> 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-advanced-activations.md).

# Advanced Activations

### Keras Advanced Activation Layer Import

Advanced activation layers in Keras are parameterized activation functions implemented as full layers (with optional learnable parameters). Support is in the [layers/advanced/activations](https://github.com/eclipse/deeplearning4j/tree/master/deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/layers/advanced/activations) package.

#### Support Summary

| Keras Layer     | DL4J Equivalent                   | Supported |
| --------------- | --------------------------------- | --------- |
| LeakyReLU       | ActivationLayer (LeakyReLU)       | Yes       |
| PReLU           | PReLULayer                        | Yes       |
| ELU             | ActivationLayer (ELU)             | Yes       |
| ThresholdedReLU | ActivationLayer (ThresholdedReLU) | Yes       |

***

### KerasLeakyReLU

[source](https://github.com/eclipse/deeplearning4j/tree/master/deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/layers/advanced/activations/KerasLeakyReLU.java)

Imports a Keras `LeakyReLU` layer as a DL4J `ActivationLayer` with a `LeakyReLU` activation function. The slope of the negative part (`alpha`) is read from the Keras configuration.

#### Constructor

```java
public KerasLeakyReLU(Map<String, Object> layerConfig)
        throws InvalidKerasConfigurationException, UnsupportedKerasConfigurationException
```

**Parameters:**

* `layerConfig` — dictionary containing the Keras layer configuration

#### getActivationLayer

```java
public ActivationLayer getActivationLayer()
```

Returns the DL4J `ActivationLayer` configured with a `LeakyReLU` activation.

#### getOutputType

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

Returns the same `InputType` as the input (activations are shape-preserving).

***

### KerasPReLU

[source](https://github.com/eclipse/deeplearning4j/tree/master/deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/layers/advanced/activations/KerasPReLU.java)

Imports a Keras `PReLU` layer as a DL4J `PReLULayer`. PReLU has per-channel (or per-element) learnable slope parameters; these are loaded from the HDF5 weights.

#### Constructor

```java
public KerasPReLU(Map<String, Object> layerConfig)
        throws InvalidKerasConfigurationException, UnsupportedKerasConfigurationException
```

#### getPReLULayer

```java
public PReLULayer getPReLULayer()
```

Returns the DL4J `PReLULayer` with learned slope weights.

#### getOutputType

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

#### setWeights

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

Loads the PReLU slope parameters from the weight map.

**Parameters:**

* `weights` — map from parameter name to `INDArray` containing the slope tensor

***

### KerasThresholdedReLU

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

Imports a Keras `ThresholdedReLU` layer as a DL4J `ActivationLayer` with a thresholded ReLU activation. The threshold value is read from the Keras configuration.

`ThresholdedReLU(x) = x if x > theta, else 0`

#### Constructor

```java
public KerasThresholdedReLU(Map<String, Object> layerConfig)
        throws InvalidKerasConfigurationException, UnsupportedKerasConfigurationException
```

#### getActivationLayer

```java
public ActivationLayer getActivationLayer()
```

Returns the DL4J `ActivationLayer` configured with the threshold value.

#### getOutputType

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

***

### ELU

ELU (Exponential Linear Unit) is supported as an activation function in standard layers (e.g., `Dense(activation='elu')`) and also as an explicit `ELU` layer. The importer handles both forms. When used as a standalone layer, it is mapped to a DL4J `ActivationLayer` with `ActivationELU`.

***

### Example

```python
# Python: model with LeakyReLU and PReLU layers
from keras.models import Sequential
from keras.layers import Dense, LeakyReLU, PReLU

model = Sequential()
model.add(Dense(64, input_dim=100))
model.add(LeakyReLU(alpha=0.1))
model.add(Dense(64))
model.add(PReLU())
model.add(Dense(10, activation='softmax'))
model.save('advanced_act_model.h5')
```

```java
// Java: import
MultiLayerNetwork model = KerasModelImport
    .importKerasSequentialModelAndWeights("advanced_act_model.h5");
```

***

### Notes

* Unlike Keras, where advanced activations are standalone layers, DL4J maps them to `ActivationLayer` (for fixed-parameter activations) or `PReLULayer` (for learned parameters). The layer count in `model.summary()` may therefore differ between DL4J and Keras.
* `Softmax` used as a standalone `Activation` layer rather than a parameter is also supported and maps to `ActivationLayer(ActivationSoftmax)`.


---

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