动手学深度学习 12.LeNet,AlexNet,VGG NiN 中文教材：zh.d2.ai 英文教材：ww.d2.ai 教学视频：https://courses.d2L.ai/berkeley-stat-157/units/lenet..html D2L.ai
动手学深度学习 12.LeNet, AlexNet, VGG 和 NiN 中文教材：zh.d2l.ai 英文教材：www.d2l.ai 教学视频：https://courses.d2l.ai/berkeley-stat-157/units/lenet.html
概要 ·LeNet（第一个卷积神经网络) ·AlexNet ·升级版的LeNet ·ReLu激活，丢弃法，平移不变性 ·VGG ·升华版的AlexNet ·重复的VGG块 ·NiN ·1x1卷积+全局池化 D2L.ai
概要 • LeNet （第一个卷积神经网络） • AlexNet • 升级 版的 LeNet • ReLu 激活， 丢弃法，平移不变性 • VGG • 升华版的 AlexNet • 重复的 VGG 块 • NiN • 1x1 卷积 + 全局池化
LeNet架构 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
LeNet 架构
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手写的数字识别
MNIST bD000O00000 ·居中和缩放 22 2上QQ2.2282 ·50,000个训练数据 6 333 3333 3 3 3 3 ·10,000个测试数据 图像大小28*28 H 444 4 ·10类 5 5 5 55 5 5 s o 6 o 6 o(0 (0 b 6 G a 7 7 77?7 7 子 7 n 8 888g 8 888 飞 8 99 999 9 9
MNIST • 居中和缩放 • 50,000 个训练数据 • 10,000 个测试数据 • 图像大小28*28 • 10 类
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