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LABBOO

# What is wrong with my Code for Exercise 6 in the Deep Language and Natural Processing Module?

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?

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?

LABBOOFinally figured this out myself - issue was the Targets tensor and size of it. Needed to be 1D. ## All Answers

LABBOOFinally figured this out myself - issue was the Targets tensor and size of it. Needed to be 1D. Brian KesslerHi Labboo,

I'm also stuck here, but I don't know what to do with your answer.

How should "1D" be reflected in the code?

Thanks,

-Brian.

LABBOOHi Brian, in the exercise when you build classApoints you create a tensor of size torch[100][2] The 1D means that the size needs to be torch[100] for classA - using fill & zero as I did above were correct, however since I'm just replacing values in the classApoints I was getting the wrong size ([100][2] instead of just [100]). If I just create a tensor using one of the other functions that were taught previously in the workbook (like in the Initializing Tensors section, maybe the 2nd example), I can built it the right size and then using dot notation extend it and still use the fill or zero (one for classA and one for classB) to derive my target tensors.

Brian KesslerThanks for the response.
I'll try again later. Johan KarlsteenMy code is very similor to @LABBOO but I get this answer:

Anwswer to Exercise 6: Loss after 100 epochs: -54.190528869628906@Brian did you manage to get past this challenge? I have redone the exercises in a new notebook and I get the exact same result so I'm out of ideas.

LABBOO@[Johan Karlstee] & @Brian,
I can tell you that to get the correct answer for Exercise 6 I had to use the CrossEntropyLoss not the NLLLoss that I had started with in the code I originally had. (I actually tried each type of loss and I remember getting your result from one of them...)
Lynda (@LABBOO) Johan KarlsteenThank you Lynda! Badge completed 🥳 Mitesh Sharma 55When I started this I was not knowing that it will be such a long tasks.... I do not know about others but I learned so many things by attempting this Badge... at last I complete (though not many points) but my request to all you expert who attempted and completed the badge is to share the details you have about the loss func which were there. Will save me time to google and then I would not be able to establish the context like you guys will Mitesh Sharma 55Lynda, Brian and Johan any of you data scientist or R python specialist? Daniel BallingerI found the learning curve towards the end of this badge a bit step as well. To help others I've recorded the state of various tensors as I moved through exercises 6 and 7.

The details are in http://bit.ly/31g6ek4

BugudeLittle difficulty in understanding the language. "passes x through linear_to_hidden, then a tanh activation function, and then hidden_to_linear and returns the output"

Not sure what this means...Need help in understanding this.

Daniel Ballinger@Bugude I found the instructions for Exercise 7 misleading. I covered this in http://bit.ly/31g6ek4.

- Call linear_to_hidden with x as the argument
- Send the output of that into tanh
- Send the output of that into hidden_to_output
- return the result.

I believe it should have said: So you need to expand out

`def forward`

.- It should accept two arguments - self and x
- The return should chain together the two methods defined above (linear_to_hidden and hidden_to_output) with the torch tanh function in the middle.

I.e.Elkin CordobaHi, I need some help. I have followed the post from Daniel Ballinger and I think the only TODO that I could have wrong is

loss = TODO

I used this for TODO

loss = loss_fn(preds, targets)

and I get 0.031865194439888 using CrossEntropyLoss.

Can you please help to get closer to one of the options in the trailhead.

Regards