This repository is related to the video about principles of neural networks, which demonstrates how to write a simple neural network and implement handwritten digit recognition without using a framework.

The neural network has a single hidden layer, and one output, you can change some arguments in the config file to adjust the settings for model training/simplification/preview detection

git clone https://github.com/josStorer/nn_example.git --depth=1 cd nn_example pip install -r requirements.txt python cli.py -h

python cli.py -h # get help python cli.py -test # test accuracy python cli.py -m # mini example test python cli.py -train # start training the model python cli.py -s # simplify trained weights python cli.py -p # start the real-time preview detection, open img.jpg in usps folder with a paint software, then edit and save, the result will be automatically refreshed python cli.py -p -pycharm # pycharm preview mode, automatically refresh with SciView. When "right-click" running ./usps/realtime_predict.py in pycharm, this mode is True python cli.py -p -pf [filename] # specify the image filename of the real-time preview

The number of classes can be modified in config.py, and the data source and data annotation can be modified in train.py

The data label value starts from 1, indicating category 1, 2, 3, ... 99, 100 ...

Finally, networks with the number of [class_num] are generated, each of which is used to predict whether the given data is of category 1, 2, 3, ... 99, 100 ...

When implementing classification, the network index with the prediction result closest to 1 is the currently judged category. The implementation code is as follows:

results = np.zeros(config.class_num) for i in range(config.class_num): results[i] = network[i].predict(data) return results.argmax() # This result starts from 0, so you have to add 1 to get the above category labelled value

reference: https://victorzhou.com/blog/intro-to-neural-networks/