Neuroscience_Project_Arousal_Training.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cQQ2QmmVMyJa",
        "outputId": "5fe28213-a5d8-4fec-89a2-ad42a92de0fb"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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          ]
        }
      ],
      "source": [
        "!pip install torcheeg"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "4MwikNtDMjis"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from scipy import signal\n",
        "from rich.pretty import pprint\n",
        "from torcheeg.datasets import DREAMERDataset\n",
        "from torcheeg.datasets.constants.emotion_recognition.dreamer import DREAMER_CHANNEL_LOCATION_DICT\n",
        "from torcheeg import transforms\n",
        "from torch import nn\n",
        "import torch\n",
        "import random"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "He4HfZ9INBqd",
        "outputId": "9213accc-88bf-47fe-df23-4047872a448f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive/\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive/')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ROctHay3Mjiv"
      },
      "outputs": [],
      "source": [
        "dataset_path = \"/content/drive/MyDrive/Neuroscience/DREAMER.mat\"\n",
        "base_path = \"/content/drive/MyDrive/Neuroscience/Arousal1/\"\n",
        "\n",
        "# dataset_path = \"/content/drive/MyDrive/Neuroscience/DREAMER.mat\"\n",
        "# base_path = \"/content/drive/MyDrive/Neuroscience/\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zeS8rzjgMjiv",
        "outputId": "dbcf4a59-2af4-41e1-ce13-2703235de1c5"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The target folder already exists, if you need to regenerate the database IO, please delete the path /content/drive/MyDrive/Neuroscience/Arousal1/dreamer8sec.\n"
          ]
        }
      ],
      "source": [
        "dataset = DREAMERDataset(\n",
        "    io_path=base_path + 'dreamer8sec',\n",
        "    mat_path=dataset_path,\n",
        "    offline_transform=transforms.Compose([\n",
        "        transforms.BaselineRemoval(),\n",
        "        transforms.MeanStdNormalize(),\n",
        "        transforms.To2d()\n",
        "    ]),\n",
        "    # online_transform=transforms.ToTensor(),\n",
        "    label_transform=transforms.Compose(\n",
        "        [transforms.Select('arousal'),\n",
        "         transforms.Binary(3.0)]),\n",
        "    chunk_size=976,\n",
        "    baseline_chunk_size=976,\n",
        "    num_baseline=8\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "DYg8kdfcMjix"
      },
      "outputs": [],
      "source": [
        "def get_tf_feature(eeg, sr, n_channels = 14):\n",
        "    WinLength = int(0.5*sr) # 500 points (0.5 sec, 500 ms)\n",
        "    step = int(0.025*sr) # 25 points (or 25 ms)\n",
        "    final_features = None\n",
        "    for i in range(n_channels):\n",
        "        eeg_single = eeg[i].squeeze()\n",
        "        myparams = dict(nperseg = WinLength, noverlap = WinLength-step, return_onesided=True, mode='magnitude')\n",
        "        f, nseg, Sxx = signal.spectrogram(x = eeg_single, fs = sr, **myparams)\n",
        "        if(isinstance(final_features, np.ndarray)):\n",
        "            final_features = np.concatenate((final_features, Sxx), axis=0)\n",
        "        else:\n",
        "            final_features = Sxx\n",
        "    return final_features"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XxRp3C3nMjiy",
        "outputId": "147f02d9-980d-45f1-84f5-3cce9764f56b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Using cuda device\n"
          ]
        }
      ],
      "source": [
        "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
        "# device = \"cpu\"\n",
        "print(f\"Using {device} device\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "l1a8bHcFMjiy"
      },
      "outputs": [],
      "source": [
        "def convert_data_to_tensor(data):\n",
        "    data = data.astype(\"float32\")\n",
