lrsched.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "3BJM-yhXQACv",
        "outputId": "722f20a1-9511-47bb-a618-85bec2e8737a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting torch==1.12.1\n",
            "  Downloading torch-1.12.1-cp310-cp310-manylinux1_x86_64.whl (776.3 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m776.3/776.3 MB\u001b[0m \u001b[31m1.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: typing-extensions in /usr/local/lib/python3.10/dist-packages (from torch==1.12.1) (4.5.0)\n",
            "Installing collected packages: torch\n",
            "  Attempting uninstall: torch\n",
            "    Found existing installation: torch 2.0.0+cu118\n",
            "    Uninstalling torch-2.0.0+cu118:\n",
            "      Successfully uninstalled torch-2.0.0+cu118\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "torchvision 0.15.1+cu118 requires torch==2.0.0, but you have torch 1.12.1 which is incompatible.\n",
            "torchtext 0.15.1 requires torch==2.0.0, but you have torch 1.12.1 which is incompatible.\n",
            "torchdata 0.6.0 requires torch==2.0.0, but you have torch 1.12.1 which is incompatible.\n",
            "torchaudio 2.0.1+cu118 requires torch==2.0.0, but you have torch 1.12.1 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0mSuccessfully installed torch-1.12.1\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting torchmetrics\n",
            "  Downloading torchmetrics-0.11.4-py3-none-any.whl (519 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m519.2/519.2 kB\u001b[0m \u001b[31m14.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from torchmetrics) (23.1)\n",
            "Requirement already satisfied: numpy>=1.17.2 in /usr/local/lib/python3.10/dist-packages (from torchmetrics) (1.22.4)\n",
            "Requirement already satisfied: torch>=1.8.1 in /usr/local/lib/python3.10/dist-packages (from torchmetrics) (1.12.1)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.10/dist-packages (from torch>=1.8.1->torchmetrics) (4.5.0)\n",
            "Installing collected packages: torchmetrics\n",
            "Successfully installed torchmetrics-0.11.4\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'1.12.1+cu102'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 1
        }
      ],
      "source": [
        "!pip install torch==1.12.1\n",
        "!pip install torchmetrics\n",
        "import torch\n",
        "torch.__version__"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "17ApMUsQQACy",
        "outputId": "31c25e05-fbf8-463e-9699-1c5d049b7695"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/torchvision/io/image.py:13: UserWarning: Failed to load image Python extension: '/usr/local/lib/python3.10/dist-packages/torchvision/image.so: undefined symbol: _ZN3c104impl8GPUTrace13gpuTraceStateE'If you don't plan on using image functionality from `torchvision.io`, you can ignore this warning. Otherwise, there might be something wrong with your environment. Did you have `libjpeg` or `libpng` installed before building `torchvision` from source?\n",
            "  warn(\n"
          ]
        }
      ],
      "source": [
        "#Import torch dependencies\n",
        "import torchvision\n",
        "import torchvision.transforms as transforms\n",
        "from torchmetrics.classification import MulticlassAccuracy\n",
        "from torchmetrics import Accuracy\n",
        "# file unpickle \n",
        "def unpickle(file):\n",
        "    import pickle\n",
        "    with open(file, 'rb') as fo:\n",
