Loss functions play a vital role in any statistical model - they define an objective which the model's performance is evaluated against, and the parameters learned by the model are determined by minimizing a chosen loss function. Loss functions define what a good prediction is and isn't.
Please check the below code to learn how to define the loss function in PyTorch.
Fig: Preview of the output that you will get on running this code from your IDE
In this solution we're using pytorch library
import torch U = 300 # number of users M = 30 # number of movies D = 4 # dimension of embedding vectors source = torch.randint(0, 2, (U, M)) # users' ratings X = source.transpose(0, 1) @ source # your `preprocessed_data` # initial values for your embedding. This is what your algorithm needs to learn v = torch.randn(M, D, requires_grad=True) X = X.to(torch.float32) # necessary to be in line with `v` # this is the `(viT vj − Xi,j )**2` part loss_elementwise = (v @ v.transpose(0, 1) - X).pow(2) # now we need to get rid of the diagonal. Notice that we can equally # well get rid of the diagonal and the whole upper triangular part, # as well, since both V @ V.T and source.T @ source are symmetric, so # the upper triangular part contains just # a mirror reflection of the lower triangular part. # This means that we actually implement a bit different summation: # sum(i=1,M) sum(j=1,i-1) stuff(i, j) # instead of # sum(i=1,M) sum(j=1,M) indicator[i̸=j] stuff(i, j) # and get exactly half the original value masked = torch.tril(loss_elementwise, -1) # finally we sum it up, multiplying by 2 to make up # for the "lost" upper triangular part loss = 2 * masked.sum()
Follow the steps carefully to get the output easily.
- Install pytorch on your IDE(Any of your favorite IDE).
- Import pytorch(refer preview)
- Copy the snippet using the 'copy' and paste it in your IDE.
- Run the file to generate the output.
I hope you found this useful. I have added the link to dependent library, version information in the following sections.
I found this code snippet by searching for 'PyTorch loss function referencing model parameters' in kandi. You can try any such use case!
I tested this solution in the following versions. Be mindful of changes when working with other versions.
- The solution is created in pycharm 2022.3.3(Community edition).
- The solution is tested on Python 3.8.10.
- Pytorch version 1.0.2.
Using this solution, we are able to understand how to define loss function in pytorch with simple steps. This process also facilities an easy way to use, hassle-free method to create a hands-on working version of code which would help us how to how to define loss function in pytorch
Python 458 Version:Current License: Permissive (MIT)