I’m getting NaN values for train loss and valid loss when training with a tabular learner, with training time of 0:00, and I’m getting an accuracy of about 0.55. The only main difference between my notebook and the lesson4-tabular notebook is my dataframe only has continuous variables (with the exception of dep_var, which is a boolean).
If anyone has any idea as to how I could go about troubleshooting this problem, any help would be appreciated.
Here’s some of my code:
cont_names = ['A','B','C','D'] # (where A, B,C, D are columns of the dataframe) cat_names =  procs = [FillMissing, Categorify, Normalize] # Percent of original dataframe test_pct = 0.1 valid_pct = 0.2 # Masks for separating dataframe sets cut_test = int(test_pct * len(df))+1 cut_valid = int(valid_pct*len(df))+cut_test valid_indx = range(cut_test,cut_valid) # range of validation indices, used for fastai dep_var = 'result' test = TabularList.from_df(df.iloc[cut_test:cut_valid].copy(), cat_names=cat_names, cont_names=cont_names) data = (TabularList.from_df(df=df, path=path, cat_names=cat_names, cont_names=cont_names) # .split_by_idx(valid_indx) .split_by_rand_pct(0.2) .label_from_df(cols=dep_var) .add_test(test) .databunch()) data.show_batch() learn = tabular_learner(data, layers=[200,100],metrics=accuracy) learn.fit(1, 1e-2)