Memory Issues Using Keras Convolutional Network
While using train_generator(), you should also set the max_q_size parameter. It's set at 10 by default, which means you're loading in 10 batches while using only 1 (since train_generator() was designed to stream data from outside sources that can be delayed like network, not to save memory). I'd recommend setting max_q_size=1for your purposes.
I am very new to ML using Big Data and I have played with Keras generic convolutional examples for the dog/cat classification before, however when applying a similar approach to my set of images, I run into memory issues.
My dataset consists of very long images that are 10048 x1687 pixels in size. To circumvent the memory issues, I am using a batch size of 1, feeding in one image at a time to the model. 
The model has two convolutional layers, each followed by max-pooling which together make the flattened layer roughly 290,000 inputs right before the fully-connected layer.
Immediately after running however, Memory usage chokes at its limit (8Gb).
So my questions are the following:
1) What is the best approach to process computations of such size in Python locally (no Cloud utilization)? Are there additional python libraries that I need to utilize?