task 35 - 副本.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
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    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torchvision\n",
    "import torchvision.transforms as transforms\n",
    "from torchvision.datasets import ImageFolder\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import datetime\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "import torch.utils.model_zoo as model_zoo\n",
    "import torchvision.models as models\n",
    "%matplotlib inline\n",
    "\n",
    "transform = transforms.Compose(\n",
    "    [transforms.Resize(224,224),\n",
    "     transforms.ToTensor(), \n",
    "     transforms.Normalize((0.5,0.5,0.5),(0.5,0.5,0.5))\n",
    "\n",
    "     ])\n",
    "\n",
    "test_transform = transforms.Compose(\n",
    "    [transforms.Resize(224,224),\n",
    "     transforms.ToTensor(), \n",
    "     transforms.Normalize((0.5,0.5,0.5),(0.5,0.5,0.5))\n",
    "     ])\n",
    "\n",
    "TrainSet = ImageFolder('./release/release/train', transform=transform) \n",
    "trainloader = torch.utils.data.DataLoader(TrainSet, batch_size=4,\n",
    "                                          shuffle=True, num_workers=2)\n",
    "\n",
    "TestSet = ImageFolder('./release/release/val', transform=test_transform)\n",
    "testloader = torch.utils.data.DataLoader(TestSet , batch_size=4,\n",
    "                                         shuffle=True, num_workers=2)\n",
    "\n",
    "classes =('apple','avocado','banana','blueberry','cherry','dragonfruit','grape','kiwifruit','lemon','orange','papaya',\n",
    "          'pineapple','plum','pear','peach','pomegranate','rockmelon','strawberry')\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_val_size = 2417\n",
    "train_size = 2000\n",
    "val_size = 417\n",
    "\n",
    "all_index = np.random.permutation(train_size+val_size)\n",
    "train_index = all_index[:train_size].tolist()\n",
    "train_sampler = torch.utils.data.SubsetRandomSampler(train_index)\n",
    "val_index = all_index[train_size:train_size+val_size].tolist()\n",
    "val_sampler = torch.utils.data.SubsetRandomSampler(val_index)\n",
    "valset =TrainSet\n",
    "valloader = torch.utils.data.DataLoader(TrainSet,batch_size=4,sampler=val_sampler)\n",
    "train_loader = torch.utils.data.DataLoader(TrainSet,batch_size=4,sampler=train_sampler)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "VGG16(\n",
       "  (features): Sequential(\n",
       "    (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (1): ReLU(inplace=True)\n",
       "    (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (3): ReLU(inplace=True)\n",
       "    (4): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
       "    (5): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (6): ReLU(inplace=True)\n",
       "    (7): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (8): ReLU(inplace=True)\n",
       "    (9): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
       "    (10): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (11): ReLU(inplace=True)\n",
       "    (12): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (13): ReLU(inplace=True)\n",
       "    (14): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (15): ReLU(inplace=True)\n",
       "    (16): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
       "    (17): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (18): ReLU(inplace=True)\n",
       "    (19): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (20): ReLU(inplace=True)\n",
       "    (21): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (22): ReLU(inplace=True)\n",
       "    (23): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
       "    (24): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (25): ReLU(inplace=True)\n",
       "    (26): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (27): ReLU(inplace=True)\n",
       "    (28): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (29): ReLU(inplace=True)\n",
       "    (30): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
       "  )\n",
       "  (classifier): Sequential(\n",
       "    (0): Linear(in_features=25088, out_features=4096, bias=True)\n",
       "    (1): ReLU(inplace=True)\n",
