ops.py 文件源码

def deconv2d(input_, output_shape,
       k_h=5, k_w=5, d_h=2, d_w=2, stddev=0.02,
       name="deconv2d", with_w=False):
    with tf.variable_scope(name):
        # filter : [height, width, output_channels, in_channels]
        w = tf.get_variable('w', [k_h, k_w, output_shape[-1], input_.get_shape()[-1]],
                  initializer=tf.random_normal_initializer(stddev=stddev))

        tf_output_shape=tf.stack(output_shape)
        deconv = tf.nn.conv2d_transpose(input_, w, output_shape=tf_output_shape,
                strides=[1, d_h, d_w, 1])

        biases = tf.get_variable('biases', [output_shape[-1]], initializer=tf.constant_initializer(0.0))
        #deconv = tf.reshape(tf.nn.bias_add(deconv, biases), deconv.get_shape())
        deconv = tf.reshape(tf.nn.bias_add(deconv, biases), tf_output_shape)

        if with_w:
            return deconv, w, biases
        else:
            return deconv