Model accuracy

Once training is complete, the next step is to validate the model accuracy. Let's get started:

1.  Validate the accuracy of the model:

score = cnn_model.evaluate(X_test_gray_norm, y_test,verbose=0)
print('Test Accuracy : {:.4f}'.format(score[1]))

2. The accuracy of the model is as follows:

Test Accuracy : 0.9523

3. The training loss versus validation loss graph looks like this:

import matplotlib.pyplot as plt

history_dict = history.history
loss_values = history_dict['loss']
val_loss_values = history_dict['val_loss']
epochs = range(1, len(loss_values)+ 1)
line1 = plt.plot(epochs, val_loss_values, label = 'Validation/Test Loss')
line2 = plt.plot(epochs, loss_values, label= 'Training Loss')
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('Loss')
plt.grid(True)
plt.legend()
plt.show()

The...