def test_perceptron_init_errors(self):
with self.assertRaises(ValueError):
algorithms.Perceptron((2, 2, 1), verbose=False)
with self.assertRaises(ValueError):
algorithms.Perceptron((2, 2.5), verbose=False)
with self.assertRaises(NetworkConnectionError):
algorithms.Perceptron(layers.Sigmoid(2) > layers.Output(1),
verbose=False)
def test_gd(self):
x_train, _, y_train, _ = simple_classification()
network = algorithms.GradientDescent(
(layers.Tanh(10) > layers.Tanh(20) > layers.Output(1)),
step=0.3,
verbose=False
)
network.train(x_train, y_train, epochs=500)
self.assertAlmostEqual(network.errors.last(), 0.02, places=3)
def test_predict_different_inputs(self):
for bp_algorithm_class in self.bp_algorithms:
network = bp_algorithm_class(
[
layers.Linear(
size=2, bias=np.zeros(1), weight=np.zeros((2, 1))),
layers.Output(1),
],
verbose=False,
)
self.assertInvalidVectorPred(network,
input_vector=np.array([0, 0]),
target=0,
is_feature1d=False)
def test_mixture_of_experts(self):
dataset = datasets.load_diabetes()
data, target = asfloat(dataset.data), asfloat(dataset.target)
insize, outsize = data.shape[1], 1
input_scaler = preprocessing.MinMaxScaler((-1, 1))
output_scaler = preprocessing.MinMaxScaler()
x_train, x_test, y_train, y_test = cross_validation.train_test_split(
input_scaler.fit_transform(data),
output_scaler.fit_transform(target.reshape(-1, 1)),
train_size=0.8)
n_epochs = 10
scaled_y_test = output_scaler.inverse_transform(y_test)
scaled_y_test = scaled_y_test.reshape((y_test.size, 1))
# -------------- Train single GradientDescent -------------- #
bpnet = algorithms.GradientDescent((insize, 20, outsize),
step=0.1,
verbose=False)
bpnet.train(x_train, y_train, epochs=n_epochs)
network_output = bpnet.predict(x_test)
network_error = rmsle(output_scaler.inverse_transform(network_output),
scaled_y_test)
# -------------- Train ensemlbe -------------- #
moe = algorithms.MixtureOfExperts(
networks=[
algorithms.Momentum((insize, 20, outsize),
step=0.1,
batch_size=1,
verbose=False),
algorithms.Momentum((insize, 20, outsize),
step=0.1,
batch_size=1,
verbose=False),
],
gating_network=algorithms.Momentum(
layers.Softmax(insize) > layers.Output(2),
step=0.1,
verbose=False))
moe.train(x_train, y_train, epochs=n_epochs)
ensemble_output = moe.predict(x_test)
ensemlbe_error = rmsle(
output_scaler.inverse_transform(ensemble_output), scaled_y_test)
self.assertGreater(network_error, ensemlbe_error)
def test_minibatch_gd(self):
x_train, _, y_train, _ = simple_classification()
compare_networks(
# Test classes
algorithms.GradientDescent,
partial(algorithms.MinibatchGradientDescent, batch_size=1),
# Test data
(x_train, y_train),
# Network configurations
connection=(layers.Tanh(10) > layers.Tanh(20) > layers.Output(1)),
step=0.1,
shuffle_data=True,
verbose=False,
# Test configurations
epochs=40,
show_comparison_plot=False
)
def test_hessdiag(self):
x_train, x_test, y_train, y_test = simple_classification()
nw = algorithms.HessianDiagonal(
connection=[
layers.Sigmoid(10, init_method='bounded', bounds=(-1, 1)),
layers.Sigmoid(20, init_method='bounded', bounds=(-1, 1)),
layers.Output(1)
],
step=0.1,
shuffle_data=False,
verbose=False,
min_eigval=0.01,
)
nw.train(x_train / 2, y_train, epochs=10)
y_predict = nw.predict(x_test)
self.assertAlmostEqual(0.10, nw.errors.last(), places=2)
def test_bfgs(self):
