gan_conf.py

# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from paddle.trainer_config_helpers import *

mode = get_config_arg("mode", str, "generator")
assert mode in set(["generator",
                    "discriminator",
                    "generator_training",
                    "discriminator_training"])

is_generator_training = mode == "generator_training"
is_discriminator_training = mode == "discriminator_training"
is_generator = mode == "generator"
is_discriminator = mode == "discriminator"

# The network structure below follows the ref https://arxiv.org/abs/1406.2661
# Here we used two hidden layers and batch_norm

print('mode=%s' % mode)
# the dim of the noise (z) as the input of the generator network
noise_dim = 10
# the dim of the hidden layer
hidden_dim = 10
# the dim of the generated sample
sample_dim = 2

settings(
    batch_size=128,
    learning_rate=1e-4,
    learning_method=AdamOptimizer(beta1=0.5)
)

def discriminator(sample):
    """
    discriminator ouputs the probablity of a sample is from generator
    or real data.
    The output has two dimenstional: dimension 0 is the probablity
    of the sample is from generator and dimension 1 is the probabblity
    of the sample is from real data.
    """
    param_attr = ParamAttr(is_static=is_generator_training)
    bias_attr = ParamAttr(is_static=is_generator_training,
                          initial_mean=1.0,
                          initial_std=0)

    hidden = fc_layer(input=sample, name="dis_hidden", size=hidden_dim,
                    bias_attr=bias_attr,
                    param_attr=param_attr,
                    act=ReluActivation())

    hidden2 = fc_layer(input=hidden, name="dis_hidden2", size=hidden_dim,
                    bias_attr=bias_attr,
                    param_attr=param_attr,
                    act=LinearActivation())
    
    hidden_bn = batch_norm_layer(hidden2, 
                     act=ReluActivation(), 
                     name="dis_hidden_bn", 
                     bias_attr=bias_attr, 
                     param_attr=ParamAttr(is_static=is_generator_training,
                           initial_mean=1.0,
                           initial_std=0.02),
                     use_global_stats=False)
    
    return fc_layer(input=hidden_bn, name="dis_prob", size=2,
                    bias_attr=bias_attr,
                    param_attr=param_attr,
                    act=SoftmaxActivation())

def generator(noise):
    """
    generator generates a sample given noise
    """
    param_attr = ParamAttr(is_static=is_discriminator_training)
    bias_attr = ParamAttr(is_static=is_discriminator_training,
                           initial_mean=1.0,
                           initial_std=0)
    
    hidden = fc_layer(input=noise,
                    name="gen_layer_hidden",
                    size=hidden_dim,
                    bias_attr=bias_attr,
                    param_attr=param_attr,
                    act=ReluActivation())

    hidden2 = fc_layer(input=hidden, name="gen_hidden2", size=hidden_dim,
                    bias_attr=bias_attr,
                    param_attr=param_attr,
                    act=LinearActivation())
    
    hidden_bn = batch_norm_layer(hidden2, 
                     act=ReluActivation(), 
                     name="gen_layer_hidden_bn", 
                     bias_attr=bias_attr, 
                     param_attr=ParamAttr(is_static=is_discriminator_training,
                           initial_mean=1.0,
                           initial_std=0.02),
                     use_global_stats=False)
    
    return fc_layer(input=hidden_bn,
                    name="gen_layer1",
                    size=sample_dim,
                    bias_attr=bias_attr,
                    param_attr=param_attr,
                    act=LinearActivation())

if is_generator_training:
    noise = data_layer(name="noise", size=noise_dim)
    sample = generator(noise)

if is_discriminator_training:
    sample = data_layer(name="sample", size=sample_dim)

if is_generator_training or is_discriminator_training:
    label = data_layer(name="label", size=1)
    prob = discriminator(sample)
    cost = cross_entropy(input=prob, label=label)
    classification_error_evaluator(input=prob, label=label, name=mode+'_error')
    outputs(cost)

if is_generator:
    noise = data_layer(name="noise", size=noise_dim)
    outputs(generator(noise))