Deploying caffe RNNs - c++

Question

I'm trying to deploy a caffe model containing a RNN layer. The issue I'm having is how to compute the output from the network. My assumption was that I could call
net->Forward();
to update the network and then
net->output_blobs()[0]->mutable_cpu_data()[x];
once every timestep to read the output. However, using a constant input and then running "net-Forward()" multiple times does not affect the output as one would expect. I've tried to use different weights/biases, which changes the output, but no matter what configuration I'm using the output will still be static. Does anyone know what the proper procedure for deploying caffe RNNs with C++ is?
Edit:
This was tested with a single neuron RNN layer like below.
model.prototxt:
layer {
name: "input"
type: "Input"
top: "states"
input_param {
shape: {
dim: 1
dim: 1
}
}
}
input: "clip"
input_shape { dim: 1 dim: 1 dim: 1}
layer {
name: "rnn"
type: "RNN"
top: "rnn"
bottom: "clip"
bottom: "states"
recurrent_param {
num_output: 1
}
}
And the .cpp:
caffe::Blob<float>* input_layer = test_net->input_blobs()[0];
float* input_data;
input_data = input_layer->mutable_cpu_data();
input_data[0] = 1.0;
for (int i=0; i<5; i++)
{
test_net->Forward();
cout << "Ouput: " << net->output_blobs().back()->mutable_cpu_data()[0] << endl;
}