Neural Network sample 1

nn1.py

count = 10
coefficient = 2

inputs = [n + 1 for n in range(count)]
targets = [i * coefficient for i in inputs]

EPOACH = 25

w = 0.1
learning_rate = 0.1

def predict(i):
    return w * i

for _ in range(EPOACH):
    pred = [predict(i) for i in inputs]
    errors = [t - p for p, t in zip(pred, targets)]
    cost = sum(errors) / len(targets)
    
    print(f"Weight: {w:.2f}, Cost: {cost:.2f}")
    
    w += learning_rate * cost

# Network Tests
test_inputs = [5, 6, 7, 8, 9]
test_targets = [10, 12, 14, 16, 18]

print(f"W: {w:.2f}")

pred = [predict(i) for i in test_inputs]
for i, t, p in zip(test_inputs, test_targets, pred):
    print(f"inputs: {i}, targets: {t}, pred: {p:.4f}")