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NeuralNetworkFileReader.cpp
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NeuralNetworkFileReader.cpp
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/*
* Copyright (c) 2014, Shogun Toolbox Foundation
* All rights reserved.
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions are met:
* 1. Redistributions of source code must retain the above copyright notice,
* this list of conditions and the following disclaimer.
*
* 2. 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.
*
* 3. Neither the name of the copyright holder 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.
*
* Written (W) 2014 Khaled Nasr
*/
#include <shogun/lib/config.h>
#ifdef HAVE_JSON
#include <shogun/io/NeuralNetworkFileReader.h>
#include <shogun/neuralnets/NeuralNetwork.h>
#include <shogun/neuralnets/NeuralLayer.h>
#include <shogun/neuralnets/NeuralInputLayer.h>
#include <shogun/neuralnets/NeuralLinearLayer.h>
#include <shogun/neuralnets/NeuralLogisticLayer.h>
#include <shogun/neuralnets/NeuralSoftmaxLayer.h>
#include <shogun/neuralnets/NeuralRectifiedLinearLayer.h>
#include <shogun/lib/DynamicObjectArray.h>
#include <shogun/lib/SGVector.h>
using namespace shogun;
CNeuralNetwork* CNeuralNetworkFileReader::read_file(const char* file_path)
{
json_object* json_network = json_object_from_file(file_path);
if (is_error(json_network))
{
SG_ERROR("Error while opening file: %s!\n", file_path);
return NULL;
}
CNeuralNetwork* network = parse_network(json_network);
json_object_put(json_network);
return network;
}
CNeuralNetwork* CNeuralNetworkFileReader::read_string(const char* str)
{
json_object* json_network = json_tokener_parse(str);
if (is_error(json_network))
{
SG_ERROR("Error while parsing the given string\n");
return NULL;
}
CNeuralNetwork* network = parse_network(json_network);
json_object_put(json_network);
return network;
}
CNeuralNetwork* CNeuralNetworkFileReader::parse_network(json_object* json_network)
{
CNeuralNetwork* network = new CNeuralNetwork;
// find the layers
json_object_iter iter;
json_object* json_layers = NULL;
json_object_object_foreachC(json_network, iter)
{
if (string_equal(iter.key, "layers"))
json_layers = iter.val;
}
if (json_layers)
network->set_layers(parse_layers(iter.val));
else
SG_ERROR("No layers found in file\n");
// set the connections
json_object_iter layers_iter;
json_object_object_foreachC(json_layers, layers_iter)
{
json_object_iter layer_iter;
json_object_object_foreachC(layers_iter.val, layer_iter)
{
if (string_equal(layer_iter.key, "inputs"))
{
int32_t len = json_object_array_length(layer_iter.val);
for (int32_t i=0; i<len; i++)
{
const char* input_key = json_object_get_string(
json_object_array_get_idx(layer_iter.val, i));
int32_t from = find_layer_index(json_layers, input_key);
int32_t to = find_layer_index(json_layers, layers_iter.key);
if (from == -1)
SG_ERROR("Invalid layer identifier (%s) in layer (%s)\n",
input_key, layers_iter.key);
network->connect(from, to);
}
}
}
}
// set the training parameters
float sigma = 0.01;
json_object_object_foreachC(json_network, iter)
{
if (string_equal(iter.key, "sigma"))
sigma = json_object_get_double(iter.val);
else if (string_equal(iter.key, "optimization_method"))
{
const char* method = json_object_get_string(iter.val);
if (string_equal(method, "NNOM_LBFGS"))
network->set_optimization_method(NNOM_LBFGS);
else if (string_equal(method, "NNOM_GRADIENT_DESCENT"))
network->set_optimization_method(NNOM_GRADIENT_DESCENT);
