How to use non-image input for conv-neural network in tensorflow?
conv-neural-network, tensorflow
Solution
This does not look like data that is suited for a convnet - convnets assume that it makes sense to share the weights locally, so they make sense when there's some form of locality in data. So it makes sense for other things than image if there is a connection from a feature to "nearby" features - for example in time series, or audio, where features that are adjacent happened at nearby time point. Your data looks like the columns are unconnected (even on different scales).
Your data is also really low-dimensional so you can just go with fully connected layers, if you want to go for a deep learning approach - I would try out other approaches on your data as well (boosting & random forests).
Problem
I have a data set ([n][13]) like : ``` 1) -3.3 -15.0 41 1026.3 16.1 0 25.9 37.0 0 0 0 280 0 2) -3.9 -13.9 46 1028.0 16.1 0 20.4 0 0 0 0 280 0 3) -3.9 -13.3 49 1028.8 16.1 0 22.2 0 0 0 0 270 0 4) -4.4 -12.2 55 1029.5 16.1 0 11.1 0 0 0 0 260 0 ... n) -1.1 -10.6 49 1030.0 16.1 0 14.8 0 0 0 0 280 0 ``` Using this data set, I want a create convolutional neural network in Tensorflow and make a prediction. I know convolutional neural network actually for images but I saw some example with non-image inputs. Can I create a convolutional neural network with using this data and How can I create? Can you give me any clue or tutorial or source?