custom_ops.py 文件源码

def __call__(self, input_layer, output_shape,
                 k_h=5, k_w=5, d_h=2, d_w=2, stddev=0.02,
                 name="deconv2d"):
        output_shape[0] = input_layer.shape[0]
        ts_output_shape = tf.pack(output_shape)
        with tf.variable_scope(name):
            # filter : [height, width, output_channels, in_channels]
            w = self.variable('w', [k_h, k_w, output_shape[-1], input_layer.shape[-1]],
                              init=tf.random_normal_initializer(stddev=stddev))

            try:
                deconv = tf.nn.conv2d_transpose(input_layer, w,
                                                output_shape=ts_output_shape,
                                                strides=[1, d_h, d_w, 1])

            # Support for versions of TensorFlow before 0.7.0
            except AttributeError:
                deconv = tf.nn.deconv2d(input_layer, w, output_shape=ts_output_shape,
                                        strides=[1, d_h, d_w, 1])

            # biases = self.variable('biases', [output_shape[-1]], init=tf.constant_initializer(0.0))
            # deconv = tf.reshape(tf.nn.bias_add(deconv, biases), [-1] + output_shape[1:])
            deconv = tf.reshape(deconv, [-1] + output_shape[1:])

            return deconv