Add result of DenseNet

7_deep_learning/1_CNN/02-LeNet5.ipynb

@@ -102,10 +102,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import numpy as np\n",
     "from torchvision.datasets import mnist\n",
     "from torch.utils.data import DataLoader\n",
-    "from torchvision.datasets import mnist \n",
     "from torchvision import transforms as tfs\n",
     "from utils import train\n",
     "\n",
@@ -122,8 +120,7 @@
     "train_set  = mnist.MNIST('../../data/mnist', train=True,  transform=data_tf, download=True) \n",
     "train_data = torch.utils.data.DataLoader(train_set, batch_size=64, shuffle=True)\n",
     "test_set   = mnist.MNIST('../../data/mnist', train=False, transform=data_tf, download=True) \n",
-    "test_data  = torch.utils.data.DataLoader(test_set, batch_size=128, shuffle=False)\n",
-    "\n"
+    "test_data  = torch.utils.data.DataLoader(test_set, batch_size=128, shuffle=False)"
    ]
   },
   {
@@ -250,9 +247,7 @@
     "optimizer = torch.optim.Adam(net.parameters(), lr=1e-3)\n",
     "criterion = nn.CrossEntropyLoss()\n",
     "\n",
-    "res = train(net, train_data, test_data, 20, \n",
-    "            optimizer, criterion,\n",
-    "            use_cuda=True)"
+    "res = train(net, train_data, test_data, 20, optimizer, criterion)"
    ]
   },
   {

