If I'm dealing with a sequence of images as the input (frame by frame), and I want to output a matrix at each timestamp, can the hidden state be a matrix?

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    $\begingroup$ Please add more context, so that it's easier for people to help you. $\endgroup$ Feb 20 '21 at 4:59
  • $\begingroup$ Hi Paul. Welcome to Artificial Intelligence Stack Exchange! As someone suggested above, could you please explain why are you asking whether the hidden state of an RNN can be a matrix? Why do you need it to be a matrix? This doesn't seem to necessarily follow from what you wrote in your post so far. $\endgroup$
    – nbro
    Feb 20 '21 at 10:26

Yes, I would say more, that hidden state can be a tensor of arbitrary dimensionality. For vanilla RNN the update rules of the hidden state and the output are: $$ h_t = \sigma_h(W_h x_t + U_h h_{t-1} + b_h) $$ $$ y_t = \sigma_y(W_y h_t + b_y) $$ Here $W_h$ is input to hidden state matrix, and $U_h$ is hidden state to hidden state, $W_y$ is hidden state to output. One can simply upgrade these matrix products to more general case of tensor products, such that they could handle multidimensional input data and hidden state.

The contraction of the $m$-dimensional tensor $X$ with $n$-dimensional Y (we assume $m \geq n$) by maximal amount of indices will be $m-n$ dimensional tensor $Z$: $$ X_{i_1 \ldots i_m} Y_{i_1 \ldots i_n} = Z_{i_{n+1} \ldots i_m} $$

Namely, imagine, that the $x_t = (x_t)_{i_1 \ldots i_X}$ is an $d_x$-dimensional tensor. Then take $W_h$ to be $d_{wh}$ dimensional tensor, hidden state $h$ to be $d_h = d_{wh} - d_{x}$ -dimensional, $U_h$ is $d_{uh}$ - dimensional, $W_y$ is $d_{wy}$ -dimensional. The output $y_t$ of the RNN will be $d_{wy} - d_{h}$ dimensional tensor.

Here note, that in order for the sum to be make sense in the first equation, dimensions need to match: $$ d_{uh} - d_{h} = d_{h} $$


The question is, why you specifically want a matrix? I assume you mean per-frame features. In such case you can use a ConvNet as a feature extractor, i.e. it outputs a vector of features of fixed size. This vector is an input in your LSTM. If you have $C$ frames, the output of LSTM is $$ Output, (h, c) = LSTM (frame \ features) $$ where frame features are size $(1, C, v)$, where $v$ is the feature vector length from ConvNet. $Output$ is size $(1, C, w)$ and $h$ is $(1, 1, w)$ where $w$ is the size of the hidden layer in LSTM, and $h$ is the last hidden layer. Now, you can resize $Output$ to $(C, w)$ and make it an input in some linear layer that outputs a batch size $C$. This is your predictions per frame.


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