        "    data = data.reshape(1, data.shape[0], data.shape[1])\n",
        "    return torch.from_numpy(data)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "J_xdabRlMjiz"
      },
      "outputs": [],
      "source": [
        "class NeuralNetwork(nn.Module):\n",
        "    def __init__(self):\n",
        "        super().__init__()\n",
        "        self.conv2D_1 = nn.Sequential(\n",
        "            nn.Conv2d(1, 1024, 11, stride=3),\n",
        "            nn.Conv2d(1024, 512, 7, stride=3),\n",
        "            nn.Conv2d(512, 128, 7, stride=3),\n",
        "        )\n",
        "        self.flatten = nn.Flatten()\n",
        "        self.linear_relu_stack = nn.Sequential(\n",
        "            # nn.Linear(14550, 2048),\n",
        "            # nn.ReLU(),\n",
        "            nn.Linear(17280, 1024),\n",
        "            nn.ReLU(),\n",
        "            nn.Linear(1024, 512),\n",
        "            nn.ReLU(),\n",
        "            nn.Linear(512, 128),\n",
        "            nn.ReLU(),\n",
        "            nn.Linear(128, 2),\n",
        "        )\n",
        "\n",
        "    def forward(self, x):\n",
        "        x = self.conv2D_1(x)\n",
        "        # x = self.flatten(x)\n",
        "        x = x.view(1, -1)\n",
        "        # print(x.shape)\n",
        "        logits = self.linear_relu_stack(x)\n",
        "        return logits"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "CQi1-d9WMjiz"
      },
      "outputs": [],
      "source": [
        "random.seed(42)\n",
        "\n",
        "test_size = 2000\n",
        "test_index = random.sample(range(0, 11000), test_size)\n",
        "train_index = []\n",
        "\n",
        "for i in range(11040):\n",
        "    if i not in test_index:\n",
        "        train_index.append(i)\n",
        "random.shuffle(train_index)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2H8L58PHMjiz"
      },
      "outputs": [],
      "source": [
        "def train_loop(dataset,  model, loss_fn, optimizer):\n",
        "    # size = len(dataset)\n",
        "    model.train()\n",
        "    sample_size = len(train_index)\n",
        "    j=0\n",
        "    correct = 0\n",
        "    for i in train_index:\n",
        "        # Compute prediction and loss\n",
        "        # print(i)\n",
        "        # Backpropagation\n",
        "        optimizer.zero_grad()\n",
        "        \n",
        "        X, y = dataset[i][0][0], dataset[i][1]\n",
        "        X = get_tf_feature(X, sr=128)\n",
        "        X = convert_data_to_tensor(X)\n",
        "        if y == 0:\n",
        "            y = [0]\n",
        "        else:\n",
        "            y = [1]\n",
        "        y = torch.tensor(y)\n",
        "        X, y = X.to(device), y.to(device)\n",
        "        pred = model(X)\n",
        "        # print(y.shape, pred.shape)\n",
        "        loss = loss_fn(pred, y)\n",
        "        \n",
        "        correct += (pred.argmax(1) == y).type(torch.float).sum().item()\n",
        "\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "        if j % 100 == 0:\n",
        "            loss, current = loss.item(), j + 1\n",
        "            print(f\"loss: {loss}  [{current:>5d}/{sample_size:>5d}]\")\n",
        "            # if j%1000 == 0:\n",
        "              # torch.save(model.state_dict(), \"eeg_model_1000.pth\")\n",
        "        j=j+1\n",
        "    correct /= sample_size\n",
        "    str = (f\"Train Accuracy: {(100*correct):>0.1f}\\n\")\n",
        "    print(str)\n",
        "    with open(base_path + \"log.txt\", \"a\") as f:\n",
        "        f.write(str)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "M0Yj50GzMji0"
      },
      "outputs": [],
      "source": [
        "\n",
        "val_error = 999999999.9\n",
        "best_model_parameter = None\n",
        "\n",
        "def test_loop(dataset, model, loss_fn):\n",
        "    global val_error\n",
        "    global best_model_parameter\n",
        "    # size = len(dataset)\n",
