设置LSTM层的初始状态

import numpy as np
import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import LSTM, Dense

# 定义参数
batch_size = 32
vocab_size = 10000
embedding_dim = 256
lstm_units = 128
num_classes = 10

# 创建模型
model = Sequential([
    LSTM(lstm_units, return_sequences=True, stateful=True),
    Dense(num_classes, activation='softmax')
])

# 设置LSTM层的初始状态
initial_state = [tf.zeros((batch_size, lstm_units))] * 2 # 初始化状态为全零向量

# 编译模型
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])

# 使用tf.Variable来创建一个可训练的初始状态
initial_state = [
    tf.Variable(tf.zeros(lstm_units), trainable=False) for _ in range(2)
]

# 将初始状态设置到模型中
model.layers[0].reset_states(states=initial_state)

# train_data 和 train_labels 是训练数据和标签
# 这里只是一个示例，具体实现取决于您的数据来源
model.fit(train_data, train_labels, batch_size=batch_size, epochs=10)