MaximumEntropy/cudnn_rnn_theano_benchmarks

Results will be integrated into the above repository eventually. The Recurrent Networks take as input a 3D Tensor batch_size x seq_length x hidden_size and output the last hidden state, compute a MSE loss and compute the gradients of error with respect to each parameter. The hidden_size specifies the size of the output and input layer of the networks. The code of the scripts we ran are available. The code for the regular theano RNN implementations were borrowed from the rnn-benchmarks repository.