metadata
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.
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 |
List of labels to get the caption from the inferred logits. The argmax value will be used if labels are not provided. |
num_images |
Number of images to log in the validation data |
Logging metrics
# Logging loss and accuracy for each epoch in Keras
from vessl.integration.keras import ExperimentCallback
...
model.fit(..., callbacks=[ExperimentCallback()])
...
Logging image objects
# 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,
)]
)
...