        "    test_loss, correct = 0, 0\n",
        "    sample_size = len(test_index)\n",
        "    # l = random.sample(range(0, 11000), 1)\n",
        "    # j=0\n",
        "    with torch.no_grad():\n",
        "        model.eval()\n",
        "        for i in test_index:\n",
        "            X, y = dataset[i][0][0], dataset[i][1]\n",
        "            X = get_tf_feature(X, sr=128)\n",
        "            X = convert_data_to_tensor(X)\n",
        "            if y == 0:\n",
        "                y = [0]\n",
        "            else:\n",
        "                y = [1]\n",
        "            y = torch.tensor(y)\n",
        "            \n",
        "            X, y = X.to(device), y.to(device)\n",
        "            pred = model(X)\n",
        "            test_loss += loss_fn(pred, y).item()\n",
        "            correct += (pred.argmax(1) == y).type(torch.float).sum().item()\n",
        "\n",
        "    test_loss /= sample_size\n",
        "    correct /= sample_size\n",
        "\n",
        "    if val_error > test_loss:\n",
        "        val_error = test_loss\n",
        "        best_model_parameter = model.state_dict()\n",
        "\n",
        "    str = (f\"Test Error: \\n Accuracy: {(100*correct):>0.1f}%, Avg loss: {test_loss:>8f} \\n\")\n",
        "    print(str)\n",
        "    with open(base_path + \"log.txt\", \"a\") as f:\n",
        "        f.write(str)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "f9Rdio0rMji1"
      },
      "outputs": [],
      "source": [
        "learning_rate = 3e-6"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "last_epoch = 9\n",
        "model = NeuralNetwork().to(device)\n",
        "if last_epoch >= 0:\n",
        "  model.load_state_dict(torch.load(base_path + f'eeg_model_{last_epoch}.pth', map_location=torch.device('cpu')))\n",
        "model.eval()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZT3mi6dj5Dn5",
        "outputId": "0fe31c82-0cde-4a87-c876-4761fe4e52ff"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "NeuralNetwork(\n",
              "  (conv2D_1): Sequential(\n",
              "    (0): Conv2d(1, 1024, kernel_size=(11, 11), stride=(3, 3))\n",
              "    (1): Conv2d(1024, 512, kernel_size=(7, 7), stride=(3, 3))\n",
              "    (2): Conv2d(512, 128, kernel_size=(7, 7), stride=(3, 3))\n",
              "  )\n",
              "  (flatten): Flatten(start_dim=1, end_dim=-1)\n",
              "  (linear_relu_stack): Sequential(\n",
              "    (0): Linear(in_features=17280, out_features=1024, bias=True)\n",
              "    (1): ReLU()\n",
              "    (2): Linear(in_features=1024, out_features=512, bias=True)\n",
              "    (3): ReLU()\n",
              "    (4): Linear(in_features=512, out_features=128, bias=True)\n",
              "    (5): ReLU()\n",
              "    (6): Linear(in_features=128, out_features=2, bias=True)\n",
              "  )\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "wBQGnn7TMji1",
        "outputId": "00f5b6a3-b4e1-4e38-fbee-a6eae3357c3b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 11\n",
            "-------------------------------\n",
            "\n",
            "loss: 4.768370445162873e-07  [    1/ 9040]\n",
            "loss: 1.1920928244535389e-07  [  101/ 9040]\n",
            "loss: 9.16677454370074e-05  [  201/ 9040]\n",
            "loss: 0.003986389376223087  [  301/ 9040]\n",
            "loss: 1.1920928244535389e-07  [  401/ 9040]\n",
            "loss: 0.00010179955279454589  [  501/ 9040]\n",
            "loss: 0.0  [  601/ 9040]\n",
            "loss: 0.022461412474513054  [  701/ 9040]\n",
            "loss: 0.014150167815387249  [  801/ 9040]\n",
            "loss: 0.022915199398994446  [  901/ 9040]\n",
            "loss: 0.002562693553045392  [ 1001/ 9040]\n",
            "loss: 7.152555099310121e-07  [ 1101/ 9040]\n",
            "loss: 0.053099375218153  [ 1201/ 9040]\n",
            "loss: 0.04856870695948601  [ 1301/ 9040]\n",
            "loss: 0.012437872588634491  [ 1401/ 9040]\n",
            "loss: 0.03686638921499252  [ 1501/ 9040]\n",
