Gesture_CNN.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Gesture_CNN.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PSKvTGOp0G5s",
        "outputId": "3114832b-1c41-4b21-d0cd-aa786485c045"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gFv8FHxi6T7I",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "22322bc8-02e4-46f5-b61d-6277b187c2d3"
      },
      "source": [
        "!unzip '/content/drive/MyDrive/gesture.zip' -d 'data'"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Archive:  /content/drive/MyDrive/gesture.zip\n",
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          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lp-V9OrO7aAG"
      },
      "source": [
        "import os\n",
        "import torch\n",
        "import torchvision\n",
        "from torchvision.datasets.utils import download_url\n",
        "from torch.utils.data import random_split"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z8Xp0sXv6yMk",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1aa39ace-cc45-46dc-cc6d-95b568f0cc6f"
      },
      "source": [
        "data_dir  = '/content/data/images/'\n",
        "classes = os.listdir(data_dir)\n",
        "print(classes)"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['3-fist', '4-moved_fist', '2-L', '11-none', '1-palm', '9-c', '7-ok', '6-index', '10-down', '5-thumb', '8-moved_palm']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i0DSPZwF7jSX"
      },
      "source": [
        "from torchvision.datasets import ImageFolder\n",
        "from torchvision.transforms import ToTensor\n",
        "from torchvision import datasets, models, transforms\n",
        "data_tfms = (transforms.Compose([transforms.Resize((256,256)),transforms.ToTensor()]))"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3ib_wdgI7tAR"
      },
      "source": [
        "dataset = ImageFolder(data_dir, data_tfms)"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CLqSusI58aLx"
      },
      "source": [
        "random_seed = 42\n",
        "torch.manual_seed(random_seed);"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zckWoelA8cc9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "393cfc69-c0b7-402c-d094-afec3409fb1c"
      },
      "source": [
        "val_size = 500\n",
        "train_size = len(dataset) - val_size\n",
        "\n",
        "train_ds, val_ds = random_split(dataset, [train_size, val_size])\n",
        "len(train_ds), len(val_ds)"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2800, 500)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jL_r9ji_8faq"
      },
      "source": [
        "from torch.utils.data.dataloader import DataLoader\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "batch_size=32\n",
        "\n",
        "train_dl = DataLoader(train_ds, batch_size, shuffle=True, num_workers=4, pin_memory=True)\n",
        "val_dl = DataLoader(val_ds, batch_size*2, num_workers=4, pin_memory=True)"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yODZc3eJ8iig",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 118
        },
        "outputId": "23da9436-6338-4486-a04a-769e991c5d7c"
      },
      "source": [
        "from torchvision.utils import make_grid\n",
        "\n",
        "def show_batch(dl):\n",
        "    for images, labels in dl:\n",
        "        fig, ax = plt.subplots(figsize=(12, 6))\n",
        "        ax.set_xticks([]); ax.set_yticks([])\n",
        "        ax.imshow(make_grid(images, nrow=16).permute(1, 2, 0))\n",
        "        break\n",
        "\n",
        "show_batch(train_dl)"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FuHw2K5g8kpc"
      },
      "source": [
        "import torch.nn as nn\n",
        "import torch.nn.functional as F"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FvL1y_DU8uXL"
      },
      "source": [
        "def accuracy(outputs, labels):\n",
        "    _, preds = torch.max(outputs, dim=1)\n",
        "    return torch.tensor(torch.sum(preds == labels).item() / len(preds))\n",
        "\n",
        "class ImageClassificationBase(nn.Module):\n",
        "    def training_step(self, batch):\n",
        "        images, labels = batch \n",
