def fit(self, X, y=None):
if issparse(X):
X = X.todense()
ds = TensorDataset(torch.from_numpy(X.astype(np.float32)))
self.autoencoder = StackedDenoisingAutoEncoder(self.dimensions, final_activation=self.final_activation)
if self.cuda:
self.autoencoder.cuda()
ae.pretrain(
ds,
self.autoencoder,
cuda=self.cuda,
epochs=self.pretrain_epochs,
batch_size=self.batch_size,
optimizer=self.optimiser_pretrain,
scheduler=self.scheduler,
corruption=0.2,
silent=True
)
ae_optimizer = self.optimiser_train(self.autoencoder)
ae.train(
ds,
self.autoencoder,
cuda=self.cuda,
epochs=self.finetune_epochs,
batch_size=self.batch_size,
optimizer=ae_optimizer,
scheduler=self.scheduler(ae_optimizer),
corruption=self.corruption,
silent=True
)
return self
def test_train_with_prediction():
autoencoder = Mock()
autoencoder.return_value = torch.zeros(10, 1000).requires_grad_()
autoencoder.encode.return_value = torch.zeros(100, 1000)
optimizer = Mock()
dataset = TensorDataset(torch.zeros(100, 1000), torch.zeros(100, 1))
train(dataset=dataset,
autoencoder=autoencoder,
epochs=1,
batch_size=10,
optimizer=optimizer,
cuda=False)
autoencoder.train.assert_called_once()
assert autoencoder.call_count == 10
assert optimizer.zero_grad.call_count == 10
assert optimizer.step.call_count == 10
def main(data_dir, cuda, batch_size, pretrain_epochs, finetune_epochs,
testing_mode):
writer = SummaryWriter() # create the TensorBoard object
# callback function to call during training, uses writer from the scope
def training_callback(epoch, lr, loss, validation_loss):
writer.add_scalars('data/autoencoder', {
'lr': lr,
'loss': loss,
'validation_loss': validation_loss,
}, epoch)
device = 'cuda' if torch.cuda.is_available() else 'cpu'
ds_train = CachedMNIST(data_dir,
is_train=True,
device=device,
testing_mode=testing_mode) # training dataset
ds_val = CachedMNIST(data_dir,
is_train=False,
device=device,
testing_mode=testing_mode) # evaluation dataset
autoencoder = StackedDenoisingAutoEncoder([28 * 28, 500, 500, 2000, 10],
final_activation=None)
autoencoder = autoencoder.to(device)
print('Pretraining stage.')
ae.pretrain(
ds_train,
autoencoder,
device=device,
validation=ds_val,
epochs=pretrain_epochs,
batch_size=batch_size,
silent=True,
optimizer=lambda model: SGD(model.parameters(), lr=0.1, momentum=0.9),
scheduler=lambda x: StepLR(x, 20000, gamma=0.1),
corruption=0.2)
print('Training stage.')
ae_optimizer = SGD(params=autoencoder.parameters(), lr=0.1, momentum=0.9)
ae.train(ds_train,
autoencoder,
device=device,
validation=ds_val,
epochs=finetune_epochs,
batch_size=batch_size,
silent=True,
optimizer=ae_optimizer,
scheduler=StepLR(ae_optimizer, 20000, gamma=0.1),
corruption=0.2,
update_callback=training_callback)
print('DEC stage.')
model = DEC(cluster_number=10,
embedding_dimension=28 * 28,
hidden_dimension=10,
encoder=autoencoder.encoder)
model = model.to(device)
dec_optimizer = SGD(model.parameters(), lr=0.01, momentum=0.9)
train(dataset=ds_train,
model=model,
epochs=20000,
batch_size=256,
silent=True,
optimizer=dec_optimizer,
stopping_delta=0.000001,
cuda=cuda)
predicted, actual = predict(ds_train,
model,
1024,
silent=True,
return_actual=True,
cuda=cuda)
actual = actual.cpu().numpy()
predicted = predicted.cpu().numpy()
reassignment, accuracy = cluster_accuracy(actual, predicted)
print('Final DEC accuracy: %s' % accuracy)
if not testing_mode:
predicted_reassigned = [reassignment[item]
for item in predicted] # TODO numpify
confusion = confusion_matrix(actual, predicted_reassigned)
normalised_confusion = confusion.astype('float') / confusion.sum(
axis=1)[:, np.newaxis]
confusion_id = uuid.uuid4().hex
sns.heatmap(normalised_confusion).get_figure().savefig(
'confusion_%s.png' % confusion_id)
print('Writing out confusion diagram with UUID: %s' % confusion_id)
writer.close()
def main(cuda, batch_size, pretrain_epochs, finetune_epochs):
writer = SummaryWriter() # create the TensorBoard object
# callback function to call during training, uses writer from the scope
def training_callback(epoch, lr, loss, validation_loss):
writer.add_scalars('data/autoencoder', {
'lr': lr,
'loss': loss,
'validation_loss': validation_loss,
}, epoch)
ds_train = CachedMNIST(train=True, cuda=cuda) # training dataset
ds_val = CachedMNIST(train=False, cuda=cuda) # evaluation dataset
autoencoder = StackedDenoisingAutoEncoder([28 * 28, 500, 500, 2000, 10],
final_activation=None)
if cuda:
autoencoder.cuda()
print('Pretraining stage.')
ae.pretrain(
ds_train,
autoencoder,
cuda=cuda,
validation=ds_val,
epochs=pretrain_epochs,
batch_size=batch_size,
optimizer=lambda model: SGD(model.parameters(), lr=0.1, momentum=0.9),
scheduler=lambda x: StepLR(x, 100, gamma=0.1),
corruption=0.2)
print('Training stage.')
