# tf.compat.v1.layers.batch_normalization

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| [![](https://www.tensorflow.org/images/GitHub-Mark-32px.png) View source on GitHub](https://github.com/keras-team/keras/tree/v2.14.0/keras/legacy_tf_layers/normalization.py#L240-L474) |

Functional interface for the batch normalization layer from_config(Ioffe et al., 2015).

    tf.compat.v1.layers.batch_normalization(
        inputs,
        axis=-1,
        momentum=0.99,
        epsilon=0.001,
        center=True,
        scale=True,
        beta_initializer=https://www.tensorflow.org/versions/r2.14/api_docs/python/tf/compat/v1/zeros_initializer,
        gamma_initializer=https://www.tensorflow.org/versions/r2.14/api_docs/python/tf/compat/v1/ones_initializer,
        moving_mean_initializer=https://www.tensorflow.org/versions/r2.14/api_docs/python/tf/compat/v1/zeros_initializer,
        moving_variance_initializer=https://www.tensorflow.org/versions/r2.14/api_docs/python/tf/compat/v1/ones_initializer,
        beta_regularizer=None,
        gamma_regularizer=None,
        beta_constraint=None,
        gamma_constraint=None,
        training=False,
        trainable=True,
        name=None,
        reuse=None,
        renorm=False,
        renorm_clipping=None,
        renorm_momentum=0.99,
        fused=None,
        virtual_batch_size=None,
        adjustment=None
    )

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## Migrate to TF2

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> [!CAUTION]
> **Caution:** This API was designed for TensorFlow v1. Continue reading for details on how to migrate from this API to a native TensorFlow v2 equivalent. See the [TensorFlow v1 to TensorFlow v2 migration guide](https://www.tensorflow.org/guide/migrate) for instructions on how to migrate the rest of your code.

This API is a legacy api that is only compatible with eager execution and
[`tf.function`](https://www.tensorflow.org/versions/r2.14/api_docs/python/tf/function) if you combine it with
`tf.compat.v1.keras.utils.track_tf1_style_variables`

Please refer to [tf.layers model mapping section of the migration guide](https://www.tensorflow.org/guide/migrate/model_mapping)
to learn how to use your TensorFlow v1 model in TF2 with Keras.

The corresponding TensorFlow v2 layer is
[`tf.keras.layers.BatchNormalization`](https://www.tensorflow.org/versions/r2.14/api_docs/python/tf/keras/layers/BatchNormalization).

The batch updating pattern with
[`tf.control_dependencies(tf.GraphKeys.UPDATE_OPS)`](https://www.tensorflow.org/versions/r2.14/api_docs/python/tf/control_dependencies) should not be used in
native TF2. Consult the [`tf.keras.layers.BatchNormalization`](https://www.tensorflow.org/versions/r2.14/api_docs/python/tf/keras/layers/BatchNormalization) documentation
for further information.

#### Structural Mapping to Native TF2

None of the supported arguments have changed name.

Before:

     x_norm = tf.compat.v1.layers.batch_normalization(x)

After:

To migrate code using TF1 functional layers use the [Keras Functional API](https://www.tensorflow.org/guide/keras/functional):

     x = tf.keras.Input(shape=(28, 28, 1),)
     y = tf.keras.layers.BatchNormalization()(x)
     model = tf.keras.Model(x, y)

#### How to Map Arguments

| TF1 Arg Name | TF2 Arg Name | Note |
|---|---|---|
| `name` | `name` | Layer base class |
| `trainable` | `trainable` | Layer base class |
| `axis` | `axis` | - |
| `momentum` | `momentum` | - |
| `epsilon` | `epsilon` | - |
| `center` | `center` | - |
| `scale` | `scale` | - |
| `beta_initializer` | `beta_initializer` | - |
| `gamma_initializer` | `gamma_initializer` | - |
| `moving_mean_initializer` | `moving_mean_initializer` | - |
| `beta_regularizer` | \`beta_regularizer' | - |
| `gamma_regularizer` | \`gamma_regularizer' | - |
| `beta_constraint` | \`beta_constraint' | - |
| `gamma_constraint` | \`gamma_constraint' | - |
| `renorm` | Not supported | - |
| `renorm_clipping` | Not supported | - |
| `renorm_momentum` | Not supported | - |
| `fused` | Not supported | - |
| `virtual_batch_size` | Not supported | - |
| `adjustment` | Not supported | - |

