# Torch tensor indexing

Two days in the past, I launched torch, an R package deal that gives the native performance that is dropped at Python customers by PyTorch. In that put up, I assumed fundamental familiarity with TensorFlow/Keras. Consequently, I portrayed torch in a method I figured can be useful to somebody who "grew up" with the Keras […]

Best options for creating tensors in PyTorch. Given all of these details, these two are the best options: torch.tensor () torch.as_tensor () The torch.tensor () call is the sort of go-to call, while torch.as_tensor () should be employed when tuning our code for performance.1. A replacement for NumPy to use the power of GPUs. 2. A deep learning research platform that provides maximum flexibility and speed. Deep Learning with PyTorch: A 60 Minute Blitz. PyTorch uses Tensor as its core data structure, similar to a Numpy array. You may wonder about this specific choice of data structure.

torch.Tensor.index_add¶ Tensor.index_add (tensor1, dim, index, tensor2) → Tensor¶ Out-of-place version of torch.Tensor.index_add_(). tensor1 corresponds to self in torch.Tensor.index_add_().torch.Tensor.index_fill_. Tensor.index_fill_(dim, index, value) → Tensor. Fills the elements of the self tensor with value value by selecting the indices in the order given in index. Parameters. dim ( int) - dimension along which to index. index ( LongTensor) - indices of self tensor to fill in. value ( float) - the value to fill with.