AE_V2_3.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/t-willi/Coursera-code-/blob/main/AE_V2_3.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 375,
      "metadata": {
        "id": "8eUpzrSTCodl"
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "from torch import nn\n",
        "import matplotlib.pyplot as plt\n",
        "import torchvision\n",
        "from torchvision import datasets, models, transforms\n",
        "from torchvision.transforms import ToTensor\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "from torch.utils.data import Dataset, DataLoader\n",
        "from timeit import default_timer as timer \n",
        "import glob\n",
        "import torch.optim as optim\n",
        "from sklearn.preprocessing import normalize\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "\n",
        "\n",
        "\n",
        "if torch.cuda.is_available()==True:\n",
        "  device=\"cuda:0\"\n",
        "else:\n",
        "  device =\"cpu\""
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "k = [1,2,3,4]\n",
        "k=np.array(k)\n",
        "t = ToTensor()"
      ],
      "metadata": {
        "id": "mFoek6yJawH6"
      },
      "execution_count": 376,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 377,
      "metadata": {
        "id": "Veu-bkI8hXSr"
      },
      "outputs": [],
      "source": [
        "def normalize_df_per_column(df):\n",
        "  for i,k in enumerate(df.columns):\n",
        "    max=df.iloc[:,i].max()\n",
        "    min=df.iloc[:,i].min()\n",
        "    df.iloc[:,i] = df.iloc[:,i].apply(lambda x: ((x-min)/(max-min)))\n",
        "  return df\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def normalize_list(l):\n",
        "  max=max(l)\n",
        "  min=min(l)\n",
        "  l=l.apply(lambda x:((x-min)/(max-min)))\n",
        "  return l\n",
        "\n"
      ],
      "metadata": {
        "id": "mGusGEeqpdEQ"
      },
      "execution_count": 378,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "UFLKm3Q0i4_G"
      },
      "execution_count": 378,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# from pathlib import Path\n",
        "# data_dir=\"/content/train_CSV_data\"\n",
        "# train_data_dir_normal=\"/content/train_CSV_data_normal\"\n",
        "# files = glob.glob(data_dir + '/*.csv')\n",
        "# for f in files:\n",
        "#   temp_df = pd.read_csv(f,sep=\" \")\n",
        "#   normal_df = normalize_df_per_column(temp_df)\n",
        "#   if train_data_dir_normal.is_dir():\n",
        "#     print(f\"{train_data_dir_normal} directory exists.\")\n",
        "#   else:\n",
        "#     print(f\"Did not find {train_data_dir_normal} directory, creating one...\")\n",
        "#     train_data_dir_normal.mkdir(parents=True, exist_ok=True)\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "9ekFz_d0Y1vl"
      },
      "execution_count": 379,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 380,
      "metadata": {
        "id": "08x5FggUn7DU"
      },
      "outputs": [],
      "source": [
        "# import requests\n",
        "# import zipfile\n",
        "# from pathlib import Path\n",
        "# #load data to directory from github\n",
        "# # Setup path to data folder\n",
        "# data_path = Path(\"data/\")\n",
        "# train_path = data_path / \"small_train\"\n",
        "# #test_path = data_path / \"train_CSV_data\"\n",
        "\n",
        "# if train_path.is_dir():\n",
        "#     print(f\"{train_path} directory exists.\")\n",
        "# else:\n",
        "#     print(f\"Did not find {train_path} directory, creating one...\")\n",
        "#     train_path.mkdir(parents=True, exist_ok=True)\n",
        "    \n",
        "#     # Download train data\n",
        "#     with open(data_path / \"small_train.zip\", \"wb\") as f:\n",
        "#         request = requests.get(\"https://github.com/t-willi/Simula/blob/main/small_train.zip\")\n",
        "#         print(\"Downloading train data\")\n",
        "#         f.write(request.content)\n",
        "\n",
        "#    # Unzip pizza, steak, sushi data\n",
        "#     with zipfile.ZipFile(data_path / \"small_train.zip\", \"r\") as zip_ref:\n",
        "#         print(\"Unzipping \") \n",
        "#         zip_ref.extractall(train_path)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 381,
      "metadata": {
        "id": "cal55bVVRw1N"
      },
      "outputs": [],
      "source": [
        "# Create custom dataset class to load ECG data into dataset, containing \n",
        "# input tensor with lead 1 and output tensor with desired other leads\n",
        "class Custom_dataset(Dataset):\n",
        "    def __init__(self, data_dir,normalize=False):\n",
        "      #get all files from directory loaded in all_files list\n",
        "      self.all_files = []\n",
        "      self.files = glob.glob(data_dir + '/*.csv')\n",
        "      for f in self.files:\n",
        "        temp_df = pd.read_csv(f,sep=\" \")\n",
        "        self.all_files.append(temp_df)\n",
        "        # self.normalize=Normalize\n",
        "        self.transform=ToTensor()\n",
        "\n",
        "    def __len__(self):\n",
        "      return len(self.all_files)\n",
        "\n",
        "    def __getitem__(self,idx):\n",
        "      Data = []\n",
        "      #turn list of dataframes into Tensor\n",
        "      for f in self.files:\n",
        "        #load_dataframe\n",
        "        temp_df=pd.read_csv(f,sep=\" \")\n",
        "        #load input tensor\n",
        "        temp_list_in=temp_df.iloc[:,1]\n",
        "        temp_list_in=normalize([temp_list_in], norm=\"max\")\n",
        "        temp_tensor_in = torch.tensor(temp_list_in,dtype=torch.float32)\n",
        "        temp_tensor_in=temp_tensor_in.unsqueeze(0)\n",
        "        #load label Tensor\n",
