optimizer.apply_gradients(zip(gradients, vars_to_fine_tune))
TypeError: apply_gradients() missing 1 required positional argument: 'grads_and_vars'
I tried several different things, I have no idea why this is not working
optimizer.apply_gradients(zip(gradients, vars_to_fine_tune))
TypeError: apply_gradients() missing 1 required positional argument: 'grads_and_vars'
I tried several different things, I have no idea why this is not working
This line looks fine but check that gradients are calculated correctly above it (check all the steps above) . Also the vars_to_fine_tune when you choose them have a look are you choosing the right layers.
Thank you for the answer!
I show you what I did, I do not see anything weird, although I in exercise 9 says 4 variables to fine tune and I just can see these two:
No need to show solutions here, its against the rules.
When you choose the layers I would suggest to use layer indexing. For the train-step_fn function I would check the whole function if something is not right. From those lines you posted here it seems ok.
Maybe it does not have anything to do with it, but just in case. And I hope this is not considered a solution. When loading the checkpoint, at ex6.2, I don’t get the exact expected value the task says:
what I get:
‘_box_predictor’: <tensorflow.python.training.tracking.util.Checkpoint at 0x7f4820653850>,
‘_feature_extractor’: object_detection.models.ssd_resnet_v1_fpn_keras_feature_extractor.SSDResNet50V1FpnKerasFeatureExtractor at 0x7f48204b78d0>
Expected:
'_box_predictor': <tensorflow.python.training.tracking.util.Checkpoint at 0x7fefac044a20>,
'_feature_extractor': <object_detection.models.ssd_resnet_v1_fpn_keras_feature_extractor.SSDResNet50V1FpnKerasFeatureExtractor at 0x7fefac0240b8>'
You know this is the most complex lab of the entire specialization. Its quite hard to debug it. I would think that given these checkpoints representing layers that we want to take ouputs from, I think that those values should be as expected.
So you should check the creation of the checkpoint, however thats a long way to the optimizer but it could be dependant on it. I would also suggest the rest of the train_step_fn function. Especially the provide model groundtruth part.
If you cannot fix the issue you could message me the notebook on private I will have a look on it when I have time, hopefully I find some problem with it.
Thank you so much for your attention. I just solved it!!
Interestingly, I just solved it by setting up momentum in the Ex8, and setting the hyperparameters, I forgot to do it before, and somehow that was causing some trouble, no idea why.
It was a hard day.
The problem for me was indeed with the optimizer in ex 8. I didn’t set it properly, and received no error msgs, so I couldn’t find what I was doing wrong. Thank you guys for the help