Demo of very basic Neural Network in Python 3 (Siraj Raval - Build a Neural Net in 4 Minutes)

neural_net_demo.py

# Credits: https://www.youtube.com/watch?v=h3l4qz76JhQ
# Basic description: teach NN to output [0, 1, 1, 0] for different inputs

import numpy as np


def nonlin(x, deriv=False):
    if(deriv == True):
        return x*(1-x)
    return 1/(1+np.exp(-x))


# input data
x = np.array([[0, 0, 1],
              [0, 1, 1],
              [1, 0, 1],
              [1, 1, 1]])

# output data
y = np.array([[0],
              [1],
              [1],
              [0]])

np.random.seed(1)

# synapses
syn0 = 2 * np.random.random((3, 4))-1  # input
syn1 = 2 * np.random.random((4, 1))-1  # output

# training step
for j in range(60000):
    l0 = x
    l1 = nonlin(np.dot(l0, syn0))
    l2 = nonlin(np.dot(l1, syn1))

    l2_error = y - l2

    if j % 10000 == 0:
        print("Error:" + str(np.mean(np.abs(l2_error))))

    l2_delta = l2_error * nonlin(l2, deriv=True)

    l1_error = l2_delta.dot(syn1.T)
    l1_delta = l1_error * nonlin(l1, deriv=True)

    # update weights
    syn1 += l1.T.dot(l2_delta)
    syn0 += l0.T.dot(l1_delta)

print("Output after training:")
print(l2)