Dear all,

I am doing my first steps in RNN models (in fact I am interested in convolutional 2D RNN/LSTM models). My goal is to use them in a way similar to conditional random fields (CRF), for image segmentation refinement. So in the RNN case I am interested in the regression / time-series forecasting perspective. Could please someone give me a simple example of a single forward function for a `gluon.contrib.Conv2DRNNCell/Conv2DLSTMCell`

?

Something like:

```
nbatch = 10
nfilters = 32
shape = [nbatch, nfilters, 64,64]
xx = nd.random_uniform(shape = shape)
net = gluon.contrib.rnn.Conv2DLSTMCell( some_params) # I am not completely sure about the params.
states = ... # ? What goes in here?
temp = net(xx, states) # This is where I am getting
```

My ultimate goal is to create a custom `Conv2DLSTM`

layer to use for fixed length iterations.

I’ve been through various input sources on the web about RNNs, most notably this and this but I am having a bit of trouble understanding the particulars of the implementation. E.g. from my understanding, in the `some_params`

argument of `Conv2DLSTM`

`input_shape`

should be `(nbatch,nfilters,64,64)`

but according to documentation this should be `(nfilters,64,64)`

? And this messes up my understanding of dimensions of `states`

(which should be - for a convolutional layer - the same as xx? But I get errors when trying to run a simple example.

Any pointers to documentation / examples for RNN/convRNNs, anything will be extremely appreciated. Thank you very much.