Training the Model

The training process in Keras involves fitting the model to the data. We use the fit method, which is the most critical part of training a neural network in Keras. Here's an overview of the fit method with explanations of its parameters:

• fit(data, labels, epochs, batch_size, validation_data): This method trains the model for a fixed number of epochs (iterations over the dataset).
  • data: Training data. In our case, it should be a NumPy array or a list of arrays if the model has multiple inputs.
  • labels: Target data (labels corresponding to your input data). Like the input data, it should be a NumPy array or a list of arrays.
  • epochs (optional): An integer specifying the number of times to iterate over the training data arrays. One epoch is a single pass through the entire dataset.
  • batch_size (optional): The number of samples per gradient update. It's a crucial parameter that can affect the performance of your model. If unspecified, batch_size will default to 32.
  • validation_data (optional): Data on which to evaluate the loss and any model metrics at the end of each epoch. It should be a tuple (val_data, val_labels) or (val_data, val_labels, val_sample_weights). Including this allows you to monitor the performance of your model during training.

Here's how we implement the fit method with our model:

Displaying the History

After training the model, Keras provides a History object. This object contains a record of training loss values and metrics values at successive epochs, as well as validation loss values and validation metrics values (if applicable).

We can visualize this training history using matplotlib to understand how our model is performing during training. Here's how you can do it:

1. Import Matplotlib: First, we need to import the matplotlib library.
2. Extracting History Data: The history object has a history attribute, which is a dictionary containing the metrics.
3. Plotting the Training and Validation Loss: We can then plot these metrics to visualize the learning process.
4. Plotting Other Metrics (Optional): Similarly, you can plot other metrics like mean_squared_error or mean_absolute_error by replacing the loss in the above code with the respective metric.

This visualization is essential for understanding how your model performs over time and for diagnosing issues in training, like overfitting or underfitting.