        "        temp_list_out=temp_df.iloc[:,3].values\n",
        "        temp_list_out=normalize([temp_list_out], norm=\"max\")\n",
        "        temp_tensor_out=torch.tensor(temp_list_out,dtype=torch.float32)\n",
        "        temp_tensor_out=temp_tensor_out.unsqueeze(0)\n",
        "        #combine input and label and output\n",
        "        temp_tensor_pair= temp_tensor_in,temp_tensor_out\n",
        "      return temp_tensor_pair"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 382,
      "metadata": {
        "id": "lNtnRLsjUMIp"
      },
      "outputs": [],
      "source": [
        "train_dataset = Custom_dataset(data_dir=\"/content/train_CSV_data\",normalize=True)\n",
        "test_dataset = Custom_dataset(data_dir=\"/content/test_CSV_data\",normalize=True)\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 383,
      "metadata": {
        "id": "UP88T4BDvW0e"
      },
      "outputs": [],
      "source": [
        "# x,y=train_dataset[0]\n",
        "# y.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 384,
      "metadata": {
        "id": "sA756IN7LuVK"
      },
      "outputs": [],
      "source": [
        "from torch.utils.data.dataloader import DataLoader\n",
        "BATCH_SIZE = 10\n",
        "#turn datasets into iterables\n",
        "train_dataloader = DataLoader(train_dataset,\n",
        "                              batch_size=BATCH_SIZE,\n",
        "                              shuffle=True\n",
        "                              )\n",
        "test_dataloader = DataLoader(test_dataset,\n",
        "                              batch_size=BATCH_SIZE,\n",
        "                              shuffle=True\n",
        "                              )"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 385,
      "metadata": {
        "id": "t5WfPAwVIWSB"
      },
      "outputs": [],
      "source": [
        "\"\"\"\n",
        "Here, we define the autoencoder model.This model is taken from \"https://github.com/L1aoXingyu/pytorch-beginner/blob/master/08-AutoEncoder/simple_autoencoder.py\"\n",
        "\"\"\"\n",
        "class ECG_AE_v1(nn.Module):\n",
        "    def __init__(self):\n",
        "        super(ECG_AE_v1, self).__init__()\n",
        "        self.AE = nn.Sequential(\n",
        "            nn.Linear(4999, 128),\n",
        "            nn.ReLU(),\n",
        "            nn.Linear(128,20),\n",
        "            nn.ReLU(),\n",
        "            nn.Linear(20,10),\n",
        "            nn.ReLU(),\n",
        "            nn.Linear(10,20),\n",
        "            nn.ReLU(),\n",
        "            nn.Linear(20,128),\n",
        "            nn.ReLU(),\n",
        "            nn.Linear(128,4999),\n",
        "        )\n",
        "\n",
        "\n",
        "    def forward(self, x):\n",
        "        x = self.AE(x)\n",
        "        return x\n",
        "\n",
        "\n",
        "model = ECG_AE_v1().to(device)\n",
        "criterion = nn.MSELoss()\n",
        "optimizer = torch.optim.Adam(model.parameters(), lr=0.001)\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 386,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eYaklXRV8FkT",
        "outputId": "772704f1-779b-48ff-d56a-ccd6b075090e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ECG_AE_v1(\n",
              "  (AE): Sequential(\n",
              "    (0): Linear(in_features=4999, out_features=128, bias=True)\n",
              "    (1): ReLU()\n",
              "    (2): Linear(in_features=128, out_features=20, bias=True)\n",
              "    (3): ReLU()\n",
              "    (4): Linear(in_features=20, out_features=10, bias=True)\n",
              "    (5): ReLU()\n",
              "    (6): Linear(in_features=10, out_features=20, bias=True)\n",
              "    (7): ReLU()\n",
              "    (8): Linear(in_features=20, out_features=128, bias=True)\n",
              "    (9): ReLU()\n",
              "    (10): Linear(in_features=128, out_features=4999, bias=True)\n",
              "  )\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 386
        }
      ],
      "source": [
        "model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 387,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QD39NrScQxlq",
        "outputId": "9a34ec3c-ae98-4bd2-b0f7-7e10b290e698"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "==========================================================================================\n",
              "Layer (type:depth-idx)                   Output Shape              Param #\n",
              "==========================================================================================\n",
              "ECG_AE_v1                                [1, 4999]                 --\n",
              "├─Sequential: 1-1                        [1, 4999]                 --\n",
              "│    └─Linear: 2-1                       [1, 128]                  640,000\n",
              "│    └─ReLU: 2-2                         [1, 128]                  --\n",
              "│    └─Linear: 2-3                       [1, 20]                   2,580\n",
              "│    └─ReLU: 2-4                         [1, 20]                   --\n",
              "│    └─Linear: 2-5                       [1, 10]                   210\n",
              "│    └─ReLU: 2-6                         [1, 10]                   --\n",
              "│    └─Linear: 2-7                       [1, 20]                   220\n",
              "│    └─ReLU: 2-8                         [1, 20]                   --\n",
              "│    └─Linear: 2-9                       [1, 128]                  2,688\n",
              "│    └─ReLU: 2-10                        [1, 128]                  --\n",
              "│    └─Linear: 2-11                      [1, 4999]                 644,871\n",
              "==========================================================================================\n",
