| import numpy as np | |
| np.random.seed(42) | |
| # SpTn-Model-only-1P | |
| # Exactly ONE trainable parameter | |
| w = np.array(0.5) | |
| text = "hello world this is a one parameter model" | |
| chars = np.array(list("abcdefghijklmnopqrstuvwxyz ")) | |
| # Training target: | |
| # frequency of letters in the text | |
| counts = np.array([text.count(c) for c in chars], dtype=float) | |
| target = counts / counts.sum() | |
| # One parameter controls the sharpness of the distribution | |
| lr = 0.01 | |
| for step in range(1000): | |
| temperature = np.exp(w) | |
| logits = np.log(target + 1e-8) / temperature | |
| probs = np.exp(logits - np.max(logits)) | |
| probs /= probs.sum() | |
| loss = -np.sum(target * np.log(probs + 1e-8)) | |
| gradient = 0.01 * (loss - 1.0) | |
| w -= lr * gradient | |
| print("SpTn-Model-only-1P") | |
| print("Trainable parameters:", 1) | |
| print("Parameter:", w) | |
| print("Loss:", loss) | |
| temperature = np.exp(w) | |
| logits = np.log(target + 1e-8) / temperature | |
| probs = np.exp(logits - np.max(logits)) | |
| probs /= probs.sum() | |
| output = "" | |
| for _ in range(200): | |
| output += np.random.choice(chars, p=probs) | |
| print("\nGenerated:") | |
| print(output) | |
| with open("generated.txt", "w") as f: | |
| f.write(output) | |
| print("\nCharacters generated:", len(output)) | |
| print("File: generated.txt") | |