training.py

# training the head of the network

H = model.fit(
	aug.flow(trainX, trainY, batch_size=BS),
	steps_per_epoch=len(trainX) // BS,
	validation_data=(testX, testY),
	validation_steps=len(testX) // BS,
	epochs=EPOCHS)

# making predictions on the testing set

predIdxs = model.predict(testX, batch_size=BS)

# for each image in the testing set we need to find the index of the label with corresponding largest predicted probability
predIdxs = np.argmax(predIdxs, axis=1)

# Creating a classification report
print(classification_report(testY.argmax(axis=1), predIdxs,
	target_names=lb.classes_))