Original article was published on Deep Learning on Medium
5 pytorch tensor functions
This post is a part of the Jovian ML’s, “Deep Learning : Zero to GANs”course. pytorch developed by Facebook is a widely used deep learning framework besides Google’s Tensorflow. We’ll look at 5 different tensor functions from the pytorch library.
torch.cumsum(input, dim, out=None, dtype=None) → Tensor
Example: Let’s suppose you hold AAPL stock for 5 years. You want to find out the cumulative return you made at the end of each holding year. The code below provides solution in 2 steps.
torch.randn(input, dim, out=None, dtype=None) → Tensor
Example: Let’s suppose you are learning about images and would like to create an image with random noise to view it. The code below provides a way to generate random values from a normal distribution. Observe output below the code.
torch.index_select(input, dim, index, out=None) → Tensor
Example: Let’s suppose you have a RGB image and would like to view only the R channel using matplotlib. The code below provides a solution.
torch.from_numpy(ndarray) → Tensor
This function could come in handy when dealing with numerical datasets. The returned tensor and
ndarray share the same memory. This doesn’t consume excess memory once loaded.
output : array([10, 99, 12, 13])
torch.flip(input, dims) → Tensor
Let’s suppose for an image augmentation problem you are looking to flip image. The code below provides the solution to flip the image.
pytorch documentation : https://pytorch.org/docs/stable/tensors.html