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  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "FgB3hhqT3g-k"
      },
      "source": [
        "!pip install torch torchvision wandb -qqq"
      ],
      "execution_count": 93,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3Ir9W_e234Zd"
      },
      "source": [
        "!git clone https://github.com/jgamper/intrinsic-dimensionality.git -q"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VLYo98tidUs4",
        "outputId": "8c863207-c93f-4def-f18b-f81916adb50b"
      },
      "source": [
        "import wandb\n",
        "!wandb login\n",
        "# wandb.init(project='id_test_toy', entity='nayannkote')"
      ],
      "execution_count": 109,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33mnayannkote\u001b[0m (use `wandb login --relogin` to force relogin)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O05qQQJr3Wre"
      },
      "source": [
        "import os\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "import torch.optim as optim\n",
        "from torchvision import datasets, transforms\n",
        "from torch.optim.lr_scheduler import StepLR\n",
        "\n",
        "import importlib\n",
        "fastfood = importlib.import_module(\"intrinsic-dimensionality.intrinsic.fastfood\")"
      ],
      "execution_count": 112,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gR6gEdcFRHXK"
      },
      "source": [
        "class FCNet(nn.Module):\n",
        "    def __init__(self):\n",
        "        super(FCNet, self).__init__()\n",
        "        # 784–200–200–10 from uber-research paper\n",
        "        self.fc1 = nn.Linear(784,200)\n",
        "        self.fc2 = nn.Linear(200,200)\n",
        "        self.fc3 = nn.Linear(200,10)\n",
        "\n",
        "    def forward(self,x):\n",
        "        x = nn.Flatten(start_dim=1)(x)\n",
        "        x = self.fc1(x)\n",
        "        x = F.relu(x)\n",
        "        x = self.fc2(x)\n",
        "        x = F.relu(x)\n",
        "        x = self.fc3(x)\n",
        "        output = F.log_softmax(x,dim=1)\n",
        "\n",
        "        return output"
      ],
      "execution_count": 113,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Rvy_oywAOvKl"
      },
      "source": [
        "def train(\n",
        "    model,\n",
        "    device,\n",
        "    train_loader,\n",
        "    optimizer,\n",
        "    epoch,\n",
        "    log_interval=100\n",
        "):\n",
        "    model.train()\n",
        "    for batch_idx, (data, target) in enumerate(train_loader):\n",
        "        data, target = data.to(device), target.to(device)\n",
        "        optimizer.zero_grad()\n",
        "        output = model(data)\n",
        "        loss = F.nll_loss(output, target)\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "        \n",
        "        if batch_idx%log_interval == 0:\n",
        "            print(f\"Epoch:{epoch+1}\\t\\t Batch Number:{batch_idx}\\t\\t Loss:{loss.item()}\")\n",
        "            wandb.log({\"batch loss\":loss.item()})\n",
        "\n",
        "def test(\n",
        "    model,\n",
        "    device,\n",
        "    test_loader,\n",
        "    test_batch_size,\n",
        "    ID,\n",
        "    log_interval=100\n",
        "):\n",
        "    model.eval()\n",
        "    test_loss = 0\n",
        "    num_correct = 0\n",
        "\n",
        "    with torch.no_grad():\n",
        "        for data, target in test_loader:\n",
        "            data, target = data.to(device), target.to(device)\n",
        "            output = model(data)\n",
        "            test_loss+=F.nll_loss(output,target)\n",
        "            pred = output.argmax(dim=1,keepdim=True)\n",
        "            num_correct += pred.eq(target.view_as(pred)).sum().item()\n",
        "\n",
        "    test_loss /= (len(test_loader)*test_batch_size) # Dividing by num_batchs*batch_size\n",
        "    acc = num_correct*100/(len(test_loader)*test_batch_size)\n",
        "    wandb.log({\"test_loss\":test_loss, \"test_accuracy\":acc}) \n",
        "\n",
        "    print(f\"\\nTest Loss: {test_loss}\\t Accuarcy:{acc}%\\n\")"
      ],
      "execution_count": 114,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aBACQIiA4Yvo"
      },
      "source": [
        "def main(\n",
