2.5 Summarize history for accuracy

# summarize history for accuracy
plt.plot(results.history['accuracy'])
plt.plot(results.history['val_accuracy'])
plt.title('model accuracy')
plt.ylabel('accuracy')
plt.xlabel('epoch')
plt.legend(['train', 'test'])

<matplotlib.legend.Legend at 0x23508457400>

2.6 Summarize history for loss

# summarize history for loss
plt.plot(results.history['loss'])
plt.plot(results.history['val_loss'])
plt.title('model loss')
plt.ylabel('loss')
plt.xlabel('epoch')
plt.legend(['train', 'test'])

max_loss = np.max(results.history['loss'])
min_loss = np.min(results.history['loss'])
print("Maximum Loss : {:.4f}".format(max_loss))
print("")
print("Minimum Loss : {:.4f}".format(min_loss))
print("")
print("Loss difference : {:.4f}".format((max_loss - min_loss)))

Maximum Loss : 0.4401

Minimum Loss : 0.0175

Loss difference : 0.4226