### 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**