In [13]:
plt.figure(figsize =(12,6))
# summarize history for accuracy
plt.subplot(121)
plt.plot(results.history['acc'])
plt.plot(results.history['val_acc'])
plt.title('model accuracy')
plt.ylabel('accuracy')
plt.xlabel('epoch')
plt.legend(['train', 'test'], loc='lower right')

# summarize history for loss
plt.subplot(122)
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'], loc='upper right')

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 : 0.6004

Minimum Loss : 0.0003

Loss difference : 0.6001