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  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "g61AWW6qEGFN",
        "outputId": "9ca13f05-9d72-4ff1-8510-d6ca1a8f50af"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/drive/MyDrive/DL_Project_2\n"
          ]
        }
      ],
      "source": [
        "cd /content/drive/MyDrive/DL_Project_2"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import zipfile\n",
        "import pandas as pd\n",
        "import librosa as lib\n",
        "from tqdm import tqdm\n",
        "import numpy as np\n",
        "import cv2\n",
        "import os\n",
        "import matplotlib.pyplot as plt\n",
        "import librosa as lib\n",
        "import torch\n",
        "from torchvision import datasets, transforms, models\n",
        "from torch.utils.data import Dataset\n",
        "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
        "device"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vnEPI4ijE7P5",
        "outputId": "0731c9ff-4811-4ec5-f1eb-cc19bc191d95"
      },
      "execution_count": 109,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "device(type='cuda')"
            ]
          },
          "metadata": {},
          "execution_count": 109
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Data-related Code Snippets"
      ],
      "metadata": {
        "id": "WrYMOPn-8K94"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "with zipfile.ZipFile('/content/drive/MyDrive/DL_Project_2/archive.zip', 'r') as zip_ref:\n",
        "  zip_ref.extractall('/content/drive/MyDrive/DL_Project_2')"
      ],
      "metadata": {
        "id": "WrK8L0PzHwVK"
      },
      "execution_count": 110,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def rename_files(root_path):\n",
        "  for emotion in [\"Sad\",\"Romantic\",\"Devotional\",\"Party\",\"Happy\"]:\n",
        "    emotional_path = os.path.join(root_path,str(emotion)+'/'+str(emotion))\n",
        "    list_emotional = os.listdir(emotional_path)\n",
        "    for emotion_piece in list_emotional:\n",
        "      piece = os.path.join(emotional_path,emotion_piece)\n",
        "      new_piece = os.path.join(emotional_path,emotion+str(\"_\")+emotion_piece)\n",
        "      os.rename(piece, new_piece)\n",
        "    print(\"done\")"
      ],
      "metadata": {
        "id": "mDnK0SUVHIhi"
      },
      "execution_count": 111,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "rename_files('/content/drive/MyDrive/DL_Project_2')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gMbL7N8hIluV",
        "outputId": "21336cde-f7cf-4105-fc3f-0fde7e66f96e"
      },
      "execution_count": 112,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "done\n",
            "done\n",
            "done\n",
            "done\n",
            "done\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def dataframe_creation(root_path):\n",
        "  label=0\n",
        "  audio_name = []\n",
        "  label_list = []\n",
        "  for emotion in [\"Sad\",\"Romantic\",\"Devotional\",\"Party\",\"Happy\"]:\n",
        "    emotional_path = os.path.join(root_path,str(emotion)+'/'+str(emotion))\n",
        "    list_emotional = os.listdir(emotional_path)\n",
        "    for emotion_piece in list_emotional:\n",
        "      piece = os.path.join(emotional_path,emotion_piece)\n",
        "      audio_name.append(piece)\n",
        "      label_list.append(label)\n",
        "    label = label+1\n",
        "  df = pd.DataFrame(columns=[\"Audio_Name\",\"Emotion_Name\"])\n",
        "  df[\"Audio_Name\"] = audio_name\n",
        "  df[\"Emotion_Name\"] = label_list\n",
        "  return df"
      ],
      "metadata": {
        "id": "bwObJVWnIuyK"
      },
      "execution_count": 113,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "audio_df = dataframe_creation('/content/drive/MyDrive/DL_Project_2')"
      ],
      "metadata": {
        "id": "jSZ6yV4WLwW7"
      },
      "execution_count": 114,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "audio_df.to_csv('/content/drive/MyDrive/DL_Project_2/Audio_metafile.csv')"
      ],
      "metadata": {
        "id": "pgoqiNxfL1ai"
      },
      "execution_count": 115,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Main Function"
      ],
      "metadata": {
        "id": "00m3EpyT8QhU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "audio_df = pd.read_csv('/content/drive/MyDrive/DL_Project_2/Audio_metafile.csv')"
      ],
      "metadata": {
        "id": "3PC-52XUMbz9"
      },
      "execution_count": 116,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "audio_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "hvbZSVT19EIU",
        "outputId": "7e48b81b-8cd9-4733-a1c5-5b6612959af5"
      },
      "execution_count": 117,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Unnamed: 0                                         Audio_Name  \\\n",
              "0             0  /content/drive/MyDrive/DL_Project_2/Sad/Sad/Sa...   \n",
              "1             1  /content/drive/MyDrive/DL_Project_2/Sad/Sad/Sa...   \n",
              "2             2  /content/drive/MyDrive/DL_Project_2/Sad/Sad/Sa...   \n",
              "3             3  /content/drive/MyDrive/DL_Project_2/Sad/Sad/Sa...   \n",
              "4             4  /content/drive/MyDrive/DL_Project_2/Sad/Sad/Sa...   \n",
              "..          ...                                                ...   \n",
              "489         489  /content/drive/MyDrive/DL_Project_2/Happy/Happ...   \n",
              "490         490  /content/drive/MyDrive/DL_Project_2/Happy/Happ...   \n",
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              "489             4  \n",
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              "491             4  \n",
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              "493             4  \n",
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              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 117
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "audio_df = audio_df.drop(columns=['Unnamed: 0'])"
      ],
      "metadata": {
        "id": "v9TlC-i8-HnO"
      },
      "execution_count": 119,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sample_1 = lib.load(audio_df[\"Audio_Name\"][0])\n",
        "x,y = sample_1\n",
        "x.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8ldkJl_dYlWR",
        "outputId": "93bdeca8-05d7-4cc4-82c2-4cc84d6c7838"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(485304,)"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "hl = 512 # number of samples per time-step in spectrogram\n",
        "hi = 224 # Height of image\n",
        "wi = 224 # Width of image"
      ],
      "metadata": {
        "id": "USIPuFPbY9LL"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "window = x[0:wi*hl]"
      ],
      "metadata": {
        "id": "p-F_KBGMZAVx"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "S = lib.feature.melspectrogram(y=window, sr=y, n_mels=hi, fmax=8000, hop_length=hl)\n",
        "S_dB = lib.power_to_db(S, ref=np.max)"
      ],
      "metadata": {
        "id": "eHBarl5SZP05"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.imshow(S_dB)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 287
        },
        "id": "Ycqs1bMnZf57",
        "outputId": "368ebec4-fc37-4f0e-ab1f-2bd3d1701668"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7fd4d6ba2950>"
            ]
          },
          "metadata": {},
          "execution_count": 16
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Dataset Class and Function"
      ],
      "metadata": {
        "id": "vL_ncEE2ckXV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from torch.utils.data.sampler import SubsetRandomSampler\n",
        "\n",
        "class Data_Prepare(Dataset):\n",
        "  \"\"\"\n",
        "  The Class will act as the container for our dataset. It will take your dataframe, the root path, and also the transform function for transforming the dataset.\n",
        "  \"\"\"\n",
        "  def __init__(self, data_frame, transform=None):\n",
        "    self.data_frame = data_frame\n",
        "    self.transform = transform\n",
        "    self.hl = 512 # number of samples per time-step in spectrogram\n",
        "    self.hi = 224 # Height of image\n",
        "    self.wi = 224 # Width of image\n",
        "  def __len__(self):\n",
        "    # Return the length of the dataset\n",
        "    return len(self.data_frame)\n",
        "  def __getitem__(self, idx):\n",
        "    # Return the observation based on an index. Ex. dataset[0] will return the first element from the dataset, in this case the image and the label.\n",
        "    if torch.is_tensor(idx):\n",
        "      idx = idx.tolist()    \n",
        "    audio_name = self.data_frame.iloc[idx, 0]\n",
        "    audio_file, y = lib.load(audio_name)\n",
        "    audio_window = audio_file[0:self.wi*self.hl]\n",
        "    spectrogram = lib.feature.melspectrogram(y=audio_window, sr=y, n_mels=self.hi, fmax=8000, hop_length=self.hl)\n",
        "    plot = lib.power_to_db(spectrogram, ref=np.max)\n",
        "    plot = cv2.resize(cv2.cvtColor(plot,cv2.COLOR_GRAY2RGB),(224,224))\n",
        "    label = (self.data_frame.iloc[idx, -1])\n",
        "    if self.transform:\n",
        "      plot = self.transform(plot)\n",
        "    return (plot, label)\n",
