Neural Network Basic

Working neural network taking XOR as an example with Decision boundary

Example

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

from dense import Dense
from activations import Tanh
from losses import mse, mse_prime
from network import train, predict

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

network = [
    Dense(2, 3),
    Tanh(),
    Dense(3, 1),
    Tanh()
]

# train
train(network, mse, mse_prime, X, Y, epochs=10000, learning_rate=0.1)

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