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BatchNormalization

keras.layers.normalization.BatchNormalization(axis=-1, momentum=0.99, epsilon=0.001, center=True, scale=True, beta_initializer='zeros', gamma_initializer='ones', moving_mean_initializer='zeros', moving_variance_initializer='ones', beta_regularizer=None, gamma_regularizer=None, beta_constraint=None, gamma_constraint=None)

Batch normalization layer (Ioffe and Szegedy, 2014).

Normalize the activations of the previous layer at each batch, i.e. applies a transformation that maintains the mean activation close to 0 and the activation standard deviation close to 1.

Arguments

  • axis: Integer, the axis that should be normalized (typically the features axis). For instance, after a Conv2D layer with data_format="channels_first", set axis=1 in BatchNormalization.
  • momentum: Momentum for the moving average.
  • epsilon: Small float added to variance to avoid dividing by zero.
  • center: If True, add offset of beta to normalized tensor. If False, beta is ignored.
  • scale: If True, multiply by gamma. If False, gamma is not used. When the next layer is linear (also e.g. nn.relu), this can be disabled since the scaling will be done by the next layer.
  • beta_initializer: Initializer for the beta weight.
  • gamma_initializer: Initializer for the gamma weight.
  • moving_mean_initializer: Initializer for the moving mean.
  • moving_variance_initializer: Initializer for the moving variance.
  • beta_regularizer: Optional regularizer for the beta weight.
  • gamma_regularizer: Optional regularizer for the gamma weight.
  • beta_constraint: Optional constraint for the beta weight.
  • gamma_constraint: Optional constraint for the gamma weight.

Input shape

Arbitrary. Use the keyword argument input_shape (tuple of integers, does not include the samples axis) when using this layer as the first layer in a model.

Output shape

Same shape as input.

References