        "        out = self(images)                  # Generate predictions\n",
        "        loss = F.cross_entropy(out, labels) # Calculate loss\n",
        "        return loss\n",
        "    \n",
        "    def validation_step(self, batch):\n",
        "        images, labels = batch \n",
        "        out = self(images)                    # Generate predictions\n",
        "        loss = F.cross_entropy(out, labels)   # Calculate loss\n",
        "        acc = accuracy(out, labels)           # Calculate accuracy\n",
        "        return {'val_loss': loss.detach(), 'val_acc': acc}\n",
        "        \n",
        "    def validation_epoch_end(self, outputs):\n",
        "        batch_losses = [x['val_loss'] for x in outputs]\n",
        "        epoch_loss = torch.stack(batch_losses).mean()   # Combine losses\n",
        "        batch_accs = [x['val_acc'] for x in outputs]\n",
        "        epoch_acc = torch.stack(batch_accs).mean()      # Combine accuracies\n",
        "        return {'val_loss': epoch_loss.item(), 'val_acc': epoch_acc.item()}\n",
        "    \n",
        "    def epoch_end(self, epoch, result):\n",
        "        print(\"Epoch [{}], train_loss: {:.4f}, val_loss: {:.4f}, val_acc: {:.4f}\".format(\n",
        "            epoch, result['train_loss'], result['val_loss'], result['val_acc']))"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G7Kvkhtj8wiW"
      },
      "source": [
        "class gestureModel(ImageClassificationBase):\n",
        "    def __init__(self):\n",
        "        super().__init__()\n",
        "        self.network = nn.Sequential(\n",
        "            nn.Conv2d(3, 16, kernel_size=3, padding=1),\n",
        "            nn.ReLU(),\n",
        "            nn.MaxPool2d(2, 2),   #128\n",
        "\n",
        "            nn.Conv2d(16,32, kernel_size=3, stride=1, padding=1),\n",
        "            nn.ReLU(),\n",
        "            nn.MaxPool2d(2, 2), #64\n",
        "\n",
        "            nn.Conv2d(32,32, kernel_size=3, stride=1, padding=1),\n",
        "            nn.ReLU(),\n",
        "            nn.MaxPool2d(2, 2), #32\n",
        "\n",
        "            nn.Conv2d(32,64, kernel_size=3, stride=1, padding=1),\n",
        "            nn.ReLU(),\n",
        "            nn.MaxPool2d(2, 2), #16\n",
        "\n",
        "            nn.Conv2d(64,128, kernel_size=3, stride=1, padding=1),\n",
        "            nn.ReLU(),\n",
        "            nn.MaxPool2d(2, 2), #8\n",
        "\n",
        "            nn.Flatten(), \n",
        "            nn.Linear(128*8*8,1024),\n",
        "            nn.ReLU(),\n",
        "            nn.Linear(1024, 512),\n",
        "            nn.ReLU(),\n",
        "            nn.Linear(512, 11))\n",
        "        \n",
        "    def forward(self, xb):\n",
        "        return self.network(xb)"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rK_T9Q9p9SOG",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a9caf2da-5b58-4ac8-8f84-80bfa9f34efb"
      },
      "source": [
        "model = gestureModel()\n",
        "model"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "gestureModel(\n",
              "  (network): Sequential(\n",
              "    (0): Conv2d(3, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "    (1): ReLU()\n",
              "    (2): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
              "    (3): Conv2d(16, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "    (4): ReLU()\n",
              "    (5): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
              "    (6): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "    (7): ReLU()\n",
              "    (8): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
              "    (9): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "    (10): ReLU()\n",
              "    (11): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
              "    (12): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "    (13): ReLU()\n",
              "    (14): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
              "    (15): Flatten(start_dim=1, end_dim=-1)\n",
