July 25, 2019
Architecture of LeNet-5, a Convolutional Neural Network, in this paper used for handwritten digit recognition. Each plane is a feature map, i.e. a set of units whose weights are constrained to be identical. LeNet-5 comprises 7 layers, not counting the input, all of which contain trainable parameters (weights). The input is a 32x32 pixel image. This is significantly larger than the largest character in the (MNIST) database (at most 20x20 pixels centered in a 28x28 field). The reason is that it is desirable that potential distinctive features such as stroke end-points or corner can appear in the center of the receptive field of the highest-level feature detectors. In LeNet-5 the set of centers of the receiptive fields of the last convolutional layer form a 20x20 area in the center of the 32x32 input. The values of the input pixels are normalized so that the background level (white) corresponds to a value of -0.1 and the foreground (black) corresponds to 1.175. This makes the mean input roughly 0, and the variance roughly 1 which accelerates learning.
Source: LeCun, Yann, et al. "Gradient-based learning applied to document recognition." Proceedings of the IEEE 86.11 (1998) 2278-2324.
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