       "    (2): Dropout(p=0.5, inplace=False)\n",
       "    (3): Linear(in_features=4096, out_features=4096, bias=True)\n",
       "    (4): ReLU(inplace=True)\n",
       "    (5): Dropout(p=0.5, inplace=False)\n",
       "  )\n",
       "  (classifier1): Sequential(\n",
       "    (0): Linear(in_features=4096, out_features=18, bias=True)\n",
       "  )\n",
       ")"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "class VGG16(nn.Module):\n",
    "\n",
    "    def __init__(self, num_classes=18):\n",
    "        super(VGG16, self).__init__()\n",
    "        self.features = nn.Sequential(\n",
    "            nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1),\n",
    "            nn.ReLU(inplace=True),\n",
    "            nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1),\n",
    "            nn.ReLU(inplace=True),\n",
    "            nn.MaxPool2d(kernel_size=2, stride=2),\n",
    "            nn.Conv2d(64, 128, kernel_size=3, stride=1, padding=1),\n",
    "            nn.ReLU(inplace=True),\n",
    "            nn.Conv2d(128, 128, kernel_size=3, stride=1, padding=1),\n",
    "            nn.ReLU(inplace=True),\n",
    "            nn.MaxPool2d(kernel_size=2, stride=2),\n",
    "            nn.Conv2d(128, 256, kernel_size=3, stride=1, padding=1),\n",
    "            nn.ReLU(inplace=True),\n",
    "            nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1),\n",
    "            nn.ReLU(inplace=True),\n",
    "            nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1),\n",
    "            nn.ReLU(inplace=True),\n",
    "            nn.MaxPool2d(kernel_size=2, stride=2),\n",
    "            nn.Conv2d(256, 512, kernel_size=3, stride=1, padding=1),\n",
    "            nn.ReLU(inplace=True),\n",
    "            nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1),\n",
    "            nn.ReLU(inplace=True),\n",
    "            nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1),\n",
    "            nn.ReLU(inplace=True),\n",
    "            nn.MaxPool2d(kernel_size=2, stride=2),\n",
    "            nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1),\n",
    "            nn.ReLU(inplace=True),\n",
    "            nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1),\n",
    "            nn.ReLU(inplace=True),\n",
    "            nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1),\n",
    "            nn.ReLU(inplace=True),\n",
    "            nn.MaxPool2d(kernel_size=2, stride=2)        \n",
    "        )\n",
    "      #  self.classifier0 = nn.Sequential(\n",
    "      #      nn.Linear(512*9*12, 4096)\n",
    "      #  )\n",
    "        self.classifier = nn.Sequential(\n",
    "            nn.Linear(25088, 4096),   \n",
    "            nn.ReLU(inplace=True),\n",
    "            nn.Dropout(),\n",
    "            nn.Linear(4096, 4096),\n",
    "            nn.ReLU(inplace=True),\n",
    "            nn.Dropout()\n",
    "        )\n",
    "        self.classifier1 = nn.Sequential(\n",
    "            nn.Linear(4096, num_classes)\n",
    "        )\n",
    "\n",
    "    def forward(self, x):\n",
    "        \n",
    "        x = self.features(x)\n",
    "        \n",
    "        x = x.view(x.size(0), 25088)\n",
    "      #  x = self.classifier0(x)\n",
    "        x = self.classifier(x)\n",
    "        x = self.classifier1(x)\n",
    "        return x\n",
    "\n",
    "    \n",
    "net = VGG16()\n",
    "net.cuda()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Downloading: \"https://download.pytorch.org/models/vgg16-397923af.pth\" to C:\\Users\\JAMESY/.cache\\torch\\checkpoints\\vgg16-397923af.pth\n",
      "100%|██████████| 528M/528M [03:22<00:00, 2.73MB/s] \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<All keys matched successfully>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vgg16 = models.vgg16(pretrained=True)\n",
    "pretrained_dict = vgg16.state_dict()\n",
    "net_dict = net.state_dict()\n",
    "\n",
    "# 1. filter out unnecessary keys\n",
    "pretrained_dict = {k: v for k, v in pretrained_dict.items() if k in net_dict}\n",
    "# 2. overwrite entries in the existing state dict\n",
    "net_dict.update(pretrained_dict) \n",
    "# 3. load the new state dict\n",
