Training Neural Networks with Keras: Understanding the fit Method

The cnn.fit method in Keras is used to train the model. Here’s a simple breakdown:

1. Training the Model: The main purpose of cnn.fit is to start the iterative training process. During each iteration (or epoch), the model processes the input data, makes a prediction, calculates the error (or loss) by comparing the prediction to the actual label, and then updates the model’s weights in an attempt to reduce this error.
2. Parameters: It usually takes several important parameters:
   x: Input data (e.g., your images).
   y: Target data (e.g., labels or ground truth associated with your images).
   epochs: Number of times the model should iterate over the entire dataset.
   batch_size: Number of samples processed before the model is updated.
   validation_data: Data on which to evaluate the model’s loss and any other metrics at the end of each epoch. It’s not used for training.
3. Returns: The fit method returns a History object, which contains details about the training process. This includes the loss and any other metrics specified (like accuracy) recorded after each epoch for both training and validation datasets.

In the context of your previous code snippet, cnn.fit(**fit_params) is unpacking the fit_params dictionary and using its contents as parameters to the fit method. This trains the cnn model based on the configuration specified in fit_params.