(inputs, filters, kernel_size, strides, data_format, kernel_initializer=tf.variance_scaling_initializer)
return tf.layers.conv2d(
      inputs=inputs, filters=filters, kernel_size=kernel_size, strides=strides,
      padding=('SAME' if strides == 1 else 'VALID'), use_bias=False,
      kernel_initializer=kernel_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 conv2d_fixed_padding(inputs, filters, kernel_size, strides, data_format, kernel_initializer=tf.variance_scaling_initializer):
  """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).
  if strides > 1:
    inputs = fixed_padding(inputs, kernel_size, data_format)

  return tf.layers.conv2d(
      inputs=inputs, filters=filters, kernel_size=kernel_size, strides=strides,
      padding=('SAME' if strides == 1 else 'VALID'), use_bias=False,
      kernel_initializer=kernel_initializer(),
      data_format=data_format)
def conv2d_fixed_padding ( inputs , filters , kernel_size , strides , data_format , kernel_initializer = tf . variance_scaling_initializer ) : # 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). if strides > 1 : inputs = fixed_padding ( inputs , kernel_size , data_format ) return tf . layers . conv2d ( inputs = inputs , filters = filters , kernel_size = kernel_size , strides = strides , padding = ( 'SAME' if strides == 1 else 'VALID' ) , use_bias = False , kernel_initializer = kernel_initializer ( ) , data_format = data_format )