numpy_4-layer-nn.py

import numpy as np
def sigmoid(x, deriv=False):
    if (deriv == True):
        return x*(1-x)
    return 1/(1+np.exp(-x))
# input
X = np.array([[0,0,1],[0,1,0],[0,1,1],[1,0,0]])
# expected output (supervised learning)
y = np.array([[1],[1],[0],[0]])

np.random.seed(1)

# randomly initialize our weights with mean 0
w0 = 2*np.random.random((3,4))-1
w1 = 2*np.random.random((4,4))-1
w2 = 2*np.random.random((4,1))-1

for i in range(2000):
    
    # Feed forward through layers 0, 1, 2 and 3
    l0 = X
    l1 = sigmoid(np.dot(l0, w0))
    l2 = sigmoid(np.dot(l1, w1))
    l3 = sigmoid(np.dot(l2, w2))
    # how much did we miss the target value?
    l3_error = y - l3
    l3_delta = l3_error*sigmoid(l3, deriv=True)
    # how much did each l2 value contribute to the l3 error?
    l2_error = l3_delta.dot(w2.T)
    l2_delta = l2_error*sigmoid(l2, deriv=True)
    # how much did each l1 value contribute to the l2 error?
    l1_error = l2_delta.dot(w1.T)
    l1_delta = l1_error*sigmoid(l1, deriv=True)
    # recalculate the weights
    w2 += l2.T.dot(l3_delta)
    w1 += l1.T.dot(l2_delta)
    w0 += l0.T.dot(l1_delta)
print(l3)