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fully_connected_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/layers/layer.h"
#include "tiny_dnn/core/kernels/fully_connected_op.h"
#include "tiny_dnn/core/kernels/fully_connected_grad_op.h"
namespace tiny_dnn {
template<typename Activation>
class fully_connected_layer : public feedforward_layer<Activation> {
public:
typedef feedforward_layer<Activation> Base;
CNN_USE_LAYER_MEMBERS;
fully_connected_layer(serial_size_t in_dim,
serial_size_t out_dim,
bool has_bias = true,
backend_t backend_type = core::default_engine())
: Base(std_input_order(has_bias)) {
set_params(in_dim, out_dim, has_bias);
init_backend(backend_type);
Base::set_backend_type(backend_type);
}
// move constructor
fully_connected_layer(fully_connected_layer&& other)
: Base(std::move(other))
, params_(std::move(other.params_))
, kernel_fwd_(std::move(other.kernel_fwd_))
, kernel_back_(std::move(other.kernel_back_)) {
init_backend(std::move(other.engine()));
}
serial_size_t fan_in_size() const override {
return params_.in_size_;
}
serial_size_t fan_out_size() const override {
return params_.out_size_;
}
std::vector<index3d<serial_size_t>> in_shape() const override {
if (params_.has_bias_) {
return { index3d<serial_size_t>(params_.in_size_, 1, 1),
index3d<serial_size_t>(params_.in_size_,
params_.out_size_, 1),
index3d<serial_size_t>(params_.out_size_, 1, 1) };
} else {
return { index3d<serial_size_t>(params_.in_size_, 1, 1),
index3d<serial_size_t>(params_.in_size_,
params_.out_size_, 1) };
}
}
std::vector<index3d<serial_size_t>> out_shape() const override {
return { index3d<serial_size_t>(params_.out_size_, 1, 1),
index3d<serial_size_t>(params_.out_size_, 1, 1) };
}
void forward_propagation(const std::vector<tensor_t*>& in_data,
std::vector<tensor_t*>& out_data) override {
// forward convolutional op context
auto ctx = OpKernelContext(in_data, out_data);
ctx.setParallelize(layer::parallelize());
ctx.setEngine(layer::engine());
// launch convolutional kernel
kernel_fwd_->compute(ctx);
// activations
this->forward_activation(*out_data[0], *out_data[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 {
// activations
// TODO(edgar/nyanp): refactor and move activations outside
this->backward_activation(*out_grad[0], *out_data[0], *out_grad[1]);
// backward convolutional op context
auto ctx = OpKernelContext(in_data, out_data, out_grad, in_grad);
ctx.setParallelize(layer::parallelize());
ctx.setEngine(layer::engine());
// launch convolutional kernel
kernel_back_->compute(ctx);
}
std::string layer_type() const override { return "fully-connected"; }
template <class Archive>
static void load_and_construct(Archive & ar, cereal::construct<fully_connected_layer> & construct) {
serial_size_t in_dim, out_dim;
bool has_bias;
ar(cereal::make_nvp("in_size", in_dim),
cereal::make_nvp("out_size", out_dim),
cereal::make_nvp("has_bias", has_bias));
construct(in_dim, out_dim, has_bias);
}
template <class Archive>
void serialize(Archive & ar) {
layer::serialize_prolog(ar);
ar(cereal::make_nvp("in_size", params_.in_size_),
cereal::make_nvp("out_size", params_.out_size_),
cereal::make_nvp("has_bias", params_.has_bias_));
}
protected:
void set_params(const serial_size_t in_size,
const serial_size_t out_size,
bool has_bias) {
params_.in_size_ = in_size;
params_.out_size_ = out_size;
params_.has_bias_ = has_bias;
}
void init_backend(backend_t backend_type) {
core::OpKernelConstruction ctx =
core::OpKernelConstruction(layer::device(), &params_);
if (backend_type == backend_t::internal ||
backend_type == backend_t::avx||
backend_type == backend_t::nnpack
) {
kernel_fwd_.reset(new FullyConnectedOp(ctx));
kernel_back_.reset(new FullyConnectedGradOp(ctx));
return;
}
else {
throw nn_error("Not supported engine: " + to_string(backend_type));
}
}
private:
/* The layer parameters */
fully_params params_;
/* Forward and backward ops */
std::shared_ptr<core::OpKernel> kernel_fwd_;
std::shared_ptr<core::OpKernel> kernel_back_;
};
} // namespace tiny_dnn
tiny_dnn::feedforward_layer
single-input, single-output network with activation function
Definition feedforward_layer.h:37
tiny_dnn::fully_connected_layer
compute fully-connected(matmul) operation
Definition fully_connected_layer.h:39
tiny_dnn::fully_connected_layer::layer_type
std::string layer_type() const override
name of layer, should be unique for each concrete class
Definition fully_connected_layer.h:125
tiny_dnn::fully_connected_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 fully_connected_layer.h:108
tiny_dnn::fully_connected_layer::fully_connected_layer
fully_connected_layer(serial_size_t in_dim, serial_size_t out_dim, bool has_bias=true, backend_t backend_type=core::default_engine())
Definition fully_connected_layer.h:49
tiny_dnn::fully_connected_layer::out_shape
std::vector< index3d< serial_size_t > > out_shape() const override
array of output shapes (width x height x depth)
Definition fully_connected_layer.h:89
tiny_dnn::fully_connected_layer::forward_propagation
void forward_propagation(const std::vector< tensor_t * > &in_data, std::vector< tensor_t * > &out_data) override
Definition fully_connected_layer.h:94
tiny_dnn::fully_connected_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 fully_connected_layer.h:72
tiny_dnn::fully_connected_layer::fan_in_size
serial_size_t fan_in_size() const override
number of incoming connections for each output unit used only for weight/bias initialization methods ...
Definition fully_connected_layer.h:68
tiny_dnn::fully_connected_layer::in_shape
std::vector< index3d< serial_size_t > > in_shape() const override
array of input shapes (width x height x depth)
Definition fully_connected_layer.h:76
tiny_dnn::image
Simple image utility class.
Definition image.h:94
tiny_dnn::layer::out_size
serial_size_t out_size() const
!
Definition layer.h:181
tiny_dnn::layer::in_size
serial_size_t in_size() const
!
Definition layer.h:176