else
SG_ERROR("Invalid optimization method (%s)\n", method);
}
else if (string_equal(iter.key, "l2_coefficient"))
network->set_l2_coefficient(json_object_get_double(iter.val));
else if (string_equal(iter.key, "l1_coefficient"))
network->set_l1_coefficient(json_object_get_double(iter.val));
else if (string_equal(iter.key, "dropout_hidden"))
network->set_dropout_hidden(json_object_get_double(iter.val));
else if (string_equal(iter.key, "dropout_input"))
network->set_dropout_input(json_object_get_double(iter.val));
else if (string_equal(iter.key, "max_norm"))
network->set_max_norm(json_object_get_double(iter.val));
else if (string_equal(iter.key, "epsilon"))
network->set_epsilon(json_object_get_double(iter.val));
else if (string_equal(iter.key, "max_num_epochs"))
network->set_max_num_epochs(json_object_get_int(iter.val));
else if (string_equal(iter.key, "gd_mini_batch_size"))
network->set_gd_mini_batch_size(json_object_get_int(iter.val));
else if (string_equal(iter.key, "gd_learning_rate"))
network->set_gd_learning_rate(json_object_get_double(iter.val));
else if (string_equal(iter.key, "gd_learning_rate_decay"))
network->set_gd_learning_rate_decay(json_object_get_double(iter.val));
else if (string_equal(iter.key, "gd_momentum"))
network->set_gd_momentum(json_object_get_double(iter.val));
else if (string_equal(iter.key, "gd_error_damping_coeff"))
network->set_gd_error_damping_coeff(json_object_get_double(iter.val));
else if (!string_equal(iter.key, "layers"))
SG_ERROR("Invalid parameter (%s)\n", iter.key);
}
network->initialize_neural_network(sigma);
return network;
}
CDynamicObjectArray* CNeuralNetworkFileReader::parse_layers(
json_object* json_layers)
{
CDynamicObjectArray* layers = new CDynamicObjectArray();
json_object_iter iter;
json_object_object_foreachC(json_layers, iter)
{
layers->append_element(parse_layer(iter.val));
}
return layers;
}
CNeuralLayer* CNeuralNetworkFileReader::parse_layer(json_object* json_layer)
{
json_object_iter iter;
CNeuralLayer* layer = NULL;
const char* type = NULL;
// find the layer type and create a appropriate instance
json_object_object_foreachC(json_layer, iter)
{
if (string_equal(iter.key, "type"))
{
type = json_object_get_string(iter.val);
if (string_equal(type, "NeuralInputLayer"))
layer = new CNeuralInputLayer();
else if (string_equal(type, "NeuralLinearLayer"))
layer = new CNeuralLinearLayer();
else if (string_equal(type, "NeuralLogisticLayer"))
layer = new CNeuralLogisticLayer();
else if (string_equal(type, "NeuralSoftmaxLayer"))
layer = new CNeuralSoftmaxLayer();
else if (string_equal(type, "NeuralRectifiedLinearLayer"))
layer = new CNeuralRectifiedLinearLayer();
else
SG_ERROR("Unknown layer type: %s", type);
}
}
// fill in the fields
json_object_object_foreachC(json_layer, iter)
{
if(string_equal(iter.key, "num_neurons"))
{
layer->set_num_neurons(json_object_get_int(iter.val));
}
else if(string_equal(type,"NeuralInputLayer") &&
string_equal(iter.key, "start_index"))
{
((CNeuralInputLayer*)layer)->set_start_index(
json_object_get_int(iter.val));
}
}
return layer;
}
int32_t CNeuralNetworkFileReader::find_layer_index(json_object* json_layers,
const char* layer_key)
{
int32_t index = 0;
json_object_iter iter;
json_object_object_foreachC(json_layers, iter)
{
if (string_equal(iter.key, layer_key))
return index;
else
index++;
}
return -1;
}
bool CNeuralNetworkFileReader::string_equal(const char* str1, const char* str2)
{
return (strcmp(str1, str2) == 0);
}
#endif