"ValueError" in Week 2 Assignment

Course Q&A TensorFlow Developer Professional Natural Language Processing in TensorFlow
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1

While completing the Week 2 assignment, all was going well until I finally fit the model:

When I was faced with this error:


Epoch 1/30

---------------------------------------------------------------------------

ValueError                                Traceback (most recent call last)

<ipython-input-53-873a88cb4a24> in <module>
      1 model = create_model(NUM_WORDS, EMBEDDING_DIM, MAXLEN)
      2 
----> 3 history = model.fit(train_padded_seq, train_label_seq, epochs=30, validation_data=(val_padded_seq, val_label_seq))


/opt/conda/lib/python3.8/site-packages/keras/utils/traceback_utils.py in error_handler(*args, **kwargs)
     65     except Exception as e:  # pylint: disable=broad-except
     66       filtered_tb = _process_traceback_frames(e.__traceback__)
---> 67       raise e.with_traceback(filtered_tb) from None
     68     finally:
     69       del filtered_tb

/opt/conda/lib/python3.8/site-packages/tensorflow/python/framework/func_graph.py in autograph_handler(*args, **kwargs)
   1127           except Exception as e:  # pylint:disable=broad-except
   1128             if hasattr(e, "ag_error_metadata"):
-> 1129               raise e.ag_error_metadata.to_exception(e)
   1130             else:
   1131               raise

ValueError: in user code:

    File "/opt/conda/lib/python3.8/site-packages/keras/engine/training.py", line 878, in train_function  *
        return step_function(self, iterator)
    File "/opt/conda/lib/python3.8/site-packages/keras/engine/training.py", line 867, in step_function  **
        outputs = model.distribute_strategy.run(run_step, args=(data,))
    File "/opt/conda/lib/python3.8/site-packages/keras/engine/training.py", line 860, in run_step  **
        outputs = model.train_step(data)
    File "/opt/conda/lib/python3.8/site-packages/keras/engine/training.py", line 809, in train_step
        loss = self.compiled_loss(
    File "/opt/conda/lib/python3.8/site-packages/keras/engine/compile_utils.py", line 201, in __call__
        loss_value = loss_obj(y_t, y_p, sample_weight=sw)
    File "/opt/conda/lib/python3.8/site-packages/keras/losses.py", line 141, in __call__
        losses = call_fn(y_true, y_pred)
    File "/opt/conda/lib/python3.8/site-packages/keras/losses.py", line 245, in call  **
        return ag_fn(y_true, y_pred, **self._fn_kwargs)
    File "/opt/conda/lib/python3.8/site-packages/keras/losses.py", line 1664, in categorical_crossentropy
        return backend.categorical_crossentropy(
    File "/opt/conda/lib/python3.8/site-packages/keras/backend.py", line 4994, in categorical_crossentropy
        target.shape.assert_is_compatible_with(output.shape)

    ValueError: Shapes (None, 1) and (None, 5) are incompatible

What does this mean?

2

Check your model loss function.

4

No. Pick the loss function for a multi class classification where the labels are not one-hot encoded.

Don’t forget to remove code from your post above.

2 Likes
5

Thanks, this did the trick! :grinning: