models.py 文件源码

def __init__(self, namespace, input_state, opts):
    super(ValueNetwork, self).__init__(namespace)

    with tf.variable_scope(namespace):
      # do potential horizontal flipping of input state
      # recall input is (batch, height, width, rgb) and we want to flip on width
      flipped_input_state = tf.cond(base_network.FLIP_HORIZONTALLY,
                                    lambda: tf.reverse(input_state, dims=[False, False, True, False]),
                                    lambda: input_state)

      # expose self.input_state_representation since it will be the network "shared"
      # by l_value & output_action network when running --share-input-state-representation
      self.conv_net_output = self.conv_net_on(flipped_input_state, opts)
      self.hidden_layers = self.hidden_layers_on(self.conv_net_output, [100, 50])
      self.value = slim.fully_connected(scope='fc',
                                        inputs=self.hidden_layers,
                                        num_outputs=1,
                                        weights_regularizer=tf.contrib.layers.l2_regularizer(0.01),
                                        activation_fn=None)  # (batch, 1)