(inputs, filters, kernel_size, dilation_rate, data_format)
return tf.layers.conv2d(
      inputs=inputs, filters=filters, kernel_size=kernel_size, strides=1, dilation_rate = dilation_rate,
      padding='SAME', use_bias=False,#True,
      kernel_initializer=tf.variance_scaling_initializer(),
      bias_initializer=None,#tf.zeros_initializer(),
      data_format=data_format)
Strided 2-D convolution with explicit padding.
Strided 2-D convolution with explicit padding.
Strided 2 - D convolution with explicit padding .
def dilate_conv2d(inputs, filters, kernel_size, dilation_rate, data_format):
  """Strided 2-D convolution with explicit padding."""
  # The padding is consistent and is based only on `kernel_size`, not on the
  # dimensions of `inputs` (as opposed to using `tf.layers.conv2d` alone).
  return tf.layers.conv2d(
      inputs=inputs, filters=filters, kernel_size=kernel_size, strides=1, dilation_rate = dilation_rate,
      padding='SAME', use_bias=False,#True,
      kernel_initializer=tf.variance_scaling_initializer(),
      bias_initializer=None,#tf.zeros_initializer(),
      data_format=data_format)
def dilate_conv2d ( inputs , filters , kernel_size , dilation_rate , data_format ) : # The padding is consistent and is based only on `kernel_size`, not on the # dimensions of `inputs` (as opposed to using `tf.layers.conv2d` alone). return tf . layers . conv2d ( inputs = inputs , filters = filters , kernel_size = kernel_size , strides = 1 , dilation_rate = dilation_rate , padding = 'SAME' , use_bias = False , #True, kernel_initializer = tf . variance_scaling_initializer ( ) , bias_initializer = None , #tf.zeros_initializer(), data_format = data_format )