# Connection between the first pooling layer and the second convolutional layer

This is a follow-up question regarding this question. I managed to verify in python that the output of the first pooling layer will be a $14 \times 14\times 32$ tensor.

I interpret this as having 32 $14 \times 14$ images. Maybe my interpretation is mistaken. In the second convolutional layer we apply $64$ filters.

When I type in python conv2.shape I get

TensorShape([Dimension(55000), Dimension(14), Dimension(14), Dimension(64)]).

How that comes? I mean I am in interested in the outcome of the depth, which is $64.$