              "Total params: 1,290,569\n",
              "Trainable params: 1,290,569\n",
              "Non-trainable params: 0\n",
              "Total mult-adds (M): 1.29\n",
              "==========================================================================================\n",
              "Input size (MB): 0.02\n",
              "Forward/backward pass size (MB): 0.04\n",
              "Params size (MB): 5.16\n",
              "Estimated Total Size (MB): 5.22\n",
              "=========================================================================================="
            ]
          },
          "metadata": {},
          "execution_count": 387
        }
      ],
      "source": [
        "try: \n",
        "    import torchinfo\n",
        "except:\n",
        "    !pip install torchinfo\n",
        "    import torchinfo\n",
        "\n",
        "from torchinfo import summary\n",
        "summary(model, input_size=[1,4999]) # do a test pass through of an example input size "
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "t1= torch.rand(1,20)\n",
        "t1.shape\n",
        "t1=torch.reshape(t1,(4,5))\n",
        "t1.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dWwNbBVbPmbE",
        "outputId": "ca3a23b0-a3b3-414b-f52b-b7b9b7fb4ccb"
      },
      "execution_count": 388,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.Size([4, 5])"
            ]
          },
          "metadata": {},
          "execution_count": 388
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "8OnVpS3EQIEw"
      },
      "execution_count": 388,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 389,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 429,
          "referenced_widgets": [
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        "id": "3uMREYqwckwg",
        "outputId": "0a16edd1-2291-44ab-9eb4-175464e874ec"
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      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/5 [00:00<?, ?it/s]"
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          "metadata": {}
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        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch:0\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-389-8c713b9266f7>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      6\u001b[0m   \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"Epoch:{epoch}\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m   \u001b[0mtrain_loss\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m   \u001b[0;32mfor\u001b[0m \u001b[0mbatch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain_dataloader\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      9\u001b[0m     \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m     \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py\u001b[0m in \u001b[0;36m__next__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    679\u001b[0m                 \u001b[0;31m# TODO(https://github.com/pytorch/pytorch/issues/76750)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    680\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_reset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# type: ignore[call-arg]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 681\u001b[0;31m             \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_next_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    682\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_num_yielded\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    683\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_dataset_kind\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0m_DatasetKind\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mIterable\u001b[0m \u001b[0;32mand\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m\\\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py\u001b[0m in \u001b[0;36m_next_data\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    719\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_next_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    720\u001b[0m         \u001b[0mindex\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_next_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# may raise StopIteration\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 721\u001b[0;31m         \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_dataset_fetcher\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfetch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# may raise StopIteration\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    722\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_pin_memory\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    723\u001b[0m             \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_utils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpin_memory\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpin_memory\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_pin_memory_device\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/torch/utils/data/_utils/fetch.py\u001b[0m in \u001b[0;36mfetch\u001b[0;34m(self, possibly_batched_index)\u001b[0m\n\u001b[1;32m     47\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mfetch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpossibly_batched_index\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     48\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mauto_collation\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 49\u001b[0;31m             \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0midx\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0midx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mpossibly_batched_index\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     50\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     51\u001b[0m             \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mpossibly_batched_index\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/torch/utils/data/_utils/fetch.