Can anyone explain how to do concatenation of flatten outputs from CNN with the outputs from DNN?

I have defined two layers

1 CNN_Layer which takes input size of 12*25*1 and implment successive Con2D filters, MaxPooling ,etc and finally flatten the inputs from the final layer.

2 DNN_Layer which takes input size of 4*1 and ouput the same as the input(Just takes raw input and give us the raw input without changing it) .

My goal is to concatenat the faltten outputs from CNN_Layers and the outputs from DNN_Layers for further training using FCC layers.

The output from CNN_Layer after flatten is dim 864, so I expact 868 after concatenation .

Is there any way we can do that in matlab?

I know how to do that in Python.

combined_model= concatenate([CNN_Layers.output, DNN_Layers.output])

[Poorya Khanali]

Hi, could you find any solution to your problem? This is exactly the problem that I am currently facing? Any help would be appreciated.

Answer 1

[Mohammad Sami]

Try using the concatenation Layer, introduced since R2019a.

https://www.mathworks.com/help/releases/R2020a/deeplearning/ref/nnet.cnn.layer.concatenationlayer.html

[Wabi Demeke]

This is multiple inputs(input1 dim(12*25*1) & Input 2 dim(1*4*1)) problem. The data need to be combined before it feed into and I don't want that at the beginning( https://www.mathworks.com/help/deeplearning/ug/multiple-input-and-multiple-output-networks.html#mw_4f57107d-31b4-4432-8d4f-2065ce9008f0). I beleive concatenationlayer/depthconcatenationlayer works as long as the input layer takes input1 or input2.

Probably for the task I am looking is explaied here very well(https://www.mathworks.com/matlabcentral/fileexchange/74760-image-classification-using-cnn-with-multi-input-cnn)