train_model.py

# training the model
history = model.fit(train_generator,
                    steps_per_epoch = 1000,  # len(train) / BS
                    epochs = 100,
                    validation_data = validation_generator,
                    validation_steps = 500,  # len(test) / BS
                    verbose = 2
)

# visualising the loss and accuracy
import matplotlib.pyplot as plt
acc = history.history['accuracy']
val_acc = history.history['val_accuracy']
loss = history.history['loss']
val_loss = history.history['val_loss']

epochs = range(len(acc))

plt.plot(epochs, acc, 'bo', label='Training accuracy')
plt.plot(epochs, val_acc, 'b', label='Validation accuracy')
plt.title('Training and validation accuracy')

plt.figure()

plt.plot(epochs, loss, 'bo', label='Training Loss')
plt.plot(epochs, val_loss, 'b', label='Validation Loss')
plt.title('Training and validation loss')
plt.legend()

plt.show()