用python写一个2层的LSTM

以下是一个2层的LSTM的Python代码示例：

import numpy as np
import tensorflow as tf

# 定义输入数据的维度和LSTM网络的参数
input_dim = 10
hidden_dim = 20
num_layers = 2
batch_size = 32
seq_length = 50

# 定义输入数据和标签占位符
inputs = tf.placeholder(tf.float32, [batch_size, seq_length, input_dim])
targets = tf.placeholder(tf.float32, [batch_size, hidden_dim])

# 定义LSTM网络的第一层
with tf.variable_scope("lstm1"):
    lstm1 = tf.contrib.rnn.BasicLSTMCell(hidden_dim)
    output1, _ = tf.nn.dynamic_rnn(lstm1, inputs, dtype=tf.float32)

# 定义LSTM网络的第二层
with tf.variable_scope("lstm2"):
    lstm2 = tf.contrib.rnn.BasicLSTMCell(hidden_dim)
    output2, _ = tf.nn.dynamic_rnn(lstm2, output1, dtype=tf.float32)

# 将LSTM网络的输出展平成二维数组
output_flat = tf.reshape(output2, [-1, hidden_dim])

# 定义输出层
with tf.variable_scope("output"):
    W = tf.get_variable("W", [hidden_dim, hidden_dim])
    b = tf.get_variable("b", [hidden_dim])
    output = tf.matmul(output_flat, W) + b

# 计算损失函数和优化器
loss = tf.reduce_mean(tf.square(output - targets))
optimizer = tf.train.AdamOptimizer().minimize(loss)

# 定义用于生成随机输入和标签的函数
def generate_data(batch_size, seq_length, input_dim, hidden_dim):
    inputs = np.random.randn(batch_size, seq_length, input_dim)
    targets = np.random.randn(batch_size, hidden_dim)
    return inputs, targets

# 开始训练
sess = tf.Session()
sess.run(tf.global_variables_initializer())
for i in range(1000):
    inputs, targets = generate_data(batch_size, seq_length, input_dim, hidden_dim)
    loss_val, _ = sess.run([loss, optimizer], feed_dict={inputs: inputs, targets: targets})
    if i % 100 == 0:
        print("Iteration %d: loss = %.4f" % (i, loss_val))

此代码示例创建了一个包含两个LSTM层的神经网络，每个LSTM层有20个隐藏单元。该网络接收一个形状为(batch_size, seq_length, input_dim)的输入张量，并输出一个形状为(batch_size, hidden_dim)的输出张量。在训练期间，该网络使用Adam优化器最小化输出和目标之间的平方误差