x_train, x_test, y_train, y_test = simple_classification()
qnnet = algorithms.QuasiNewton(
connection=[
layers.Sigmoid(10, init_method='ortho'),
layers.Sigmoid(25, init_method='ortho'),
layers.Output(1)
],
shuffle_data=True,
show_epoch='20 times',
verbose=False,
)
qnnet.train(x_train, y_train, x_test, y_test, epochs=20)
result = qnnet.predict(x_test).round().astype(int)
roc_curve_score = metrics.roc_auc_score(result, y_test)
self.assertAlmostEqual(0.92, roc_curve_score, places=2)
def test_compare_bp_and_hessian(self):
x_train, _, y_train, _ = simple_classification()
compare_networks(
# Test classes
algorithms.GradientDescent,
partial(algorithms.HessianDiagonal, min_eigval=0.01),
# Test data
(x_train, y_train),
# Network configurations
connection=[
layers.Sigmoid(10, init_method='bounded', bounds=(-1, 1)),
layers.Sigmoid(20, init_method='bounded', bounds=(-1, 1)),
layers.Output(1)
],
step=0.1,
shuffle_data=True,
verbose=False,
# Test configurations
epochs=50,
show_comparison_plot=False
)
def test_linear_search(self):
methods = [
('golden', 0.34202),
('brent', 0.34942),
]
for method_name, valid_error in methods:
np.random.seed(self.random_seed)
dataset = datasets.load_boston()
data, target = dataset.data, dataset.target
data_scaler = preprocessing.MinMaxScaler()
target_scaler = preprocessing.MinMaxScaler()
x_train, x_test, y_train, y_test = train_test_split(
data_scaler.fit_transform(data),
target_scaler.fit_transform(target.reshape(-1, 1)),
train_size=0.85)
cgnet = algorithms.ConjugateGradient(
connection=[
layers.Sigmoid(13),
layers.Sigmoid(50),
layers.Output(1),
],
show_epoch=1,
verbose=False,
search_method=method_name,
tol=0.1,
addons=[algorithms.LinearSearch],
)
cgnet.train(x_train, y_train, epochs=4)
y_predict = cgnet.predict(x_test).round(1)
error = rmsle(target_scaler.inverse_transform(y_test),
target_scaler.inverse_transform(y_predict))
self.assertAlmostEqual(valid_error, error, places=5)
def test_quasi_newton_sr1(self):
x_train, x_test, y_train, y_test = simple_classification()
qnnet = algorithms.QuasiNewton(
connection=[
layers.Sigmoid(10, init_method='ortho'),
layers.Sigmoid(30, init_method='ortho'),
layers.Output(1)
],
shuffle_data=True,
show_epoch=20,
verbose=False,
update_function='sr1',
h0_scale=2,
gradient_tol=1e-5,
)
qnnet.train(x_train, y_train, x_test, y_test, epochs=10)
result = qnnet.predict(x_test).round()
roc_curve_score = metrics.roc_auc_score(result, y_test)
self.assertAlmostEqual(0.92, roc_curve_score, places=2)
data = dataset.data
target = dataset.target.reshape((-1, 1))
data_scaler = preprocessing.MinMaxScaler((-3, 3))
target_scaler = preprocessing.MinMaxScaler()
data = data_scaler.fit_transform(data)
target = target_scaler.fit_transform(target)
x_train, x_test, y_train, y_test = train_test_split(data,
target,
train_size=0.85)
cgnet = algorithms.Hessian(
connection=[
layers.Sigmoid(13),
layers.Sigmoid(50),
layers.Sigmoid(10),
layers.Output(1),
],
verbose=True,
)
cgnet.train(x_train, y_train, x_test, y_test, epochs=3)
y_predict = cgnet.predict(x_test)
y_test = target_scaler.inverse_transform(y_test.reshape((-1, 1)))
y_predict = target_scaler.inverse_transform(y_predict).T.round(1)
error = estimators.rmsle(y_predict, y_test)
print("RMSLE = {}".format(error))
def test_handle_errors(self):
networks = [
algorithms.GradientDescent((1, 20, 1), step=0.2, verbose=False),
