You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
117 lines
3.5 KiB
117 lines
3.5 KiB
# 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 *
|
|
import numpy as np
|
|
|
|
is_generating = get_config_arg("is_generating", bool, False)
|
|
|
|
settings(batch_size=32, learning_rate=1e-3, learning_method=AdamOptimizer())
|
|
|
|
X_dim = 28 * 28
|
|
h_dim = 128
|
|
z_dim = 100
|
|
|
|
|
|
def reparameterization(mu, logvar):
|
|
eps = ParamAttr(initial_mean=0., initial_std=1)
|
|
with mixed_layer() as sigma:
|
|
sigma += dotmul_projection(layer_math.exp(logvar) * 0.5, param_attr=eps)
|
|
return mu + sigma
|
|
|
|
|
|
def q_func(X):
|
|
"""
|
|
xavier initialization
|
|
"""
|
|
param_attr = ParamAttr(
|
|
name='share.w', initial_mean=0., initial_std=1. / np.sqrt(X_dim / 2.))
|
|
mu_param = ParamAttr(
|
|
name='mu.w', initial_mean=0., initial_std=1. / np.sqrt(h_dim / 2.))
|
|
logvar_param = ParamAttr(
|
|
name='logvar.w', initial_mean=0., initial_std=1. / np.sqrt(h_dim / 2.))
|
|
|
|
bias_attr = ParamAttr(name='share.bias', initial_mean=0., initial_std=0.)
|
|
mu_bias = ParamAttr(name='mu.bias', initial_mean=0., initial_std=0.)
|
|
logvar_bias = ParamAttr(name='logvar.bias', initial_mean=0., initial_std=0.)
|
|
|
|
share_layer = fc_layer(
|
|
X,
|
|
size=h_dim,
|
|
param_attr=param_attr,
|
|
bias_attr=bias_attr,
|
|
act=ReluActivation())
|
|
|
|
return (fc_layer(
|
|
share_layer,
|
|
size=z_dim,
|
|
param_attr=mu_param,
|
|
bias_attr=mu_bias,
|
|
act=LinearActivation()), fc_layer(
|
|
share_layer,
|
|
size=z_dim,
|
|
param_attr=logvar_param,
|
|
bias_attr=logvar_bias,
|
|
act=LinearActivation()))
|
|
|
|
|
|
def generator(z):
|
|
|
|
hidden_param = ParamAttr(
|
|
name='hidden.w', initial_mean=0., initial_std=1. / np.sqrt(z_dim / 2.))
|
|
hidden_bias = ParamAttr(name='hidden.bias', initial_mean=0., initial_std=0.)
|
|
prob_param = ParamAttr(
|
|
name='prob.w', initial_mean=0., initial_std=1. / np.sqrt(h_dim / 2.))
|
|
prob_bias = ParamAttr(name='prob.bias', initial_mean=0., initial_std=0.)
|
|
|
|
hidden_layer = fc_layer(
|
|
z,
|
|
size=h_dim,
|
|
act=ReluActivation(),
|
|
param_attr=hidden_param,
|
|
bias_attr=hidden_bias)
|
|
prob = fc_layer(
|
|
hidden_layer,
|
|
size=X_dim,
|
|
act=SigmoidActivation(),
|
|
param_attr=prob_param,
|
|
bias_attr=prob_bias)
|
|
|
|
return prob
|
|
|
|
|
|
def reconstruct_error(prob, X):
|
|
cost = multi_binary_label_cross_entropy(input=prob, label=X)
|
|
return cost
|
|
|
|
|
|
def KL_loss(mu, logvar):
|
|
with mixed_layer() as mu_square:
|
|
mu_square += dotmul_operator(mu, mu, scale=1.)
|
|
|
|
cost = 0.5 * sum_cost(layer_math.exp(logvar) + mu_square - 1. - logvar)
|
|
|
|
return cost
|
|
|
|
|
|
if not is_generating:
|
|
x_batch = data_layer(name='x_batch', size=X_dim)
|
|
mu, logvar = q_func(x_batch)
|
|
z_samples = reparameterization(mu, logvar)
|
|
prob = generator(z_samples)
|
|
outputs(reconstruct_error(prob, x_batch) + KL_loss(mu, logvar))
|
|
else:
|
|
z_samples = data_layer(name='noise', size=z_dim)
|
|
outputs(generator(z_samples))
|