visualize/plot keras metrics with model.fit_generator()

Question

The usage of model.fit_generator() in keras works for me, however I'd like to have a live visualisation of accuracy, loss etc. like it is easily possible with model.fit(). Couldn't find any explanations on how to do that with the model.fit_generator() function. The training method of my model looks like this:

def train_model(model, args, X_train, X_valid, y_train, y_valid):

    checkpoint = ModelCheckpoint('model-{epoch:03d}.h5',
                                 monitor='val_loss',
                                 verbose=0,
                                 save_best_only=args.save_best_only,
                                 mode='auto')

    model.compile(optimizer=Adam(lr=args.learning_rate), loss='mean_squared_error', metrics=['accuracy'])


    model.fit_generator(batch_generator(args.data_dir, X_train, y_train, args.batch_size, True),
                        args.samples_per_epoch,
                        args.nb_epoch,
                        max_q_size=1,
                        validation_data=batch_generator(args.data_dir, X_valid, y_valid, args.batch_size, False),
                        nb_val_samples=len(X_valid),
                        callbacks=[checkpoint],
                        verbose=1)

Thanks for help