XOR.py

import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D

from dense import Dense
from activations import Tanh,Relu,Sigmoid
from losses import mse, mse_prime,binary_cross_entropy,binary_cross_entropy_prime

X = np.reshape([[0, 0], [0, 1], [1, 0], [1, 1]], (4, 2, 1))
Y = np.reshape([[0], [1], [1], [0]], (4, 1, 1))

epochs = 10000
learning_rate = 0.01

network = [
    Dense(2, 3),
    Relu(),
    Dense(3, 1),
    Sigmoid()
]

# train
for e in range(epochs):
    error = 0
    for x, y in zip(X, Y):
        # forward
        output = x
        for layer in network:
            output = layer.forward(output)

        # error
        error += binary_cross_entropy(y, output)

        # backward
        grad = binary_cross_entropy_prime(y, output)
        for layer in reversed(network):
            grad = layer.backward(grad, learning_rate)

    error /= len(X)
    print(f"{e + 1}/{epochs}, error={error}")

# prediction
print()
for x in X:
    z = x
    for layer in network:
        z = layer.forward(z)
    print(x.tolist(), "->", z)

# decision boundary plot
points = []
for x in np.linspace(0, 1, 20):
    for y in np.linspace(0, 1, 20):
        z = [[x], [y]]
        for layer in network:
            z = layer.forward(z)
        points.append([x, y, z[0,0]])

points = np.array(points)

fig = plt.figure()
ax = fig.add_subplot(111, projection="3d")
ax.scatter(points[:, 0], points[:, 1], points[:, 2], c=points[:, 2], cmap="winter")
plt.show()