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Exercise

Defining the feature columns and input function

Last chapter, you created a DNN Classifer using the Banknote_Authentication dataset, in which you were able to fairly accurately assess whether a banknote was genuine or forged (response attribute: Class) based on several feature columns, including Var,Skew, Kurt, and Entropy.

The next task is to use a dropout technique to assess whether you can increase the accuracy of, and lower the chances of overfitting to, our DNN Classifier model using the TFEstimators API.

Instructions
100 XP
  • Define the four feature columns from the Banknote_Authentication dataset.
  • Create an input function, banknote_input_fn, by identifying the correct features and response variables.