{ "nbformat": 4, "nbformat_minor": 0, "metadata": { "colab": { "name": "demoTA.ipynb", "provenance": [], "collapsed_sections": [], "toc_visible": true, "include_colab_link": true }, "kernelspec": { "name": "python3", "display_name": "Python 3" } }, "cells": [ { "cell_type": "markdown", "metadata": { "id": "view-in-github", "colab_type": "text" }, "source": [ "\"Open" ] }, { "cell_type": "markdown", "metadata": { "id": "hT2sm0_4Ywa8", "colab_type": "text" }, "source": [ "# A typically demo to structure basic consepts\n", "Source: https://pytorch.org/tutorials/beginner/blitz/cifar10_tutorial.html" ] }, { "cell_type": "code", "metadata": { "id": "N7Mpiy75B5RZ", "colab_type": "code", "outputId": "0cd85195-537f-4b1b-a8a1-5ff8fd3ac38d", "colab": { "base_uri": "https://localhost:8080/", "height": 85 } }, "source": [ "import torch\n", "import torchvision\n", "import torchvision.transforms as transforms\n", "transform = transforms.Compose(\n", " [transforms.ToTensor(),\n", " transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])\n", "\n", "trainset = torchvision.datasets.CIFAR10(root='./data', train=True,\n", " download=True, transform=transform)\n", "trainloader = torch.utils.data.DataLoader(trainset, batch_size=4,\n", " shuffle=True, num_workers=2)\n", "\n", "testset = torchvision.datasets.CIFAR10(root='./data', train=False,\n", " download=True, transform=transform)\n", "testloader = torch.utils.data.DataLoader(testset, batch_size=4,\n", " shuffle=False, num_workers=2)\n", "\n", "classes = ('plane', 'car', 'bird', 'cat',\n", " 'deer', 'dog', 'frog', 'horse', 'ship', 'truck')" ], "execution_count": 0, "outputs": [ { "output_type": "stream", "text": [ "\r0it [00:00, ?it/s]" ], "name": "stderr" }, { "output_type": "stream", "text": [ "Downloading https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to ./data/cifar-10-python.tar.gz\n" ], "name": "stdout" }, { "output_type": "stream", "text": [ "170500096it [00:04, 42444516.33it/s] \n" ], "name": "stderr" }, { "output_type": "stream", "text": [ "Extracting ./data/cifar-10-python.tar.gz to ./data\n", "Files already downloaded and verified\n" ], "name": "stdout" } ] }, { "cell_type": "code", "metadata": { "id": "B7CrwV7TFurd", "colab_type": "code", "outputId": "00989d10-e72a-4e17-a460-062eef913903", "colab": { "base_uri": "https://localhost:8080/", "height": 155 } }, "source": [ "import matplotlib.pyplot as plt\n", "import numpy as np\n", "\n", "# functions to show an image\n", "\n", "\n", "def imshow(img):\n", " img = img / 2 + 0.5 # unnormalize\n", " npimg = img.numpy()\n", " plt.imshow(np.transpose(npimg, (1, 2, 0)))\n", " plt.show()\n", "\n", "\n", "# get some random training images\n", "dataiter = iter(trainloader)\n", "images, labels = dataiter.next()\n", "\n", "# show images\n", "imshow(torchvision.utils.make_grid(images))\n", "# print labels\n", "print(' '.join('%5s' % classes[labels[j]] for j in range(4)))" ], "execution_count": 0, "outputs": [ { "output_type": "display_data", "data": { "image/png": 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EdUmfqlJXcYLOP3hE+7G+RuRsu0u/6nt2HUiP/czPEHl5+20H9fxVIjmLeXUj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\n60T/I5DN9qccTTgnjNUEUlc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"text/plain": [ "
" ] }, "metadata": { "tags": [] } }, { "output_type": "stream", "text": [ "truck ship cat ship\n" ], "name": "stdout" } ] }, { "cell_type": "code", "metadata": { "id": "AJwqGu4AGaea", "colab_type": "code", "colab": {} }, "source": [ "#Define a Convolutional Neural Network\n", "'''\n", "https://pytorch.org/docs/stable/nn.html?highlight=conv2d#torch.nn.Conv2d\n", "torch.nn.Conv2d(in_channels, out_channels, kernel_size, \n", "stride=1, padding=0, dilation=1, groups=1, bias=True, padding_mode='zeros')\n", "\n", "'''\n", "import pdb\n", "import torch.nn as nn\n", "import torch.nn.functional as F\n", "import torch.optim as optim\n", "\n", "class Net(nn.Module):\n", " def __init__(self):\n", " super(Net, self).__init__()\n", " self.conv1 = nn.Conv2d(3, 6, 5)\n", " self.pool = nn.MaxPool2d(2, 2)\n", " self.conv2 = nn.Conv2d(6, 16, 5)\n", " self.fc1 = nn.Linear(16 * 5 * 5, 120)\n", " self.fc2 = nn.Linear(120, 84)\n", " self.fc3 = nn.Linear(84, 10)\n", "\n", " def forward(self, x):\n", " x = self.pool(F.relu(self.conv1(x)))\n", " x = self.pool(F.relu(self.conv2(x))) \n", " x = x.view(-1, 16 * 5 * 5)\n", " x = F.relu(self.fc1(x))\n", " x = F.relu(self.fc2(x))\n", " x = self.fc3(x)\n", " return x \n", "net = Net()\n", "\n", "#Define a Loss function and optimizer \n", "\n", "criterion = nn.CrossEntropyLoss()\n", "#all optim https://pytorch.org/docs/stable/optim.html?highlight=optim#module-torch.optim\n", "optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)\n", "#optimizer = optim.Adam(net.parameters(), lr=0.001)#, betas=(0.5, 0.99))\n", "\n" ], "execution_count": 0, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "sdTJfyQDV5aS", "colab_type": "code", "outputId": "4d4decff-2e3a-48a6-cd9f-f6f780ff6d08", "colab": { "base_uri": "https://localhost:8080/", "height": 238 } }, "source": [ "#train the network\n", "for epoch in range(2): # loop over