广东工业大学：《机器学习》课程教学资源（课件讲义）第12讲 卷积神经网络（LeNet, AlexNet, VGG和NiN）

ATET LeNet 5 RESEARCH answer: 0 0 163 Y.LeCun,L Bottou,Y.Bengio P.Haffner,1998 Gradient-based learning applied to document recognition 2L.ai

Y. LeCun, L. Bottou, Y. Bengio, P. Haffner, 1998 Gradient-based learning applied to document recognition

如果我们有很多输出，那开销就很 convolution pooling full convolution pooling full Gauss 6@14x14 S2 feature map 32x32 image 16@5x5 6@28x28 16@10x10 C1 feature map C3 feature map S4 feature map D2L.ai

如果我们有很多输出，那开销就很大 了

MXNet中的LeNet net gluon.nn.Sequential() with net.name_scope(): net.add(gluon.nn.Conv2D(channels=20,kernel_size=5, activation='tanh')) net.add(gluon.nn.AvgPool2D(pool_size=2)) net.add(gluon.nn.Conv2D(channels=50,kernel_size=5, activation='tanh')) net.add(gluon.nn.AvgPool2D(pool_size=2)) ● net.add(gluon.nn.Flatten()) net.add(gluon.nn.Dense(500,activation='tanh')) ● net.add(gluon.nn.Dense(10)) loss gluon.loss.SoftmaxCrossEntropyLoss() .(size and shape inference is automatic) D2L.ai

MXNet 中的 LeNet • net = gluon.nn.Sequential() • with net.name_scope(): • net.add(gluon.nn.Conv2D(channels=20, kernel_size=5, activation='tanh')) • net.add(gluon.nn.AvgPool2D(pool_size=2)) • net.add(gluon.nn.Conv2D(channels=50, kernel_size=5, activation='tanh')) • net.add(gluon.nn.AvgPool2D(pool_size=2)) • net.add(gluon.nn.Flatten()) • net.add(gluon.nn.Dense(500, activation='tanh')) • net.add(gluon.nn.Dense(10)) • loss = gluon.loss.SoftmaxCrossEntropyLoss() • (size and shape inference is automatic)

AlexNet 2048 2048 dense 192 192 128 11 48 128 3 13 *28 224 5 I3. 03 dense dense 27 11 755 1000 192 192 128Ma× 2048 224 Max pooling 2048 Stride 128 Max of 4 pooling pooling 3 48 D2L.ai

AlexNet

2001 机器学习 In the 1990s,a new type of learning algorithm was developed,based on results from statistical learning theory: Learning with Kernels the Support Vector Machine(SVM).This gave rise to a new class of theoretically elegant learning machines that use a central concept of SVMs--kernels-for a number of Support Vector Machines,Regularization, Optimization,and Beyond 提取特征 ·选择内核以获得相似性 Bernhard Scholkopf and Alexander J.Smola ·凸优化问题 米 米 。许多完美的定理… D2L.ai 米

机器学习 • 提取特征 • 选择内核以获得相似性 • 凸优化问题 • 许多完美的定理 ...... 2001