              "    (16): Linear(in_features=8192, out_features=1024, bias=True)\n",
              "    (17): ReLU()\n",
              "    (18): Linear(in_features=1024, out_features=512, bias=True)\n",
              "    (19): ReLU()\n",
              "    (20): Linear(in_features=512, out_features=11, bias=True)\n",
              "  )\n",
              ")"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bR3-ywaT9VmC"
      },
      "source": [
        "def get_default_device():\n",
        "    \"\"\"Pick GPU if available, else CPU\"\"\"\n",
        "    if torch.cuda.is_available():\n",
        "        return torch.device('cuda')\n",
        "    else:\n",
        "        return torch.device('cpu')\n",
        "    \n",
        "def to_device(data, device):\n",
        "    \"\"\"Move tensor(s) to chosen device\"\"\"\n",
        "    if isinstance(data, (list,tuple)):\n",
        "        return [to_device(x, device) for x in data]\n",
        "    return data.to(device, non_blocking=True)\n",
        "\n",
        "class DeviceDataLoader():\n",
        "    \"\"\"Wrap a dataloader to move data to a device\"\"\"\n",
        "    def __init__(self, dl, device):\n",
        "        self.dl = dl\n",
        "        self.device = device\n",
        "        \n",
        "    def __iter__(self):\n",
        "        \"\"\"Yield a batch of data after moving it to device\"\"\"\n",
        "        for b in self.dl: \n",
        "            yield to_device(b, self.device)\n",
        "\n",
        "    def __len__(self):\n",
        "        \"\"\"Number of batches\"\"\"\n",
        "        return len(self.dl)"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sHwvnpQF9ZFP",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "24959bcf-54ae-4e15-8cb2-171b43d19507"
      },
      "source": [
        "device = get_default_device()\n",
        "device"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "device(type='cuda')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_Oj02pzh9cZD"
      },
      "source": [
        "train_dl = DeviceDataLoader(train_dl, device)\n",
        "val_dl = DeviceDataLoader(val_dl, device)\n",
        "to_device(model, device);      #shifting data onto gpu"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dohPNsrP9gQo"
      },
      "source": [
        "@torch.no_grad()\n",
        "def evaluate(model, val_loader):\n",
        "    model.eval()\n",
        "    outputs = [model.validation_step(batch) for batch in val_loader]\n",
        "    return model.validation_epoch_end(outputs)\n",
        "\n",
        "def fit(epochs, lr, model, train_loader, val_loader, opt_func=torch.optim.SGD):\n",
        "    history = []\n",
        "    optimizer = opt_func(model.parameters(), lr)\n",
        "    for epoch in range(epochs):\n",
        "        # Training Phase \n",
        "        model.train()\n",
        "        train_losses = []\n",
        "        for batch in train_loader:\n",
        "            loss = model.training_step(batch)\n",
        "            train_losses.append(loss)\n",
        "            loss.backward()\n",
        "            optimizer.step()\n",
        "            optimizer.zero_grad()\n",
        "        # Validation phase\n",
        "        result = evaluate(model, val_loader)\n",
        "        result['train_loss'] = torch.stack(train_losses).mean().item()\n",
        "        model.epoch_end(epoch, result)\n",
        "        history.append(result)\n",
        "    return history"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8fhOQQkP9i2A"
      },
      "source": [
        "model = to_device(gestureModel(), device)"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "12esQG3u9mdg",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ed021136-a79c-4bb9-dace-01bfdd88f5f5"
      },
      "source": [
        "evaluate(model, val_dl)"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'val_acc': 0.08728966116905212, 'val_loss': 2.396811008453369}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6ic-kMk29p0q",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4d1080f6-2632-4489-bec9-9db5aa96b99f"
      },
