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---
title: Keras
version: EN
---
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).
## ExperimentCallback
`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`.
| Parameter | Description |
| ----------------- | ---------------------------------------------------------------------------------------------------------------------------------- |
| `data_type` | Use `image` to log image objects |
| `validation_data` | Tuple of `(validation_data, validation_labels)` |
| `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> |
| `num_images` | Number of images to log in the validation data |
### Logging metrics
```python
# Logging loss and accuracy for each epoch in Keras
from vessl.integration.keras import ExperimentCallback
...
model.fit(..., callbacks=[ExperimentCallback()])
...
```
### Logging image objects
```python
# Logging images along with the loss and accuracy for each epoch in Keras
from vessl.keras import ExperimentCallback
...
model.fit(
...,
callbacks=[ExperimentCallback(
data_type='image',
validation_data=(x_val, y_val),
num_images=5,
)]
)
...
```
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