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152 lines
4.4 KiB
152 lines
4.4 KiB
# Copyright (c) 2016 PaddlePaddle Authors. 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([
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"generator", "discriminator", "generator_training", "discriminator_training"
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])
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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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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(
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is_static=is_generator_training, initial_mean=1.0, initial_std=0)
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hidden = fc_layer(
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input=sample,
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name="dis_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(
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input=hidden,
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name="dis_hidden2",
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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=LinearActivation())
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hidden_bn = batch_norm_layer(
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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(
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is_static=is_generator_training, 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(
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input=hidden_bn,
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name="dis_prob",
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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(
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is_static=is_discriminator_training, initial_mean=1.0, initial_std=0)
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hidden = fc_layer(
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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(
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input=hidden,
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name="gen_hidden2",
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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=LinearActivation())
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hidden_bn = batch_norm_layer(
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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(
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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(
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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(
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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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