the dataset multiple times\n", "\n", " running_loss = 0.0\n", " for i, data in enumerate(trainloader, 0):\n", " # get the inputs; data is a list of [inputs, labels]\n", " inputs, labels = data\n", "\n", " # zero the parameter gradients\n", " optimizer.zero_grad()\n", "\n", " # forward + backward + optimize\n", " outputs = net(inputs)\n", " loss = criterion(outputs, labels)\n", " loss.backward()\n", " optimizer.step()\n", "\n", " # print statistics\n", " running_loss += loss.item()\n", " if i % 2000 == 1999: # print every 2000 mini-batches\n", " print('[%d, %5d] loss: %.3f' %\n", " (epoch + 1, i + 1, running_loss / 2000))\n", " running_loss = 0.0\n", "\n", "print('Finished Training')" ], "execution_count": 0, "outputs": [ { "output_type": "stream", "text": [ "[1, 2000] loss: 2.191\n", "[1, 4000] loss: 1.865\n", "[1, 6000] loss: 1.700\n", "[1, 8000] loss: 1.600\n", "[1, 10000] loss: 1.534\n", "[1, 12000] loss: 1.491\n", "[2, 2000] loss: 1.432\n", "[2, 4000] loss: 1.394\n", "[2, 6000] loss: 1.394\n", "[2, 8000] loss: 1.328\n", "[2, 10000] loss: 1.319\n", "[2, 12000] loss: 1.304\n", "Finished Training\n" ], "name": "stdout" } ] }, { "cell_type": "code", "metadata": { "id": "gSFo3JGQW394", "colab_type": "code", "outputId": "21fda441-aa79-486e-d4ea-ebc45325ede7", "colab": { "base_uri": "https://localhost:8080/", "height": 172 } }, "source": [ "#save model\n", "PATH = './cifar_net.pth'\n", "torch.save(net.state_dict(), PATH)\n", "#Test the network on the test data\n", "dataiter = iter(testloader)\n", "images, labels = dataiter.next()\n", "\n", "# print images\n", "imshow(torchvision.utils.make_grid(images))\n", "print('GroundTruth: ', ' '.join('%5s' % classes[labels[j]] for j in range(4)))\n", "\n", "net = Net()\n", "net.load_state_dict(torch.load(PATH))\n", "outputs = net(images)\n", "_, predicted = torch.max(outputs, 1) \n", "print('Predicted: ', ' '.join('%5s' % classes[predicted[j]]\n", " for j in range(4)))\n" ], "execution_count": 0, "outputs": [ { "output_type": "display_data", "data": { "image/png": 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udM3GlYGUNaxTTm8VXgL38PDw6FH4F7iHh4dHj2LbTSguOm7nDvHJdjXyEkUG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\nlBNLpKLPxioVZ4ZU6qClVI7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" ] }, "metadata": { "tags": [] } }, { "output_type": "stream", "text": [ "GroundTruth: cat ship ship plane\n", "Predicted: cat plane car ship\n" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "id": "EhQaj7PvYlHb", "colab_type": "text" }, "source": [ "# 讲解dataloader部分" ] }, { "cell_type": "markdown", "metadata": { "id": "F6Q4_7OudweH", "colab_type": "text" }, "source": [ "" ] }, { "cell_type": "code", "metadata": { "id": "xRGfjdsVks_K", "colab_type": "code", "outputId": "b79cecc0-cf64-448c-be3f-4128e85e61d4", "colab": { "base_uri": "https://localhost:8080/", "height": 34 } }, "source": [ "cd ./data" ], "execution_count": 0, "outputs": [ { "output_type": "stream", "text": [ "/content/data\n" ], "name": "stdout" } ] }, { "cell_type": "code", "metadata": { "id": "3CJGDOqcfreL", "colab_type": "code", "outputId": "209fa42f-e92f-41b8-de9d-02631eee4527", "colab": { "base_uri": "https://localhost:8080/", "height": 153 } }, "source": [ "!git clone https://github.com/YoongiKim/CIFAR-10-images.git" ], "execution_count": 0, "outputs": [ { "output_type": "stream", "text": [ "Cloning into 'CIFAR-10-images'...\n", "remote: Enumerating objects: 60027, done.\u001b[K\n", "remote: Counting objects: 100% (60027/60027), done.\u001b[K\n", "remote: Compressing objects: 100% (37/37), done.\u001b[K\n", "remote: Total 60027 (delta 59990), reused 60024 (delta 59990), pack-reused 0\n", "Receiving objects: 100% (60027/60027), 19.94 MiB | 37.47 MiB/s, done.\n", "Resolving deltas: 100% (59990/59990), done.\n", "Checking out files: 100% (60001/60001), done.\n" ], "name": "stdout" } ] }, { "cell_type": "code", "metadata": { "id": "IpR-4a44vv3B", "colab_type": "code", "outputId": "95041075-bfd3-4051-eff4-2ba867070493", "colab": { "base_uri": "https://localhost:8080/", "height": 34 } }, "source": [ "cd .." ], "execution_count": 0, "outputs": [ { "output_type": "stream", "text": [ "/content\n" ], "name": "stdout" } ] }, { "cell_type": "code", "metadata": { "id": "RrAOru6ih_i9", "colab_type": "code", "outputId": "d506eb1d-f9b2-48f0-dca2-0f041980a0fc", "colab": { "base_uri": "https://localhost:8080/", "height": 206 } }, "source": [ "from __future__ import print_function, division\n", "import os\n", "import torch\n", "import pandas as pd\n", "from skimage import io, transform\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from torch.utils.data import Dataset, DataLoader\n", "from torchvision import transforms, utils\n", "import pdb\n", "\n", "# dataset load\n", "class MyDataset(Dataset): \n", " def __init__(self, root_dir,istraining=True,transform=None):\n", " \"\"\"\n", " Args:\n", " root_dir (string): Directory with all the images.\n", " transform (callable, optional): Optional transform to be applied\n", " on a sample.