ALEXNET.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Sohan M\\anaconda3\\envs\\pytorch\\lib\\site-packages\\tqdm\\auto.py:22: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cuda:0\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
    "print(device)\n",
    "\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import torchvision\n",
    "from torch import nn\n",
    "from torchvision import transforms, datasets\n",
    "from torch.utils.data import DataLoader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_dir = \"./concrete_cracked_images/train/\"\n",
    "test_dir = \"./concrete_cracked_images/test/\"\n",
    "\n",
    "# Setup ImageNet normalization levels (turns all images into similar distribution as ImageNet)\n",
    "normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],\n",
    "                                 std=[0.229, 0.224, 0.225])\n",
    "\n",
    "# Create starter transform\n",
    "train_transform = transforms.Compose([\n",
    "    transforms.Resize((224, 224)),\n",
    "    transforms.RandomHorizontalFlip(),\n",
    "    transforms.RandomRotation(degrees=90),\n",
    "    \n",
    "    transforms.ToTensor(),\n",
    "    normalize\n",
    "])\n",
    "\n",
    "test_transform = transforms.Compose([transforms.Resize((224, 224)),\n",
    "    transforms.ToTensor(),\n",
    "    normalize\n",
    "])           \n",
    "\n",
    "# Create datasets\n",
    "\n",
    "train_dataset = torchvision.datasets.ImageFolder(root = train_dir, transform = train_transform)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Dataset ImageFolder\n",
       "    Number of datapoints: 28718\n",
       "    Root location: ./concrete_cracked_images/train/\n",
       "    StandardTransform\n",
       "Transform: Compose(\n",
       "               Resize(size=(224, 224), interpolation=bilinear, max_size=None, antialias=None)\n",
       "               RandomHorizontalFlip(p=0.5)\n",
       "               RandomRotation(degrees=[-90.0, 90.0], interpolation=nearest, expand=False, fill=0)\n",
       "               ToTensor()\n",
       "               Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])\n",
       "           )"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_dataset = torchvision.datasets.ImageFolder(root = test_dir, transform = test_transform)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Dataset ImageFolder\n",
       "    Number of datapoints: 11282\n",
       "    Root location: ./concrete_cracked_images/test/\n",
       "    StandardTransform\n",
       "Transform: Compose(\n",
       "               Resize(size=(224, 224), interpolation=bilinear, max_size=None, antialias=None)\n",
       "               ToTensor()\n",
       "               Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])\n",
       "           )"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_dataloader = DataLoader(train_dataset, batch_size=64, shuffle=True)\n",
    "test_dataloader = DataLoader(test_dataset, batch_size=64, shuffle=False)\n",
    "classes = (\"negative\", \"positive\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from torch import conv2d, max_pool2d\n",
    "from torch.nn import Dropout, Linear\n",
    "from torch.nn.modules import MaxPool2d, ReLU, linear, padding\n",
    "from torch.nn.modules.pooling import MaxPool3d\n",
    "\n",
    "\n",
    "class AlexNet(nn.Module):\n",
    "    def __init__(self,n_classes) :\n",
    "        super(AlexNet,self).__init__()\n",
    "        \n",
    "        self.featuresex = nn.Sequential(\n",
    "            nn.Conv2d(in_channels= 3, out_channels= 64,kernel_size = (11,11) ,stride= (3,3), padding = (2,2)),\n",
    "            nn.ReLU(),\n",
    "            nn.MaxPool2d(kernel_size= 3, stride=2, padding = 0, dilation=1, ceil_mode=False),\n",
    "            nn.Conv2d(in_channels= 64, out_channels= 192, kernel_size= (5,5), stride=(2,2), padding = (1,1)),\n",
    "            nn.ReLU(),\n",
    "            nn.MaxPool2d(kernel_size= 3, stride=2, padding = 0, dilation=1, ceil_mode=False),\n",