        "\n",
        "def data_preparation(Data_Class, Dataframe, Mean, Std, Batch_Size = 128, Shuffle = False):\n",
        "  transform = transforms.Compose([transforms.ToTensor(),transforms.Normalize(Mean, Std)])\n",
        "  dataset_whole = Data_Class(data_frame=Dataframe,transform = transform)\n",
        "  test_split = 0.2\n",
        "  random_seed= 42\n",
        "  dataset_size = len(dataset_whole)\n",
        "  indices = list(range(dataset_size))\n",
        "  split = int(np.floor(test_split * dataset_size))\n",
        "  if Shuffle==True:\n",
        "    np.random.seed(random_seed)\n",
        "    np.random.shuffle(indices)\n",
        "  train_indices, test_indices = indices[split:], indices[:split]\n",
        "  train_sampler = SubsetRandomSampler(train_indices)\n",
        "  test_sampler = SubsetRandomSampler(test_indices)\n",
        "  train_loader = torch.utils.data.DataLoader(dataset = dataset_whole, batch_size = Batch_Size, pin_memory = True, num_workers=2, sampler=train_sampler)\n",
        "  test_loader = torch.utils.data.DataLoader(dataset = dataset_whole, batch_size = Batch_Size, pin_memory = True, num_workers=2, sampler=test_sampler)\n",
        "  return train_loader, test_loader"
      ],
      "metadata": {
        "id": "jwIkjMqqa6W3"
      },
      "execution_count": 125,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train, test = data_preparation(Data_Prepare, audio_df,(0,0,0),(1,1,1),Batch_Size=64, Shuffle=True)"
      ],
      "metadata": {
        "id": "wwFJTh6GfwbO"
      },
      "execution_count": 126,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Helper Function"
      ],
      "metadata": {
        "id": "tDfKXro6cT47"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def bifurcation(model_name):\n",
        "  model = model_name(pretrained=True)\n",
        "  param = model.state_dict()\n",
        "  for i in param.keys():\n",
        "    print(i)\n",
        "\n",
        "def selective_finetuning_single_layer(model_name, layer_name):\n",
        "  model = model_name(pretrained=True)\n",
        "  for name, param in model.named_parameters():\n",
        "    if param.requires_grad and layer_name in name:\n",
        "      param.requires_grad = True\n",
        "    else:\n",
        "      param.requires_grad = False\n",
        "  optimizer = torch.optim.Adam(filter(lambda p: p.requires_grad, model.parameters()))\n",
        "  return model.cuda(), torch.nn.CrossEntropyLoss().cuda(), optimizer"
      ],
      "metadata": {
        "id": "_iXQewLXgTf5"
      },
      "execution_count": 94,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def grad_change(Loss_Function, Optimizer, Label = None, Predicted = None):\n",
        "  Optimizer.zero_grad()\n",
        "  loss = Loss_Function(Predicted, Label)\n",
        "  loss.backward()\n",
        "  Optimizer.step()\n",
        "  return loss, Optimizer\n",
        "\n",
        "def model(Train_Loader, Test_Loader, Epochs, Model_Class=None, Loss_Function=None, Optimizer=None):\n",
        "  outputs_train=[]\n",
        "  outputs_test=[]\n",
        "  y_true=[]\n",
        "  y_pred=[]\n",
        "  for Epoch in range(Epochs):\n",
        "    running_loss_train=0\n",
        "    running_loss_test=0\n",
        "    correct_train=0\n",
        "    correct_test=0\n",
        "    for (image, label) in tqdm(Train_Loader):\n",
        "      image = image.cuda()\n",
        "      label = torch.tensor(label).cuda()\n",
        "      out = Model_Class(image)\n",
        "      loss, Optimizer = grad_change(Loss_Function = Loss_Function, Optimizer = Optimizer, Label = label, Predicted = out)\n",
        "      running_loss_train += loss.item()\n",
        "      predicted_train = out.data.max(1, keepdim=True)[1]\n",
        "      correct_train += predicted_train.eq(label.data.view_as(predicted_train)).sum()\n",
        "      outputs_train.append((Epoch, running_loss_train/len(Train_Loader.dataset), 100*correct_train/len(Train_Loader.dataset)))\n",
        "    with torch.no_grad():\n",
        "      for (image, label) in Test_Loader:\n",
        "        image = image.cuda()\n",
        "        label = torch.tensor(label).cuda()\n",
        "        out = Model_Class(image)\n",
        "        loss = Loss_Function(out,label)\n",
        "        running_loss_test += loss.item()\n",
        "        predicted_test = out.data.max(1, keepdim=True)[1]\n",
        "        if Epoch==(Epochs-1):\n",
        "          y_pred.extend(predicted_test.cpu().numpy())\n",
        "          y_true.extend(label.data.cpu().numpy())\n",
        "        correct_test += predicted_test.eq(label.data.view_as(predicted_test)).sum()\n",
        "        outputs_test.append((Epoch, running_loss_test/len(Test_Loader.dataset), 100*correct_test/len(Test_Loader.dataset)))\n",
        "  return Model_Class, outputs_train, outputs_test, y_pred, y_true"
      ],
      "metadata": {
        "id": "UYu0x3fuhRa0"
      },
      "execution_count": 95,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# VGG16 Training and Inference"
      ],
      "metadata": {
        "id": "SWqlpq4xbmD_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "bifurcation(models.vgg16)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "A13siv7wjmZ8",
        "outputId": "58ecb037-763f-4d50-f0c0-988309516fa0"
      },
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "features.0.weight\n",
            "features.0.bias\n",
            "features.2.weight\n",
            "features.2.bias\n",
            "features.5.weight\n",
            "features.5.bias\n",
            "features.7.weight\n",
            "features.7.bias\n",
            "features.10.weight\n",
            "features.10.bias\n",
            "features.12.weight\n",
            "features.12.bias\n",
            "features.14.weight\n",
            "features.14.bias\n",
            "features.17.weight\n",
            "features.17.bias\n",
            "features.19.weight\n",
            "features.19.bias\n",
            "features.21.weight\n",
            "features.21.bias\n",
            "features.24.weight\n",
            "features.24.bias\n",
            "features.26.weight\n",
            "features.26.bias\n",
            "features.28.weight\n",
            "features.28.bias\n",
            "classifier.0.weight\n",
            "classifier.0.bias\n",
            "classifier.3.weight\n",
            "classifier.3.bias\n",
            "classifier.6.weight\n",
            "classifier.6.bias\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")\n",
        "\n",
        "model_vgg16, loss_vgg16, optim_vgg16 = selective_finetuning_single_layer(models.vgg16, 'classifier')\n",
        "model_vgg16 ,vgg16_train ,vgg16_test, vgg16_pred, vgg16_true = model(train,test,5,model_vgg16, loss_vgg16, optim_vgg16)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Jn8fXuiGj8aR",
        "outputId": "c8f17c15-5df7-4438-802c-bbf3c658241f"
      },
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 7/7 [02:32<00:00, 21.76s/it]\n",
            "100%|██████████| 7/7 [01:58<00:00, 16.94s/it]\n",
            "100%|██████████| 7/7 [02:01<00:00, 17.37s/it]\n",
            "100%|██████████| 7/7 [02:11<00:00, 18.77s/it]\n",
            "100%|██████████| 7/7 [03:37<00:00, 31.13s/it]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns \n",
        "from sklearn.metrics import confusion_matrix\n",
        "cf_matrix=confusion_matrix(vgg16_true,vgg16_pred)\n",
        "cf_matrix"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fjAlI8Qy6pxE",
        "outputId": "81ca904a-9473-4e59-8626-b4302d3f8f31"
      },
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[13,  5,  2,  1,  6,  0,  0],\n",
              "       [11,  1,  2,  0,  2,  0,  0],\n",
              "       [ 6,  4,  3,  0,  0,  2,  1],\n",
              "       [ 7,  0,  3,  2,  3,  0,  0],\n",
              "       [ 8,  3,  4,  3,  6,  0,  0],\n",
              "       [ 0,  0,  0,  0,  0,  0,  0],\n",
              "       [ 0,  0,  0,  0,  0,  0,  0]])"
            ]
          },
          "metadata": {},
          "execution_count": 81
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(9,9))\n",
        "sns.heatmap(cf_matrix, cbar=False, fmt='d', annot=True, cmap='Blues')\n",
        "plt.xlabel('Predicted')\n",
        "plt.ylabel('Actual')\n",
        "plt.title('Confusion matrix of the CNN Classifier')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 567
        },
        "id": "NFuIMLMS69Mq",
        "outputId": "2b19ef73-05dd-4464-c6a6-9db2b1ded751"
      },
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 648x648 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Mobilenet-V3-Small Training and Inference"
      ],
      "metadata": {
        "id": "IJRa2MOfbZ-h"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "bifurcation(models.mobilenet_v3_small)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
          "referenced_widgets": [
            "a428ff01efb447dda0f11d0ceca15bf4",
            "c44b65367e3c46bda7aecfd1b9838a5f",
            "59aa95c5545a4e1f82d8186bade5d017",
            "fa769a6df54f44a19dd33862bb59c24a",
            "1cd9b497dab0436b8de25b5e6c53c115",
            "5cf888d4b1bb49da9702320996f53e1b",
            "cd59ebb7db1e4a97a9faabfddf1a7bc7",
            "bbd68421ecd24dbcb7d2c7b7c745fec4",
            "666a9c9f3d004db1baac35164935a894",
            "77d4754c153840f2bdb0585a21eab2dd",
