def _new_nmt_criterion(vocab_size):
weight = torch.ones(vocab_size)
weight[Constants.PAD] = 0
criterion = nn.NLLLoss(weight, size_average=False)
if torch_is_using_cuda():
criterion.cuda()
return criterion
def _new_nmt_criterion(self, vocab_size):
weight = torch.ones(vocab_size)
weight[Constants.PAD] = 0
criterion = nn.NLLLoss(weight, size_average=False)
if self._gpu_ids is not None:
criterion.cuda()
return criterion
def _inversion_coef(self, constants):
X, y, permuted_y, XTX, XTy, XTmy = constants.values()
feature_sparsity = self.feature_sparsity
if self.GPU:
identity = torch.diag(torch.ones(XTX.shape[0])).float().cuda()
else:
identity = torch.diag(torch.ones(XTX.shape[0])).float()
penality = torch.exp(self._params["lambda"]) * identity
if self.elastic_feature_sparsity:
mu = torch.sigmoid(self._params["feature_elastic_coef"])
coef = self._inversion_coef_without_sparsity(penality, XTX,
XTy) * mu
coef += self._inversion_coef_with_sparsity(penality, XTX,
XTy) * (1 - mu)
elif feature_sparsity:
coef = self._inversion_coef_with_sparsity(penality, XTX, XTy)
else:
coef = self._inversion_coef_without_sparsity(penality, XTX, XTy)
self.coef_ = self._tensor_to_array(coef)
def _inversion_forward(self, constants, feature_sparsity):
X, y, XTX, XTy = constants.values()
if feature_sparsity:
sparse_vector = torch.diag(self._sparsify("feature"))
sparse_X = X @ sparse_vector
sparse_XTX = sparse_vector @ XTX @ sparse_vector
sparse_XTy = sparse_vector @ XTy
penality = torch.exp(self._params["lambda"]) * torch.diag(
torch.ones(XTX.shape[0])).float().cuda()
inv = torch.inverse(sparse_XTX + penality)
projection_matrix = sparse_X @ inv
y_hat = projection_matrix @ sparse_XTy
return y_hat, permuted_y_hat
else:
penality = torch.exp(self._params["lambda"]) * torch.diag(
torch.ones(XTX.shape[0])).float().cuda()
inv = torch.inverse(XTX + penality)
projection_matrix = X @ inv
y_hat = projection_matrix @ XTy
return y_hat
def get_mask(attentions, lengths):
"""
Construct mask for padded itemsteps, based on lengths
"""
max_len = max(lengths.data)
mask = Variable(torch.ones(attentions.size())).detach()
mask = mask.to(DEVICE)
for i, l in enumerate(lengths.data): # skip the first sentence
if l < max_len:
mask[i, l:] = 0
return mask
def get_mask(attentions, lengths):
"""
Construct mask for padded itemsteps, based on lengths
"""
max_len = max(lengths.data)
mask = Variable(torch.ones(attentions.size())).detach()
mask = mask.to(DEVICE)
for i, l in enumerate(lengths.data): # skip the first sentence
if l < max_len:
mask[i, l:] = 0
return mask
def get_mask(attentions, lengths):
"""
Construct mask for padded itemsteps, based on lengths
"""
max_len = max(lengths.data)
# Detaches the Tensor from the graph that created it, making it a leaf.
# Views cannot be detached in-place.
mask = Variable(torch.ones(
attentions.size())).detach() # Variable/Tensor
mask = mask.to(DEVICE)
for i, l in enumerate(lengths.data): # skip the first sentence
if l < max_len:
mask[i, l:] = 0 # [1 1 1 0 0 0 0 ...]
return mask