Let's say that we have a CNN with two convolutional layers (https://www.tensorflow.org/tutorials/layers). My question regards the dimension of the tensor, which is the output of the pooling layer 1.
In the first convolutional layer, we apply $32$ filters to the input image (let's say that the output will be $28\times 28 \times 32)$, so as far as I can understand we will get $32$ separate feature maps, because of the number of filters.
In the next step, we can apply an activation function, which does not change the dimensionality.
The $\max$ pooling layer takes as input a tensor of $28\times 28\times 32$ and the output is going to be a $14 \times 14 \times \color{red}{1}$ tensor (according to the link above).
I cannot understand the unit as the depth, since we apply $32$ filters and we apply the $\max$ pooling layer to every feature map. So, why is the output tensor $14 \times 14 \times 1$?
According to my understanding the input in the second convolutional layer should be a $14 \times 14 \times 32$ tensor. Probably, I am missing something here.