            "2a4c4cadd4c2401ea3b78b9a75a88922"
          ]
        },
        "id": "gOKZE5RwlDbn",
        "outputId": "dbb493b4-59c4-4867-819a-cbddb752e71b"
      },
      "execution_count": 123,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Downloading: \"https://download.pytorch.org/models/mobilenet_v3_small-047dcff4.pth\" to /root/.cache/torch/hub/checkpoints/mobilenet_v3_small-047dcff4.pth\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0.00/9.83M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a428ff01efb447dda0f11d0ceca15bf4"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_mobilenet_v3_small, loss_mobilenet_v3_small, optim_mobilenet_v3_small = selective_finetuning_single_layer(models.mobilenet_v3_small, 'classifier')\n",
        "model_mobilenet_v3_small ,mobilenet_v3_small_train ,mobilenet_v3_small_test, mobilenet_v3_small_pred, mobilenet_v3_small_true = model(train,test,5,model_mobilenet_v3_small, loss_mobilenet_v3_small, optim_mobilenet_v3_small)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4oxZjH8c8lJR",
        "outputId": "0b561509-7083-4ed3-e874-99e363287021"
      },
      "execution_count": 127,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 7/7 [02:13<00:00, 19.08s/it]\n",
            "100%|██████████| 7/7 [02:15<00:00, 19.42s/it]\n",
            "100%|██████████| 7/7 [02:03<00:00, 17.68s/it]\n",
            "100%|██████████| 7/7 [02:02<00:00, 17.51s/it]\n",
            "100%|██████████| 7/7 [01:58<00:00, 16.93s/it]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cf_matrix_mobilenet=confusion_matrix(mobilenet_v3_small_true,mobilenet_v3_small_pred)\n",
        "cf_matrix_mobilenet"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "r86TDYKD_AE9",
        "outputId": "2ddbf6f8-652a-4a9d-d42e-9cfc095f4b2b"
      },
      "execution_count": 128,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[14,  5,  6,  1,  1],\n",
              "       [ 3,  6,  5,  2,  0],\n",
              "       [ 1,  5,  7,  2,  1],\n",
              "       [ 2,  3,  1,  8,  1],\n",
              "       [ 1,  5,  3, 12,  3]])"
            ]
          },
          "metadata": {},
          "execution_count": 128
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(9,9))\n",
        "sns.heatmap(cf_matrix_mobilenet, cbar=False, fmt='d', annot=True, cmap='Blues')\n",
        "plt.xlabel('Predicted')\n",
        "plt.ylabel('Actual')\n",
        "plt.title('Confusion matrix of the CNN Classifier')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 567
        },
        "id": "tiSynbvB_Rox",
        "outputId": "f008e821-2d9e-4838-a038-214349a3728b"
      },
      "execution_count": 129,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 648x648 with 1 Axes>"
            ],
            "image/png": 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AAKEoIwAAIBRlBAAAhKKMAACAUJQRAAAQijICAABCUUYAAEAoyggAAAhFGQEAAKEoIwAAIBRlBAAAhKKMAACAUJQRAAAQytw9OkO9XvpgZfMMllH/nL0oOgLq2L19q+gIyPPZ7jtHR0CebuVtoiOgjtYtZA2NY8sIAAAIRRkBAAChKCMAACAUZQQAAISijAAAgFCUEQAAEIoyAgAAQlFGAABAKMoIAAAIRRkBAAChKCMAACAUZQQAAISijAAAgFCUEQAAEIoyAgAAQlFGAABAKMoIAAAIRRkBAAChKCMAACAUZQQAAISijAAAgFCUEQAAEIoyAgAAQlFGAABAKMoIAAAIRRkBAAChKCMAACAUZQQAAISijAAAgFCUEQAAEIoyAgAAQlFGAABAKMoIAAAIRRkBAAChKCMAACAUZQQAAISijAAAgFCUEQAAEIoyAgAAQlFGAABAKMoIAAAIRRkBAAChKCMAACAUZQQAAIRqER1gR1VZuUFXjRmtqqoq5XLV+uzQCo0YdU50rEzbsHa1nvrtJC2b94FkpqGjvq2ue/aNjpVpky4aqVZtSmVFRSoqKtbZEyZHR8qsRQvm6/oJY7V86VKZSUcdd5KOH3FqdKzMe/rJJ/TjiRNUk6vRiSeN0Jlnj46OVBCUkQIpKWmpsddOVus2paqurta4i8/SAQd+Xnv13S86WmY9N+Vmddt3oCrOuUK56ipVV26IjgRJo664XqXtO0THyLzi4mKddf4Y9e7TV2vXrtG3zvyaPjNosHr0+lR0tMzK5XK6esJVuvnW29W1a1eNPOUrOuzwI/Sp3r2jo2117KYpEDNT6zalkqRcdbVyuWqZLDhVdlWuW6P577ymvYd8SZJU3KJErUrbBqcCmo/yTp3Vu0+ypbC0tEzde+6pJYsXBqfKttdmTFf37nuoW/fuKmnZUkcd82U99ui/omMVBFtGCqgml9Pl55+m+fM+0hePG6HefftHR8qsVYvnq3XbDnryNzdoydzZ6tSjtwaffK5KWrWOjpZpZqY7J14qk2lAxTANrBgWHQmSFnw8V7Pfnqk+/diSG2nhggXaZdddNl7v0rWrZkyfHpiocLb5lhEz+0Yj40ab2TQzm/bnu27flrEKoqi4WBNvuku/uOt+vfvW65rz3qzoSJlVU5PTkjmztM+hx+jEK36uFi1ba/pDU6JjZd7pV/5Uo6++WSMvu0bTHv6LPnhzx3yj3Z6sW7tWE8Z+V2dfdIlKy9h6iG0jYjfN+IZGuPst7j7I3QcNH9lgZ9nulLVtp377D9Sr056NjpJZZTt1UtlOndSl1z6SpF4DDtbiD98NToX25Z0lSWUdOqrPoIM1992ZwYmyrbq6SlePHaPDv3CMhhxaER0n87p07ar5H8/feH3hggXq2rVrYKLCKUgZMbPpDVxmSNoxH8k6Vi5fpjWrV0mSKjes14yXXtBu3XvGhsqw0g7lKivvrOXzP5IkzZv5ijru2iM4VbZVrl+nDevWbvx99oxp6sJrJIy7a9LE8eres5dO/Opp0XEgad/+++nDD9/XRx/NUVVlpR584H4devgR0bEKolDHjHSV9CVJy+oMN0nPFGiezcqypYs1+bpxqqmpkdfUaPChR2rA4KHRsTLtc6ecq8d/da1yuWq167SLDhl1cXSkTFuzYpmm3HClpOT4qv5DKtR7/4OCU2XXGzNe0SMP3aeee+6lC75xsiTp66Mv1IGf430rSosWLfT9K36gb44+SzU1OZ1w4knq3Xuv6FgFYe6+9Sdq9ktJt7v7U/WMu8vdR25qGi99sHLrB8MW++fsRdERUMfu7VtFR0Cez3bfOToC8nQrbxMdAXW0btHwKaUF2TLi7mc2Mm6TRQQAAGQHnzMCAABCUUYAAEAoyggAAAhFGQEAAKEoIwAAIBRlBAAAhKKMAACAUJQRAAAQijICAABCUUYAAEAoyggAAAhFGQEAAKEoIwAAIBRlBAAAhKKMAACAUJQRAAAQijICAABCUUYAAEAoyggAAAhFGQEAAKEoIwAAIBRlBAAAhKKMAACAUJQRAAAQijICAABCUUYAAEAoyggAAAhFGQEAAKEoIwAAIBRlBAAAhKKMAACAUJQRAAAQijICAABCUUYAAEAoyggAAAhFGQEAAKEoIwAAIBRlBAAAhKKMAACAUJQRAAAQijICAABCUUYAAEAoyggAAAjVIjpAQ7qVt4mOgDy7L24VHQF1PPPhqugIyPPZ7jtHR0CeleuqoiOgjtbtShocx5YRAAAQijICAABCUUYAAEAoyggAAAhFGQEAAKEoIwAAIBRlBAAAhKKMAACAUJQRAAAQijICAABCUUYAAEAoyggAAAhFGQEAAKEoIwAAIBRlBAAAhKKMAACAUJQRAAAQijICAABCUUYAAEAoyggAAAhFGQEAAKEoIwAAIBRlBAAAhKKMAACAUJQRAAAQijICAABCUUYAAEAoyggAAAhFGQEAAKEoIwAAIBRlBAAAhKKMAACAUJQRAAAQijICAABCUUYAAEAoyggAAAjVoqERZvYzSd7QeHe/qCCJAABApjRYRiRN22YpAABAZjVYRtz9N9syCAAAyKbGtoxIksyss6TLJPWT1Lp2uLsfUcBcAAAgI5pyAOvvJL0pqZek8ZLelzS1gJkAAECGNKWM7Ozuv5RU5e6Pu/sZktgqAgAAtopN7qaRVJX+/NjMvixpnqTywkUCAABZ0pQy8iMz6yBpjKSfSWov6eKCpgIAAJmxyTLi7velv66QdHhh4+w4rhk/Vs889YQ6dizXHVPujY4DSZMuGqlWbUplRUUqKirW2RMmR0fKtC5tW+rMA3ffeH3nshLd/+YiPfrussBU2bVowXxdP2Gsli9dKjPpqONO0vEjTo2OlWlZWo805Wya21XPh5+lx46gAUcfe4KGnzJSE35weXQU5Bl1xfUqbd8hOgYkLVxdqWsefU+SZJKuPnovvTpvVWyoDCsuLtZZ549R7z59tXbtGn3rzK/pM4MGq0evT0VHy6wsrUeaspvmvrzfW0s6UclxI2jEAQMG6eN5c6NjANuFPl3KtGhNpZauq46OklnlnTqrvFNnSVJpaZm699xTSxYvpIwEytJ6pCm7af6Uf93M7pb01KbuZ2b7SNpd0vPuvjpv+FHu/uAWZAU+ETPTnRMvlck0oGKYBlYMi46E1KBu7fXiRyujYyC14OO5mv32TPXpt190FGTElnxR3l6SujR2AzO7SNJfJF0o6TUzOz5v9NWN3G+0mU0zs2l33H7bFkQDGnb6lT/V6Ktv1sjLrtG0h/+iD96cHh0JkopN2m+XtnppLmWkOVi3dq0mjP2uzr7oEpWWtY2Og4xoyjEjq/Sfx4zMV/KJrI05W9JAd19tZj0l/dHMerr7JCW7h+vl7rdIukWSFq6qavBL+oAt0b482QRd1qGj+gw6WHPfnak9+n46OBX23aWt5ixfr1UbctFRMq+6ukpXjx2jw79wjIYcWhEdBxnSlN007bZgukW1u2bc/X0zO0xJIdlDjZQRoFAq16+Tu6tVm1JVrl+n2TOm6ZDhp0XHgqSB3dprGrtowrm7Jk0cr+49e+nEr/LawLbVlC0j/3L3ik0Nq2OBmR3g7q9IUrqFZJikX0nKxE7IcZdfopdfnKoVy5dr+DEVOmP0eRp2wknRsTJrzYplmnLDlZKkmlxO/YdUqPf+BwWnQsti0z5dynT3y/Ojo2TeGzNe0SMP3aeee+6lC75xsiTp66Mv1IGfGxqcLLuytB4x9/r3hphZa0mlkh6VdJj+vUWjvaQH3X2fBidq1k1Stbv/1zuMmQ1x96c3FYzdNM3Lw28viI6AOp75kNNgm5OLh/SMjoA87ds05WRRbEtd2pU0uGeksaV1jqRvS9pN0ov6dxlZKennjc3Q3T9qZNwmiwgAAMiOBstIerDpJDO70N1/tg0zAQCADGnKqb01ZrZT7RUz62hm5xUwEwAAyJCmlJGz3X157RV3X6bk1F0AAIBPrCllpNjMNh50YmbFkloWLhIAAMiSphxu/KCke8zs5vT6OZL+XrhIAAAgS5pSRi6TNFrSuen16ZJ2KVgiAACQKZvcTePuNZKel/S+pIMkHSHpzcLGAgAAWdHglhEz21vS19LLYkn3SJK7H75togEAgCxobDfNTElPShrm7rMkycwu3iapAABAZjS2m2a4pI8lPWpmt5pZhfiSOwAAsJU1WEbc/V53/6qkfZR8P823JXUxs8lm9sVtFRAAAOzYmnIA6xp3v8vdj5XUTdLLSs6wAQAA+MSa8qFnG7n7Mne/xd0rChUIAABky2aVEQAAgK2NMgIAAEJRRgAAQCjKCAAACEUZAQAAoSgjAAAgFGUEAACEoowAAIBQlBEAABCKMgIAAEJRRgAAQCjKCAAACEUZAQAAoSgjAAAgFGUEAACEoowAAIBQlBEAABCKMgIAAEJRRgAAQCjKCAAACEUZAQAAoSgjAAAgFGUEAACEoowAAIBQlBEAABCKMgIAAEJRRgAAQCjKCAAACEUZAQAAoSgjAAAglLl7dIZ6zVq4rnkGy6iV66qiI6CObuVtoiMgz3l/nBEdAXlu/Mp+0RFQR5d2JdbQOLaMAACAUJQRAAAQijICAABCUUYAAEAoyggAAAhFGQEAAKEoIwAAIBRlBAAAhKKMAACAUJQRAAAQijICAABCUUYAAEAoyggAAAhFGQEAAKEoIwAAIBRlBAAAhKKMAACAUJQRAAAQijICAABCUUYAAEAoyggAAAhFGQEAAKEoIwAAIBRlBAAAhKKMAACAUJQRAAAQijICAABCUUYAAEAoyggAAAhFGQEAAKEoIwAAIBRlBAAAhKKMAACAUJQRAAAQijICAABCUUYAAEAoyggAAAhFGQEAAKEoIwAAIBRlBAAAhKKMAACAUJQRAAAQijICAABCUUYAAECoFtEBdlSLFszX9RPGavnSpTKTjjruJB0/4tToWJlVWblBV40ZraqqKuVy1frs0AqNGHVOdKxMu2b8WD3z1BPq2LFcd0y5NzoOJA3r10UVe+8sl/ThsnX6xVMfqCrn0bEyK0uvEbaMFEhxcbHOOn+Mbrrzz7r+5t/qvj/fow/fezc6VmaVlLTU2Gsn68c33aWJk+/Sq1Of1TtvzoiOlWlHH3uCfvKzm6JjIFVeWqKj+3XWZX+bqe/c+6aKzDSkV8foWJmWpdcIZaRAyjt1Vu8+fSVJpaVl6t5zTy1ZvDA4VXaZmVq3KZUk5aqrlctVy2TBqbLtgAGD1L59h+gYyFNcZGpZXKQik1q1KNKytVXRkTItS6+Rgu2mMbODJLm7TzWzfpKOkjTT3R8o1DybqwUfz9Xst2eqT7/9oqNkWk0up8vPP03z532kLx43Qr379o+OBDQbS9dW6a+vLdDkk/urMlej6XNX6dV5q6JjISMKsmXEzK6U9H8lTTazayT9XFKZpO+Z2RWN3G+0mU0zs2m/v+OXhYi2za1bu1YTxn5XZ190iUrL2kbHybSi4mJNvOku/eKu+/XuW69rznuzoiMBzUZZy2Id2GMnnf+H1zX69zPUqkWRhu5ZHh0LGVGoLSNfkXSApFaS5kvq5u4rzewnkp6XNKG+O7n7LZJukaRZC9dt90dNVVdX6eqxY3T4F47RkEMrouMgVda2nfrtP1CvTntW3Xv1jo4DNAuf3q2dFq7aoJUbqiVJz3+wXH26lOnJ2UuDkyELCnXMSLW759x9raR33X2lJLn7Okk1BZpns+LumjRxvLr37KUTv3padJzMW7l8mdasTjY5V25YrxkvvaDduveMDQU0I4tXV2rvzmVqWZwcS7Xfbu00d8X64FTIikJtGak0s9K0jAysHWhmHZSRMvLGjFf0yEP3qeeee+mCb5wsSfr66At14OeGBifLpmVLF2vydeNUU1Mjr6nR4EOP1IDBLItI4y6/RC+/OFUrli/X8GMqdMbo8zTshJOiY2XWO4vX6tn3l+u64/oq5673lqzVw28tjo6VaVl6jZj71t8bYmat3H1DPcM7SdrV3Td5TuWOsJtmR7JyHUfVNzfdyttER0Ce8/7IqeLNyY1f4YSB5qZLu5IGT2EsyJaR+opIOnyxJKo2AADYiM8ZAQAAoSgjAAAgFGUEAACEoowAAFK9j8oAAAtGSURBVIBQlBEAABCKMgIAAEJRRgAAQCjKCAAACEUZAQAAoSgjAAAgFGUEAACEoowAAIBQlBEAABCKMgIAAEJRRgAAQCjKCAAACEUZAQAAoSgjAAAgFGUEAACEoowAAIBQlBEAABCKMgIAAEJRRgAAQCjKCAAACEUZAQAAoSgjAAAgFGUEAACEoowAAIBQlBEAABCKMgIAAEJRRgAAQCjKCAAACEUZAQAAoSgjAAAgFGUEAACEoowAAIBQlBEAABCKMgIAAEJRRgAAQCjKCAAACEUZAQAAoSgjAAAgVIvoAA1p36bZRsuk5+csiY4ANGsP/n16dATk2fPv0/XEdcOjYyBPl3YlDY5jywgAYIdDEdm+UEYAAEAoyggAAAhFGQEAAKEoIwAAIBRlBAAAhKKMAACAUJQRAAAQijICAABCUUYAAEAoyggAAAhFGQEAAKEoIwAAIBRlBAAAhKKMAACAUJQRAAAQijICAABCUUYAAEAoyggAAAhFGQEAAKEoIwAAIBRlBAAAhKKMAACAUJQRAAAQijICAABCUUYAAEAoyggAAAhFGQEAAKEoIwAAIBRlBAAAhKKMAACAUJQRAAAQijICAABCUUYAAEAoyggAAAhFGQEAAKEoIwAAIBRlBAAAhKKMAACAUJQRAAAQijICAABCUUYAAEAoyggAAAhFGQEAAKEoIwAAIFSL6AA7qmvGj9UzTz2hjh3LdceUe6PjQNKki0aqVZtSWVGRioqKdfaEydGRMq2ycoOuGjNaVVVVyuWq9dmhFRox6pzoWJly4zeH6OgB3bRoxXod9N2/SJJ+9D+DdMzA7qqszum9Bat07o1Pa8XayuCk2ZSl1whlpECOPvYEDT9lpCb84PLoKMgz6orrVdq+Q3QMSCopaamx105W6zalqq6u1riLz9IBB35ee/XdLzpaZvzusVm6+cE3dev5QzcOe2T6PF1514vK1biuOnWgxpy4n37wuxcDU2ZXll4j7KYpkAMGDFJ7VnpAg8xMrduUSpJy1dXK5aplsuBU2fL0mwu0bPV/bvV4ZPo85WpckjT17UXavbw0IhqUrdfINtsyYmZ3uPuobTU/oC4z050TL5XJNKBimAZWDIuOlHk1uZwuP/80zZ/3kb543Aj17ts/OhLynHbEXvrTM+9Fx8i0rLxGClJGzOyvdQdJOtzMdpIkdz+ugfuNljRakq6bdKNGfeOsQsRDRp1+5U/Vvryz1qxYpjuvuVSdduuhPfp+OjpWphUVF2viTXdpzepV+t/xl2jOe7PUvVfv6FiQdMmJn1YuV6N7npwdHSXTsvIaKdSWkW6S3pB0myRXUkYGSbq+sTu5+y2SbpGkhauqvEDZkFHtyztLkso6dFSfQQdr7rszKSPNRFnbduq3/0C9Ou3ZHfKNdntz6qG9ddTAbhp21UPRUZDa0V8jhTpmZJCkFyVdIWmFuz8maZ27P+7ujxdonkCDKtev04Z1azf+PnvGNHXp3jM2VMatXL5Ma1avkiRVblivGS+9oN1YJuGO3H93XXx8f53y439pXWUuOk6mZek1UpAtI+5eI+kGM/tD+nNBoebVXI27/BK9/OJUrVi+XMOPqdAZo8/TsBNOio6VWWtWLNOUG66UlOyD7T+kQr33Pyg4VbYtW7pYk68bp5qaGnlNjQYfeqQGDB666Ttiq7n9W4doaL9dtHO71npr8ghNmPKKxpy4n1q1KNZf/8+XJElT31mkb936bHDSbMrSa8TcC783xMy+LGmIuzf5PFd20zQvD7+9IDoC6ujbqX10BOQ55JI/R0dAnieuGx4dAXUM2KN9g6cCbZOtFe5+v6T7t8W8AADA9oXPGQEAAKEoIwAAIBRlBAAAhKKMAACAUJQRAAAQijICAABCUUYAAEAoyggAAAhFGQEAAKEoIwAAIBRlBAAAhKKMAACAUJQRAAAQijICAABCUUYAAEAoyggAAAhFGQEAAKEoIwAAIBRlBAAAhKKMAACAUJQRAAAQijICAABCUUYAAEAoyggAAAhFGQEAAKEoIwAAIBRlBAAAhKKMAACAUJQRAAAQijICAABCUUYAAEAoyggAAAhFGQEAAKEoIwAAIBRlBAAAhKKMAACAUJQRAAAQijICAABCUUYAAEAoyggAAAhFGQEAAKEoIwAAIBRlBAAAhDJ3j86wQzOz0e5+S3QO/BvLpHlheTQ/LJPmJQvLgy0jhTc6OgD+C8ukeWF5ND8sk+Zlh18elBEAABCKMgIAAEJRRgpvh97Pt51imTQvLI/mh2XSvOzwy4MDWAEAQCi2jAAAgFCUkQIys6PM7C0zm2Vm34vOk3Vm9iszW2hmr0VngWRm3c3sUTN7w8xeN7NvRWfKMjNrbWYvmNmr6fIYH50JkpkVm9nLZnZfdJZCoowUiJkVS/qFpKMl9ZP0NTPrF5sq834t6ajoENioWtIYd+8nabCk83mNhNog6Qh331/SAZKOMrPBwZkgfUvSm9EhCo0yUjgHSZrl7rPdvVLS7yUdH5wp09z9CUlLo3Mg4e4fu/tL6e+rlLzh7h6bKrs8sTq9WpJeOKgwkJl1k/RlSbdFZyk0ykjh7C5pTt71j8QbLVAvM+sp6TOSno9Nkm3pLoFXJC2U9LC7szxi/VTSpZJqooMUGmUEQCgzayvpT5K+7e4ro/Nkmbvn3P0ASd0kHWRm/aMzZZWZDZO00N1fjM6yLVBGCmeupO5517ulwwCkzKxESRH5nbv/OToPEu6+XNKj4hirSEMkHWdm7yvZzX+Emd0ZG6lwKCOFM1XSXmbWy8xaSvqqpL8GZwKaDTMzSb+U9Ka7/290nqwzs85mtlP6extJX5A0MzZVdrn79929m7v3VLL+eMTd/yc4VsFQRgrE3aslXSDpISUH5k1x99djU2Wbmd0t6VlJfczsIzM7MzpTxg2RdJqS//heSS/HRIfKsF0lPWpm05X8M/Wwu+/Qp5Oi+eATWAEAQCi2jAAAgFCUEQAAEIoyAgAAQlFGAABAKMoIAAAIRRkB0CRmlktPv33NzP5gZqWfYFq/NrOvpL/f1tgX5JnZYWb2+S2Yx/tm1mlLMwLYdigjAJpqnbsf4O79JVVKOjd/pJm12JKJuvtZ7v5GIzc5TNJmlxEA2w/KCIAt8aSk3ulWiyfN7K+S3ki/aO06M5tqZtPN7Bwp+bRVM/u5mb1lZv+U1KV2Qmb2mJkNSn8/ysxeMrNXzexf6RfonSvp4nSrzND0k0L/lM5jqpkNSe+7s5n9w8xeN7PbJNm2fUgAbKkt+k8GQHalW0COlvRgOmiApP7u/p6ZjZa0wt0PNLNWkp42s38o+UbePpL6Seoq6Q1Jv6oz3c6SbpV0SDqtcndfamY3SVrt7j9Jb3eXpBvc/Skz66HkU477SrpS0lPufpWZfVkSn7ALbCcoIwCaqk369fJSsmXkl0p2n7zg7u+lw78o6dO1x4NI6iBpL0mHSLrb3XOS5pnZI/VMf7CkJ2qn5e5LG8hxpKR+yVfbSJLap9/8e4ik4el97zezZVv4dwLYxigjAJpqXfr18hulhWBN/iBJF7r7Q3VutzW/c6ZI0mB3X19PFgDbIY4ZAbA1PSTpm2ZWIklmtreZlUl6QtIp6TElu0o6vJ77PifpEDPrld63PB2+SlK7vNv9Q9KFtVfMrLYgPSFpZDrsaEkdt9pfBaCgKCMAtqbblBwP8pKZvSbpZiVbYP+fpHfScXco+fbk/+DuiySNlvRnM3tV0j3pqL9JOrH2AFZJF0kalB4g+4b+fVbPeCVl5nUlu2s+LNDfCGAr41t7AQBAKLaMAACAUJQRAAAQijICAABCUUYAAEAoyggAAAhFGQEAAKEoIwAAIBRlBAAAhPr/UMplXRARX/UAAAAASUVORK5CYII=\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Densenet-121 Training and Inference"
      ],
      "metadata": {
        "id": "W6tpuZYsbRMC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "bifurcation(models.densenet121)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8DVlg5Sk8fhr",
        "outputId": "0f9c481b-fcab-4ff4-cf79-776434ef46a7"
      },
      "execution_count": 130,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "features.conv0.weight\n",
            "features.norm0.weight\n",
            "features.norm0.bias\n",
            "features.norm0.running_mean\n",
            "features.norm0.running_var\n",
            "features.norm0.num_batches_tracked\n",
            "features.denseblock1.denselayer1.norm1.weight\n",
            "features.denseblock1.denselayer1.norm1.bias\n",
            "features.denseblock1.denselayer1.norm1.running_mean\n",
            "features.denseblock1.denselayer1.norm1.running_var\n",
            "features.denseblock1.denselayer1.norm1.num_batches_tracked\n",
            "features.denseblock1.denselayer1.conv1.weight\n",
            "features.denseblock1.denselayer1.norm2.weight\n",
            "features.denseblock1.denselayer1.norm2.bias\n",
            "features.denseblock1.denselayer1.norm2.running_mean\n",
            "features.denseblock1.denselayer1.norm2.running_var\n",
            "features.denseblock1.denselayer1.norm2.num_batches_tracked\n",
            "features.denseblock1.denselayer1.conv2.weight\n",
            "features.denseblock1.denselayer2.norm1.weight\n",
            "features.denseblock1.denselayer2.norm1.bias\n",
            "features.denseblock1.denselayer2.norm1.running_mean\n",
            "features.denseblock1.denselayer2.norm1.running_var\n",
            "features.denseblock1.denselayer2.norm1.num_batches_tracked\n",
            "features.denseblock1.denselayer2.conv1.weight\n",
            "features.denseblock1.denselayer2.norm2.weight\n",
            "features.denseblock1.denselayer2.norm2.bias\n",
            "features.denseblock1.denselayer2.norm2.running_mean\n",
            "features.denseblock1.denselayer2.norm2.running_var\n",
            "features.denseblock1.denselayer2.norm2.num_batches_tracked\n",
            "features.denseblock1.denselayer2.conv2.weight\n",
            "features.denseblock1.denselayer3.norm1.weight\n",
            "features.denseblock1.denselayer3.norm1.bias\n",
            "features.denseblock1.denselayer3.norm1.running_mean\n",
            "features.denseblock1.denselayer3.norm1.running_var\n",
            "features.denseblock1.denselayer3.norm1.num_batches_tracked\n",
            "features.denseblock1.denselayer3.conv1.weight\n",
            "features.denseblock1.denselayer3.norm2.weight\n",
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            "features.denseblock1.denselayer3.norm2.running_mean\n",
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          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_densenet121, loss_densenet121, optim_densenet121 = selective_finetuning_single_layer(models.densenet121, 'classifier')\n",
        "model_densenet121 ,densenet121_train ,densenet121_test, densenet121_pred, densenet121_true = model(train,test,5,model_densenet121, loss_densenet121, optim_densenet121)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "84QJLBao_j9t",
        "outputId": "45bafee5-dc0e-40f3-d823-bd7c6b2ceb8c"
      },
      "execution_count": 131,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 7/7 [01:57<00:00, 16.85s/it]\n",
            "100%|██████████| 7/7 [02:02<00:00, 17.47s/it]\n",
            "100%|██████████| 7/7 [02:13<00:00, 19.13s/it]\n",
            "100%|██████████| 7/7 [02:12<00:00, 18.93s/it]\n",
            "100%|██████████| 7/7 [02:07<00:00, 18.20s/it]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cf_matrix_densenet=confusion_matrix(densenet121_true,densenet121_pred)\n",
        "cf_matrix_densenet"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "E_qr1Zbq_W_5",
        "outputId": "40219f4c-00e9-4aa0-ac0d-2f845756683a"
      },
      "execution_count": 132,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 9,  3,  2,  2, 11],\n",
              "       [ 4,  2,  4,  2,  4],\n",
              "       [ 4,  2,  5,  3,  2],\n",
              "       [ 1,  5,  2,  3,  4],\n",
              "       [ 4,  7,  7,  3,  3]])"
            ]
          },
          "metadata": {},
          "execution_count": 132
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(9,9))\n",
        "sns.heatmap(cf_matrix_densenet, cbar=False, fmt='d', annot=True, cmap='Blues')\n",
        "plt.xlabel('Predicted')\n",
        "plt.ylabel('Actual')\n",
        "plt.title('Confusion matrix of the CNN Classifier')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 567
        },
        "id": "ccL4YArZ_b-Y",
        "outputId": "912a96d4-f42e-4f50-c1ed-6888bd0fc9f5"
      },
      "execution_count": 133,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 648x648 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Efficientnet-B0 Training and Inference"
      ],
      "metadata": {
        "id": "_XaTmcSdbIuc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "bifurcation(models.efficientnet.efficientnet_b0)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
          "referenced_widgets": [
            "7f26381524dc4c1c9a43eb385ae46e31",
            "71a9b533334c4b13b300b7e49484a7f6",
            "568ccbb49ef04e1c8f18a7ed89bc189c",
            "44d8671a02f14baaba6a8e60d61583f0",
            "2899e529f2644bb29ac4a3b2fc19bb1b",
            "2861f210b7a141518798aa332a8a37c4",
            "eaa866a3b5bf48f7b3ff49b3a0d9ba74",
            "1cdc4a0381cd40708015d5e91d896e82",
            "9a43518a34b24deaa62900c48fa0f6cf",
            "e08e3c61bc1645ebaca7e5c060a4edfd",
            "e5c91554644a4af0be81e8aa25f2e8bd"
          ]
        },
        "id": "qSvNvqGr8fTZ",
        "outputId": "c80ed64e-bf5d-4d59-bfb0-9c24ada0f529"
      },
