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117 lines
3.5 KiB
117 lines
3.5 KiB
8 years ago
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# 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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import numpy as np
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is_generating = get_config_arg("is_generating", bool, False)
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settings(batch_size=32, learning_rate=1e-3, learning_method=AdamOptimizer())
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X_dim = 28 * 28
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h_dim = 128
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z_dim = 100
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def reparameterization(mu, logvar):
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eps = ParamAttr(initial_mean=0., initial_std=1)
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with mixed_layer() as sigma:
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sigma += dotmul_projection(layer_math.exp(logvar) * 0.5, param_attr=eps)
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return mu + sigma
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def q_func(X):
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"""
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xavier initialization
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"""
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param_attr = ParamAttr(
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name='share.w', initial_mean=0., initial_std=1. / np.sqrt(X_dim / 2.))
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mu_param = ParamAttr(
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name='mu.w', initial_mean=0., initial_std=1. / np.sqrt(h_dim / 2.))
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logvar_param = ParamAttr(
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name='logvar.w', initial_mean=0., initial_std=1. / np.sqrt(h_dim / 2.))
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bias_attr = ParamAttr(name='share.bias', initial_mean=0., initial_std=0.)
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mu_bias = ParamAttr(name='mu.bias', initial_mean=0., initial_std=0.)
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logvar_bias = ParamAttr(name='logvar.bias', initial_mean=0., initial_std=0.)
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share_layer = fc_layer(
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X,
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size=h_dim,
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param_attr=param_attr,
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bias_attr=bias_attr,
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act=ReluActivation())
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return (fc_layer(
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share_layer,
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size=z_dim,
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param_attr=mu_param,
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bias_attr=mu_bias,
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act=LinearActivation()), fc_layer(
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share_layer,
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size=z_dim,
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param_attr=logvar_param,
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bias_attr=logvar_bias,
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act=LinearActivation()))
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def generator(z):
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hidden_param = ParamAttr(
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name='hidden.w', initial_mean=0., initial_std=1. / np.sqrt(z_dim / 2.))
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hidden_bias = ParamAttr(name='hidden.bias', initial_mean=0., initial_std=0.)
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prob_param = ParamAttr(
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name='prob.w', initial_mean=0., initial_std=1. / np.sqrt(h_dim / 2.))
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prob_bias = ParamAttr(name='prob.bias', initial_mean=0., initial_std=0.)
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hidden_layer = fc_layer(
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z,
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size=h_dim,
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act=ReluActivation(),
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param_attr=hidden_param,
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bias_attr=hidden_bias)
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prob = fc_layer(
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hidden_layer,
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size=X_dim,
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act=SigmoidActivation(),
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param_attr=prob_param,
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bias_attr=prob_bias)
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return prob
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def reconstruct_error(prob, X):
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cost = multi_binary_label_cross_entropy(input=prob, label=X)
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return cost
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def KL_loss(mu, logvar):
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with mixed_layer() as mu_square:
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mu_square += dotmul_operator(mu, mu, scale=1.)
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cost = 0.5 * sum_cost(layer_math.exp(logvar) + mu_square - 1. - logvar)
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return cost
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if not is_generating:
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x_batch = data_layer(name='x_batch', size=X_dim)
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mu, logvar = q_func(x_batch)
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z_samples = reparameterization(mu, logvar)
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prob = generator(z_samples)
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outputs(reconstruct_error(prob, x_batch) + KL_loss(mu, logvar))
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else:
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z_samples = data_layer(name='noise', size=z_dim)
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outputs(generator(z_samples))
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