OASIS_3.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "OASIS 3",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "It-UDOTVcA9h"
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "import torchvision\n",
        "from torchvision.transforms import transforms\n",
        "import torchvision.datasets as dsets\n",
        "from torch.autograd import Variable\n",
        "import torch.nn.functional as F\n",
        "import torch.optim as optim\n",
        "import numpy as np\n",
        "import nibabel as nib\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "%matplotlib inline"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "X8TVKgr7cXcV",
        "outputId": "6a82a92c-d021-4adb-a2eb-8d1f9b9a1038"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "mean = torch.load(\"/content/drive/MyDrive/OASIS2/OAS2_RAW_PART1/mean_t.pt\")\n",
        "std = torch.load( \"/content/drive/MyDrive/OASIS2/OAS2_RAW_PART1/std_t.pt\")\n",
        "mean.reshape(256,256,128)\n",
        "mean.size()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TacqCy8Zcc1z",
        "outputId": "2c9653b1-5d69-467d-ac05-5e2b74e060f2"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.Size([1, 256, 256, 128])"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "my_dataframe = pd.read_csv(\"/content/drive/MyDrive/OASIS2/OAS2_RAW_PART1/Oasis_Demo_2 - Oasis_Demo.csv\")\n",
        "\n",
        "from torch.utils.data import Dataset\n",
        "from PIL import Image\n",
        "from torchvision import models, transforms\n",
        "import cv2\n",
        "\n",
        "class createDataset(Dataset):\n",
        "    def __init__(self, dataframe):\n",
        "        self.dataframe = dataframe\n",
        "        self.transform = transforms.Compose([transforms.Normalize(torch.reshape(mean,(256,256,128,1)), torch.reshape(std,(256,256,128,1))), transforms.RandomHorizontalFlip(), transforms.RandomVerticalFlip()])\n",
        "\n",
        "    def __getitem__(self, index):\n",
        "        image = self.dataframe.iloc[index, 0]\n",
        "        image = nib.load(image).get_fdata()  \n",
        "        image = torch.from_numpy(np.asarray(image))\n",
        "        image = image.double()\n",
        "        image = self.transform(image)\n",
        "        #image = image.squeeze()\n",
        "        image = image.double()\n",
        "\n",
        "        label = self.dataframe.iloc[index, 1]\n",
        "        label = torch.from_numpy(np.array(label, dtype='int32'))\n",
        "        return image, label\n",
        "\n",
        "    def __len__(self):\n",
        "        return self.dataframe.shape[0]\n",
        "        \n",
        "      \n",
        "my_dataset = createDataset(dataframe = my_dataframe)\n",
        "\n",
        "from torch.utils.data import DataLoader\n",
        "\n",
        "my_dataloader = DataLoader(dataset = my_dataset, batch_size=3, shuffle = True, )"
      ],
      "metadata": {
        "id": "otf3KzFdce2W"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class ResBlock(nn.Module):\n",
        "    def __init__(self, in_channels, out_channels, downsample):\n",
        "        super().__init__()\n",
        "        if downsample:\n",
        "            self.conv1 = nn.Conv2d(\n",
        "                in_channels, out_channels, kernel_size=3, stride=2, padding=1)\n",
        "            self.shortcut = nn.Sequential(\n",
        "                nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=2),\n",
        "                nn.BatchNorm2d(out_channels)\n",
        "            )\n",
        "        else:\n",
        "            self.conv1 = nn.Conv2d(\n",
        "                in_channels, out_channels, kernel_size=3, stride=1, padding=1)\n",
        "            self.shortcut = nn.Sequential()\n",
        "\n",
        "        self.conv2 = nn.Conv2d(out_channels, out_channels,\n",
        "                               kernel_size=3, stride=1, padding=1)\n",
