As a newbie to tensorflow, I am using this tutorial from google for binary classification using a simple dense neural network.
The slightly annoying thing about this (and a few other) tutorials is they completely gloss over the part of how to actually make a prediction from a dataframe of features, and directly move to model evaluation using some method of the trained model which hides the actual prediction procedure. So basically, I finished the model training, but even after that, I see no way mentioned on how to actually use the model to predict classes of unknown samples.
To put concretely, I have a trained model, a pandas dataframe called test, and a list of columnames which correspond to feature names. Based on the variables declared in the tutorial, I tried
feature_names=['age', 'sex', 'cp', 'trestbps', 'chol', 'fbs', 'restecg', 'thalach', 'exang', 'oldpeak', 'slope', 'ca', 'thal'] model.predict_proba(x=test[feature_names])
But it is throwing a type error. Basically, I need a function which will give me the classes, or ideally, the softmax probabilities of the classes from the feature frames without the label, because that is how we use any model.