    "net.load_state_dict(net_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch.optim as optim\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "optimizer = optim.SGD(net.parameters(), lr=0.00005, momentum=0.9)\n",
    "scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=5, gamma=0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\JAMESY\\Anaconda3\\lib\\site-packages\\torch\\optim\\lr_scheduler.py:82: UserWarning: Detected call of `lr_scheduler.step()` before `optimizer.step()`. In PyTorch 1.1.0 and later, you should call them in the opposite order: `optimizer.step()` before `lr_scheduler.step()`.  Failure to do this will result in PyTorch skipping the first value of the learning rate schedule.See more details at https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate\n",
      "  \"https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate\", UserWarning)\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "Caught ValueError in DataLoader worker process 0.\nOriginal Traceback (most recent call last):\n  File \"C:\\Users\\JAMESY\\Anaconda3\\lib\\site-packages\\torch\\utils\\data\\_utils\\worker.py\", line 178, in _worker_loop\n    data = fetcher.fetch(index)\n  File \"C:\\Users\\JAMESY\\Anaconda3\\lib\\site-packages\\torch\\utils\\data\\_utils\\fetch.py\", line 44, in fetch\n    data = [self.dataset[idx] for idx in possibly_batched_index]\n  File \"C:\\Users\\JAMESY\\Anaconda3\\lib\\site-packages\\torch\\utils\\data\\_utils\\fetch.py\", line 44, in <listcomp>\n    data = [self.dataset[idx] for idx in possibly_batched_index]\n  File \"C:\\Users\\JAMESY\\Anaconda3\\lib\\site-packages\\torchvision\\datasets\\folder.py\", line 140, in __getitem__\n    sample = self.transform(sample)\n  File \"C:\\Users\\JAMESY\\Anaconda3\\lib\\site-packages\\torchvision\\transforms\\transforms.py\", line 61, in __call__\n    img = t(img)\n  File \"C:\\Users\\JAMESY\\Anaconda3\\lib\\site-packages\\torchvision\\transforms\\transforms.py\", line 198, in __call__\n    return F.resize(img, self.size, self.interpolation)\n  File \"C:\\Users\\JAMESY\\Anaconda3\\lib\\site-packages\\torchvision\\transforms\\functional.py\", line 253, in resize\n    return img.resize((ow, oh), interpolation)\n  File \"C:\\Users\\JAMESY\\Anaconda3\\lib\\site-packages\\PIL\\Image.py\", line 1869, in resize\n    message + \" Use \" + \", \".join(filters[:-1]) + \" or \" + filters[-1]\nValueError: Unknown resampling filter (224). Use Image.NEAREST (0), Image.LANCZOS (1), Image.BILINEAR (2), Image.BICUBIC (3), Image.BOX (4) or Image.HAMMING (5)\n",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-6-a21bb9e34538>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      6\u001b[0m     \u001b[0mrunning_val_loss\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m0.0\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      7\u001b[0m     \u001b[0mscheduler\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstep\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 8\u001b[1;33m     \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata\u001b[0m \u001b[1;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtrainloader\u001b[0m \u001b[1;33m,\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      9\u001b[0m         \u001b[1;31m# get the inputs\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     10\u001b[0m         \u001b[0mimages\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlabels\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\torch\\utils\\data\\dataloader.py\u001b[0m in \u001b[0;36m__next__\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    817\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    818\u001b[0m                 \u001b[1;32mdel\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtask_info\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0midx\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 819\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_process_data\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    820\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    821\u001b[0m     \u001b[0mnext\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m__next__\u001b[0m  \u001b[1;31m# Python 2 compatibility\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\torch\\utils\\data\\dataloader.py\u001b[0m