Category Archives: PyTorch

The convolution layer uses filters that perform convolution operations as it is scanning the input with respect to its dimensions. The resulting output is called a feature map or activation map. 

That max pooling isn’t the only way you can achieve downsampling. You can also use strides in the prior convolution layer or you can use average pooling instead of max pooling,

Average pooling was a common approach early on but has fallen out of favor somewhat. As you might expect, it works exactly like a max pooling layer, except it computes the mean rather than the max.

Calling view on a tensor returns a new tensor that changes the number of dimensions and the striding information, without changing the storage.

Contiguous memory allocation, all the free memory space can stay in the same place concurrently. In non-contiguous memory allocation, the operation is allowed to different memory sections at multiple memory positions in the memory.

For a multiclass classification problem, you can use the one-versus-all design and find a ROC curve for each class. The one-versus-all treats a multiclass classification problem as a set of binary classification problems

If you try to backward you will get an error. It is just harder to detect during the forward. The .backward() did NOT catch the in-place operation on a tensor that is in the forward computation graph. It only detects it during the backward.

In this article, we will explain the difference between detach(), clone(), copy.deepcopy(), detach().clone(), and clone().detach() for a tensor.

In this section, we will demonstrate how you can use DataPipe with DataLoader. For the most part, you should be able to use it just by passing dataset=datapipe as an input argument into the DataLoader2.

In this tutorial, We will Install the Transformers library, set up your cache, and download the pre-train Transformers model to run offline and load the model from a local directory.