\n", " \"\"\"\n", " \n", " self.root_dir = root_dir\n", " self.istraining = istraining\n", " self.transform = transform\n", " self.classes = ('airplane', 'automobile', 'bird', 'cat',\n", " 'deer', 'dog', 'frog', 'horse', 'ship', 'truck')\n", " self.sampleAll=[] #each item is imgPtah and imgClass\n", " \n", " \n", " def readAllPath(self): \n", " for classImg in range(len(self.classes)) :\n", " classi=self.classes[classImg]\n", " classCurrentPath=self.root_dir+'/'+classi \n", " classCurrent= os.listdir(classCurrentPath) #得到文件夹下的所有文件名称\n", " # classImg=self.classes[classi]\n", " for imgs in classCurrent: #遍历文件夹\n", " imgPath=classCurrentPath+'/'+imgs \n", " self.sampleAll.append([imgPath,classImg]) \n", " #print(len(self.sampleAll))\n", " \n", " def __len__(self):\n", " if self.istraining:\n", " return 50000#len(self.landmarks_frame)\n", " else:\n", " return 10000\n", "\n", " def __getitem__(self, idx):\n", " imgP,imgC=self.sampleAll[idx]\n", " image = io.imread(imgP) \n", " # sample = {'image': image, 'landmarks': landmarks} \n", " if self.transform:\n", " image = self.transform(image)\n", "\n", " return image,imgC\n", "\n", "# data_transform = transforms.Compose([\n", "# transforms.RandomSizedCrop(224),\n", "# transforms.RandomHorizontalFlip(),\n", "# transforms.ToTensor(),\n", "# transforms.Normalize(mean=[0.485, 0.456, 0.406],\n", "# std=[0.229, 0.224, 0.225])\n", "# ])\n", " \n", "batch_size=64\n", "train_dataset = MyDataset(root_dir= './data/CIFAR-10-images/train',istraining=True,transform=transforms.ToTensor())\n", "train_loader = DataLoader(train_dataset,\n", " batch_size=batch_size, shuffle=True, num_workers=2)\n", "train_loader.dataset.readAllPath()\n", "\n", "test_dataset = MyDataset(root_dir= './data/CIFAR-10-images/test',istraining=False,transform=transforms.ToTensor())\n", "test_loader = DataLoader(test_dataset,\n", " batch_size=batch_size, shuffle=True)\n", "test_loader.dataset.readAllPath()\n", "\n", "def imshow(img):\n", " img = img / 2 + 0.5 # unnormalize\n", " npimg = img.numpy()\n", " plt.imshow(np.transpose(npimg, (1, 2, 0)))\n", " plt.show() \n", "# get some random training images\n", "dataiter = iter(trainloader)\n", "images, labels = dataiter.next()\n", "\n", "# show images\n", "imshow(torchvision.utils.make_grid(images))\n", "# print labels\n", "print(' '.join('%5s' % classes[labels[j]] for j in range(4)))\n", "\n", "\n", "for i, data in enumerate(train_loader):\n", " # get the inputs; data is a list of [inputs, labels]\n", " inputs, labels = data\n", " print(inputs)\n", " break\n", " \n" ], "execution_count": 0, "outputs": [ { "output_type": "display_data", "data": { "image/png": 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892mXarkoZPRwlsfSKHu4tmyS48XhJXAPDw+POoV/gXt4eHjUKa67CSXsfHSV\nT+XgYaoG0tYuZEWJK5tU2bwyPSMmhoYGUmHn55WvcJHOO3xMfJstq4f960iNqqmq9ElOg6p/0Vzt\nu0hNVNKY0z65H9o3O8p2nrBKkgVOtxkpiGlhZJjMP8cHqW/7DkkfI0zWxeNy3dUTpGoa0fouwqnR\n08HnV379IgCgvUHUs2iE+pEtiCo9Ok5+y7PzHO2oIhtdqt2uLiGYIqAxFFT0WIJNF2kmTDV56D5H\nVARfkBRKkcVll5yKyTijfODdGoSVucuZrbpaO4O2FiayXaTi1IxUHc9MUtRgc0xv96VNKEVWb7UZ\nIcSRqVEVO1BKMxkd4mjUsq6zyFGDeSG6wlUyCVZULltXJzFq6dhWlX72djad5GNCppaYXGtTJqjQ\neXoWkqNkdsir7M7lEU6+1iR7OOLy6i6SqMwsYlpybdpcWOAo5p/94LtB25bVqxZ8b1KZwkpM3PZ2\nCBFfa6c9c6oiprvKNNWJeZvXb3hExtLIEZOrNt4RtBXDZDadUe+D4b0UD5Jn5/cHt20NjkX42Ty2\nW4jNBl63iYr0N81k52yE9ncNMvZmNg1pgr/IpsNku+zJApvgSly/d74i76c9I+RXnlZVjsphWvul\nE/ReDC+Be3h4eNQprr8EHqZfxGpJfv2e+RlVUL//gYeCtrYOikY0LAklFFFTKRf5r0h/LqpvXKWp\nbWQpcZUrpBAX0jNTcCSLSDYu3apOKxqOkATU093C/VDV1ZlAKylJdv9BqpD9/DOvBG2v734dALBv\nHxUEyJVFov2tL30ZAJBWqWAnxzjh/DJVqzNlkVDfOEgpdG/ffmfQtnmA8mPMjokUkOUItRTnRIkq\n6W9sjCSf1aslKsylutWhpnGWPNpbSZLIq/wyJZ7TqigfQXDj+LjUNG1gF8S2NiKsimo+nDReVbly\n4ry2HW2S/nN1L+W5ifD6DJ0TYuzUNLmgZhUR2hhdWnZpTnCtUl1Dk0noWlQR5RxFWeTq8cZIf4bG\nWRpWrplxltSLahnDhsa6vov6s65b9nVLA+eoqcg1ohy1mIjqVMgc0dtO65Pa0R8cyyVoX2TOyHyk\nWaKOKnfDQPJeRDExTPDrCGPLBGFxWqI+cYEEbpTLaphfNZGUbIYEk76ZjEjgRXZcaOb6kY1QUdC8\neSYKqhgDk9xrlFtxX47ma5Ql4JByN02l6Z6Tu6V+aSFLz2uxUblwckT0mg89DAAYzsr7KRSn63Ws\nWR20jbPb6MY7dgVtv36LCN7uGPU3Y6SW7LFzlN43OSXuvx/7ONWOPzMhhOyl4CVwDw8PjzrFdZfA\nXeECo376DduVx5T0PDNPUn10wycAACAASURBVI4redbaKhKqM83VytoFkF2rlCtiln/pD+4/AAC4\neYcUjJjhQJjmRnHYBwfmaKnywD5y1+vsoPuvXS3Z2Gr8i/9n3/h/grZfPvMcACAzJ1Lllo2UbyXL\nJdiSjSLVzc3QL3+hILbWo1XO/fGxD2MpaBvd6CTN27PPPB20le+5HwCQahIpMcO2b+fqmFCBBFmW\n6GOqknbaaRsVESEjfNzlYnF2cgAIpV3hAJFsZubJtjmeFjdQl4WwtZm0mnnlzjjDLmFx1Y++XtIK\n2tp0IQy6fw+LiQNrxdVsaoq0icyk3FN5612ELzxEhT/CKgNihLWTs6NK09lLgTxzXCChKa0KQLTQ\nPEejKjdMmCSrdRGR5j66jvbPB5PU/8YxVRBgmMZ+Oq3KrGVo/rbEZJ5roPkNp+lvNK1ccjtoDIUZ\nWdtC0o1ezks7zZPTEFajoom683RAVpiP37RuadfWARUs49Zv8NA7QZuzJXc3y1jc2hreC6ti8pwP\nTtO+2HNOpNbpCj3ffS3yjG5rpbmvDNNz0KZq6RnObplWnFuSc7JEVZ6bBL8/TrMmelKVh7uF93o+\nLmPfe4LcVvMjsn6vnCCJ2+VpaVS5Vqpc2jDdJTzftlupIM2Z595DCdwYs8YY84Ix5qAx5oAx5ne4\nvc0Y84wx5hj/bb3UtTw8PDw83jusxIRSAfAvrLXbAewC8GVjzHYAXwHwnLV2E4Dn+N8eHh4eHtcI\nKympNgxgmD/PG2MOAVgN4DFQrUwAeArAiwD+5eV2IMQkREeHRFzdcw+lg1zVJ1Wwh8dJdTw/TGTM\nmXNSzbmRc0Z0dgiJEuLIMv0LdewIpZJ84VkmdD4rR+MJusbZc0KubeCK8iPKlFNgd8Z975IZZnJK\n3NUmx0m1+873pMZzmdXEZhVxNTZN39m8jYpDbNwkqmaNzROO6ASAzaxml4tCjl6IiBE1MclpQs+d\nOBi0vTRNqt19Dz4q92KSbmyKiKjRGamb6MxGrto2ADTH2WVQEYANTaRGJji6r0Hla4kkSNWMJcVE\nFBmlLZdbvS5om8+QeprlmoDZrKjDaSadujsVSbWayKOcKpiYy3MqVXYV7GqT/dTTTfN3fF7WqmDF\nRHUh+rmQQllFz6aYcGtSeVlthd3T4rTvqsr0E4nQ+SGVC6WFVfTNVkwAzUyWJY7S+hTn5Z5VJrJt\nSsx0cR5zclyIzWKIxjLbSfu09qq4Lo6ep+dk9ZbbpG9skqnUtMmR9qmVRMlyjPeJdjF0dWhv3iIk\n90HJuEpQUzzDa3z3nXcFbc2c/jZRlHVZz84JNY4Ybi7JWMbZHS8/qqorxOm56uoTc0aSn9cGJsNj\nqijE+Cly2W1UbqnNTErmVORtnNPqHmU35EmdajZCz2F5WMwlb50h89zR3KtBmzU0ls3dtD/u/8h9\nwbEjT5N5s2GVEKE9PeKCuFJcFolpjOkHcBuA1wF088sdAEYAdC/xnSeMMXuMMXtyyi/Uw8PDw+Pq\nsGIS0xjTAOAHAP65tXZO/xpba60xZtFAfmvt1wF8HQB6e3svOscl2+9dLb9EjZxbYnJSXJQcsbmO\ng3B0IM/oKP30T07Ir3WKA0yaFCn56ivkyvfWm0REJlSGu089/psAgPOKRGriDGeDJ08FbRWWqJJc\nBT6Rll/+tUwQPv6Zvxe0DQ9T37qUg/+aPpJaujuJwGhuEqn1jT1vUj/OCpHR3UwSQnWZDHrG6lwh\nNFcNiqQqzRAZc3bwUNDWsIY0nJksSXMTar6beXyruyQPR4aDK44fl2u0sgQ+yS5prWqcm2+hohsN\nLSK9zM5yDhm1XVxZvQnWZnTGungLSVgptVYl1kROnJIAqBl2+dy2lbSalHKJTCepjxHVVsmppBUX\noBimMZRUUYhwiaSphogQsgM99Aw09/QDAIzKvhdzWsqcSH/J49TvnhGZ59mTJE1WOGAklpDMlDVX\n82xBThbObqmI4WyN+unyhzQkRPsIcxBaKSIaWprHYFWBAbe1DEviEaXsVdldUrsAxvj5b1SFRICF\nc6qz753h3EV3bJVoNMMFR6pFHaTFQUMuYK4qx2yMi3ZElLswk8WluHR4IkN7Pc65g8JN0u+udfTM\n5cdPBG01Q/tzaEJI7i1xeiYb+F00o0TdDTtI84oY2U+r+Pbv7D8ctH30IdJ2E0nOc9OsMiby2s5P\nyvtmfk40s5ViRRK4MSYKenl/y1r7Q24eNcas4uOrABX+5eHh4eHxvmMlXigGwJ8DOGSt/UN16GkA\nX+DPXwDw4wu/6+Hh4eHx/mElJpT7AHwewLvGmH3c9n8B+LcAvmuM+SKA0wA+cyUdcEpcW6vkBHAE\n2vCoqBc5ztO4YwdXg+8Qf+Y01xHMzav8Gi53ioqKnBglks6ljn31Vy8Gxz76MKk7RZVj4uwQmfi3\nbpFcCk3sbxxLkEpaVuptLkf3uvOOu4O2RIrMHxWlJrootjFWpeMJXZiA1OHWZjEZNLOZwi6TbrKi\n1X32C06oWpRxrqY+NSmK0niVjo+MUpuuRP6/3P9hAMDHH3okaPvlz/8GAHBGmS6m+EtJQ9dvUial\nAquELauE6BqbJDNJWUVFRrlqfZR9hUMVJVewP26tJvPsrjF4StTgETajuYjQiNranc1kUmhMialq\nel5IwIuQJDXbqb4AEOMo2LlhSVNrmNCsFTj1qSqQYDkHTjUs10jO0xhas2L+iFo6L8HpfrV/etWZ\nNVQ+lQRH9amyoQG5fXKKnpeoKhBS4jXOZsSUU2MCL614yhT3PcLrElbRxy4AOK/iLKad+aOqXyEX\nmKVUzU0U6Px335CI5J5Geoa2qHqWIY64rWU4urpVnvMKDzo/JKaOKpuNzo5rcwbNUTub7nIqRgKz\nRALP1GSPjWZoL9gmMf/t+PinAAATZynHUHHkXHBswx23AwDOqZTWG7eSOfLYoOyPMNf/PHCKnCY6\nD8v1z07QHt52261BWyp++QUdVuKF8gqWzvzz0cu+o4eHh4fHe4LrHonpkEiKSOES0m9Y3x+0/eSn\n5HaT5jwVa7gMEgCsYvfB6ZBIKrNc3Xp6QsjOIpfI2rCeSi6Nj4nb3LtvvwUAaO0WafHcEP1ylueF\nXHC/22MT9N2zQ/LLnC+QhNW3ZiBo+9AHyXXo1KkjQdvUBElKWc549uKh/cGxt98iErO3RySPzk4i\n8oLiBougUZF8aVfSraL8uFjiaGsSTQftJJkOjVPEWDUvEmRLilzYGpqEpJqfpzlNKmk4amg9ggyM\nqszUqbdI2ioekmi6oqW17du8LWhr5qyCBRb1dFENwwU08soVbCZH61hQ/ShzlO3gCdIOulqFyHOJ\nHRsSoh1MhpaOxezfRBqXUVpC9gRJ+EOTotFFOklicjlTqkp7q4SYPFSPWIWr1+fyIv1V1pE7ZZFJ\nuNSseGrNM7lbmhbJNs7ubCFVyq+hkfZ/77ZbAAA/3y3FPSZGaG3XlKTf5UPkWtihNIa/84mPAwDW\nMWlcUtpYhffd3gPvBm17OWP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" ] }, "metadata": { "tags": [] } }, { "output_type": "stream", "text": [ " car cat truck truck\n", "tensor([4, 0, 6, 6, 4, 7, 7, 3, 8, 7, 8, 1, 5, 4, 7, 5, 4, 2, 2, 1, 8, 5, 2, 1,\n", " 7, 2, 8, 1, 1, 3, 3, 8, 1, 4, 5, 2, 6, 7, 4, 7, 8, 5, 0, 4, 1, 3, 4, 4,\n", " 9, 3, 4, 9, 0, 3, 7, 2, 5, 7, 5, 9, 6, 9, 0, 1])\n" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "id": "G_THhIUJ9yAX", "colab_type": "text" }, "source": [ "#Complex CNN\n", "Transfer learning:\n", "https://cs231n.github.io/transfer-learning/\n" ] }, { "cell_type": "code", "metadata": { "id": "F64Gld2Z3odX", "colab_type": "code", "colab": { "base_uri": "https://localhost:8080/", "height": 34 }, "outputId": "a67841a6-3ea0-429f-f5af-c07d82551046" }, "source": [ "cd ./data" ], "execution_count": 15, "outputs": [ { "output_type": "stream", "text": [ "/content/data\n" ], "name": "stdout" } ] }, { "cell_type": "code", "metadata": { "id": "bjmgFcmEzJQd", "colab_type": "code", "colab": { "base_uri": "https://localhost:8080/", "height": 204 }, "outputId": "0beba7dd-27cf-42d3-da0a-707e4db72c3d" }, "source": [ "!wget https://download.pytorch.org/tutorial/hymenoptera_data.zip" ], "execution_count": 14, "outputs": [ { "output_type": "stream", "text": [ "--2019-11-14 03:01:22-- https://download.pytorch.org/tutorial/hymenoptera_data.zip\n", "Resolving download.pytorch.org (download.pytorch.org)... 99.84.74.83, 99.84.74.105, 99.84.74.5, ...\n", "Connecting to download.pytorch.org (download.pytorch.org)|99.84.74.83|:443... connected.\n", "HTTP request sent, awaiting response... 200 OK\n", "Length: 47286322 (45M) [application/zip]\n", "Saving to: ‘hymenoptera_data.zip’\n", "\n", "hymenoptera_data.zi 100%[===================>] 45.10M 40.3MB/s in 1.1s \n", "\n", "2019-11-14 03:01:24 (40.3 MB/s) - ‘hymenoptera_data.zip’ saved [47286322/47286322]\n", "\n" ], "name": "stdout" } ] }, { "cell_type": "code", "metadata": { "id": "T-9NsHtk3tOI", "colab_type": "code", "colab": {} }, "source": [ "!unzip hymenoptera_data.zip" ], "execution_count": 0, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "vRgeWh2h344I", "colab_type": "code", "colab": { "base_uri": "https://localhost:8080/", "height": 34 }, "outputId": "24cb1d04-e8dd-42bd-8324-5917ef17cd0d" }, "source": [ "cd .." ], "execution_count": 18, "outputs": [ { "output_type": "stream", "text": [ "/content\n" ], "name": "stdout" } ] }, { "cell_type": "code", "metadata": { "id": "3IcI3Bum95gD", "colab_type": "code", "colab": {} }, "source": [ "\n", "from __future__ import print_function, division\n", "\n", "import torch\n", "import torch.nn as nn\n", "import torch.optim as optim\n", "from torch.optim import