      "execution_count": 137,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Downloading: \"https://download.pytorch.org/models/efficientnet_b0_rwightman-3dd342df.pth\" to /root/.cache/torch/hub/checkpoints/efficientnet_b0_rwightman-3dd342df.pth\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0.00/20.5M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "7f26381524dc4c1c9a43eb385ae46e31"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "features.0.0.weight\n",
            "features.0.1.weight\n",
            "features.0.1.bias\n",
            "features.0.1.running_mean\n",
            "features.0.1.running_var\n",
            "features.0.1.num_batches_tracked\n",
            "features.1.0.block.0.0.weight\n",
            "features.1.0.block.0.1.weight\n",
            "features.1.0.block.0.1.bias\n",
            "features.1.0.block.0.1.running_mean\n",
            "features.1.0.block.0.1.running_var\n",
            "features.1.0.block.0.1.num_batches_tracked\n",
            "features.1.0.block.1.fc1.weight\n",
            "features.1.0.block.1.fc1.bias\n",
            "features.1.0.block.1.fc2.weight\n",
            "features.1.0.block.1.fc2.bias\n",
            "features.1.0.block.2.0.weight\n",
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          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_efficientnet_b0, loss_efficientnet_b0, optim_efficientnet_b0 = selective_finetuning_single_layer(models.efficientnet_b0, 'classifier')\n",
        "model_efficientnet_b0 ,efficientnet_b0_train ,efficientnet_b0_test, efficientnet_b0_pred, efficientnet_b0_true = model(train,test,5,model_efficientnet_b0, loss_efficientnet_b0, optim_efficientnet_b0)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eX-p5yzz_wwF",
        "outputId": "31c73c46-459d-491f-97af-44994a147e7b"
      },
      "execution_count": 138,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 7/7 [02:02<00:00, 17.54s/it]\n",
            "100%|██████████| 7/7 [02:03<00:00, 17.71s/it]\n",
            "100%|██████████| 7/7 [02:00<00:00, 17.24s/it]\n",
            "100%|██████████| 7/7 [02:00<00:00, 17.16s/it]\n",
            "100%|██████████| 7/7 [01:58<00:00, 16.99s/it]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cf_matrix_efficientnet_b0=confusion_matrix(efficientnet_b0_true,efficientnet_b0_pred)\n",
        "cf_matrix_efficientnet_b0"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ynQwmlGe_wwF",
        "outputId": "212d4030-4d9e-4b2d-e40b-07b24484d956"
      },
      "execution_count": 139,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 8, 10,  2,  3,  4,  0,  0,  0],\n",
              "       [ 3,  2,  4,  2,  4,  1,  0,  0],\n",
              "       [ 4,  0,  8,  1,  1,  0,  1,  1],\n",
              "       [ 3,  4,  1,  4,  3,  0,  0,  0],\n",
              "       [ 0,  3,  2, 14,  5,  0,  0,  0],\n",
              "       [ 0,  0,  0,  0,  0,  0,  0,  0],\n",
              "       [ 0,  0,  0,  0,  0,  0,  0,  0],\n",
              "       [ 0,  0,  0,  0,  0,  0,  0,  0]])"
            ]
          },
          "metadata": {},
          "execution_count": 139
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(9,9))\n",
        "sns.heatmap(cf_matrix_efficientnet_b0, cbar=False, fmt='d', annot=True, cmap='Blues')\n",
        "plt.xlabel('Predicted')\n",
        "plt.ylabel('Actual')\n",
        "plt.title('Confusion matrix of the CNN Classifier')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 567
        },
        "id": "AyAAPkXl_wwF",
        "outputId": "a4e5acf6-538e-4e19-ea3d-a936b2deb98b"
      },
      "execution_count": 140,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 648x648 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Alexnet Training and Inference"
      ],
      "metadata": {
        "id": "ggyXW-VYa-Po"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "bifurcation(models.alexnet)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 359,
          "referenced_widgets": [
            "8ecf3fb1382e48a1a9bde23bedf88945",
            "b38e343c1a3d4877acd9d51e8ed6745e",
            "e8cac91bf77f4198840a99e55e2d8e99",
            "bbb221246cdb4bbcaffc5a62ab61a167",
            "fd1f884eeb884e76986ffc0780e4cc2c",
            "1a6a2c0debd447299cd5df63fed89644",
            "85b571b32e8e4bac96ea00b32f9231a2",
            "3510a95d20c0438daef2c5c0b440d2bc",
            "fef6510cc5c6402ea9c5c7f1c848a33d",
            "2015f85e78c24de094d024056ffa4daf",
            "8026bf79b3e84960bb87400085a2da26"
          ]
        },
        "id": "S2jGUrVk_tal",
        "outputId": "6a9c4387-720e-4fda-a355-5e92619b9cc1"
      },
      "execution_count": 148,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Downloading: \"https://download.pytorch.org/models/alexnet-owt-7be5be79.pth\" to /root/.cache/torch/hub/checkpoints/alexnet-owt-7be5be79.pth\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0.00/233M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "8ecf3fb1382e48a1a9bde23bedf88945"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "features.0.weight\n",
            "features.0.bias\n",
            "features.3.weight\n",
            "features.3.bias\n",
            "features.6.weight\n",
            "features.6.bias\n",
            "features.8.weight\n",
            "features.8.bias\n",
            "features.10.weight\n",
            "features.10.bias\n",
            "classifier.1.weight\n",
            "classifier.1.bias\n",
            "classifier.4.weight\n",
            "classifier.4.bias\n",
            "classifier.6.weight\n",
            "classifier.6.bias\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_alexnet, loss_alexnet, optim_alexnet = selective_finetuning_single_layer(models.alexnet, 'classifier')\n",
        "model_alexnet ,alexnet_train ,alexnet_test, alexnet_pred, alexnet_true = model(train,test,5,model_alexnet, loss_alexnet, optim_alexnet)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "18rfgdIf_x8R",
        "outputId": "9ea813a6-02e5-422d-9998-72d219c7dc72"
      },
      "execution_count": 149,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 7/7 [02:06<00:00, 18.02s/it]\n",
            "100%|██████████| 7/7 [01:59<00:00, 17.02s/it]\n",
            "100%|██████████| 7/7 [01:57<00:00, 16.76s/it]\n",
            "100%|██████████| 7/7 [02:11<00:00, 18.76s/it]\n",
            "100%|██████████| 7/7 [02:05<00:00, 17.90s/it]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cf_matrix_alexnet=confusion_matrix(alexnet_true,alexnet_pred)\n",
        "cf_matrix_alexnet"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JnQxatrV_x8S",
        "outputId": "e75fc4c2-4689-4e17-d84e-00fbbcb50334"
      },
      "execution_count": 150,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[10,  5, 10,  0,  2],\n",
              "       [ 3,  3,  6,  0,  4],\n",
              "       [ 0,  1, 11,  0,  4],\n",
              "       [ 1,  1,  6,  2,  5],\n",
              "       [ 1,  2, 11,  0, 10]])"
            ]
          },
          "metadata": {},
          "execution_count": 150
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(9,9))\n",
        "sns.heatmap(cf_matrix_alexnet, cbar=False, fmt='d', annot=True, cmap='Blues')\n",
        "plt.xlabel('Predicted')\n",
        "plt.ylabel('Actual')\n",
        "plt.title('Confusion matrix of the CNN Classifier')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 567
        },
        "id": "FA_estjm_x8S",
        "outputId": "f4908888-f3c7-48c3-a24a-f2982eb1620e"
      },
      "execution_count": 151,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 648x648 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# ResNet18 Training and Inference"
      ],
      "metadata": {
        "id": "Is9xs4hwa3Pg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "bifurcation(models.resnet18)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
          "referenced_widgets": [
            "f27c9f669812421cbc7e998bc9814563",
            "e50ad5749acf4f18949a6d8bc96f987a",
            "35556492e918415eaa8b0e34ab2b5074",
            "232b2319eed54653a27af94ca64b7610",
            "83c52cc77def4c56842bcbd15a856200",
            "523cec551c684bc1a0bbb0a3b1a84bcf",
            "dbd2447f49ad4209bbcd66135b93c036",
            "6e8dbbdcf8e3466fae856ef820fcd155",
            "bba9f86208d040cdb6374fd1eb69dd6b",
            "12a848cdccff48189de11438b2aba8c1",
            "d31e0967fd02445fa54a72bd00307fa2"
          ]
        },
        "id": "A1iPt3HlDbxe",
        "outputId": "f510803b-1524-417f-aefb-916b570bc323"
      },
      "execution_count": 134,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Downloading: \"https://download.pytorch.org/models/resnet18-f37072fd.pth\" to /root/.cache/torch/hub/checkpoints/resnet18-f37072fd.pth\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0.00/44.7M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f27c9f669812421cbc7e998bc9814563"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "conv1.weight\n",
            "bn1.weight\n",
            "bn1.bias\n",
            "bn1.running_mean\n",
            "bn1.running_var\n",
            "bn1.num_batches_tracked\n",
            "layer1.0.conv1.weight\n",
            "layer1.0.bn1.weight\n",
            "layer1.0.bn1.bias\n",
            "layer1.0.bn1.running_mean\n",
            "layer1.0.bn1.running_var\n",
            "layer1.0.bn1.num_batches_tracked\n",
            "layer1.0.conv2.weight\n",
            "layer1.0.bn2.weight\n",
            "layer1.0.bn2.bias\n",
            "layer1.0.bn2.running_mean\n",
