def test_model_training():
train, validation = dataset_preparation()
# TODO : Modify the Model to improve performances !
model = Models.create(input_shape=(28, 28, 1), num_classes=10)
Models.print_model_summary(model)
Models.train(model, train, validation)
Models.save_model(model, './my_model')
# evaluate the model
scores = model.evaluate_generator(validation,
steps=validation.n,
verbose=0)
accuracy_percentage = scores[1] * 100
print("evaluation on unseen dataset : {} = {}".format(
model.metrics_names[1], accuracy_percentage))
# TODO : add batch normalization to Models.create_model() to gain some accuracy
# https://keras.io/layers/normalization/
assert accuracy_percentage >= 50, "Bad accuracy ({}%) : {}".format(
accuracy_percentage, "something is wrong :(")
def test_create_model():
model = Models.create(input_shape=(28, 28, 1), num_classes=10)
Models.save_model(model, model_path)
assert os.path.exists(os.path.join(model_path, 'model.h5'))