Loss Function with Simple Python

Python Simulation Without Libraries

# Simple loss calculator: y = mx + b
def predict(x, m, b):
    return m * x + b

def mse_loss(y_true, y_pred):
    error = 0
    for yt, yp in zip(y_true, y_pred):
        error += (yt - yp) ** 2
    return error / len(y_true)

# Data
x_data = [1, 2, 3]
y_data = [2, 4, 6]  # Ideal: y = 2x

# Let's predict with wrong values
m, b = 1.5, 0.5
predictions = [predict(x, m, b) for x in x_data]

loss = mse_loss(y_data, predictions)
print("Loss:", loss)

Summary — Relevancy of Loss Function

  • Core metric guiding learning.
  • Without it, no feedback — no learning.
  • Shapes how we optimize the network.
  • Helps detect underfitting/overfitting.
  • Used to compare multiple models objectively.

Next – Learning Rate adjustment Impact in Neural Network