a3c_network.py 文件源码

def __create_network(self, scope, img_shape=(80, 80)):
        with tf.variable_scope(self.task_name):
            with tf.variable_scope(scope):
                with tf.variable_scope('input_data'):
                    self.inputs = tf.placeholder(shape=[None, *img_shape, cfg.HIST_LEN], dtype=tf.float32)
                with tf.variable_scope('networks'):
                    with tf.variable_scope('conv_1'):
                        self.conv_1 = slim.conv2d(activation_fn=tf.nn.relu, inputs=self.inputs, num_outputs=32,
                                                  kernel_size=[8, 8], stride=4, padding='SAME', trainable=self.is_train)
                    with tf.variable_scope('conv_2'):
                        self.conv_2 = slim.conv2d(activation_fn=tf.nn.relu, inputs=self.conv_1, num_outputs=64,
                                                  kernel_size=[4, 4], stride=2, padding='SAME', trainable=self.is_train)
                    with tf.variable_scope('conv_3'):
                        self.conv_3 = slim.conv2d(activation_fn=tf.nn.relu, inputs=self.conv_2, num_outputs=64,
                                                  kernel_size=[3, 3], stride=1, padding='SAME', trainable=self.is_train)
                    with tf.variable_scope('f_c'):
                        self.fc = slim.fully_connected(slim.flatten(self.conv_3), 512,
                                                       activation_fn=tf.nn.elu, trainable=self.is_train)