X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.2,random_state=0) y_train import torch import torch.nn as nn import torch.nn.functional as F when I Run this code I got this error: How to can I solve this error? Answer To transfer the variables to GPU, try the following:
Tag: pytorch
Convert 5D tensor to 4D tensor in PyTorch
In PyTorch I have a 5D tensor X of dimensions B x 9 x C x H x W. I want to convert it into a 4D tensor Y with dimensions B x 9C x H x W such that concatenation happens channel wise. To illustrate let, Then in the tensor Y, a to i should be channel wise concatenated.
Changing batch,height,width,alpha to batch,alpha,height,width for pytorch
I have batch of images like torch.Size([10, 512, 512, 3]) I can loop to the images and can see 10 images. But to feed this batch to pytorch i have to convert it to torch.Size([10, 3, 512, 512]) I tried lot of ways but unable to get the solution for this How can we do that ? Answer Use permute:
Pytorch’s nn.TransformerEncoder “src_key_padding_mask” not functioning as expected
Im working with Pytorch’s nn.TransformerEncoder module. I got input samples with (as normal) the shape (batch-size, seq-len, emb-dim). All samples in one batch have been zero-padded to the size of the biggest sample in this batch. Therefore I want the attention of the all zero values to be ignored. The documentation says, to add an argument src_key_padding_mask to the forward
How to get the last index of model’s prediction?
I am new to PyTorch. I have a variable pred which has a list of a tensor. So I wanted to access the last element which is the class. I did that by first converting the list into a tensor. Now, how do I access the last element or is there any better/efficient way of doing this? EDIT: For further
BERT DataLoader: Difference between shuffle=True vs Sampler?
I trained a DistilBERT model with DistilBertForTokenClassification on ConLL data fro predicting NER. Training seem to have completed with no problems but I have 2 problems during evaluation phase. I’m getting negative loss value During training, I used shuffle=True for DataLoader. But during evaluation, when I do shuffle=True for DataLoader, I get very poor metric results(f_1, accuracy, recall etc). But
How to understand creating leaf tensors in PyTorch?
From PyTorch documentation: But why are e and f leaf Tensors, when they both were also cast from a CPU Tensor, into a Cuda Tensor (an operation)? Is it because Tensor e was cast into Cuda before the in-place operation requires_grad_()? And because f was cast by assignment device=”cuda” rather than by method .cuda()? Answer When a tensor is first
How to use ‘collate_fn’ with dataloaders?
I am trying to train a pretrained roberta model using 3 inputs, 3 input_masks and a label as tensors of my training dataset. I do this using the following code: However this gives me the following error: TypeError: vars() argument must have dict attribute Now I have found out that it is probably because I don’t use collate_fn when using
torch.unique does not work for float tensors
I am trying to extract the unique elements from a float tensor. I have tried : However this method only works for int/long tensor. My tensor is quantizied tensor in a non-uniform way, thus its guaranteed to have a small set of float values. Answer You could using numpy.unique instead Outputs:
Multiple values for argument
I am trying to convert this code passing it with pysyft refference like this : But when I try to create a instance of the model I got a TypeError TypeError: multiple values for argument ‘torch_ref’ I tried to change the order of the arguments but i got an error about positional arguments . Can you help me , I