Once we have created a model, we can train it using the fit method. The fit method takes two arguments: the training data and the training labels.
The training data is a NumPy array that contains the input data for the model. The training labels are a NumPy array that contains the output labels for the model.
The fit method will train the model by iteratively adjusting the model's parameters to minimize the loss function. The loss function is a measure of how well the model is predicting the output labels.
The fit method will continue to train the model until it converges, which means that the loss function is no longer decreasing significantly.
Now that the model is built, we can train it. The model.fit method trains the model using the training data. The epochs argument specifies the number of times that the model will be trained on the data.
model.fit(train_images, train_labels, epochs=10)
The epochs argument specifies the number of times that the model will be trained on the data. In this example, the model will be trained 10 times.
