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Keras
Use our callback to automatically save all the metrics and the loss values tracked in
model.fit.1
import wandb
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from wandb.keras import WandbCallback
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wandb.init(config={"hyper": "parameter"})
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... # code to set up your model in Keras
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# 🧙 magic
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model.fit(X_train, y_train, validation_data=(X_test, y_test),
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callbacks=[WandbCallback()])
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Usage Examples
Try our integration out in a colab notebook (with video walkthrough below) or see our example repo for scripts, including a Fashion MNIST example and the W&B Dashboard it generates.
Configuring the WandbCallback
WandbCallbackThe
WandbCallback class supports a wide variety of logging configuration options: specifying a metric to monitor, tracking of weights and gradients, logging of predictions on training_data and validation_data, and more.Frequently Asked Questions
How do I use Keras multiprocessing with wandb?
Keras multiprocessing with wandb?If you're setting
use_multiprocessing=True and seeing an error like:1
Error('You must call wandb.init() before wandb.config.batch_size')
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then try this:
- 1.In the
Sequenceclass construction, add:wandb.init(group='...') - 2.In your main program, make sure you're using
if __name__ == "__main__":and then put the rest of your script logic inside that.
Last modified 2mo ago