difference in predictions between model.predict() and model.predict_generator() in keras
@petezurich Thanks for your comment. Generator.reset() before model.predict_generator() and turning off the shuffle in predict_generator() fixed the problem
When I use model.predict_generator() on my test_set (images) I am getting a different prediction and when I  use mode.predict() on the same test_Set I am getting a different set of predictions.
For using model.predict_generator I followed the below steps to create a generator:

Imagedatagenerator(no arguments here) and used flow_from_directory 
with shuffle = False.
There are no augmentations nor preprocessing of 
images(normalization,zero-centering etc) while training the model.

I am working on a binary classification problem involving dogs and cats (from kaggle).On the test set, I have 1000 cat images.
and by using model.predict_generator() I am able to get 87% accuracy()i.e 870 images are classified correctly.
But while using model.predict I am getting 83% accuracy.
This is confusing because both should give identical results right?
Thanks in advance :)