| import numpy as np | |
| # SpTn-Model-only-1P | |
| # Text -> 1 parameter -> generated text | |
| np.random.seed(42) | |
| # Exactly ONE trainable parameter | |
| w = np.float32(0.5) | |
| text = input("Enter text: ").lower() | |
| if not text: | |
| text = "hello world" | |
| chars = np.array(list("abcdefghijklmnopqrstuvwxyz ")) | |
| # Target = average character code, normalized | |
| codes = np.array([ | |
| (ord(c) - 32) / 95.0 | |
| for c in text | |
| if 32 <= ord(c) <= 126 | |
| ]) | |
| target = np.mean(codes) | |
| # Train ONE parameter | |
| lr = 0.05 | |
| for step in range(1000): | |
| prediction = w | |
| error = prediction - target | |
| loss = error ** 2 | |
| gradient = 2 * error | |
| w -= lr * gradient | |
| print("\nSpTn-Model-only-1P") | |
| print("Trainable parameters:", 1) | |
| print("Parameter:", w) | |
| print("Loss:", loss) | |
| # Generate text | |
| output = "" | |
| for _ in range(200): | |
| value = np.random.random() | |
| index = int( | |
| np.clip( | |
| w * len(chars) + value * 3, | |
| 0, | |
| len(chars) - 1 | |
| ) | |
| ) | |
| output += chars[index] | |
| print("\nGenerated:") | |
| print(output) | |