How to build a hybrid graph with CNN and RNN Architecture? For an instance, we have M matrix of size n*k, I would like to treat each matrix independently and perform the convolution operation at each one in order to introduce to an LSTM layer the outputs of the convolution layer. can I treat each matrix independently or do I have to use the channel technique and how I can provide each LSTM cell? you find the architecture link below, thank you in advance!!
I write an answer here since I don't have enough reputation to write a comment.

What about treating your M matrices as sequence, feeding them to a TimeDistributed(Conv2D) layer(s) and then to an LSTM layer? In this way the Conv layer should extract feature maps that are treated by the LSTM in a sequence-like fashion, updating/accumulating the states up to the M matrix. Afterwards you should put a Dense layer with sigmoid/softmax activation and get class probabilities.