lr_scheduler\n", "import numpy as np\n", "import torchvision\n", "from torchvision import datasets, models, transforms\n", "import matplotlib.pyplot as plt\n", "import time\n", "import os\n", "import copy\n", "\n", "plt.ion() # interactive mode" ], "execution_count": 0, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "LuMWOVQ23_iH", "colab_type": "code", "colab": {} }, "source": [ "# Just normalization for validation\n", "data_transforms = {\n", " 'train': transforms.Compose([\n", " transforms.RandomResizedCrop(224),\n", " transforms.RandomHorizontalFlip(),\n", " transforms.ToTensor(),\n", " transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])\n", " ]),\n", " 'val': transforms.Compose([\n", " transforms.Resize(256),\n", " transforms.CenterCrop(224),\n", " transforms.ToTensor(),\n", " transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])\n", " ]),\n", "}\n", "\n", "data_dir = 'data/hymenoptera_data'\n", "image_datasets = {x: datasets.ImageFolder(os.path.join(data_dir, x),\n", " data_transforms[x])\n", " for x in ['train', 'val']}\n", "dataloaders = {x: torch.utils.data.DataLoader(image_datasets[x], batch_size=4,\n", " shuffle=True, num_workers=4)\n", " for x in ['train', 'val']}\n", "dataset_sizes = {x: len(image_datasets[x]) for x in ['train', 'val']}\n", "class_names = image_datasets['train'].classes\n", "\n", "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n" ], "execution_count": 0, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "mssuVcRt4Caz", "colab_type": "code", "colab": { "base_uri": "https://localhost:8080/", "height": 148 }, "outputId": "c37893df-d508-44f6-a0d2-2e93c3ae896b" }, "source": [ "def imshow(inp, title=None):\n", " \"\"\"Imshow for Tensor.\"\"\"\n", " inp = inp.numpy().transpose((1, 2, 0))\n", " mean = np.array([0.485, 0.456, 0.406])\n", " std = np.array([0.229, 0.224, 0.225])\n", " inp = std * inp + mean\n", " inp = np.clip(inp, 0, 1)\n", " plt.imshow(inp)\n", " if title is not None:\n", " plt.title(title)\n", " plt.pause(0.001) # pause a bit so that plots are updated\n", "\n", "\n", "# Get a batch of training data\n", "inputs, classes = next(iter(dataloaders['train']))\n", "\n", "# Make a grid from batch\n", "out = torchvision.utils.make_grid(inputs)\n", "\n", "imshow(out, title=[class_names[x] for x in classes])" ], "execution_count": 22, "outputs": [ { "output_type": "display_data", "data": { "image/png": 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0VLGkpIYiUqwVaC404i/71q+1PRYea1B4JOK+bX+w8nEiBKxJyQZDBkdWWTk2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" ] }, "metadata": { "tags": [] } } ] }, { "cell_type": "code", "metadata": { "id": "n7Z27vqs4nHQ", "colab_type": "code", "colab": { "base_uri": "https://localhost:8080/", "height": 1000 }, "outputId": "8d004e2a-e42b-4f83-a4bf-14cfa73d30b9" }, "source": [ "from __future__ import print_function, division\n", "\n", "import torch\n", "import torch.nn as nn\n", "import torch.optim as optim\n", "from torch.optim import lr_scheduler\n", "import numpy as np\n", "import torchvision\n", "from torchvision import datasets, models, transforms\n", "import matplotlib.pyplot as plt\n", "import time\n", "import os\n", "import copy\n", "\n", "plt.ion() # interactive mode\n", "\n", "######################################################################\n", "# Load Data\n", "# ---------\n", "#\n", "# We will use torchvision and torch.utils.data packages for loading the\n", "# data.\n", "#\n", "# The problem we're going to solve today is to train a model to classify\n", "# **ants** and **bees**. We have about 120 training images each for ants and bees.\n", "# There are 75 validation images for each class. Usually, this is a very\n", "# small dataset to generalize upon, if trained from scratch. Since we\n", "# are using transfer learning, we should be able to generalize reasonably\n", "# well.\n", "#\n", "# This dataset is a very small subset of imagenet.\n", "#\n", "# .. Note ::\n", "# Download the data from\n", "# `here `_\n", "# and extract it to the current directory.\n", "\n", "# Data augmentation and normalization for training\n", "# Just normalization for validation\n", "data_transforms = {\n", " 'train': transforms.Compose([\n", " transforms.RandomResizedCrop(224),\n", " transforms.RandomHorizontalFlip(),\n", " transforms.ToTensor(),\n", " transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])\n", " ]),\n", " 'val': transforms.Compose([\n", " transforms.Resize(256),\n", " transforms.CenterCrop(224),\n", " transforms.ToTensor(),\n", " transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])\n", " ]),\n", "}\n", "\n", "data_dir = 'data/hymenoptera_data'\n", "image_datasets = {x: datasets.ImageFolder(os.path.join(data_dir, x),\n", " data_transforms[x])\n", " for x in ['train', 'val']}\n", "dataloaders = {x: torch.utils.data.DataLoader(image_datasets[x], batch_size=4,\n", " shuffle=True, num_workers=4)\n", " for x in ['train', 'val']}\n", "dataset_sizes = {x: len(image_datasets[x]) for x in ['train', 'val']}\n", "class_names = image_datasets['train'].classes\n", "\n", "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n", "\n", "######################################################################\n", "# Visualize a few images\n", "# ^^^^^^^^^^^^^^^^^^^^^^\n", "# Let's visualize a few training images so as to understand the data\n", "# augmentations.\n", "\n", "def imshow(inp, title=None):\n", " \"\"\"Imshow for Tensor.\"\"\"\n", " inp = inp.numpy().transpose((1, 2, 0))\n", " mean = np.array([0.485, 0.456, 0.406])\n", " std = np.array([0.229, 0.224, 0.225])\n", " inp = std * inp + mean\n", " inp = np.clip(inp, 0, 1)\n", " plt.imshow(inp)\n", " if title is not None:\n", " plt.title(title)\n", " plt.pause(0.001) # pause a bit so that plots are updated\n", "\n", "\n", "# Get a batch of training data\n", "inputs, classes = next(iter(dataloaders['train']))\n", "\n", "# Make a grid from batch\n", "out = torchvision.utils.make_grid(inputs)\n", "\n", "imshow(out, title=[class_names[x] for x in classes])\n", "\n", "\n", "######################################################################\n", "# Training the model\n", "# ------------------\n", "#\n", "# Now, let's write a general function to train a model. Here, we will\n", "# illustrate:\n", "#\n", "# - Scheduling the learning rate\n", "# - Saving the best model\n", "#\n", "# In the following, parameter ``scheduler`` is an LR scheduler object from\n", "# ``torch.optim.lr_scheduler``.\n", "\n", "\n", "def train_model(model, criterion, optimizer, scheduler, num_epochs=25):\n", " since = time.time()\n", "\n", " best_model_wts = copy.deepcopy(model.state_dict())\n", " best_acc = 0.0\n", "\n", " for epoch in range(num_epochs):\n", " print('Epoch {}/{}'.format(epoch, num_epochs - 1))\n", " print('-' * 10)\n", "\n", " # Each epoch has a training and validation phase\n", " for phase in ['train', 'val']:\n", " if phase == 'train':\n", " model.train() # Set model to training mode\n", " else:\n", " model.eval() # Set model to evaluate mode\n", "\n", " running_loss = 0.0\n", " running_corrects = 0\n", "\n", " # Iterate over data.\n", " for inputs, labels in dataloaders[phase]:\n", " inputs = inputs.to(device)\n", " labels = labels.to(device)\n", "\n", " # zero the parameter gradients\n", " optimizer.zero_grad()\n", "\n", " # forward\n", " # track history if only in train\n", " with torch.set_grad_enabled(phase == 'train'):\n", " outputs = model(inputs)\n", " _, preds = torch.max(outputs, 1)\n", " loss = criterion(outputs, labels)\n", "\n", " # backward + optimize only if in training phase\n", " if phase == 'train':\n", " loss.backward()\n", " optimizer.step()\n", "\n", " # statistics\n", " running_loss += loss.item() * inputs.size(0)\n", " running_corrects += torch.sum(preds == labels.data)\n", " if phase == 'train':\n", " scheduler.step()\n", "\n", " epoch_loss = running_loss / dataset_sizes[phase]\n", " epoch_acc = running_corrects.double() / dataset_sizes[phase]\n", "\n", " print('{} Loss: {:.4f} Acc: {:.4f}'.format(\n", " phase, epoch_loss, epoch_acc))\n", "\n", " # deep copy the model\n", " if phase == 'val' and epoch_acc > best_acc:\n", " best_acc = epoch_acc\n", " best_model_wts = copy.deepcopy(model.state_dict())\n", "\n", " print()\n", "\n", " time_elapsed = time.time() - since\n", " print('Training complete in {:.0f}m {:.0f}s'.format(\n", " time_elapsed // 60, time_elapsed % 60))\n", " print('Best val Acc: {:4f}'.format(best_acc))\n", "\n", " # load best model weights\n", " model.load_state_dict(best_model_wts)\n", " return model\n", "\n", "\n", "######################################################################\n", "# Visualizing the model predictions\n", "# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", "#\n", "# Generic function to display predictions for a few images\n", "#\n", "\n", "def visualize_model(model, num_images=6):\n", " was_training = model.training\n", " model.eval()\n", " images_so_far = 0\n", " fig = plt.figure()\n", "\n", " with torch.no_grad():\n", " for i, (inputs, labels) in enumerate(dataloaders['val']):\n", " inputs = inputs.to(device)\n", " labels = labels.to(device)\n", "\n", " outputs = model(inputs)\n", " _, preds = torch.max(outputs, 1)\n", "\n", " for j in range(inputs.size()[0]):\n", " images_so_far += 1\n", " ax = plt.subplot(num_images//2, 2, images_so_far)\n", " ax.axis('off')\n", " ax.set_title('predicted: {}'.format(class_names[preds[j]]))\n", " imshow(inputs.cpu().data[j])\n", "\n", " if images_so_far == num_images:\n", " model.train(mode=was_training)\n", " return\n", " model.train(mode=was_training)\n", "\n", "######################################################################\n", "# Finetuning the convnet\n", "# ----------------------\n", "#\n", "# Load a pretrained model and reset final fully connected layer.\n", "#\n", "\n", "model_ft = models.resnet18(pretrained=True)\n", "num_ftrs = model_ft.fc.in_features\n", "# Here the size of each output sample is set to 2.\n", "# Alternatively, it can be generalized to nn.Linear(num_ftrs, len(class_names)).