNX format.

mlask::SoftMax::backward
vectorIn backward(vectorOut) override
derived version of softmax
Definition SoftMax.cpp:13

mlask::SoftMax::forward
vectorOut forward(vectorIn) override
softmax definition
Definition SoftMax.cpp:6

mlask
Definition LeakyRelu.hpp:4

mlask::vectorOut
Eigen::Matrix< float_t, Eigen::Dynamic, 1 > vectorOut
Definition types.hpp:17

mlask::float_t
float float_t
Definition types.hpp:12

mlask::vectorIn
Eigen::Matrix< float_t, Eigen::Dynamic, 1 > vectorIn
Definition types.hpp:16