I have test data test1 = TabularList.from_df(test, cat_names=cat_names, cont_names=cont_names)
and databunch with train and test data data1 = (TabularList.from_df(train.reset_index().drop('index',axis=1).iloc[0:1000], cat_names=cat_names, cont_names=cont_names, procs=procs) .random_split_by_pct(0.33) .label_from_df(cols = 'age') .add_test(test1, label='age') .databunch())
I made a learner like in lesson 4 and couldn’t understand how to infere all test data to neural net. learn.get_preds() get prediction for validation data. learn.pred_batch() get prediction for validation data for one batch. learn.predict(test.iloc) get prediction only for 1 row. And throw an error when I try to put there a slice.
learn.get_preds(test1) surpisingly get prediction for validation data. again.
Of course I can do prediction row by row in cycle (and this is very slow!), but there should be faster and better way?