Category Archives: PyTorch

I use Windows OS machines for most of my work, but I also use MacOS machines and Linux machines too. I try to keep in practice with all three platforms, and so one morning, I figured I’d run the latest … Continue reading →

I regularly use Windows OS (most of the time), MacOS (less often), and Linux (occasionally). I try to keep in practice with all three platforms, and so one morning, I figured I’d run the latest version of one of my … Continue reading →

In a PyTorch multi-class classification problem, the basic architecture is to apply log_softmax() activation on the output nodes, in conjunction with NLLLoss() during training. It’s possible to compute softmax() and then apply log() but it’s slightly more efficient to compute … Continue reading →

The PyTorch neural network code library has built-in loss functions that can handle most scenarios. Examples include NLLLoss() and CrossEntropyLoss() for multi-class classification, BCELoss() for binary classification, and MSELoss() and L1Loss() for regression. Because PyTorch works at a low level … Continue reading →

I do most of my work on Windows OS machines. One morning I noticed that my MacBook laptop in my office was collecting dust so I figured I’d upgrade the existing PyTorch 1.10.0 to version 1.12.1 to make sure there … Continue reading →

I wrote an article titled “Regression Using PyTorch, Part 1: New Best Practices” in the November 2022 edition of Microsoft Visual Studio Magazine. See https://visualstudiomagazine.com/articles/2022/11/01/pytorch-regression.aspx. A regression problem is one where the goal is to predict a single numeric value. … Continue reading →

I hadn’t used the torch.utils.bottleneck tool for quite some time, so just for hoots I figured I’d see if anything had changed since my last use of the tool. The bottleneck tool analyzes a program run using the Python profiler … Continue reading →

In most cases, I train a PyTorch model using inline code inside a main() function. For example: import torch as T . . . def main(): # 0. get started print(“Begin People predict politics type “) T.manual_seed(1) np.random.seed(1) # 1. … Continue reading →

Bottom line: Hyperparameter random search can be effective but the difficult part is determining what to parameterize and the range of possible parameter values. When creating a neural network prediction model there are many architecture hyperparameters (number hidden layers, number … Continue reading →