def generator(name, in_data, training=True):
'''生成器，通过name来获得具体的生成器'''
# 1050 gtx 2G 约100ms一幅(256x256x3)
with tf.variable_scope(name, reuse=tf.AUTO_REUSE):
features = encode(in_data, training)
transformed_features = transform(features, training)
genImg = decode(transformed_features, training)
return genImg
def g(X):
return encode(X, self.D, self.I, self.cell_type, self.n_hidden,
self.recurrent)
def autoencoder(x, dropout, noise_std):
#Variables - filters
with tf.name_scope('weights'):
#weights={'h1': tf.Variable(tf.truncated_normal([3,3,1,10],stddev=0.1)),
# 'h2': tf.Variable(tf.truncated_normal([3,3,10,10],stddev=0.1)),
# 'h3': tf.Variable(tf.truncated_normal([3,3,10,10],stddev=0.1))}
weights = {
'h1':
tf.Variable(
tf.random_uniform([3, 3, 1, 10], -1.0 / np.sqrt(10),
1.0 / np.sqrt(10))),
'h2':
tf.Variable(
tf.random_uniform([3, 3, 10, 10], -1.0 / np.sqrt(10),
1.0 / np.sqrt(10))),
'h3':
tf.Variable(
tf.random_uniform([3, 3, 10, 10], -1.0 / np.sqrt(10),
1.0 / np.sqrt(10)))
}
for a_layer in weights:
with tf.name_scope('weights_' + a_layer):
variable_summaries(weights[a_layer])
#Variables - biases
with tf.name_scope('biases'):
biases = {
'h1': tf.Variable(tf.zeros([10])),
'h2': tf.Variable(tf.zeros([10])),
'h3': tf.Variable(tf.zeros([10])),
'h4': tf.Variable(tf.zeros([10])),
'h5': tf.Variable(tf.zeros([10])),
'h6': tf.Variable(tf.zeros([1]))
}
for a_layer in biases:
with tf.name_scope('bias_' + a_layer):
variable_summaries(biases[a_layer])
#reshape input
with tf.name_scope('input'):
x = tf.reshape(x, shape=[-1, 28, 28, 1])
tf.summary.image('input', x, 10)
#noise (training_time)
with tf.name_scope('add_noise'):
noise = tf.random_normal(shape=tf.shape(x),
mean=0.0,
stddev=noise_std,
dtype=tf.float32)
x_noised = x + noise
x = x_noised
#encoding
with tf.name_scope('encoding'):
code = encode(x, weights, biases)
#dropout (training_time)
code = tf.nn.dropout(code, dropout)
#decoding
with tf.name_scope('decoding'):
reconstruction = decode(code, weights, biases)
#reshape
tf.summary.image('output', reconstruction, 10)
code = tf.reshape(
code,
[-1, tf.shape(code)[1] * tf.shape(code)[2] * tf.shape(code)[3]])
reconstruction = tf.reshape(reconstruction, [-1, n_input])
#logging
tf.summary.histogram('code', code)
tf.summary.histogram('reconstruction', reconstruction)
return (code, reconstruction)