def test_numerical_accuracy(self):
prob_vector = np.array([[0.3, 0.7], [0.6, 0.4]])
true_vector = [1, 1]
self.assertAlmostEqual(numerical_accuracy(prob_vector, true_vector),
0.5)
true_vector = [1, 0]
self.assertAlmostEqual(numerical_accuracy(prob_vector, true_vector),
1.0)
def test_xor(self):
X = np.array([0, 0, 1, 1, 0, 1, 1, 0], dtype=np.float32).reshape(4, 2)
Y = np.array([0, 0, 1, 1], dtype=np.float32)
mlp = MLP(hidden_layer_sizes=(2, ), epoch_num=1600, learning_rate=0.22)
np.random.seed(2020)
mlp.train(X, Y)
self.assertAlmostEqual(numerical_accuracy(mlp.predict(X), Y), 1.0)
def test_multiple_layer_with_regulation(self):
# test on UCI ML hand-written digits datasets
mlp = MLP(hidden_layer_sizes=(30, ),
epoch_num=600,
batch_size=32,
learning_rate=0.2,
_lambda=0.05)
digits = load_digits()
n_samples = len(digits.images)
x_train = digits.data[:n_samples // 2]
y_train = digits.target[:n_samples // 2]
np.random.seed(2020)
mlp.train(x_train, y_train)
self.assertTrue(
numerical_accuracy(mlp.predict(x_train), y_train) > 0.99)
x_test = digits.data[n_samples // 2:]
y_test = digits.target[n_samples // 2:]
self.assertTrue(numerical_accuracy(mlp.predict(x_test), y_test) > 0.93)
def test_iris(self):
# test softmax on Iris dataset
iris = load_iris()
x_train = iris.data
batch_size = int(x_train.shape[0] / 3)
y_train = iris.target
mlp = MLP(epoch_num=400, batch_size=batch_size, learning_rate=0.1)
np.random.seed(2020)
mlp.train(x_train, y_train)
y_predict = mlp.predict(x_train)
self.assertTrue(numerical_accuracy(y_predict, y_train) > 0.95)