LSTM预测模型建立
时间: 2023-11-27 20:47:50 浏览: 43
以下是使用Keras建立LSTM预测模型的步骤:
1.导入所需的库和模块
```python
from keras.models import Sequential
from keras.layers import LSTM, Dense
```
2.准备数据集并进行预处理
```python
# 假设我们有一个形状为(100, 2)的输入序列
import numpy as np
data = np.random.random((100, 2))
# 将数据集划分为训练集和测试集
train_data = data[:80]
test_data = data[80:]
# 将数据集转换为3D张量,以便LSTM层可以接受
def create_dataset(dataset, look_back=1):
X, Y = [], []
for i in range(len(dataset)-look_back):
X.append(dataset[i:i+look_back, :])
Y.append(dataset[i+look_back, 0])
return np.array(X), np.array(Y)
look_back = 3
train_X, train_Y = create_dataset(train_data, look_back)
test_X, test_Y = create_dataset(test_data, look_back)
# 将数据集重塑为适合LSTM层的形状
train_X = np.reshape(train_X, (train_X.shape[0], train_X.shape[1], 2))
test_X = np.reshape(test_X, (test_X.shape[0], test_X.shape[1], 2))
```
3.定义LSTM模型并进行训练
```python
# 定义模型
model = Sequential()
model.add(LSTM(5, input_shape=(look_back, 2), return_sequences=True))
model.add(LSTM(5))
model.add(Dense(1))
model.compile(loss='mean_squared_error', optimizer='adam')
# 训练模型
model.fit(train_X, train_Y, epochs=100, batch_size=1, verbose=2)
```
4.使用模型进行预测
```python
# 使用模型进行预测
train_predict = model.predict(train_X)
test_predict = model.predict(test_X)
```