      "source": [
        "num_epochs = 60\n",
        "opt_func = torch.optim.Adam\n",
        "lr = 0.00001\n",
        "\n",
        "history = fit(num_epochs, lr, model, train_dl, val_dl, opt_func)"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch [0], train_loss: 2.3983, val_loss: 2.3972, val_acc: 0.0873\n",
            "Epoch [1], train_loss: 2.3967, val_loss: 2.3956, val_acc: 0.0847\n",
            "Epoch [2], train_loss: 2.3913, val_loss: 2.3857, val_acc: 0.1345\n",
            "Epoch [3], train_loss: 2.3679, val_loss: 2.3408, val_acc: 0.2227\n",
            "Epoch [4], train_loss: 2.2746, val_loss: 2.1643, val_acc: 0.2572\n",
            "Epoch [5], train_loss: 2.0122, val_loss: 1.8850, val_acc: 0.3595\n",
            "Epoch [6], train_loss: 1.7018, val_loss: 1.5964, val_acc: 0.5224\n",
            "Epoch [7], train_loss: 1.4044, val_loss: 1.2790, val_acc: 0.5999\n",
            "Epoch [8], train_loss: 1.2129, val_loss: 1.1605, val_acc: 0.6364\n",
            "Epoch [9], train_loss: 1.0717, val_loss: 1.1472, val_acc: 0.6846\n",
            "Epoch [10], train_loss: 0.9683, val_loss: 0.9234, val_acc: 0.7194\n",
            "Epoch [11], train_loss: 0.8617, val_loss: 0.8579, val_acc: 0.7139\n",
            "Epoch [12], train_loss: 0.8056, val_loss: 0.8080, val_acc: 0.7734\n",
            "Epoch [13], train_loss: 0.7600, val_loss: 0.7215, val_acc: 0.7811\n",
            "Epoch [14], train_loss: 0.6864, val_loss: 0.7170, val_acc: 0.7925\n",
            "Epoch [15], train_loss: 0.6194, val_loss: 0.7176, val_acc: 0.7556\n",
            "Epoch [16], train_loss: 0.5655, val_loss: 0.5909, val_acc: 0.8158\n",
            "Epoch [17], train_loss: 0.5356, val_loss: 0.6074, val_acc: 0.8080\n",
            "Epoch [18], train_loss: 0.4769, val_loss: 0.4791, val_acc: 0.8377\n",
            "Epoch [19], train_loss: 0.4335, val_loss: 0.4200, val_acc: 0.8801\n",
            "Epoch [20], train_loss: 0.4314, val_loss: 0.4421, val_acc: 0.8422\n",
            "Epoch [21], train_loss: 0.4000, val_loss: 0.4331, val_acc: 0.8953\n",
            "Epoch [22], train_loss: 0.3555, val_loss: 0.3679, val_acc: 0.8855\n",
            "Epoch [23], train_loss: 0.3414, val_loss: 0.3394, val_acc: 0.9035\n",
            "Epoch [24], train_loss: 0.3114, val_loss: 0.2963, val_acc: 0.9211\n",
            "Epoch [25], train_loss: 0.2991, val_loss: 0.3370, val_acc: 0.8905\n",
            "Epoch [26], train_loss: 0.3069, val_loss: 0.3013, val_acc: 0.9020\n",
            "Epoch [27], train_loss: 0.2633, val_loss: 0.2313, val_acc: 0.9250\n",
            "Epoch [28], train_loss: 0.2516, val_loss: 0.2377, val_acc: 0.9357\n",
            "Epoch [29], train_loss: 0.2270, val_loss: 0.2562, val_acc: 0.9094\n",
            "Epoch [30], train_loss: 0.2333, val_loss: 0.3228, val_acc: 0.8966\n",
            "Epoch [31], train_loss: 0.2000, val_loss: 0.1802, val_acc: 0.9474\n",
            "Epoch [32], train_loss: 0.1830, val_loss: 0.1863, val_acc: 0.9367\n",
            "Epoch [33], train_loss: 0.1774, val_loss: 0.1737, val_acc: 0.9455\n",
            "Epoch [34], train_loss: 0.1650, val_loss: 0.2405, val_acc: 0.9007\n",
            "Epoch [35], train_loss: 0.1475, val_loss: 0.1241, val_acc: 0.9694\n",
            "Epoch [36], train_loss: 0.1339, val_loss: 0.1246, val_acc: 0.9659\n",
            "Epoch [37], train_loss: 0.1201, val_loss: 0.1283, val_acc: 0.9669\n",
            "Epoch [38], train_loss: 0.1130, val_loss: 0.1622, val_acc: 0.9367\n",
            "Epoch [39], train_loss: 0.1185, val_loss: 0.1537, val_acc: 0.9396\n",
            "Epoch [40], train_loss: 0.1191, val_loss: 0.1012, val_acc: 0.9820\n",
            "Epoch [41], train_loss: 0.0982, val_loss: 0.0864, val_acc: 0.9776\n",
            "Epoch [42], train_loss: 0.1042, val_loss: 0.0796, val_acc: 0.9811\n",
            "Epoch [43], train_loss: 0.0731, val_loss: 0.0806, val_acc: 0.9815\n",
            "Epoch [44], train_loss: 0.0665, val_loss: 0.0586, val_acc: 0.9898\n",
            "Epoch [45], train_loss: 0.0539, val_loss: 0.0566, val_acc: 0.9878\n",
            "Epoch [46], train_loss: 0.0564, val_loss: 0.0474, val_acc: 0.9917\n",
            "Epoch [47], train_loss: 0.0502, val_loss: 0.0417, val_acc: 0.9937\n",