            "layer1.0.bn2.running_var\n",
            "layer1.0.bn2.num_batches_tracked\n",
            "layer1.1.conv1.weight\n",
            "layer1.1.bn1.weight\n",
            "layer1.1.bn1.bias\n",
            "layer1.1.bn1.running_mean\n",
            "layer1.1.bn1.running_var\n",
            "layer1.1.bn1.num_batches_tracked\n",
            "layer1.1.conv2.weight\n",
            "layer1.1.bn2.weight\n",
            "layer1.1.bn2.bias\n",
            "layer1.1.bn2.running_mean\n",
            "layer1.1.bn2.running_var\n",
            "layer1.1.bn2.num_batches_tracked\n",
            "layer2.0.conv1.weight\n",
            "layer2.0.bn1.weight\n",
            "layer2.0.bn1.bias\n",
            "layer2.0.bn1.running_mean\n",
            "layer2.0.bn1.running_var\n",
            "layer2.0.bn1.num_batches_tracked\n",
            "layer2.0.conv2.weight\n",
            "layer2.0.bn2.weight\n",
            "layer2.0.bn2.bias\n",
            "layer2.0.bn2.running_mean\n",
            "layer2.0.bn2.running_var\n",
            "layer2.0.bn2.num_batches_tracked\n",
            "layer2.0.downsample.0.weight\n",
            "layer2.0.downsample.1.weight\n",
            "layer2.0.downsample.1.bias\n",
            "layer2.0.downsample.1.running_mean\n",
            "layer2.0.downsample.1.running_var\n",
            "layer2.0.downsample.1.num_batches_tracked\n",
            "layer2.1.conv1.weight\n",
            "layer2.1.bn1.weight\n",
            "layer2.1.bn1.bias\n",
            "layer2.1.bn1.running_mean\n",
            "layer2.1.bn1.running_var\n",
            "layer2.1.bn1.num_batches_tracked\n",
            "layer2.1.conv2.weight\n",
            "layer2.1.bn2.weight\n",
            "layer2.1.bn2.bias\n",
            "layer2.1.bn2.running_mean\n",
            "layer2.1.bn2.running_var\n",
            "layer2.1.bn2.num_batches_tracked\n",
            "layer3.0.conv1.weight\n",
            "layer3.0.bn1.weight\n",
            "layer3.0.bn1.bias\n",
            "layer3.0.bn1.running_mean\n",
            "layer3.0.bn1.running_var\n",
            "layer3.0.bn1.num_batches_tracked\n",
            "layer3.0.conv2.weight\n",
            "layer3.0.bn2.weight\n",
            "layer3.0.bn2.bias\n",
            "layer3.0.bn2.running_mean\n",
            "layer3.0.bn2.running_var\n",
            "layer3.0.bn2.num_batches_tracked\n",
            "layer3.0.downsample.0.weight\n",
            "layer3.0.downsample.1.weight\n",
            "layer3.0.downsample.1.bias\n",
            "layer3.0.downsample.1.running_mean\n",
            "layer3.0.downsample.1.running_var\n",
            "layer3.0.downsample.1.num_batches_tracked\n",
            "layer3.1.conv1.weight\n",
            "layer3.1.bn1.weight\n",
            "layer3.1.bn1.bias\n",
            "layer3.1.bn1.running_mean\n",
            "layer3.1.bn1.running_var\n",
            "layer3.1.bn1.num_batches_tracked\n",
            "layer3.1.conv2.weight\n",
            "layer3.1.bn2.weight\n",
            "layer3.1.bn2.bias\n",
            "layer3.1.bn2.running_mean\n",
            "layer3.1.bn2.running_var\n",
            "layer3.1.bn2.num_batches_tracked\n",
            "layer4.0.conv1.weight\n",
            "layer4.0.bn1.weight\n",
            "layer4.0.bn1.bias\n",
            "layer4.0.bn1.running_mean\n",
            "layer4.0.bn1.running_var\n",
            "layer4.0.bn1.num_batches_tracked\n",
            "layer4.0.conv2.weight\n",
            "layer4.0.bn2.weight\n",
            "layer4.0.bn2.bias\n",
            "layer4.0.bn2.running_mean\n",
            "layer4.0.bn2.running_var\n",
            "layer4.0.bn2.num_batches_tracked\n",
            "layer4.0.downsample.0.weight\n",
            "layer4.0.downsample.1.weight\n",
            "layer4.0.downsample.1.bias\n",
            "layer4.0.downsample.1.running_mean\n",
            "layer4.0.downsample.1.running_var\n",
            "layer4.0.downsample.1.num_batches_tracked\n",
            "layer4.1.conv1.weight\n",
            "layer4.1.bn1.weight\n",
            "layer4.1.bn1.bias\n",
            "layer4.1.bn1.running_mean\n",
            "layer4.1.bn1.running_var\n",
            "layer4.1.bn1.num_batches_tracked\n",
            "layer4.1.conv2.weight\n",
            "layer4.1.bn2.weight\n",
            "layer4.1.bn2.bias\n",
            "layer4.1.bn2.running_mean\n",
            "layer4.1.bn2.running_var\n",
            "layer4.1.bn2.num_batches_tracked\n",
            "fc.weight\n",
            "fc.bias\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_resnet18, loss_resnet18, optim_resnet18 = selective_finetuning_single_layer(models.resnet18, 'fc')\n",
        "model_resnet18 ,resnet18_train ,resnet18_test, resnet18_pred, resnet18_true = model(train,test,5,model_resnet18, loss_resnet18, optim_resnet18)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eoGJ2l1sDgDJ",
        "outputId": "56bc958e-a400-481a-dcd3-2e5197476503"
      },
      "execution_count": 135,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 7/7 [01:59<00:00, 17.14s/it]\n",
            "100%|██████████| 7/7 [01:59<00:00, 17.06s/it]\n",
            "100%|██████████| 7/7 [01:57<00:00, 16.81s/it]\n",
            "100%|██████████| 7/7 [01:59<00:00, 17.05s/it]\n",
            "100%|██████████| 7/7 [01:57<00:00, 16.79s/it]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cf_matrix_resnet18=confusion_matrix(resnet18_true,resnet18_pred)\n",
        "cf_matrix_resnet18"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zveb66BzDhIg",
        "outputId": "0219549d-dfb4-4b14-8c7a-9e5d970af30f"
      },
      "execution_count": 136,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[5, 7, 5, 4, 6],\n",
              "       [4, 2, 4, 1, 5],\n",
              "       [4, 5, 1, 2, 4],\n",
              "       [4, 3, 1, 4, 3],\n",
              "       [6, 7, 3, 6, 2]])"
            ]
          },
          "metadata": {},
          "execution_count": 136
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(9,9))\n",
        "sns.heatmap(cf_matrix_resnet18, cbar=False, fmt='d', annot=True, cmap='Blues')\n",
        "plt.xlabel('Predicted')\n",
        "plt.ylabel('Actual')\n",
        "plt.title('Confusion matrix of the CNN Classifier')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 567
        },
        "id": "u2GedBoYDiRd",
        "outputId": "d3dd1ccf-13d3-4cf0-c859-47c644d4b58a"
      },
      "execution_count": 143,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 648x648 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Loss v/s Epochs and Accuracy v/s Epochs Curves"
      ],
      "metadata": {
        "id": "IxQwYmV0axWP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10,5))\n",
        "plt.plot([j for j in range(1,6)],[resnet18_train[i][1] for i in range(0,5)])\n",
        "plt.plot([j for j in range(1,6)],[vgg16_train[i][1] for i in range(0,5)])\n",
        "plt.plot([j for j in range(1,6)],[densenet121_train[i][1] for i in range(0,5)])\n",
        "plt.plot([j for j in range(1,6)],[mobilenet_v3_small_train[i][1] for i in range(0,5)])\n",
        "plt.plot([j for j in range(1,6)],[efficientnet_b0_train[i][1] for i in range(0,5)])\n",
        "plt.plot([j for j in range(1,6)],[alexnet_train[i][1] for i in range(0,5)])\n",
        "plt.xlabel(\"Epochs\")\n",
        "plt.ylabel(\"Average Loss\")\n",
        "plt.title(\"Loss v/s Epochs for Training\")\n",
        "plt.legend([\"ResNet18\",\"VGG16\",\"DenseNet121\",\"Mobilenet_v3_small\",\"Efficientnet_b0\",\"Alexnet\"])\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        },
        "id": "pDjBr3UkCym1",
        "outputId": "6abf2ebb-28af-4525-ca9b-bf934c716b42"
      },
      "execution_count": 152,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10,5))\n",
        "plt.plot([j for j in range(1,6)],[resnet18_test[i][1] for i in range(0,5)])\n",
        "plt.plot([j for j in range(1,6)],[vgg16_test[i][1] for i in range(0,5)])\n",
        "plt.plot([j for j in range(1,6)],[densenet121_test[i][1] for i in range(0,5)])\n",
        "plt.plot([j for j in range(1,6)],[mobilenet_v3_small_test[i][1] for i in range(0,5)])\n",
        "plt.plot([j for j in range(1,6)],[efficientnet_b0_test[i][1] for i in range(0,5)])\n",
        "plt.plot([j for j in range(1,6)],[alexnet_test[i][1] for i in range(0,5)])\n",
        "plt.xlabel(\"Epochs\")\n",
        "plt.ylabel(\"Average Loss\")\n",
        "plt.title(\"Loss v/s Epochs for Testing\")\n",
        "plt.legend([\"ResNet18\",\"VGG16\",\"DenseNet121\",\"Mobilenet_v3_small\",\"Efficientnet_b0\",\"Alexnet\"])\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        },
        "id": "lHjLVb7SCzB3",
        "outputId": "bde4cc1e-36f0-494c-ba86-3020ecda6e26"
      },
      "execution_count": 153,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10,5))\n",
        "plt.plot([j for j in range(1,6)],[resnet18_train[i][2].cpu().numpy() for i in range(0,5)])\n",
        "plt.plot([j for j in range(1,6)],[vgg16_train[i][2].cpu().numpy() for i in range(0,5)])\n",
        "plt.plot([j for j in range(1,6)],[densenet121_train[i][2].cpu().numpy() for i in range(0,5)])\n",
        "plt.plot([j for j in range(1,6)],[mobilenet_v3_small_train[i][2].cpu().numpy() for i in range(0,5)])\n",
        "plt.plot([j for j in range(1,6)],[efficientnet_b0_train[i][2].cpu().numpy() for i in range(0,5)])\n",
        "plt.plot([j for j in range(1,6)],[alexnet_train[i][2].cpu().numpy() for i in range(0,5)])\n",
        "plt.xlabel(\"Epochs\")\n",
