afd954e65f15aea4d438d0a219136fc4a63a573d,tensorflow/python/keras/layers/preprocessing/normalization.py,Normalization,build,#Normalization#Any#,120

Before Change

    super(Normalization, self).build(input_shape)

    if (self.mean_val is not None and self.variance_val is not None):
      mean_val = self.mean_val * np.ones(mean_and_var_shape)
      variance_val = self.variance_val * np.ones(mean_and_var_shape)
      self.set_weights([mean_val, variance_val])

    self.built = True

  def update_state(self, data):
    if not self.built:

After Change

    ]
    mean_and_var_shape = tuple(input_shape[d] for d in self._keep_axis)

    if self.input_mean is None:
      self.adapt_mean = self.add_weight(
          name="mean",
          shape=mean_and_var_shape,
          dtype=self.dtype,
          initializer=init_ops.zeros_initializer,
          trainable=False)
      self.adapt_variance = self.add_weight(
          name="variance",
          shape=mean_and_var_shape,
          dtype=self.dtype,
          initializer=init_ops.ones_initializer,
          trainable=False)
      self.count = self.add_weight(
          name="count",
          shape=(),
          dtype=dtypes.int64,
          initializer=init_ops.zeros_initializer,
          trainable=False)
      self.finalize_state()
    else:
      // In the no adapt case, make constant tensors for mean and variance with
      // proper broadcast shape for use during call.
      mean = self.input_mean * np.ones(mean_and_var_shape)
      variance = self.input_variance * np.ones(mean_and_var_shape)
      mean = array_ops.reshape(mean, self._broadcast_shape)
      variance = array_ops.reshape(variance, self._broadcast_shape)
      self.mean = math_ops.cast(mean, self.dtype)
      self.variance = math_ops.cast(variance, self.dtype)

  def update_state(self, data):
    if self.input_mean is not None:
      raise ValueError(
          "Cannot `adapt` a Normalization layer that is initialized with "

Instances