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1.msl任务也会赋值domain_mask为None
2.validation 阶段给入的val_x有问题,predict报错; best_model = model.fit(train_model_input, train[target].values, batch_size=train_batch_size, epochs=epochs, validation_data=(test_model_input, test[target].values)) 改为: best_model = model.fit(train_model_input, train[target].values, batch_size=train_batch_size, epochs=epochs, validation_split=0.)
3.未引入sklearn.metrics的roc_auc_score, log_loss 在main.py新增from sklearn.metrics import roc_auc_score, log_loss
The text was updated successfully, but these errors were encountered:
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1.msl任务也会赋值domain_mask为None
2.validation 阶段给入的val_x有问题,predict报错;
best_model = model.fit(train_model_input, train[target].values, batch_size=train_batch_size, epochs=epochs, validation_data=(test_model_input, test[target].values))
改为:
best_model = model.fit(train_model_input, train[target].values, batch_size=train_batch_size, epochs=epochs, validation_split=0.)
3.未引入sklearn.metrics的roc_auc_score, log_loss
在main.py新增from sklearn.metrics import roc_auc_score, log_loss
The text was updated successfully, but these errors were encountered: