#evaluate the network
print("[INFO] evaluating network...")
predictions = model.predict(testX, batch_size=32)

#Uncomment to see the predicted probabilty for each class in every test image
# print ("predictions---------------->",predictions)
#Uncomment to print the predicted labels in each image
# print("predictions.argmax(axis=1)",predictions.argmax(axis=1))

# print the performance report of the prediction
print(classification_report(testY.argmax(axis=1),
	predictions.argmax(axis=1), target_names=lb.classes_))

# plot the training loss and accuracy for each epoch
N = np.arange(0, EPOCHS)
plt.style.use("ggplot")
plt.figure()
plt.plot(N, H.history["loss"], label="train_loss")
plt.plot(N, H.history["val_loss"], label="val_loss")
plt.plot(N, H.history["acc"], label="train_acc")
plt.plot(N, H.history["val_acc"], label="val_acc")
plt.title("Training Loss and Accuracy (simple_multiclass_classifcation)")
plt.xlabel("Epoch #")
plt.ylabel("Loss/Accuracy")
plt.legend()
plt.savefig("training_performance.png")