        "    ID,\n",
        "    num_epochs,\n",
        "    train_batch_size,\n",
        "    test_batch_size,\n",
        "    output_dir,\n",
        "    learning_rate=1.0,\n",
        "    gamma=0.1,\n",
        "    use_cuda=True,\n",
        "    seed=69,\n",
        "    log_interval=100,\n",
        "):\n",
        "    \n",
        "    # Use GPU or not\n",
        "    use_cuda = torch.cuda.is_available() and use_cuda\n",
        "    device = torch.device(\"cuda\" if use_cuda else \"cpu\")\n",
        "\n",
        "    # Setting seed\n",
        "    torch.manual_seed(seed)\n",
        "\n",
        "    # Setting train and test kwargs\n",
        "    train_kwargs = {\"batch_size\":train_batch_size}\n",
        "    test_kwargs = {\"batch_size\":test_batch_size}\n",
        "\n",
        "    if use_cuda:\n",
        "        cuda_kwargs={\n",
        "            \"num_workers\":1, \n",
        "            \"pin_memory\":True, # Why pinning is faster, ref : https://forums.developer.nvidia.com/t/why-using-pinned-memory-is-faster/1948\n",
        "            \"shuffle\":True\n",
        "        }\n",
        "        train_kwargs.update(cuda_kwargs)\n",
        "        test_kwargs.update(cuda_kwargs)\n",
        "\n",
        "    # Defining image transform, ref : https://stackoverflow.com/questions/63746182/correct-way-of-normalizing-and-scaling-the-mnist-dataset\n",
        "    transform = transforms.Compose(\n",
        "        [\n",
        "         transforms.ToTensor(), # Converts input image to 3 dim tensor and values b/w [0,1]\n",
        "         transforms.Normalize((0.1307,), (0.3081, )) # Mean 0.1307 and StdDev 0.3081\n",
        "        ]\n",
        "    )\n",
        "\n",
        "    # Getting train and test datasets\n",
        "    train_data = datasets.MNIST(\"/content/\", train=True, download=True, transform=transform)\n",
        "    test_data = datasets.MNIST(\"/content/\", train=False, download=True, transform=transform)\n",
        "\n",
        "    # Dataloaders\n",
        "    train_loader = torch.utils.data.DataLoader(train_data,**train_kwargs)\n",
        "    test_loader = torch.utils.data.DataLoader(test_data,**test_kwargs)\n",
        "\n",
        "    # Model, Optimizer and Learning Rate Scheduler\n",
        "    model = FCNet().to(device)\n",
        "    if ID:\n",
        "        model = fastfood.FastfoodWrap(model, intrinsic_dimension=ID, device=device)\n",
        "    optimizer = optim.Adadelta(model.parameters(), lr=learning_rate)\n",
        "    lr_scheduler = StepLR(optimizer, step_size=1, gamma=gamma)\n",
        "\n",
        "    # Training model\n",
        "    print(f\"Training model with ID: {ID}\\n\")\n",
        "    for epoch in range(num_epochs):\n",
        "        train(model,device,train_loader,optimizer,epoch,log_interval)\n",
        "        test(model,device,test_loader,test_batch_size,ID)\n",
        "        lr_scheduler.step()\n",
        "\n",
        "    if output_dir!=None:\n",
        "        torch.save({\n",
        "            'model_state_dict': model.state_dict(),\n",
        "            'optimizer_state_dict': optimizer.state_dict(),\n",
        "            'scheduler_state_dict': lr_scheduler.state_dict(),\n",
        "            'epoch': epoch+1\n",
        "        }, os.path.join(output_dir,f\"mnist_fc_ID{ID}.pt\"))"
      ],
      "execution_count": 115,
      "outputs": []
    },
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          "height": 1000,
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        "id": "pyvzEo2E43mF",
        "outputId": "9b3287da-4d83-4083-8e2f-0e94bac6ffb7"
      },
      "source": [
        "for ID in range(0,1001,100):\n",
        "    # # Custom x axis\n",
        "    # wandb.define_metric(\"ID\")\n",
        "    # # Values which are plotted with custom x axis\n",
        "    # wandb.define_metric(\"test*\", step_metric=\"ID\")\n",
        "    wandb.init(reinit=True, project='id_test_toy', entity='nayannkote', name=f\"ID_{ID}\", resume=None)\n",
        "    main(ID=ID,num_epochs=1,train_batch_size=128,test_batch_size=128,output_dir=\"/content/\")"
      ],
      "execution_count": 118,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "Finishing last run (ID:3oead8y6) before initializing another..."
            ],
            "text/plain": [
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            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<br/>Waiting for W&B process to finish, PID 1867<br/>Program ended successfully."
            ],
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              "<IPython.core.display.HTML object>"
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          },
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            "text/plain": [
              "VBox(children=(Label(value=' 0.00MB of 0.00MB uploaded (0.00MB deduped)\\r'), FloatProgress(value=1.0, max=1.0)…"
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "text": [
            "\u001b[34m\u001b[1mwandb\u001b[0m: \u001b[32m\u001b[41mERROR\u001b[0m Control-C detected -- Run data was not synced\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "...Successfully finished last run (ID:3oead8y6). Initializing new run:<br/><br/>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Tracking run with wandb version 0.12.1<br/>\n",
              "                Syncing run <strong style=\"color:#cdcd00\">ID_0</strong> to <a href=\"https://wandb.ai\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://wandb.ai/nayannkote/id_test_toy\" target=\"_blank\">https://wandb.ai/nayannkote/id_test_toy</a><br/>\n",
              "                Run page: <a href=\"https://wandb.ai/nayannkote/id_test_toy/runs/d901ua75\" target=\"_blank\">https://wandb.ai/nayannkote/id_test_toy/runs/d901ua75</a><br/>\n",
              "                Run data is saved locally in <code>/content/wandb/run-20210827_081357-d901ua75</code><br/><br/>\n",
              "            "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "text": [
            "Training model with ID: 0\n",
            "\n",
            "Epoch:1\t\t Batch Number:0\t\t Loss:2.2965505123138428\n",
            "Epoch:1\t\t Batch Number:100\t\t Loss:0.18629644811153412\n",
            "Epoch:1\t\t Batch Number:200\t\t Loss:0.1204913854598999\n",
            "Epoch:1\t\t Batch Number:300\t\t Loss:0.20646442472934723\n",
            "Epoch:1\t\t Batch Number:400\t\t Loss:0.18631988763809204\n",
            "\n",
            "Test Loss: 0.0015600861515849829\t Accuarcy:92.72151898734177%\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "Finishing last run (ID:d901ua75) before initializing another..."
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<br/>Waiting for W&B process to finish, PID 1909<br/>Program ended successfully."
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              "                Project page: <a href=\"https://wandb.ai/nayannkote/id_test_toy\" target=\"_blank\">https://wandb.ai/nayannkote/id_test_toy</a><br/>\n",
              "                Run page: <a href=\"https://wandb.ai/nayannkote/id_test_toy/runs/39lwb62n\" target=\"_blank\">https://wandb.ai/nayannkote/id_test_toy/runs/39lwb62n</a><br/>\n",
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            "Epoch:1\t\t Batch Number:200\t\t Loss:2.097813367843628\n",
            "Epoch:1\t\t Batch Number:300\t\t Loss:1.8429356813430786\n",
            "Epoch:1\t\t Batch Number:400\t\t Loss:1.7076603174209595\n",
            "\n",
            "Test Loss: 0.01227465458214283\t Accuarcy:49.61431962025316%\n",
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              "                Tracking run with wandb version 0.12.1<br/>\n",
              "                Syncing run <strong style=\"color:#cdcd00\">ID_200</strong> to <a href=\"https://wandb.ai\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://wandb.ai/nayannkote/id_test_toy\" target=\"_blank\">https://wandb.ai/nayannkote/id_test_toy</a><br/>\n",
              "                Run page: <a href=\"https://wandb.ai/nayannkote/id_test_toy/runs/3egzx1qk\" target=\"_blank\">https://wandb.ai/nayannkote/id_test_toy/runs/3egzx1qk</a><br/>\n",
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            "Epoch:1\t\t Batch Number:100\t\t Loss:2.138045072555542\n",
            "Epoch:1\t\t Batch Number:200\t\t Loss:1.7940938472747803\n",
            "Epoch:1\t\t Batch Number:300\t\t Loss:1.4335778951644897\n",
            "Epoch:1\t\t Batch Number:400\t\t Loss:1.336256504058838\n",
            "\n",
            "Test Loss: 0.009130015037953854\t Accuarcy:62.78678797468354%\n",
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              "<tr><td>batch loss</td><td>1.33626</td></tr><tr><td>_runtime</td><td>58</td></tr><tr><td>_timestamp</td><td>1630052202</td></tr><tr><td>_step</td><td>5</td></tr><tr><td>test_loss</td><td>0.00913</td></tr><tr><td>test_accuracy</td><td>62.78679</td></tr></table>"
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              "                Tracking run with wandb version 0.12.1<br/>\n",
              "                Syncing run <strong style=\"color:#cdcd00\">ID_300</strong> to <a href=\"https://wandb.ai\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://wandb.ai/nayannkote/id_test_toy\" target=\"_blank\">https://wandb.ai/nayannkote/id_test_toy</a><br/>\n",
              "                Run page: <a href=\"https://wandb.ai/nayannkote/id_test_toy/runs/1o9yyz0a\" target=\"_blank\">https://wandb.ai/nayannkote/id_test_toy/runs/1o9yyz0a</a><br/>\n",
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            "Epoch:1\t\t Batch Number:0\t\t Loss:2.2965505123138428\n",
            "Epoch:1\t\t Batch Number:100\t\t Loss:1.9123228788375854\n",
            "Epoch:1\t\t Batch Number:200\t\t Loss:1.1883898973464966\n",
            "Epoch:1\t\t Batch Number:300\t\t Loss:0.933940052986145\n",
            "Epoch:1\t\t Batch Number:400\t\t Loss:0.9550138115882874\n",
            "\n",
            "Test Loss: 0.006449082400649786\t Accuarcy:72.83425632911393%\n",
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              "<tr><td>batch loss</td><td>0.95501</td></tr><tr><td>_runtime</td><td>58</td></tr><tr><td>_timestamp</td><td>1630052264</td></tr><tr><td>_step</td><td>5</td></tr><tr><td>test_loss</td><td>0.00645</td></tr><tr><td>test_accuracy</td><td>72.83426</td></tr></table>"
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              "                Project page: <a href=\"https://wandb.ai/nayannkote/id_test_toy\" target=\"_blank\">https://wandb.ai/nayannkote/id_test_toy</a><br/>\n",
              "                Run page: <a href=\"https://wandb.ai/nayannkote/id_test_toy/runs/210iuq76\" target=\"_blank\">https://wandb.ai/nayannkote/id_test_toy/runs/210iuq76</a><br/>\n",
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            "Epoch:1\t\t Batch Number:100\t\t Loss:1.7507984638214111\n",
            "Epoch:1\t\t Batch Number:200\t\t Loss:0.8930684328079224\n",
            "Epoch:1\t\t Batch Number:300\t\t Loss:0.8180958032608032\n",
            "Epoch:1\t\t Batch Number:400\t\t Loss:0.7962849736213684\n",
            "\n",
            "Test Loss: 0.00542991328984499\t Accuarcy:76.99762658227849%\n",
            "\n"
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              "                Syncing run <strong style=\"color:#cdcd00\">ID_500</strong> to <a href=\"https://wandb.ai\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://wandb.ai/nayannkote/id_test_toy\" target=\"_blank\">https://wandb.ai/nayannkote/id_test_toy</a><br/>\n",
              "                Run page: <a href=\"https://wandb.ai/nayannkote/id_test_toy/runs/3qaau1df\" target=\"_blank\">https://wandb.ai/nayannkote/id_test_toy/runs/3qaau1df</a><br/>\n",
              "                Run data is saved locally in <code>/content/wandb/run-20210827_081846-3qaau1df</code><br/><br/>\n",
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            "Epoch:1\t\t Batch Number:100\t\t Loss:1.6568373441696167\n",
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            "Epoch:1\t\t Batch Number:300\t\t Loss:0.6454060077667236\n",
            "Epoch:1\t\t Batch Number:400\t\t Loss:0.7094405293464661\n",
            "\n",
            "Test Loss: 0.004516534972935915\t Accuarcy:80.50830696202532%\n",
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              "                Tracking run with wandb version 0.12.1<br/>\n",
              "                Syncing run <strong style=\"color:#cdcd00\">ID_600</strong> to <a href=\"https://wandb.ai\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://wandb.ai/nayannkote/id_test_toy\" target=\"_blank\">https://wandb.ai/nayannkote/id_test_toy</a><br/>\n",
              "                Run page: <a href=\"https://wandb.ai/nayannkote/id_test_toy/runs/230wivo8\" target=\"_blank\">https://wandb.ai/nayannkote/id_test_toy/runs/230wivo8</a><br/>\n",
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            "Epoch:1\t\t Batch Number:200\t\t Loss:0.6697553396224976\n",
            "Epoch:1\t\t Batch Number:300\t\t Loss:0.5585227608680725\n",
            "Epoch:1\t\t Batch Number:400\t\t Loss:0.6850382685661316\n",
            "\n",
            "Test Loss: 0.00452557485550642\t Accuarcy:80.9434335443038%\n",
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              "                Tracking run with wandb version 0.12.1<br/>\n",
              "                Syncing run <strong style=\"color:#cdcd00\">ID_700</strong> to <a href=\"https://wandb.ai\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://wandb.ai/nayannkote/id_test_toy\" target=\"_blank\">https://wandb.ai/nayannkote/id_test_toy</a><br/>\n",
              "                Run page: <a href=\"https://wandb.ai/nayannkote/id_test_toy/runs/gxfy7fkq\" target=\"_blank\">https://wandb.ai/nayannkote/id_test_toy/runs/gxfy7fkq</a><br/>\n",
              "                Run data is saved locally in <code>/content/wandb/run-20210827_082050-gxfy7fkq</code><br/><br/>\n",
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            "Epoch:1\t\t Batch Number:0\t\t Loss:2.2965505123138428\n",
            "Epoch:1\t\t Batch Number:100\t\t Loss:1.3173428773880005\n",
            "Epoch:1\t\t Batch Number:200\t\t Loss:0.5984770059585571\n",
            "Epoch:1\t\t Batch Number:300\t\t Loss:0.549479067325592\n",
            "Epoch:1\t\t Batch Number:400\t\t Loss:0.6874551773071289\n",
            "\n",
            "Test Loss: 0.0039199963212013245\t Accuarcy:83.14873417721519%\n",
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              "<br/>Waiting for W&B process to finish, PID 2219<br/>Program ended successfully."
