learnrate.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "0sIiWEnGfIAd",
        "outputId": "89e8f694-c22d-49dd-c2f5-c84fd2bf4903"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "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[31m995.9 kB/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[31m10.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: numpy>=1.17.2 in /usr/local/lib/python3.10/dist-packages (from torchmetrics) (1.24.3)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from torchmetrics) (23.1)\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"
          ]
        },
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'1.12.1+cu102'"
            ]
          },
          "execution_count": 1,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "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": "aMNg4xACfIAe",
        "outputId": "b7779924-d276-4e8a-f2bb-d6aec896c0d2"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "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": "dWjViSy9fIAe"
      },
      "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": [
            "9f4f8a142c4b47c6bd7bdcee21bbd007",
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            "febd12c6591748aa8d4e044ae5a001ac",
            "8c64488919964c03b3ec650efe088562",
            "363220d9d14c49c1905987eee1f21095",
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            "d57e9c4bd64744e1957b85967c3a0be1",
            "55df8be2552049d0b1390061997bfe0a"
          ]
        },
        "id": "5T1FulwlfIAf",
        "outputId": "85680154-9337-4828-9764-ad6929c2d118"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Downloading https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to ./data/cifar-10-python.tar.gz\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "9f4f8a142c4b47c6bd7bdcee21bbd007",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/170498071 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "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": "SSxtnigNfIAf"
      },
      "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": "p5MZvuWPfIAg",
        "outputId": "1b52b478-2725-4ee2-d359-166ef669a4c8"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "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": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "train_x.to('cuda:0')\n",
        "train_y.to('cuda:0')\n",
        "test_x.to('cuda:0')\n",
        "test_y.to('cuda:0')\n",
        "encode_y.to('cuda:0')\n",
        "encode_test.to('cuda:0')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "mwm83f3EfIAg"
      },
      "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": "4hPM31GcfIAg"
      },
      "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"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "J-KXGiGEfjn2",
        "outputId": "24682275-a95e-4a6e-abe0-828709b5bf8d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "----EPOCH 1-----\n",
            "VAL ACC EPOCH = 1: 0.5899999737739563\n",
            "TRAIN ACC EPOCH 1: 0.5699999928474426\n",
            "----EPOCH 2-----\n",
            "VAL ACC EPOCH = 2: 0.6729999780654907\n",
            "TRAIN ACC EPOCH 2: 0.6610000133514404\n",
            "----EPOCH 3-----\n",
            "VAL ACC EPOCH = 3: 0.6909999847412109\n",
            "TRAIN ACC EPOCH 3: 0.7020000219345093\n",
            "----EPOCH 4-----\n",
            "VAL ACC EPOCH = 4: 0.7009999752044678\n",
            "TRAIN ACC EPOCH 4: 0.7329999804496765\n",
            "----EPOCH 5-----\n",
            "VAL ACC EPOCH = 5: 0.7200000286102295\n",
            "TRAIN ACC EPOCH 5: 0.7440000176429749\n",
            "----EPOCH 6-----\n",
            "VAL ACC EPOCH = 6: 0.6949999928474426\n",
            "TRAIN ACC EPOCH 6: 0.7369999885559082\n",
            "----EPOCH 7-----\n",
            "VAL ACC EPOCH = 7: 0.6909999847412109\n",
            "TRAIN ACC EPOCH 7: 0.746999979019165\n",
            "----EPOCH 8-----\n",
            "VAL ACC EPOCH = 8: 0.7039999961853027\n",
            "TRAIN ACC EPOCH 8: 0.7580000162124634\n",
            "----EPOCH 9-----\n",
            "VAL ACC EPOCH = 9: 0.7139999866485596\n",
            "TRAIN ACC EPOCH 9: 0.8029999732971191\n",
            "----EPOCH 10-----\n",
            "VAL ACC EPOCH = 10: 0.7099999785423279\n",
            "TRAIN ACC EPOCH 10: 0.8190000057220459\n",
            "----EPOCH 11-----\n",
            "VAL ACC EPOCH = 11: 0.6949999928474426\n",
            "TRAIN ACC EPOCH 11: 0.8059999942779541\n",
            "----EPOCH 12-----\n",
            "VAL ACC EPOCH = 12: 0.6650000214576721\n",
            "TRAIN ACC EPOCH 12: 0.7870000004768372\n",
            "----EPOCH 13-----\n",
            "VAL ACC EPOCH = 13: 0.6690000295639038\n",
            "TRAIN ACC EPOCH 13: 0.7940000295639038\n",
            "----EPOCH 14-----\n",
            "VAL ACC EPOCH = 14: 0.6679999828338623\n",
            "TRAIN ACC EPOCH 14: 0.8240000009536743\n",
            "----EPOCH 15-----\n",
            "VAL ACC EPOCH = 15: 0.6669999957084656\n",
            "TRAIN ACC EPOCH 15: 0.8370000123977661\n",
            "----EPOCH 16-----\n",
            "VAL ACC EPOCH = 16: 0.6940000057220459\n",
