关于lstm模型预测的python代码
时间: 2023-10-02 16:09:49 浏览: 42
以下是一个简单的LSTM模型预测的Python代码示例:
```python
import numpy as np
import pandas as pd
from keras.models import Sequential
from keras.layers import Dense, LSTM
# 读取数据集
data = pd.read_csv('data.csv')
# 将数据集转换为适合LSTM的格式
def create_dataset(dataset):
X, Y = [], []
for i in range(len(dataset) - 1):
X.append(dataset[i])
Y.append(dataset[i + 1])
return np.array(X), np.array(Y)
train_data = data.values.astype('float32')
train_data = np.reshape(train_data, (len(train_data), 1))
# 划分训练集和测试集
train_size = int(len(train_data) * 0.67)
test_size = len(train_data) - train_size
train, test = train_data[0:train_size,:], train_data[train_size:len(train_data),:]
# 创建数据集
train_X, train_Y = create_dataset(train)
test_X, test_Y = create_dataset(test)
# 创建并拟合LSTM模型
model = Sequential()
model.add(LSTM(4, input_shape=(1, 1)))
model.add(Dense(1))
model.compile(loss='mean_squared_error', optimizer='adam')
model.fit(train_X, train_Y, epochs=100, batch_size=1, verbose=2)
# 使用模型进行预测
train_predict = model.predict(train_X)
test_predict = model.predict(test_X)
# 可视化预测结果
train_predict_plot = np.empty_like(train_data)
train_predict_plot[:, :] = np.nan
train_predict_plot[1:len(train_predict)+1, :] = train_predict
test_predict_plot = np.empty_like(train_data)
test_predict_plot[:, :] = np.nan
test_predict_plot[len(train_predict)+(1*2)+1:len(train_data)-1, :] = test_predict
plt.plot(train_data)
plt.plot(train_predict_plot)
plt.plot(test_predict_plot)
plt.show()
```
该代码使用Keras库构建了一个LSTM模型,并使用该模型进行了训练和预测。首先,读取数据集并将其转换为适合LSTM的格式。然后,将数据集划分为训练集和测试集,并使用函数`create_dataset`创建数据集。接下来,创建并拟合LSTM模型。最后,使用模型进行预测并可视化预测结果。