nets.py 文件源码

def encZ(x, ACTIVATION):

   conv1 = tcl.conv2d(x, 32, 5, 2, activation_fn=tf.identity, normalizer_fn=tcl.batch_norm, weights_initializer=tf.random_normal_initializer(stddev=0.02), scope='conv1')
   conv1 = activate(conv1, ACTIVATION)

   conv2 = tcl.conv2d(conv1, 64, 5, 2, activation_fn=tf.identity, normalizer_fn=tcl.batch_norm, weights_initializer=tf.random_normal_initializer(stddev=0.02), scope='conv2')
   conv2 = activate(conv2, ACTIVATION)

   conv3 = tcl.conv2d(conv2, 128, 5, 2, activation_fn=tf.identity, normalizer_fn=tcl.batch_norm, weights_initializer=tf.random_normal_initializer(stddev=0.02), scope='conv3')
   conv3 = activate(conv3, ACTIVATION)

   conv4 = tcl.conv2d(conv3, 256, 5, 2, activation_fn=tf.identity, normalizer_fn=tcl.batch_norm, weights_initializer=tf.random_normal_initializer(stddev=0.02), scope='conv4')
   conv4 = activate(conv4, ACTIVATION)

   conv4_flat = tcl.flatten(conv4)

   fc1 = tcl.fully_connected(conv4_flat, 4096, activation_fn=tf.identity, normalizer_fn=tcl.batch_norm, weights_initializer=tf.random_normal_initializer(stddev=0.02), scope='fc1')
   fc1 = activate(fc1, ACTIVATION)
   #fc1 = tcl.dropout(fc1, 0.5)

   fc2 = tcl.fully_connected(fc1, 100, activation_fn=tf.identity, weights_initializer=tf.random_normal_initializer(stddev=0.02), scope='fc2')

   print 'input:',x
   print 'conv1:',conv1
   print 'conv2:',conv2
   print 'conv3:',conv3
   print 'conv4:',conv4
   print 'fc1:',fc1
   print 'fc2:',fc2
   print 'END ENCODER\n'

   tf.add_to_collection('vars', conv1)
   tf.add_to_collection('vars', conv2)
   tf.add_to_collection('vars', conv3)
   tf.add_to_collection('vars', conv4)
   tf.add_to_collection('vars', fc1)
   tf.add_to_collection('vars', fc2)

   return fc2