        "        dict = pickle.load(fo, encoding='bytes')\n",
        "    return dict"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "2Di8JkoqQACy"
      },
      "outputs": [],
      "source": [
        "#Data loader: \n",
        "transform = transforms.Compose([transforms.ToTensor(),transforms.RandomHorizontalFlip(p=1)])\n",
        "batch_size = 10000"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 101,
          "referenced_widgets": [
            "039c8c83743f4f8c873fddb31495cd94",
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            "bb91dd27a29f4bd9bc46fcb359a29abc",
            "68bce11ce30546fb84286cc64323371d",
            "a73bd9370c354ee686c496315e8b38c7",
            "d692cfff636f4a58bd8e20888b31ef8e",
            "79e6a284cec64b7c92fbb349ce6aca98",
            "7035dfe1c72e444d98a0e7d00954a8eb",
            "efb2241c9d444bae886bb2f642fbd013",
            "c3105923dcb14ca18a4500758a94ce3a",
            "23bcd7a0b71840bd81b5b36adf8a988b"
          ]
        },
        "id": "ixmG8W7QQACy",
        "outputId": "69d07647-b47a-45d5-8c85-b4604914f34c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to ./data/cifar-10-python.tar.gz\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/170498071 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "039c8c83743f4f8c873fddb31495cd94"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting ./data/cifar-10-python.tar.gz to ./data\n",
            "Files already downloaded and verified\n"
          ]
        }
      ],
      "source": [
        "trainset = torchvision.datasets.CIFAR10(root='./data', train=True,download=True, transform=transform)\n",
        "trainloader = torch.utils.data.DataLoader(trainset, batch_size=batch_size,shuffle=True, num_workers=2)\n",
        "testset = torchvision.datasets.CIFAR10(root='./data', train=False,download=True, transform=transform)\n",
        "testloader = torch.utils.data.DataLoader(testset, batch_size=batch_size,shuffle=False, num_workers=2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "gmQus7RNQACz"
      },
      "outputs": [],
      "source": [
        "#dropout\n",
        "#batch norm\n",
        "#group norm\n",
        "#resnet\n",
        "#dla\n",
        "#vgg"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FbuUFDONQACz",
        "outputId": "2280a692-fc41-4efe-a2fe-6e79814cae2e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-5-043a4d172dbf>:26: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
            "  encode_y = torch.tensor(encode_y, dtype=torch.float)\n",
            "<ipython-input-5-043a4d172dbf>:28: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
            "  encode_test = torch.tensor(encode_test, dtype=torch.float)\n"
          ]
        }
      ],
      "source": [
        "dict1 = unpickle(file='./data/cifar-10-batches-py/data_batch_1')\n",
        "dict2 = unpickle(file='./data/cifar-10-batches-py/data_batch_2')\n",
        "dict3 = unpickle(file='./data/cifar-10-batches-py/data_batch_3')\n",
        "dict4 = unpickle(file='./data/cifar-10-batches-py/data_batch_4')\n",
        "dict5 = unpickle(file='./data/cifar-10-batches-py/data_batch_5')\n",
        "dict_test = unpickle(file='./data/cifar-10-batches-py/test_batch')\n",
        "train_x1 = torch.tensor(dict1[b'data'])\n",
        "train_y1 = torch.tensor(dict1[b'labels'])\n",
        "train_x2 = torch.tensor(dict2[b'data'])\n",
        "train_y2 = torch.tensor(dict2[b'labels'])\n",
        "train_x3 = torch.tensor(dict3[b'data'])\n",
