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 $64.$
Connection between the first pooling layer and the second convolutional layer
deep learning;tensorflow;computer vision;image recognition