Python编写深度学习预测全球云量代码
时间: 2023-07-05 07:28:12 浏览: 98
下面是一个简单的使用Keras库编写CNN模型来预测全球云量的Python代码示例:
```python
import numpy as np
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Dropout, Conv1D, MaxPooling1D, Flatten
# 加载数据集
df = pd.read_csv('cloud_data.csv')
# 数据预处理和特征提取
scaler = MinMaxScaler(feature_range=(0, 1))
scaled_data = scaler.fit_transform(df['cloud_index'].values.reshape(-1, 1))
# 划分训练集和测试集
train_size = int(len(scaled_data) * 0.8)
train_data = scaled_data[:train_size, :]
test_data = scaled_data[train_size:, :]
# 构建数据集
def create_dataset(dataset, time_steps=1):
X, Y = [], []
for i in range(len(dataset) - time_steps):
X.append(dataset[i:(i + time_steps), 0])
Y.append(dataset[i + time_steps, 0])
return np.array(X), np.array(Y)
time_steps = 60
X_train, Y_train = create_dataset(train_data, time_steps)
X_test, Y_test = create_dataset(test_data, time_steps)
# 构建CNN模型
model = Sequential()
model.add(Conv1D(filters=64, kernel_size=3, activation='relu', input_shape=(time_steps, 1)))
model.add(Conv1D(filters=32, kernel_size=3, activation='relu'))
model.add(MaxPooling1D(pool_size=2))
model.add(Flatten())
model.add(Dense(50, activation='relu'))
model.add(Dense(1, activation='linear'))
# 编译模型
model.compile(optimizer='adam', loss='mse')
# 模型训练
model.fit(X_train, Y_train, epochs=100, batch_size=64)
# 模型预测
y_pred = model.predict(X_test)
# 结果逆缩放
y_pred = scaler.inverse_transform(y_pred)
Y_test = scaler.inverse_transform(Y_test.reshape(-1, 1))
# 评估模型
rmse = np.sqrt(np.mean((y_pred - Y_test) ** 2))
print('RMSE:', rmse)
```
在这个示例中,使用了一个简单的CNN模型,包括两个卷积层、一个最大池化层、一个Flatten层和两个全连接层。数据集加载后,使用MinMaxScaler进行归一化处理,并使用create_dataset函数构建训练集和测试集。然后,使用模型编译和训练方法对模型进行训练,最后对模型进行预测和评估。
阅读全文