Python Deep Learning Projects

Evaluating the model

Now, we need to evaluate the model:

# transform/augment test datafor test_img, test_lab in train_datagen.flow(X_test, y_test, batch_size = X_test.shape[0]):    break# evaluate model on test data loss,acc = model.evaluate(test_img, test_lab)print('Test loss:', loss)print('Accuracy:', acc)

The following is the output of the preceding code:

Figure 8.35: Printout of the evaluation of the deep convolution classifier on augmented data

Then, we need to plot the deep convolution classifier:

# plot the learningloss_plot(history)

The following is the output of the preceding code:

Figure 8.36: Loss/accuracy plot of the deep convolution ...