\n", "model_ft.fc = nn.Linear(num_ftrs, 2)\n", "\n", "model_ft = model_ft.to(device)\n", "\n", "criterion = nn.CrossEntropyLoss()\n", "\n", "# Observe that all parameters are being optimized\n", "optimizer_ft = optim.SGD(model_ft.parameters(), lr=0.001, momentum=0.9)\n", "\n", "# Decay LR by a factor of 0.1 every 7 epochs\n", "exp_lr_scheduler = lr_scheduler.StepLR(optimizer_ft, step_size=7, gamma=0.1)\n", "\n", "######################################################################\n", "# Train and evaluate\n", "# ^^^^^^^^^^^^^^^^^^\n", "#\n", "# It should take around 15-25 min on CPU. On GPU though, it takes less than a\n", "# minute.\n", "#\n", "\n", "model_ft = train_model(model_ft, criterion, optimizer_ft, exp_lr_scheduler,\n", " num_epochs=25)\n", "\n", "######################################################################\n", "#\n", "\n", "visualize_model(model_ft)\n", "\n", "\n", "######################################################################\n", "# ConvNet as fixed feature extractor\n", "# ----------------------------------\n", "#\n", "# Here, we need to freeze all the network except the final layer. We need\n", "# to set ``requires_grad == False`` to freeze the parameters so that the\n", "# gradients are not computed in ``backward()``.\n", "#\n", "# You can read more about this in the documentation\n", "# `here `__.\n", "#\n", "\n", "model_conv = torchvision.models.resnet18(pretrained=True)\n", "for param in model_conv.parameters():\n", " param.requires_grad = False\n", "\n", "# Parameters of newly constructed modules have requires_grad=True by default\n", "num_ftrs = model_conv.fc.in_features\n", "model_conv.fc = nn.Linear(num_ftrs, 2)\n", "\n", "model_conv = model_conv.to(device)\n", "\n", "criterion = nn.CrossEntropyLoss()\n", "\n", "# Observe that only parameters of final layer are being optimized as\n", "# opposed to before.\n", "optimizer_conv = optim.SGD(model_conv.fc.parameters(), lr=0.001, momentum=0.9)\n", "\n", "# Decay LR by a factor of 0.1 every 7 epochs\n", "exp_lr_scheduler = lr_scheduler.StepLR(optimizer_conv, step_size=7, gamma=0.1)\n", "\n", "\n", "######################################################################\n", "# Train and evaluate\n", "# ^^^^^^^^^^^^^^^^^^\n", "#\n", "# On CPU this will take about half the time compared to previous scenario.\n", "# This is expected as gradients don't need to be computed for most of the\n", "# network. However, forward does need to be computed.\n", "#\n", "\n", "model_conv = train_model(model_conv, criterion, optimizer_conv,\n", " exp_lr_scheduler, num_epochs=25)\n", "\n", "######################################################################\n", "#\n", "\n", "visualize_model(model_conv)\n", "\n", "plt.ioff()\n", "plt.show()" ], "execution_count": 28, "outputs": [ { "output_type": "display_data", "data": { "image/png": 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IMb2MOLpgtaeRSAHuA7v3Yxu3sbAA1CgfWl1hraIN1sJGtGs9UCpKUytvfN+v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gg9aVBKpdaM9wQREiFSKUQX6EEMRJ/2fytSarvMNJpA1i/QvVFic3hPFII3ft/fxe3s0T98nMP/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" ] }, "metadata": { "tags": [] } }, { "output_type": "stream", "text": [ "Downloading: \"https://download.pytorch.org/models/resnet18-5c106cde.pth\" to /root/.cache/torch/checkpoints/resnet18-5c106cde.pth\n", "100%|██████████| 44.7M/44.7M [00:00<00:00, 70.2MB/s]\n" ], "name": "stderr" }, { "output_type": "stream", "text": [ "Epoch 0/24\n", "----------\n", "train Loss: 0.5122 Acc: 0.7582\n", "val Loss: 0.1954 Acc: 0.9150\n", "\n", "Epoch 1/24\n", "----------\n", "train Loss: 0.5054 Acc: 0.8033\n", "val Loss: 0.2249 Acc: 0.8889\n", "\n", "Epoch 2/24\n", "----------\n", "train Loss: 0.4297 Acc: 0.8320\n", "val Loss: 0.2097 Acc: 0.9150\n", "\n", "Epoch 3/24\n", "----------\n", "train Loss: 0.5679 Acc: 0.7869\n", "val Loss: 0.2405 Acc: 0.9216\n", "\n", "Epoch 4/24\n", "----------\n", "train Loss: 0.6131 Acc: 0.7623\n", "val Loss: 0.5041 Acc: 0.8497\n", "\n", "Epoch 5/24\n", "----------\n", "train Loss: 0.4580 Acc: 0.8525\n", "val Loss: 0.2435 Acc: 0.9085\n", "\n", "Epoch 6/24\n", "----------\n", "train Loss: 0.5001 Acc: 0.8361\n", "val Loss: 0.5966 Acc: 0.7843\n", "\n", "Epoch 7/24\n", "----------\n", "train Loss: 0.4210 Acc: 0.7951\n", "val Loss: 0.2872 Acc: 0.9020\n", "\n", "Epoch 8/24\n", "----------\n", "train Loss: 0.4167 Acc: 0.7992\n", "val Loss: 0.2368 Acc: 0.9216\n", "\n", "Epoch 9/24\n", "----------\n", "train Loss: 0.3458 Acc: 0.8443\n", "val Loss: 0.2380 Acc: 0.9150\n", "\n", "Epoch 10/24\n", "----------\n", "train Loss: 0.3176 Acc: 0.8607\n", "val Loss: 0.2514 Acc: 0.9085\n", "\n", "Epoch 11/24\n", "----------\n", "train Loss: 0.3037 Acc: 0.8811\n", "val Loss: 0.2125 Acc: 0.9216\n", "\n", "Epoch 12/24\n", "----------\n", "train Loss: 0.2358 Acc: 0.9221\n", "val Loss: 0.2281 Acc: 0.9150\n", "\n", "Epoch 13/24\n", "----------\n", "train Loss: 0.3110 Acc: 0.8730\n", "val Loss: 0.1923 Acc: 0.9346\n", "\n", "Epoch 14/24\n", "----------\n", "train Loss: 0.2603 Acc: 0.8852\n", "val Loss: 0.1858 Acc: 0.9346\n", "\n", "Epoch 15/24\n", "----------\n", "train Loss: 0.2726 Acc: 0.8893\n", "val Loss: 0.2044 Acc: 0.9150\n", "\n", "Epoch 16/24\n", "----------\n", "train Loss: 0.3231 Acc: 0.8484\n", "val Loss: 0.1893 Acc: 0.9346\n", "\n", "Epoch 17/24\n", "----------\n", "train Loss: 0.2949 Acc: 