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Build a Complete Neural Network in Less Than 2,000 Lines of Code
A minimized construction of neural network components. Supports both Fully Connected Neural Network (FCNN) and Convolutional Neural Network (CNN) for learning purposes.
python version > 3.8
In your working directory, run: python setup.py install
In the examples directory, there are many sample codes:
├─examples
│ 00-download_mnist.py # Download the mnist dataset
│ 01-simple_forward.py # Understand the simplest forward computation
│ 02-simple_backward.py # Understand backward computation based on numerical differentiation
│ 03-simple_network.py # Implement a two-layer neural network with both forward and backward computations
│ 04-introduce_layer.py # Introduce the concept of the layer
│ 05-simple_network_with_layer.py # Implement a neural network based on the layer concept
│ 06-introduce_optimizer.py # Introduce the concept of the optimizer and compare based on a specific function
│ 07-introduce_optimizer_2.py # Apply the optimizer in a real network
│ 08-introduce_weight_decay.py # Introduce the concept of weight decay
│ 09-introduce_batch_normalization.py # Introduce the concept of batch normalization
│ 10-introduce_dropout.py # Introduce the concept of dropout
│ 11-hyperparam_search.py # Implement hyperparameter search
│ 12-CNN_and_digits_recognition.py # Recognize handwritten digits using CNN