## This is my error.

IndexError Traceback (most recent call last)

in

----> 1 results, indices = predict_and_sample(inference_model, x_initializer, a_initializer, c_initializer)

2

3 print(“np.argmax(results[12]) =”, np.argmax(results[12]))

4 print(“np.argmax(results[17]) =”, np.argmax(results[17]))

5 print(“list(indices[12:18]) =”, list(indices[12:18]))

in predict_and_sample(inference_model, x_initializer, a_initializer, c_initializer)

21 indices = np.argmax(pred, axis=-1)

22 # Step 3: Convert indices to one-hot vectors, the shape of the results should be (1, )

—> 23 results = to_categorical(indices, num_classes=78)

24 ### END CODE HERE ###

25

/opt/conda/lib/python3.7/site-packages/tensorflow/python/keras/utils/np_utils.py in to_categorical(y, num_classes, dtype)

76 n = y.shape[0]

77 categorical = np.zeros((n, num_classes), dtype=dtype)

—> 78 categorical[np.arange(n), y] = 1

79 output_shape = input_shape + (num_classes,)

80 categorical = np.reshape(categorical, output_shape)

IndexError: index 82 is out of bounds for axis 1 with size 78

I suspect it to be either in step 2 or 3, but can exactly diagnose the issue, and I’m confused why it’s arbitrarily bringing up the number 82. I also believe my music_inference_model should be correct.

Thanks for the help in advance!

Edit1:

I just read one of the discourse messages from one of the mentors.

(step 3 of predict and sample)

results = … num_classes=n_values)

I originally had it set to

results = … num_classes=78)

I just wanted to ask why in the instructions, it specifies not to use num_classes=n_values when the grader only deems it correct once you have it as results = … num_classes=n_values)?

Edit2: Source Code Removed