I have trained a convolutional neural network on images to detect emotions. Now I need to use the same network to extract features from the images and use them to train an LSTM. The problem is: the dimensions of the top layers are:
[None, 4, 4, 512] or
[None, 4, 4, 1024]. Therefore, extracting features from this layer will result in a
4 x 4 x 512 = 8192 or
4 x 4 x 1024 = 16384 dimensional vector for each image. Clearly, this is not what I want.
Therefore, I would like to know what to do in this case and how to extract features that are of reasonable size. Should I apply global average pooling to the activation or what?
Any help is much appreciated!