基于CNN-LSTM寿命预测的代码
时间: 2023-11-02 08:05:44 浏览: 42
以下是基于CNN-LSTM的寿命预测模型的示例代码:
```python
import pandas as pd
import numpy as np
import tensorflow as tf
from keras.models import Sequential
from keras.layers import Dense, Conv1D, MaxPooling1D, Flatten, LSTM, Dropout
# 读取数据
data = pd.read_csv('data.csv')
# 数据预处理
x_train = []
y_train = []
for i in range(60, len(data)):
x_train.append(data[i-60:i, 0])
y_train.append(data[i, 0])
x_train = np.array(x_train)
y_train = np.array(y_train)
x_train = np.reshape(x_train, (x_train.shape[0], x_train.shape[1], 1))
# 建立模型
model = Sequential()
model.add(Conv1D(filters=64, kernel_size=3, activation='relu', input_shape=(60, 1)))
model.add(Conv1D(filters=32, kernel_size=3, activation='relu'))
model.add(MaxPooling1D(pool_size=2))
model.add(Dropout(0.2))
model.add(LSTM(100, return_sequences=True))
model.add(Dropout(0.2))
model.add(LSTM(100))
model.add(Dense(1))
# 编译模型
model.compile(loss='mean_squared_error', optimizer='adam')
# 训练模型
model.fit(x_train, y_train, epochs=50, batch_size=64)
# 预测
test_data = pd.read_csv('test_data.csv')
x_test = []
for i in range(60, len(test_data)):
x_test.append(test_data[i-60:i, 0])
x_test = np.array(x_test)
x_test = np.reshape(x_test, (x_test.shape[0], x_test.shape[1], 1))
predictions = model.predict(x_test)
# 展示结果
plt.plot(test_data['Date'], test_data['Lifespan'], label='Actual')
plt.plot(test_data['Date'], predictions, label='Predicted')
plt.legend()
plt.show()
```
代码中,我们首先读取并预处理数据。然后,我们建立了一个包含卷积层、最大池化层、Dropout层和LSTM层的模型,并使用均方误差作为损失函数进行编译。最后,我们使用测试数据进行预测并展示结果。
注意:这只是一个示例代码,实际应用中需要根据数据情况对模型进行调整。