def test_vader_recur(self):
X_train, W_train, y_train = generate_x_w_y(7, 400)
# Note: y_train is used purely for monitoring performance when a ground truth clustering is available.
# It can be omitted if no ground truth is available.
# noinspection PyTypeChecker
vader = VADER(X_train=X_train,
W_train=W_train,
y_train=y_train,
save_path=None,
n_hidden=[12, 2],
k=4,
learning_rate=1e-3,
output_activation=None,
recurrent=True,
batch_size=16)
# pre-train without latent loss
vader.pre_fit(n_epoch=10, verbose=True)
# train with latent loss
vader.fit(n_epoch=10, verbose=True)
# get the clusters
clustering = vader.cluster(X_train)
assert any(clustering)
assert len(clustering) == len(X_train)
# get the re-constructions
prediction = vader.predict(X_train)
assert prediction.shape == X_train.shape
# compute the loss given the network
loss = vader.get_loss(X_train)
assert loss
assert "reconstruction_loss" in loss
assert "latent_loss" in loss
assert loss["reconstruction_loss"] >= 0
assert loss["latent_loss"] >= 0
def test_vader_save_load_transfer_learning(self):
save_folder = "test_vader_save_load_transfer_learning"
save_path = f"{save_folder}//weights"
if os.path.exists(save_folder):
shutil.rmtree(save_folder)
X_train, W_train, y_train = generate_x_w_y(7, 400)
# noinspection PyTypeChecker
vader = VADER(X_train=X_train,
W_train=W_train,
y_train=y_train,
save_path=save_path,
n_hidden=[12, 2],
k=4,
learning_rate=1e-3,
output_activation=None,
recurrent=True,
batch_size=16)
vader.pre_fit(n_epoch=10, verbose=True)
vader.fit(n_epoch=10, verbose=True)
clustering_before_loading = vader.cluster(X_train)
X_train_ft, W_train_ft, y_train_ft = generate_x_w_y(7, 400)
vader = VADER(X_train=X_train_ft,
W_train=W_train_ft,
y_train=y_train_ft,
save_path=None,
n_hidden=[12, 2],
k=4,
learning_rate=1e-3,
output_activation=None,
recurrent=True,
batch_size=16)
vader.load_weights(save_path)
vader.pre_fit(n_epoch=10, verbose=True)
vader.fit(n_epoch=10, verbose=True)
# get the clusters
clustering = vader.cluster(X_train_ft)
if os.path.exists(save_folder):
shutil.rmtree(save_folder)
assert any(clustering)
assert len(clustering) == len(X_train_ft)
# get the re-constructions
prediction = vader.predict(X_train_ft)
assert prediction.shape == X_train_ft.shape
# compute the loss given the network
loss = vader.get_loss(X_train_ft)
assert loss
assert "reconstruction_loss" in loss
assert "latent_loss" in loss
assert loss["reconstruction_loss"] >= 0
assert loss["latent_loss"] >= 0
def test2():
x_train, y_train = get_dete_for_seconed_test()
vader = VADER(x_train=x_train,
y_train=y_train,
n_hidden=[12, 2],
k=2,
learning_rate=1e-3,
output_activation=None,
recurrent=False,
batch_size=16)
# pre-train without latent loss
vader.pre_fit(n_epoch=50, verbose=True)
# train with latent loss
vader.fit(n_epoch=50, verbose=True)
# get the clusters
c = vader.cluster(x_train)
# get the re-constructions
p = vader.predict(x_train)
# compute the loss given the network
l = vader.get_loss(x_train)
# generate some samples
g = vader.generate(10)
# compute the loss given the network
l = vader.get_loss(x_train)
def test_vader_nonrecur(self):
NUM_OF_TIME_POINTS = 7
X_train, y_train = generate_x_y_for_nonrecur(NUM_OF_TIME_POINTS, 400)
# Run VaDER non-recurrently (ordinary VAE with GM prior)
# noinspection PyTypeChecker
vader = VADER(X_train=X_train,
y_train=y_train,
n_hidden=[12, 2],
k=2,
learning_rate=1e-3,
output_activation=None,
recurrent=False,
batch_size=16)
# pre-train without latent loss
vader.pre_fit(n_epoch=10, verbose=True)
# train with latent loss
vader.fit(n_epoch=10, verbose=True)
# get the clusters
clustering = vader.cluster(X_train)
assert any(clustering)
assert len(clustering) == len(X_train)
# get the re-constructions
prediction = vader.predict(X_train)
assert prediction.shape == X_train.shape
# compute the loss given the network
loss = vader.get_loss(X_train)
assert loss
assert "reconstruction_loss" in loss
assert "latent_loss" in loss
assert loss["reconstruction_loss"] >= 0
assert loss["latent_loss"] >= 0
# generate some samples
NUM_OF_GENERATED_SAMPLES = 10
generated_samples = vader.generate(NUM_OF_GENERATED_SAMPLES)
assert generated_samples
assert "clusters" in generated_samples
assert "samples" in generated_samples
assert len(generated_samples["clusters"]) == NUM_OF_GENERATED_SAMPLES
assert generated_samples["samples"].shape == (NUM_OF_GENERATED_SAMPLES,
NUM_OF_TIME_POINTS)
def test_vader_transfer_learning(self):
X_train, W_train, y_train = generate_x_w_y(7, 400)
# noinspection PyTypeChecker
vader = VADER(X_train=X_train,
W_train=W_train,
y_train=y_train,
save_path=None,
n_hidden=[12, 2],
k=4,
learning_rate=1e-3,
output_activation=None,
recurrent=True,
batch_size=16)
# pre-train without latent loss
vader.pre_fit(n_epoch=10, verbose=True)
# train with latent loss
vader.fit(n_epoch=10, verbose=True)
X_train_ft, W_train_ft, y_train_ft = generate_x_w_y(7, 400)
vader.set_inputs(X_train_ft, W_train_ft, y_train_ft)
# pre-train without latent loss
vader.pre_fit(n_epoch=10, verbose=True)
# train with latent loss
vader.fit(n_epoch=10, verbose=True)
# get the clusters
clustering = vader.cluster(X_train_ft)
assert any(clustering)
assert len(clustering) == len(X_train_ft)
# get the re-constructions
prediction = vader.predict(X_train_ft)
assert prediction.shape == X_train_ft.shape
# compute the loss given the network
loss = vader.get_loss(X_train_ft)
assert loss
assert "reconstruction_loss" in loss
assert "latent_loss" in loss
assert loss["reconstruction_loss"] >= 0
assert loss["latent_loss"] >= 0
k=4,
learning_rate=1e-3,
output_activation=None,
recurrent=True,
batch_size=16)
# pre-train without latent loss
vader.pre_fit(n_epoch=50, verbose=True)
# train with latent loss
vader.fit(n_epoch=50, verbose=True)
# get the clusters
c = vader.cluster(X_train)
# get the re-constructions
p = vader.predict(X_train)
# compute the loss given the network
l = vader.get_loss(X_train)
# Run VaDER non-recurrently (ordinary VAE with GM prior)
nt = int(8)
ns = int(2e2)
sigma = np.diag(np.repeat(2, nt))
mu1 = np.repeat(-1, nt)
mu2 = np.repeat(1, nt)
a1 = np.random.multivariate_normal(mu1, sigma, ns)
a2 = np.random.multivariate_normal(mu2, sigma, ns)
X_train = np.concatenate((a1, a2), axis=0)
y_train = np.repeat([0, 1], ns)
ii = np.random.permutation(ns * 2)
X_train = X_train[ii, :]
y_train = y_train[ii]
# normalize (better for fitting)