The model training is done in one single method call called fit that takes few parameters as seen in the code below −

history = model.fit(X_train, Y_train,
   batch_size=128, epochs=20,
   verbose=2,
   validation_data=(X_test, Y_test)))

The first two parameters to the fit method specify the features and the output of the training dataset.

The epochs is set to 20; we assume that the training will converge in max 20 epochs – the iterations. The trained model is validated on the test data as specified in the last parameter.

The partial output of running the above command is shown here −

Train on 60000 samples, validate on 10000 samples
Epoch 1/20
- 9s - loss: 0.2488 - acc: 0.9252 - val_loss: 0.1059 - val_acc: 0.9665
Epoch 2/20
- 9s - loss: 0.1004 - acc: 0.9688 - val_loss: 0.0850 - val_acc: 0.9715
Epoch 3/20
- 9s - loss: 0.0723 - acc: 0.9773 - val_loss: 0.0717 - val_acc: 0.9765
Epoch 4/20
- 9s - loss: 0.0532 - acc: 0.9826 - val_loss: 0.0665 - val_acc: 0.9795
Epoch 5/20
- 9s - loss: 0.0457 - acc: 0.9856 - val_loss: 0.0695 - val_acc: 0.9792

The screenshot of the output is given below for your quick reference −

Now, as the model is trained on our training data, we will evaluate its performance.