import tensorflow as tf
import numpy as np

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

y = np.array([sum(i) % 2 for i in x])

model = tf.keras.Sequential([
    tf.keras.layers.Input(shape = (3,)),
    tf.keras.layers.Dense(3, activation = "tanh"),
    tf.keras.layers.Dense(1, activation = "sigmoid")
])

model.compile(
    optimizer = tf.keras.optimizers.Adam(learning_rate = 0.05),
    loss = "binary_crossentropy",
    metrics = ["accuracy"]
)

for _ in range(100):
    model.fit(x, y, epochs = 10, verbose = 0)
    loss, accuracy = model.evaluate(x, y, verbose = 0)

    print(f"\rAchieved accuracy={accuracy}, loss={loss}", end = '')

    if accuracy == 1.0:
        print()
        break

model.save_weights("mod1_final.weights.h5")