Comments (2)
Yes, it is calculated based on the previous layer, to know total number of parameters. Basically, you have:
input: (28, 28, 1) Pictures of size 28x28 with 1 color channel (grayscale)
conv1, 32 filters, strides=1:
(28, 28, 32)
maxpool1, kernel_size=2:
(14, 14, 32)
conv2, 64 filters, strides=1:
(14, 14, 64)
maxpool2, kernel_size=2:
(7, 7, 64) => 7x7x64 outputs
(that we flatten to connect to a dense layer)
(3136)
For further understanding, you can have a look at http://cs231n.github.io/convolutional-networks/, it gives a good explanation and some examples.
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Thankyou.
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