Keras Implementation of DenseRecNet

Adding recurrent connections to the DenseNet model.

Usage guide:

python run_cifar10.py

optional arguments:

-h, --help show this help message and exit
--batch_size BATCH_SIZE Batch size
--nb_epoch NB_EPOCH  Number of epochs
--depth DEPTH  Network depth
--nb_dense_block NB_DENSE_BLOCK Number of dense blocks
--nb_filter NB_FILTER Initial number of conv filters
--growth_rate GROWTH_RATE Number of new filters added by conv layers
--dropout_rate DROPOUT_RATE  Dropout rate
--learning_rate LEARNING_RATE Learning rate
--weight_decay WEIGHT_DECAY L2 regularization on weights
--plot_architecture PLOT_ARCHITECTURE Save a plot of the network architecture

Output

Trains a network for the specified number of epochs. Save the traing/validation loss in a .json file.