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## Description

> [!NOTE]
> **Note:** when training, the moving_mean and moving_variance need to be updated. By default the update ops are placed in `tf.GraphKeys.UPDATE_OPS`, so they need to be executed alongside the `train_op`. Also, be sure to add any batch_normalization ops before getting the update_ops collection. Otherwise, update_ops will be empty, and training/inference will not work properly. For example:

      x_norm = tf.compat.v1.layers.batch_normalization(x, training=training)

      # ...

      update_ops = tf.compat.v1.get_collection(tf.GraphKeys.UPDATE_OPS)
      train_op = optimizer.minimize(loss)
      train_op = tf.group([train_op, update_ops])

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| ## Args ||
|---|---|
| `inputs` | Tensor input. |
| `axis` | An `int`, the axis that should be normalized (typically the features axis). For instance, after a `Convolution2D` 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`](https://www.tensorflow.org/api_docs/python/tf/nn/relu)), this can be disabled since the scaling can 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` | An optional projection function to be applied to the `beta` weight after being updated by an `Optimizer` (e.g. used to implement norm constraints or value constraints for layer weights). The function must take as input the unprojected variable and must return the projected variable (which must have the same shape). Constraints are not safe to use when doing asynchronous distributed training. |
| `gamma_constraint` | An optional projection function to be applied to the `gamma` weight after being updated by an `Optimizer`. |
| `training` | Either a Python boolean, or a TensorFlow boolean scalar tensor (e.g. a placeholder). Whether to return the output in training mode (normalized with statistics of the current batch) or in inference mode (normalized with moving statistics). **NOTE**: make sure to set this parameter correctly, or else your training/inference will not work properly. |
| `trainable` | Boolean, if `True` also add variables to the graph collection `GraphKeys.TRAINABLE_VARIABLES` (see tf.Variable). |
| `name` | String, the name of the layer. |
| `reuse` | Boolean, whether to reuse the weights of a previous layer by the same name. |
| `renorm` | Whether to use Batch Renormalization (Ioffe, 2017). This adds extra variables during training. The inference is the same for either value of this parameter. |
| `renorm_clipping` | A dictionary that may map keys 'rmax', 'rmin', 'dmax' to scalar `Tensors` used to clip the renorm correction. The correction `(r, d)` is used as `corrected_value = normalized_value * r + d`, with `r` clipped to \[rmin, rmax\], and `d` to \[-dmax, dmax\]. Missing rmax, rmin, dmax are set to inf, 0, inf, respectively. |
| `renorm_momentum` | Momentum used to update the moving means and standard deviations with renorm. Unlike `momentum`, this affects training and should be neither too small (which would add noise) nor too large (which would give stale estimates). Note that `momentum` is still applied to get the means and variances for inference. |
| `fused` | if `None` or `True`, use a faster, fused implementation if possible. If `False`, use the system recommended implementation. |
| `virtual_batch_size` | An `int`. By default, `virtual_batch_size` is `None`, which means batch normalization is performed across the whole batch. When `virtual_batch_size` is not `None`, instead perform "Ghost Batch Normalization", which creates virtual sub-batches which are each normalized separately (with shared gamma, beta, and moving statistics). Must divide the actual batch size during execution. |
| `adjustment` | A function taking the `Tensor` containing the (dynamic) shape of the input tensor and returning a pair (scale, bias) to apply to the normalized values (before gamma and beta), only during training. For example, if axis==-1, `adjustment = lambda shape: ( tf.random.uniform(shape[-1:], 0.93, 1.07), tf.random.uniform(shape[-1:], -0.1, 0.1))` will scale the normalized value by up to 7% up or down, then shift the result by up to 0.1 (with independent scaling and bias for each feature but shared across all examples), and finally apply gamma and/or beta. If `None`, no adjustment is applied. Cannot be specified if virtual_batch_size is specified. |

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| ## Returns ||
|---|---|
| Output tensor. ||

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| ## Raises ||
|---|---|
| `ValueError` | if eager execution is enabled. |

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| ## References ||
|---|---|
| Batch Normalization - Accelerating Deep Network Training by Reducing Internal Covariate Shift: [Ioffe et al., 2015](http://proceedings.mlr.press/v37/ioffe15.html) ([pdf](http://proceedings.mlr.press/v37/ioffe15.pdf)) Batch Renormalization - Towards Reducing Minibatch Dependence in Batch-Normalized Models: [Ioffe, 2017](http://papers.nips.cc/paper/6790-batch-renormalization-towards-reducing-minibatch-dependence-in-batch-normalized-models) ([pdf](http://papers.nips.cc/paper/6790-batch-renormalization-towards-reducing-minibatch-dependence-in-batch-normalized-models.pdf)) ||

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