for i in range(epochs):
print("Epoch %i" % i)
optimizer.zero_grad()
W_out, C = model.forward()
# Transpose to match n x W
W_in = model.W_in
loss = autoExtend_loss(W_out, W_in, model.E, model.D, C,\
num_words, num_classes, num_lexemes, num_dims, alpha, beta)
print("LOSS: %4f" % loss.data[0])
if loss.data[0] < last_loss:# and i > 5:
print("Normalizing Maps...")
model.normalize_columns()
print("Computing backward...")
loss.backward()
optimizer.step()
last_loss = loss.data[0]
W = model.W_in
E = model.E.data
D = model.D.data
torch.save(W, "./parameters/scoped_embeds.%i_%i"\
% (dim_start, dim_end))
torch.save(E, "./parameters/E.%i_%i"\
% (dim_start, dim_end))
