Hi,
I am getting an error when evaluating the model with example batch. The error message is:
Your model is not compatible with the dataset you defined earlier. Check that the loss function and last layer are compatible with one another.
I have used sparse_categorical_entropy with softmax as the last layer of the model. Don’t understand why there is an error.
codes removed, against community guidelines
All the unit tests up to the previous step of preprocessing is passed.
Please explain the meaning of this (see quoted strings):
Since the test prints the error message when catching an exception, how about printing the stack trace and figuring out where the mistake is? You can create a new cell and run code without catching the exception and see the results. Odds are good that the final layer has incorrect shape.
For reference, see other multi class classification problems and observe the shape of output layer via model.summary().
Thank you so much for your hint. Since column name is not assigned to the tensor slice (dataset variable), extracting columns using name won’t work. I have changed the map/lambda function to access by position and it works with no issues.