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--- |
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title: Keras |
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version: EN |
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--- |
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VESSL provides integrations for Keras, an interface for the TensorFlow library. You can find a complete example using Keras in our [GitHub repository](https://github.com/savvihub/examples/blob/main/mnist/keras/main.py). |
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## ExperimentCallback |
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`ExperimentCallback` extends Keras' callback class. Add `ExperimentCallback` as a callback parameter in the `fit` function to automatically track Keras metrics at the end of each epoch. You can also log image objects using `ExperimentCallback`. |
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| Parameter | Description | |
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| ----------------- | ---------------------------------------------------------------------------------------------------------------------------------- | |
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| `data_type` | Use `image` to log image objects | |
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| `validation_data` | Tuple of `(validation_data, validation_labels)` | |
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| `labels` | <p>List of labels to get the caption from the inferred logits.</p><p>The argmax value will be used if labels are not provided.</p> | |
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| `num_images` | Number of images to log in the validation data | |
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### Logging metrics |
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```python |
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# Logging loss and accuracy for each epoch in Keras |
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from vessl.integration.keras import ExperimentCallback |
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... |
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model.fit(..., callbacks=[ExperimentCallback()]) |
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... |
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``` |
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### Logging image objects |
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```python |
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# Logging images along with the loss and accuracy for each epoch in Keras |
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from vessl.keras import ExperimentCallback |
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... |
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model.fit( |
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..., |
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callbacks=[ExperimentCallback( |
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data_type='image', |
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validation_data=(x_val, y_val), |
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num_images=5, |
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)] |
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) |
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... |
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``` |
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