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weight_init.h
/*
Copyright (c) 2015, Taiga Nomi
All rights reserved.
Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:
* Redistributions of source code must retain the above copyright
notice, this list of conditions and the following disclaimer.
* Redistributions in binary form must reproduce the above copyright
notice, this list of conditions and the following disclaimer in the
documentation and/or other materials provided with the distribution.
* Neither the name of the <organization> nor the
names of its contributors may be used to endorse or promote products
derived from this software without specific prior written permission.
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY
EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY
DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
(INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
*/
#pragma once
#include "tiny_dnn/util/util.h"
namespace tiny_dnn {
namespace weight_init {
class function {
public:
virtual void fill(vec_t *weight, serial_size_t fan_in, serial_size_t fan_out) = 0;
};
class scalable : public function {
public:
scalable(float_t value) : scale_(value) {}
void scale(float_t value) {
scale_ = value;
}
protected:
float_t scale_;
};
class xavier : public scalable {
public:
xavier() : scalable(float_t(6)) {}
explicit xavier(float_t value) : scalable(value) {}
void fill(vec_t *weight, serial_size_t fan_in, serial_size_t fan_out) override {
const float_t weight_base = std::sqrt(scale_ / (fan_in + fan_out));
uniform_rand(weight->begin(), weight->end(), -weight_base, weight_base);
}
};
class lecun : public scalable {
public:
lecun() : scalable(float_t(1)) {}
explicit lecun(float_t value) : scalable(value) {}
void fill(vec_t *weight, serial_size_t fan_in, serial_size_t fan_out) override {
CNN_UNREFERENCED_PARAMETER(fan_out);
const float_t weight_base = scale_ / std::sqrt(float_t(fan_in));
uniform_rand(weight->begin(), weight->end(), -weight_base, weight_base);
}
};
class gaussian : public scalable {
public:
gaussian() : scalable(float_t(1)) {}
explicit gaussian(float_t sigma) : scalable(sigma) {}
void fill(vec_t *weight, serial_size_t fan_in, serial_size_t fan_out) override {
CNN_UNREFERENCED_PARAMETER(fan_in);
CNN_UNREFERENCED_PARAMETER(fan_out);
gaussian_rand(weight->begin(), weight->end(), float_t(0), scale_);
}
};
class constant : public scalable {
public:
constant() : scalable(float_t(0)) {}
explicit constant(float_t value) : scalable(value) {}
void fill(vec_t *weight, serial_size_t fan_in, serial_size_t fan_out) override {
CNN_UNREFERENCED_PARAMETER(fan_in);
CNN_UNREFERENCED_PARAMETER(fan_out);
std::fill(weight->begin(), weight->end(), scale_);
}
};
class he : public scalable {
public:
he() : scalable(float_t(2)) {}
explicit he(float_t value) : scalable(value) {}
void fill(vec_t *weight, serial_size_t fan_in, serial_size_t fan_out) override {
CNN_UNREFERENCED_PARAMETER(fan_out);
const float_t sigma = std::sqrt(scale_ /fan_in);
gaussian_rand(weight->begin(), weight->end(), float_t(0), sigma);
}
};
} // namespace weight_init
} // namespace tiny_dnn
tiny_dnn::image
Simple image utility class.
Definition image.h:94
tiny_dnn::weight_init::constant
Definition weight_init.h:102
tiny_dnn::weight_init::function
Definition weight_init.h:33
tiny_dnn::weight_init::gaussian
Definition weight_init.h:89
tiny_dnn::weight_init::he
Definition weight_init.h:115
tiny_dnn::weight_init::lecun
Use fan-in(number of input weight for each neuron) for scaling.
Definition weight_init.h:75
tiny_dnn::weight_init::scalable
Definition weight_init.h:38
tiny_dnn::weight_init::xavier
Use fan-in and fan-out for scaling.
Definition weight_init.h:56