            "loss: 0.0012317459331825376  [ 1601/ 9040]\n",
            "loss: 3.576278118089249e-07  [ 1701/ 9040]\n",
            "loss: 0.004210892133414745  [ 1801/ 9040]\n",
            "loss: 0.15342797338962555  [ 1901/ 9040]\n",
            "loss: 0.09003865718841553  [ 2001/ 9040]\n",
            "loss: 1.0251946150674485e-05  [ 2101/ 9040]\n",
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            "loss: 0.00025948495022021234  [ 2301/ 9040]\n",
            "loss: 2.3841855067985307e-07  [ 2401/ 9040]\n",
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            "loss: 1.1920928244535389e-07  [ 2601/ 9040]\n",
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            "loss: 0.0001961992384167388  [ 3601/ 9040]\n",
            "loss: 0.0034661947283893824  [ 3701/ 9040]\n",
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            "loss: 6.198863957251888e-06  [ 3901/ 9040]\n",
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            "loss: 1.2636104656849056e-05  [ 4101/ 9040]\n",
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            "loss: 9.810443589231e-05  [ 4901/ 9040]\n",
            "loss: 1.7881377516459906e-06  [ 5001/ 9040]\n",
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            "loss: 4.768370445162873e-07  [ 5401/ 9040]\n",
            "loss: 5.638440416078083e-05  [ 5501/ 9040]\n",
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            "loss: 1.261828899383545  [ 5701/ 9040]\n",
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            "loss: 2.2411095415009186e-05  [ 5901/ 9040]\n",
            "loss: 1.1920928244535389e-07  [ 6001/ 9040]\n",
            "loss: 1.1920928244535389e-07  [ 6101/ 9040]\n",
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            "loss: 0.0018952994141727686  [ 6401/ 9040]\n",
            "loss: 8.046303264563903e-05  [ 6501/ 9040]\n",
            "loss: 0.00012134769349358976  [ 6601/ 9040]\n",
            "loss: 0.0014293702552095056  [ 6701/ 9040]\n",
            "loss: 1.0728830375228426e-06  [ 6801/ 9040]\n",
            "loss: 0.0003830652858596295  [ 6901/ 9040]\n",
            "loss: 0.00013147920253686607  [ 7001/ 9040]\n",
            "loss: 0.0003564914222806692  [ 7101/ 9040]\n",
            "loss: 0.00985034555196762  [ 7201/ 9040]\n",
            "loss: 0.0  [ 7301/ 9040]\n",
            "loss: 0.0006611545104533434  [ 7401/ 9040]\n",
            "loss: 6.981983661651611  [ 7501/ 9040]\n",
            "loss: 0.22139117121696472  [ 7601/ 9040]\n",
            "loss: 0.0020260538440197706  [ 7701/ 9040]\n",
            "loss: 2.586808113846928e-05  [ 7801/ 9040]\n",
            "loss: 5.960462772236497e-07  [ 7901/ 9040]\n",
            "loss: 0.00017617580306250602  [ 8001/ 9040]\n",
            "loss: 2.9802276912960224e-06  [ 8101/ 9040]\n",
            "loss: 3.3378546504536644e-06  [ 8201/ 9040]\n",
            "loss: 1.5497195136049413e-06  [ 8301/ 9040]\n",
            "loss: 0.004270956851541996  [ 8401/ 9040]\n",
            "loss: 7.271740287251305e-06  [ 8501/ 9040]\n",
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            "loss: 9.536738616588991e-07  [ 8801/ 9040]\n",
            "loss: 0.047173015773296356  [ 8901/ 9040]\n",
            "loss: 0.0  [ 9001/ 9040]\n",
            "Train Accuracy: 98.1\n",
            "\n",
            "Test Error: \n",
            " Accuracy: 89.3%, Avg loss: 0.443244 \n",
            "\n",
            "Epoch 12\n",
            "-------------------------------\n",
            "\n",
            "loss: 8.344646857949556e-07  [    1/ 9040]\n",
            "loss: 1.1920928244535389e-07  [  101/ 9040]\n",
            "loss: 7.30726242181845e-05  [  201/ 9040]\n",
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            "loss: 0.007427813485264778  [  701/ 9040]\n",
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            "loss: 0.020971694961190224  [  901/ 9040]\n",
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            "loss: 4.0531076592742465e-06  [ 1101/ 9040]\n",
