PyTorch_ANN_MNIST.ipynb

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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/selimunal/Pytorch/blob/main/PyTorch_ANN_MNIST.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "h0pE4l4iIBFr"
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "import torchvision.transforms as transforms\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from torchvision import datasets\n",
        "from torchvision.transforms import ToTensor"
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "YrDFRrIhxWbx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_dataset = datasets.MNIST(\n",
        "    root='.',\n",
        "    train=True,\n",
        "    transform=transforms.ToTensor(),\n",
        "    download=True)"
      ],
      "metadata": {
        "id": "dN8w-lXtIP-7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423,
          "referenced_widgets": [
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        },
        "outputId": "0b7edf75-7c07-4994-a69d-47150f693fd9"
      },
      "execution_count": null,
      "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 ./MNIST/raw/train-images-idx3-ubyte.gz\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/9912422 [00:00<?, ?it/s]"
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting ./MNIST/raw/train-images-idx3-ubyte.gz to ./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 ./MNIST/raw/train-labels-idx1-ubyte.gz\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/28881 [00:00<?, ?it/s]"
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        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting ./MNIST/raw/train-labels-idx1-ubyte.gz to ./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 ./MNIST/raw/t10k-images-idx3-ubyte.gz\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/1648877 [00:00<?, ?it/s]"
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          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting ./MNIST/raw/t10k-images-idx3-ubyte.gz to ./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 ./MNIST/raw/t10k-labels-idx1-ubyte.gz\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/4542 [00:00<?, ?it/s]"
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            "application/vnd.jupyter.widget-view+json": {
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting ./MNIST/raw/t10k-labels-idx1-ubyte.gz to ./MNIST/raw\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "torch.__version__"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "HScq_60uJtx6",
        "outputId": "33d6903c-618b-4cd2-cac1-dd0f84a8c821"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'1.12.1+cu113'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_dataset.data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eWuzcwVTKsXp",
        "outputId": "0ab92a4d-607e-41c3-c36c-ab7cdccf21cb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[[0, 0, 0,  ..., 0, 0, 0],\n",
              "         [0, 0, 0,  ..., 0, 0, 0],\n",
              "         [0, 0, 0,  ..., 0, 0, 0],\n",
              "         ...,\n",
              "         [0, 0, 0,  ..., 0, 0, 0],\n",
              "         [0, 0, 0,  ..., 0, 0, 0],\n",
              "         [0, 0, 0,  ..., 0, 0, 0]],\n",
              "\n",
              "        [[0, 0, 0,  ..., 0, 0, 0],\n",
              "         [0, 0, 0,  ..., 0, 0, 0],\n",
              "         [0, 0, 0,  ..., 0, 0, 0],\n",
              "         ...,\n",
              "         [0, 0, 0,  ..., 0, 0, 0],\n",
              "         [0, 0, 0,  ..., 0, 0, 0],\n",
              "         [0, 0, 0,  ..., 0, 0, 0]],\n",
              "\n",
              "        [[0, 0, 0,  ..., 0, 0, 0],\n",
              "         [0, 0, 0,  ..., 0, 0, 0],\n",
              "         [0, 0, 0,  ..., 0, 0, 0],\n",
              "         ...,\n",
              "         [0, 0, 0,  ..., 0, 0, 0],\n",
              "         [0, 0, 0,  ..., 0, 0, 0],\n",
              "         [0, 0, 0,  ..., 0, 0, 0]],\n",
              "\n",
              "        ...,\n",
              "\n",
              "        [[0, 0, 0,  ..., 0, 0, 0],\n",
              "         [0, 0, 0,  ..., 0, 0, 0],\n",
              "         [0, 0, 0,  ..., 0, 0, 0],\n",
              "         ...,\n",
              "         [0, 0, 0,  ..., 0, 0, 0],\n",
