The code is organized as follows:

• data_provider.py abstracts away the datasets and provides uniform API for the code.
• utils.py is what it sounds like it is :)
• solver.py: the solver class doesn't know anything about images or sentences, it gets a model and the gradients and performs a step update
• generic_batch_generator.py handles batching across a batch of image/sentences that need to be forwarded through the networks. It calls the
• lstm_generator.py, which is an implementation of the Google LSTM for generating images.
• imagernn_utils.py contains some image-rnn specific utilities, such as evaluation function etc. These come in handy when we want to use some functionality across different scripts (e.g. driver and evaluator)
• rnn_generator.py has a simple RNN implementation for now, an alternative to LSTM