def transform(self, x):
'''
x is a pytorch Variable tensor, this works for combine
x is the output of previous trainer.transform
'''
x = to_np(x)
clusters = self.clf.predict(x)
clusters = onehotize(to_var(torch.from_numpy(clusters)).view(-1, 1), self.k)
return clusters
def forward(self, x):
if self.mtl: # the last one is task number
if len(x.size()) == 1:
t = np.zeros(self.input_size)
t[int(to_np(x[-1])[0])] = 1
x = to_var(torch.from_numpy(t).float()).view(1, -1)
else:
x = x[:, -1:]
x = onehotize(x, self.input_size)
o = self.i2o(x)
return self.logsoftmax(o)