You’ll then be presented with the following directory structure. To follow along with this tutorial, be sure to access the “Downloads” section of this guide to retrieve the source code. Gain access to Jupyter Notebooks for this tutorial and other PyImageSearch guides that are pre-configured to run on Google Colab’s ecosystem right in your web browser! No installation required.Īnd best of all, these Jupyter Notebooks will run on Windows, macOS, and Linux! Project structure Then join PyImageSearch University today! Ready to run the code right now on your Windows, macOS, or Linux system?.Wanting to skip the hassle of fighting with the command line, package managers, and virtual environments?.Learning on your employer’s administratively locked system?. Having problems configuring your development environment?įigure 1: Having trouble configuring your dev environment? Want access to pre-configured Jupyter Notebooks running on Google Colab? Be sure to join PyImageSearch University - you’ll be up and running with this tutorial in a matter of minutes. If you need help configuring your development environment for PyTorch, I highly recommend that you read the PyTorch documentation - PyTorch’s documentation is comprehensive and will have you up and running quickly. Luckily, both PyTorch and scikit-learn are extremely easy to install using pip: $ pip install torch torchvision To follow this guide, you need to have the PyTorch deep learning library and the scikit-machine learning package installed on your system. Let’s get started! Configuring your development environment We’ll wrap up the tutorial with a discussion of our results. With our two Python scripts implemented, we’ll move on to training our network. The second script will then load our example dataset and demonstrate how to train the network architecture we just implemented using PyTorch.The first script will be our simple feedforward neural network architecture, implemented with Python and the PyTorch library.We’ll start by reviewing our project directory structure and then configuring our development environment.įrom there, we’ll implement two Python scripts: Telling PyTorch to train your network with a GPU (if a GPU is available on your machine, of course).Telling your optimizer to update the gradients of your network.Making predictions and computing the loss on the current batch of data.Looping over data batches inside each epoch.Looping over your number of training epochs.Initializing your optimizer and loss function.Defining your neural network architecture.Inside this guide, you will become familiar with common procedures in PyTorch, including: Looking for the source code to this post? Jump Right To The Downloads Section Intro to PyTorch: Training your first neural network using PyTorch
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