### 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.

**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.

- 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**: 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.

**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**