Hello @tom ,
Thank you for your quick reply, well I thought about " spreading " my data (seq_len * batch_size) but it will even complicated things making it [25*100] , I was thinking of maybe creating something like this: a feedforward net for each time step so my initial input will be split into seq_dim * [batch_size , features] pass each of this new tensors through the same FFN , we’ll call this model_in then combines the outputs of the FFNs to create our input for the RNN [,batch_size, seq_len , features (maybe I will change this depending on the output from FFN ) ] , these are my thoughts on the matter , can you give me your feedback ( plausibility , complexity … )
And Thank you
Cheers