Good model but bad confusion matrix? I am trying to understand the code here.
The output [12] shows that the model accuracy is above 90% even for the validation set, but the confusion matrix in [16] ca not even achieve 50%  accuracy, and it is also on the validation set, so I do not understand this low accuracy on the confusion matrix. I think it may be due to data augmentation, but I would be thankful if someone could explain it to me and tell me how I could then have a confusion matrix in adequacy with the learning curves. Thanks in advance.
I found the answer in https://stackoverflow.com/questions/55868975/keras-how-to-evaluate-model-accuracy-evaluate-generator-vs-predict-generator/57630536#57630536
I just had to add shuffle = False in the test_datagen.flow_from_dataframe, and the confusion matrix was good