np.argmax(results[12]) = 0
np.argmax(results[17]) = 0
list(indices[12:18]) = [array([88]), array([66]), array([14]), array([53]), array([42]), array([39])]
Expected (Approximate) Output:
Your results may likely differ because Keras' results are not completely predictable.
However, if you have trained your LSTM_cell with model.fit() for exactly 100 epochs as described above:
You should very likely observe a sequence of indices that are not all identical. Perhaps with the following values:
**np.argmax(results[12])** = 26
**np.argmax(results[17])** = 7
**list(indices[12:18])** = [array([26]), array([18]), array([53]), array([27]), array([40]), array([7])]

The grader says the function is incorrect, but I don’t see what’s wrong.

I should add that the unit tests for Inference Model did pass and the grader seems to also give it a pass.

Code Cell UNQ_C1: Function ‘djmodel’ is correct.
Code Cell UNQ_C2: Function ‘music_inference_model’ is correct.
Code Cell UNQ_C3: Unexpected error (ValueError('operands could not be broadcast together with shapes (13,1,90,90) (13,90) ')) occurred during function check. We expected function predict_and_sample to return Test 3 failed. Please check that this function is defined properly.
If you see many functions being marked as incorrect, try to trace back your steps & identify if there is an incorrect function that is being used in other steps.
This dependency may be the cause of the errors.