(inputs, num_outputs, kernel_shape, strides=[1, 1], scope="deconv2d")
Args:
    inputs: nhwc
    num_outputs: int
    kernel_shape: [kernel_h, kernel_w]
    strides: [stride_h, stride_w]
  Returns:
    outputs: nhwc
Args:
    inputs: nhwc
    num_outputs: int
    kernel_shape: [kernel_h, kernel_w]
    strides: [stride_h, stride_w]
  Returns:
    outputs: nhwc
Args : inputs : nhwc num_outputs : int kernel_shape : [ kernel_h kernel_w ] strides : [ stride_h stride_w ] Returns : outputs : nhwc
def deconv2d(inputs, num_outputs, kernel_shape, strides=[1, 1], scope="deconv2d"):
  """
  Args:
    inputs: nhwc
    num_outputs: int
    kernel_shape: [kernel_h, kernel_w]
    strides: [stride_h, stride_w]
  Returns:
    outputs: nhwc
  """
  with tf.variable_scope(scope) as scope:
    return tf.contrib.layers.convolution2d_transpose(inputs, num_outputs, kernel_shape, strides, \
          padding='SAME', weights_initializer=tf.truncated_normal_initializer(stddev=0.1), \
          biases_initializer=tf.constant_initializer(0.0))
def deconv2d ( inputs , num_outputs , kernel_shape , strides = [ 1 , 1 ] , scope = "deconv2d" ) : with tf . variable_scope ( scope ) as scope : return tf . contrib . layers . convolution2d_transpose ( inputs , num_outputs , kernel_shape , strides , padding = 'SAME' , weights_initializer = tf . truncated_normal_initializer ( stddev = 0.1 ) , biases_initializer = tf . constant_initializer ( 0.0 ) )