Hi,

In week3 “Convolutional Implementation of Sliding Windows” video, I understood the concept well but there are two things that are not clear.

1 ) What are these 4 numbers are correspond to in the third dimension of 1x1x4 output?.

2 ) How changing FC layers to CONV layers help performance?

Thanks for any answer in advance…

I am okay with the second answer thanks again.

On the other hand, for the first one; that was not my question actually. I understood how the shape became like that in the first place. I was curious about contents of that 1x1x4 tensor like are they probabilities or the first one binary and the others are some other probabilities etc.

Apply softmax on the last dimension of 1x1x4 to interpret the output as probabilities. Each of the 4 units would corresponds to the probability of a certain class of object (eg: bus, human etc.) detected within the input image of dimension 14x14x3.

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