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BatchNormalization

keras.layers.normalization.BatchNormalization(epsilon=1e-06, mode=0, axis=-1, momentum=0.9, weights=None, beta_init='zero', gamma_init='one')

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.

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.

Arguments

  • epsilon: small float > 0. Fuzz parameter.
  • mode: integer, 0 or 1.
    • 0: feature-wise normalization. Each feature map in the input will be normalized separately. The axis on which to normalize is specified by the axis argument. Note that if the input is a 4D image tensor using Theano conventions (samples, channels, rows, cols) then you should set axis to 1 to normalize along the channels axis.
    • 1: sample-wise normalization. This mode assumes a 2D input.
  • axis: integer, axis along which to normalize in mode 0. For instance, if your input tensor has shape (samples, channels, rows, cols), set axis to 1 to normalize per feature map (channels axis).
  • momentum: momentum in the computation of the exponential average of the mean and standard deviation of the data, for feature-wise normalization.
  • weights: Initialization weights. List of 2 numpy arrays, with shapes: [(input_shape,), (input_shape,)]
  • beta_init: name of initialization function for shift parameter (see initializations), or alternatively, Theano/TensorFlow function to use for weights initialization. This parameter is only relevant if you don't pass a weights argument.

  • gamma_init: name of initialization function for scale parameter (see initializations), or alternatively, Theano/TensorFlow function to use for weights initialization. This parameter is only relevant if you don't pass a weights argument. References

  • Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift