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Understanding Convolutional Neural Networks with CNNs
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[QUOTE="Future Bruno, post: 6883617, member: 552905"] Yes, that is correct. The 3 kernels K4, K5, K6 in the 2nd conv layer are applied to the 3 feature maps FP1, FP2, FP3 generated in the 1st conv layer. K4 is convolved with FP1, FP2, FP3, then K5 is convolved with FP1, FP2, FP3, and finally K6 is convolved with FP1, FP2, FP3. This process will result in a volume containing 9 new feature maps.At the end, the 9 new features maps from the last convolutional layer are all flattened into a vector (1D array) with as many elements as the nodes in the input layer of the artificial neural network. Starting with the first feature map, its rows are concatenated one by one in a straight line and this process continues for all other 8 feature maps. This results in a very long 1D vector that is then fed into the input layer of the ANN. [/QUOTE]
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Understanding Convolutional Neural Networks with CNNs
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