        "        self.bn1 = nn.BatchNorm2d(out_channels)\n",
        "        self.bn2 = nn.BatchNorm2d(out_channels)\n",
        "\n",
        "    def forward(self, input):\n",
        "        shortcut = self.shortcut(input)\n",
        "        input = nn.ReLU()(self.bn1(self.conv1(input)))\n",
        "        input = nn.ReLU()(self.bn2(self.conv2(input)))\n",
        "        input = input + shortcut\n",
        "        return nn.ReLU()(input)\n",
        "class ResBottleneckBlock(nn.Module):\n",
        "    def __init__(self, in_channels, out_channels, downsample):\n",
        "        super().__init__()\n",
        "        self.downsample = downsample\n",
        "        self.conv1 = nn.Conv2d(in_channels, out_channels//4,\n",
        "                               kernel_size=1, stride=1)\n",
        "        self.conv2 = nn.Conv2d(\n",
        "            out_channels//4, out_channels//4, kernel_size=3, stride=2 if downsample else 1, padding=1)\n",
        "        self.conv3 = nn.Conv2d(out_channels//4, out_channels, kernel_size=1, stride=1)\n",
        "\n",
        "        if self.downsample or in_channels != out_channels:\n",
        "            self.shortcut = nn.Sequential(\n",
        "                nn.Conv2d(in_channels, out_channels, kernel_size=1,\n",
        "                          stride=2 if self.downsample else 1),\n",
        "                nn.BatchNorm2d(out_channels)\n",
        "            )\n",
        "        else:\n",
        "            self.shortcut = nn.Sequential()\n",
        "\n",
        "        self.bn1 = nn.BatchNorm2d(out_channels//4)\n",
        "        self.bn2 = nn.BatchNorm2d(out_channels//4)\n",
        "        self.bn3 = nn.BatchNorm2d(out_channels)\n",
        "\n",
        "    def forward(self, input):\n",
        "        shortcut = self.shortcut(input)\n",
        "        input = nn.ReLU()(self.bn1(self.conv1(input)))\n",
        "        input = nn.ReLU()(self.bn2(self.conv2(input)))\n",
        "        input = nn.ReLU()(self.bn3(self.conv3(input)))\n",
        "        input = input + shortcut\n",
        "        return nn.ReLU()(input)\n",
        "\n",
        "class ResNet(nn.Module):\n",
        "    def __init__(self, in_channels, resblock, repeat, useBottleneck=False, outputs=1000):\n",
        "        super().__init__()\n",
        "        self.layer0 = nn.Sequential(\n",
        "            nn.Conv2d(in_channels, 64, kernel_size=7, stride=2, padding=3),\n",
        "            nn.MaxPool2d(kernel_size=3, stride=2, padding=1),\n",
        "            nn.BatchNorm2d(64),\n",
        "            nn.ReLU()\n",
        "        )\n",
        "\n",
        "        if useBottleneck:\n",
        "            filters = [64, 256, 512, 1024, 2048]\n",
        "        else:\n",
        "            filters = [64, 64, 128, 256, 512]\n",
        "\n",
        "        self.layer1 = nn.Sequential()\n",
        "        self.layer1.add_module('conv2_1', resblock(filters[0], filters[1], downsample=False))\n",
        "        for i in range(1, repeat[0]):\n",
        "                self.layer1.add_module('conv2_%d'%(i+1,), resblock(filters[1], filters[1], downsample=False))\n",
        "\n",
        "        self.layer2 = nn.Sequential()\n",
        "        self.layer2.add_module('conv3_1', resblock(filters[1], filters[2], downsample=True))\n",
        "        for i in range(1, repeat[1]):\n",
        "                self.layer2.add_module('conv3_%d' % (\n",
        "                    i+1,), resblock(filters[2], filters[2], downsample=False))\n",
        "\n",
        "        self.layer3 = nn.Sequential()\n",
        "        self.layer3.add_module('conv4_1', resblock(filters[2], filters[3], downsample=True))\n",
        "        for i in range(1, repeat[2]):\n",
        "            self.layer3.add_module('conv2_%d' % (\n",
        "                i+1,), resblock(filters[3], filters[3], downsample=False))\n",
        "\n",