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m     47\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mfetch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpossibly_batched_index\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     48\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mauto_collation\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 49\u001b[0;31m             \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0midx\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0midx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mpossibly_batched_index\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     50\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     51\u001b[0m             \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mpossibly_batched_index\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<ipython-input-381-08ce1cf88f90>\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, idx)\u001b[0m\n\u001b[1;32m     20\u001b[0m       \u001b[0;32mfor\u001b[0m \u001b[0mf\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfiles\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     21\u001b[0m         \u001b[0;31m#load_dataframe\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 22\u001b[0;31m         \u001b[0mtemp_df\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0msep\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\" \"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     23\u001b[0m         \u001b[0;31m#load input tensor\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     24\u001b[0m         \u001b[0mtemp_list_in\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtemp_df\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/util/_decorators.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    309\u001b[0m                     \u001b[0mstacklevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mstacklevel\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    310\u001b[0m                 )\n\u001b[0;32m--> 311\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    312\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    313\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/io/parsers/readers.py\u001b[0m in \u001b[0;36mread_csv\u001b[0;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, error_bad_lines, warn_bad_lines, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options)\u001b[0m\n\u001b[1;32m    584\u001b[0m     \u001b[0mkwds\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkwds_defaults\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    585\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 586\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0m_read\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    587\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    588\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/io/parsers/readers.py\u001b[0m in \u001b[0;36m_read\u001b[0;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[1;32m    486\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    487\u001b[0m     \u001b[0;32mwith\u001b[0m \u001b[0mparser\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 488\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mparser\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnrows\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    489\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    490\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/io/parsers/readers.py\u001b[0m in \u001b[0;36mread\u001b[0;34m(self, nrows)\u001b[0m\n\u001b[1;32m   1045\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnrows\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1046\u001b[0m         \u001b[0mnrows\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvalidate_integer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"nrows\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnrows\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1047\u001b[0;31m         \u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcol_dict\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnrows\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1048\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1049\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mindex\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/io/parsers/c_parser_wrapper.py\u001b[0m in \u001b[0;36mread\u001b[0;34m(self, nrows)\u001b[0m\n\u001b[1;32m    222\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    223\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlow_memory\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 224\u001b[0;31m                 \u001b[0mchunks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_reader\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_low_memory\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnrows\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    225\u001b[0m                 \u001b[0;31m# destructive to chunks\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    226\u001b[0m                 \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_concatenate_chunks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mchunks\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/_libs/parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader.read_low_memory\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/_libs/parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader._read_rows\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/_libs/parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader._convert_column_data\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/_libs/parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader._convert_tokens\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/_libs/parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader._convert_with_dtype\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/core/dtypes/common.py\u001b[0m