I have a list of tensors and want to convert it to I tried this code But got this error I am not sure how to get around this Answer You should use list comprehension, and a little “outer product” trick:
Tag: pytorch
The shuffling order of DataLoader in pytorch
I am really confused about the shuffle order of DataLoader in pytorch. Supposed I have a dataset: In scenario I, the code is: the shuffling result is 0,4,2,3,1. In scenario II, the code is: the shuffling result is 1,3,4,0,2. In scenario III, the code is: the shuffling result is 4,1,3,0,2. Can someone explain what is going on here? Answer Based
Select pytorch tensor elements by list of indices
I guess I have a pretty simple problem. Let’s take the following tensor of length 6 Now I would like to to access only the elements at specific indices, lets say at [0, 3, 4]. So I would like to return I found torch.index_select which worked great for a tensor of two dimensions, e.g. dimension (2, 4), but not for
How to implement batch normalization merging in python?
I have defined the model as in the code below, and I used batch normalization merging to make 3 layers into 1 linear layer. The first layer of the model is a linear layer and there is no bias. The second layer of the model is a batch normalization and there is no weight and bias ( affine is false
Installed Pytorch 1.12 in the environment but detects version 1.10.0+cpu
Recently, I have installed pytorch 1.12.1 in the conda environment. After installation, I checked the version of pytorch using print(torch.__version__), it returns 1.10.0+cpu. I also checked the available packages in the environment. It shows pytorch version 1.12.1 as shown in figure below. I am unable to understand why it is detecting version 1.10.0+cpu. I even reinstalled Anaconda python in Windows,
How to extract a specific digit from the MNIST dataset with dataloader?
I am feeding the MNIST dataset to train my neural network in the following manner However, since the training is taking huge time to complete I have decided to train the model with only a specific digit from the MNIST dataset, for example the digit 4. How can I just extract the digit 4 and feed it to my neural
In PyTorch, how do I update a neural network via the average gradient from a list of losses?
I have a toy reinforcement learning project based on the REINFORCE algorithm (here’s PyTorch’s implementation) that I would like to add batch updates to. In RL, the “target” can only be created after a “prediction” has been made, so standard batching techniques do not apply. As such, I accrue losses for each episode and append them to a list l_losses
In pytorch, how to calculate gradient for a element in a tensor when it is used to calculate another element in this tensor?
In this pytorch code: I want y[0]’s gradient to consist 2 parts: loss backward to y[0] itself. y[0] is used to calculate y[1], so it should have the part of y[1]’s gradient. but when I run this code, there is only part 1 in y[0]’s gradient. So how to make y[0]’s gradient to have all 2 parts? edit: the output
What is the most efficient way to make a method that is able to process single and multi dimensional arrays in python?
I was using pytorch and realized that for a linear layer you could pass not only 1d tensors but multidmensional tensors as long as the last dimensions matched. Multi dimensional inputs in pytorch Linear method? I tried looping over each item, but is that what pytorch does? I’m having trouble thinking how you would program looping with more dimensions, maybe
How to replace PyTorch model layer’s tensor with another layer of same shape in Huggingface model?
Given a Huggingface model, e.g. I can access a layer’s tensor as such: [out]: Given the another tensor of the same shape that I’ve pre-defined from somewhere else, in this case, for illustration, I’m creating a random tensor but this can be any tensor that is pre-defined. Note: I’m not trying to replace a layer with a random tensor but