Validation versus test accuracy

In this section, we will plot the graph for validation and test accuracy.

In this step, we will compare validation and test accuracy:

import matplotlib.pyplot as plt

history_dict = history.history
acc_values = history_dict['acc']
val_acc_values = history_dict['val_acc']
epochs = range(1, len(loss_values)+ 1)
line1 = plt.plot(epochs, val_acc_values, label = 'Validation/Test Accuracy')
line2 = plt.plot(epochs, acc_values, label= 'Training Accuracy')
plt.setp(line1, linewidth=2.0, marker = '+', markersize=10.0)
plt.setp(line2, linewidth=2.0, marker= '4', markersize=10.0)
plt.xlabel('Epochs')
plt.ylabel('Accuracy')
plt.grid(True)
plt.legend()
plt.show()

Here's our output:

Fig 6.23: Validation versus train accuracy plot

We can see that our training accuracy is around 96% and the test accuracy is around 97.84%; this shows that our model performed well.