        "train_y3 = torch.tensor(dict3[b'labels'])\n",
        "train_x4 = torch.tensor(dict4[b'data'])\n",
        "train_y4 = torch.tensor(dict4[b'labels'])\n",
        "train_x5 = torch.tensor(dict5[b'data'])\n",
        "train_y5 = torch.tensor(dict5[b'labels'])\n",
        "test_x = torch.tensor(dict_test[b'data'])\n",
        "test_y = torch.tensor(dict_test[b'labels'])\n",
        "train_x = torch.cat((train_x1, train_x2, train_x3, train_x4, train_x5), dim=0)\n",
        "train_y = torch.cat((train_y1, train_y2, train_y3, train_y4, train_y5), dim=0)\n",
        "train_x = torch.reshape(train_x, (50000, 3, 32, 32))\n",
        "train_x = train_x/255\n",
        "test_x = torch.reshape(test_x, (10000, 3, 32, 32))\n",
        "test_x = test_x/255\n",
        "encode_y = torch.nn.functional.one_hot(train_y.clone().detach(), num_classes=10)\n",
        "encode_y = torch.tensor(encode_y, dtype=torch.float)\n",
        "encode_test = torch.nn.functional.one_hot(test_y.clone().detach(), num_classes=10)\n",
        "encode_test = torch.tensor(encode_test, dtype=torch.float)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "UwXGbUzbQACz"
      },
      "outputs": [],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "#(For plots and losses)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "1BxNnbElQAC0"
      },
      "outputs": [],
      "source": [
        "class CNN_Model(torch.nn.Module):\n",
        "    def __init__(self) -> None:\n",
        "        super().__init__()\n",
        "        self.conv1 = torch.nn.Conv2d(in_channels=3,out_channels=32,kernel_size=(3,3),stride=(1,1), padding=(1,1), padding_mode='zeros', bias=True)\n",
        "        self.maxpool = torch.nn.MaxPool2d((2,2), stride=(2,2), padding=0)\n",
        "        self.conv2 = torch.nn.Conv2d(32,64,(5,5),stride=(1,1), padding=(1,1), padding_mode='zeros', bias=True)\n",
        "        self.conv3 = torch.nn.Conv2d(64,64,(3,3),stride=(1,1), padding=(1,1), padding_mode='zeros', bias=True)\n",
        "        self.fc1 = torch.nn.Linear(64*7*7, 64)\n",
        "        self.fc2 = torch.nn.Linear(64, 10)\n",
        "        self.softmax = torch.nn.LogSoftmax(dim=1)\n",
        "\n",
        "    def forward(self, x, batch_size):\n",
        "        x = self.conv1(x)\n",
        "        x = torch.nn.functional.relu(x)\n",
        "        x = self.maxpool(x)\n",
        "        x = self.conv2(x)\n",
        "        x = torch.nn.functional.relu(x)\n",
        "        x = self.maxpool(x)\n",
        "        x = self.conv3(x)\n",
        "        x = torch.nn.functional.relu(x)\n",
        "        x = torch.reshape(x, (batch_size, -1))\n",
        "        x = self.fc1(x)\n",
        "        x = torch.nn.functional.relu(x)\n",
        "        x = self.fc2(x)\n",
        "        logits = self.softmax(x)\n",
        "        return logits\n",
        "    \n",
        "import torch.optim.lr_scheduler as lr_scheduler\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eX8oeQhEQAC0",
        "outputId": "fd7bc97d-37dc-4bf5-e25a-f31a0cc99093"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "----EPOCH 1-----\n",
            "LR BEFORE: 0.001\n",
            "LR AFTER: 0.00099\n",
            "VAL ACC EPOCH = 1: 0.5559999942779541\n",
            "TRAIN ACC EPOCH 1: 0.5490000247955322\n",
            "----EPOCH 2-----\n",
            "LR BEFORE: 0.00099\n",
            "LR AFTER: 0.0009801\n",
            "VAL ACC EPOCH = 2: 0.6190000176429749\n",
            "TRAIN ACC EPOCH 2: 0.6150000095367432\n",