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        {
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              "Find user logs for this run at: <code>/content/wandb/run-20210827_082050-gxfy7fkq/logs/debug.log</code>"
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            "text/plain": [
              "<IPython.core.display.HTML object>"
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          "metadata": {}
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        {
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          "data": {
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              "Find internal logs for this run at: <code>/content/wandb/run-20210827_082050-gxfy7fkq/logs/debug-internal.log</code>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
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              "<h3>Run summary:</h3><br/><style>\n",
              "    table.wandb td:nth-child(1) { padding: 0 10px; text-align: right }\n",
              "    </style><table class=\"wandb\">\n",
              "<tr><td>batch loss</td><td>0.68746</td></tr><tr><td>_runtime</td><td>58</td></tr><tr><td>_timestamp</td><td>1630052511</td></tr><tr><td>_step</td><td>5</td></tr><tr><td>test_loss</td><td>0.00392</td></tr><tr><td>test_accuracy</td><td>83.14873</td></tr></table>"
            ],
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              "<IPython.core.display.HTML object>"
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              "<h3>Run history:</h3><br/><style>\n",
              "    table.wandb td:nth-child(1) { padding: 0 10px; text-align: right }\n",
              "    </style><table class=\"wandb\">\n",
              "<tr><td>batch loss</td><td>█▄▁▁▂</td></tr><tr><td>_runtime</td><td>▁▂▄▅▇█</td></tr><tr><td>_timestamp</td><td>▁▂▄▅▇█</td></tr><tr><td>_step</td><td>▁▂▄▅▇█</td></tr><tr><td>test_loss</td><td>▁</td></tr><tr><td>test_accuracy</td><td>▁</td></tr></table><br/>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {}
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        {
          "output_type": "display_data",
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            "text/html": [
              "Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)"
            ],
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              "<IPython.core.display.HTML object>"
            ]
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            "text/html": [
              "\n",
              "                    <br/>Synced <strong style=\"color:#cdcd00\">ID_700</strong>: <a href=\"https://wandb.ai/nayannkote/id_test_toy/runs/gxfy7fkq\" target=\"_blank\">https://wandb.ai/nayannkote/id_test_toy/runs/gxfy7fkq</a><br/>\n",
              "                "
            ],
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              "<IPython.core.display.HTML object>"
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          "metadata": {}
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        {
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            "text/html": [
              "...Successfully finished last run (ID:gxfy7fkq). Initializing new run:<br/><br/>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {}
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        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Tracking run with wandb version 0.12.1<br/>\n",
              "                Syncing run <strong style=\"color:#cdcd00\">ID_800</strong> to <a href=\"https://wandb.ai\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://wandb.ai/nayannkote/id_test_toy\" target=\"_blank\">https://wandb.ai/nayannkote/id_test_toy</a><br/>\n",
              "                Run page: <a href=\"https://wandb.ai/nayannkote/id_test_toy/runs/y1duegln\" target=\"_blank\">https://wandb.ai/nayannkote/id_test_toy/runs/y1duegln</a><br/>\n",
              "                Run data is saved locally in <code>/content/wandb/run-20210827_082151-y1duegln</code><br/><br/>\n",
              "            "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
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          "metadata": {}
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        {
          "output_type": "stream",
          "text": [
            "Training model with ID: 800\n",
            "\n",
            "Epoch:1\t\t Batch Number:0\t\t Loss:2.2965505123138428\n",
            "Epoch:1\t\t Batch Number:100\t\t Loss:1.1542048454284668\n",
            "Epoch:1\t\t Batch Number:200\t\t Loss:0.49162694811820984\n",
            "Epoch:1\t\t Batch Number:300\t\t Loss:0.5034583210945129\n",
            "Epoch:1\t\t Batch Number:400\t\t Loss:0.5398048162460327\n",
            "\n",
            "Test Loss: 0.003861360251903534\t Accuarcy:83.34651898734177%\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "Finishing last run (ID:y1duegln) before initializing another..."
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<br/>Waiting for W&B process to finish, PID 2258<br/>Program ended successfully."
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {}
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        {
          "output_type": "display_data",
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              "VBox(children=(Label(value=' 0.00MB of 0.00MB uploaded (0.00MB deduped)\\r'), FloatProgress(value=1.0, max=1.0)…"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "Find user logs for this run at: <code>/content/wandb/run-20210827_082151-y1duegln/logs/debug.log</code>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "Find internal logs for this run at: <code>/content/wandb/run-20210827_082151-y1duegln/logs/debug-internal.log</code>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<h3>Run summary:</h3><br/><style>\n",
              "    table.wandb td:nth-child(1) { padding: 0 10px; text-align: right }\n",
              "    </style><table class=\"wandb\">\n",
              "<tr><td>batch loss</td><td>0.5398</td></tr><tr><td>_runtime</td><td>58</td></tr><tr><td>_timestamp</td><td>1630052573</td></tr><tr><td>_step</td><td>5</td></tr><tr><td>test_loss</td><td>0.00386</td></tr><tr><td>test_accuracy</td><td>83.34652</td></tr></table>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<h3>Run history:</h3><br/><style>\n",
              "    table.wandb td:nth-child(1) { padding: 0 10px; text-align: right }\n",
              "    </style><table class=\"wandb\">\n",
              "<tr><td>batch loss</td><td>█▄▁▁▁</td></tr><tr><td>_runtime</td><td>▁▂▄▅▆█</td></tr><tr><td>_timestamp</td><td>▁▂▄▅▆█</td></tr><tr><td>_step</td><td>▁▂▄▅▇█</td></tr><tr><td>test_loss</td><td>▁</td></tr><tr><td>test_accuracy</td><td>▁</td></tr></table><br/>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)"
            ],
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              "<IPython.core.display.HTML object>"
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          },
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          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                    <br/>Synced <strong style=\"color:#cdcd00\">ID_800</strong>: <a href=\"https://wandb.ai/nayannkote/id_test_toy/runs/y1duegln\" target=\"_blank\">https://wandb.ai/nayannkote/id_test_toy/runs/y1duegln</a><br/>\n",
              "                "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
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          },
          "metadata": {}
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        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "...Successfully finished last run (ID:y1duegln). Initializing new run:<br/><br/>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Tracking run with wandb version 0.12.1<br/>\n",
              "                Syncing run <strong style=\"color:#cdcd00\">ID_900</strong> to <a href=\"https://wandb.ai\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://wandb.ai/nayannkote/id_test_toy\" target=\"_blank\">https://wandb.ai/nayannkote/id_test_toy</a><br/>\n",
              "                Run page: <a href=\"https://wandb.ai/nayannkote/id_test_toy/runs/3scmpzv8\" target=\"_blank\">https://wandb.ai/nayannkote/id_test_toy/runs/3scmpzv8</a><br/>\n",
              "                Run data is saved locally in <code>/content/wandb/run-20210827_082253-3scmpzv8</code><br/><br/>\n",
              "            "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
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        {
          "output_type": "stream",
          "text": [
            "Training model with ID: 900\n",
            "\n",
            "Epoch:1\t\t Batch Number:0\t\t Loss:2.2965505123138428\n",
            "Epoch:1\t\t Batch Number:100\t\t Loss:1.0084731578826904\n",
            "Epoch:1\t\t Batch Number:200\t\t Loss:0.5628271102905273\n",
            "Epoch:1\t\t Batch Number:300\t\t Loss:0.6070367693901062\n",
            "Epoch:1\t\t Batch Number:400\t\t Loss:0.5674901008605957\n",
            "\n",
            "Test Loss: 0.003637476358562708\t Accuarcy:84.59256329113924%\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "Finishing last run (ID:3scmpzv8) before initializing another..."
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<br/>Waiting for W&B process to finish, PID 2303<br/>Program ended successfully."
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
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              "VBox(children=(Label(value=' 0.00MB of 0.00MB uploaded (0.00MB deduped)\\r'), FloatProgress(value=1.0, max=1.0)…"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "Find user logs for this run at: <code>/content/wandb/run-20210827_082253-3scmpzv8/logs/debug.log</code>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "Find internal logs for this run at: <code>/content/wandb/run-20210827_082253-3scmpzv8/logs/debug-internal.log</code>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<h3>Run summary:</h3><br/><style>\n",
              "    table.wandb td:nth-child(1) { padding: 0 10px; text-align: right }\n",
              "    </style><table class=\"wandb\">\n",
              "<tr><td>batch loss</td><td>0.56749</td></tr><tr><td>_runtime</td><td>58</td></tr><tr><td>_timestamp</td><td>1630052634</td></tr><tr><td>_step</td><td>5</td></tr><tr><td>test_loss</td><td>0.00364</td></tr><tr><td>test_accuracy</td><td>84.59256</td></tr></table>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<h3>Run history:</h3><br/><style>\n",
              "    table.wandb td:nth-child(1) { padding: 0 10px; text-align: right }\n",
              "    </style><table class=\"wandb\">\n",
              "<tr><td>batch loss</td><td>█▃▁▁▁</td></tr><tr><td>_runtime</td><td>▁▂▄▅▆█</td></tr><tr><td>_timestamp</td><td>▁▂▄▅▆█</td></tr><tr><td>_step</td><td>▁▂▄▅▇█</td></tr><tr><td>test_loss</td><td>▁</td></tr><tr><td>test_accuracy</td><td>▁</td></tr></table><br/>"
            ],
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              "<IPython.core.display.HTML object>"
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          "metadata": {}
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        {
          "output_type": "display_data",
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            "text/html": [
              "Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
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          },
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        {
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            "text/html": [
              "\n",
              "                    <br/>Synced <strong style=\"color:#cdcd00\">ID_900</strong>: <a href=\"https://wandb.ai/nayannkote/id_test_toy/runs/3scmpzv8\" target=\"_blank\">https://wandb.ai/nayannkote/id_test_toy/runs/3scmpzv8</a><br/>\n",
              "                "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "...Successfully finished last run (ID:3scmpzv8). Initializing new run:<br/><br/>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
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          },
          "metadata": {}
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        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Tracking run with wandb version 0.12.1<br/>\n",
              "                Syncing run <strong style=\"color:#cdcd00\">ID_1000</strong> to <a href=\"https://wandb.ai\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://wandb.ai/nayannkote/id_test_toy\" target=\"_blank\">https://wandb.ai/nayannkote/id_test_toy</a><br/>\n",
              "                Run page: <a href=\"https://wandb.ai/nayannkote/id_test_toy/runs/3dwpnhnl\" target=\"_blank\">https://wandb.ai/nayannkote/id_test_toy/runs/3dwpnhnl</a><br/>\n",
              "                Run data is saved locally in <code>/content/wandb/run-20210827_082354-3dwpnhnl</code><br/><br/>\n",
              "            "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "text": [
            "Training model with ID: 1000\n",
            "\n",
            "Epoch:1\t\t Batch Number:0\t\t Loss:2.2965505123138428\n",
            "Epoch:1\t\t Batch Number:100\t\t Loss:0.9048476219177246\n",
            "Epoch:1\t\t Batch Number:200\t\t Loss:0.4023589789867401\n",
            "Epoch:1\t\t Batch Number:300\t\t Loss:0.48732519149780273\n",
            "Epoch:1\t\t Batch Number:400\t\t Loss:0.5104105472564697\n",
            "\n",
            "Test Loss: 0.003414246952161193\t Accuarcy:85.5320411392405%\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "t9Nk9nxXZaMi",
        "outputId": "70d7d665-d893-4265-d12a-b6262e560157"
      },
      "source": [
        "baseline_model = FCNet()\n",
        "trainable_params = 0\n",
        "layers = 0\n",
        "for name, param in baseline_model.named_parameters():\n",
        "    layers+=1\n",
        "    # print(name, param.size())\n",
        "    if param.requires_grad : \n",
        "        trainable_params+=np.prod(param.size())\n",
        "\n",
        "print(f\"Trainable params : {trainable_params} and layers : {layers}\")"
      ],
      "execution_count": 119,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Trainable params : 199210 and layers : 6\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "USt1yRnSxVzn"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}