nn_34.py

import numpy as np
import os

# Features and labels
training_set_inputs = np.array([[0, 0, 1], [1, 1, 1], [1, 0, 1], [0, 1, 1]])
training_set_outputs = np.transpose(np.array([[0, 1, 1, 1]]))
# Predict
predict = np.array([[0, 0, 1]])
# Check if model has been saved
if not os.path.exists('model.npy'):
    # No saved model
    print("Couldn't find saved model...")
    # Seed
    np.random.seed(1)
    # Random initial weights
    synaptic_weights = 2 * np.random.random((3, 1)) - 1
    # Training loop
    for i in range(50000):
        # Layer
        output = 1 / (1 + np.exp(-(np.dot(training_set_inputs, synaptic_weights))))
        # Update weights
        synaptic_weights += np.dot(np.transpose(training_set_inputs), (training_set_outputs - output) * output * (1 - output))
    # Save model
    np.save('model.npy', synaptic_weights)
    # Weights
    weights = synaptic_weights
else:
    # Load saved model
    print('Loading saved model...')
    weights = np.load('model.npy')
# Predict
prediction = 1 / (1 + np.exp(-(np.dot(predict, weights))))
print(prediction)