I’m reaching to you for some help on the assignment lab.
I can’t reach the .95 grading threshold on the saliency map model even after matching with the ungraded lab, tweaking the model parameters, adding a last maxpooling layer as I saw in one of the Topics here, not shuffling, paying attention to the loss function choice
(I think I maybe need to choose a different one that I won’t quote here but when I try it with the non sparse version of it I get an error that I can’t find how to solve :
ValueError: Shapes (None, 1) and (None, 2) are incompatible)
But maybe this is not on the loss function.
I also tried with the loss adapted to binary situations but ended with this error :
ValueError: logits and labels must have the same shape, received ((None, 2) vs (None, 1)).
The labels are one hot encoded as asked.
The absolute value of the gradients is done.
In the GradientTape, the loss is the ‘non sparse’ version.
I’m on it for 4 hours now and it seems to be a minor error so that is driving me crazy. Thanks for your consideration have a great day!
did not mean to do that - im not getting an error code - im just under the .95 by a margin now i got it to run - i thought it might be the expected output or the loss function im using sparse categotrical when i attempt to change it at the compile stage i get an error
some of the text got cut off Sorry Deepti- im not encountering an error im just a little perplexed by what im seeing as opaque criteria and maybe redundant instructions im not sure what my exact constraints are im currently tweaking momentum
If the issue not getting the desirable accuracy, then look into your model again, value error incompatible shows you to look if you have recalled correctly your input shape as per the assignment’s instructions.
If unable to find solution, kindly send your notebook via DM. Click on my name and then message.
You didn’t define label but define only image in def augment image grader cell. after resizing the image, define label with tensor one_hot code.
While defining model, you do not require to import files again in the cell separately
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Conv2D, MaxPooling2D, GlobalAveragePooling2D, Dense, Dropout
from tensorflow.keras.optimizers import RMSprop
Incorrect activation for the last dense layer, it is not a binary classification but probably for each of the 2 classes(categories), so which activation would you use for multi class category??
The below code needs correction, make sure you used correct image shape while one_hot encoding.
Define the expected output array by one-hot encoding the label
Although they have mentioned it has optional step, but they want you to write the code for the below statement which you have left blank
Optional: superimpose the saliency map with the original image, then display it
the most important issue with your notebook, in your configuration model training, you have applied two optimisers also you have used model load weight. You only need to compile model according to the instructions mentioned above the grader cell which clearly states you to use RMSprop optimizer with a learning rate of 0.001, even loss you have used two types, kindly use only categorical_crossentropy. I am sharing a screenshot of your incorrect model compile
overall, I also got a doubt if you have edited something outside of ##START AND END CODE HERE##, so I sincerely suggest you to do these correction with a fresh copy of your assignment for better comparison, learning and successful submission of your assignment.
i found a couple of things more challenging about this week- it is the course finale- so I’m not shocked that the code cells were almost blank for this assignment -plus typically there is an exhaustive Colab with Laurence doing a walk through and that did not happen -we got a much more abbreviated variety of that - and the final assignment seems to also focus on a small percentage of the ideas that were embraced - one dimension of the week is almost an afterthought unless we are to employ gradcam for instance in this scenario - it is possible to elicit salience mapping on 18 epochs without doing a model.fit - since we have the weights/// i was wondering (as its not entirely spelled out or i missed it somehow) -but am i supposed to just get the 2nd salience mapping without running the longer epochs ? i hope this isn’t too much of a bother and if i figure this out I’ll update to let u know so as not to trouble you-further - thanks again I just went through your instructions again
How can you do salience mapping without training your correct model (which you need to correct)
Kindly read the comments point by point, so you don’t get such doubts. Debugging is not corrected codes but correction, re execution, re training for better outcome of successful submission.
its clear to me the three epochs have to be trained - i’m still unsure about the 18 i was
with the provided weights if it was necessary to train the latter - ill keep at it -
Kindly be brief and point in the right direction of your doubt.
If you are stating about the optional step then you could try once without that step and download your model for submission. If you didn’t clear, then try the optional step as the next step mention you to do so