              "         [0, 0, 0,  ..., 0, 0, 0],\n",
              "         [0, 0, 0,  ..., 0, 0, 0]],\n",
              "\n",
              "        [[0, 0, 0,  ..., 0, 0, 0],\n",
              "         [0, 0, 0,  ..., 0, 0, 0],\n",
              "         [0, 0, 0,  ..., 0, 0, 0],\n",
              "         ...,\n",
              "         [0, 0, 0,  ..., 0, 0, 0],\n",
              "         [0, 0, 0,  ..., 0, 0, 0],\n",
              "         [0, 0, 0,  ..., 0, 0, 0]],\n",
              "\n",
              "        [[0, 0, 0,  ..., 0, 0, 0],\n",
              "         [0, 0, 0,  ..., 0, 0, 0],\n",
              "         [0, 0, 0,  ..., 0, 0, 0],\n",
              "         ...,\n",
              "         [0, 0, 0,  ..., 0, 0, 0],\n",
              "         [0, 0, 0,  ..., 0, 0, 0],\n",
              "         [0, 0, 0,  ..., 0, 0, 0]]], dtype=torch.uint8)"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_dataset.data.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "z39Gyf8FU0yr",
        "outputId": "08827d29-e9f3-42fc-8149-caeb4d75b243"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.Size([60000, 28, 28])"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_dataset.data.max()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2msqwDmbU4Yy",
        "outputId": "d57b6eca-77fa-4fc0-fa3e-9307762350ea"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor(255, dtype=torch.uint8)"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "type(train_dataset)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ygV-PM5LU7aS",
        "outputId": "af2a1990-3eca-4d46-ab20-54321ff6729e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torchvision.datasets.mnist.MNIST"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_dataset.targets.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "s6_gbJDoVAZT",
        "outputId": "82e63342-2f3c-410e-8bc4-82266a799092"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.Size([60000])"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_dataset.targets.max()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "spmf4OtzVPqD",
        "outputId": "fac56cc8-3501-4542-c760-dde54f0593f7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor(9)"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_dataset=datasets.MNIST(\n",
        "    root='.',\n",
        "    train=False,\n",
        "    transform=transforms.ToTensor(),\n",
        "    download=True)"
      ],
      "metadata": {
        "id": "IAZi-EAlVSh7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test_dataset.data.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vVYDtKAQVd_j",
        "outputId": "d71ee151-e8c2-4b64-af98-4d29f34e5d74"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.Size([10000, 28, 28])"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_dataset.targets.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5BPn3WuRVhSc",
        "outputId": "1ba7914c-5a65-4928-b048-56da121bb4cc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.Size([10000])"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#BULD THE MODEL\n",
        "model=nn.Sequential(\n",
        "    nn.Linear(784,128),\n",
        "    nn.ReLU(),\n",
        "    nn.Linear(128,10)\n",
        ")\n",
        "#NO NEED FOR FINAL SOFTMAX"
      ],
      "metadata": {
        "id": "x-cWqq7BVjPS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "device=torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')\n",
        "print(device)\n",
        "model.to(device)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "20a6ByZeZLHU",
        "outputId": "e6475f99-dc20-4537-966e-68db8d59da67"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "cpu\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Sequential(\n",
              "  (0): Linear(in_features=784, out_features=128, bias=True)\n",
              "  (1): ReLU()\n",
              "  (2): Linear(in_features=128, out_features=10, bias=True)\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Loss AND OPTIMIZER\n",
        "criterion=nn.CrossEntropyLoss()\n",
        "optimizer=torch.optim.Adam(model.parameters())"
      ],
      "metadata": {
        "id": "roVnJVTSZ_rs"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#DATA LOADER\n",
        "#useful beacause it automatically generates batches in the training loop\n",
