pytorch实现手动数字识别

# NumberRecognition - [NumberRecognition](#numberrecognition) - [Quick start](#quick-start) - [MNIST Digit Recognition with PyTorch](#mnist-digit-recognition-with-pytorch) - [Overview](#overview) - [Contents](#contents) - [parse\_train\_images\_labels.py](#parse_train_images_labelspy) - [parse\_t10k\_images\_labels.py](#parse_t10k_images_labelspy) - [model\_train.py](#model_trainpy) - [model\_inference.py](#model_inferencepy) - [How to Use](#how-to-use) - [Test result (97.77%)](#test-result-9777) - [Dependencies](#dependencies) - [License](#license) - [Acknowledgements](#acknowledgements) ## Quick start ``` pip install -r requirements.txt python parse_train_images_labels.py python parse_t10k_images_labels.py python model_training.py python model_test.py ``` ## MNIST Digit Recognition with PyTorch This is a "Hello World" program in the field of artificial intelligence (AI) and deep learning, written for learning purposes. This repository contains a complete implementation of a neural network for recognizing handwritten digits from the MNIST dataset using PyTorch. The project includes both training and inference scripts, along with utilities for preparing the dataset. ### Overview The project consists of the following scripts: - model_train.py: This script is responsible for training the neural network model on the MNIST dataset. - model_inference.py: This script performs inference using the trained model on a test dataset. - parse_train_images_labels.py: Reads and saves the MNIST training images, organized by digit labels. - parse_t10k_images_labels.py: Reads and saves the MNIST test labels and images, organized by digit labels. ### Contents #### parse_train_images_labels.py This script handles the following tasks: - Reads the MNIST training images from the .idx3-ubyte file. - Parses the images and saves them into the mnist_train directory, organized by digit labels. #### parse_t10k_images_labels.py This script performs the following tasks: - Reads the MNIST test labels from the .idx1-ubyte file. - Parses the labels and saves the corresponding images into the mnist_test directory, organized by digit labels. #### model_train.py This script handles the following tasks: - Loads the training and test datasets from the ./mnist_train and ./mnist_test directories respectively. - Applies transformations to the images, including converting them to grayscale and tensor format. - Initializes and trains a neural network using the Adam optimizer and cross-entropy loss. - Logs the progress of the training process, including dataset lengths, batch information, and loss values. - Saves the trained model to mnist.pth. #### model_inference.py This script performs the following tasks: - Loads the test dataset from the ./mnist_test directory and applies necessary transformations. - Loads the pre-trained model from mnist.pth. - Performs inference on the test dataset and prints cases where the model's predictions do not match the actual labels. - Calculates and prints the accuracy of the model on the test dataset. #### How to Use 1. Dataset Preparation: Ensure the MNIST dataset is available in the ./mnist_train and ./mnist_test directories. The dataset should be organized such that each digit (0-9) has its own subdirectory containing corresponding images. 2. Training the Model: ``` python model_train.py ``` This command will train the model and save the trained weights to mnist.pth. 3. Running Inference: ``` python model_inference.py ``` This command will evaluate the trained model on the test dataset and print the accuracy along with any misclassified images. #### Test result (97.77%) ``` C:\Users\kz\.conda\envs\nn\python.exe C:\Users\kz\Documents\Code\NumberRecognition\model_inference.py test_dataset length: 10000 wrong case: predict = 9 actual = 0 img_path = ./mnist_test\0\mnist_test_126.png wrong case: predict = 2 actual = 0 img_path = ./mnist_test\0\mnist_test_1748.png wrong case: predict = 9 actual = 0 img_path = ./mnist_test\0\mnist_test_1987.png wrong case: predict = 4 actual = 0 img_path = ./mnist_test\0\mnist_test_2033.png wrong case: predict = 9 actual = 0 img_path = ./mnist_test\0\mnist_test_3251.png wrong case: predict = 4 actual = 0 img_path = ./mnist_test\0\mnist_test_3818.png wrong case: predict = 8 actual = 0 img_path = ./mnist_test\0\mnist_test_4065.png wrong case: predict = 8 actual = 0 img_path = ./mnist_test\0\mnist_test_4880.png wrong case: predict = 4 actual = 0 img_path = ./mnist_test\0\mnist_test_6400.png wrong case: predict = 7 actual = 0 img_path = ./mnist_test\0\mnist_test_6597.png wrong case: predict = 6 actual = 0 img_path = ./mnist_test\0\mnist_test_7216.png wrong case: predict = 6 actual = 0 img_path = ./mnist_test\0\mnist_test_8325.png wrong case: predict = 3 actual = 0 img_path = ./mnist_test\0\mnist_test_9634.png wrong case: predict = 2 actual = 1 img_path = ./mnist_test\1\mnist_test_2182.png wrong case: predict = 2 actual = 1 img_path = ./mnist_test\1\mnist_test_3073.png wrong case: predict = 3 actual = 1 img_path = ./mnist_test\1\mnist_test_3906.png wrong case: predict = 7 actual = 1 img_path = ./mnist_test\1\mnist_test_4201.png wrong case: predict = 6 actual = 1 img_path = ./mnist_test\1\mnist_test_5331.png wrong case: predict = 8 actual = 1 img_path = ./mnist_test\1\mnist_test_5457.png wrong case: predict = 5 actual = 1 img_path = ./mnist_test\1\mnist_test_5642.png wrong case: predict = 8 actual = 1 img_path = ./mnist_test\1\mnist_test_619.png wrong case: predict = 6 actual = 1 img_path = ./mnist_test\1\mnist_test_6783.png wrong case: predict = 0 actual = 1 img_path = ./mnist_test\1\mnist_test_7928.png wrong case: predict = 8 actual = 1 img_path = ./mnist_test\1\mnist_test_8020.png wrong case: predict = 2 actual = 1 img_path = ./mnist_test\1\mnist_test_956.png wrong case: predict = 3 actual = 2 img_path = ./mnist_test\2\mnist_test_1395.png wrong case: predict = 6 actual = 2 img_path = ./mnist_test\2\mnist_test_1609.png wrong case: predict = 7 actual = 2 img_path = ./mnist_test\2\mnist_test_1790.png wrong case: predict = 0 actual = 2 img_path = ./mnist_test\2\mnist_test_2098.png wrong case: predict = 4 actual = 2 img_path = ./mnist_test\2\mnist_test_2488.png wrong case: predict = 7 actual = 2 img_path = ./mnist_test\2\mnist_test_321.png wrong case: predict = 8 actual = 2 img_path = ./mnist_test\2\mnist_test_3796.png wrong case: predict = 4 actual = 2 img_path = ./mnist_test\2\mnist_test_3817.png wrong case: predict = 8 actual = 2 img_path = ./mnist_test\2\mnist_test_4248.png wrong case: predict = 7 actual = 2 img_path = ./mnist_test\2\mnist_test_4289.png wrong case: predict = 4 actual = 2 img_path = ./mnist_test\2\mnist_test_4615.png wrong case: predict = 4 actual = 2 img_path = ./mnist_test\2\mnist_test_4876.png wrong case: predict = 4 actual = 2 img_path = ./mnist_test\2\mnist_test_5086.png wrong case: predict = 7 actual = 2 img_path = ./mnist_test\2\mnist_test_583.png wrong case: predict = 8 actual = 2 img_path = ./mnist_test\2\mnist_test_613.png wrong case: predict = 6 actual = 2 img_path = ./mnist_test\2\mnist_test_6574.png wrong case: predict = 1 actual = 2 img_path = ./mnist_test\2\mnist_test_659.png wrong case: predict = 7 actual = 2 img_path = ./mnist_test\2\mnist_test_7457.png wrong case: predict = 4 actual = 2 img_path = ./mnist_test\2\mnist_test_7886.png wrong case: predict = 8 actual = 2 img_path = ./mnist_test\2\mnist_test_8094.png wrong case: predict = 4 actual = 2 img_path = ./mnist_test\2\mnist_test_8198.png wrong case: predict = 7 actual = 2 img_path = ./mnist_test\2\mnist_test_9664.png wrong case: predict = 0 actual = 2 img_path = ./mnist_test\2\mnist_test_9768.png wrong case: predict = 8 actual = 2 img_path = ./mnist_test\2\mnist_test_9811.png wrong case: predict = 3 actual = 2 img_path = ./mnist_test\2\mnist_test_9839.png wrong case: predict = 7 actual = 3 img_path = ./mnist_test\3\mnist_test_1128.png wrong case: predict