            "----EPOCH 3-----\n",
            "LR BEFORE: 0.0009801\n",
            "LR AFTER: 0.000970299\n",
            "VAL ACC EPOCH = 3: 0.6480000019073486\n",
            "TRAIN ACC EPOCH 3: 0.6520000100135803\n",
            "----EPOCH 4-----\n",
            "LR BEFORE: 0.000970299\n",
            "LR AFTER: 0.0009605960099999999\n",
            "VAL ACC EPOCH = 4: 0.6690000295639038\n",
            "TRAIN ACC EPOCH 4: 0.6980000138282776\n",
            "----EPOCH 5-----\n",
            "LR BEFORE: 0.0009605960099999999\n",
            "LR AFTER: 0.0009509900498999999\n",
            "VAL ACC EPOCH = 5: 0.6809999942779541\n",
            "TRAIN ACC EPOCH 5: 0.7279999852180481\n",
            "----EPOCH 6-----\n",
            "LR BEFORE: 0.0009509900498999999\n",
            "LR AFTER: 0.0009414801494009999\n",
            "VAL ACC EPOCH = 6: 0.6819999814033508\n",
            "TRAIN ACC EPOCH 6: 0.75\n",
            "----EPOCH 7-----\n",
            "LR BEFORE: 0.0009414801494009999\n",
            "LR AFTER: 0.0009320653479069899\n",
            "VAL ACC EPOCH = 7: 0.675000011920929\n",
            "TRAIN ACC EPOCH 7: 0.7459999918937683\n",
            "----EPOCH 8-----\n",
            "LR BEFORE: 0.0009320653479069899\n",
            "LR AFTER: 0.00092274469442792\n",
            "VAL ACC EPOCH = 8: 0.671999990940094\n",
            "TRAIN ACC EPOCH 8: 0.7459999918937683\n",
            "----EPOCH 9-----\n",
            "LR BEFORE: 0.00092274469442792\n",
            "LR AFTER: 0.0009135172474836408\n",
            "VAL ACC EPOCH = 9: 0.6549999713897705\n",
            "TRAIN ACC EPOCH 9: 0.753000020980835\n",
            "----EPOCH 10-----\n",
            "LR BEFORE: 0.0009135172474836408\n",
            "LR AFTER: 0.0009043820750088043\n",
            "VAL ACC EPOCH = 10: 0.6690000295639038\n",
            "TRAIN ACC EPOCH 10: 0.7749999761581421\n",
            "----EPOCH 11-----\n",
            "LR BEFORE: 0.0009043820750088043\n",
            "LR AFTER: 0.0008953382542587163\n",
            "VAL ACC EPOCH = 11: 0.6710000038146973\n",
            "TRAIN ACC EPOCH 11: 0.800000011920929\n",
            "----EPOCH 12-----\n",
            "LR BEFORE: 0.0008953382542587163\n",
            "LR AFTER: 0.0008863848717161291\n",
            "VAL ACC EPOCH = 12: 0.6790000200271606\n",
            "TRAIN ACC EPOCH 12: 0.8220000267028809\n",
            "----EPOCH 13-----\n",
            "LR BEFORE: 0.0008863848717161291\n",
            "LR AFTER: 0.0008775210229989678\n",
            "VAL ACC EPOCH = 13: 0.6880000233650208\n",
            "TRAIN ACC EPOCH 13: 0.8309999704360962\n",
            "----EPOCH 14-----\n",
            "LR BEFORE: 0.0008775210229989678\n",
            "LR AFTER: 0.0008687458127689781\n",
            "VAL ACC EPOCH = 14: 0.6850000023841858\n",
            "TRAIN ACC EPOCH 14: 0.8489999771118164\n",
            "----EPOCH 15-----\n",
            "LR BEFORE: 0.0008687458127689781\n",
            "LR AFTER: 0.0008600583546412883\n",
            "VAL ACC EPOCH = 15: 0.6700000166893005\n",
            "TRAIN ACC EPOCH 15: 0.8209999799728394\n",
            "----EPOCH 16-----\n",
            "LR BEFORE: 0.0008600583546412883\n",
            "LR AFTER: 0.0008514577710948754\n",
            "VAL ACC EPOCH = 16: 0.671999990940094\n",
            "TRAIN ACC EPOCH 16: 0.8479999899864197\n",
            "----EPOCH 17-----\n",
            "LR BEFORE: 0.0008514577710948754\n",
            "LR AFTER: 0.0008429431933839266\n",
            "VAL ACC EPOCH = 17: 0.6800000071525574\n",
