# Problem extracting features from convolutional layer where the dimensions are big for feature maps

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 the features 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 gloabl average pooling to the activation or what?

Any help is much appreciated!!