            "TRAIN ACC EPOCH 16: 0.8799999952316284\n",
            "----EPOCH 17-----\n",
            "VAL ACC EPOCH = 17: 0.6859999895095825\n",
            "TRAIN ACC EPOCH 17: 0.8989999890327454\n",
            "----EPOCH 18-----\n",
            "VAL ACC EPOCH = 18: 0.6779999732971191\n",
            "TRAIN ACC EPOCH 18: 0.8790000081062317\n",
            "----EPOCH 19-----\n",
            "VAL ACC EPOCH = 19: 0.6890000104904175\n",
            "TRAIN ACC EPOCH 19: 0.8849999904632568\n",
            "----EPOCH 20-----\n",
            "VAL ACC EPOCH = 20: 0.6859999895095825\n",
            "TRAIN ACC EPOCH 20: 0.9049999713897705\n",
            "tensor([2320.8611, 1691.8088, 1412.4016, 1235.2119, 1102.7893, 1000.4318,\n",
            "         907.3509,  825.9540,  765.4579,  688.0619,  638.7194,  585.1105,\n",
            "         562.6476,  512.4421,  466.1303,  428.9043,  399.7799,  384.0531,\n",
            "         358.1672,  329.2685])\n",
            "tensor([1.1596, 0.9463, 0.8834, 0.8476, 0.8354, 0.8823, 0.9423, 0.9424, 0.9440,\n",
            "        1.0265, 1.1563, 1.3823, 1.4400, 1.4801, 1.6012, 1.5168, 1.5237, 1.6540,\n",
            "        1.7389, 1.8095])\n",
            "tensor([4.6432, 3.3847, 2.8257, 2.4712, 2.2063, 2.0015, 1.8153, 1.6524, 1.5314,\n",
            "        1.3766, 1.2778, 1.1706, 1.1257, 1.0252, 0.9326, 0.8581, 0.7998, 0.7684,\n",
            "        0.7166, 0.6587])\n",
            "tensor([0.1160, 0.0946, 0.0883, 0.0848, 0.0835, 0.0882, 0.0942, 0.0942, 0.0944,\n",
            "        0.1026, 0.1156, 0.1382, 0.1440, 0.1480, 0.1601, 0.1517, 0.1524, 0.1654,\n",
            "        0.1739, 0.1810])\n",
            "tensor([0.5700, 0.6610, 0.7020, 0.7330, 0.7440, 0.7370, 0.7470, 0.7580, 0.8030,\n",
            "        0.8190, 0.8060, 0.7870, 0.7940, 0.8240, 0.8370, 0.8800, 0.8990, 0.8790,\n",
            "        0.8850, 0.9050])\n",
            "tensor([0.5900, 0.6730, 0.6910, 0.7010, 0.7200, 0.6950, 0.6910, 0.7040, 0.7140,\n",
            "        0.7100, 0.6950, 0.6650, 0.6690, 0.6680, 0.6670, 0.6940, 0.6860, 0.6780,\n",
            "        0.6890, 0.6860])\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).to('cpu')\n",
        "    finalacc_val = acc_met(pred_val, test_y[:1000])\n",
        "    score_tr = model.forward(train_x[:1000].to('cuda:0'),1000).to('cuda:0')\n",
        "    pred_tr = torch.argmax(score_tr, dim=1).to('cpu')\n",
        "    finalacc_tr = acc_met(pred_tr, train_y[:1000])  \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()\n",
        "print(trainaccs)\n",
        "print(valaccs)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "C98rHY0YfIAh",
        "outputId": "209478e5-30a9-4466-a36e-07d51ac0fc37"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "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": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "nbDgFMnifIAh",
        "outputId": "44e70d1a-d536-4fa7-9dd9-7776dae14a4d"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "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",
      "execution_count": 14,
      "metadata": {
        "id": "U_QNx7HlfIAh"
      },
      "outputs": [],
      "source": [
        "###COMPARE DIFFERENT LEARNING RATES:\n",
        "loss1 = np.array([6.6126, 5.6835, 5.4495, 5.3000, 5.1790, 5.0811, 5.0014, 4.9343, 4.8771,\n",
        "        4.8264, 4.7791, 4.7340, 4.6905, 4.6481, 4.6070])\n",
        "loss2 = 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",
        "loss3 = np.array([69.0353,  7.2338,  7.2340,  7.2340,  7.2340,  7.2340,  7.2340,  7.2340,\n",
        "         7.2340,  7.2340,  7.2340,  7.2340,  7.2340,  7.2340,  7.2340,  7.2340,\n",
        "         7.2340,  7.2340,  7.2340,  7.2340])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "7MyNXoB0fIAh",
        "outputId": "5d5f6b31-ed71-4631-b533-f27fdcff926d"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(8,6))\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Train Data: Cross-entropy Loss')\n",
        "eps = np.arange(1,16)\n",
        "plt.ylim(0,15)\n",
        "plt.xticks(np.arange(0,16))\n",
        "plt.plot(eps,loss1[:15],'m',label=\"LR=0.00001\")\n",
        "plt.plot(eps,loss2[:15], 'g', label='LR=0.001')\n",
        "plt.plot(eps, loss3[:15],'cyan',label='LR=0.1')\n",
        "plt.legend()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 611
        },
        "id": "FrjaeF_ffIAi",
        "outputId": "cda65a08-2edb-42c4-aff6-2b8d11d3d0eb"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "tensor(0.6860)\n",
            "tensor([0.6214, 0.7753, 0.5200, 0.6990, 0.5667, 0.5349, 0.8036, 0.7549, 0.7358,\n",
            "        0.7982])\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "WARNING:matplotlib.legend:No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n"
          ]
        },
        {
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "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',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": "TVrym4UBfIAi"
      },
      "outputs": [],
      "source": []
    }
  ],
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      "language": "python",
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