How do I train a simple neural network with PyTorch on a pandas dataframe df? The column df[“Target”] is the target (e.g. labels) of the network. This doesn’t work: Answer I’m referring to the question in the title as you haven’t really specified anything else in the text, so just converting the DataFrame into a PyTorch tensor. Without information about
Tag: pytorch
Is there an efficient way to create a random bit mask in Pytorch?
I want to have a random bit mask that has some specified percent of 0s. The function I devised is: To illustrate: The main issue I have with this method is it requires the rate to divide the shape. I want a function that accepts an arbitrary decimal and gives approximately rate percent of 0s in the bitmask. Furthermore, I
Pytorch softmax: What dimension to use?
The function torch.nn.functional.softmax takes two parameters: input and dim. According to its documentation, the softmax operation is applied to all slices of input along the specified dim, and will rescale them so that the elements lie in the range (0, 1) and sum to 1. Let input be: Suppose I want the following, so that every entry in that array
What does .contiguous() do in PyTorch?
What does x.contiguous() do for a tensor x? Answer There are a few operations on Tensors in PyTorch that do not change the contents of a tensor, but change the way the data is organized. These operations include: narrow(), view(), expand() and transpose() For example: when you call transpose(), PyTorch doesn’t generate a new tensor with a new layout, it
What’s the reason of the error ValueError: Expected more than 1 value per channel?
reference fast.ai github repository of fast.ai (as the code elevates the library which is built on top of PyTorch) Please scroll the discussion a bit I am running the following code, and get an error while trying to pass the data to the predict_array function The code is failing when i am trying to use it to predict directly on
How do I check if PyTorch is using the GPU?
How do I check if PyTorch is using the GPU? The nvidia-smi command can detect GPU activity, but I want to check it directly from inside a Python script. Answer These functions should help: This tells us: CUDA is available and can be used by one device. Device 0 refers to the GPU GeForce GTX 950M, and it is currently
Why do we need to call zero_grad() in PyTorch?
Why does zero_grad() need to be called during training? Answer In PyTorch, for every mini-batch during the training phase, we typically want to explicitly set the gradients to zero before starting to do backpropragation (i.e., updating the Weights and biases) because PyTorch accumulates the gradients on subsequent backward passes. This accumulating behaviour is convenient while training RNNs or when we
NumPy/PyTorch extract subsets of images
In Numpy, given a stack of large images A of size(N,hl,wl), and coordinates x of size(N) and y of size(N) I want to get smaller images of size (N,16,16) In a for loop it would look like this: But can I do this just with indexing? Bonus question: Will this indexing also work in pytorch? If not how can I
How to run Pytorch model in normal non-parallel way?
I am going through this script, and there is a code block which takes 2 options into account, DataParallel and DistributedDataParallel here: What if I don’t want either of these options, and I want to run it without even DataParallel. How do I do it? How do I define my model so that it runs as a plain nn and
PyTorch Linear layer input dimension mismatch
Im getting this error when passing the input data to the Linear (Fully Connected Layer) in PyTorch: I fully understand the problem since the input data has a shape (N,C,H,W) (from a Convolutional+MaxPool layer) where: N: Data Samples C: Channels of the data H,W: Height and Width Nevertheless I was expecting PyTorch to do the “reshaping” of the data form: