MetaSpore
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meta-soul
A unified end-to-end machine intelligence platform
* [DONE] optimizer_abstraction Add the following PyTorchEstimator parameters. **loss and metric** + loss_function + metric_class (and builtin ModelMetric classes) **dataset and minibatch transformers** + training_dataset_transformer & validation_dataset_transformer + training_minibatch_transformer &...
MLPlayers中的dense_layers重复添加了input的Normalization
All negative samples have the same category(genre) as the corresponding positive samples. The category should correspond to the item id (movie_id):
For example, t1.movie_id is negative sampled item id. But t2.genre is one feature which belongs to the related original positive sample: Please verify the code:
Implement scorecard demo based on loan default probability.
**metaspore-serving-bin** tries each model load type sequentially, which will leave extra error logs. A better way would be checking model load type according to directory structure in advance.
Currently, metaspore-serving-bin init and load use different directory structure, where the latter contains model version in the path, which makes it impossible for metaspore-serving-bin to init from a previously loaded...
Currently, the Python preprocessor uses stdout to pass input_names and output_names back to metaspore-serving-bin, which will make exceptions thrown in the Python preprocessor invisible in the log of metaspore-serving-bin. Another...
