• Gauthier Muguerza
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  • Deloitte


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I'm working on the Word Meaning and Word2vec badge and the Hands-on: Construct examples for each W2V variant is taking forever (it's already been running for 2 hours).  Has anyone else gotten through this?  How long did this part take?

My best guess is that I have something wrong but I'm not sure what since I've gotten no error messages...but when I stoped it it looks like it's still in the while loop - do can anyone provide some guidance on where I'm wrong and what I might want to look at to get back on track?

while True:
    # TODO: select a random sentence index using random.randint and get that
    # sentence. Be careful to avoid indexing errors.
    sentence_idx = random.randint(0,len(numericalized_sentences)-1)
    sentence = numericalized_sentences[sentence_idx]
    # TODO: Select a random window index using random.randint
    # and obtain that window of size n. Be careful to avoid indexing errors.
    window_idx = random.randint(0,len(sentence)-1)
    window = sentence[window_idx:k]
    
    if len(window) <= n//2:
      continue

Thanks!
Lynda

 
  • January 02, 2019
  • Like
  • 1
I'm really stuck on this one.

For the first part I have:
torch.manual_seed(123)

# TODO: Generate 2 clusters of 100 2d vectors, each one distributed normally, using
# only two calls of randn()
classApoints = torch.randn(100,2)
classBpoints = torch.randn(100,2)
#println(classApoints)

# TODO: Add the vector [1.0,3.0] to the first cluster and [3.0,1.0] to the second.
classApoints += torch.tensor([1.0,3.0])
classBpoints += torch.tensor([3.0,1.0])
#println(classApoints)

# TODO: Concatenate these two clusters along dimension 0 so that the points
# distributed around [1.0, 3.0] all come first
inputs = torch.cat([classApoints, classBpoints],0)
#println(inputs) - I suspect U might be missing something in here but I'm not certain

# TODO: Create a tensor of target values, 0 for points for the first cluster and
# 1 for the points in the second cluster. Make sure that these are LongTensors.
classA = classApoints.zero_().long()
classB = classBpoints.fill_(1).long()
targets = torch.cat([classA, classB])
#println(targets.type()) - pretty sure this is correct and I've confirmed they are LongTensors

For the 2nd part (where I'm having error) I've got:
# TODO: Set the random seed to 123 using manual_seed
torch.manual_seed(123)


# TODO: Initialize a Linear layer to output scores 
# for each class given the 2d examples
model = nn.Linear(2, 2)

# TODO: Define your loss function
loss_fn = nn.NLLLoss()

# TODO: Initialize an SGD optimizer with learning rate 0.1
optimizer = torch.optim.SGD(model.parameters(), lr=0.1)

# Train the model for 100 epochs 
n_epochs = 1
losses = []
for _ in range(n_epochs):
  optimizer.zero_grad() 
  preds = model(inputs)
  #println(preds)
  #println(targets)
  loss = loss_fn(preds, targets)
  losses.append(loss)
  loss.backward()
  optimizer.step()
print(f'Anwswer to Exercise 6: Loss after {n_epochs} epochs: {losses[-1]}')
      
iterations = np.arange(len(losses))
_ = plt.plot(iterations, losses, '', iterations, losses, '-')

The error I'm getting:
--------------------------------------------------------------------------- RuntimeError Traceback (most recent call last) <ipython-input-65-b59a439a8791> in <module>() 20 #println(preds) 21 #println(targets) ---> 22 loss = loss_fn(preds, targets) 23 losses.append(loss) 24 loss.backward() /usr/local/lib/python3.6/dist-packages/torch/nn/modules/module.py in __call__(self, *input, **kwargs) 489 result = self._slow_forward(*input, **kwargs) 490 else: --> 491 result = self.forward(*input, **kwargs) 492 for hook in self._forward_hooks.values(): 493 hook_result = hook(self, input, result) /usr/local/lib/python3.6/dist-packages/torch/nn/modules/loss.py in forward(self, input, target) 191 _assert_no_grad(target) 192 return F.nll_loss(input, target, self.weight, self.size_average, --> 193 self.ignore_index, self.reduce) 194 195 /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py in nll_loss(input, target, weight, size_average, ignore_index, reduce) 1330 .format(input.size(0), target.size(0))) 1331 if dim == 2: -> 1332 return torch._C._nn.nll_loss(input, target, weight, size_average, ignore_index, reduce) 1333 elif dim == 4: 1334 return torch._C._nn.nll_loss2d(input, target, weight, size_average, ignore_index, reduce) RuntimeError: multi-target not supported at /pytorch/aten/src/THNN/generic/ClassNLLCriterion.c:22

Can anyone assist on this?
  • December 28, 2018
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I'm trying to get through the service cloud superbadge, and challenge 3 is giving me this issue

"We can't find Entitlements on the Case Lightning Page. Ensure Entitlements are visible on Cases in Lightning."

I've got entitlements on the case record page, via the parent account, and I've got entitlements history via the parent Entitlement Name.. 

What is it actually looking for? 
I'm working on the Word Meaning and Word2vec badge and the Hands-on: Construct examples for each W2V variant is taking forever (it's already been running for 2 hours).  Has anyone else gotten through this?  How long did this part take?

My best guess is that I have something wrong but I'm not sure what since I've gotten no error messages...but when I stoped it it looks like it's still in the while loop - do can anyone provide some guidance on where I'm wrong and what I might want to look at to get back on track?

while True:
    # TODO: select a random sentence index using random.randint and get that
    # sentence. Be careful to avoid indexing errors.
    sentence_idx = random.randint(0,len(numericalized_sentences)-1)
    sentence = numericalized_sentences[sentence_idx]
    # TODO: Select a random window index using random.randint
    # and obtain that window of size n. Be careful to avoid indexing errors.
    window_idx = random.randint(0,len(sentence)-1)
    window = sentence[window_idx:k]
    
    if len(window) <= n//2:
      continue

Thanks!
Lynda

 
  • January 02, 2019
  • Like
  • 1