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Model compression techniques are inspired by the fault tolerance property to network damage conditions, seen in larger networks.
Lenet 300 100 architecture. For the AlexNet, VGG-16, and ResNet-50 architectures, we reduce network parameters (FLOPs) by 15.7× (4.6×), 33.2× (8.9×), and 4.1× (2.1×) respectively. $ python lenet-300-100.py 0.0001 0 0.0005 0.5 0.01:. LeNet-300-100 is a fully connected network with two hidden layers.
Deeper networks like LeNet-300-100 MLP (Multi Layer Perceptron) or LeNet-5 12 CNN (Convolutional Neural Network) form complex decision boundaries but prevents itself from overfitting. We use the Lenet-300-100 architecture (LeCun et al., 1998) as described in Figure 2. We directly train the LeNet-5 using the settings for the training parameters provided by Caffe.
After randomly initializing and training a network, we prune the network and reset the remaining connections to their original initializations. $ sparsity of w_fc1= 9. % $ sparsity of w_fc2= 11.16 % $ sparsity of w_out= 54.5 % $ Total Sparsity= / 2660 = 9. % $ Compression Rate = 10. $ Accuracy without prune:. 0.9813 $ Accuracy with prune:.
Node pruning for five benchmark networks and SIMD-aware weight pruning for LeNet-300-100 and LeNet-5 is included. We follow the outline from Section 1:. The baseline models are trained on the Caffe 24 deep learning platform without any data augmentation.
This is a PyTorch implementation of the Scalpel. There are already plenty of literature review talk about the LeNet which is a classical image classification deep learning convolutional neural network.
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