APPLICATION OF SUPERVISED LEARNING
DEEP LEARNING
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Question
[CLICK ON ANY CHOICE TO KNOW THE RIGHT ANSWER]
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Detailed explanation-1: -POOL layer: This has got no learnable parameters because all it does is calculate a specific number, no backprop learning involved! Thus number of parameters = 0.
Detailed explanation-2: -Max pooling does not have any learnable parameters. The initialization of these layers is probably just for convenience, e.g. if you want easily change the pooling operation without changing your forward method.
Detailed explanation-3: -MaxPool-1: The maxpool layer following Conv-1 consists of pooling size of 3×3 and stride 2. Conv-2: The second conv layer consists of 256 kernels of size 5×5 applied with a stride of 1 and padding of 2. MaxPool-2: The maxpool layer following Conv-2 consists of pooling size of 3×3 and a stride of 2.