Tensorflow how pooling layer is connected to convolutional layer

Question

Is there a way to selectively connect the feature maps of a pooling layer to the feature maps of a (sequential) convolutional layer? In the paper "Gradient-Based Learning Applied To Document Recognition" [Proc. of the IEEE, Nov 1998] LeCun et al. describe how a subsampling layer is selectively connected to a subsequent convolutional layer. In the basic TF example (A guide to TF layers: Building a CNN) conv2 is connected to pool1 as:

conv2 = tf.layers.conv2d(inputs=pool1, ...

however, I'd like to selectively connect pool1 to conv2 in a similar way the LeCun paper connects S2 to C3 (see Table 1).

Thanks!

Answer 1

I haven't examined this paper. Just want to share that you can manipulate your pool1 tensor any way your like before passing it to the conv2d layer, eg split it by two and connect each one to its own conv layer.

For instance, that's what LSTM cell is doing (see the implementation here ).