tiny_dnn
1.0.0
A header only, dependency-free deep learning framework in C++11
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tiny_dnn
models
alexnet.h
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/*
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Copyright (c) 2013, Taiga Nomi
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Copyright (c) 2016, Taiga Nomi, Edgar Riba
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All rights reserved.
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Redistribution and use in source and binary forms, with or without
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modification, are permitted provided that the following conditions are met:
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* Redistributions of source code must retain the above copyright
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notice, this list of conditions and the following disclaimer.
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* Redistributions in binary form must reproduce the above copyright
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notice, this list of conditions and the following disclaimer in the
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documentation and/or other materials provided with the distribution.
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* Neither the name of the <organization> nor the
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names of its contributors may be used to endorse or promote products
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derived from this software without specific prior written permission.
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THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY
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EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
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WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
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DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY
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DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
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(INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
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LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
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ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
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(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
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SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
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*/
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//#include "tiny_dnn/tiny_dnn.h"
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using namespace
tiny_dnn::activation;
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using namespace
tiny_dnn::layers;
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namespace
models {
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// Based on: https://github.com/DeepMark/deepmark/blob/master/torch/image%2Bvideo/alexnet.lua
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class
alexnet
:
public
network
<sequential> {
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public
:
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explicit
alexnet
(
const
std::string& name =
""
)
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:
network<sequential>
(name) {
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*
this << conv<relu>
(224, 224, 11, 11, 3, 64, padding::valid,
true
, 4, 4);
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*
this << max_pool<identity>
(54, 54, 64, 2);
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*
this << conv<relu>
(27, 27, 5, 5, 64, 192, padding::valid,
true
, 1, 1);
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*
this << max_pool<identity>
(23, 23, 192, 1);
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*
this << conv<relu>
(23, 23, 3, 3, 192, 384, padding::valid,
true
, 1, 1);
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*
this << conv<relu>
(21, 21, 3, 3, 384, 256, padding::valid,
true
, 1, 1);
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*
this << conv<relu>
(19, 19, 3, 3, 256, 256, padding::valid,
true
, 1, 1);
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*
this << max_pool<identity>
(17, 17, 256, 1);
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}
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};
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}
// namespace models
models::alexnet
Definition
alexnet.h:37
tiny_dnn::image
Simple image utility class.
Definition
image.h:94
tiny_dnn::network
A model of neural networks in tiny-dnn.
Definition
network.h:167
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