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Add sample code of LeNet5 and AlexNet

pull/8/MERGE
bushuhui 3 years ago
parent
63fed11e3f
commit
5377fc5be5
3 changed files with 108 additions and 58 deletions
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  1. +67
    -42
      7_deep_learning/1_CNN/02-LeNet5.ipynb
  2. +38
    -0
      7_deep_learning/1_CNN/03-AlexNet.ipynb
  3. +3
    -16
      7_deep_learning/1_CNN/08-batch-normalization.ipynb

+ 67
- 42
7_deep_learning/1_CNN/02-LeNet5.ipynb View File

@@ -11,6 +11,13 @@
"在LeNet5提出的时候，没有 GPU 帮助训练，甚至 CPU 的速度也很慢，因此，LeNet5的规模并不大。其包含七个处理层，每一层都包含可训练参数（权重），当时使用的输入数据是 $32 \\times 32$ 像素的图像。LeNet-5 这个网络虽然很小，但是它包含了深度学习的基本模块：卷积层，池化层，全连接层。它是其他深度学习模型的基础，这里对LeNet5进行深入分析和讲解，通过实例分析，加深对与卷积层和池化层的理解。" "在LeNet5提出的时候，没有 GPU 帮助训练，甚至 CPU 的速度也很慢，因此，LeNet5的规模并不大。其包含七个处理层，每一层都包含可训练参数（权重），当时使用的输入数据是 $32 \\times 32$ 像素的图像。LeNet-5 这个网络虽然很小，但是它包含了深度学习的基本模块：卷积层，池化层，全连接层。它是其他深度学习模型的基础，这里对LeNet5进行深入分析和讲解，通过实例分析，加深对与卷积层和池化层的理解。"
] ]
}, },
{
"cell_type": "markdown",
"metadata": {},
"source": [
"定义网络为："
]
},
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 1, "execution_count": 1,
@@ -26,30 +33,57 @@
"import torch\n", "import torch\n",
"from torch import nn\n", "from torch import nn\n",
"from torch.autograd import Variable\n", "from torch.autograd import Variable\n",
"import torch.nn.functional as F\n",
"from torchvision.datasets import CIFAR10\n", "from torchvision.datasets import CIFAR10\n",
"from torchvision import transforms as tfs"
"from torchvision import transforms as tfs\n",
"\n",
"\n",
"class LeNet5(nn.Module):\n",
" def __init__(self):\n",
" super(LeNet5, self).__init__()\n",
" # 1-input channel, 6-output channels, 5x5-conv\n",
" self.conv1 = nn.Conv2d(1, 6, 5)\n",
" # 6-input channel, 16-output channels, 5x5-conv\n",
" self.conv2 = nn.Conv2d(6, 16, 5)\n",
" # 16x5x5-input, 120-output\n",
" self.fc1 = nn.Linear(16 * 5 * 5, 120) \n",
" # 120-input, 84-output\n",
" self.fc2 = nn.Linear(120, 84)\n",
" # 84-input, 10-output\n",
" self.fc3 = nn.Linear(84, 10)\n",
"\n",
" def forward(self, x):\n",
" x = F.max_pool2d(F.relu(self.conv1(x)), (2, 2))\n",
" x = F.max_pool2d(F.relu(self.conv2(x)), (2, 2))\n",
" x = torch.flatten(x, 1) # 将结果拉升成1维向量，除了批次的维度\n",
" x = F.relu(self.fc1(x))\n",
" x = F.relu(self.fc2(x))\n",
" x = self.fc3(x)\n",
" return x"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"LeNet5(\n",
" (conv1): Conv2d(1, 6, kernel_size=(5, 5), stride=(1, 1))\n",
" (conv2): Conv2d(6, 16, kernel_size=(5, 5), stride=(1, 1))\n",
" (fc1): Linear(in_features=400, out_features=120, bias=True)\n",
" (fc2): Linear(in_features=120, out_features=84, bias=True)\n",
" (fc3): Linear(in_features=84, out_features=10, bias=True)\n",
")\n"
]
}
],
"source": [ "source": [
"import torch\n",
"from torch import nn\n",
"\n",
"lenet5 = nn.Sequential(\n",
" nn.Conv2d(1, 6, kernel_size=5, padding=2), nn.Sigmoid(),\n",
" nn.AvgPool2d(kernel_size=2, stride=2),\n",
" nn.Conv2d(6, 16, kernel_size=5), nn.Sigmoid(),\n",
" nn.AvgPool2d(kernel_size=2, stride=2),\n",
" nn.Flatten(),\n",
" nn.Linear(16 * 5 * 5, 120), nn.Sigmoid(),\n",
" nn.Linear(120, 84), nn.Sigmoid(),\n",
" nn.Linear(84, 10) )"
