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Introducing TensorFlow Feature Columns

Monday, November 20, 2017
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Labels: Datasets , Estimators , TensorFlow

4 comments :

  1. UnknownNovember 20, 2017 at 8:03 PM

    Looks like the example Jupyter notebook works in Colaboratory with TensorFlow 1.4; no installation or git checkout required --
    https://colab.research.google.com/notebook#fileId=1QhSnbh-WJVGZjQJF8u974msOL_vAgMeS

    The external images are missing, but all the code seems to work.

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  2. windmapleNovember 21, 2017 at 1:07 AM

    Great article!

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  3. NYAMUKKURUSNovember 26, 2017 at 7:40 AM

    owh yeah, thanks feature columns

    download game android gratis

    ReplyDelete
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  4. UnknownNovember 28, 2017 at 2:10 PM

    Interesting post. I find the feature crosses (which is equivalent to what is usually called factor interaction in more classical classification methods such as logistic regression) and hash buckets (akin to a 'random-ish grouping') particularly interesting. Computing interaction terms is not new but it was somewhat limited before the advent of deep learning. Now we can afford to create a variable with, say, 10 thousands levels as in this example!

    However I am surprised by the comments on the bucketised columns -that using n weights instead of one creates a richer model. My first remark would be that in most cases you have to decide on the buckets definition in an arbitrary manner (or, at best, using knowledge and intuition about the case). But imagine that in your example there was a 'real' underlying grouping of years say, over the period 1970-1990, that affects the target. Then your configuration would struggle to capture it!

    What's more, this method increases the number of input since you're creating indicators per level of the bucketised variable. But because neural nets are a highly non-linear model -able to capture non linear relations- it seems nothing prevents us from simply defining one 'numerical' variable (with a finite number of values). Yes, there would be only one weight associated to this variable, but one weight per unit! This is thanks to the large number of units that the non linear
    relation will be captured. And note that this apply to categorical variables in general.

    Last, bucketising numeric variables can have an adverse effect as well if the true, underlying effect of the variable on the target is actually continuous (say in your example that prices do go up every year). By bucketising the variable you will end up averaging the effects over the year that are grouped together (like transforming a linear function to a step one) and lose some accuracy. And again, if the true relation is non-linear, the neural model is supposed to be
    able to capture it anyway.

    At the end of the day, the answer is probably that there is no true answer. The only option is to try several configurations
    with variables bucketised or not and keep the best model!

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