in \u001b[0;36m_process_data\u001b[1;34m(self, data)\u001b[0m\n\u001b[0;32m    844\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_try_put_index\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    845\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mExceptionWrapper\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 846\u001b[1;33m             \u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreraise\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    847\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    848\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\torch\\_utils.py\u001b[0m in \u001b[0;36mreraise\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    367\u001b[0m             \u001b[1;31m# (https://bugs.python.org/issue2651), so we work around it.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    368\u001b[0m             \u001b[0mmsg\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mKeyErrorMessage\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmsg\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 369\u001b[1;33m         \u001b[1;32mraise\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mexc_type\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmsg\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m: Caught ValueError in DataLoader worker process 0.\nOriginal Traceback (most recent call last):\n  File \"C:\\Users\\JAMESY\\Anaconda3\\lib\\site-packages\\torch\\utils\\data\\_utils\\worker.py\", line 178, in _worker_loop\n    data = fetcher.fetch(index)\n  File \"C:\\Users\\JAMESY\\Anaconda3\\lib\\site-packages\\torch\\utils\\data\\_utils\\fetch.py\", line 44, in fetch\n    data = [self.dataset[idx] for idx in possibly_batched_index]\n  File \"C:\\Users\\JAMESY\\Anaconda3\\lib\\site-packages\\torch\\utils\\data\\_utils\\fetch.py\", line 44, in <listcomp>\n    data = [self.dataset[idx] for idx in possibly_batched_index]\n  File \"C:\\Users\\JAMESY\\Anaconda3\\lib\\site-packages\\torchvision\\datasets\\folder.py\", line 140, in __getitem__\n    sample = self.transform(sample)\n  File \"C:\\Users\\JAMESY\\Anaconda3\\lib\\site-packages\\torchvision\\transforms\\transforms.py\", line 61, in __call__\n    img = t(img)\n  File \"C:\\Users\\JAMESY\\Anaconda3\\lib\\site-packages\\torchvision\\transforms\\transforms.py\", line 198, in __call__\n    return F.resize(img, self.size, self.interpolation)\n  File \"C:\\Users\\JAMESY\\Anaconda3\\lib\\site-packages\\torchvision\\transforms\\functional.py\", line 253, in resize\n    return img.resize((ow, oh), interpolation)\n  File \"C:\\Users\\JAMESY\\Anaconda3\\lib\\site-packages\\PIL\\Image.py\", line 1869, in resize\n    message + \" Use \" + \", \".join(filters[:-1]) + \" or \" + filters[-1]\nValueError: Unknown resampling filter (224). Use Image.NEAREST (0), Image.LANCZOS (1), Image.BILINEAR (2), Image.BICUBIC (3), Image.BOX (4) or Image.HAMMING (5)\n"
     ]
    }
   ],
   "source": [
    "val_loss = []\n",
    "train_loss = []\n",
    "\n",
    "for epoch in range(8):  # loop over the trainset（45000 each epoch） multiple times\n",
    "    running_train_loss = 0.0\n",
    "    running_val_loss = 0.0\n",
    "    scheduler.step()\n",
    "    for i, data in enumerate(trainloader , 0):\n",
    "        # get the inputs\n",
    "        images, labels = data\n",
    "        inputs, labels = images.cuda(),labels.cuda()\n",
    "        # zero the parameter gradients\n",
    "        optimizer.zero_grad()\n",
    "        # forward + backward + optimize\n",
    "        outputs = net(inputs)\n",
    "        loss = criterion(outputs, labels)\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "        # print statistics\n",
    "        running_train_loss += loss.item()\n",
    "        if i % 500 == 499:    # print 1 epoch\n",
    "             print('[%d, %5d] train_loss: %.3f' %\n",
    "                  (epoch + 1, i + 1, running_train_loss / 500))\n",
    "             train_loss.append(running_train_loss/500)\n",
    "                \n",
    "    for i, data in enumerate(testloader, 0):\n",
    "        # get the inputs\n",
    "        images, labels = data\n",
    "        inputs, labels = images.cuda(),labels.cuda()\n",
    "        # forward + backward + optimize\n",
