How to Interpret H2o deep learning output vector?

Question

I want to predict tags using an H2o deep learning model, and I can't interpret my H2o deep learning output.

That's my model parameters of the H2o deep learning model.

dl_model = deeplearning.H2ODeepLearningEstimator(hidden =[200,200],
                                    epochs  = 10,
                                    missing_values_handling= 'MeanImputation',
                                    activation = "Tanh", 
                            )

I pass the word2vec vectors of Blog Content which names as Content.vecs and Y is also word2vec of Tags.

Train the model

dl_model.train(x= Content_vecs.names,
               y= 'Y',
               training_frame   = data_split[0],
               validation_frame = data_split[1]
               )

and the output is

**predict
-0.700515
-0.700515
-0.700515
-0.700515
-0.700515
-0.700515
-0.700515
-0.700515
-0.700515
-0.700515**

In Original Data Predictor variable is Content and response variable is tags. I passing Word2vec vectors of Contents as x and Tags as y of in deep learning Figure. I want to predict single or multiple tag using H2o deep learning and word2vec

Answer 1

首先请确保您指定distribution =“ multinomial”。如果您没有太多标签，则可以将原始标签用作响应级别。否则，如果您保留数值级别，则将需要进行一些映射，以查看与原始标签对应的值。

这也是如何将word2vec与H2O算法结合使用的示例，以使您了解目标的外观：https://github.com/h2oai/h2o-3/blob/master/h2o-py/demos/word2vec_craigslistjobtitles.ipynb以及深度学习教程：https://github.com/h2oai/h2o-tutorials/tree/master/tutorials/deeplearning