"net = LeNet5()\n",
"print(net)"
] ]
}, },
{ {
@@ -60,34 +94,27 @@
}, },
"outputs": [], "outputs": [],
"source": [ "source": [
"from torchvision.datasets import mnist\n",
"from torch.utils.data import DataLoader\n",
"from utils import train\n", "from utils import train\n",
"\n", "\n",
"# 使用数据增强\n", "# 使用数据增强\n",
"def train_tf(x):\n",
" im_aug = tfs.Compose([\n",
" tfs.Resize(224),\n",
" tfs.ToTensor(),\n",
" tfs.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])\n",
" ])\n",
" x = im_aug(x)\n",
" return x\n",
"\n",
"def test_tf(x):\n",
"def data_tf(x):\n",
" im_aug = tfs.Compose([\n", " im_aug = tfs.Compose([\n",
" tfs.Resize(224),\n",
" tfs.Resize(32),\n",
" tfs.ToTensor(),\n", " tfs.ToTensor(),\n",
" tfs.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])\n", " tfs.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])\n",
" ])\n", " ])\n",
" x = im_aug(x)\n", " x = im_aug(x)\n",
" return x\n", " return x\n",
" \n", " \n",
"train_set = CIFAR10('../../data', train=True, transform=train_tf)\n",
"train_set = CIFAR10('../../data', train=True, transform=data_tf)\n",
"train_data = torch.utils.data.DataLoader(train_set, batch_size=64, shuffle=True)\n", "train_data = torch.utils.data.DataLoader(train_set, batch_size=64, shuffle=True)\n",
"test_set = CIFAR10('../../data', train=False, transform=test_tf)\n",
"test_set = CIFAR10('../../data', train=False, transform=data_tf)\n",
"test_data = torch.utils.data.DataLoader(test_set, batch_size=128, shuffle=False)\n", "test_data = torch.utils.data.DataLoader(test_set, batch_size=128, shuffle=False)\n",
"\n", "\n",
"net = lenet5\n",
"optimizer = torch.optim.SGD(net.parameters(), lr=1e-1)\n",
"net = LeNet5()\n",
"optimizer = torch.optim.Adam(net.parameters(), lr=1e-1)\n",
"criterion = nn.CrossEntropyLoss()" "criterion = nn.CrossEntropyLoss()"
] ]
}, },
@@ -99,11 +126,9 @@
}, },
"outputs": [], "outputs": [],
"source": [ "source": [
"(l_train_loss, l_train_acc, l_valid_loss, l_valid_acc) = train(net, \n",
" train_data, test_data, \n",
" 20, \n",
" optimizer, criterion,\n",
" use_cuda=False)"
"res = train(net, train_data, test_data, 20, \n",
" optimizer, criterion,\n",
" use_cuda=False)"
] ]
}, },
{ {
@@ -117,15 +142,15 @@
"import matplotlib.pyplot as plt\n", "import matplotlib.pyplot as plt\n",
"%matplotlib inline\n", "%matplotlib inline\n",
"\n", "\n",
"plt.plot(l_train_loss, label='train')\n",
"plt.plot(l_valid_loss, label='valid')\n",
"plt.plot(res[0], label='train')\n",
"plt.plot(res[2], label='valid')\n",
"plt.xlabel('epoch')\n", "plt.xlabel('epoch')\n",
"plt.legend(loc='best')\n", "plt.legend(loc='best')\n",
"plt.savefig('fig-res-lenet5-train-validate-loss.pdf')\n", "plt.savefig('fig-res-lenet5-train-validate-loss.pdf')\n",
"plt.show()\n", "plt.show()\n",
"\n", "\n",
"plt.plot(l_train_acc, label='train')\n",
"plt.plot(l_valid_acc, label='valid')\n",
"plt.plot(res[1], label='train')\n",
"plt.plot(res[3], label='valid')\n",
"plt.xlabel('epoch')\n", "plt.xlabel('epoch')\n",
"plt.legend(loc='best')\n", "plt.legend(loc='best')\n",
"plt.savefig('fig-res-lenet5-train-validate-acc.pdf')\n", "plt.savefig('fig-res-lenet5-train-validate-acc.pdf')\n",


