def ml_example():
x_train, x_test, y_train, y_test = get_data(data_path=data_path)
models = [NN, RF, SVM]
for M in models:
model = M()
print('Starting', model.name)
model.train(x_train, y_train)
model.evaluate(x_test, y_test)
print(model.name, 'Done')
def extract_data(flatten):
data, labels = get_data(_DATA_PATH,
class_labels=_CLASS_LABELS,
flatten=flatten)
x_train, x_test, y_train, y_test = train_test_split(data,
labels,
test_size=0.2,
random_state=42)
return np.array(x_train), np.array(x_test), np.array(y_train), np.array(
y_test), len(_CLASS_LABELS)
def dnn_example():
x_train, x_test, y_train, y_test = get_data(dataset_path=dataset_path, flatten=False)
y_train = np_utils.to_categorical(y_train)
y_test = np_utils.to_categorical(y_test)
print('Starting LSTM')
model = LSTM(input_shape=x_train[0].shape, num_classes=len(class_labels))
model.train(x_train, y_train, x_test, y_test)
model.evaluate(x_test, y_test)
print('LSTM Done\n Starting CNN')
in_shape = x_train[0].shape
x_train = x_train.reshape(x_train.shape[0], in_shape[0], in_shape[1], 1)
x_test = x_test.reshape(x_test.shape[0], in_shape[0], in_shape[1], 1)
model = CNN(input_shape=x_train[0].shape, num_classes=len(class_labels))
model.train(x_train, y_train, x_test, y_test)
model.evaluate(x_test, y_test)
print('CNN Done')