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Some tests are better than none!

The model creation functions defined in nn_utils.network_utils include an optional parameter num_recurrent_units=1

None of the calls to these functions from train.py and generate.py specify a value for this parameter, so by default there is only ever 1 hidden layer.

Add a parameter to config.nn_config for the number of hidden layers and then use the value of that parameter wherever the model is created.

Currently the model is created in both train and generate

Consequently whenever a change is made to one creation call, it needs to be manually replicated in the other file. Much better if there was a common function in network_utils.create_model()

Dropout between model layers is a regularization technique for reducing overfitting.

Allow two distinct dropout parameters dropout_W and dropout_U:

• dropout_W: float between 0 and 1. Fraction of the input units to drop for input gates.
• dropout_U: float between 0 and 1. Fraction of the input units to drop for recurrent connections.

See Keras GRU and LSTM documentation.

Parameters to be specified in config.nn_config and used when creating the model.