            "TRAIN ACC EPOCH 17: 0.8519999980926514\n",
            "----EPOCH 18-----\n",
            "LR BEFORE: 0.0008429431933839266\n",
            "LR AFTER: 0.0008345137614500873\n",
            "VAL ACC EPOCH = 18: 0.6800000071525574\n",
            "TRAIN ACC EPOCH 18: 0.8980000019073486\n",
            "----EPOCH 19-----\n",
            "LR BEFORE: 0.0008345137614500873\n",
            "LR AFTER: 0.0008261686238355864\n",
            "VAL ACC EPOCH = 19: 0.6759999990463257\n",
            "TRAIN ACC EPOCH 19: 0.8809999823570251\n",
            "----EPOCH 20-----\n",
            "LR BEFORE: 0.0008261686238355864\n",
            "LR AFTER: 0.0008179069375972306\n",
            "VAL ACC EPOCH = 20: 0.6589999794960022\n",
            "TRAIN ACC EPOCH 20: 0.890999972820282\n",
            "tensor([2381.3975, 1843.8719, 1584.5974, 1404.6610, 1264.7582, 1152.2657,\n",
            "        1056.7312,  977.2145,  898.7598,  831.3088,  772.5734,  706.2683,\n",
            "         650.0825,  607.1200,  568.0565,  545.0364,  492.0146,  466.5000,\n",
            "         427.3419,  402.8243])\n",
            "tensor([1.2808, 1.1336, 1.0440, 0.9579, 0.9270, 0.9369, 0.9811, 1.0447, 1.1164,\n",
            "        1.1194, 1.1509, 1.1768, 1.2630, 1.3312, 1.4344, 1.4750, 1.5144, 1.5466,\n",
            "        1.6641, 1.8166])\n",
            "tensor([4.7643, 3.6889, 3.1702, 2.8102, 2.5303, 2.3053, 2.1141, 1.9551, 1.7981,\n",
            "        1.6631, 1.5456, 1.4130, 1.3006, 1.2146, 1.1365, 1.0904, 0.9843, 0.9333,\n",
            "        0.8550, 0.8059])\n",
            "tensor([0.1281, 0.1134, 0.1044, 0.0958, 0.0927, 0.0937, 0.0981, 0.1045, 0.1116,\n",
            "        0.1119, 0.1151, 0.1177, 0.1263, 0.1331, 0.1434, 0.1475, 0.1514, 0.1547,\n",
            "        0.1664, 0.1817])\n"
          ]
        }
      ],
      "source": [
        "epoch = 20\n",
        "batch_size = 32\n",
        "batches = 1562\n",
        "acc_met = Accuracy(task=\"multiclass\", num_classes=10).to('cuda:0')\n",
        "model = CNN_Model().to('cuda:0')\n",
        "criterion = torch.nn.CrossEntropyLoss()\n",
        "optimizer = torch.optim.Adam(model.parameters(), lr=0.001)\n",
        "scheduler_LR = lr_scheduler.ExponentialLR(optimizer, gamma=0.99)\n",
        "trainaccs = torch.empty(epoch)\n",
        "valaccs = torch.empty(epoch)\n",
        "train_loss = torch.empty(epoch)\n",
        "val_loss = torch.empty(epoch)\n",
        "for epochs in range(epoch):\n",
        "    print(f'----EPOCH {epochs+1}-----')\n",
        "    tloss = 0\n",
        "    for b in range(batches):\n",
        "      data = train_x[b*batch_size:(b+1)*batch_size,:,:,:].to('cuda:0')\n",
        "      target = encode_y[b*batch_size:(b+1)*batch_size,:].to('cuda:0')\n",
        "      scores = model.forward(data,32)\n",
        "      loss = criterion(scores,target)\n",
        "      # print(f'EPOCH: {epochs+1}, BATCH: {b+1}, LOSS: {loss}')\n",
        "      tloss+=loss.item()\n",
        "      optimizer.zero_grad()\n",
        "      loss.backward()\n",
        "      optimizer.step()\n",
        "    print(f'LR BEFORE: {optimizer.param_groups[0][\"lr\"]}')\n",
        "    scheduler_LR.step()\n",
        "    print(f'LR AFTER: {optimizer.param_groups[0][\"lr\"]}')\n",
        "    score_val = model.forward(test_x[:1000].to('cuda:0'),1000)\n",
        "    pred_val = torch.argmax(score_val, dim=1)\n",
        "    finalacc_val = acc_met(pred_val, test_y[:1000].to('cuda:0'))\n",
