src/deep_learning/pipeline/impl/text_generation/sample_functions.py

import keras
from keras import ops


# 按照最大概率采样
def greedy_search(preds):
    return ops.argmax(preds)


# 温度采样
def random_sample(preds, temperature=1.0):
    preds = preds / temperature
    return keras.random.categorical(preds[None, :], num_samples=1)[0]


# 只从前 k 个元素中采用温度采样
def top_k(preds, k=5, temperature=1.0):
    preds = preds / temperature
    top_preds, top_indices = ops.top_k(preds, k=k, sorted=False)
    choice = keras.random.categorical(top_preds[None, :], num_samples=1)[0]
    return ops.take_along_axis(top_indices, choice, axis=-1)