        "plt.ylabel(\"Accuracy\")\n",
        "plt.title(\"Accuracy v/s Epochs for Training\")\n",
        "plt.legend([\"ResNet18\",\"VGG16\",\"DenseNet121\",\"Mobilenet_v3_small\",\"Efficientnet_b0\",\"Alexnet\"])\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        },
        "id": "5af6Yo1sCzLf",
        "outputId": "17886838-d1df-48c6-ef0e-910d4c9c367a"
      },
      "execution_count": 154,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10,5))\n",
        "plt.plot([j for j in range(1,6)],[resnet18_test[i][2].cpu().numpy() for i in range(0,5)])\n",
        "plt.plot([j for j in range(1,6)],[vgg16_test[i][2].cpu().numpy() for i in range(0,5)])\n",
        "plt.plot([j for j in range(1,6)],[densenet121_test[i][2].cpu().numpy() for i in range(0,5)])\n",
        "plt.plot([j for j in range(1,6)],[mobilenet_v3_small_test[i][2].cpu().numpy() for i in range(0,5)])\n",
        "plt.plot([j for j in range(1,6)],[efficientnet_b0_test[i][2].cpu().numpy() for i in range(0,5)])\n",
        "plt.plot([j for j in range(1,6)],[alexnet_test[i][2].cpu().numpy() for i in range(0,5)])\n",
        "plt.xlabel(\"Epochs\")\n",
        "plt.ylabel(\"Accuracy\")\n",
        "plt.title(\"Accuracy v/s Epochs for Testing\")\n",
        "plt.legend([\"ResNet18\",\"VGG16\",\"DenseNet121\",\"Mobilenet_v3_small\",\"Efficientnet_b0\",\"Alexnet\"])\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        },
        "id": "Pzz2iWU4CzTo",
        "outputId": "fbe8d744-01e6-4f2d-d99f-fb508d121c2c"
      },
      "execution_count": 155,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Wandb Saves"
      ],
      "metadata": {
        "id": "UKuG0U1wan_h"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import wandb\n",
        "\n",
        "torch.save(model_vgg16, '/content/drive/MyDrive/DL_Project_2/model_vgg16.pth')\n",
        "run = wandb.init(project='DL_Project_2')\n",
        "artifact = wandb.Artifact('VGG16', type='VGG16')\n",
        "artifact.add_file('/content/drive/MyDrive/DL_Project_2/model_vgg16.pth')\n",
        "run.log_artifact(artifact)\n",
        "run.join()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 140,
          "referenced_widgets": [
            "a01de48d0c24442885dffcda58e66182",
            "ef2df8f4e5764befabaf6a048edd6ccc",
            "b3060b53b4314527a8dd6ecf14ee7225",
            "ceb8628dd101426b87def98dcbb46025",
            "0c880df760b547f98828985dfa35f098",
            "6d68bc70b3034181a1bfa93f574a0b2b",
            "1a3d0a278f824496ac896c5e9d790715",
            "f9d780c9911c4191b45ff20b445e0e00"
          ]
        },
        "id": "wD2JriC_Uci7",
        "outputId": "2458d637-6e99-4cf8-dcf9-5665f06fa2da"
      },
      "execution_count": 177,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "Tracking run with wandb version 0.12.15"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "Run data is saved locally in <code>/content/wandb/run-20220502_212044-3cv15uec</code>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "Syncing run <strong><a href=\"https://wandb.ai/kwanit1142/DL_Project_2/runs/3cv15uec\" target=\"_blank\">swept-rain-7</a></strong> to <a href=\"https://wandb.ai/kwanit1142/DL_Project_2\" target=\"_blank\">Weights & Biases</a> (<a href=\"https://wandb.me/run\" target=\"_blank\">docs</a>)<br/>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "Waiting for W&B process to finish... <strong style=\"color:green\">(success).</strong>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "VBox(children=(Label(value='527.810 MB of 527.810 MB uploaded (0.000 MB deduped)\\r'), FloatProgress(value=1.0,…"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a01de48d0c24442885dffcda58e66182"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "Synced <strong style=\"color:#cdcd00\">swept-rain-7</strong>: <a href=\"https://wandb.ai/kwanit1142/DL_Project_2/runs/3cv15uec\" target=\"_blank\">https://wandb.ai/kwanit1142/DL_Project_2/runs/3cv15uec</a><br/>Synced 4 W&B file(s), 0 media file(s), 1 artifact file(s) and 0 other file(s)"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "Find logs at: <code>./wandb/run-20220502_212044-3cv15uec/logs</code>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "torch.save(model_alexnet, '/content/drive/MyDrive/DL_Project_2/model_alexnet.pth')\n",
        "run = wandb.init(project='DL_Project_2')\n",
        "artifact = wandb.Artifact('alexnet', type='alexnet')\n",
        "artifact.add_file('/content/drive/MyDrive/DL_Project_2/model_alexnet.pth')\n",
        "run.log_artifact(artifact)\n",
        "run.join()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 140,
          "referenced_widgets": [
            "b9e4037fe3d545aebac25c727009771a",
            "53bf4d59be64425480573b80a5003466",
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      },
      "execution_count": 178,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
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            "text/html": [
              "Tracking run with wandb version 0.12.15"
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              "Run data is saved locally in <code>/content/wandb/run-20220502_212127-2w0pu770</code>"
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              "Syncing run <strong><a href=\"https://wandb.ai/kwanit1142/DL_Project_2/runs/2w0pu770\" target=\"_blank\">sweet-elevator-8</a></strong> to <a href=\"https://wandb.ai/kwanit1142/DL_Project_2\" target=\"_blank\">Weights & Biases</a> (<a href=\"https://wandb.me/run\" target=\"_blank\">docs</a>)<br/>"
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          },
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        {
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            "text/html": [
              "Waiting for W&B process to finish... <strong style=\"color:green\">(success).</strong>"
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    {
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        "torch.save(model_efficientnet_b0, '/content/drive/MyDrive/DL_Project_2/model_efficientnet_b0.pth')\n",
        "run = wandb.init(project='DL_Project_2')\n",
        "artifact = wandb.Artifact('efficientnet_b0', type='efficientnet_b0')\n",
        "artifact.add_file('/content/drive/MyDrive/DL_Project_2/model_efficientnet_b0.pth')\n",
        "run.log_artifact(artifact)\n",
        "run.join()"
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      "execution_count": 179,
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
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            "text/html": [
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              "Run data is saved locally in <code>/content/wandb/run-20220502_212155-3esyh43p</code>"
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              "Syncing run <strong><a href=\"https://wandb.ai/kwanit1142/DL_Project_2/runs/3esyh43p\" target=\"_blank\">glad-monkey-9</a></strong> to <a href=\"https://wandb.ai/kwanit1142/DL_Project_2\" target=\"_blank\">Weights & Biases</a> (<a href=\"https://wandb.me/run\" target=\"_blank\">docs</a>)<br/>"
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              "Waiting for W&B process to finish... <strong style=\"color:green\">(success).</strong>"
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              "Find logs at: <code>./wandb/run-20220502_212155-3esyh43p/logs</code>"
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    {
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        "torch.save(model_mobilenet_v3_small, '/content/drive/MyDrive/DL_Project_2/model_mobilenet_v3_small.pth')\n",
        "run = wandb.init(project='DL_Project_2')\n",
        "artifact = wandb.Artifact('mobilenet_v3_small', type='mobilenet_v3_small')\n",
        "artifact.add_file('/content/drive/MyDrive/DL_Project_2/model_mobilenet_v3_small.pth')\n",
        "run.log_artifact(artifact)\n",
        "run.join()"
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      "outputs": [
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          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
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            "text/html": [
              "Tracking run with wandb version 0.12.15"
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              "Run data is saved locally in <code>/content/wandb/run-20220502_212212-20skqwma</code>"
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              "Syncing run <strong><a href=\"https://wandb.ai/kwanit1142/DL_Project_2/runs/20skqwma\" target=\"_blank\">summer-eon-10</a></strong> to <a href=\"https://wandb.ai/kwanit1142/DL_Project_2\" target=\"_blank\">Weights & Biases</a> (<a href=\"https://wandb.me/run\" target=\"_blank\">docs</a>)<br/>"
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              "Waiting for W&B process to finish... <strong style=\"color:green\">(success).</strong>"
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              "Synced <strong style=\"color:#cdcd00\">summer-eon-10</strong>: <a href=\"https://wandb.ai/kwanit1142/DL_Project_2/runs/20skqwma\" target=\"_blank\">https://wandb.ai/kwanit1142/DL_Project_2/runs/20skqwma</a><br/>Synced 4 W&B file(s), 0 media file(s), 1 artifact file(s) and 0 other file(s)"
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          "output_type": "display_data",
          "data": {