ae_optimizer = SGD(params=autoencoder.parameters(), lr=0.1, momentum=0.9)
ae.train(ds_train,
autoencoder,
cuda=cuda,
validation=ds_val,
epochs=finetune_epochs,
batch_size=batch_size,
optimizer=ae_optimizer,
scheduler=StepLR(ae_optimizer, 100, gamma=0.1),
corruption=0.2,
update_callback=training_callback)
print('k-Means stage')
dataloader = DataLoader(ds_train, batch_size=1024, shuffle=False)
kmeans = KMeans(n_clusters=10, n_init=20)
autoencoder.eval()
features = []
actual = []
for index, batch in enumerate(dataloader):
if (isinstance(batch, tuple)
or isinstance(batch, list)) and len(batch) == 2:
batch, value = batch # if we have a prediction label, separate it to actual
actual.append(value)
if cuda:
batch = batch.cuda(async=True)
batch = batch.squeeze(1).view(batch.size(0), -1)
features.append(autoencoder.encoder(batch).detach().cpu())
actual = torch.cat(actual).long().cpu().numpy()
predicted = kmeans.fit_predict(torch.cat(features).numpy())
reassignment, accuracy = cluster_accuracy(predicted, actual)
print('Final k-Means accuracy: %s' % accuracy)
predicted_reassigned = [reassignment[item]
for item in predicted] # TODO numpify
confusion = confusion_matrix(actual, predicted_reassigned)
normalised_confusion = confusion.astype('float') / confusion.sum(
axis=1)[:, np.newaxis]
confusion_id = uuid.uuid4().hex
sns.heatmap(normalised_confusion).get_figure().savefig('confusion_%s.png' %
confusion_id)
print('Writing out confusion diagram with UUID: %s' % confusion_id)
writer.add_embedding(
torch.cat(features),
metadata=predicted,
label_img=ds_train.ds.train_data.float().unsqueeze(1), # TODO bit ugly
tag='predicted')
writer.close()
corruption=0.2,
silent=True,
)
print('SDAE Training stage.')
print(f'@ {time.time() - start_time}\n')
ae_optimizer = SGD(params=autoencoder.parameters(),
lr=0.1,
momentum=0.9)
ae.train(
ds_train,
autoencoder,
cuda=cuda,
validation=ds_val,
epochs=finetune_epochs,
batch_size=batch_size,
optimizer=ae_optimizer,
scheduler=StepLR(ae_optimizer, 100, gamma=0.1),
corruption=0.2,
update_callback=training_callback,
silent=True,
)
pickle.dump(autoencoder, open(autoencoder_path, 'wb'))
ds_train.output = 6
# Train a weighted DEC
# We further re-initialize our DEC by weighted
# K-means every I epochs
I = 6
# with the new weights set by Sk normalized by the
# number of positive samples in the cluster,
def main(cuda, batch_size, pretrain_epochs, finetune_epochs, testing_mode):
writer = SummaryWriter() # create the TensorBoard object
# callback function to call during training, uses writer from the scope
def training_callback(epoch, lr, loss, validation_loss):
writer.add_scalars(
"data/autoencoder",
{
"lr": lr,
"loss": loss,
"validation_loss": validation_loss,
},
epoch,
)
ds_train = CachedMNIST(train=True, cuda=cuda,
testing_mode=testing_mode) # training dataset
ds_val = CachedMNIST(train=False, cuda=cuda,
testing_mode=testing_mode) # evaluation dataset
autoencoder = StackedDenoisingAutoEncoder([28 * 28, 500, 500, 2000, 10],
final_activation=None)
if cuda:
autoencoder.cuda()
print("Pretraining stage.")
ae.pretrain(
ds_train,
autoencoder,
cuda=cuda,
validation=ds_val,
epochs=pretrain_epochs,
batch_size=batch_size,
optimizer=lambda model: SGD(model.parameters(), lr=0.1, momentum=0.9),
scheduler=lambda x: StepLR(x, 100, gamma=0.1),
corruption=0.2,
)
print("Training stage.")
ae_optimizer = SGD(params=autoencoder.parameters(), lr=0.1, momentum=0.9)
ae.train(
ds_train,
autoencoder,
cuda=cuda,
validation=ds_val,
epochs=finetune_epochs,
batch_size=batch_size,
optimizer=ae_optimizer,
scheduler=StepLR(ae_optimizer, 100, gamma=0.1),
corruption=0.2,
update_callback=training_callback,
)
print("DEC stage.")
model = DEC(cluster_number=10,
hidden_dimension=10,
encoder=autoencoder.encoder)
if cuda:
model.cuda()
dec_optimizer = SGD(model.parameters(), lr=0.01, momentum=0.9)
train(
dataset=ds_train,
model=model,
epochs=100,
batch_size=256,
optimizer=dec_optimizer,
stopping_delta=0.000001,
cuda=cuda,
)
predicted, actual = predict(ds_train,
model,
1024,
silent=True,
return_actual=True,
cuda=cuda)
actual = actual.cpu().numpy()
predicted = predicted.cpu().numpy()
reassignment, accuracy = cluster_accuracy(actual, predicted)
print("Final DEC accuracy: %s" % accuracy)
if not testing_mode:
predicted_reassigned = [reassignment[item]
for item in predicted] # TODO numpify
confusion = confusion_matrix(actual, predicted_reassigned)
normalised_confusion = (confusion.astype("float") /
confusion.sum(axis=1)[:, np.newaxis])
confusion_id = uuid.uuid4().hex
sns.heatmap(normalised_confusion).get_figure().savefig(
"confusion_%s.png" % confusion_id)
print("Writing out confusion diagram with UUID: %s" % confusion_id)
writer.close()
