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tiny_dnn.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/config.h"
#include "tiny_dnn/network.h"
#include "tiny_dnn/nodes.h"
#include "tiny_dnn/core/framework/tensor.h"
#include "tiny_dnn/core/framework/device.h"
#include "tiny_dnn/core/framework/program_manager.h"
#include "tiny_dnn/layers/input_layer.h"
#include "tiny_dnn/layers/feedforward_layer.h"
#include "tiny_dnn/layers/convolutional_layer.h"
#include "tiny_dnn/layers/quantized_convolutional_layer.h"
#include "tiny_dnn/layers/deconvolutional_layer.h"
#include "tiny_dnn/layers/quantized_deconvolutional_layer.h"
#include "tiny_dnn/layers/fully_connected_layer.h"
#include "tiny_dnn/layers/quantized_fully_connected_layer.h"
#include "tiny_dnn/layers/average_pooling_layer.h"
#include "tiny_dnn/layers/max_pooling_layer.h"
#include "tiny_dnn/layers/linear_layer.h"
#include "tiny_dnn/layers/lrn_layer.h"
#include "tiny_dnn/layers/dropout_layer.h"
#include "tiny_dnn/layers/arithmetic_layer.h"
#include "tiny_dnn/layers/concat_layer.h"
#include "tiny_dnn/layers/max_unpooling_layer.h"
#include "tiny_dnn/layers/average_unpooling_layer.h"
#include "tiny_dnn/layers/batch_normalization_layer.h"
#include "tiny_dnn/layers/slice_layer.h"
#include "tiny_dnn/layers/power_layer.h"
#include "tiny_dnn/activations/activation_function.h"
#include "tiny_dnn/lossfunctions/loss_function.h"
#include "tiny_dnn/optimizers/optimizer.h"
#include "tiny_dnn/util/weight_init.h"
#include "tiny_dnn/util/image.h"
#include "tiny_dnn/util/deform.h"
#include "tiny_dnn/util/product.h"
#include "tiny_dnn/util/graph_visualizer.h"
#include "tiny_dnn/io/mnist_parser.h"
#include "tiny_dnn/io/cifar10_parser.h"
#include "tiny_dnn/io/display.h"
#include "tiny_dnn/io/layer_factory.h"
#include "tiny_dnn/util/serialization_helper.h"
#include "tiny_dnn/util/deserialization_helper.h"
#ifdef CNN_USE_CAFFE_CONVERTER
// experimental / require google protobuf
#include "tiny_dnn/io/caffe/layer_factory.h"
#endif
// shortcut version of layer names
namespace tiny_dnn {
namespace layers {
template <class T>
using conv = tiny_dnn::convolutional_layer<T>;
template <class T>
using q_conv = tiny_dnn::quantized_convolutional_layer<T>;
template <class T>
using max_pool = tiny_dnn::max_pooling_layer<T>;
template <class T>
using ave_pool = tiny_dnn::average_pooling_layer<T>;
template <class T>
using fc = tiny_dnn::fully_connected_layer<T>;
template <class T>
using dense = tiny_dnn::fully_connected_layer<T>;
using add = tiny_dnn::elementwise_add_layer;
using dropout = tiny_dnn::dropout_layer;
using input = tiny_dnn::input_layer;
template <class T>
using lrn = tiny_dnn::lrn_layer<T>;
using input = tiny_dnn::input_layer;
using concat = tiny_dnn::concat_layer;
template <class T>
using deconv = tiny_dnn::deconvolutional_layer<T>;
template <class T>
using max_unpool = tiny_dnn::max_unpooling_layer<T>;
template <class T>
using ave_unpool = tiny_dnn::average_unpooling_layer<T>;
}
#include "tiny_dnn/models/alexnet.h"
using batch_norm = tiny_dnn::batch_normalization_layer;
using slice = tiny_dnn::slice_layer;
using power = tiny_dnn::power_layer;
using batch_norm = tiny_dnn::batch_normalization_layer;
using slice = tiny_dnn::slice_layer;
using power = tiny_dnn::power_layer;
}
tiny_dnn::batch_normalization_layer
Batch Normalization.
Definition batch_normalization_layer.h:42
tiny_dnn::concat_layer
concat N layers along depth
Definition concat_layer.h:44
tiny_dnn::dropout_layer
applies dropout to the input
Definition dropout_layer.h:37
tiny_dnn::elementwise_add_layer
element-wise add N vectors y_i = x0_i + x1_i + ... + xnum_i
Definition arithmetic_layer.h:36
tiny_dnn::image
Simple image utility class.
Definition image.h:94
tiny_dnn::input_layer
Definition input_layer.h:32
tiny_dnn::power_layer
element-wise pow: y = scale*x^factor
Definition power_layer.h:38
tiny_dnn::slice_layer
slice an input data into multiple outputs along a given slice dimension.
Definition slice_layer.h:42