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              "<IPython.core.display.HTML object>"
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            "text/html": [
              "Find logs at: <code>./wandb/run-20220502_212212-20skqwma/logs</code>"
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    {
      "cell_type": "code",
      "source": [
        "torch.save(model_resnet18, '/content/drive/MyDrive/DL_Project_2/model_resnet18.pth')\n",
        "run = wandb.init(project='DL_Project_2')\n",
        "artifact = wandb.Artifact('resnet18', type='resnet18')\n",
        "artifact.add_file('/content/drive/MyDrive/DL_Project_2/model_resnet18.pth')\n",
        "run.log_artifact(artifact)\n",
        "run.join()"
      ],
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            "text/plain": [
              "<IPython.core.display.HTML object>"
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            "text/html": [
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              "Syncing run <strong><a href=\"https://wandb.ai/kwanit1142/DL_Project_2/runs/3nsr8676\" target=\"_blank\">grateful-wind-11</a></strong> to <a href=\"https://wandb.ai/kwanit1142/DL_Project_2\" target=\"_blank\">Weights & Biases</a> (<a href=\"https://wandb.me/run\" target=\"_blank\">docs</a>)<br/>"
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              "VBox(children=(Label(value='44.675 MB of 44.675 MB uploaded (0.000 MB deduped)\\r'), FloatProgress(value=1.0, m…"
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            "text/html": [
              "Synced <strong style=\"color:#cdcd00\">grateful-wind-11</strong>: <a href=\"https://wandb.ai/kwanit1142/DL_Project_2/runs/3nsr8676\" target=\"_blank\">https://wandb.ai/kwanit1142/DL_Project_2/runs/3nsr8676</a><br/>Synced 4 W&B file(s), 0 media file(s), 1 artifact file(s) and 0 other file(s)"
            ]
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          "output_type": "display_data",
          "data": {
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              "<IPython.core.display.HTML object>"
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            "text/html": [
              "Find logs at: <code>./wandb/run-20220502_212230-3nsr8676/logs</code>"
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          },
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    },
    {
      "cell_type": "code",
      "source": [
        "torch.save(model_densenet121, '/content/drive/MyDrive/DL_Project_2/model_densenet121.pth')\n",
        "run = wandb.init(project='DL_Project_2')\n",
        "artifact = wandb.Artifact('densenet121', type='densenet121')\n",
        "artifact.add_file('/content/drive/MyDrive/DL_Project_2/model_densenet121.pth')\n",
        "run.log_artifact(artifact)\n",
        "run.join()"
      ],
      "metadata": {
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          "height": 140,
          "referenced_widgets": [
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      },
      "execution_count": 182,
      "outputs": [
        {
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          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
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              "Syncing run <strong><a href=\"https://wandb.ai/kwanit1142/DL_Project_2/runs/3g5r6r7v\" target=\"_blank\">sparkling-yogurt-12</a></strong> to <a href=\"https://wandb.ai/kwanit1142/DL_Project_2\" target=\"_blank\">Weights & Biases</a> (<a href=\"https://wandb.me/run\" target=\"_blank\">docs</a>)<br/>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "Waiting for W&B process to finish... <strong style=\"color:green\">(success).</strong>"
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          },
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        {
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          "data": {
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              "VBox(children=(Label(value='31.035 MB of 31.035 MB uploaded (0.000 MB deduped)\\r'), FloatProgress(value=1.0, m…"
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        {
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          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "Synced <strong style=\"color:#cdcd00\">sparkling-yogurt-12</strong>: <a href=\"https://wandb.ai/kwanit1142/DL_Project_2/runs/3g5r6r7v\" target=\"_blank\">https://wandb.ai/kwanit1142/DL_Project_2/runs/3g5r6r7v</a><br/>Synced 4 W&B file(s), 0 media file(s), 1 artifact file(s) and 0 other file(s)"
            ]
          },
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "Find logs at: <code>./wandb/run-20220502_212250-3g5r6r7v/logs</code>"
            ]
          },
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install wandb"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nTex0b07ZCBc",
        "outputId": "cb0edd3d-2f58-4f9c-ac65-7a8e9c7e5fb3"
      },
      "execution_count": 157,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting wandb\n",
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            "  Building wheel for pathtools (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for pathtools: filename=pathtools-0.1.2-py3-none-any.whl size=8806 sha256=7000b086845d00a151506e08761deceb53fad6e3e8d0d19098a482fda49f31c9\n",
            "  Stored in directory: /root/.cache/pip/wheels/3e/31/09/fa59cef12cdcfecc627b3d24273699f390e71828921b2cbba2\n",
            "Successfully built pathtools\n",
            "Installing collected packages: smmap, gitdb, shortuuid, setproctitle, sentry-sdk, pathtools, GitPython, docker-pycreds, wandb\n",
            "Successfully installed GitPython-3.1.27 docker-pycreds-0.4.0 gitdb-4.0.9 pathtools-0.1.2 sentry-sdk-1.5.10 setproctitle-1.2.3 shortuuid-1.0.8 smmap-5.0.0 wandb-0.12.15\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# End-To-End Pipeline"
      ],
      "metadata": {
        "id": "TqwdR_pZe4EZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "path = input(\"Enter the File-Path for 10-Seconds Hindi-Music Based Emotion Classification :- \")\n",
        "input_audio , y = lib.load(path)\n",
        "hl = 512 # number of samples per time-step in spectrogram\n",
        "hi = 224 # Height of image\n",
        "wi = 224 # Width of image\n",
        "audio_window = input_audio[0:wi*hl]\n",
        "spectrogram = lib.feature.melspectrogram(y=audio_window, sr=y, n_mels=hi, fmax=8000, hop_length=hl)\n",
        "plot = lib.power_to_db(spectrogram, ref=np.max)\n",
        "plt.imshow(plot)\n",
        "final_plot = cv2.resize(cv2.cvtColor(plot,cv2.COLOR_GRAY2RGB),(224,224))\n",
        "\n",
        "print(\" \")\n",
        "print(\"Pre-Trained Models List\\n\")\n",
        "print(\"1. Alexnet\\n\")\n",
        "print(\"2. EfficientNet-B0\\n\")\n",
        "print(\"3. ResNet18\\n\")\n",
        "print(\"4. VGG16\\n\")\n",
        "print(\"5. MobileNet_V3_Small\\n\")\n",
        "print(\"6. DenseNet121\\n\\n\")\n",
        "model_choice = int(input(\"Enter the Choice of Model :- \\n\"))\n",
        "if model_choice==1:\n",
        "  model = torch.load('/content/drive/MyDrive/DL_Project_2/model_alexnet.pth', map_location='cuda:0')\n",
        "if model_choice==2:\n",
        "  model = torch.load('/content/drive/MyDrive/DL_Project_2/model_eddicientnet_b0.pth', map_location='cuda:0')\n",
        "if model_choice==3:\n",
        "  model = torch.load('/content/drive/MyDrive/DL_Project_2/model_resnet18.pth', map_location='cuda:0')\n",
        "if model_choice==4:\n",
        "  model = torch.load('/content/drive/MyDrive/DL_Project_2/model_vgg16.pth', map_location='cuda:0')\n",
        "if model_choice==5:\n",
        "  model = torch.load('/content/drive/MyDrive/DL_Project_2/model_mobilenet_v3_small.pth', map_location='cuda:0')\n",
        "if model_choice==6:\n",
        "  model = torch.load('/content/drive/MyDrive/DL_Project_2/model_densenet121.pth', map_location='cuda:0')\n",
        "\n",
        "class_map = {0:'Sad',1:'Romantic',2:'Devotional',3:'Party',4:'Happy'}\n",
        "model_input = transforms.Compose([transforms.ToTensor(),transforms.Normalize((0,0,0), (1,1,1))])(final_plot)[None]\n",
        "with torch.no_grad():\n",
        "  output = model(model_input.cuda())\n",
        "  class_predict = output.data.max(1, keepdim=True)[1]\n",
        "  print(\"\\nPredicted Emotion -->\"+class_map[class_predict.cpu().numpy()[0][0]])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 635
        },
        "id": "qMVJb6cZe0xD",
        "outputId": "25ef97da-a8ce-4496-b358-732bacdc3594"
      },
      "execution_count": 187,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Enter the File-Path for 10-Seconds Hindi-Music Based Emotion Classification :- /content/drive/MyDrive/DL_Project_2/Happy/Happy/Happy_1.wav\n",
            " \n",
            "Pre-Trained Models List\n",
            "\n",
            "1. Alexnet\n",
            "\n",
            "2. EfficientNet-B0\n",
            "\n",
            "3. ResNet18\n",
            "\n",
            "4. VGG16\n",
            "\n",
            "5. MobileNet_V3_Small\n",
            "\n",
            "6. DenseNet121\n",
            "\n",
            "\n",
            "Enter the Choice of Model :- \n",
            "1\n",
            "Predicted Emotion -->Happy\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "47BkgGoqlHku"
      },
      "execution_count": 172,
      "outputs": []
    }
  ]
}