            "Epoch [48], train_loss: 0.0501, val_loss: 0.0430, val_acc: 0.9908\n",
            "Epoch [49], train_loss: 0.0476, val_loss: 0.0558, val_acc: 0.9883\n",
            "Epoch [50], train_loss: 0.0524, val_loss: 0.0409, val_acc: 0.9913\n",
            "Epoch [51], train_loss: 0.0409, val_loss: 0.0385, val_acc: 0.9922\n",
            "Epoch [52], train_loss: 0.0342, val_loss: 0.0261, val_acc: 0.9956\n",
            "Epoch [53], train_loss: 0.0522, val_loss: 0.0288, val_acc: 0.9976\n",
            "Epoch [54], train_loss: 0.0456, val_loss: 0.0280, val_acc: 0.9961\n",
            "Epoch [55], train_loss: 0.0245, val_loss: 0.0213, val_acc: 0.9980\n",
            "Epoch [56], train_loss: 0.0227, val_loss: 0.0438, val_acc: 0.9869\n",
            "Epoch [57], train_loss: 0.0271, val_loss: 0.0206, val_acc: 0.9956\n",
            "Epoch [58], train_loss: 0.0189, val_loss: 0.0138, val_acc: 1.0000\n",
            "Epoch [59], train_loss: 0.0232, val_loss: 0.0293, val_acc: 0.9961\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O8s9msDX_-g3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "outputId": "b04656e5-fea8-4803-cb59-56f3bc451b52"
      },
      "source": [
        "def plot_accuracies(history):\n",
        "    accuracies = [x['val_acc'] for x in history]\n",
        "    plt.plot(accuracies, '-x')\n",
        "    plt.xlabel('epoch')\n",
        "    plt.ylabel('accuracy')\n",
        "    plt.title('Accuracy vs. No. of epochs');\n",
        "\n",
        "plot_accuracies(history)"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "I3Wqbp-kAKfr",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "outputId": "26931dd3-fd3a-4fc2-88a3-8d7861dcc06d"
      },
      "source": [
        "def plot_losses(history):\n",
        "    train_losses = [x.get('train_loss') for x in history]\n",
        "    val_losses = [x['val_loss'] for x in history]\n",
        "    plt.plot(train_losses, '-bx')\n",
        "    plt.plot(val_losses, '-rx')\n",
        "    plt.xlabel('epoch')\n",
        "    plt.ylabel('loss')\n",
        "    plt.legend(['Training', 'Validation'])\n",
        "    plt.title('Loss vs. No. of epochs');\n",
        "\n",
        "plot_losses(history)"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vJTFC_w2AOO3"
      },
      "source": [
        "torch.save(model, '/content/drive/My Drive/WEIGHTS.pth')"
      ],
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9sXDXV-pAlom",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "584d2750-dc2d-43ba-f7fb-9ba0223cd36d"
      },
      "source": [
        "model = torch.load('/content/drive/My Drive/WEIGHTS.pth')\n",
        "model.eval()"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "gestureModel(\n",
              "  (network): Sequential(\n",
              "    (0): Conv2d(3, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "    (1): ReLU()\n",
              "    (2): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
              "    (3): Conv2d(16, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "    (4): ReLU()\n",
              "    (5): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
              "    (6): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "    (7): ReLU()\n",
              "    (8): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
              "    (9): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "    (10): ReLU()\n",
              "    (11): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
              "    (12): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "    (13): ReLU()\n",
              "    (14): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
              "    (15): Flatten(start_dim=1, end_dim=-1)\n",
              "    (16): Linear(in_features=8192, out_features=1024, bias=True)\n",
              "    (17): ReLU()\n",
              "    (18): Linear(in_features=1024, out_features=512, bias=True)\n",
              "    (19): ReLU()\n",
              "    (20): Linear(in_features=512, out_features=11, bias=True)\n",
              "  )\n",
              ")"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    }
  ]
}