    "            nn.Conv2d(in_channels= 192, out_channels= 384, kernel_size=(3,3), stride=(1,1),padding =(1,1)),\n",
    "            nn.ReLU(),\n",
    "            nn.Conv2d(in_channels= 384, out_channels= 256, kernel_size=(3,3), stride=(1,1),padding =(1,1)),\n",
    "            nn.ReLU(),\n",
    "            nn.Conv2d(in_channels= 256, out_channels= 256 , kernel_size=(3,3), stride=(1,1),padding =(1,1)),\n",
    "            nn.ReLU(),\n",
    "            nn.MaxPool2d(kernel_size= 3, stride=2, padding = 0, dilation=1, ceil_mode=False),\n",
    "            nn.AdaptiveAvgPool2d(output_size=(6,6))\n",
    "            \n",
    "        )\n",
    "        self.classifier = nn.Sequential(\n",
    "            nn.Dropout(p=0.5, inplace= False),\n",
    "            nn.Linear(in_features= 9216, out_features=4096, bias = True),\n",
    "            nn.ReLU(),\n",
    "            nn.Dropout(p=0.5, inplace=False),\n",
    "            nn.Linear(in_features= 4096, out_features= 1024, bias = True),\n",
    "            nn.ReLU(),\n",
    "            nn.Linear(in_features= 1024, out_features= 2, bias = True)\n",
    "\n",
    "            \n",
    "        )\n",
    "    def forward(self, x):\n",
    "        x = self.featuresex(x)\n",
    "        x = torch.flatten(x, 1)\n",
    "        logits = self.classifier(x)\n",
    "        # probs = F.softmax(logits, dim=1)\n",
    "        return logits\n",
    "        \n",
    "     "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "positive positive positive positive\n"
     ]
    }
   ],
   "source": [
    "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",
    "#Get some random training images\n",
    "dataiter = iter(train_dataloader)\n",
    "images, labels = dataiter.next()\n",
    "imshow(torchvision.utils.make_grid(images))\n",
    "# print labels\n",
    "print(' '.join('%5s' % classes[labels[j]] for j in range(4)))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = AlexNet(2).to(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch.optim as optim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "torch.manual_seed(99)\n",
    "\n",
    "optimizer = optim.Adam(model.parameters(), lr = 0.01)\n",
    "criterion = nn.CrossEntropyLoss()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "avg_testacc = []\n",
    "avg_trnacc = []\n",
    "avg_trnloss = []\n",
    "avg_tesloss = []\n",
    "\n",
    "loss_history = []\n",
    "\n",
    "\n",
    "acc_hist = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train(trainloader,model):\n",
    "    for epoch in range(10):  # loop over the dataset multiple times\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[0].to(device), data[1].to(device)\n",
    "            \n",
    "\n",
    "            # zero the parameter gradients\n",
    "            optimizer.zero_grad()\n",
    "            \n",
    "\n",
    "            # forward + backward + optimize\n",
    "            output = model(inputs)\n",
    "            loss = criterion(output, labels)\n",
    "            loss.backward()\n",
    "            optimizer.step()\n",
    "\n",
    "            # print statistics\n",
    "            running_loss += loss.item()\n",
    "            loss_history.append(loss.item()/64)\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 of AlexNet')\n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "from torchvision.transforms import ToTensor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "train(train_dataloader,model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of the network on the 10000 test images: 0 %\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32mc:\\Users\\Sohan M\\Desktop\\110121103_SPML_Task1\\ALEXNET.ipynb Cell 15\u001b[0m in \u001b[0;36m<cell line: 16>\u001b[1;34m()\u001b[0m\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/Sohan%20M/Desktop/110121103_SPML_Task1/ALEXNET.ipynb#X20sZmlsZQ%3D%3D?line=14'>15</a>\u001b[0m class_total \u001b[39m=\u001b[39m \u001b[39mlist\u001b[39m(\u001b[39m0.\u001b[39m \u001b[39mfor\u001b[39;00m i \u001b[39min\u001b[39;00m \u001b[39mrange\u001b[39m(\u001b[39m2\u001b[39m))\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/Sohan%20M/Desktop/110121103_SPML_Task1/ALEXNET.ipynb#X20sZmlsZQ%3D%3D?line=15'>16</a>\u001b[0m \u001b[39mwith\u001b[39;00m torch\u001b[39m.