        "#and takes care of shuffling\n",
        "batch_size=128\n",
        "train_loader=torch.utils.data.DataLoader(\n",
        "    dataset=train_dataset,\n",
        "    batch_size=batch_size,\n",
        "    shuffle=True\n",
        ")\n",
        "test_loader=torch.utils.data.DataLoader(\n",
        "    dataset=test_dataset,\n",
        "    batch_size=batch_size,\n",
        "    shuffle=False\n",
        ")"
      ],
      "metadata": {
        "id": "TZOKySz1aOQT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#check out what data loader does \n",
        "#maps the values to (0,1)\n",
        "#makes the data of shape (batch size, color, height width)\n",
        "\n",
        "tmp_loader=torch.utils.data.DataLoader(\n",
        "    dataset=train_dataset,\n",
        "    batch_size=1,\n",
        "    shuffle=True\n",
        ")\n",
        "\n",
        "for x,y in tmp_loader:\n",
        "  print(x)\n",
        "  print(x.shape)\n",
        "  print(y.shape)\n",
        "  break"
      ],
      "metadata": {
        "id": "LbKN59nSfASd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fRgrv70Sfl8t",
        "outputId": "f63742ad-893b-451a-f595-3a6a62b42300"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.Size([1, 1, 28, 28])"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y.shape"
      ],
      "metadata": {
        "id": "VRCjgEiFnLO2",
        "outputId": "9dd651c1-16c0-49e7-f225-094f7704f017",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.Size([1])"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "torch.max(x)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "r0k0ldZqf8bs",
        "outputId": "8217f216-4086-452e-9ed2-0db25283ca39"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor(1.)"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "torch.min(x)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "L5U_QWwFgMu8",
        "outputId": "660ae9ae-49a8-4bfb-f315-412a940c31e3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor(0.)"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_dataset.transform(train_dataset.data.numpy()).max()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9myB9BEtgjk0",
        "outputId": "a9e9a5df-9e66-47eb-e10a-c356a84eaacd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor(1.)"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#TRAIN THE MODEL\n",
        "n_epochs=35\n",
        "\n",
        "#Stuff to store\n",
        "train_losses=np.zeros(n_epochs)\n",
        "test_losses=np.zeros(n_epochs)\n",
        "\n",
        "for it in range(n_epochs):\n",
        "  train_loss=[]\n",
        "  for inputs,targets in train_loader:\n",
        "    #move data to GPU\n",
        "    inputs,targets=inputs.to(device),targets.to(device)\n",
        "\n",
        "    #reshape the input\n",
        "    inputs=inputs.view(-1,784)\n",
        "\n",
        "    #zero the parameter gradients\n",
        "    optimizer.zero_grad()\n",
        "\n",
        "    #forward pass\n",
        "    outpus=model(inputs)\n",
        "    loss=criterion(outpus,targets)\n",
        "\n",
        "    #backward and optimize\n",
        "    loss.backward()\n",
        "    optimizer.step()\n",
        "\n",
        "    train_loss.append(loss.item())\n",
        "\n",
        "  #get train loss and test loss\n",
        "  train_loss=np.mean(train_loss) #a little misleading\n",
        "\n",
        "  test_loss=[]\n",
        "  for inputs,targets in test_loader:\n",
        "    inputs,targets=inputs.to(device), targets.to(device)\n",
        "    inputs=inputs.view(-1,784)\n",
        "    outputs=model(inputs)\n",
        "    loss=criterion(outputs,targets)\n",
        "    test_loss.append(loss.item())\n",
        "  \n",
        "  test_loss=np.mean(test_loss)\n",
        "\n",
        "  #save losses\n",
        "  train_losses[it]=train_loss\n",
        "  test_losses[it]=test_loss\n",
        "\n",
        "  print(f'Epoch {it+1}/{n_epochs},Train Loss: {train_loss:.4f},Test Loss:{test_loss:.4f}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pUpOxA3hguJs",
        "outputId": "3f2c89ac-65a8-4af0-f84f-a3ef40685ccf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/35,Train Loss: 0.4102,Test Loss:0.2139\n",
            "Epoch 2/35,Train Loss: 0.1904,Test Loss:0.1562\n",
