写出lstm进行轨迹预测的代码
时间: 2023-12-09 07:04:11 浏览: 25
以下是一个简单的LSTM模型用于轨迹预测的代码示例:
```python
import numpy as np
import tensorflow as tf
# 构建LSTM模型
class LSTMModel(object):
def __init__(self, num_layers, hidden_size, input_shape, output_shape, learning_rate):
self.num_layers = num_layers
self.hidden_size = hidden_size
self.input_shape = input_shape
self.output_shape = output_shape
self.learning_rate = learning_rate
self.build()
def build(self):
# 输入数据
self.inputs = tf.placeholder(tf.float32, shape=[None, self.input_shape[0], self.input_shape[1]], name='inputs')
# 输出数据
self.targets = tf.placeholder(tf.float32, shape=[None, self.output_shape[0], self.output_shape[1]], name='targets')
# 定义LSTM单元
lstm_cell = tf.nn.rnn_cell.BasicLSTMCell(self.hidden_size)
# 堆叠多个LSTM单元
stacked_lstm = tf.nn.rnn_cell.MultiRNNCell([lstm_cell]*self.num_layers)
# 对输入序列进行处理
outputs, state = tf.nn.dynamic_rnn(stacked_lstm, self.inputs, dtype=tf.float32)
# 预测结果
self.prediction = tf.layers.dense(outputs[:, -1], self.output_shape[1])
# 定义损失函数
self.loss = tf.losses.mean_squared_error(self.targets, self.prediction)
# 定义优化器
self.optimizer = tf.train.AdamOptimizer(learning_rate=self.learning_rate).minimize(self.loss)
# 数据处理
def process_data(data):
# 将数据分为输入序列和输出序列
input_data = data[:, :-1]
output_data = data[:, 1:]
return input_data, output_data
# 训练模型
def train(model, input_data, output_data, epochs, batch_size):
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
num_batches = input_data.shape[0] // batch_size
for epoch in range(epochs):
epoch_loss = 0
for batch in range(num_batches):
batch_input = input_data[batch*batch_size:(batch+1)*batch_size, :]
batch_output = output_data[batch*batch_size:(batch+1)*batch_size, :]
_, loss = sess.run([model.optimizer, model.loss], feed_dict={model.inputs: batch_input, model.targets: batch_output})
epoch_loss += loss
if epoch % 100 == 0:
print('Epoch {}/{}: Loss = {}'.format(epoch, epochs, epoch_loss/num_batches))
# 保存模型
saver = tf.train.Saver()
saver.save(sess, './lstm_model.ckpt')
# 预测
def predict(model, input_data):
with tf.Session() as sess:
saver = tf.train.Saver()
saver.restore(sess, './lstm_model.ckpt')
prediction = sess.run(model.prediction, feed_dict={model.inputs: input_data})
return prediction
# 示例数据
data = np.array([[1, 2, 3, 4], [2, 4, 6, 8], [3, 6, 9, 12], [4, 8, 12, 16], [5, 10, 15, 20]])
# 处理数据
input_data, output_data = process_data(data)
# 定义模型
model = LSTMModel(num_layers=2, hidden_size=32, input_shape=[input_data.shape[1], input_data.shape[2]], output_shape=[output_data.shape[1], output_data.shape[2]], learning_rate=0.001)
# 训练模型
train(model, input_data, output_data, epochs=1000, batch_size=2)
# 预测结果
input_data = np.array([[1, 2, 3, 4]])
prediction = predict(model, input_data)
print(prediction)
```