Edit on GitHub

Keras

DVCLive allows you to easily add experiment tracking capabilities to your Keras projects.

About Keras

Keras is a central part of the tightly-connected TensorFlow 2.0 ecosystem, covering every step of the machine learning workflow, from data management to hyperparameter training to deployment solutions.

Usage

To start using DVCLive you just need to add a few lines to your training code in any Keras project.

You just need to add the DvcLiveCallback to the callbacks list passed to your Model:

+from dvclive.keras import DvcLiveCallback

. . .

model.fit(
    train_dataset,
    epochs=num_epochs,
-    validation_data=validation_dataset)
+    validation_data=validation_dataset,
+    callbacks=[DvcLiveCallback()])

This will generate the metrics logs and summaries as described in the Get Started.

💡Without requiring additional modifications to your training code, you can use DVCLive alongside DVC. See DVCLive with DVC for more info.

Parameters

  • model_file - (None by default) - The name of the file where the model will be saved at the end of each step.

  • save_weights_only (False by default) - if True, then only the model's weights will be saved (model.save_weights(model_file)), else the full model is saved (model.save(model_file))

  • **kwargs - Any additional arguments will be passed to Live.

Examples

  • Using model_file and save_weights_only.
from dvclive.keras import DvcLiveCallback

model.fit(
    train_dataset,
    epochs=num_epochs,
    validation_data=validation_dataset,
    callbacks=[DvcLiveCallback(
        model_file="my_model_weights.h5",
        save_weights_only=True)])
  • Using **kwargs to customize Live.
from dvclive.keras import DvcLiveCallback

model.fit(
    train_dataset,
    epochs=num_epochs,
    validation_data=validation_dataset,
    callbacks=[DvcLiveCallback(
        path="custom_path",
        summary=False)])
Content

🐛 Found an issue? Let us know! Or fix it:

Edit on GitHub

Have a question? Join our chat, we will help you:

Discord Chat