        "        self.layer4 = nn.Sequential()\n",
        "        self.layer4.add_module('conv5_1', resblock(filters[3], filters[4], downsample=True))\n",
        "        for i in range(1, repeat[3]):\n",
        "            self.layer4.add_module('conv3_%d'%(i+1,),resblock(filters[4], filters[4], downsample=False))\n",
        "\n",
        "        self.gap = torch.nn.AdaptiveAvgPool2d(1)\n",
        "        self.fc = torch.nn.Linear(filters[4], outputs)\n",
        "\n",
        "    def forward(self, input):\n",
        "        input = self.layer0(input)\n",
        "        input = self.layer1(input)\n",
        "        input = self.layer2(input)\n",
        "        input = self.layer3(input)\n",
        "        input = self.layer4(input)\n",
        "        input = self.gap(input)\n",
        "        # torch.flatten()\n",
        "        # https://stackoverflow.com/questions/60115633/pytorch-flatten-doesnt-maintain-batch-size\n",
        "        input = torch.flatten(input, start_dim=1)\n",
        "        input = self.fc(input)\n",
        "\n",
        "        return input\n",
        "from torchsummary import summary\n",
        "\n",
        "resnet18 = ResNet(256, ResBottleneckBlock, [\n",
        "                  3, 4, 6, 3], useBottleneck=True, outputs=1000)\n",
        "resnet18.to(torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\"))\n",
        "summary(resnet18, (256, 224, 224))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-p_GpxYLci_n",
        "outputId": "deb64408-890d-4295-90c1-cb170f148a4c"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "----------------------------------------------------------------\n",
            "        Layer (type)               Output Shape         Param #\n",
            "================================================================\n",
            "            Conv2d-1         [-1, 64, 112, 112]         802,880\n",
            "         MaxPool2d-2           [-1, 64, 56, 56]               0\n",
            "       BatchNorm2d-3           [-1, 64, 56, 56]             128\n",
            "              ReLU-4           [-1, 64, 56, 56]               0\n",
            "            Conv2d-5          [-1, 256, 56, 56]          16,640\n",
            "       BatchNorm2d-6          [-1, 256, 56, 56]             512\n",
            "            Conv2d-7           [-1, 64, 56, 56]           4,160\n",
            "       BatchNorm2d-8           [-1, 64, 56, 56]             128\n",
            "            Conv2d-9           [-1, 64, 56, 56]          36,928\n",
            "      BatchNorm2d-10           [-1, 64, 56, 56]             128\n",
            "           Conv2d-11          [-1, 256, 56, 56]          16,640\n",
            "      BatchNorm2d-12          [-1, 256, 56, 56]             512\n",
            "ResBottleneckBlock-13          [-1, 256, 56, 56]               0\n",
            "           Conv2d-14           [-1, 64, 56, 56]          16,448\n",
            "      BatchNorm2d-15           [-1, 64, 56, 56]             128\n",
            "           Conv2d-16           [-1, 64, 56, 56]          36,928\n",
            "      BatchNorm2d-17           [-1, 64, 56, 56]             128\n",
            "           Conv2d-18          [-1, 256, 56, 56]          16,640\n",
            "      BatchNorm2d-19          [-1, 256, 56, 56]             512\n",
            "ResBottleneckBlock-20          [-1, 256, 56, 56]               0\n",
            "           Conv2d-21           [-1, 64, 56, 56]          16,448\n",
            "      BatchNorm2d-22           [-1, 64, 56, 56]             128\n",
            "           Conv2d-23           [-1, 64, 56, 56]          36,928\n",
            "      BatchNorm2d-24           [-1, 64, 56, 56]             128\n",
            "           Conv2d-25          [-1, 256, 56, 56]          16,640\n",
            "      BatchNorm2d-26          [-1, 256, 56, 56]             512\n",
            "ResBottleneckBlock-27          [-1, 256, 56, 56]               0\n",
            "           Conv2d-28          [-1, 512, 28, 28]         131,584\n",
            "      BatchNorm2d-29          [-1, 512, 28, 28]           1,024\n",
            "           Conv2d-30          [-1, 128, 56, 56]          32,896\n",
            "      BatchNorm2d-31          [-1, 128, 56, 56]             256\n",
            "           Conv2d-32          [-1, 128, 28, 28]         147,584\n",
            "      BatchNorm2d-33          [-1, 128, 28, 28]             256\n",
            "           Conv2d-34          [-1, 512, 28, 28]          66,048\n",
            "      BatchNorm2d-35          [-1, 512, 28, 28]           1,024\n",
            "ResBottleneckBlock-36          [-1, 512, 28, 28]               0\n",
            "           Conv2d-37          [-1, 128, 28, 28]          65,664\n",
            "      BatchNorm2d-38          [-1, 128, 28, 28]             256\n",
            "           Conv2d-39          [-1, 128, 28, 28]         147,584\n",
            "      BatchNorm2d-40          [-1, 128, 28, 28]             256\n",
            "           Conv2d-41          [-1, 512, 28, 28]          66,048\n",
            "      BatchNorm2d-42          [-1, 512, 28, 28]           1,024\n",
            "ResBottleneckBlock-43          [-1, 512, 28, 28]               0\n",
            "           Conv2d-44          [-1, 128, 28, 28]          65,664\n",
            "      BatchNorm2d-45          [-1, 128, 28, 28]             256\n",
            "           Conv2d-46          [-1, 128, 28, 28]         147,584\n",
            "      BatchNorm2d-47          [-1, 128, 28, 28]             256\n",
            "           Conv2d-48          [-1, 512, 28, 28]          66,048\n",
            "      BatchNorm2d-49          [-1, 512, 28, 28]           1,024\n",
            "ResBottleneckBlock-50          [-1, 512, 28, 28]               0\n",
            "           Conv2d-51          [-1, 128, 28, 28]          65,664\n",
            "      BatchNorm2d-52          [-1, 128, 28, 28]             256\n",
            "           Conv2d-53          [-1, 128, 28, 28]         147,584\n",
            "      BatchNorm2d-54          [-1, 128, 28, 28]             256\n",
            "           Conv2d-55          [-1, 512, 28, 28]          66,048\n",
            "      BatchNorm2d-56          [-1, 512, 28, 28]           1,024\n",
            "ResBottleneckBlock-57          [-1, 512, 28, 28]               0\n",
            "           Conv2d-58         [-1, 1024, 14, 14]         525,312\n",
            "      BatchNorm2d-59         [-1, 1024, 14, 14]           2,048\n",
            "           Conv2d-60          [-1, 256, 28, 28]         131,328\n",
            "      BatchNorm2d-61          [-1, 256, 28, 28]             512\n",
            "           Conv2d-62          [-1, 256, 14, 14]         590,080\n",
            "      BatchNorm2d-63          [-1, 256, 14, 14]             512\n",
            "           Conv2d-64         [-1, 1024, 14, 14]         263,168\n",
            "      BatchNorm2d-65         [-1, 1024, 14, 14]           2,048\n",
            "ResBottleneckBlock-66         [-1, 1024, 14, 14]               0\n",
            "           Conv2d-67          [-1, 256, 14, 14]         262,400\n",
            "      BatchNorm2d-68          [-1, 256, 14, 14]             512\n",
            "           Conv2d-69          [-1, 256, 14, 14]         590,080\n",
            "      BatchNorm2d-70          [-1, 256, 14, 14]             512\n",
            "           Conv2d-71         [-1, 1024, 14, 14]         263,168\n",
            "      BatchNorm2d-72         [-1, 1024, 14, 14]           2,048\n",
            "ResBottleneckBlock-73         [-1, 1024, 14, 14]               0\n",
            "           Conv2d-74          [-1, 256, 14, 14]         262,400\n",
            "      BatchNorm2d-75          [-1, 256, 14, 14]             512\n",
            "           Conv2d-76          [-1, 256, 14, 14]         590,080\n",
            "      BatchNorm2d-77          [-1, 256, 14, 14]             512\n",
            "           Conv2d-78         [-1, 1024, 14, 14]         263,168\n",
            "      BatchNorm2d-79         [-1, 1024, 14, 14]           2,048\n",
            "ResBottleneckBlock-80         [-1, 1024, 14, 14]               0\n",
            "           Conv2d-81          [-1, 256, 14, 14]         262,400\n",
            "      BatchNorm2d-82          [-1, 256, 14, 14]             512\n",
            "           Conv2d-83          [-1, 256, 14, 14]         590,080\n",
            "      BatchNorm2d-84          [-1, 256, 14, 14]             512\n",
            "           Conv2d-85         [-1, 1024, 14, 14]         263,168\n",
            "      BatchNorm2d-86         [-1, 1024, 14, 14]           2,048\n",
            "ResBottleneckBlock-87         [-1, 1024, 14, 14]               0\n",
            "           Conv2d-88          [-1, 256, 14, 14]         262,400\n",
            "      BatchNorm2d-89          [-1, 256, 14, 14]             512\n",
            "           Conv2d-90          [-1, 256, 14, 14]         590,080\n",
            "      BatchNorm2d-91          [-1, 256, 14, 14]             512\n",
            "           Conv2d-92         [-1, 1024, 14, 14]         263,168\n",
            "      BatchNorm2d-93         [-1, 1024, 14, 14]           2,048\n",
            "ResBottleneckBlock-94         [-1, 1024, 14, 14]               0\n",
            "           Conv2d-95          [-1, 256, 14, 14]         262,400\n",
            "      BatchNorm2d-96          [-1, 256, 14, 14]             512\n",
            "           Conv2d-97          [-1, 256, 14, 14]         590,080\n",
            "      BatchNorm2d-98          [-1, 256, 14, 14]             512\n",
            "           Conv2d-99         [-1, 1024, 14, 14]         263,168\n",
            "     BatchNorm2d-100         [-1, 1024, 14, 14]           2,048\n",
            "ResBottleneckBlock-101         [-1, 1024, 14, 14]               0\n",
            "          Conv2d-102           [-1, 2048, 7, 7]       2,099,200\n",
            "     BatchNorm2d-103           [-1, 2048, 7, 7]           4,096\n",
            "          Conv2d-104          [-1, 512, 14, 14]         524,800\n",
            "     BatchNorm2d-105          [-1, 512, 14, 14]           1,024\n",
            "          Conv2d-106            [-1, 512, 7, 7]       2,359,808\n",
            "     BatchNorm2d-107            [-1, 512, 7, 7]           1,024\n",
            "          Conv2d-108           [-1, 2048, 7, 7]       1,050,624\n",
            "     BatchNorm2d-109           [-1, 2048, 7, 7]           4,096\n",
            "ResBottleneckBlock-110           [-1, 2048, 7, 7]               0\n",
            "          Conv2d-111            [-1, 512, 7, 7]       1,049,088\n",
            "     BatchNorm2d-112            [-1, 512, 7, 7]           1,024\n",
            "          Conv2d-113            [-1, 512, 7, 7]       2,359,808\n",
            "     BatchNorm2d-114            [-1, 512, 7, 7]           1,024\n",
            "          Conv2d-115           [-1, 2048, 7, 7]       1,050,624\n",
            "     BatchNorm2d-116           [-1, 2048, 7, 7]           4,096\n",
            "ResBottleneckBlock-117           [-1, 2048, 7, 7]               0\n",
            "          Conv2d-118            [-1, 512, 7, 7]       1,049,088\n",
            "     BatchNorm2d-119            [-1, 512, 7, 7]           1,024\n",
            "          Conv2d-120            [-1, 512, 7, 7]       2,359,808\n",
            "     BatchNorm2d-121            [-1, 512, 7, 7]           1,024\n",
            "          Conv2d-122           [-1, 2048, 7, 7]       1,050,624\n",
            "     BatchNorm2d-123           [-1, 2048, 7, 7]           4,096\n",
            "ResBottleneckBlock-124           [-1, 2048, 7, 7]               0\n",
            "AdaptiveAvgPool2d-125           [-1, 2048, 1, 1]               0\n",
            "          Linear-126                 [-1, 1000]       2,049,000\n",
            "================================================================\n",
            "Total params: 26,377,000\n",
            "Trainable params: 26,377,000\n",
            "Non-trainable params: 0\n",
            "----------------------------------------------------------------\n",
            "Input size (MB): 49.00\n",
            "Forward/backward pass size (MB): 210.19\n",
            "Params size (MB): 100.62\n",
            "Estimated Total Size (MB): 359.81\n",
            "----------------------------------------------------------------\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "criterion = nn.CrossEntropyLoss()\n",
        "optimizer = optim.SGD(resnet18.parameters(), lr= 0.1, momentum = 0.9)\n",
        "num_epochs = 2\n",
        "resnet18 = resnet18.double()"
      ],
      "metadata": {
        "id": "1sGWewVscxA2"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "device = 'cuda:0' if torch.cuda.is_available() else 'cpu'\n",
        "print(device)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bnXIGWY7c4Ex",
        "outputId": "c45ad13c-bd39-412a-d9f8-cffcb0b4ea5b"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "cpu\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "count = 0\n",
        "loss_list = []\n",
        "iteration_list = []\n",
        "accuracy_list = []\n",
        "for epoch in range(num_epochs):\n",
        "    for i, (images, labels) in enumerate(my_dataloader):\n",
        "        \n",
        "        train = Variable(images).to(device)\n",
        "        labels = Variable(labels).to(device)\n",
        "        # Clear gradients\n",
        "        optimizer.zero_grad()\n",
        "        # Forward propagation\n",
        "        train = train.squeeze()\n",
        "        outputs = resnet18(train)\n",
        "        # Calculate softmax and ross entropy loss\n",
        "        loss = criterion(outputs, labels.long())\n",
        "        # Calculating gradients\n",
        "        loss.backward()\n",
        "        # Update parameters\n",
        "        optimizer.step()\n",
        "        \n",
        "        count += 1\n",
        "        print(count)\n",
        "        if count % 50 == 0:\n",
        "            # Calculate Accuracy         \n",
        "            correct = 0\n",
        "            total = 0\n",
        "            # Iterate through test dataset\n",
        "            for images, labels in my_dataloader:\n",
        "                \n",
        "                test = Variable(images).to(device)\n",
        "                test = test.squeeze()\n",
        "                labels = Variable(labels).to(device)\n",
        "\n",
        "                # Forward propagation\n",
        "                outputs = resnet18(test)\n",
        "\n",
        "                # Get predictions from the maximum value\n",
        "                predicted = torch.max(outputs.data, 1)[1]\n",
        "                \n",
        "                # Total number of labels\n",
        "                total += len(labels)\n",
        "                correct += (predicted == labels).sum()\n",
        "                print(count)\n",
        "            accuracy = 100 * correct / float(total)\n",
        "            \n",
        "            # store loss and iteration\n",
        "            loss_list.append(loss.data)\n",
        "            iteration_list.append(count)\n",
        "            accuracy_list.append(accuracy)\n",
        "        if count % 500 == 0:\n",
        "            # Print Loss\n",
        "            print('Iteration: {}  Loss: {}  Accuracy: {} %'.format(count, loss.data, accuracy))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sVoaGdyHc6Ue",
        "outputId": "f7689f1a-90a1-4ffd-d6f8-4379ab142265"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
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}