> For the complete documentation index, see [llms.txt](https://deeplearning4j.konduit.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://deeplearning4j.konduit.ai/en-1.0.0-rewrite/model-import/overview-1/layers-normalization.md).

# Normalization Layers

### Keras Normalization Layer Import

Normalization layer support is implemented in the [layers/normalization](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/layers/normalization) package.

#### Support Summary

| Keras Layer        | DL4J Equivalent    | Supported |
| ------------------ | ------------------ | --------- |
| BatchNormalization | BatchNormalization | Yes       |

***

### KerasBatchNormalization

[source](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/layers/normalization/KerasBatchNormalization.java)

Imports a Keras `BatchNormalization` layer as a DL4J `BatchNormalization` layer. Both training-mode (with learned gamma/beta) and inference-mode (with running mean/variance) parameters are loaded from the HDF5 file.

#### Constructor

```java
public KerasBatchNormalization(Integer kerasVersion) throws UnsupportedKerasConfigurationException
```

Pass-through constructor. `kerasVersion` is the major Keras version (1 or 2).

#### getBatchNormalizationLayer

```java
public BatchNormalization getBatchNormalizationLayer()
```

Returns the configured DL4J `BatchNormalization` layer. Configuration includes:

* `epsilon` — small constant added to the variance for numerical stability (default `1e-3` in Keras)
* `momentum` — momentum for updating running statistics (called `decay` in Keras 1, `momentum` in Keras 2)
* `center` — whether a bias (beta) term is used
* `scale` — whether a scale (gamma) term is used

#### getOutputType

```java
public InputType getOutputType(InputType... inputType) throws InvalidKerasConfigurationException
```

Returns the output `InputType`. For `BatchNormalization`, the output shape is the same as the input shape.

**Parameters:**

* `inputType` — array of input `InputType` objects

**Returns:** output `InputType`

#### getNumParams

```java
public int getNumParams()
```

Returns the number of trainable parameters (4: gamma, beta, running mean, running variance).

#### setWeights

```java
public void setWeights(Map<String, INDArray> weights) throws InvalidKerasConfigurationException
```

Loads the four weight tensors from the weight map:

| DL4J parameter name | Keras HDF5 tensor |
| ------------------- | ----------------- |
| `gamma`             | scale parameter   |
| `beta`              | center parameter  |
| `mean`              | running mean      |
| `var`               | running variance  |

**Parameters:**

* `weights` — map from parameter name to `INDArray`

***

### Keras BatchNormalization Parameters

The following Keras parameters are mapped during import:

| Keras parameter | DL4J configuration method                |
| --------------- | ---------------------------------------- |
| `axis`          | determines which dimension is normalized |
| `momentum`      | `decay()` in DL4J builder                |
| `epsilon`       | `eps()` in DL4J builder                  |
| `center`        | whether beta is included                 |
| `scale`         | whether gamma is included                |

**Note on `axis`**: Keras BatchNormalization normalizes along the feature axis. In Keras with channels-last (`NHWC`), this is axis 3. DL4J uses channels-first (`NCHW`), where the feature axis is 1. The importer translates the axis value automatically.

***

### Example

```python
# Python: define and save a model with BatchNormalization
from keras.models import Sequential
from keras.layers import Dense, BatchNormalization

model = Sequential()
model.add(Dense(128, input_dim=64))
model.add(BatchNormalization())
model.add(Dense(10, activation='softmax'))
model.save('bn_model.h5')
```

```java
// Java: import and run inference
MultiLayerNetwork model = KerasModelImport.importKerasSequentialModelAndWeights("bn_model.h5");
INDArray input = Nd4j.randn(32, 64);
INDArray output = model.output(input);
```

***

### Notes

* At inference time, DL4J uses the running mean and variance (not batch statistics), which matches Keras inference behavior.
* `LayerNormalization` (Keras) has no direct equivalent in DL4J core and is not currently supported by the importer.


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