Open CaseyHaralson opened 11 months ago
Replace df[indep_cols].values
with df[indep_cols].to_numpy(dtype=np.float32)
indep_cols = ['Age', 'SibSp', 'Parch', 'LogFare'] + added_cols
t_indep = tensor(df[indep_cols].to_numpy(dtype=np.float32), dtype=torch.float)
t_indep
This is the recommended way: https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.values.html
Replace
df[indep_cols].values
withdf[indep_cols].to_numpy(dtype=np.float32)
indep_cols = ['Age', 'SibSp', 'Parch', 'LogFare'] + added_cols t_indep = tensor(df[indep_cols].to_numpy(dtype=np.float32), dtype=torch.float) t_indep
This is the recommended way: https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.values.html
Thanks! That worked
FYI: the original code is working in kaggle and it is using panda with older version 1.3.5
When running the 5th notebook "Linear Model and Neural Network From Scratch", there is a type error when trying to convert the dataframe to a tensor.
This code gives the error:
The error: TypeError: can't convert np.ndarray of type numpy.object_. The only supported types are: float64, float32, float16, complex64, complex128, int64, int32, int16, int8, uint8, and bool.
I was able to get around the error by converting the values into a list, but that threw a warning that that method was very slow and to try using an np array. Changing the code to use a np array gives the original error so that didn't help.
What is the best way to do the conversion?