\u001b[39mno_grad():\n\u001b[1;32m---> <a href='vscode-notebook-cell:/c%3A/Users/Sohan%20M/Desktop/110121103_SPML_Task1/ALEXNET.ipynb#X20sZmlsZQ%3D%3D?line=16'>17</a>\u001b[0m     \u001b[39mfor\u001b[39;00m data \u001b[39min\u001b[39;00m test_dataloader:\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/Sohan%20M/Desktop/110121103_SPML_Task1/ALEXNET.ipynb#X20sZmlsZQ%3D%3D?line=17'>18</a>\u001b[0m         images, labels \u001b[39m=\u001b[39m data[\u001b[39m0\u001b[39m]\u001b[39m.\u001b[39mto(device), data[\u001b[39m1\u001b[39m]\u001b[39m.\u001b[39mto(device)\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/Sohan%20M/Desktop/110121103_SPML_Task1/ALEXNET.ipynb#X20sZmlsZQ%3D%3D?line=18'>19</a>\u001b[0m         outputs \u001b[39m=\u001b[39m model(images)\n",
      "File \u001b[1;32mc:\\Users\\Sohan M\\anaconda3\\envs\\pytorch\\lib\\site-packages\\torch\\utils\\data\\dataloader.py:530\u001b[0m, in \u001b[0;36m_BaseDataLoaderIter.__next__\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    528\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_sampler_iter \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[0;32m    529\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_reset()\n\u001b[1;32m--> 530\u001b[0m data \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_next_data()\n\u001b[0;32m    531\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_num_yielded \u001b[39m+\u001b[39m\u001b[39m=\u001b[39m \u001b[39m1\u001b[39m\n\u001b[0;32m    532\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_dataset_kind \u001b[39m==\u001b[39m _DatasetKind\u001b[39m.\u001b[39mIterable \u001b[39mand\u001b[39;00m \\\n\u001b[0;32m    533\u001b[0m         \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_IterableDataset_len_called \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m \u001b[39mand\u001b[39;00m \\\n\u001b[0;32m    534\u001b[0m         \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_num_yielded \u001b[39m>\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_IterableDataset_len_called:\n",
      "File \u001b[1;32mc:\\Users\\Sohan M\\anaconda3\\envs\\pytorch\\lib\\site-packages\\torch\\utils\\data\\dataloader.py:570\u001b[0m, in \u001b[0;36m_SingleProcessDataLoaderIter._next_data\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    568\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m_next_data\u001b[39m(\u001b[39mself\u001b[39m):\n\u001b[0;32m    569\u001b[0m     index \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_next_index()  \u001b[39m# may raise StopIteration\u001b[39;00m\n\u001b[1;32m--> 570\u001b[0m     data \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_dataset_fetcher\u001b[39m.\u001b[39;49mfetch(index)  \u001b[39m# may raise StopIteration\u001b[39;00m\n\u001b[0;32m    571\u001b[0m     \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_pin_memory:\n\u001b[0;32m    572\u001b[0m         data \u001b[39m=\u001b[39m _utils\u001b[39m.\u001b[39mpin_memory\u001b[39m.\u001b[39mpin_memory(data)\n",
      "File \u001b[1;32mc:\\Users\\Sohan M\\anaconda3\\envs\\pytorch\\lib\\site-packages\\torch\\utils\\data\\_utils\\fetch.py:49\u001b[0m, in \u001b[0;36m_MapDatasetFetcher.fetch\u001b[1;34m(self, possibly_batched_index)\u001b[0m\n\u001b[0;32m     47\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mfetch\u001b[39m(\u001b[39mself\u001b[39m, possibly_batched_index):\n\u001b[0;32m     48\u001b[0m     \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mauto_collation:\n\u001b[1;32m---> 49\u001b[0m         data \u001b[39m=\u001b[39m [\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mdataset[idx] \u001b[39mfor\u001b[39;00m idx \u001b[39min\u001b[39;00m possibly_batched_index]\n\u001b[0;32m     50\u001b[0m     \u001b[39melse\u001b[39;00m:\n\u001b[0;32m     51\u001b[0m         data \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mdataset[possibly_batched_index]\n",