algorithms.GradientDescent((1, 20, 1), step=0.2, verbose=False),
]
with self.assertRaises(ValueError):
# Ivalid network (not GradientDescent)
algorithms.MixtureOfExperts(
networks=networks + [algorithms.GRNN(verbose=False)],
gating_network=algorithms.GradientDescent(
layers.Sigmoid(1) > layers.Output(3),
verbose=False,
))
with self.assertRaises(ValueError):
# Ivalid number of outputs in third network
algorithms.MixtureOfExperts(
networks=networks + [
algorithms.GradientDescent(
(1, 20, 2), step=0.2, verbose=False)
],
gating_network=algorithms.GradientDescent(
layers.Sigmoid(1) > layers.Output(3),
verbose=False,
))
with self.assertRaises(ValueError):
# Ivalid gating network output layer size
algorithms.MixtureOfExperts(
networks=networks,
gating_network=algorithms.GradientDescent(
layers.Softmax(1) > layers.Output(1),
verbose=False,
))
with self.assertRaises(ValueError):
# Ivalid gating network input layer
algorithms.MixtureOfExperts(
networks=networks,
gating_network=algorithms.GradientDescent(
layers.Sigmoid(1) > layers.Output(2),
verbose=False,
))
with self.assertRaises(ValueError):
# Ivalid gating network output layer
algorithms.MixtureOfExperts(
networks=networks,
gating_network=algorithms.GradientDescent(
layers.Softmax(1) > layers.RoundedOutput(2),
verbose=False,
))
with self.assertRaises(ValueError):
# Ivalid network error function
algorithms.MixtureOfExperts(
networks=networks + [
algorithms.GradientDescent(
(1, 20, 1),
step=0.2,
error='rmsle',
verbose=False,
)
],
gating_network=algorithms.GradientDescent(
layers.Sigmoid(1) > layers.Output(3),
verbose=False,
),
)
with self.assertRaises(ValueError):
moe = algorithms.MixtureOfExperts(
# Ivalid gating error function
networks=networks,
gating_network=algorithms.GradientDescent(
layers.Softmax(1) > layers.Output(2),
error='rmsle',
verbose=False),
)
moe = algorithms.MixtureOfExperts(
# Ivalid gating network output layer
networks=networks,
gating_network=algorithms.GradientDescent(
layers.Softmax(1) > layers.Output(2), verbose=False),
)
with self.assertRaises(ValueError):
# Wrong number of train input features
moe.train(np.array([[1, 2]]), np.array([[0]]))
with self.assertRaises(ValueError):
# Wrong number of train output features
moe.train(np.array([[1]]), np.array([[0, 0]]))
def get_connection():
""" Generate new connections every time when we call it.
"""
input_layer = NoBiasSigmoid(2, weight=default_weight.copy())
output_layer = layers.Output(1)
return input_layer > output_layer
mnist = datasets.fetch_mldata('MNIST original')
data = mnist.data / 255.
features_mean = data.mean(axis=0)
data = (data - features_mean).astype(np.float32)
np.random.shuffle(data)
x_train, x_test = data[:60000], data[60000:]
autoencoder = algorithms.Momentum(
[
layers.Dropout(proba=0.5),
layers.Sigmoid(784),
layers.Sigmoid(100),
layers.Output(784),
],
step=0.25,
verbose=True,
momentum=0.99,
nesterov=True,
)
autoencoder.train(x_train, x_train, x_test, x_test, epochs=100)
n_samples = 4
image_vectors = x_test[:n_samples, :]
images = (image_vectors + features_mean) * 255.
predicted_images = autoencoder.predict(image_vectors)
predicted_images = (predicted_images + features_mean) * 255.
# Compare real and reconstructed images
def test_valid_cases(self):
algorithms.Perceptron(layers.Step(2) > layers.Output(1), verbose=False)
