Output 0 – training loss: 0.0837 | test loss: 0.0806 100 – training loss: 0.0433 | test loss: 0.0431 200 – training loss: 0.0426 | test loss: 0.0425 300 – training loss: 0.042 | test loss: 0.0419 400 – training loss: 0.0414 | test loss: 0.0414 500 – training loss: 0.0408 | test loss: 0.0408 600 – training loss: 0.0403
Tag: pytorch
Reshape original X from LSTM with predictions
I have a tensor with my LSTM inputs X (in PyTorch) as well as the matching predictions Y_hat I want to add the Y_hat as a column to the original X. The problem is that LSTM a sliding window with seq_length. In case seq. length is 3 and I have 6 variables in X, and 2 variables in Y_hat, I
RuntimeError: Found dtype Char but expected Float
I am using PyTorch in my program(Binary Classification). The output from my model and actual labels are When I calculate the Binary Cross Entropy, it gives me the error I have no idea how it is finding the Char dtype. Even If calculate it manually, it gives me this error. My DataLoader is my training loop is And my Model
Shuffling two 2D tensors in PyTorch and maintaining same order correlation
Is it possible to shuffle two 2D tensors in PyTorch by their rows, but maintain the same order for both? I know you can shuffle a 2D tensor by rows with the following code: To elaborate: If I had 2 tensors And ran them through some function/block of code to shuffle randomly but maintain correlation and produce something like the
Accessing a specific layer in a pretrained model in PyTorch
I want to extract the features from certain blocks of the TimeSformer model and also want to remove the last two layers. The print of the model is as follows: ) ) Specifically, I want to extract the outputs of the 4th, 8th and 11th blocks of the model and removing the lats two layers. How can I do this.
Conda is installing a very old version of pytorch-lightning
I tried installing pytorch lightning by runnning: as described here: https://anaconda.org/conda-forge/pytorch-lightning This link seems updated to version 1.6.5 However, when I run this command, an old version of pytorch-lightning is installed, as can be seen here: As you can see, version 0.8.5 is being installed. Is there a way for me to use conda and get a newer version of
Why does this custom function cost too much time while backward in pytorch?
I’m revising a baseline method in pytorch. But when I add a custom function in the training phase, the cost time of backward increases 4x on a single V100. Here is an example of the custom function: where b is the batch size, 16; h and w are the spatial dimensions, 100; k is equal to 21. I’m not sure
Imported package searches for modules in my code
Can someone explain me what is going on here and how to prevent this? I have a main.py with the following code: I outsourced some functions into a module named utils.py: When I run this I get the following output: So it seems like the torch package I imported has also a utils resource (package) and searches for a module
How do I create a model from a state dict?
I am trying to load a checkpoint pth file from the faster_rcnn_resnet101 model which is not currently in the PyTorch model zoo. This causes PyTorch to throw a KeyError saying that I the layers in the state dict does not match the model architecture of faster_rcnn_fpn_resnet50 that I’ve loaded from the model zoo. Note: I tried posting the architecture of
`torch.gather` without unbroadcasting
I have some batched input x of shape [batch, time, feature], and some batched indices i of shape [batch, new_time] which I want to gather into the time dim of x. As output of this operation I want a tensor y of shape [batch, new_time, feature] with values like this: In Tensorflow, I can accomplish this by using the batch_dims: