C3_W2 BBC assignment grading/coding guidance

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

Hi knowlegable Moderators,

Can you help me out with my grader/coding issue?
It is the Last Step for me to finish the course.
Would restarting in a new workbook help?
One of the locked Cells has an issue, search for „else:any“.
Hope for your guidance.
Thanks
Fabian

2

can you share screenshot of the locked cell issue you encountered, that locked cell must be the unittest cell you run down after you wrote down the code in grade cell. So just share the error screenshot and not grade cell codes as posting codes is against community guidelines.

3

See my Screenshot, all Code is from the assignement, no Solution Lines are included.
Thank you for looking into my issue.

IMG_1767
IMG_17671967×2625 631 KB

4

indentation error is related to space error while you write your codes. it can be an extra space or usually encountered while writing a code in loop statements.

So you need figure out in your assignment where you might have done this error?

if you cannot figure out, then get a fresh copy and re-do the assignment. Make sure when you re-do, do not copy paste your codes as the error will continue to pass in this way. Re-write the codes again line by line, referring to the previous copy and making sure to only write codes between ###START AND END CODE HERE### and not editing, deleting anything outside of these markers in grade cells where you write your code.

Regards
DP

5

Look at else:any at cell with run [24]. Remove the any word.

6

Well spotted, the “any” !
Regrettably the cell is part of the assignment and locked.
This is why I asked for help as apparently the results of the the code I wrote are good, but e.g. the “any” in a locked cell is causing the grader to be unhappy.

7

As @Deepti_Prasad suggested, please get a fresh copy of the notebook and submit your work again.

Here’s the code I’m observing in starter code:

example_batch = train_proc_dataset.take(1)

try:
	model.evaluate(example_batch, verbose=False)
except:
	print("Your model is not compatible with the dataset you defined earlier. Check that the loss function and last layer are compatible with one another.")
else:
	predictions = model.predict(example_batch, verbose=False)
	print(f"predictions have shape: {predictions.shape}")