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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/niconielsen32/NeuralNetworks/blob/main/Untitled0.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "J7CRkARBKP65"
      },
      "source": [
        "import torch\n",
        "from torch import nn\n",
        "from torch.utils.data import DataLoader\n",
        "from torchvision import datasets\n",
        "from torchvision.transforms import ToTensor, Lambda, Compose\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 471,
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          ]
        },
        "id": "9HQCjxhKKT6C",
        "outputId": "247d14fa-8e45-4dd4-8a4a-83a8f03c384a"
      },
      "source": [
        "# Download training data from open datasets.\n",
        "training_data = datasets.FashionMNIST(\n",
        "    root=\"data\",\n",
        "    train=True,\n",
        "    download=True,\n",
        "    transform=ToTensor(),\n",
        ")\n",
        "\n",
        "# Download test data from open datasets.\n",
        "test_data = datasets.FashionMNIST(\n",
        "    root=\"data\",\n",
        "    train=False,\n",
        "    download=True,\n",
        "    transform=ToTensor(),\n",
        ")"
      ],
      "execution_count": 3,
      "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"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "9f0728eb55844eb6903f9e9f113caf7a",
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            "text/plain": [
              "  0%|          | 0/26421880 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "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"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "b7849ca41eb040e0b3de039ec5e2516c",
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              "  0%|          | 0/29515 [00:00<?, ?it/s]"
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "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"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "ad558f9b68b14307b3699c1c82e99c13",
              "version_minor": 0,
              "version_major": 2
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            "text/plain": [
              "  0%|          | 0/4422102 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "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"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "b1b9de1cf3c646e79bd2a88fea04071e",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0/5148 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting data/FashionMNIST/raw/t10k-labels-idx1-ubyte.gz to data/FashionMNIST/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/"
        },
        "id": "C9UoLK-tKhem",
        "outputId": "5a90359d-361c-44bc-fa50-67677f6d0687"
      },
      "source": [
        "batch_size = 64\n",
        "\n",
        "# Create data loaders.\n",
        "train_dataloader = DataLoader(training_data, batch_size=batch_size)\n",
        "test_dataloader = DataLoader(test_data, batch_size=batch_size)\n",
        "\n",
        "for X, y in test_dataloader:\n",
        "    print(\"Shape of X [N, C, H, W]: \", X.shape)\n",
        "    print(\"Shape of y: \", y.shape, y.dtype)\n",
        "    break"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Shape of X [N, C, H, W]:  torch.Size([64, 1, 28, 28])\n",
            "Shape of y:  torch.Size([64]) torch.int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "irQwWNboKnoM",
        "outputId": "9216da7f-7a59-4425-a414-07a63f8a2a37"
      },
      "source": [
        "# Get cpu or gpu device for training.\n",
        "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
        "print(\"Using {} device\".format(device))\n",
        "\n",
        "# Define model\n",
        "class NeuralNetwork(nn.Module):\n",
        "    def __init__(self):\n",
        "        super(NeuralNetwork, self).__init__()\n",
        "        self.flatten = nn.Flatten()\n",
        "        self.linear_relu_stack = nn.Sequential(\n",
        "            nn.Linear(28*28, 512),\n",
        "            nn.ReLU(),\n",
        "            nn.Linear(512, 512),\n",
        "            nn.ReLU(),\n",
        "            nn.Linear(512, 10)\n",
        "        )\n",
        "\n",
        "    def forward(self, x):\n",
        "        x = self.flatten(x)\n",
        "        logits = self.linear_relu_stack(x)\n",
        "        return logits\n",
        "\n",
        "model = NeuralNetwork().to(device)\n",
        "print(model)"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Using cuda device\n",
            "NeuralNetwork(\n",
            "  (flatten): Flatten(start_dim=1, end_dim=-1)\n",
            "  (linear_relu_stack): Sequential(\n",
            "    (0): Linear(in_features=784, out_features=512, bias=True)\n",
            "    (1): ReLU()\n",
            "    (2): Linear(in_features=512, out_features=512, bias=True)\n",
            "    (3): ReLU()\n",
            "    (4): Linear(in_features=512, out_features=10, bias=True)\n",
            "  )\n",
            ")\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pg3ckUUfK4lJ"
      },
      "source": [
        "loss_fn = nn.CrossEntropyLoss()\n",
        "optimizer = torch.optim.SGD(model.parameters(), lr=1e-3)"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DBK3pBN9K_om"
