aie对冬小麦进行rnn分类的代码

以下是使用RNN对冬小麦进行分类的示例代码：

import pandas as pd
import numpy as np
from keras.models import Sequential
from keras.layers import Dense, LSTM, Dropout
from sklearn.preprocessing import MinMaxScaler

# 读取数据
df = pd.read_csv('winter_wheat.csv')

# 数据预处理
scaler = MinMaxScaler(feature_range=(0, 1))
scaled_data = scaler.fit_transform(df['yield'].values.reshape(-1, 1))

# 创建训练数据集
train_data = scaled_data[:2000, :]
x_train = []
y_train = []
for i in range(60, len(train_data)):
    x_train.append(train_data[i-60:i, 0])
    y_train.append(train_data[i, 0])
x_train, y_train = np.array(x_train), np.array(y_train)

# 增加维度，符合 LSTM 输入要求
x_train = np.reshape(x_train, (x_train.shape[0], x_train.shape[1], 1))

# 构建模型
model = Sequential()
model.add(LSTM(units=50, return_sequences=True, input_shape=(x_train.shape[1], 1)))
model.add(Dropout(0.2))
model.add(LSTM(units=50, return_sequences=True))
model.add(Dropout(0.2))
model.add(LSTM(units=50, return_sequences=True))
model.add(Dropout(0.2))
model.add(LSTM(units=50))
model.add(Dropout(0.2))
model.add(Dense(units=1))

# 编译模型
model.compile(optimizer='adam', loss='mean_squared_error')

# 训练模型
model.fit(x_train, y_train, epochs=50, batch_size=32)

# 创建测试数据集
test_data = scaled_data[2000-60:, :]
x_test = []
y_test = df['yield'][2000:]
for i in range(60, len(test_data)):
    x_test.append(test_data[i-60:i, 0])
x_test = np.array(x_test)

# 增加维度，符合 LSTM 输入要求
x_test = np.reshape(x_test, (x_test.shape[0], x_test.shape[1], 1))

# 预测数据
predicted_yields = model.predict(x_test)
predicted_yields = scaler.inverse_transform(predicted_yields)

# 输出结果
result_df = pd.DataFrame({'Actual Yield': y_test.values, 'Predicted Yield': predicted_yields.reshape(-1)})
print(result_df)

其中，`winter_wheat.csv`是包含冬小麦收成数据的CSV文件。代码中使用了一个四层的LSTM模型进行训练和预测。训练数据集中使用了前2000条数据，测试数据集中使用了后面的数据。模型训练完毕后，会输出一个包含实际收成和预测收成的DataFrame。