image_processing.ipynb

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  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "AA60ZBLfYwz7"
      },
      "source": [
        "import torch\n",
        "from torch.utils.data import Dataset\n",
        "from torchvision import datasets\n",
        "from torchvision.transforms import ToTensor\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "device = torch.device(\"cuda:0\")"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KAlujRb9ZoSf"
      },
      "source": [
        "1. Data prepatation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 495,
          "referenced_widgets": [
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            "ca874d5709e54d8a94a477ccfdc8d5c3",
            "268ba6b7aca9412bbffa0e6753e521bd",
            "34c782552df34d46b4e20db8680d75c1"
          ]
        },
        "id": "7OHoFzUtZ54O",
        "outputId": "49999980-4d06-4389-b565-5df0215e1b46"
      },
      "source": [
        "training_data = datasets.MNIST(root=\"data\", train=True, download=True, transform=ToTensor())\n",
        "test_data = datasets.MNIST(root=\"data\", train=False, download=True, transform=ToTensor())"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz\n",
            "Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz to data/MNIST/raw/train-images-idx3-ubyte.gz\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "813c24eca99a4da7af37020a53dc2b32",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0/9912422 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting data/MNIST/raw/train-images-idx3-ubyte.gz to data/MNIST/raw\n",
            "\n",
            "Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz\n",
            "Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz to data/MNIST/raw/train-labels-idx1-ubyte.gz\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "f0bacdf893d847aab7979a21ff24bad5",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0/28881 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting data/MNIST/raw/train-labels-idx1-ubyte.gz to data/MNIST/raw\n",
            "\n",
            "Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz\n",
            "Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz to data/MNIST/raw/t10k-images-idx3-ubyte.gz\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "69c194543edd449ea80e4e96d86e3320",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0/1648877 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting data/MNIST/raw/t10k-images-idx3-ubyte.gz to data/MNIST/raw\n",
            "\n",
            "Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz\n",
            "Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz to data/MNIST/raw/t10k-labels-idx1-ubyte.gz\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "1bb65132f2804fe491a860b647978549",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0/4542 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting data/MNIST/raw/t10k-labels-idx1-ubyte.gz to data/MNIST/raw\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/torchvision/datasets/mnist.py:498: UserWarning: The given NumPy array is not writeable, and PyTorch does not support non-writeable tensors. This means you can write to the underlying (supposedly non-writeable) NumPy array using the tensor. You may want to copy the array to protect its data or make it writeable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at  /pytorch/torch/csrc/utils/tensor_numpy.cpp:180.)\n",
            "  return torch.from_numpy(parsed.astype(m[2], copy=False)).view(*s)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 482
        },
        "id": "E9HKMNKdaGhH",
        "outputId": "325c145d-e0b4-4939-c692-efab5077902c"
      },
      "source": [
        "figure = plt.figure(figsize=(8, 8))\n",
        "cols, rows = 3, 3\n",
        "for i in range(1, cols * rows + 1):\n",
        "    sample_idx = torch.randint(len(training_data), size=(1,)).item()\n",
        "    img, label = training_data[sample_idx]\n",
        "    figure.add_subplot(rows, cols, i)\n",
        "    plt.title(label)\n",
        "    plt.axis(\"off\")\n",
        "    plt.imshow(img.squeeze(), cmap=\"gray\")\n",
        "plt.show()"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x576 with 9 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aT4JZ4UCaG1Y"
      },
      "source": [
        "from torch.utils.data import DataLoader\n",
        "train_loader = DataLoader(dataset=training_data, batch_size=64, shuffle=True)"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2z3mVXL1bcEM"
      },
      "source": [
        "2. Defining a neural network"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "54fZh6v6be62"
      },
      "source": [
        "import torch.nn as nn\n",
        "\n",
        "class Net(nn.Module):\n",
        "    def __init__(self):\n",
        "        super(Net,self).__init__()\n",
        "        self.fc1 = nn.Linear(784,256)\n",
        "        self.fc2 = nn.Linear(256,64)\n",
        "        self.fc3 = nn.Linear(64,10)\n",
        "        self.relu = nn.ReLU()\n",
        "\n",
        "    def forward(self, x):\n",
        "        x = x.view(-1, 784)\n",
        "        x = self.fc1(x)\n",
        "        x = self.relu(x)\n",
        "        x = self.fc2(x)\n",
        "        x = self.relu(x)\n",
        "        x = self.fc3(x)\n",
        "        return x"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Tt4SYqhNdHox"
      },
      "source": [
        "net = Net().to(device)"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Bxgi_MCzdcc7"
      },
      "source": [
        "3. Loss function and optimization method"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "axWnUoYidf6E"
      },
      "source": [
        "loss_fuction = nn.CrossEntropyLoss()\n",
        "optimizer = torch.optim.Adam(net.parameters(), lr = 0.001)"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ESdAEA22dxkG"
      },
      "source": [
        "4. Training of the neural network"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6PNAWdRvd0yP",
        "outputId": "cb4d61f8-3a23-4fd0-fec3-3ad67c166862"
      },
      "source": [
        "for epoch in range(20):\n",
        "    loss_val = 0\n",
        "    for itr, data in enumerate(train_loader):\n",
        "        optimizer.zero_grad()\n",
        "\n",
        "        inputs, labels = data\n",
        "        \n",
        "        inputs = inputs.to(device)\n",
        "        labels = labels.to(device)\n",
        "\n",
        "        pred = net(inputs)\n",
        "        loss = loss_fuction(pred, labels)\n",
        "\n",
        "        loss.backward()\n",
        "        #optimizer.zero_grad()\n",
        "        optimizer.step()\n",
        "\n",
        "        loss_val += loss.item()\n",
        "\n",
        "    print(\"Loss: \", loss_val)"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Loss:  287.79841139540076\n",
            "Loss:  106.448620993644\n",
            "Loss:  70.66131340153515\n",
            "Loss:  50.87681229272857\n",
            "Loss:  39.908418313309085\n",
            "Loss:  29.937699520844035\n",
            "Loss:  24.02156528746127\n",
            "Loss:  20.16139099645079\n",
            "Loss:  17.257103534866474\n",
            "Loss:  14.611268945081974\n",
            "Loss:  12.295275918528205\n",
            "Loss:  12.90077480351465\n",
            "Loss:  9.932105171418698\n",
            "Loss:  10.966258399752405\n",
            "Loss:  8.491200962484754\n",
            "Loss:  8.625546608372133\n",
            "Loss:  8.233395194899003\n",
            "Loss:  7.574995734166009\n",
            "Loss:  10.446848280435006\n",
            "Loss:  5.713167069815881\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CJz13gm03noE"
      },
      "source": [
        "5. Prediction and Evaluation for test set "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dv2fcyTO3wbv",
        "outputId": "97e334aa-70e3-4d2c-bbc0-7cecf40c58ed"
      },
      "source": [
        "input_test = test_data.data.float().to(device)\n",
        "\n",
        "pred_test = net(input_test)\n",
        "pred_category = torch.argmax(pred_test, dim=1)\n",
        "label_test = test_data.targets.to(device)\n",
        "accu = torch.mean((pred_category == label_test).float())\n",
        "print(\"Test accuracy:\", accu)"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test accuracy: tensor(0.9792, device='cuda:0')\n"
          ]
        }
      ]
    }
  ]
}