'None' gradients in pytorch

I am trying to implement a simple MDN that predicts the parameters of a distribution over a target variable instead of a point value, and then assigns probabilities to discrete bins of the point value. Narrowing down the issue, the code from which the 'None' springs is:

import torch

# params
tte_bins = np.linspace(
start=0,
stop=399,
num=400,
dtype='float32'
).reshape(1, 1, -1)
bins = torch.tensor(tte_bins, dtype=torch.float32)
x_train = np.random.randn(1, 1024, 3)
y_labels = np.random.randint(low=0, high=399, size=(1, 1024))
y_train = np.eye(400)[y_labels]

# data
in_train = torch.tensor(x_train[0:1, :, :], dtype=torch.float)
in_train = (in_train - torch.mean(in_train)) / torch.std(in_train)
out_train = torch.tensor(y_train[0:1, :, :], dtype=torch.float)

# model
linear = torch.nn.Linear(in_features=3, out_features=2)
lin = linear(in_train)
preds = torch.exp(lin)

# intermediate values
alpha = torch.clamp(preds[0:1, :, 0:1], 0, 500)
beta = torch.clamp(preds[0:1, :, 1:2], 0, 100)

# probs
p1 = torch.exp(-torch.pow(bins / alpha, beta))
p2 = torch.exp(-torch.pow((bins + 1.0) / alpha, beta))
probs = p1 - p2

# loss
loss = torch.mean(torch.pow(out_train - probs, 2))

# gradients
loss.backward()
for p in linear.parameters():
print(p.grad, 'gradient')


in_train has shape: [1, 1024, 3], out_train has shape: [1, 1024, 400], bins has shape: [1, 1, 400]. All the broadcasting etc.. appears find, the resulting matrices (like alpha/beta/loss) are the right shape and have the right values - there's simply no gradients

edit: added loss.backward() and x_train/y_train, now I have nans

loss.backward()

x_train/y_train

nans

x_train

y_train

y_labels

test

p1

p2

preds.sum().backward()

p1

alpha

bins**(beta)

1 Answer
1

You simply forgot to compute the gradients. While you calculate the loss, you never tell pytorch with respect to which function it should calculate the gradients.

pytorch

Simply adding

loss.backward()


to your code should fix the problem.

Additionally, in your code some intermediate results like alpha are sometimes zero but are in a denominator when computing the gradient. This will lead to the nan results you observed.

alpha

nan

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