7_deep_learning/1_CNN/04-vgg.ipynb

@@ -85,12 +85,9 @@
    },
    "outputs": [],
    "source": [
-    "import numpy as np\n",
     "import torch\n",
     "from torch import nn\n",
     "from torch.autograd import Variable\n",
-    "from torchvision.datasets import CIFAR10\n",
-    "from torchvision import transforms as tfs\n",
     "\n",
     "def VGG_Block(num_convs, in_channels, out_channels):\n",
     "    # 定义第一层\n",
@@ -364,19 +361,12 @@
    },
    "outputs": [],
    "source": [
+    "from torchvision.datasets import CIFAR10\n",
+    "from torchvision import transforms as tfs\n",
     "from utils import train\n",
     "\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",
+    "# 使用数据变换\n",
+    "def data_tf(x):\n",
     "    im_aug = tfs.Compose([\n",
     "        #tfs.Resize(224),\n",
     "        tfs.ToTensor(),\n",
@@ -385,10 +375,10 @@
     "    x = im_aug(x)\n",
     "    return x\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",
-    "test_set  = CIFAR10('../../data', train=False, transform=test_tf)\n",
-    "test_data = torch.utils.data.DataLoader(test_set, batch_size=128, shuffle=False)\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 = VGG_Net()\n",
     "optimizer = torch.optim.Adam(net.parameters(), lr=1e-3)\n",
@@ -409,26 +399,26 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Epoch 0. Train Loss: 1.746658, Train Acc: 0.313979, Valid Loss: 1.441544, Valid Acc: 0.459454, Time 00:00:24\n",
-      "Epoch 1. Train Loss: 1.293454, Train Acc: 0.532868, Valid Loss: 1.064248, Valid Acc: 0.630934, Time 00:00:27\n",
-      "Epoch 2. Train Loss: 0.976450, Train Acc: 0.663643, Valid Loss: 0.865004, Valid Acc: 0.693730, Time 00:00:27\n",
-      "Epoch 3. Train Loss: 0.797400, Train Acc: 0.734835, Valid Loss: 0.752283, Valid Acc: 0.745847, Time 00:00:27\n",
-      "Epoch 4. Train Loss: 0.669900, Train Acc: 0.782389, Valid Loss: 0.654101, Valid Acc: 0.787085, Time 00:00:27\n",
-      "Epoch 5. Train Loss: 0.573636, Train Acc: 0.815837, Valid Loss: 0.637741, Valid Acc: 0.797765, Time 00:00:27\n",
-      "Epoch 6. Train Loss: 0.499975, Train Acc: 0.839174, Valid Loss: 0.571682, Valid Acc: 0.809237, Time 00:00:27\n",
-      "Epoch 7. Train Loss: 0.428868, Train Acc: 0.862372, Valid Loss: 0.505937, Valid Acc: 0.831982, Time 00:00:27\n",
-      "Epoch 8. Train Loss: 0.373129, Train Acc: 0.882133, Valid Loss: 0.559180, Valid Acc: 0.821203, Time 00:00:27\n",
-      "Epoch 9. Train Loss: 0.323578, Train Acc: 0.895920, Valid Loss: 0.582161, Valid Acc: 0.827927, Time 00:00:27\n",
-      "Epoch 10. Train Loss: 0.274420, Train Acc: 0.913163, Valid Loss: 0.623256, Valid Acc: 0.813390, Time 00:00:27\n",
-      "Epoch 11. Train Loss: 0.239558, Train Acc: 0.924692, Valid Loss: 0.561333, Valid Acc: 0.840882, Time 00:00:27\n",
-      "Epoch 12. Train Loss: 0.207196, Train Acc: 0.935042, Valid Loss: 0.537481, Valid Acc: 0.843256, Time 00:00:27\n",
-      "Epoch 13. Train Loss: 0.180536, Train Acc: 0.943454, Valid Loss: 0.697694, Valid Acc: 0.818631, Time 00:00:27\n",
-      "Epoch 14. Train Loss: 0.157166, Train Acc: 0.950607, Valid Loss: 0.542898, Valid Acc: 0.857298, Time 00:00:27\n",
-      "Epoch 15. Train Loss: 0.138539, Train Acc: 0.957201, Valid Loss: 0.602181, Valid Acc: 0.847112, Time 00:00:27\n",
-      "Epoch 16. Train Loss: 0.130947, Train Acc: 0.960418, Valid Loss: 0.607590, Valid Acc: 0.852453, Time 00:00:27\n",
-      "Epoch 17. Train Loss: 0.109348, Train Acc: 0.966972, Valid Loss: 0.636679, Valid Acc: 0.848497, Time 00:00:27\n",
-      "Epoch 18. Train Loss: 0.099000, Train Acc: 0.970009, Valid Loss: 0.640463, Valid Acc: 0.849684, Time 00:00:27\n",
-      "Epoch 19. Train Loss: 0.083908, Train Acc: 0.974604, Valid Loss: 0.630587, Valid Acc: 0.859771, Time 00:00:27\n"
+      "[ 0] Train:(L=1.689224, Acc=0.340493), Valid:(L=1.514729, Acc=0.449664), T: 00:00:27\n",
+      "[ 1] Train:(L=1.211734, Acc=0.572111), Valid:(L=1.043950, Acc=0.638944), T: 00:00:27\n",
+      "[ 2] Train:(L=0.939749, Acc=0.680647), Valid:(L=0.795742, Acc=0.731408), T: 00:00:27\n",
+      "[ 3] Train:(L=0.776414, Acc=0.742987), Valid:(L=0.773068, Acc=0.741792), T: 00:00:27\n",
+      "[ 4] Train:(L=0.655303, Acc=0.784607), Valid:(L=0.697191, Acc=0.759296), T: 00:00:27\n",
+      "[ 5] Train:(L=0.565006, Acc=0.816956), Valid:(L=0.634936, Acc=0.791436), T: 00:00:27\n",
+      "[ 6] Train:(L=0.487787, Acc=0.842172), Valid:(L=0.643098, Acc=0.788766), T: 00:00:27\n",
+      "[ 7] Train:(L=0.422939, Acc=0.866129), Valid:(L=0.539120, Acc=0.828323), T: 00:00:27\n",
+      "[ 8] Train:(L=0.365580, Acc=0.882413), Valid:(L=0.598219, Acc=0.808248), T: 00:00:27\n",
+      "[ 9] Train:(L=0.316299, Acc=0.899097), Valid:(L=0.601980, Acc=0.821005), T: 00:00:27\n",
+      "[10] Train:(L=0.271955, Acc=0.914043), Valid:(L=0.664353, Acc=0.803402), T: 00:00:27\n",
+      "[11] Train:(L=0.240455, Acc=0.923633), Valid:(L=0.555360, Acc=0.831191), T: 00:00:27\n",
+      "[12] Train:(L=0.199894, Acc=0.937100), Valid:(L=0.514384, Acc=0.857793), T: 00:00:27\n",
+      "[13] Train:(L=0.175543, Acc=0.944973), Valid:(L=0.641336, Acc=0.842860), T: 00:00:27\n",
+      "[14] Train:(L=0.149222, Acc=0.953285), Valid:(L=0.600546, Acc=0.847706), T: 00:00:27\n",
+      "[15] Train:(L=0.137142, Acc=0.957221), Valid:(L=0.597016, Acc=0.851958), T: 00:00:27\n",
+      "[16] Train:(L=0.121605, Acc=0.962456), Valid:(L=0.638970, Acc=0.850870), T: 00:00:27\n",
+      "[17] Train:(L=0.101306, Acc=0.970149), Valid:(L=0.602158, Acc=0.851760), T: 00:00:27\n",
+      "[18] Train:(L=0.103552, Acc=0.970009), Valid:(L=0.619986, Acc=0.844640), T: 00:00:27\n",
+      "[19] Train:(L=0.092559, Acc=0.972047), Valid:(L=0.692679, Acc=0.841772), T: 00:00:27\n"
      ]
     }
    ],