        "    score_tr = model.forward(train_x[:1000].to('cuda:0'),1000)\n",
        "    pred_tr = torch.argmax(score_tr, dim=1)\n",
        "    finalacc_tr = acc_met(pred_tr, train_y[:1000].to('cuda:0'))  \n",
        "    valaccs[epochs]=finalacc_val\n",
        "    trainaccs[epochs]=finalacc_tr\n",
        "    val_loss[epochs]=criterion(score_val,encode_test[:1000].to('cuda:0')).item()\n",
        "    train_loss[epochs]=tloss\n",
        "    print(f'VAL ACC EPOCH = {epochs+1}: {finalacc_val}')\n",
        "    print(f'TRAIN ACC EPOCH {epochs+1}: {finalacc_tr}')\n",
        "print(train_loss)\n",
        "print(val_loss)\n",
        "train_loss = train_loss/499.84\n",
        "val_loss = val_loss/10\n",
        "print(train_loss)\n",
        "print(val_loss)\n",
        "loss_t = train_loss.detach().numpy()\n",
        "loss_v = val_loss.detach().numpy()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "bEsAJtQcQAC1",
        "outputId": "9bea8646-983b-435e-d717-7d4492968eb6"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "#Plots of accuracy:\n",
        "plt.figure(figsize=(8,6))\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Accuracy')\n",
        "eps = np.arange(1,21)\n",
        "# plt.ylim(90,95)\n",
        "plt.xticks(np.arange(0,20))\n",
        "plt.plot(eps,trainaccs,'orange',label=\"Train data\")\n",
        "plt.plot(eps,valaccs, 'green', label='Validation data')\n",
        "plt.legend()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "6Dr9dGb_QAC2",
        "outputId": "85a29293-f97d-4367-8443-261238e56856"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "#Plots of loss:\n",
        "plt.figure(figsize=(8,6))\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Cross Entropy Loss')\n",
        "eps = np.arange(1,21)\n",
        "# plt.ylim(90,95)\n",
        "plt.xticks(np.arange(0,20))\n",
        "plt.plot(eps,loss_t,'purple',label=\"Train data\")\n",
        "plt.plot(eps,loss_v, 'green', label='Validation data')\n",
        "plt.legend()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Plots of loss:\n",
        "plt.figure(figsize=(8,6))\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Cross Entropy Loss')\n",
        "eps = np.arange(1,21)\n",
        "# plt.ylim(90,95)\n",
        "plt.xticks(np.arange(0,20))\n",
        "# plt.plot(eps,loss_t,'purple',label=\"Train data\")\n",
        "plt.plot(eps,loss_v, 'green', label='Validation data')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "ngPs3OmvSo3t",
        "outputId": "e3f976d6-5943-4a25-fa14-1164720ea3b2"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "fjspYK2VQAC2"
      },
      "outputs": [],
      "source": [
        "#Comparision of scheduler\n",
        "lr_train = np.array([4.8267446,3.6123343, 3.0997598,2.751738,2.4861553,2.2644663,2.068952,1.8973457,1.7478659,1.5998421,1.4763336,1.3568488,1.2573795,1.1618962,1.0666112,0.9850452,0.89684457,0.8208717,\n",
        " 0.76688,0.72068715])\n",
        "lr_val = np.array([0.12529357,0.10450778,0.09618945,0.09385785,0.09412806,0.09069528,0.08882987,0.08804294,0.08979881,0.09351919,0.10316746,0.11604999,0.12372348,0.12584366,0.13077053,0.1317522,0.1419825,0.15237904,0.16274376,0.16384901])\n",
        "wo_train = np.array([4.8653, 3.6573, 3.1862, 2.8779, 2.6409, 2.4466, 2.2700, 2.1135, 1.9730,\n",
        "        1.8376, 1.7420, 1.6335, 1.5481, 1.4443, 1.3547, 1.3146, 1.2454, 1.1907,\n",
        "        1.1475, 1.0778, 0.9883, 0.9565, 0.8862, 0.8662, 0.8134, 0.7863, 0.7771,\n",
