> 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/optimizers.md).

# Optimizers

### Keras Optimizers

All standard Keras optimizers are supported for import. Optimizer settings are preserved when the Keras model was compiled and saved with `model.save()`. The mapping is implemented in [KerasOptimizerUtils](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/utils/KerasOptimizerUtils.java).

The `TFOptimizer` (a TensorFlow-specific wrapper) is not supported.

***

### Optimizer Mapping Table

| Keras Optimizer | DL4J Equivalent | Supported |
| --------------- | --------------- | --------- |
| SGD             | `Sgd`           | Yes       |
| RMSprop         | `RmsProp`       | Yes       |
| Adagrad         | `AdaGrad`       | Yes       |
| Adadelta        | `AdaDelta`      | Yes       |
| Adam            | `Adam`          | Yes       |
| Adamax          | `AdaMax`        | Yes       |
| Nadam           | `Nadam`         | Yes       |
| TFOptimizer     | —               | No        |

***

### Optimizer Descriptions

#### SGD

Stochastic Gradient Descent with optional momentum, learning rate decay, and Nesterov momentum.

**Keras parameters:**

| Parameter  | Default | Description                        |
| ---------- | ------- | ---------------------------------- |
| `lr`       | 0.01    | Learning rate                      |
| `momentum` | 0.0     | Momentum factor                    |
| `decay`    | 0.0     | Learning rate decay per update     |
| `nesterov` | False   | Whether to apply Nesterov momentum |

**DL4J mapping:** `org.nd4j.linalg.learning.config.Sgd`

***

#### RMSprop

Root Mean Square Propagation. Adapts the learning rate by dividing by a running average of recent gradients.

**Keras parameters:**

| Parameter | Default | Description                          |
| --------- | ------- | ------------------------------------ |
| `lr`      | 0.001   | Learning rate                        |
| `rho`     | 0.9     | Discounting factor for old gradients |
| `epsilon` | 1e-8    | Fuzz factor for numerical stability  |
| `decay`   | 0.0     | Learning rate decay                  |

**DL4J mapping:** `org.nd4j.linalg.learning.config.RmsProp`

***

#### Adagrad

Adapts the learning rate for each parameter individually based on the accumulated sum of squared gradients. Good for sparse data.

**Keras parameters:**

| Parameter | Default | Description         |
| --------- | ------- | ------------------- |
| `lr`      | 0.01    | Learning rate       |
| `epsilon` | 1e-8    | Fuzz factor         |
| `decay`   | 0.0     | Learning rate decay |

**DL4J mapping:** `org.nd4j.linalg.learning.config.AdaGrad`

***

#### Adadelta

An extension of Adagrad that adapts learning rates based on a moving window of gradient updates. No manual learning rate setting required.

**Keras parameters:**

| Parameter | Default | Description                    |
| --------- | ------- | ------------------------------ |
| `lr`      | 1.0     | Learning rate (scaling factor) |
| `rho`     | 0.95    | Decay factor                   |
| `epsilon` | 1e-8    | Fuzz factor                    |
| `decay`   | 0.0     | Learning rate decay            |

**DL4J mapping:** `org.nd4j.linalg.learning.config.AdaDelta`

***

#### Adam

Adaptive Moment Estimation. Combines the advantages of AdaGrad and RMSProp. The most commonly used optimizer for deep learning.

**Keras parameters:**

| Parameter | Default | Description                              |
| --------- | ------- | ---------------------------------------- |
| `lr`      | 0.001   | Learning rate                            |
| `beta_1`  | 0.9     | Exponential decay rate for first moment  |
| `beta_2`  | 0.999   | Exponential decay rate for second moment |
| `epsilon` | 1e-8    | Fuzz factor                              |
| `decay`   | 0.0     | Learning rate decay                      |
| `amsgrad` | False   | Whether to apply AMSGrad variant         |

**DL4J mapping:** `org.nd4j.linalg.learning.config.Adam`

***

#### Adamax

A variant of Adam based on the infinity norm. More stable than Adam in some cases.

**Keras parameters:**

| Parameter | Default | Description                              |
| --------- | ------- | ---------------------------------------- |
| `lr`      | 0.002   | Learning rate                            |
| `beta_1`  | 0.9     | Exponential decay rate for first moment  |
| `beta_2`  | 0.999   | Exponential decay rate for second moment |
| `epsilon` | 1e-8    | Fuzz factor                              |
| `decay`   | 0.0     | Learning rate decay                      |

**DL4J mapping:** `org.nd4j.linalg.learning.config.AdaMax`

***

#### Nadam

Nesterov Adam. Combines Adam with Nesterov momentum for faster convergence in some settings.

**Keras parameters:**

| Parameter        | Default | Description                              |
| ---------------- | ------- | ---------------------------------------- |
| `lr`             | 0.002   | Learning rate                            |
| `beta_1`         | 0.9     | Exponential decay rate for first moment  |
| `beta_2`         | 0.999   | Exponential decay rate for second moment |
| `epsilon`        | 1e-8    | Fuzz factor                              |
| `schedule_decay` | 0.004   | Decay for the momentum schedule          |

**DL4J mapping:** `org.nd4j.linalg.learning.config.Nadam`

***

### Notes on Training Configuration Import

Optimizer settings are only available in DL4J after import when:

1. The Keras model was compiled before saving (`model.compile(...)`)
2. The model was saved with `model.save('model.h5')` (not weights-only or config-only)
3. `enforceTrainingConfig=true` (the default) when calling the import method

If the training configuration is absent or `enforceTrainingConfig=false`, the imported model can still be used for inference but will not have an optimizer configured.

To add an optimizer to an imported model for continued training:

```java
MultiLayerNetwork model = KerasModelImport.importKerasSequentialModelAndWeights("model.h5", false);

// Re-configure for continued training with a new optimizer
model.setLearningRate(0.001);
```

Or rebuild with a `NeuralNetConfiguration.Builder` using the imported configuration as a starting point.


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