def fit(self, X, y, quiet=True):
for epoch in range(100):
running_loss = 0
_skf = StratifiedKFold(n_splits=10, shuffle=True)
for more, less in _skf.split(X, y):
# Get data and labels
inputs = torch.from_numpy(X[more])
labels = torch.FloatTensor(np.array([y[more]]).T * 1.0)
# Make sure positive and negative samples have the same number
inputs = inputs[:torch.sum(labels).type(torch.int16) * 2]
labels = labels[:torch.sum(labels).type(torch.int16) * 2]
# wrap them in Variable
inputs = Variable(inputs.cuda())
labels = Variable(labels.cuda())
# zero the parameter gradients
self.optimizer.zero_grad()
# forward + backward + optimize
outputs = self.net(inputs)
loss = self.criterion(outputs, labels)
loss.backward()
self.optimizer.step()
running_loss += loss.data
self.scheduler.step()
if not quiet:
if epoch % 10 == 0:
print(f'Epoch {epoch}: {running_loss}')
def predict(self, X):
inputs = torch.from_numpy(X)
inputs = Variable(inputs.cuda())
return self.net(inputs)