Hello Paul,
Issues with your notebook assignment.
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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.
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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??
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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.
Kindly let me know once your issue is resolved.
Regards
DP