import tensorflow as tf from tensorflow import keras from tensorflow.keras import layers # 构建神经网络模型 model = keras.Sequential([ layers.Dense(128, activation='relu', input_shape=(X_train.shape[1],)), layers.Dropout(0.3), layers.Dense(64, activation='relu'), layers.Dropout(0.3), layers.Dense(32, activation='relu'), layers.Dense(1, activation='sigmoid') # 二分类输出 ]) # 编译模型 model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy']) # 训练模型 history = model.fit(X_train, y_train, epochs=100, batch_size=32, validation_split=0.2, verbose=1) # 评估模型 test_loss, test_accuracy = model.evaluate(X_test, y_test) print(f"测试集准确率: {test_accuracy:.4f}")