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Merge pull request #516 from wangyang59/gan
Demo: Generative Adverserial Nets
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‎.gitignore‎

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.cproject
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.pydevproject
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Makefile
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.test_env/

‎demo/gan/.gitignore‎

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output/
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uniform_params/
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cifar_params/
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mnist_params/
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*.png
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.pydevproject
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.project
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*.log
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*.pyc
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data/mnist_data/
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data/cifar-10-batches-py/

‎demo/gan/README.md‎

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# Generative Adversarial Networks (GAN)
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This demo implements GAN training described in the original GAN paper (https://arxiv.org/abs/1406.2661) and DCGAN (https://arxiv.org/abs/1511.06434).
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The general training procedures are implemented in gan_trainer.py. The neural network configurations are specified in gan_conf.py (for synthetic data) and gan_conf_image.py (for image data).
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In order to run the model, first download the corresponding data by running the shell script in ./data.
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Then you can run the command below. The flag -d specifies the training data (cifar, mnist or uniform) and flag --useGpu specifies whether to use gpu for training (0 is cpu, 1 is gpu).
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$python gan_trainer.py -d cifar --use_gpu 1
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The generated images will be stored in ./cifar_samples/
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The corresponding models will be stored in ./cifar_params/

‎demo/gan/data/download_cifar.sh‎

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# Copyright (c) 2016 Baidu, Inc. All Rights Reserved
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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set -e
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wget https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz
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tar zxf cifar-10-python.tar.gz
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rm cifar-10-python.tar.gz
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‎demo/gan/data/get_mnist_data.sh‎

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#!/usr/bin/env sh
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# This script downloads the mnist data and unzips it.
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set -e
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DIR="$( cd "$(dirname "$0")" ; pwd -P )"
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rm -rf "$DIR/mnist_data"
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mkdir "$DIR/mnist_data"
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cd "$DIR/mnist_data"
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echo "Downloading..."
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for fname in train-images-idx3-ubyte train-labels-idx1-ubyte t10k-images-idx3-ubyte t10k-labels-idx1-ubyte
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do
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if [ ! -e $fname ]; then
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wget --no-check-certificate http://yann.lecun.com/exdb/mnist/${fname}.gz
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gunzip ${fname}.gz
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fi
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done
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‎demo/gan/gan_conf.py‎

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# Copyright (c) 2016 Baidu, Inc. All Rights Reserved
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from paddle.trainer_config_helpers import *
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mode = get_config_arg("mode", str, "generator")
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assert mode in set(["generator",
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"discriminator",
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"generator_training",
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"discriminator_training"])
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is_generator_training = mode == "generator_training"
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is_discriminator_training = mode == "discriminator_training"
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is_generator = mode == "generator"
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is_discriminator = mode == "discriminator"
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# The network structure below follows the ref https://arxiv.org/abs/1406.2661
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# Here we used two hidden layers and batch_norm
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print('mode=%s' % mode)
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# the dim of the noise (z) as the input of the generator network
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noise_dim = 10
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# the dim of the hidden layer
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hidden_dim = 10
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# the dim of the generated sample
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sample_dim = 2
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settings(
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batch_size=128,
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learning_rate=1e-4,
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learning_method=AdamOptimizer(beta1=0.5)
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)
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def discriminator(sample):
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"""
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discriminator ouputs the probablity of a sample is from generator
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or real data.
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The output has two dimenstional: dimension 0 is the probablity
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of the sample is from generator and dimension 1 is the probabblity
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of the sample is from real data.
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"""
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param_attr = ParamAttr(is_static=is_generator_training)
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bias_attr = ParamAttr(is_static=is_generator_training,
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initial_mean=1.0,
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initial_std=0)
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hidden = fc_layer(input=sample, name="dis_hidden", size=hidden_dim,
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bias_attr=bias_attr,
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param_attr=param_attr,
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act=ReluActivation())
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hidden2 = fc_layer(input=hidden, name="dis_hidden2", size=hidden_dim,
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bias_attr=bias_attr,
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param_attr=param_attr,
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act=LinearActivation())
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hidden_bn = batch_norm_layer(hidden2,
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act=ReluActivation(),
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name="dis_hidden_bn",
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bias_attr=bias_attr,
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param_attr=ParamAttr(is_static=is_generator_training,
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initial_mean=1.0,
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initial_std=0.02),
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use_global_stats=False)
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return fc_layer(input=hidden_bn, name="dis_prob", size=2,
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bias_attr=bias_attr,
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param_attr=param_attr,
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act=SoftmaxActivation())
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def generator(noise):
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"""
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generator generates a sample given noise
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"""
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param_attr = ParamAttr(is_static=is_discriminator_training)
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bias_attr = ParamAttr(is_static=is_discriminator_training,
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initial_mean=1.0,
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initial_std=0)
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hidden = fc_layer(input=noise,
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name="gen_layer_hidden",
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size=hidden_dim,
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bias_attr=bias_attr,
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param_attr=param_attr,
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act=ReluActivation())
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hidden2 = fc_layer(input=hidden, name="gen_hidden2", size=hidden_dim,
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bias_attr=bias_attr,
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param_attr=param_attr,
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act=LinearActivation())
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hidden_bn = batch_norm_layer(hidden2,
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act=ReluActivation(),
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name="gen_layer_hidden_bn",
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bias_attr=bias_attr,
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param_attr=ParamAttr(is_static=is_discriminator_training,
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initial_mean=1.0,
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initial_std=0.02),
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use_global_stats=False)
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return fc_layer(input=hidden_bn,
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name="gen_layer1",
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size=sample_dim,
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bias_attr=bias_attr,
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param_attr=param_attr,
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act=LinearActivation())
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if is_generator_training:
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noise = data_layer(name="noise", size=noise_dim)
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sample = generator(noise)
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if is_discriminator_training:
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sample = data_layer(name="sample", size=sample_dim)
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if is_generator_training or is_discriminator_training:
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label = data_layer(name="label", size=1)
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prob = discriminator(sample)
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cost = cross_entropy(input=prob, label=label)
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classification_error_evaluator(input=prob, label=label, name=mode+'_error')
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outputs(cost)
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if is_generator:
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noise = data_layer(name="noise", size=noise_dim)
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outputs(generator(noise))

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