            "Epoch 3/35,Train Loss: 0.1383,Test Loss:0.1267\n",
            "Epoch 4/35,Train Loss: 0.1096,Test Loss:0.1059\n",
            "Epoch 5/35,Train Loss: 0.0881,Test Loss:0.0927\n",
            "Epoch 6/35,Train Loss: 0.0732,Test Loss:0.0869\n",
            "Epoch 7/35,Train Loss: 0.0620,Test Loss:0.0845\n",
            "Epoch 8/35,Train Loss: 0.0529,Test Loss:0.0804\n",
            "Epoch 9/35,Train Loss: 0.0447,Test Loss:0.0754\n",
            "Epoch 10/35,Train Loss: 0.0381,Test Loss:0.0827\n",
            "Epoch 11/35,Train Loss: 0.0338,Test Loss:0.0800\n",
            "Epoch 12/35,Train Loss: 0.0280,Test Loss:0.0730\n",
            "Epoch 13/35,Train Loss: 0.0245,Test Loss:0.0751\n",
            "Epoch 14/35,Train Loss: 0.0203,Test Loss:0.0748\n",
            "Epoch 15/35,Train Loss: 0.0178,Test Loss:0.0701\n",
            "Epoch 16/35,Train Loss: 0.0153,Test Loss:0.0773\n",
            "Epoch 17/35,Train Loss: 0.0133,Test Loss:0.0764\n",
            "Epoch 18/35,Train Loss: 0.0112,Test Loss:0.0785\n",
            "Epoch 19/35,Train Loss: 0.0102,Test Loss:0.0774\n",
            "Epoch 20/35,Train Loss: 0.0085,Test Loss:0.0773\n",
            "Epoch 21/35,Train Loss: 0.0074,Test Loss:0.0822\n",
            "Epoch 22/35,Train Loss: 0.0062,Test Loss:0.0907\n",
            "Epoch 23/35,Train Loss: 0.0068,Test Loss:0.0844\n",
            "Epoch 24/35,Train Loss: 0.0053,Test Loss:0.0793\n",
            "Epoch 25/35,Train Loss: 0.0040,Test Loss:0.0824\n",
            "Epoch 26/35,Train Loss: 0.0028,Test Loss:0.0850\n",
            "Epoch 27/35,Train Loss: 0.0038,Test Loss:0.0881\n",
            "Epoch 28/35,Train Loss: 0.0058,Test Loss:0.0906\n",
            "Epoch 29/35,Train Loss: 0.0038,Test Loss:0.0899\n",
            "Epoch 30/35,Train Loss: 0.0015,Test Loss:0.0869\n",
            "Epoch 31/35,Train Loss: 0.0010,Test Loss:0.0878\n",
            "Epoch 32/35,Train Loss: 0.0009,Test Loss:0.0894\n",
            "Epoch 33/35,Train Loss: 0.0043,Test Loss:0.1120\n",
            "Epoch 34/35,Train Loss: 0.0063,Test Loss:0.1062\n",
            "Epoch 35/35,Train Loss: 0.0012,Test Loss:0.0946\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Plot the train loss and test Loss per iteration\n",
        "plt.plot(train_losses,label='train_losses')\n",
        "plt.plot(test_losses,label='test loss')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "aHdXdjG-jDc4",
        "outputId": "8c79c890-cc84-4fd0-8bea-80cb5418f36f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "n_correct=0.\n",
        "n_total=0.\n",
        "\n",
        "for inputs, targets in train_loader:\n",
        "  #move data to GPU\n",
        "  inputs,targets=inputs.to(device),targets.to(device)\n",
        "\n",
        "  #reshape the input\n",
        "  inputs=inputs.view(-1,784)\n",
        "\n",
        "  #forward pass\n",
        "  outputs=model(inputs)\n",
        "\n",
        "  #get prodictions \n",
        "  #torch.max returns both max and argmax\n",
        "  _, predictions=torch.max(outputs,1)\n",
        "\n",
        "  #update counts\n",
        "  n_correct += (predictions==targets).sum().item()\n",
        "  n_total += targets.shape[0]\n",
        "\n",
        "train_acc=n_correct/n_total\n",
        "\n",
        "n_correct=0.\n",
        "n_total=0.\n",
        "\n",
        "for inputs, targets in test_loader:\n",
        "\n",
        "  #move data to GPU\n",
        "  inputs,targets=inputs.to(device),targets.to(device)\n",
        "\n",
        "  #reshape the input\n",
        "  inputs=inputs.view(-1,784)\n",
        "\n",
        "  #forward pass\n",
        "  outputs=model(inputs)\n",
        "\n",
        "  #get prediction\n",
        "  #torch.max returns both max and argmax\n",
        "  _,predictions=torch.max(outputs,1)\n",
        "\n",
        "  #update counts\n",
        "  n_correct += (predictions==targets).sum().item()\n",
        "  n_total += targets.shape[0]\n",
        "\n",
        "test_acc=n_correct/n_total\n",
        "\n",
        "print(f'Train acc {train_acc:.4f}, test acc {test_acc:.4f}')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TXl3NhnwjjT1",
        "outputId": "916e711d-7e85-4567-f1c1-cb9193c34aa3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train acc 0.9999, test acc 0.9788\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot confusion matrix\n",
