I have training samples of the following shape: (1000,2). These are numeric sequences, each of length = 1000 and dimension = 2. I need to build a convolutional neural network to output predictions/sequences of the same shape (1000, 2). Since, after applying convolution and pooling, the height and width of the input is reduced. How should I then set up the fully connected layer(s) and an output layer in my CNN, so that the output dimensions match the input dimensions?
i.e. for each input sample of shape (1000, 2) how can I produce an output of same shape (1000, 2) and how can I set up the last fully connected / output layers to achieve this in PyTorch?