0.8607\n", "val Loss: 0.1856 Acc: 0.9216\n", "\n", "Epoch 18/24\n", "----------\n", "train Loss: 0.2079 Acc: 0.9303\n", "val Loss: 0.1814 Acc: 0.9281\n", "\n", "Epoch 19/24\n", "----------\n", "train Loss: 0.2354 Acc: 0.8893\n", "val Loss: 0.1920 Acc: 0.9150\n", "\n", "Epoch 20/24\n", "----------\n", "train Loss: 0.3548 Acc: 0.8443\n", "val Loss: 0.2372 Acc: 0.9020\n", "\n", "Epoch 21/24\n", "----------\n", "train Loss: 0.2831 Acc: 0.8566\n", "val Loss: 0.1872 Acc: 0.9150\n", "\n", "Epoch 22/24\n", "----------\n", "train Loss: 0.2249 Acc: 0.9139\n", "val Loss: 0.2073 Acc: 0.9150\n", "\n", "Epoch 23/24\n", "----------\n", "train Loss: 0.2353 Acc: 0.9057\n", "val Loss: 0.1953 Acc: 0.9346\n", "\n", "Epoch 24/24\n", "----------\n", "train Loss: 0.3029 Acc: 0.8770\n", "val Loss: 0.2102 Acc: 0.9150\n", "\n", "Training complete in 35m 35s\n", "Best val Acc: 0.934641\n" ], "name": "stdout" }, { "output_type": "display_data", "data": { "image/png": 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XOfj6d6AbkKU6zdYSuVwJU+qkccrywkmy1CDJImy7QKkg2HzZbd/9vjspXRQp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\nBZfM74JtEIU9RJxx5zt+gfN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xlEc8/Y4n+eVf+jKDtEu/0yWyRzQjj+tozjywxOj0mIW1NfLgSRRMlWF5MUeX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\nMuNmlLex4BrIEk1dWYxqmUr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iQlEjEYndkXt6cy27AEubGYfLxpLp41k8awB//uZHJEIIKrUcW4uBP288yKn0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\nKrFh/gwYOpbQumsM69Ofb7f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" ] }, "metadata": { "tags": [] } }, { "output_type": "stream", "text": [ "Epoch 0/24\n", "----------\n", "train Loss: 0.6579 Acc: 0.6803\n", "val Loss: 0.7584 Acc: 0.6797\n", "\n", "Epoch 1/24\n", "----------\n", "train Loss: 0.5425 Acc: 0.7295\n", "val Loss: 0.2052 Acc: 0.9412\n", "\n", "Epoch 2/24\n", "----------\n", "train Loss: 0.5104 Acc: 0.7828\n", "val Loss: 0.4443 Acc: 0.8170\n", "\n", "Epoch 3/24\n", "----------\n", "train Loss: 0.5286 Acc: 0.7787\n", "val Loss: 0.2297 Acc: 0.9412\n", "\n", "Epoch 4/24\n", "----------\n", "train Loss: 0.4917 Acc: 0.7910\n", "val Loss: 0.5569 Acc: 0.8170\n", "\n", "Epoch 5/24\n", "----------\n", "train Loss: 0.3740 Acc: 0.8402\n", "val Loss: 0.3090 Acc: 0.8954\n", "\n", "Epoch 6/24\n", "----------\n", "train Loss: 0.6099 Acc: 0.7295\n", "val Loss: 0.3015 Acc: 0.9085\n", "\n", "Epoch 7/24\n", "----------\n", "train Loss: 0.4173 Acc: 0.8238\n", "val Loss: 0.2198 Acc: 0.9412\n", "\n", "Epoch 8/24\n", "----------\n", "train Loss: 0.3585 Acc: 0.8443\n", "val Loss: 0.2287 Acc: 0.9412\n", "\n", "Epoch 9/24\n", "----------\n", "train Loss: 0.2966 Acc: 0.8648\n", "val Loss: 0.2144 Acc: 0.9477\n", "\n", "Epoch 10/24\n", "----------\n", "train Loss: 0.3279 Acc: 0.8402\n", "val Loss: 0.2041 Acc: 0.9412\n", "\n", "Epoch 11/24\n", "----------\n", "train Loss: 0.3619 Acc: 0.8443\n", "val Loss: 0.2279 Acc: 0.9412\n", "\n", "Epoch 12/24\n", "----------\n", "train Loss: 0.3983 Acc: 0.8115\n", "val Loss: 0.2129 Acc: 0.9477\n", "\n", "Epoch 13/24\n", "----------\n", "train Loss: 0.3158 Acc: 0.8443\n", "val Loss: 0.2287 Acc: 0.9412\n", "\n", "Epoch 14/24\n", "----------\n", "train Loss: 0.2961 Acc: 0.8811\n", "val Loss: 0.2293 Acc: 0.9477\n", "\n", "Epoch 15/24\n", "----------\n", "train Loss: 0.3545 Acc: 0.8443\n", "val Loss: 0.2155 Acc: 0.9477\n", "\n", "Epoch 16/24\n", "----------\n", "train Loss: 0.2797 Acc: 0.8689\n", "val Loss: 0.2172 Acc: 0.9412\n", "\n", "Epoch 17/24\n", "----------\n", "train Loss: 0.3180 Acc: 0.8648\n", "val Loss: 0.2159 Acc: 0.9477\n", "\n", "Epoch 18/24\n", "----------\n", "train Loss: 0.2510 Acc: 0.8975\n", "val Loss: 0.2023 Acc: 0.9412\n", "\n", "Epoch 19/24\n", "----------\n", "train Loss: 0.3350 Acc: 0.8566\n", "val Loss: 0.2235 Acc: 0.9346\n", "\n", "Epoch 20/24\n", "----------\n", "train Loss: 0.3051 Acc: 0.8811\n", "val Loss: 0.2094 Acc: 0.9477\n", "\n", "Epoch 21/24\n", "----------\n", "train Loss: 0.2806 Acc: 0.8730\n", "val Loss: 0.2202 Acc: 0.9412\n", "\n", "Epoch 22/24\n", "----------\n", "train Loss: 0.3528 Acc: 0.8402\n", "val Loss: 0.2342 Acc: 0.9346\n", "\n", "Epoch 23/24\n", "----------\n", "train Loss: 0.3103 Acc: 0.8689\n", "val Loss: 0.2584 Acc: 0.9346\n", "\n", "Epoch 24/24\n", "----------\n", "train Loss: 0.3632 Acc: 0.8115\n", "val Loss: 0.2025 Acc: 0.9412\n", "\n", "Training complete in 15m 21s\n", "Best val Acc: 0.947712\n" ], "name": "stdout" }, { "output_type": "display_data", "data": { "image/png": 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kaQFGEQUBtoSSCmMNeEtV5QghGWQZ3UEfXxmG6kP01YDuoMKUBq8Uua6IIk0kJNoIjLRU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\nubDIYcX8I21C1aK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hzAyr58/QP3WZQXeJdPsuKuUGjXKDPXNzOH7AE5cus77eYueBKWbGp1iuncVg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\n4vshvhegkz46XSXRPdprZ6m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hkpI0PA3lOOeW0zyzCoeiogNJ0YGgFxBKVmCyajjdHvofP0wsmeaCdeew6Q8v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\npYgIIIJInlwhT7qQRyimMSS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