I have been struggling with predicting health costs challenge. I have looked at the tutorial on tensorflow on fcc, as well as Kylie Ying’s tutorial. I’ve tried applying both formats for preparing the datasets and I get errors I don’t understand for both. The fcc version uses tf.feature_column, which seems to be deprecated and Kylie Ying’s method throws a few errors:
TypeError Traceback (most recent call last)
/usr/local/lib/python3.7/dist-packages/tensorflow/python/data/util/structure.py in normalize_element(element, element_signature)
101 if spec is None:
--> 102 spec = type_spec_from_value(t, use_fallback=False)
103 except TypeError:
12 frames
TypeError: Could not build a `TypeSpec` for age sex bmi children smoker region
637 35 female 38.1 2 no northeast
1310 42 male 26.3 1 no northwest
1076 47 female 32.0 1 no southwest
998 33 female 36.3 3 no northeast
45 55 male 37.3 0 no southwest
... ... ... ... ... ... ...
100 41 female 31.6 0 no southwest
470 27 male 32.7 0 no southeast
1328 23 female 24.2 2 no northeast
167 32 female 33.2 3 no northwest
1054 27 female 21.5 0 no northwest
[1070 rows x 6 columns] with type DataFrame
During handling of the above exception, another exception occurred:
ValueError Traceback (most recent call last)
/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/constant_op.py in convert_to_eager_tensor(value, ctx, dtype)
100 dtype = dtypes.as_dtype(dtype).as_datatype_enum
101 ctx.ensure_initialized()
--> 102 return ops.EagerTensor(value, ctx.device_name, dtype)
103
104
ValueError: Failed to convert a NumPy array to a Tensor (Unsupported object type int).
Any body point me in the right direction to find some help here?