FashionMNISTUsingNN.ipynb

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      "source": [
        "# Fashion MNIST Dataset trained using FeedForward NN and Convolution NN\n",
        "\n",
        "1.   CNN\n",
        "2.   FeedForward\n",
        "\n"
      ]
    },
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        "import torch\n",
        "import torch.nn as nn\n",
        "import torchvision.transforms as transforms\n",
        "import torchvision.datasets as dsets\n",
        "from sklearn.datasets import load_breast_cancer\n",
        "from sklearn import preprocessing\n",
        "import numpy as np\n",
        "from sklearn.model_selection import train_test_split\n",
        "from torch.utils.data import DataLoader, Dataset\n",
        "import torch\n",
        "import torchvision.transforms as transforms\n",
        "import torch\n",
        "import torch.nn as nn"
      ],
      "execution_count": 0,
      "outputs": []
    },
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      "cell_type": "markdown",
      "metadata": {
        "id": "uJfWa7aYDPZ8",
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      "source": [
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      "source": [
        "train_dataset = dsets.FashionMNIST(root='./data', \n",
        "                            train=True, \n",
        "                            transform=transforms.ToTensor(),\n",
        "                            download=True)\n",
        "\n",
        "test_dataset = dsets.FashionMNIST(root='./data', \n",
        "                           train=False, \n",
        "                           transform=transforms.ToTensor())"
      ],
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-images-idx3-ubyte.gz to ./data/FashionMNIST/raw/train-images-idx3-ubyte.gz\n"
          ],
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          "output_type": "stream",
          "text": [
            "Extracting ./data/FashionMNIST/raw/train-images-idx3-ubyte.gz to ./data/FashionMNIST/raw\n",
            "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-labels-idx1-ubyte.gz to ./data/FashionMNIST/raw/train-labels-idx1-ubyte.gz\n"
          ],
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          "text": [
            "Extracting ./data/FashionMNIST/raw/train-labels-idx1-ubyte.gz to ./data/FashionMNIST/raw\n",
            "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-images-idx3-ubyte.gz to ./data/FashionMNIST/raw/t10k-images-idx3-ubyte.gz\n"
          ],
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          "text": [
            "Extracting ./data/FashionMNIST/raw/t10k-images-idx3-ubyte.gz to ./data/FashionMNIST/raw\n",
            "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-labels-idx1-ubyte.gz to ./data/FashionMNIST/raw/t10k-labels-idx1-ubyte.gz\n",
            "\n",
            "\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
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              "model_id": "cf0257b945524200bedb80eb330b24f0",
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          "text": [
            "Extracting ./data/FashionMNIST/raw/t10k-labels-idx1-ubyte.gz to ./data/FashionMNIST/raw\n",
            "Processing...\n",
            "Done!\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WoCWh6aKSx1f",
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          "height": 850
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        "outputId": "53538f85-2f3f-4969-faef-46d479554b2d"
      },
      "source": [
        "train_dataset.data"
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      "execution_count": 57,
      "outputs": [
        {
          "output_type": "execute_result",
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              "\n",
              "        [[0, 0, 0,  ..., 0, 0, 0],\n",
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              "         [0, 0, 0,  ..., 0, 0, 0],\n",
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              "\n",
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              "         [0, 0, 0,  ..., 0, 0, 0],\n",
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              "\n",
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              "         [0, 0, 0,  ..., 0, 0, 0],\n",
              "         ...,\n",
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    {
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      "source": [
        "Explore"
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    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wvzUsKACNTIP",
        "colab_type": "code",
        "colab": {}
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      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from PIL import Image\n",
        "%matplotlib inline\n",
        "def show(img):\n",
        "    npimg = img.numpy()\n",
        "    plt.imshow(npimg, cmap='gray')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rNUpo3O3GGSc",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 384
        },
        "outputId": "3f54f809-41c9-4dca-b55c-dcdebb5c63a0"
      },
      "source": [
        "show(train_dataset.data[90])\n",
        "show(train_dataset.data[20])\n",
        "\n",
        "print(train_dataset.targets)\n",
        "train_dataset"
      ],
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "tensor([9, 0, 0,  ..., 3, 0, 5])\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Dataset FashionMNIST\n",
              "    Number of datapoints: 60000\n",
              "    Root location: ./data\n",
              "    Split: Train\n",
              "    StandardTransform\n",
              "Transform: ToTensor()"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 71
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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3heMVOhr2r9y42d66rk1X197q2pdEb63qVG88jAcyQdiBTHQ77ANdvv1IXXura18SvbWq\nI7119Tk7gM7p9pkdQIcQdiATXQm7mT1kZu+b2UEze6obPTRiZofNbL+Z7ev2/nTFHnrHzOzAmMsW\nmNkOM/uw+DzuHntd6u0ZMxsq7rt9ZvZwl3pbbGa7zOxdM3vHzH5SXN7V+y7oqyP3W8efs5vZFEkf\nSPoHSYOS9kha6+7vdrSRBszssKQV7t71N2CY2d9JOidpq7v/dXHZv0o66e7PFf9R9rr7P9Wkt2ck\nnev2Nt7FbkX9Y7cZl/SIpMfVxfsu6GuNOnC/dePMvlLSQXf/yN1HJP1O0uou9FF77v66pC8vNbNa\n0pbi6y0a/WPpuAa91YK7H3H3t4qvhyV9ts14V++7oK+O6EbYb5P0lzHfD6pe+727pD+Z2ZtmtqHb\nzYyjz92PFF9/Kqmvm82MI7mNdyd9aZvx2tx3rWx/XhYv0H3Vfe7+t5K+J+nHxcPVWvLR52B1Gjtt\nahvvThlnm/HPdfO+a3X787K6EfYhSYvHfP+N4rJacPeh4vMxSX9U/baiPvrZDrrF52Nd7udzddrG\ne7xtxlWD+66b2593I+x7JC0zs2+a2TRJP5C0vQt9fIWZzS5eOJGZzZb0XdVvK+rtktYVX6+T9HIX\ne/mCumzj3WibcXX5vuv69ufu3vEPSQ9r9BX5Q5L+uRs9NOhrqaT/LT7e6XZvkl7U6MO6Kxp9bWO9\npJsk7ZT0oaT/kbSgRr39p6T9kt7WaLD6u9TbfRp9iP62pH3Fx8Pdvu+Cvjpyv/F2WSATvEAHZIKw\nA5kg7EAmCDuQCcIOZIKwA5kg7EAm/h8ZmnmgsRsSJQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uyJ31kVYDm8i",
        "colab_type": "text"
      },
      "source": [
        "NNType: Convolution Neural\n",
        "\n",
        "\n",
        "1.   Define the CNN architecture\n",
        "2.   Select loss function\n",
        "3.   Select optimizer\n",
        "4.   Make datasets iterable\n",
        "5.   Train \n",
        "6.   Test\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TEG8YQ6yJ0KR",
        "colab_type": "text"
      },
      "source": [
        "Training and Testing for Convolutional Neural Network"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H-pXRuUCGKh9",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 799
        },
        "outputId": "c4f668ce-17ab-47a0-ee4e-0ec6e7552fb1"
      },
      "source": [
        "class CNNModel(nn.Module):\n",
        "    def __init__(self):\n",
        "        super(CNNModel, self).__init__()\n",
        "        \n",
        "        # Convolution 1\n",
        "        self.cnn1 = nn.Conv2d(in_channels=1, out_channels=16, kernel_size=5, stride=1, padding=2)\n",
        "        self.relu1 = nn.ReLU()\n",
        "        \n",
        "        # Max pool 1\n",
        "        self.maxpool1 = nn.MaxPool2d(kernel_size=2)\n",
        "     \n",
        "        # Convolution 2\n",
        "        self.cnn2 = nn.Conv2d(in_channels=16, out_channels=32, kernel_size=5, stride=1, padding=2)\n",
        "        self.relu2 = nn.ReLU()\n",
        "        \n",
        "        # Max pool 2\n",
        "        self.maxpool2 = nn.MaxPool2d(kernel_size=2)\n",
        "        \n",
        "        # Fully connected 1 (readout)\n",
        "        self.fc1 = nn.Linear(32 * 7 * 7, 10) \n",
        "    \n",
        "    def forward(self, x):\n",
        "        # Convolution 1\n",
        "        out = self.cnn1(x)\n",
        "        out = self.relu1(out)\n",
        "        \n",
        "        # Max pool 1\n",
        "        out = self.maxpool1(out)\n",
        "        \n",
        "        # Convolution 2 \n",
        "        out = self.cnn2(out)\n",
        "        out = self.relu2(out)\n",
        "        \n",
        "        # Max pool 2 \n",
        "        out = self.maxpool2(out)\n",
        "        \n",
        "        # Resize\n",
        "        # Original size: (100, 32, 7, 7)\n",
        "        # out.size(0): 100\n",
        "        # New out size: (100, 32*7*7)\n",
        "        out = out.view(out.size(0), -1)\n",
        "\n",
        "        # Linear function (readout)\n",
        "        out = self.fc1(out)\n",
        "        \n",
        "        return out\n",
        "\n",
        "## Create an instance of NN\n",
        "model = CNNModel()\n",
        "\n",
        "## Check params\n",
        "print(model.parameters())\n",
        "print(len(list(model.parameters())))\n",
        "# FC 1 Parameters \n",
        "print(list(model.parameters())[0].size())\n",
        "# FC 1 Bias Parameters\n",
        "print(list(model.parameters())[1].size())\n",
        "# FC 2 Parameters\n",
        "print(list(model.parameters())[2].size())\n",
        "# FC 2 Bias Parameters\n",
        "print(list(model.parameters())[3].size())\n",
        "\n",
        "\n",
        "## Select a Loss Function\n",
        "criterion = nn.CrossEntropyLoss()\n",
        "learning_rate = 0.10\n",
        "\n",
        "## Select an optimizer\n",
        "optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate,momentum=0.09)  \n",
        "\n",
        "num_epochs=80\n",
        "iter = 0\n",
        "\n",
        "data=train_dataset.data\n",
        "\n",
        "batch_size = 600\n",
        "n_iters = 100\n",
        "num_epochs = n_iters / (len(train_dataset) / batch_size)*2\n",
        "num_epochs = int(num_epochs)\n",
        "\n",
        "train_loader = torch.utils.data.DataLoader(dataset=train_dataset, \n",
        "                                           batch_size=batch_size, \n",
        "                                           shuffle=True)\n",
        "\n",
        "test_loader = torch.utils.data.DataLoader(dataset=test_dataset, \n",
        "                                          batch_size=batch_size, \n",
        "                                          shuffle=False)\n",
        "\n",
        "## Training...\n",
        "for epoch in range(num_epochs):\n",
        "    for i,(images,labels) in enumerate(train_loader):\n",
        "        # Load images with gradient accumulation capabilities\n",
        "\n",
        "        #print(images)\n",
        "        images = images.requires_grad_()\n",
        "        #labels=train_dataset.train_labels\n",
        "\n",
        "        #print(images.shape)\n",
        "        #break\n",
        "        # Clear gradients w.r.t. parameters\n",
        "        optimizer.zero_grad()\n",
        "        \n",
        "        # Forward pass to get output/logits\n",
        "        outputs = model(images)\n",
        "        \n",
        "        # Calculate Loss: softmax --> cross entropy loss\n",
        "        loss = criterion(outputs, labels)\n",
        "      \n",
        "        # Getting gradients w.r.t. parameters\n",
        "        loss.backward()\n",
        "       \n",
        "        # Updating parameters\n",
        "        optimizer.step()\n",
        "\n",
        "        iter += 1\n",
        "        \n",
        "        if iter % 5 == 0:\n",
        "          # Calculate Accuracy         \n",
        "          correct = 0\n",
        "          total = 0\n",
        "          # Iterate through test dataset\n",
        "          for images, labels in test_loader:\n",
        "              # Load images\n",
        "              images = images.requires_grad_()\n",
        "              \n",
        "              # Forward pass only to get logits/output\n",
        "              outputs = model(images)\n",
        "              \n",
        "              # Get predictions from the maximum value\n",
        "              _, predicted = torch.max(outputs.data, 1)\n",
        "              \n",
        "              # Total number of labels\n",
        "              total += labels.size(0)\n",
        "              \n",
        "              # Total correct predictions\n",
        "              correct += (predicted == labels).sum()\n",
        "          \n",
        "          accuracy = 100 * correct / total\n",
        "              # Print Loss\n",
        "          print('Iteration: {}. Loss: {}. Accuracy: {}.'.format(iter, loss.item(),accuracy))"
      ],
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<generator object Module.parameters at 0x7fe8240040a0>\n",
            "6\n",
            "torch.Size([16, 1, 5, 5])\n",
            "torch.Size([16])\n",
            "torch.Size([32, 16, 5, 5])\n",
            "torch.Size([32])\n",
            "Iteration: 5. Loss: 2.1694655418395996. Accuracy: 41.\n",
            "Iteration: 10. Loss: 1.8226900100708008. Accuracy: 18.\n",
            "Iteration: 15. Loss: 1.7401975393295288. Accuracy: 53.\n",
            "Iteration: 20. Loss: 1.4638408422470093. Accuracy: 50.\n",
            "Iteration: 25. Loss: 1.4704984426498413. Accuracy: 64.\n",
            "Iteration: 30. Loss: 1.3227393627166748. Accuracy: 59.\n",
            "Iteration: 35. Loss: 0.8608920574188232. Accuracy: 66.\n",
            "Iteration: 40. Loss: 1.0503071546554565. Accuracy: 68.\n",
            "Iteration: 45. Loss: 0.9342146515846252. Accuracy: 66.\n",
            "Iteration: 50. Loss: 0.8413082957267761. Accuracy: 71.\n",
            "Iteration: 55. Loss: 0.9053203463554382. Accuracy: 72.\n",
            "Iteration: 60. Loss: 0.7720800042152405. Accuracy: 71.\n",
            "Iteration: 65. Loss: 0.6921757459640503. Accuracy: 72.\n",
            "Iteration: 70. Loss: 0.7726670503616333. Accuracy: 71.\n",
            "Iteration: 75. Loss: 0.8956863284111023. Accuracy: 72.\n",
            "Iteration: 80. Loss: 0.6367461681365967. Accuracy: 73.\n",
            "Iteration: 85. Loss: 0.7344485521316528. Accuracy: 75.\n",
            "Iteration: 90. Loss: 0.8430625200271606. Accuracy: 73.\n",
            "Iteration: 95. Loss: 0.7252615094184875. Accuracy: 76.\n",
            "Iteration: 100. Loss: 0.8854538798332214. Accuracy: 74.\n",
            "Iteration: 105. Loss: 0.6552078127861023. Accuracy: 75.\n",
            "Iteration: 110. Loss: 0.6452654004096985. Accuracy: 74.\n",
            "Iteration: 115. Loss: 0.7133504748344421. Accuracy: 74.\n",
            "Iteration: 120. Loss: 0.6926556825637817. Accuracy: 74.\n",
            "Iteration: 125. Loss: 0.650583803653717. Accuracy: 72.\n",
            "Iteration: 130. Loss: 0.6956948041915894. Accuracy: 76.\n",
            "Iteration: 135. Loss: 0.6606491804122925. Accuracy: 75.\n",
            "Iteration: 140. Loss: 0.5985891222953796. Accuracy: 75.\n",
            "Iteration: 145. Loss: 0.7032730579376221. Accuracy: 77.\n",
            "Iteration: 150. Loss: 0.6379361748695374. Accuracy: 79.\n",
            "Iteration: 155. Loss: 0.7249268293380737. Accuracy: 75.\n",
            "Iteration: 160. Loss: 0.49720820784568787. Accuracy: 78.\n",
            "Iteration: 165. Loss: 0.651827871799469. Accuracy: 78.\n",
            "Iteration: 170. Loss: 0.5258407592773438. Accuracy: 80.\n",
            "Iteration: 175. Loss: 0.605777382850647. Accuracy: 75.\n",
            "Iteration: 180. Loss: 0.5018000602722168. Accuracy: 79.\n",
            "Iteration: 185. Loss: 0.5693520903587341. Accuracy: 79.\n",
            "Iteration: 190. Loss: 0.5499865412712097. Accuracy: 79.\n",
            "Iteration: 195. Loss: 0.5026928782463074. Accuracy: 79.\n",
            "Iteration: 200. Loss: 0.5995035767555237. Accuracy: 79.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zmrb2AWoJt-c",
        "colab_type": "text"
      },
      "source": [
        "NNType: FeedForward Neural\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "1.   Define the CNN architecture\n",
        "2.   Select loss function\n",
        "3.   Select optimizer\n",
        "4.   Make datasets iterable\n",
        "5.   Train\n",
        "6.   Test\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Nepf9vdBGKlA",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        },
        "outputId": "87d8914b-e8e1-44d5-e614-b7415cfc36a9"
      },
      "source": [
        "class FeedforwardNeuralNetModel(nn.Module):\n",
        "    def __init__(self, input_dim, hidden_dim, output_dim):\n",
        "        super(FeedforwardNeuralNetModel, self).__init__()\n",
        "        # Linear function\n",
        "        self.fc1 = nn.Linear(input_dim, hidden_dim) \n",
        "        # Non-linearity\n",
        "        self.sigmoid = nn.Sigmoid()\n",
        "        # Linear function (readout)\n",
        "        self.fc2 = nn.Linear(hidden_dim, output_dim)  \n",
        "    \n",
        "    def forward(self, x):\n",
        "        # Linear function  # LINEAR\n",
        "        out = self.fc1(x)\n",
        "        # Non-linearity  # NON-LINEAR\n",
        "        out = self.sigmoid(out)\n",
        "        # Linear function (readout)  # LINEAR\n",
        "        out = self.fc2(out)\n",
        "        return out\n",
        "\n",
        "\n",
        "\n",
        "input_dim = 28*28\n",
        "hidden_dim = 28*2\n",
        "output_dim = 10\n",
        "\n",
        "## Create an instance of NN\n",
        "model = FeedforwardNeuralNetModel(input_dim, hidden_dim, output_dim)\n",
        "\n",
        "## Select a Loss Function\n",
        "criterion = nn.CrossEntropyLoss()\n",
        "learning_rate = 0.10\n",
        "\n",
        "## Select an optimizer\n",
        "optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate,momentum=0.09)  \n",
        "\n",
        "num_epochs=500\n",
        "iter = 0\n",
        "\n",
        "data=train_dataset.data.numpy().reshape(60000,784)\n",
        "\n",
        "## Training...\n",
        "for epoch in range(num_epochs):\n",
        "\n",
        "        # Load images with gradient accumulation capabilities\n",
        "\n",
        "        images = torch.from_numpy(data).float().requires_grad_()\n",
        "        labels=train_dataset.train_labels\n",
        "\n",
        "        #print(images.shape)\n",
        "        # Clear gradients w.r.t. parameters\n",
        "        optimizer.zero_grad()\n",
        "        \n",
        "        # Forward pass to get output/logits\n",
        "        outputs = model(images)\n",
        "        \n",
        "        # Calculate Loss: softmax --> cross entropy loss\n",
        "        loss = criterion(outputs, labels)\n",
        "      \n",
        "        # Getting gradients w.r.t. parameters\n",
        "        loss.backward()\n",
        "       \n",
        "        # Updating parameters\n",
        "        optimizer.step()\n",
        "\n",
        "        iter += 1\n",
        "        \n",
        "        if iter % 50 == 0:\n",
        "              # Print Loss\n",
        "            print('Iteration: {}. Loss: {}'.format(iter, loss.item()))"
      ],
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/torchvision/datasets/mnist.py:45: UserWarning: train_labels has been renamed targets\n",
            "  warnings.warn(\"train_labels has been renamed targets\")\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Iteration: 50. Loss: 0.971230685710907\n",
            "Iteration: 100. Loss: 0.7214902639389038\n",
            "Iteration: 150. Loss: 0.6241992712020874\n",
            "Iteration: 200. Loss: 0.5673993229866028\n",
            "Iteration: 250. Loss: 0.5352330803871155\n",
            "Iteration: 300. Loss: 0.5269184708595276\n",
            "Iteration: 350. Loss: 0.48353591561317444\n",
            "Iteration: 400. Loss: 0.44956132769584656\n",
            "Iteration: 450. Loss: 0.4396604597568512\n",
            "Iteration: 500. Loss: 0.4702751636505127\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9cMxm1msJc6x",
        "colab_type": "text"
      },
      "source": [
        "Test for NNFeedForward Neural Network"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z17fUPcpGKod",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 333
        },
        "outputId": "8ef6c32e-d305-447d-b5e5-33f55c1e626b"
      },
      "source": [
        "with torch.no_grad():\n",
        "          # Calculate Accuracy   \n",
        "          \n",
        "          correct = 0\n",
        "          total = 0\n",
        "\n",
        "          objLabels = {\n",
        "          0:\t\"T-shirt/top\",\n",
        "          1 :\t\"Trouser\",\n",
        "          2\t:\"Pullover\",\n",
        "          3:\t\"Dress\",\n",
        "          4:\t\"Coat\",\n",
        "          5:\t\"Sandal\",\n",
        "          6:\"\tShirt\",\n",
        "          7:\t\"Sneaker\",\n",
        "          8:\t\"Bag\",\n",
        "          9:\t\"Ankle boot\"\n",
        "          }\n",
        "\n",
        "          tData=test_dataset.data.numpy().reshape(test_dataset.data.shape[0],784)\n",
        "\n",
        "          # Iterate through test dataset\n",
        "          test_images = torch.from_numpy(tData)\n",
        "          test_target=test_dataset.test_labels\n",
        "\n",
        "          # Forward pass only to get logits/output\n",
        "          outputs = model(test_images.float())\n",
        "          \n",
        "          #print(outputs.data)\n",
        "          # # Get predictions from the maximum value\n",
        "          _, predicted = torch.max(outputs.data, 1)\n",
        "\n",
        "          show(test_dataset.data[2])\n",
        "          print(\"Thats a {}\",objLabels[predicted[2].numpy().item()])\n",
        "         \n",
        "          # Total number of labels\n",
        "          total += test_target.size(0)\n",
        "          \n",
        "          # Total correct predictions\n",
        "          correct += (predicted == test_target).sum()\n",
        "          \n",
        "          accuracy = 100 * correct / total\n",
        "          print('Iteration: {}. Loss: {}. Accuracy: {}'.format(iter, loss.item(), accuracy))"
      ],
      "execution_count": 99,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Thats a {} Trouser\n",
            "Iteration: 500. Loss: 0.4702751636505127. Accuracy: 81\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/torchvision/datasets/mnist.py:50: UserWarning: test_labels has been renamed targets\n",
            "  warnings.warn(\"test_labels has been renamed targets\")\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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kf1ATj12ir8fVgOPWjDN7p6R97n7A3S9KWidpeRP6aHnuvk3Sia9sXi5pTXZ7jYb/WBou\np7eW4O597r47uz0g6Ytlxpt67BJ9NUQzwn6LpMMjfj6i1lrv3SX90czeMrPuZjczhnZ378tufyKp\nvZnNjKFwGe9G+soy4y1z7GpZ/rws3qC7Upe73yfpO5J+kD1dbUk+/BqslcZOx7WMd6OMscz4l5p5\n7Gpd/rysZoT9qKQFI37+WratJbj70ex7v6QNar2lqI99sYJu9r2/yf18qZWW8R5rmXG1wLFr5vLn\nzQj7Tkm3mdnXzaxN0nclbWpCH1cws+nZGycys+mSvq3WW4p6k6RV2e1VkjY2sZdRWmUZ77xlxtXk\nY9f05c/dveFfkh7W8Dvy+yX9czN6yOlrkaT/yb7eaXZvktZq+GndoIbf23ha0l9J2iLpA0lvSJrT\nQr29LOltSXs1HKz5TeqtS8NP0fdK2pN9PdzsY5foqyHHjY/LAkHwBh0QBGEHgiDsQBCEHQiCsANB\nEHYgCMIOBPH/eT2vutYwnNAAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "imRt35Fl6BGO",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WkVr_14E2pM9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "x = thisdict[2]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VPQxIck23gLi",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}