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Vineet Pandey
Vineet Pandey

Reputation: 1646

Understanding `torch.nn.Parameter()`

How does torch.nn.Parameter() work?

Upvotes: 133

Views: 182404

Answers (3)

Shreejan Shrestha
Shreejan Shrestha

Reputation: 195

torch.nn.Parameter is used to explicitly specify which tensors should be treated as the model's learnable parameters. So that those tensors are learned (updated) during the training process to minimize the loss function.

For example, if you are creating a simple linear regression using Pytorch then, in "W * X + b", W and b need to be nn.Parameter.

weight = torch.nn.Parameter(torch.rand(1))

bias = torch.nn.Parameter(torch.rand(1))

Here, I have randomly created 1 value for weight and bias each which will be of type float32, and assigned it to torch.nn.Parameter.

Upvotes: 7

prosti
prosti

Reputation: 46341

Recent PyTorch releases just have Tensors, it came out the concept of the Variable has been deprecated.

Parameters are just Tensors limited to the module they are defined in (in the module constructor __init__ method).

They will appear inside module.parameters(). This comes handy when you build your custom modules that learn thanks to these parameters gradient descent.

Anything that is true for the PyTorch tensors is true for parameters, since they are tensors.

Additionally, if a module goes to the GPU, parameters go as well. If a module is saved parameters will also be saved.

There is a similar concept to model parameters called buffers.

These are named tensors inside the module, but these tensors are not meant to learn via gradient descent, instead you can think these are like variables. You will update your named buffers inside module forward() as you like.

For buffers, it is also true they will go to the GPU with the module, and they will be saved together with the module.

enter image description here

Upvotes: 56

Astha Sharma
Astha Sharma

Reputation: 2049

I will break it down for you. Tensors, as you might know, are multi dimensional matrices. Parameter, in its raw form, is a tensor i.e. a multi dimensional matrix. It sub-classes the Variable class.

The difference between a Variable and a Parameter comes in when associated with a module. When a Parameter is associated with a module as a model attribute, it gets added to the parameter list automatically and can be accessed using the 'parameters' iterator.

Initially in Torch, a Variable (which could for example be an intermediate state) would also get added as a parameter of the model upon assignment. Later on there were use cases identified where a need to cache the variables instead of having them added to the parameter list was identified.

One such case, as mentioned in the documentation is that of RNN, where in you need to save the last hidden state so you don't have to pass it again and again. The need to cache a Variable instead of having it automatically register as a parameter to the model is why we have an explicit way of registering parameters to our model i.e. nn.Parameter class.

For instance, run the following code - 

import torch
import torch.nn as nn
from torch.optim import Adam

class NN_Network(nn.Module):
    def __init__(self,in_dim,hid,out_dim):
        super(NN_Network, self).__init__()
        self.linear1 = nn.Linear(in_dim,hid)
        self.linear2 = nn.Linear(hid,out_dim)
        self.linear1.weight = torch.nn.Parameter(torch.zeros(in_dim,hid))
        self.linear1.bias = torch.nn.Parameter(torch.ones(hid))
        self.linear2.weight = torch.nn.Parameter(torch.zeros(in_dim,hid))
        self.linear2.bias = torch.nn.Parameter(torch.ones(hid))

    def forward(self, input_array):
        h = self.linear1(input_array)
        y_pred = self.linear2(h)
        return y_pred

in_d = 5
hidn = 2
out_d = 3
net = NN_Network(in_d, hidn, out_d)

Now, check the parameter list associated with this model -

for param in net.parameters():
    print(type(param.data), param.size())

""" Output
<class 'torch.FloatTensor'> torch.Size([5, 2])
<class 'torch.FloatTensor'> torch.Size([2])
<class 'torch.FloatTensor'> torch.Size([5, 2])
<class 'torch.FloatTensor'> torch.Size([2])
"""

Or try,

list(net.parameters())

This can easily be fed to your optimizer - 

opt = Adam(net.parameters(), learning_rate=0.001)

Also, note that Parameters have require_grad set by default.

Upvotes: 186

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