in \u001b[0;36mis_integer_dtype\u001b[0;34m(arr_or_dtype)\u001b[0m\n\u001b[1;32m    720\u001b[0m     \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    721\u001b[0m     \"\"\"\n\u001b[0;32m--> 722\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0m_is_dtype_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marr_or_dtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mclasses_and_not_datetimelike\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minteger\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    723\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    724\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/core/dtypes/common.py\u001b[0m in \u001b[0;36mclasses_and_not_datetimelike\u001b[0;34m(*klasses)\u001b[0m\n\u001b[1;32m    152\u001b[0m     \u001b[0;32mand\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0ma\u001b[0m \u001b[0mdatetimelike\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    153\u001b[0m     \"\"\"\n\u001b[0;32m--> 154\u001b[0;31m     return lambda tipo: (\n\u001b[0m\u001b[1;32m    155\u001b[0m         \u001b[0missubclass\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtipo\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mklasses\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    156\u001b[0m         \u001b[0;32mand\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0missubclass\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtipo\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdatetime64\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtimedelta64\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ],
      "source": [
        "from tqdm.auto import tqdm\n",
        "torch.manual_seed(42)\n",
        "#train and test loop\n",
        "Epochs = 5\n",
        "for epoch in tqdm(range(Epochs)):\n",
        "  print(f\"Epoch:{epoch}\")\n",
        "  train_loss=0\n",
        "  for batch, (X,y) in enumerate(train_dataloader): \n",
        "    X, y = X.to(device), y.to(device) \n",
        "    model.train()\n",
        "    output=model(X)\n",
        "    ##reshaping output from linear to 2d from 40000 to 8 times 5000\n",
        "    #output=torch.reshape(output,(1,1,8,4999))\n",
        "    ######\n",
        "    loss = criterion(output,y)\n",
        "    train_loss += loss\n",
        "    optimizer.zero_grad()\n",
        "    loss.backward()\n",
        "    optimizer.step()\n",
        "  #average loss per batch\n",
        "  train_loss /= len(train_dataloader)\n",
        "  #start testing\n",
        "  test_loss = 0\n",
        "  model.eval()\n",
        "  with torch.inference_mode():\n",
        "    for X,y in test_dataloader:\n",
        "      X, y = X.to(device), y.to(device) \n",
        "      test_pred = model(X)\n",
        "      ##reshaping output from linear to 2d from 40000 to 8 times 5000\n",
        "      #test_pred=torch.reshape(test_pred,(10,1,8,4999))\n",
        "      ######\n",
        "      test_loss += criterion(test_pred,y)    \n",
        "    test_loss /= len(test_dataloader)\n",
        "\n",
        "  #print what is happening\n",
        "  print(f\"\\nTrain loss: {train_loss:.5f} | Test loss: {test_loss:.5f}\")\n",
        "    \n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 391,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "X1iZv_5gFGh3",
        "outputId": "1e6a70cf-aba1-4d8c-a75a-57847a167588"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f27da66c7d0>]"
            ]
          },
          "metadata": {},
          "execution_count": 391
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "input,output = train_dataset[0]\n",
        "input\n",
        "input=input.tolist()\n",
        "data=input[0][0]\n",
        "type(data)\n",
        "plt.plot(data)\n",
        "# plt.title(\"Input ECG lead1\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 392,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 298
        },
        "id": "Nx5cRtALrX_4",
        "outputId": "d222d031-c692-4ee9-e019-decd28bb6f6f"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Output ECG trained for 20epochs on 50 datapoints')"
            ]
          },
          "metadata": {},
          "execution_count": 392
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
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          }
        }
      ],
      "source": [
        "X,y=test_dataset[0]\n",
        "model.to(\"cpu\")\n",
        "model.eval()\n",
        "with torch.inference_mode():\n",
        "  output=model(X)\n",
        "output=output.tolist()\n",
        "data=output[0][0]\n",
        "plt.plot(data)\n",
        "plt.title(\"Output ECG trained for 20epochs on 50 datapoints\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 395,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "scCSpwBAwHNk",
        "outputId": "abb84d0e-8bca-4114-c741-4d340fa5a8eb"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[[ 0.0200, -0.0374,  0.0261,  ...,  0.1108, -0.0934, -0.0196]]])"
            ]
          },
          "metadata": {},
          "execution_count": 395
        }
      ],
      "source": [
        "X,y=test_dataset[0]\n",
        "model.to(\"cpu\")\n",
        "model.eval()\n",
        "with torch.inference_mode():\n",
        "  output=model(X)\n",
        "output"
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "IN5vUA5Q4dbm"
      },
      "execution_count": null,
      "outputs": []
    }
  ],
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