Pre-trained 3D LeNet-5

Our implementation of 2D LeNet-5 model achieved 98.48% accuracy on the grey-scale MNIST test set after training on its train set. To transfer the learnable parameters from pre-trained 2D LeNet-5 (MNIST) to 3D one, we duplicated 2D filters (copying them repeatedly) through the third dimension. This is possible since a video or a 3D image can be converted into a sequence of image slices. In the training process, we expect that the 3D LeNet-5 learns patterns in each frame. This model has about 260,000 learnable parameters.

simply, call "lenet5TL3Dfun()" function.