Deep Learning Quick Reference

Training our model

Now that we've defined our model, we're all set to train it. Here's how we do that:

input_features = data["train_X"].shape[1]model = build_network(input_features=input_features)model.fit(x=data["train_X"], y=data["train_y"], batch_size=32, epochs=20, verbose=1, validation_data=(data["val_X"], data["val_y"]), callbacks=callbacks)

This should look pretty familiar if you've already read Chapter 2, Using Deep Learning to Solve Regression Problems. It's really, for the most part, the same. The callback list contains the TensorBoard callback, so let's watch our network train for 20 epochs and see what happens:

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While our train ...