8.1.0.7.16. lenet5

(packages/gblearn2/net-lenet5.lsh)

LeNet5 is a convolutional network architecture described in several publications, notably in [LeCun, Bottou, Bengio and Haffner 1998]: "gradient-based learning applied to document recognition", Proc IEEE, Nov 1998. The paper is also available at http://yann.lecun.com .

8.1.0.7.16.0. (new-lenet5 image-height image-width ki0 kj0 si0 sj0 ki1 kj1 si1 sj1 hid output-size net-param)

(packages/gblearn2/net-lenet5.lsh)

create a new instance of net-cscscf implementing a LeNet-5 type convolutional neural net. This network has regular sigmoid units on the output, not an extra RBF layer as described in the Proc. IEEE paper. The network has 6 feature maps at the first layer and 16 feature maps at the second layer with a connection matrix between feature maps as described in the paper. Arguments:

 <image-height> <image-width>: height and width of input image
 <ki0> <kj0>: height and with of convolutional kernel, first layer.
 <si0> <sj0>: subsampling ratio of subsampling layer, second layer.
 <ki1> <kj1>: height and with of convolutional kernel, third layer.
 <si1> <sj1>: subsampling ratio of subsampling layer, fourth layer.
 <hid>: number of hidden units, fifth layer
 <output-size>: number of output units
 <net-param>: idx1-ddparam that will hold the trainable parameters
              of the network

example

 (setq p (new idx1-ddparam 0 0.1 0.02 0.02 80000))
 (setq z (new-lenet5 32 32 5 5 2 2 5 5 2 2 120 10 p))