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            "loss: 5.23315102327615e-05  [ 2101/ 9040]\n",
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            "loss: 4.8993817472364753e-05  [ 2301/ 9040]\n",
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            "loss: 3.576278118089249e-07  [ 7301/ 9040]\n",
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            "loss: 0.0001512651506345719  [ 7701/ 9040]\n",
            "loss: 5.8410845667822286e-05  [ 7801/ 9040]\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-15-f07b9845afa0>\u001b[0m in \u001b[0;36m<cell line: 5>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     10\u001b[0m     \u001b[0;32mwith\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbase_path\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m\"log.txt\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"a\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m         \u001b[0mf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwrite\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 12\u001b[0;31m     \u001b[0mtrain_loop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mloss_fn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moptimizer\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     13\u001b[0m     \u001b[0mtest_loop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mloss_fn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     14\u001b[0m     \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msave\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstate_dict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbase_path\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34mf\"eeg_model_{t}.pth\"\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-11-33b2056f7a2d>\u001b[0m in \u001b[0;36mtrain_loop\u001b[0;34m(dataset, model, loss_fn, optimizer)\u001b[0m\n\u001b[1;32m     19\u001b[0m             \u001b[0my\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[1;32m     20\u001b[0m         \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtensor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 21\u001b[0;31m         \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[0m\u001b[1;32m     22\u001b[0m         \u001b[0mpred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     23\u001b[0m         \u001b[0;31m# print(y.shape, pred.shape)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ],
      "source": [
        "loss_fn = nn.CrossEntropyLoss()\n",
        "optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)\n",
        "\n",
        "epochs = 100\n",
        "for t in range(epochs):\n",
        "    if t <= last_epoch:\n",
        "      continue\n",
        "    str = (f\"Epoch {t+1}\\n-------------------------------\\n\")\n",
        "    print(str)\n",
        "    with open(base_path + \"log.txt\", \"a\") as f:\n",
        "        f.write(str)\n",
        "    train_loop(dataset, model, loss_fn, optimizer)\n",
        "    test_loop(dataset, model, loss_fn)\n",
        "    torch.save(model.state_dict(), base_path + f\"eeg_model_{t}.pth\")\n",
        "print(\"Done!\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "49ySsevVMji2"
      },
      "outputs": [],
      "source": [
        "assert best_model_parameter is not None, \"No best model\"\n",
        "best_model = NeuralNetwork().to(device)\n",
        "best_model.load_state_dict(best_model_parameter)\n",
        "torch.save(best_model.state_dict(), \"eeg_model.pth\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "usegr5dqMji2"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "HynMGZX3Mji2"
      },
      "outputs": [],
      "source": [
        "!cp ./log.txt ./drive/MyDrive/Neuroscience"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "-hPFI0QXMji2"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "provenance": []
    },
    "gpuClass": "standard",
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.10.6"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
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