Failing to update nn.Parameter

I am facing problem with updating a custom defined nn.Parameter(). I tried porting it into GPU/CPU using Adam/SGD but the the parameter does not update at all over batches/epochs. The code below is written to use a custom weights kernel in F.Conv1d. Below is my network class definition. Also, I found that
print( gives None. I appreciate any suggestions to debug this. I am new to using NNs and pytorch.

class CNN(nn.Module):
  def __init__(self, h_in, w_in, h_out):
    self.filter_dur = 8e-3
    self.srate = 16000

    self.f_max = 6500
    self.C_out = 64

    self.filter_length = int(self.filter_dur*self.srate)-1
    self.K = (self.filter_length,1)
    self.m = torch.arange(0,self.K[0],1)/self.srate

    self.freq_scale = 100 = Parameter(torch.rand((self.C_out,1)))
    self.center_freq = (self.freq_scale*,self.K[0]))
    self.time_grid = self.m.repeat(self.C_out, 1)

    self.dims = (self.C_out, h_in, w_in)  # dimensions of pool layer output
    self.fc_1 = nn.Linear(, h_out)  # flattened pool output

  def forward(self, x):
    torch.pi = torch.acos(torch.zeros(1)).item() * 2
    gauss = torch.exp(-(self.time_grid - self.filter_dur/2)**2/(torch.sigmoid(self.center_freq/self.f_max -0.5)**2)*1000)
    tones = torch.cos(2*(torch.pi)*torch.relu(self.center_freq) * self.time_grid)
    filters = gauss * tones
    filters = filters.reshape(self.C_out,self.K[1],self.K[0])
    x = x.view(x.shape[0],x.shape[2],x.shape[1])
    x = F.conv1d(x, filters, stride=1, padding=self.K[0]//2)
    x = x.view(-1,  # flatten pooling layer outputs into a vector
    x = torch.relu(self.fc_1(x))  # output of fully connected layer
    return x


First you should never use .data.
When you do it returns a new Tensor and so its .grad field will always be None.

Also, you want to move self.center_freq = (self.freq_scale*,self.K[0])) to the forward funciton: every computation that is part of the forward should happen in the forward.
Otherwise, you will have part of these computations that are shared across iterations and it will lead to errors.

Thank you @albanD . Yes, your suggestion makes sense. I moved self.center_freq into the forward function but this too doesn’t help in any updates on Only the fc_1 params are updating. To give a better picture of the code, I am putting below my training block.

# train the CNN for num_epochs
train_loss = []
val_loss = []
mu_updates = []
start_time = time.time()
retain_graph = True

for epoch in range(num_epochs):
    loss_accum = []
    for dataIns in dataloader:
        # read one sample
        inputRef, outputRef = dataIns
        inputRef = inputRef.reshape(inputRef.shape[0],h_in,w_in)
        outputRef = outputRef.reshape(outputRef.shape[0], h_out)
        inputRef, outputRef =,
        # forward pass
        output = model(inputRef.float())
        # compute loss
        loss = custom_loss(output.float(), outputRef.float())
        # backward pass

    # store mu
    # compute val loss
    output = model(valData.float())
    vloss = custom_loss(output.float(), valOutput.float())

    # ===================store loss==================
    if epoch % 50 == 0:
        print('epoch [{}/{}], loss:{:.4f}, validation_loss:{:.4f}, time_elapsed:{:.4f}'
              .format(epoch + 1, num_epochs, train_loss[epoch], val_loss[epoch], time.time() - start_time))
    if <= 0.0001:

Note that the .data here is not useful either :wink:

Also you can print print( to see if you actually get gradients for it.
In particular, you want to make sure that your function structure does not make the gradients always 0 here.

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Thanks again @albanD . I see the output of print( is in range tensor([[ 5.7729e-07], ... These values are too low to reflect in an update, right? Is this do do with dynamic range of my initialization or that of inputs?

It can have many cause so hard to say without any details.
But the original value can be the issue. Or being in a region that is too flat of your loss.

A good way to debug these is to add some_tensor.register_hook(print) on Tensors in the forward pass. It will print the gradient for that Tensor. That way you can see where in the backward pass the gradients are vanishing.

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