| import numpy as np |
|
|
| MODEL_FILE = "sp_tn_1p.npz" |
|
|
| model = np.load(MODEL_FILE, allow_pickle=True) |
|
|
| w = float(model["w"]) |
| vocab = [str(x) for x in model["vocab"]] |
|
|
| char_to_id = {c: i for i, c in enumerate(vocab)} |
|
|
|
|
| def logits(parameter, input_id): |
| ids = np.arange(len(vocab), dtype=np.float32) |
| feature = np.sin((ids + 1.0) * (input_id + 1.0)) |
| return parameter * feature |
|
|
|
|
| def softmax(values): |
| values = values - np.max(values) |
| exp_values = np.exp(values) |
| return exp_values / np.sum(exp_values) |
|
|
|
|
| def next_character(previous_char): |
| if previous_char not in char_to_id: |
| previous_char = " " |
|
|
| if previous_char not in char_to_id: |
| previous_char = vocab[0] |
|
|
| input_id = char_to_id[previous_char] |
|
|
| probabilities = softmax( |
| logits(w, input_id) |
| ) |
|
|
| return vocab[ |
| np.random.choice( |
| len(vocab), |
| p=probabilities |
| ) |
| ] |
|
|
|
|
| def generate(prompt, length=80): |
| if not prompt: |
| prompt = " " |
|
|
| result = prompt |
|
|
| previous = prompt[-1] |
|
|
| for _ in range(length): |
| character = next_character(previous) |
| result += character |
| previous = character |
|
|
| return result |
|
|
|
|
| print("================================") |
| print("SpTn-Model-only-1P") |
| print("Trainable parameters: 1") |
| print("Model file:", MODEL_FILE) |
| print("Parameter:", w) |
| print("================================") |
| print("Type 'exit' to quit.") |
|
|
| while True: |
| text = input("\nYou: ") |
|
|
| if text.lower() == "exit": |
| print("Bye.") |
| break |
|
|
| if not text.strip(): |
| continue |
|
|
| print("Model:", generate(text)) |
|
|
