began_model.py 文件源码

def encoder(self, x, embedding, reuse=None):
        with tf.variable_scope("encoder", reuse=reuse):
            with slim.arg_scope([slim.conv2d],
                                stride=1, activation_fn=tf.nn.elu, padding="SAME",
                                weights_initializer=tf.contrib.layers.variance_scaling_initializer(),
                                weights_regularizer=slim.l2_regularizer(5e-4),
                                bias_initializer=tf.zeros_initializer()):
                x = slim.conv2d(x, embedding, 3)

                for i in range(self.conv_repeat_num):
                    channel_num = embedding * (i + 1)
                    x = slim.repeat(x, 2, slim.conv2d, channel_num, 3)
                    if i < self.conv_repeat_num - 1:
                        # Is using stride pooling more better method than max pooling?
                        # or average pooling
                        # x = slim.conv2d(x, channel_num, kernel_size=3, stride=2)  # sub-sampling
                        x = slim.avg_pool2d(x, kernel_size=2, stride=2)
                        # x = slim.max_pooling2d(x, 3, 2)

                x = tf.reshape(x, [-1, np.prod([8, 8, channel_num])])
        return x