[source]

### BatchNormalization

    keras.layers.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 mean and the moving variance.
- 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

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

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