[FIXED] How to iterate through all parameters in a neural network using pytorch

Issue

I have the following simple fully-connected neural network:

class Neural_net(nn.Module):
    def __init__(self):
        super(Neural_net, self).__init__()
        self.fc1    = nn.Linear(2, 2)        
        self.fc2    = nn.Linear(2, 1)
        self.fc_out = nn.Linear(1, 1)      
        
    def forward(self, x,train = True):
        x = torch.tanh(self.fc1(x))
        x = torch.tanh(self.fc2(x))
        x = self.fc_out(x)
        return x

net = Neural_net()

How can I loop through all the parameters of the network and check if for example they are greater than a certain value? I am using pytorch and if I do:

for n,p in net.named_parameters():     
       if p > value:
       ...

I get an error since p is not a single number, but rather a tensor of either weights or biases for each layer.

My goal is to check if a criterion is satisfied for each of the parameters and flag them e.g. with 1 if it is or 0 if it is not, storing it in a dictionary with the same structure as net.parameters(). Yet, I am having trouble figuring out how to loop through them.

I thought about creating a parameter vector:

param_vec  =  torch.cat([p.view(-1) for p in net.parameters()])

and then accessing the parameter values and checking them would be easy,but then I can't think of a way to go back to the dictionary form to flag them.

Thank you for any help!

Solution

First I would define the criterion as an operation on a tensor. In your case, this could look like this:

cond = lambda tensor: tensor.gt(value)

Then you just need to apply it to each tensor in net.parameters(). To keep it with the same structure, you can do it with dict comprehension:

cond_parameters = {n: cond(p) for n,p in net.named_parameters()}

---

Let's see it in practice!

net = Neural_net()
print(dict(net.parameters())
#> {'fc1.weight': Parameter containing:
#>  tensor([[-0.4767,  0.0771],
#>          [ 0.2874,  0.5474]], requires_grad=True),
#>  'fc1.bias': Parameter containing:
#>  tensor([ 0.0405, -0.1997], requires_grad=True),
#>  'fc2.weight': Parameter containing:
#>  tensor([[0.5400, 0.3241]], requires_grad=True),
#>  'fc2.bias': Parameter containing:
#>  tensor([-0.5306], requires_grad=True),
#>  'fc_out.weight': Parameter containing:
#>  tensor([[-0.9706]], requires_grad=True),
#>  'fc_out.bias': Parameter containing:
#> tensor([-0.4174], requires_grad=True)}

Let's set value to zero and get the dict of parameters:

value = 0
cond = lambda tensor: tensor.gt(value)
cond_parameters = {n: cond(p) for n,p in net.named_parameters()}
#>{'fc1.weight': tensor([[False,  True],
#>         [ True,  True]]),
#> 'fc1.bias': tensor([ True, False]),
#> 'fc2.weight': tensor([[True, True]]),
#> 'fc2.bias': tensor([False]),
#> 'fc_out.weight': tensor([[False]]),
#> 'fc_out.bias': tensor([False])}

Answered By - ibarrond