#AI Challenge Camp First Stop# Implementing MNIST handwritten digit recognition based on pytorch

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#AI Challenge Camp First Stop# Implementing MNIST handwritten digit recognition based on pytorch [Copy link]

Basic concepts :

1. The essence of model training is to learn the patterns and relationships between data through a large amount of data and algorithms, so that the model can make accurate predictions or decisions on new, unseen data. The final result of training is to obtain a trained model that can make accurate predictions and classifications on new input data.

2. PyTorch is an open source machine learning library based on Python and designed for deep neural networks. It provides powerful GPU acceleration support, dynamic graph mechanism and automatic differentiation function, making the model development, debugging and optimization process more efficient.

Supported operating systems: windows, macos, linux, etc.

Supported computing platforms: CPU, NVIDIA GPU, AMD GPU, TPU, etc.

Environment setup:

debian 12 + vscode + pytorch

pytorch installation: pip install torch torchvision torchaudio

Problem: Debian uses python3.11.X, which does not support pip installation

Solution: Use a virtual python environment, the command is as follows:

python3 -m venv myenv ------Create your own Python virtual environment, myenv is the name of your own virtual environment

source myenv/bin/active ------Activate the virtual environment

pip install XXX ------Install the required python library

deactive ------Close the virtual environment

Source code :

Download the test training set:

Define the training network:

Start training:

Test Methods:

Test Results

Trained model output: