DeepLearning_Final_Project.ipynb

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      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "from torch.autograd import Variable\n",
        "\n",
        "import torchvision\n",
        "import torchvision.transforms as transforms\n",
        "from torch.utils.data import Dataset, DataLoader"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "q6btjJ9YTXXw"
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      "source": [
        "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\") #gpu calling function"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VePPK186m6iS"
      },
      "source": [
        "### Using FashionMNIST from torchvision \n",
        "\n"
      ]
    },
    {
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        "outputId": "9fa2b58e-60f2-4d75-af77-d8158a16d740"
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      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-images-idx3-ubyte.gz\n",
            "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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          "text": [
            "Extracting ./data/FashionMNIST/raw/train-images-idx3-ubyte.gz to ./data/FashionMNIST/raw\n",
            "\n",
            "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-labels-idx1-ubyte.gz\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",
            "\n",
            "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-images-idx3-ubyte.gz\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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          "name": "stdout",
          "text": [
            "Extracting ./data/FashionMNIST/raw/t10k-images-idx3-ubyte.gz to ./data/FashionMNIST/raw\n",
            "\n",
            "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-labels-idx1-ubyte.gz\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"
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/5148 [00:00<?, ?it/s]"
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              "model_id": "0a1aefa374e6481eb216c7ae24815c31"
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          "metadata": {}
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          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting ./data/FashionMNIST/raw/t10k-labels-idx1-ubyte.gz to ./data/FashionMNIST/raw\n",
            "\n"
          ]
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      ],
      "source": [
        "#loading of dataset and transforming it to tensors\n",
        "train_set = torchvision.datasets.FashionMNIST(\"./data\", download=True, transform=\n",
        "                                                transforms.Compose([transforms.ToTensor()]))\n",
        "test_set = torchvision.datasets.FashionMNIST(\"./data\", download=True, train=False, transform=\n",
        "                                               transforms.Compose([transforms.ToTensor()]))  \n",
        "                                      "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "8s2uhlGJZOOP"
      },
      "outputs": [],
      "source": [
        "#Initialization of DataLoader\n",
        "train_loader = torch.utils.data.DataLoader(train_set, \n",
        "                                           batch_size=100)\n",
        "test_loader = torch.utils.data.DataLoader(test_set,\n",
        "                                          batch_size=100)\n",
        "                                     "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4SfsxOX4peHU"
      },
      "source": [
        "### 10 types of clothes in FashionMNIST dataset.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "uWIE3hVqOlMi"
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      "outputs": [],
      "source": [
        "def output_label(label):\n",
        "    output_mapping = {\n",
        "                 0: \"T-shirt/Top\",\n",
        "                 1: \"Trouser\",\n",
        "                 2: \"Pullover\",\n",
        "                 3: \"Dress\",\n",
        "                 4: \"Coat\", \n",
        "                 5: \"Sandal\", \n",
        "                 6: \"Shirt\",\n",
        "                 7: \"Sneaker\",\n",
        "                 8: \"Bag\",\n",
        "                 9: \"Ankle Boot\"\n",
        "                 }\n",
        "    input = (label.item() if type(label) == torch.Tensor else label)\n",
        "    return output_mapping[input]"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Description of Plots"
      ],
      "metadata": {
        "id": "ZY_LLBtmkSyp"
      }
    },
    {
      "cell_type": "code",
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        },
        "id": "RB9jenaDYZmt",
        "outputId": "9b1be579-b758-4d05-9601-c87e36a7a0bf"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.Size([100, 1, 28, 28])"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "a = next(iter(train_loader))\n",
        "a[0].size()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "D-PnIoRpjuZW",
        "outputId": "a5b896a8-e9ac-4b73-b8a7-32ba110c67cf"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "60000"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ],
      "source": [
        "len(train_set)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "2kC6CrJrlbf_",
        "outputId": "bdf458ff-140d-4c58-fada-79adbc6e934c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "9\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "image, label = next(iter(train_set))\n",
        "plt.imshow(image.squeeze(), cmap=\"gray\")\n",
        "print(label)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sgpYsgh0PY09",
        "outputId": "b45a2f50-ad16-4148-90d2-003ef623b4c3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'torch.Tensor'> <class 'torch.Tensor'>\n",
            "torch.Size([10, 1, 28, 28]) torch.Size([10])\n"
          ]
        }
      ],
      "source": [
        "#getting data from dataloader\n",
        "demo_loader = torch.utils.data.DataLoader(train_set, batch_size=10)\n",
        "\n",
        "batch = next(iter(demo_loader))\n",
        "images, labels = batch\n",
        "print(type(images), type(labels))\n",
        "print(images.shape, labels.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 156
        },
        "id": "2Z0D4BgQRW8e",
        "outputId": "481ba845-0b35-4e12-d427-93500e8cba9f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "labels:  Ankle Boot, T-shirt/Top, T-shirt/Top, Dress, T-shirt/Top, Pullover, Sneaker, Pullover, Sandal, Sandal, "
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x1440 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# Creating images from the batch of images\n",
        "grid = torchvision.utils.make_grid(images, nrow=10)\n",
        "\n",
        "plt.figure(figsize=(15, 20))\n",
        "plt.imshow(np.transpose(grid, (1, 2, 0)))\n",
        "print(\"labels: \", end=\" \")\n",
        "for i, label in enumerate(labels):\n",
        "    print(output_label(label), end=\", \")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
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        "id": "hyCH0Q4hSgFB"
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        "'''first convolutional layer has 32 filters of size 3x3 with a ReLU activation function,\n",
        " second convolutional layer has 64 filters of size 3x3 and also uses a ReLU activation function. \n",
        " 1st fully-connected layer has 600 units, and second fully-connected layer has 120 units.\n",
        "final output layer has 10 units which corresponds to the number of classes in the fashion dataset.\n",
        " network also utilizes batch normalization prevent overfitting.'''\n",
        "\n",
        "\n",
        "class My_CNN_Model(nn.Module):\n",
        "    \n",
        "    def __init__(self):\n",
        "        super(My_CNN_Model, self).__init__()\n",
        "        #1st layer\n",
        "        self.layer1 = nn.Sequential(\n",
        "            nn.Conv2d(in_channels=1, out_channels=32, kernel_size=3, padding=1),#padding-adding layers of 0 to our input images\n",
        "            nn.BatchNorm2d(32), #batch normalization (making raw data into mini data patches)\n",
        "            nn.ReLU(),\n",
        "            nn.MaxPool2d(kernel_size=2, stride=2)\n",
        "        )\n",
        "        #2nd layer\n",
        "        self.layer2 = nn.Sequential(\n",
        "            nn.Conv2d(in_channels=32, out_channels=64, kernel_size=3),\n",
        "            nn.BatchNorm2d(64),\n",
        "            nn.ReLU(),\n",
        "            nn.MaxPool2d(2)\n",
        "        )\n",
        "        \n",
        "        self.fc1 = nn.Linear(in_features=64*6*6, out_features=600)\n",
        "        self.drop = nn.Dropout2d(0.25)#dropout normalization (provides some predefined units to prevent overfitting)\n",
        "        self.fc2 = nn.Linear(in_features=600, out_features=120) \n",
        "        self.fc3 = nn.Linear(in_features=120, out_features=10)# types of products\n",
        "        \n",
        "    def forward(self, x):\n",
        "        out = self.layer1(x)\n",
        "        out = self.layer2(out)\n",
        "        out = out.view(out.size(0), -1) #reshape the output tensor to a flat vector(flatteniing)\n",
        "        out = self.fc1(out)\n",
        "        out = self.drop(out)\n",
        "        out = self.fc2(out)\n",
        "        out = self.fc3(out)\n",
        "        \n",
        "        return out\n"
      ]
    },
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      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NILDHzNgQ1Gt",
        "outputId": "c49344d8-369f-40d9-c485-7b3e2b67205b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "My_CNN_Model(\n",
            "  (layer1): Sequential(\n",
            "    (0): Conv2d(1, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "    (1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    (2): ReLU()\n",
            "    (3): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
            "  )\n",
            "  (layer2): Sequential(\n",
            "    (0): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1))\n",
            "    (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    (2): ReLU()\n",
            "    (3): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
            "  )\n",
            "  (fc1): Linear(in_features=2304, out_features=600, bias=True)\n",
            "  (drop): Dropout2d(p=0.25, inplace=False)\n",
            "  (fc2): Linear(in_features=600, out_features=120, bias=True)\n",
            "  (fc3): Linear(in_features=120, out_features=10, bias=True)\n",
            ")\n"
          ]
        }
      ],
      "source": [
        "model = My_CNN_Model()\n",
        "model.to(device) # moves the model to cpu or gpu\n",
        "\n",
        "error = nn.CrossEntropyLoss() #calculation of error function for training the model\n",
        "\n",
        "learning_rate = 0.001 #learning rate during optimization\n",
        "optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)\n",
        "print(model)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "im58ZlvMvkty"
      },
      "source": [
        "## Training a network \n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SYh_6HtpUlNl",
        "outputId": "35af5c29-dc99-4690-d37a-b4531029b163"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/torch/nn/functional.py:1331: UserWarning: dropout2d: Received a 2-D input to dropout2d, which is deprecated and will result in an error in a future release. To retain the behavior and silence this warning, please use dropout instead. Note that dropout2d exists to provide channel-wise dropout on inputs with 2 spatial dimensions, a channel dimension, and an optional batch dimension (i.e. 3D or 4D inputs).\n",
            "  warnings.warn(warn_msg)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0 0.27422717213630676\n"
          ]
        }
      ],
      "source": [
        "num_epochs = 2\n",
        "\n",
        "\n",
        "for epoch in range(num_epochs):\n",
        "    for images, labels in train_loader:\n",
        "        # Transfering images and labels to GPU if available\n",
        "        images, labels = images.to(device), labels.to(device)\n",
        "    \n",
        "        train = Variable(images.view(100, 1, 28, 28))\n",
        "        labels = Variable(labels)\n",
        "        \n",
        "        # Forward pass \n",
        "        outputs = model(train)\n",
        "        loss = error(outputs, labels)\n",
        "        \n",
        "        # Initializing a gradient as 0 so there is no mixing of gradient among the batches\n",
        "        optimizer.zero_grad()\n",
        "        \n",
        "        #Propagating the error backward\n",
        "        loss.backward()\n",
        "        \n",
        "        # Optimizing the parameters\n",
        "        optimizer.step()\n",
        "        optimizer.zero_grad()\n",
        "        loss = loss.item()\n",
        "    if not epoch % 10:\n",
        "        print(epoch, loss)"
      ]
    }
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