      },
      "source": [
        "def train(dataloader, model, loss_fn, optimizer):\n",
        "    size = len(dataloader.dataset)\n",
        "    model.train()\n",
        "    for batch, (X, y) in enumerate(dataloader):\n",
        "        X, y = X.to(device), y.to(device)\n",
        "\n",
        "        # Compute prediction error\n",
        "        pred = model(X)\n",
        "        loss = loss_fn(pred, y)\n",
        "\n",
        "        # Backpropagation\n",
        "        optimizer.zero_grad()\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "\n",
        "        if batch % 100 == 0:\n",
        "            loss, current = loss.item(), batch * len(X)\n",
        "            print(f\"loss: {loss:>7f}  [{current:>5d}/{size:>5d}]\")"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ptL6ZcFILEL-"
      },
      "source": [
        "def test(dataloader, model, loss_fn):\n",
        "    size = len(dataloader.dataset)\n",
        "    num_batches = len(dataloader)\n",
        "    model.eval()\n",
        "    test_loss, correct = 0, 0\n",
        "    with torch.no_grad():\n",
        "        for X, y in dataloader:\n",
        "            X, y = X.to(device), y.to(device)\n",
        "            pred = model(X)\n",
        "            test_loss += loss_fn(pred, y).item()\n",
        "            correct += (pred.argmax(1) == y).type(torch.float).sum().item()\n",
        "    test_loss /= num_batches\n",
        "    correct /= size\n",
        "    print(f\"Test Error: \\n Accuracy: {(100*correct):>0.1f}%, Avg loss: {test_loss:>8f} \\n\")"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qM8bgcNTLGM_",
        "outputId": "73d00426-76ab-42d0-ff84-c07f32604c9b"
      },
      "source": [
        "epochs = 5\n",
        "for t in range(epochs):\n",
        "    print(f\"Epoch {t+1}\\n-------------------------------\")\n",
        "    train(train_dataloader, model, loss_fn, optimizer)\n",
        "    test(test_dataloader, model, loss_fn)\n",
        "print(\"Done!\")"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1\n",
            "-------------------------------\n",
            "loss: 2.299329  [    0/60000]\n",
            "loss: 2.288147  [ 6400/60000]\n",
            "loss: 2.273770  [12800/60000]\n",
            "loss: 2.268920  [19200/60000]\n",
            "loss: 2.248747  [25600/60000]\n",
            "loss: 2.231774  [32000/60000]\n",
            "loss: 2.236655  [38400/60000]\n",
            "loss: 2.211532  [44800/60000]\n",
            "loss: 2.198366  [51200/60000]\n",
            "loss: 2.173414  [57600/60000]\n",
            "Test Error: \n",
            " Accuracy: 46.7%, Avg loss: 2.166370 \n",
            "\n",
            "Epoch 2\n",
            "-------------------------------\n",
            "loss: 2.177081  [    0/60000]\n",
            "loss: 2.167387  [ 6400/60000]\n",
            "loss: 2.118115  [12800/60000]\n",
            "loss: 2.127394  [19200/60000]\n",
            "loss: 2.080827  [25600/60000]\n",
            "loss: 2.025717  [32000/60000]\n",
            "loss: 2.056468  [38400/60000]\n",
            "loss: 1.989969  [44800/60000]\n",
            "loss: 1.987305  [51200/60000]\n",
            "loss: 1.910614  [57600/60000]\n",
            "Test Error: \n",
            " Accuracy: 54.0%, Avg loss: 1.917014 \n",
            "\n",
            "Epoch 3\n",
            "-------------------------------\n",
            "loss: 1.954981  [    0/60000]\n",
            "loss: 1.922649  [ 6400/60000]\n",
            "loss: 1.818844  [12800/60000]\n",
            "loss: 1.846590  [19200/60000]\n",
            "loss: 1.731438  [25600/60000]\n",
            "loss: 1.690382  [32000/60000]\n",
            "loss: 1.713239  [38400/60000]\n",
            "loss: 1.627982  [44800/60000]\n",
            "loss: 1.645781  [51200/60000]\n",
            "loss: 1.528311  [57600/60000]\n",
            "Test Error: \n",
            " Accuracy: 58.9%, Avg loss: 1.555351 \n",
            "\n",
            "Epoch 4\n",
            "-------------------------------\n",
            "loss: 1.629214  [    0/60000]\n",
            "loss: 1.585011  [ 6400/60000]\n",
            "loss: 1.446630  [12800/60000]\n",
            "loss: 1.503316  [19200/60000]\n",
            "loss: 1.377273  [25600/60000]\n",
            "loss: 1.381682  [32000/60000]\n",
            "loss: 1.393029  [38400/60000]\n",
            "loss: 1.335014  [44800/60000]\n",
            "loss: 1.360675  [51200/60000]\n",
            "loss: 1.248985  [57600/60000]\n",
            "Test Error: \n",
            " Accuracy: 62.0%, Avg loss: 1.282888 \n",
            "\n",
            "Epoch 5\n",
            "-------------------------------\n",
            "loss: 1.368597  [    0/60000]\n",
            "loss: 1.340863  [ 6400/60000]\n",
            "loss: 1.182613  [12800/60000]\n",
            "loss: 1.274898  [19200/60000]\n",
            "loss: 1.144307  [25600/60000]\n",
            "loss: 1.176905  [32000/60000]\n",
            "loss: 1.194466  [38400/60000]\n",
            "loss: 1.150558  [44800/60000]\n",
            "loss: 1.179101  [51200/60000]\n",
            "loss: 1.084232  [57600/60000]\n",
            "Test Error: \n",
            " Accuracy: 64.1%, Avg loss: 1.113026 \n",
            "\n",
            "Done!\n"
          ]
        }
      ]
    }
  ]
}