    "        outputs = net(inputs)\n",
    "        loss = criterion(outputs, labels)\n",
    "        # print statistics\n",
    "        running_val_loss += loss.item()\n",
    "        if i % 88 == 87:    # print 1 epoch\n",
    "             print('[%d, %5d] val_loss: %.3f' %\n",
    "                  (epoch + 1, i + 1, running_val_loss /88))\n",
    "             val_loss.append(running_val_loss/88)\n",
    "             running_train_loss = 0.0\n",
    "             running_val_loss = 0.0\n",
    " \n",
    "                \n",
    "# functions to show the loss\n",
    "t = np.arange(8)  \n",
    "\n",
    "plt.plot(t+1, train_loss,color='red',linestyle='--')\n",
    "plt.plot(t+1, val_loss,color='blue',linestyle='-.')\n",
    "\n",
    "plt.title('loss curve')\n",
    "plt.xlabel('epoch')\n",
    "plt.ylabel('loss')\n",
    "plt.legend(['train', 'val'])\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def eval_net(net, test_loader):\n",
    "    correct = 0\n",
    "    total = 0\n",
    "    with torch.no_grad():\n",
    "        for data in test_loader:\n",
    "            images, labels = data\n",
    "            inputs, labels = images.cuda(),labels.cuda()\n",
    "            outputs = net(inputs)\n",
    "            _, predicted = torch.max(outputs.data, 1)\n",
    "            total += labels.size(0)\n",
    "            correct += (predicted == labels).sum().item()\n",
    "\n",
    "    print('Accuracy of the network on the test images: %d %%\\n\\n' % (\n",
    "        100 * correct / total))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Caught ValueError in DataLoader worker process 0.\nOriginal Traceback (most recent call last):\n  File \"C:\\Users\\JAMESY\\Anaconda3\\lib\\site-packages\\torch\\utils\\data\\_utils\\worker.py\", line 178, in _worker_loop\n    data = fetcher.fetch(index)\n  File \"C:\\Users\\JAMESY\\Anaconda3\\lib\\site-packages\\torch\\utils\\data\\_utils\\fetch.py\", line 44, in fetch\n    data = [self.dataset[idx] for idx in possibly_batched_index]\n  File \"C:\\Users\\JAMESY\\Anaconda3\\lib\\site-packages\\torch\\utils\\data\\_utils\\fetch.py\", line 44, in <listcomp>\n    data = [self.dataset[idx] for idx in possibly_batched_index]\n  File \"C:\\Users\\JAMESY\\Anaconda3\\lib\\site-packages\\torchvision\\datasets\\folder.py\", line 140, in __getitem__\n    sample = self.transform(sample)\n  File \"C:\\Users\\JAMESY\\Anaconda3\\lib\\site-packages\\torchvision\\transforms\\transforms.py\", line 61, in __call__\n    img = t(img)\n  File \"C:\\Users\\JAMESY\\Anaconda3\\lib\\site-packages\\torchvision\\transforms\\transforms.py\", line 198, in __call__\n    return F.resize(img, self.size, self.interpolation)\n  File \"C:\\Users\\JAMESY\\Anaconda3\\lib\\site-packages\\torchvision\\transforms\\functional.py\", line 253, in resize\n    return img.resize((ow, oh), interpolation)\n  File \"C:\\Users\\JAMESY\\Anaconda3\\lib\\site-packages\\PIL\\Image.py\", line 1869, in resize\n    message + \" Use \" + \", \".join(filters[:-1]) + \" or \" + filters[-1]\nValueError: Unknown resampling filter (224). Use Image.NEAREST (0), Image.LANCZOS (1), Image.BILINEAR (2), Image.BICUBIC (3), Image.BOX (4) or Image.HAMMING (5)\n",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-8-6caf22cdcc17>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0meval_net\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnet\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtestloader\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m<ipython-input-7-cb390f591479>\u001b[0m in \u001b[0;36meval_net\u001b[1;34m(net, test_loader)\u001b[0m\n\u001b[0;32m      3\u001b[0m     \u001b[0mtotal\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m     \u001b[1;32mwith\u001b[0m \u001b[0mtorch\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mno_grad\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 5\u001b[1;33m         \u001b[1;32mfor\u001b[0m \u001b[0mdata\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mtest_loader\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      6\u001b[0m             \u001b[0mimages\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlabels\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      7\u001b[0m             \u001b[0minputs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlabels\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mimages\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcuda\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mlabels\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcuda\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\torch\\utils\\data\\dataloader.py\u001b[0m in \u001b[0;36m__next__\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    817\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    818\u001b[0m                 \u001b[1;32mdel\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtask_info\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0midx\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 819\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_process_data\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    820\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    821\u001b[0m     \u001b[0mnext\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m__next__\u001b[0m  \u001b[1;31m# Python 2 compatibility\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\torch\\utils\\data\\dataloader.py\u001b[0m in \u001b[0;36m_process_data\u001b[1;34m(self, data)\u001b[0m\n\u001b[0;32m    844\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_try_put_index\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    845\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mExceptionWrapper\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 846\u001b[1;33m             \u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreraise\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    847\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    848\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\torch\\_utils.py\u001b[0m in \u001b[0;36mreraise\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    367\u001b[0m             \u001b[1;31m# (https://bugs.python.org/issue2651), so we work around it.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    368\u001b[0m             \u001b[0mmsg\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mKeyErrorMessage\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmsg\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 369\u001b[1;33m         \u001b[1;32mraise\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mexc_type\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmsg\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m: Caught ValueError in DataLoader worker process 0.\nOriginal Traceback (most recent call last):\n  File \"C:\\Users\\JAMESY\\Anaconda3\\lib\\site-packages\\torch\\utils\\data\\_utils\\worker.py\", line 178, in _worker_loop\n    data = fetcher.fetch(index)\n  File \"C:\\Users\\JAMESY\\Anaconda3\\lib\\site-packages\\torch\\utils\\data\\_utils\\fetch.py\", line 44, in fetch\n    data = [self.dataset[idx] for idx in possibly_batched_index]\n  File \"C:\\Users\\JAMESY\\Anaconda3\\lib\\site-packages\\torch\\utils\\data\\_utils\\fetch.py\", line 44, in <listcomp>\n    data = [self.dataset[idx] for idx in possibly_batched_index]\n  File \"C:\\Users\\JAMESY\\Anaconda3\\lib\\site-packages\\torchvision\\datasets\\folder.py\", line 140, in __getitem__\n    sample = self.transform(sample)\n  File \"C:\\Users\\JAMESY\\Anaconda3\\lib\\site-packages\\torchvision\\transforms\\transforms.py\", line 61, in __call__\n    img = t(img)\n  File \"C:\\Users\\JAMESY\\Anaconda3\\lib\\site-packages\\torchvision\\transforms\\transforms.py\", line 198, in __call__\n    return F.resize(img, self.size, self.interpolation)\n  File \"C:\\Users\\JAMESY\\Anaconda3\\lib\\site-packages\\torchvision\\transforms\\functional.py\", line 253, in resize\n    return img.resize((ow, oh), interpolation)\n  File \"C:\\Users\\JAMESY\\Anaconda3\\lib\\site-packages\\PIL\\Image.py\", line 1869, in resize\n    message + \" Use \" + \", \".join(filters[:-1]) + \" or \" + filters[-1]\nValueError: Unknown resampling filter (224). Use Image.NEAREST (0), Image.LANCZOS (1), Image.BILINEAR (2), Image.BICUBIC (3), Image.BOX (4) or Image.HAMMING (5)\n"
     ]
    }
   ],
   "source": [
    "eval_net(net, testloader)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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