Keras
Use our callback to automatically save all the metrics and the loss values tracked in model.fit.
import wandbfrom wandb.keras import WandbCallbackwandb.init(config={"hyper": "parameter"})# Magicmodel.fit(X_train, y_train, validation_data=(X_test, y_test),callbacks=[WandbCallback()])
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.
The WandbCallback() class supports a number of options:
Keyword argument | Default | Description |
|
| The training metric used to measure performance for saving the best model. i.e. |
|
|
|
|
| only save the weights instead of the entire model |
|
| save the model if it's improved at each step |
|
| log the values of each layers parameters at each epoch |
|
| log the gradients of each layers parameters at each epoch |
|
| tuple |
|
| the type of data we're saving, currently only |
|
| only used if |
|
| the number of predictions to make if |
|
| if using data augmentation and |
If you're setting use_multiprocessing=True and seeing the error Error('You must call wandb.init() before wandb.config.batch_size') then try this:
In the
Sequenceclass init, add:wandb.init(group='...')In your main program, make sure you're using
if __name__ == "__main__":and then put the rest of your script logic inside that.
