使用神经网络训练UJIndoorLoc数据集的代码
时间: 2024-02-22 22:56:52 浏览: 24
以下是使用Keras框架训练UJIndoorLoc数据集的示例代码:
```
import pandas as pd
from keras.models import Sequential
from keras.layers import Dense, Dropout, BatchNormalization
from keras.optimizers import Adam
# 加载数据集
train_data = pd.read_csv('train_data.csv')
test_data = pd.read_csv('test_data.csv')
# 数据预处理
X_train = train_data.drop('location', axis=1).values
y_train = train_data['location'].values
X_test = test_data.drop('location', axis=1).values
y_test = test_data['location'].values
# 构建神经网络模型
model = Sequential()
model.add(Dense(128, activation='relu', input_dim=X_train.shape[1]))
model.add(BatchNormalization())
model.add(Dropout(0.2))
model.add(Dense(64, activation='relu'))
model.add(BatchNormalization())
model.add(Dropout(0.2))
model.add(Dense(32, activation='relu'))
model.add(BatchNormalization())
model.add(Dropout(0.2))
model.add(Dense(len(set(y_train)), activation='softmax'))
# 设置优化器、损失函数和评估指标
optimizer = Adam(lr=0.001)
model.compile(optimizer=optimizer, loss='sparse_categorical_crossentropy', metrics=['accuracy'])
# 训练模型
model.fit(X_train, y_train, epochs=50, batch_size=32, validation_data=(X_test, y_test))
# 评估模型
score = model.evaluate(X_test, y_test, verbose=0)
print('Test loss:', score[0])
print('Test accuracy:', score[1])
```
在上述代码中,首先使用Pandas库加载UJIndoorLoc数据集并进行数据预处理。然后,使用Keras框架构建了一个包含3个隐藏层和1个输出层的神经网络模型。使用Adam优化器、sparse_categorical_crossentropy损失函数和accuracy评估指标进行训练,并设置了50个epochs和32个batch_size。最后,通过evaluate方法评估模型的性能。