CODEWITHSUNDEEP
python-3.xdeep-learningword2vech2o
Sanjay Kumar
Sanjay Kumar

Reputation: 65

How to Interpret H2o deep learning output vector?

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

Upvotes: 1

Views: 163

Answers (1)

Lauren
Lauren

Reputation: 5778

First make sure that you specify distribution="multinomial". If you don't have too many tags, then you can just use the original tag as a response level. Otherwise if you leave numeric value levels, you will need to have some mapping that you can use to see what values correspond to your original tags.

here is also an example of how to use word2vec with an H2O algo, to give you a sense of what your target should look like: https://github.com/h2oai/h2o-3/blob/master/h2o-py/demos/word2vec_craigslistjobtitles.ipynb as well as a tutorial for deep learning: https://github.com/h2oai/h2o-tutorials/tree/master/tutorials/deeplearning

Upvotes: 1

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