        "from sklearn.metrics import confusion_matrix\n",
        "import numpy as np\n",
        "import itertools\n",
        "\n",
        "def plot_confusion_matrix(cm, classes,\n",
        "                          normalize=False,\n",
        "                          title='Confusion matrix',\n",
        "                          cmap=plt.cm.Blues):\n",
        "  \"\"\"\n",
        "  This function prints and plots the confusion matrix.\n",
        "  Normalization can be applied by setting `normalize=True`.\n",
        "  \"\"\"\n",
        "  if normalize:\n",
        "      cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
        "      print(\"Normalized confusion matrix\")\n",
        "  else:\n",
        "      print('Confusion matrix, without normalization')\n",
        "\n",
        "  print(cm)\n",
        "\n",
        "  plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
        "  plt.title(title)\n",
        "  plt.colorbar()\n",
        "  tick_marks = np.arange(len(classes))\n",
        "  plt.xticks(tick_marks, classes, rotation=45)\n",
        "  plt.yticks(tick_marks, classes)\n",
        "\n",
        "  fmt = '.2f' if normalize else 'd'\n",
        "  thresh = cm.max() / 2.\n",
        "  for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
        "      plt.text(j, i, format(cm[i, j], fmt),\n",
        "               horizontalalignment=\"center\",\n",
        "               color=\"white\" if cm[i, j] > thresh else \"black\")\n",
        "\n",
        "  plt.tight_layout()\n",
        "  plt.ylabel('True label')\n",
        "  plt.xlabel('Predicted label')\n",
        "  plt.show()\n",
        "\n",
        "# Do these results make sense?\n",
        "# It's easy to confuse 9 <--> 4, 9 <--> 7, 2 <--> 7, etc. "
      ],
      "metadata": {
        "id": "PavF2g2o9Mmv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# get all predictions in an array and plot confusion matrix\n",
        "\n",
        "x_test = test_dataset.data.numpy()\n",
        "y_test = test_dataset.targets.numpy()\n",
        "p_test = np.array([])\n",
        "for inputs, targets in test_loader:\n",
        "  # move data to GPU\n",
        "  inputs = inputs.to(device)\n",
        "\n",
        "  # reshape the input\n",
        "  inputs = inputs.view(-1, 784)\n",
        "\n",
        "  # Forward pass\n",
        "  outputs = model(inputs)\n",
        "\n",
        "  # Get prediction\n",
        "  _, predictions = torch.max(outputs, 1)\n",
        "  \n",
        "  # update p_test\n",
        "  p_test = np.concatenate((p_test, predictions.cpu().numpy()))\n",
        "\n",
        "cm = confusion_matrix(y_test, p_test)\n",
        "plot_confusion_matrix(cm, list(range(10)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 502
        },
        "id": "vVHT_EKr9vvW",
        "outputId": "daa9bfca-a89e-4aa6-8465-3ee5bc8235eb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Confusion matrix, without normalization\n",
            "[[ 970    0    0    0    1    0    2    1    4    2]\n",
            " [   0 1123    3    0    0    1    2    2    4    0]\n",
            " [   4    2 1004    2    1    0    2    7    9    1]\n",
            " [   0    0    3  989    0    4    0    3    3    8]\n",
            " [   2    1    3    0  960    1    3    3    2    7]\n",
            " [   2    0    0    9    1  869    3    1    6    1]\n",
            " [   5    1    2    1    5    6  936    1    1    0]\n",
            " [   1    3    8    2    2    0    0 1003    2    7]\n",
            " [   3    1    3    3    1    4    1    4  948    6]\n",
            " [   1    2    0    4    7    3    0    2    4  986]]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Show some misclassified examples\n",
        "misclassified_idx = np.where(p_test != y_test)[0]\n",
        "i = np.random.choice(misclassified_idx)\n",
        "plt.imshow(x_test[i], cmap='gray')\n",
        "plt.title(\"True label: %s Predicted: %s\" % (y_test[i], int(p_test[i])));"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "id": "-sXjvyTu9yyu",
        "outputId": "6b8382d7-943f-40b0-e184-140e61d6042b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "4VyfGbJx91Pu"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}