recurrent neural network - How can I feed last output y(t-1) as input for generating y(t) in tensorflow RNN?

Question

I want to design a single layer RNN in Tensorflow such that last output (y(t-1)) is participated in updating the hidden state.

h(t) = tanh(W_{ih} * x(t) + W_{hh} * h(t) + **W_{oh}y(t - 1)**)
y(t) = W_{ho}*h(t)

How can I feed last input y(t - 1) as input for updating the hidden state?

Answer 1

Is y(t-1) the last input or output? In both cases it is not a straight fit with the TensorFlow RNN cell abstraction. If your RNN is simple you can just write the loop on your own, then you have full control. Another way that I would use is to pre-process your RNN input, e.g., do something like:

processed_input[t] = tf.concat(input[t], input[t-1])

Then call the RNN cell with processed_input and split there.