tensorflow_probability/examples/models/bayesian_resnet.py
def _resnet_block(x, filters, kernel, stride, kernel_posterior_fn):
  """Network block for ResNet."""
  x = tf.keras.layers.BatchNormalization()(x)
  x = tf.keras.layers.Activation('relu')(x)

  if stride != 1 or filters != x.shape[1]:
    shortcut = _projection_shortcut(x, filters, stride, kernel_posterior_fn)
  else:
    shortcut = x

  x = tfp.layers.Convolution2DFlipout(
      filters,
      kernel,
      strides=stride,
      padding='same',
      kernel_posterior_fn=kernel_posterior_fn)(x)
  x = tf.keras.layers.BatchNormalization()(x)
  x = tf.keras.layers.Activation('relu')(x)

  x = tfp.layers.Convolution2DFlipout(
      filters,
      kernel,
      strides=1,
      padding='same',
      kernel_posterior_fn=kernel_posterior_fn)(x)
  x = tf.keras.layers.add([x, shortcut])
  return x
def _resnet_block(x, filters, kernel, stride, kernel_posterior_fn):
  x = tf.keras.layers.BatchNormalization()(x)
  x = tf.keras.layers.Activation('relu')(x)

  if stride != 1 or filters != x.shape[1]:
    shortcut = _projection_shortcut(x, filters, stride, kernel_posterior_fn)
  else:
    shortcut = x

  x = tfp.layers.Convolution2DFlipout(
      filters,
      kernel,
      strides=stride,
      padding='same',
      kernel_posterior_fn=kernel_posterior_fn)(x)
  x = tf.keras.layers.BatchNormalization()(x)
  x = tf.keras.layers.Activation('relu')(x)

  x = tfp.layers.Convolution2DFlipout(
      filters,
      kernel,
      strides=1,
      padding='same',
      kernel_posterior_fn=kernel_posterior_fn)(x)
  x = tf.keras.layers.add([x, shortcut])
  return x
def _resnet_block ( x , filters , kernel , stride , kernel_posterior_fn ) : x = tf . keras . layers . BatchNormalization ( ) ( x ) x = tf . keras . layers . Activation ( 'relu' ) ( x ) if stride != 1 or filters != x . shape [ 1 ] : shortcut = _projection_shortcut ( x , filters , stride , kernel_posterior_fn ) else : shortcut = x x = tfp . layers . Convolution2DFlipout ( filters , kernel , strides = stride , padding = 'same' , kernel_posterior_fn = kernel_posterior_fn ) ( x ) x = tf . keras . layers . BatchNormalization ( ) ( x ) x = tf . keras . layers . Activation ( 'relu' ) ( x ) x = tfp . layers . Convolution2DFlipout ( filters , kernel , strides = 1 , padding = 'same' , kernel_posterior_fn = kernel_posterior_fn ) ( x ) x = tf . keras . layers . add ( [ x , shortcut ] ) return x
Network block for ResNet.
Network block for ResNet .