+ 38
- 0
7_deep_learning/1_CNN/03-AlexNet.ipynb View File

@@ -73,6 +73,44 @@
" nn.init.normal_(m.weight, 0, 0.01) \n", " nn.init.normal_(m.weight, 0, 0.01) \n",
" nn.init.constant_(m.bias, 0)" " nn.init.constant_(m.bias, 0)"
] ]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import sys\n",
"sys.path.append('..')\n",
"\n",
"from torchvision.datasets import CIFAR10\n",
"from torchvision import transforms as tfs\n",
"from utils import train\n",
"\n",
"\n",
"# 数据转换\n",
"def data_tf(x):\n",
" im_aug = tfs.Compose([\n",
" tfs.Resize(227),\n",
" tfs.ToTensor(),\n",
" tfs.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])\n",
" ])\n",
" x = im_aug(x)\n",
" return x\n",
" \n",
"train_set = CIFAR10('../../data', train=True, transform=data_tf)\n",
"train_data = torch.utils.data.DataLoader(train_set, batch_size=64, shuffle=True)\n",
"test_set = CIFAR10('../../data', train=False, transform=data_tf)\n",
"test_data = torch.utils.data.DataLoader(test_set, batch_size=128, shuffle=False)\n",
"\n",
"net = AlexNet(num_classes=10)\n",
"optimizer = torch.optim.Adam(net.parameters(), lr=1e-2)\n",
"criterion = nn.CrossEntropyLoss()\n",
"\n",
"res = train(net, train_data, test_data, 20, optimizer, criterion, use_cuda=False)"
]
} }
], ],
"metadata": { "metadata": {


+ 3
- 16
7_deep_learning/1_CNN/08-batch-normalization.ipynb View File

@@ -204,7 +204,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 5,
"execution_count": 6,
"metadata": { "metadata": {
"collapsed": true "collapsed": true
}, },
@@ -214,20 +214,7 @@
"from torchvision.datasets import mnist # 导入 pytorch 内置的 mnist 数据\n", "from torchvision.datasets import mnist # 导入 pytorch 内置的 mnist 数据\n",
"from torch.utils.data import DataLoader\n", "from torch.utils.data import DataLoader\n",
"from torch import nn\n", "from torch import nn\n",
"from torch.autograd import Variable"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# 使用内置函数下载 mnist 数据集\n",
"train_set = mnist.MNIST('../../data/mnist', train=True)\n",
"test_set = mnist.MNIST('../../data/mnist', train=False)\n",
"from torch.autograd import Variable\n",
"\n", "\n",
"def data_tf(x):\n", "def data_tf(x):\n",
" x = np.array(x, dtype='float32') / 255\n", " x = np.array(x, dtype='float32') / 255\n",
@@ -236,7 +223,7 @@
" x = torch.from_numpy(x)\n", " x = torch.from_numpy(x)\n",
" return x\n", " return x\n",
"\n", "\n",
"# 重新载入数据集，申明定义的数据变换\n",
"# 下载 MNIST 数据集，载入数据集，申明定义的数据变换\n",
"train_set = mnist.MNIST('../../data/mnist', train=True, \n", "train_set = mnist.MNIST('../../data/mnist', train=True, \n",
" transform=data_tf, download=True) \n", " transform=data_tf, download=True) \n",
"test_set = mnist.MNIST('../../data/mnist', train=False, \n", "test_set = mnist.MNIST('../../data/mnist', train=False, \n",


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