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dropout_layer.h
/*
Copyright (c) 2013, 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"
#include "tiny_dnn/layers/layer.h"
#include <algorithm>
namespace tiny_dnn {
class dropout_layer : public layer {
public:
typedef activation::identity Activation;
typedef layer Base;
dropout_layer(serial_size_t in_dim, float_t dropout_rate, net_phase phase = net_phase::train)
: Base({vector_type::data}, {vector_type::data}),
phase_(phase),
dropout_rate_(dropout_rate),
scale_(float_t(1) / (float_t(1) - dropout_rate_)),
in_size_(in_dim)
{
mask_.resize(1, std::vector<uint8_t>(in_dim));
clear_mask();
}
dropout_layer(const dropout_layer& obj) = default;
virtual ~dropout_layer(){}
#ifdef CNN_USE_DEFAULT_MOVE_CONSTRUCTORS
dropout_layer(dropout_layer&& obj) = default;
dropout_layer& operator=(const dropout_layer& obj) = default;
dropout_layer& operator=(dropout_layer&& obj) = default;
#endif
void set_dropout_rate(float_t rate)
{
dropout_rate_ = rate;
scale_ = float_t(1) / (float_t(1) - dropout_rate_);
}
float_t dropout_rate() const {
return dropout_rate_;
}
serial_size_t fan_in_size() const override
{
return 1;
}
serial_size_t fan_out_size() const override
{
return 1;
}
std::vector<index3d<serial_size_t>> in_shape() const override {
return{ index3d<serial_size_t>(in_size_, 1, 1) };
}
std::vector<index3d<serial_size_t>> out_shape() const override {
return{ index3d<serial_size_t>(in_size_, 1, 1) };
}
void back_propagation(const std::vector<tensor_t*>& in_data,
const std::vector<tensor_t*>& out_data,
std::vector<tensor_t*>& out_grad,
std::vector<tensor_t*>& in_grad) override {
tensor_t& prev_delta = *in_grad[0];
const tensor_t& curr_delta = *out_grad[0];
CNN_UNREFERENCED_PARAMETER(in_data);
CNN_UNREFERENCED_PARAMETER(out_data);
for (serial_size_t sample = 0; sample < static_cast<serial_size_t>(prev_delta.size()); ++sample) {
for (serial_size_t i = 0; i < static_cast<serial_size_t>(curr_delta.size()); i++) {
prev_delta[sample][i] = mask_[sample][i] * curr_delta[sample][i];
}
}
}
void forward_propagation(const std::vector<tensor_t*>& in_data,
std::vector<tensor_t*>& out_data) override {
const tensor_t& in = *in_data[0];
tensor_t& out = *out_data[0];
const size_t sample_count = in.size();
if (mask_.size() < sample_count) {
mask_.resize(sample_count, mask_[0]);
}
for (size_t sample = 0, sample_count = in.size(); sample < sample_count; ++sample) {
std::vector<uint8_t>& mask = mask_[sample];
const vec_t& in_vec = in[sample];
vec_t& out_vec = out[sample];
if (phase_ == net_phase::train) {
for (size_t i = 0; i < in_vec.size(); i++)
mask[i] = bernoulli(dropout_rate_);
for (size_t i = 0; i < in_vec.size(); i++)
out_vec[i] = mask[i] * scale_ * in_vec[i];
}
else {
for (size_t i = 0, end = in_vec.size(); i < end; i++)
out_vec[i] = in_vec[i];
}
}
}
void set_context(net_phase ctx) override
{
phase_ = ctx;
}
std::string layer_type() const override { return "dropout"; }
// currently used by tests only
const std::vector<uint8_t>& get_mask(serial_size_t sample_index) const {
return mask_[sample_index];
}
void clear_mask() {
for (auto& sample : mask_) {
std::fill(sample.begin(), sample.end(), 0);
}
}
template <class Archive>
static void load_and_construct(Archive & ar, cereal::construct<dropout_layer> & construct) {
net_phase phase;
float_t dropout_rate;
serial_size_t in_size;
ar(cereal::make_nvp("in_size", in_size), cereal::make_nvp("dropout_rate", dropout_rate), cereal::make_nvp("phase", phase));
construct(in_size, dropout_rate, phase);
}
template <class Archive>
void serialize(Archive & ar) {
layer::serialize_prolog(ar);
ar(cereal::make_nvp("in_size", in_size_), cereal::make_nvp("dropout_rate", dropout_rate_), cereal::make_nvp("phase", phase_));
}
private:
net_phase phase_;
float_t dropout_rate_;
float_t scale_;
serial_size_t in_size_;
std::vector<std::vector<uint8_t>> mask_;
};
} // namespace tiny_dnn
tiny_dnn::activation::identity
Definition activation_function.h:67
tiny_dnn::dropout_layer
applies dropout to the input
Definition dropout_layer.h:37
tiny_dnn::dropout_layer::fan_out_size
serial_size_t fan_out_size() const override
number of outgoing connections for each input unit used only for weight/bias initialization methods w...
Definition dropout_layer.h:84
tiny_dnn::dropout_layer::back_propagation
void back_propagation(const std::vector< tensor_t * > &in_data, const std::vector< tensor_t * > &out_data, std::vector< tensor_t * > &out_grad, std::vector< tensor_t * > &in_grad) override
return delta of previous layer (delta=\frac{dE}{da}, a=wx in fully-connected layer)
Definition dropout_layer.h:97
tiny_dnn::dropout_layer::dropout_layer
dropout_layer(serial_size_t in_dim, float_t dropout_rate, net_phase phase=net_phase::train)
Definition dropout_layer.h:47
tiny_dnn::dropout_layer::in_shape
std::vector< index3d< serial_size_t > > in_shape() const override
array of input shapes (width x height x depth)
Definition dropout_layer.h:89
tiny_dnn::dropout_layer::dropout_rate
float_t dropout_rate() const
number of incoming connections for each output unit
Definition dropout_layer.h:73
tiny_dnn::dropout_layer::layer_type
std::string layer_type() const override
name of layer, should be unique for each concrete class
Definition dropout_layer.h:154
tiny_dnn::dropout_layer::fan_in_size
serial_size_t fan_in_size() const override
number of outgoing connections for each input unit
Definition dropout_layer.h:78
tiny_dnn::dropout_layer::forward_propagation
void forward_propagation(const std::vector< tensor_t * > &in_data, std::vector< tensor_t * > &out_data) override
Definition dropout_layer.h:114
tiny_dnn::dropout_layer::set_context
void set_context(net_phase ctx) override
set dropout-context (training-phase or test-phase)
Definition dropout_layer.h:149
tiny_dnn::dropout_layer::out_shape
std::vector< index3d< serial_size_t > > out_shape() const override
array of output shapes (width x height x depth)
Definition dropout_layer.h:93
tiny_dnn::image
Simple image utility class.
Definition image.h:94
tiny_dnn::layer
base class of all kind of NN layers
Definition layer.h:62
tiny_dnn::layer::in_size
serial_size_t in_size() const
!
Definition layer.h:176