基于TensorFlow的高效内存滑动窗口序列学习

import tensorflow as tf

def create_dataset(data, window_size, batch_size):
    dataset = tf.data.Dataset.from_tensor_slices(data)
    dataset = dataset.window(window_size, shift=1, drop_remainder=True)
    dataset = dataset.flat_map(lambda window: window.batch(window_size))
    dataset = dataset.shuffle(buffer_size=len(data) // batch_size)
    dataset = dataset.batch(batch_size).prefetch(1)
    return dataset

data = ...  # 你的时间序列数据
window_size = 10
batch_size = 32
dataset = create_dataset(data, window_size, batch_size)

@tf.function
def train_step(inputs, labels):
    with tf.GradientTape() as tape:
        predictions = model(inputs, training=True)
        loss = loss_object(labels, predictions)
    gradients = tape.gradient(loss, model.trainable_variables)
    optimizer.apply_gradients(zip(gradients, model.trainable_variables))
    return loss

from tensorflow.keras.layers import Dropout

model.add(Dropout(0.5))