npnet

neural net powered by numpy


Keywords
backpropagation, layer, neural-network, numpy, simple
License
MIT
Install
pip install npnet==0.0.7

Documentation

Simple Neural Networks

This is a repo for building a simple Neural Net based only on Numpy.

The usage is similar to Pytorch. There are only limited codes involved to be functional. Unlike those popular but complex packages such as Tensorflow and Pytorch, you can dig into my source codes smoothly.

The main purpose of this repo is for you to understand the code rather than implementation. So please feel free to read the codes.

Simple usage

Build a network with a python class and train it.

import npnet

class Net(npnet.Module):
    def __init__(self):
        super().__init__()
        self.l1 = npnet.layers.Dense(n_in=1, n_out=10, activation=npnet.act.tanh)
        self.out = npnet.layers.Dense(10, 1)

    def forward(self, x):
        x = self.l1(x)
        o = self.out(x)
        return o

The training procedure starts by defining an optimizer and loss.

net = Net()
opt = npnet.optim.Adam(net.params, lr=0.1)
loss_fn = npnet.losses.MSE()

for _ in range(1000):
    o = net.forward(x)
    loss = loss_fn(o, y)
    net.backward(loss)
    opt.step()

Demo

Install

pip install npnet

Download or fork

Download link

Fork this repo:

$ git clone https://github.com/MorvanZhou/npnet.git

Results

img