      "File \u001b[1;32mc:\\Users\\Sohan M\\anaconda3\\envs\\pytorch\\lib\\site-packages\\torch\\utils\\data\\_utils\\fetch.py:49\u001b[0m, in \u001b[0;36m<listcomp>\u001b[1;34m(.0)\u001b[0m\n\u001b[0;32m     47\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mfetch\u001b[39m(\u001b[39mself\u001b[39m, possibly_batched_index):\n\u001b[0;32m     48\u001b[0m     \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mauto_collation:\n\u001b[1;32m---> 49\u001b[0m         data \u001b[39m=\u001b[39m [\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mdataset[idx] \u001b[39mfor\u001b[39;00m idx \u001b[39min\u001b[39;00m possibly_batched_index]\n\u001b[0;32m     50\u001b[0m     \u001b[39melse\u001b[39;00m:\n\u001b[0;32m     51\u001b[0m         data \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mdataset[possibly_batched_index]\n",
      "File \u001b[1;32mc:\\Users\\Sohan M\\anaconda3\\envs\\pytorch\\lib\\site-packages\\torchvision\\datasets\\folder.py:230\u001b[0m, in \u001b[0;36mDatasetFolder.__getitem__\u001b[1;34m(self, index)\u001b[0m\n\u001b[0;32m    222\u001b[0m \u001b[39m\"\"\"\u001b[39;00m\n\u001b[0;32m    223\u001b[0m \u001b[39mArgs:\u001b[39;00m\n\u001b[0;32m    224\u001b[0m \u001b[39m    index (int): Index\u001b[39;00m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    227\u001b[0m \u001b[39m    tuple: (sample, target) where target is class_index of the target class.\u001b[39;00m\n\u001b[0;32m    228\u001b[0m \u001b[39m\"\"\"\u001b[39;00m\n\u001b[0;32m    229\u001b[0m path, target \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39msamples[index]\n\u001b[1;32m--> 230\u001b[0m sample \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mloader(path)\n\u001b[0;32m    231\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mtransform \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[0;32m    232\u001b[0m     sample \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mtransform(sample)\n",
      "File \u001b[1;32mc:\\Users\\Sohan M\\anaconda3\\envs\\pytorch\\lib\\site-packages\\torchvision\\datasets\\folder.py:269\u001b[0m, in \u001b[0;36mdefault_loader\u001b[1;34m(path)\u001b[0m\n\u001b[0;32m    267\u001b[0m     \u001b[39mreturn\u001b[39;00m accimage_loader(path)\n\u001b[0;32m    268\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m--> 269\u001b[0m     \u001b[39mreturn\u001b[39;00m pil_loader(path)\n",
      "File \u001b[1;32mc:\\Users\\Sohan M\\anaconda3\\envs\\pytorch\\lib\\site-packages\\torchvision\\datasets\\folder.py:248\u001b[0m, in \u001b[0;36mpil_loader\u001b[1;34m(path)\u001b[0m\n\u001b[0;32m    245\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mpil_loader\u001b[39m(path: \u001b[39mstr\u001b[39m) \u001b[39m-\u001b[39m\u001b[39m>\u001b[39m Image\u001b[39m.\u001b[39mImage:\n\u001b[0;32m    246\u001b[0m     \u001b[39m# open path as file to avoid ResourceWarning (https://github.com/python-pillow/Pillow/issues/835)\u001b[39;00m\n\u001b[0;32m    247\u001b[0m     \u001b[39mwith\u001b[39;00m \u001b[39mopen\u001b[39m(path, \u001b[39m\"\u001b[39m\u001b[39mrb\u001b[39m\u001b[39m\"\u001b[39m) \u001b[39mas\u001b[39;00m f:\n\u001b[1;32m--> 248\u001b[0m         img \u001b[39m=\u001b[39m Image\u001b[39m.\u001b[39;49mopen(f)\n\u001b[0;32m    249\u001b[0m         \u001b[39mreturn\u001b[39;00m img\u001b[39m.\u001b[39mconvert(\u001b[39m\"\u001b[39m\u001b[39mRGB\u001b[39m\u001b[39m\"\u001b[39m)\n",
      "File \u001b[1;32mc:\\Users\\Sohan M\\anaconda3\\envs\\pytorch\\lib\\site-packages\\PIL\\Image.py:3101\u001b[0m, in \u001b[0;36mopen\u001b[1;34m(fp, mode, formats)\u001b[0m\n\u001b[0;32m   3098\u001b[0m     fp \u001b[39m=\u001b[39m io\u001b[39m.\u001b[39mBytesIO(fp\u001b[39m.\u001b[39mread())\n\u001b[0;32m   3099\u001b[0m     exclusive_fp \u001b[39m=\u001b[39m \u001b[39mTrue\u001b[39;00m\n\u001b[1;32m-> 3101\u001b[0m prefix \u001b[39m=\u001b[39m fp\u001b[39m.\u001b[39;49mread(\u001b[39m16\u001b[39;49m)\n\u001b[0;32m   3103\u001b[0m preinit()\n\u001b[0;32m   3105\u001b[0m accept_warnings \u001b[39m=\u001b[39m []\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "correct = 0\n",
    "total = 0\n",
    "with torch.no_grad():\n",
    "    for data in test_dataloader:\n",
    "        images, labels = data[0].to(device), data[1].to(device)\n",
    "        outputs = model(images)\n",
    "        _, predicted = torch.max(outputs.data, 1)\n",
    "        total += labels.size(0)\n",
    "        correct += (predicted == labels).sum().item()\n",
    "        acc_hist.append(correct/total*100)\n",
    "\n",
    "print('Accuracy of the network on the 10000 test images: %d %%' % (\n",
    "    100 * correct / total))\n",
    "\n",
    "class_correct = list(0. for i in range(2))\n",
    "class_total = list(0. for i in range(2))\n",
    "with torch.no_grad():\n",
    "    for data in test_dataloader:\n",
    "        images, labels = data[0].to(device), data[1].to(device)\n",
    "        outputs = model(images)\n",
    "        _, predicted = torch.max(outputs, 1)\n",
    "        c = (predicted == labels).squeeze()\n",
    "        \n",
    "        for i in range(4):\n",
    "            label = labels[i]\n",
    "            class_correct[label] += c[i].item()\n",
    "            class_total[label] += 1\n",
    "\n",
    "for i in range(2):\n",
    "    print('Accuracy of %5s : %2d %%' % (\n",
    "        classes[i], 100 * class_correct[i] / 708))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.plot(acc_hist)\n",
    "plt.title('LOSS PER EPOCH')\n",
    "plt.xlabel('BATCH')\n",
    "plt.ylabel('LOSS')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.plot(loss_history)\n",
    "plt.title('LOSS PER EPOCH')\n",
    "plt.xlabel('BATCH')\n",
    "plt.ylabel('LOSS')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'ImageFolder' object has no attribute 'data'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32mc:\\Users\\Sohan M\\Desktop\\110121103_SPML_Task1\\ALEXNET.ipynb Cell 18\u001b[0m in \u001b[0;36m<cell line: 5>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Users/Sohan%20M/Desktop/110121103_SPML_Task1/ALEXNET.ipynb#X23sZmlsZQ%3D%3D?line=5'>6</a>\u001b[0m plt\u001b[39m.\u001b[39msubplot(N_ROWS, ROW_IMG, index)\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Users/Sohan%20M/Desktop/110121103_SPML_Task1/ALEXNET.ipynb#X23sZmlsZQ%3D%3D?line=6'>7</a>\u001b[0m plt\u001b[39m.\u001b[39maxis(\u001b[39m'\u001b[39m\u001b[39moff\u001b[39m\u001b[39m'\u001b[39m)\n\u001b[1;32m----> <a href='vscode-notebook-cell:/c%3A/Users/Sohan%20M/Desktop/110121103_SPML_Task1/ALEXNET.ipynb#X23sZmlsZQ%3D%3D?line=7'>8</a>\u001b[0m plt\u001b[39m.\u001b[39mimshow(test_dataset\u001b[39m.\u001b[39;49mdata[index], cmap\u001b[39m=\u001b[39m\u001b[39m'\u001b[39m\u001b[39mgray_r\u001b[39m\u001b[39m'\u001b[39m)\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/Sohan%20M/Desktop/110121103_SPML_Task1/ALEXNET.ipynb#X23sZmlsZQ%3D%3D?line=9'>10</a>\u001b[0m \u001b[39mwith\u001b[39;00m torch\u001b[39m.\u001b[39mno_grad():\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/Sohan%20M/Desktop/110121103_SPML_Task1/ALEXNET.ipynb#X23sZmlsZQ%3D%3D?line=10'>11</a>\u001b[0m     model\u001b[39m.\u001b[39meval()\n",
      "\u001b[1;31mAttributeError\u001b[0m: 'ImageFolder' object has no attribute 'data'"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAACoAAAAzCAYAAADhGpoIAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAAAAU0lEQVR4nO3OwQnAMAwAsTT77+yu0MdBMUgT6JmZs8H9O/CVaE20JloTrYnWRGuiNdGaaE20JloTrYnWRGuiNdGaaE20JloTrYnWRGuiNdHamugLcwYDY6ESNkMAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ROW_IMG = 10\n",
    "N_ROWS = 5\n",
    "\n",
    "fig = plt.figure()\n",
    "for index in range(1, ROW_IMG * N_ROWS + 1):\n",
    "    plt.subplot(N_ROWS, ROW_IMG, index)\n",
    "    plt.axis('off')\n",
    "    plt.imshow(test_dataset.data[index], cmap='gray_r')\n",
    "    \n",
    "    with torch.no_grad():\n",
    "        model.eval()\n",
    "        _, probs = model(test_dataset[index][0].unsqueeze(0))\n",
    "        \n",
    "    title = f'{torch.argmax(probs)} ({torch.max(probs * 100):.0f}%)'\n",
    "    \n",
    "    plt.title(title, fontsize=7)\n",
    "fig.suptitle('AlexNet - predictions')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_dataset."
   ]
  }
 ],
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