修改代码，要求以测试集F1 score和ROC-AUC为评价标准，同时考虑到标签中1要远比0少：model = Sequential() model.add(Dense(64, activation='relu', input_dim=X_train.shape[1])) model.add(Dropout(0.5)) model.add(Dense(32, activation='relu')) model.add(Dropout(0.5)) model.add(Dense(1, activation='sigmoid')) model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy']) early_stopping = EarlyStopping(monitor='val_loss', patience=5, verbose=1) model_checkpoint = ModelCheckpoint('model.h5', monitor='val_loss', save_best_only=True, verbose=1) history = model.fit(X_train, y_train_forced_turnover_nolimited, epochs=50, batch_size=32, validation_data=(X_test, y_test_forced_turnover_nolimited), callbacks=[early_stopping, model_checkpoint]) model.load_weights('model.h5') pred = model.predict(X_test) auc = roc_auc_score(y_test_forced_turnover_nolimited, pred) print('Testing AUC:', auc)

可以使用以下代码来同时考虑到标签中1要远比0少，并以测试集F1 score和ROC-AUC为评价标准：

from keras.callbacks import EarlyStopping, ModelCheckpoint
from keras.models import Sequential
from keras.layers import Dense, Dropout
from sklearn.metrics import f1_score, roc_auc_score

# 构建模型
model = Sequential()
model.add(Dense(64, activation='relu', input_dim=X_train.shape[1]))
model.add(Dropout(0.5))
model.add(Dense(32, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(1, activation='sigmoid'))

# 编译模型
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])

# 定义EarlyStopping和ModelCheckpoint回调函数
early_stopping = EarlyStopping(monitor='val_loss', patience=5, verbose=1)
model_checkpoint = ModelCheckpoint('model.h5', monitor='val_loss', save_best_only=True, verbose=1)

# 训练模型
history = model.fit(X_train, y_train_forced_turnover_nolimited, epochs=50, batch_size=32, validation_data=(X_test, y_test_forced_turnover_nolimited),
                    callbacks=[early_stopping, model_checkpoint])

# 加载最佳模型
model.load_weights('model.h5')

# 在测试集上进行预测
pred = model.predict(X_test)

# 计算F1 score和ROC-AUC
f1 = f1_score(y_test_forced_turnover_nolimited, np.round(pred))
auc = roc_auc_score(y_test_forced_turnover_nolimited, pred)

# 输出测试集F1 score和ROC-AUC
print('Testing F1 score:', f1)
print('Testing AUC:', auc)

在训练过程中，可以使用回调函数来记录训练集和测试集的F1 score和ROC-AUC，并在每个epoch结束时保存最佳模型。在测试集上选择最佳模型时，可以根据测试集的F1 score和ROC-AUC来进行比较。