        "        0.7353, 0.6883, 0.6879, 0.6746, 0.6109, 0.6046, 0.5638, 0.5293, 0.5496,\n",
        "        0.5270, 0.4853, 0.4909, 0.5053])\n",
        "wo_val = np.array([0.1260, 0.1058, 0.0973, 0.0908, 0.0889, 0.0886, 0.0931, 0.0991, 0.1038,\n",
        "        0.1157, 0.1245, 0.1341, 0.1328, 0.1441, 0.1563, 0.1432, 0.1534, 0.1530,\n",
        "        0.1569, 0.1685, 0.1889, 0.1764, 0.1897, 0.1949, 0.2098, 0.2122, 0.2234,\n",
        "        0.2219, 0.2264, 0.2453, 0.2343, 0.2533, 0.2587, 0.2688, 0.2705, 0.2745,\n",
        "        0.2731, 0.2943, 0.2705, 0.2840])"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(8,6))\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Cross-entropy Loss (Val data)')\n",
        "eps = [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20]\n",
        "plt.xticks(eps)\n",
        "# plt.xlim(0,5)\n",
        "# plt.ylim(1,3)\n",
        "plt.plot(eps,lr_train,'purple',label=\"with LR Scheduler\")\n",
        "plt.plot(eps,wo_train[:20], 'lime', label='with Fixed LR')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "gud2xRr7SvPV",
        "outputId": "e5d0260d-666c-4d1e-c6be-8a600ccc8bc4"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "fE_vGL6HQAC2",
        "outputId": "849edee3-b74e-45b8-f408-24aa58906ed3"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.figure(figsize=(8,6))\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Cross-entropy Loss (Val data)')\n",
        "eps = [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20]\n",
        "plt.xticks(eps)\n",
        "# plt.xlim(0,5)\n",
        "# plt.ylim(1,3)\n",
        "plt.plot(eps,lr_val,'purple',label=\"with LR Scheduler\")\n",
        "plt.plot(eps,wo_val[:20], 'lime', label='with Fixed LR')\n",
        "plt.legend()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 594
        },
        "id": "HQeE9dO8QAC3",
        "outputId": "94b8088e-af5e-49af-b263-693e5d1cc47f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tensor(0.6590, device='cuda:0')\n",
            "tensor([0.6408, 0.7416, 0.4600, 0.4951, 0.4889, 0.6047, 0.7054, 0.7745, 0.7925,\n",
            "        0.8440], device='cuda:0')\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "metric = MulticlassAccuracy(num_classes=10, average=None).to('cuda:0')\n",
        "pred_y = torch.argmax(score_val, dim=1)\n",
        "classwise = metric(score_val, test_y[:1000].to('cuda:0'))\n",
        "val_acc_met = Accuracy(task=\"multiclass\", num_classes=10).to('cuda:0')\n",
        "finalacc = val_acc_met(pred_y, test_y[:1000].to('cuda:0'))\n",
        "print(finalacc)\n",
        "print(classwise)\n",
        "###BAR GRAPH OF CLASSWISE ACCURACY\n",
        "plt.figure(figsize=(8,6))\n",
        "classes = np.arange(0,10)\n",
        "plt.xticks(classes)\n",
        "plt.bar(classes, classwise.to('cpu'), color='purple',label='Classwise',width=0.4)\n",
        "plt.xlabel('Classes')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.legend()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "pSuMwm-2QAC3"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3.9.12 ('base')",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.9.12"
    },
    "orig_nbformat": 4,
    "vscode": {
      "interpreter": {
        "hash": "73cba7173f8447e209b3236db8d7a4c2baa473635286a4d66150d0ea51f721be"
      }
    },
    "colab": {
      "provenance": []
    },
    "accelerator": "GPU",
    "gpuClass": "standard",
    "widgets": {
      "application/vnd.jupyter.widget-state+json": {
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