hyperparameters_tuning.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Kyriezxc/CIS522_Project/blob/main/hyperparameters_tuning.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8BFipdcUuQOe",
        "outputId": "86268742-5176-45b6-bea3-58b46f96ff72"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive', force_remount=True)"
      ],
      "id": "8BFipdcUuQOe"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "M-z94KSWuaZu",
        "outputId": "3d1571f2-1171-4b94-f091-b871483f68dd"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/drive/MyDrive/Colab Notebooks/CIS522_Project\n"
          ]
        }
      ],
      "source": [
        "%cd \"/content/drive/MyDrive/Colab Notebooks/CIS522_Project\""
      ],
      "id": "M-z94KSWuaZu"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "d1ea8790-e5c3-44da-a27e-b1929b70cbaa"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.model_selection import train_test_split\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "from torch.utils.data import Dataset, DataLoader\n",
        "from tqdm.notebook import tqdm\n",
        "\n",
        "SEED = 2023\n",
        "DEVICE = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")"
      ],
      "id": "d1ea8790-e5c3-44da-a27e-b1929b70cbaa"
    },
    {
      "cell_type": "code",
      "source": [
        "# @title Set random seed\n",
        "\n",
        "# @markdown Executing `set_seed(seed=seed)` you are setting the seed\n",
        "\n",
        "# For DL its critical to set the random seed so that students can have a\n",
        "# baseline to compare their results to expected results.\n",
        "# Read more here: https://pytorch.org/docs/stable/notes/randomness.html\n",
        "\n",
        "# Call `set_seed` function in the exercises to ensure reproducibility.\n",
        "import random\n",
        "\n",
        "def set_seed(seed=None, seed_torch=True):\n",
        "    \"\"\"\n",
        "    Function that controls randomness. NumPy and random modules must be imported.\n",
        "\n",
        "    Args:\n",
        "      seed : Integer\n",
        "        A non-negative integer that defines the random state. Default is `None`.\n",
        "      seed_torch : Boolean\n",
        "        If `True` sets the random seed for pytorch tensors, so pytorch module\n",
        "        must be imported. Default is `True`.\n",
        "\n",
        "    Returns:\n",
        "      Nothing.\n",
        "    \"\"\"\n",
        "    if seed is None:\n",
        "        seed = np.random.choice(2**32)\n",
        "    random.seed(seed)\n",
        "    np.random.seed(seed)\n",
        "    if seed_torch:\n",
        "        torch.manual_seed(seed)\n",
        "        torch.cuda.manual_seed_all(seed)\n",
        "        torch.cuda.manual_seed(seed)\n",
        "        torch.backends.cudnn.benchmark = False\n",
        "        torch.backends.cudnn.deterministic = True\n",
        "\n",
        "    print(f\"Random seed {seed} has been set.\")\n",
        "\n",
        "\n",
        "# In case that `DataLoader` is used\n",
        "def seed_worker(worker_id):\n",
        "    \"\"\"\n",
        "    DataLoader will reseed workers following randomness in\n",
        "    multi-process data loading algorithm.\n",
        "\n",
        "    Args:\n",
        "      worker_id: integer\n",
        "        ID of subprocess to seed. 0 means that\n",
        "        the data will be loaded in the main process\n",
        "        Refer: https://pytorch.org/docs/stable/data.html#data-loading-randomness for more details\n",
        "\n",
        "    Returns:\n",
        "      Nothing\n",
        "    \"\"\"\n",
        "    worker_seed = torch.initial_seed() % 2**32\n",
        "    np.random.seed(worker_seed)\n",
        "    random.seed(worker_seed)\n",
        "\n",
        "set_seed(SEED)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "cellView": "form",
        "id": "aZCMqIAqkLUT",
        "outputId": "51f9a315-c2af-46a8-b07a-0f2f60e9bda8"
      },
      "id": "aZCMqIAqkLUT",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Random seed 2023 has been set.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "e4bdab8c-0706-48d0-aa4a-24df14ff51d1"
      },
      "outputs": [],
      "source": [
        "train = pd.read_csv(\"data/train.csv\")\n",
        "# Filter missing data and useless columns\n",
        "train = train[train[\"MISSING_DATA\"] == False]\n",
        "train = train[train[\"POLYLINE\"].map(len) > 2]\n",
        "train = train[[\"POLYLINE\"]]\n",
        "# Choose 10000 rows randomly from dataset to run\n",
        "train_1 = train.sample(10000, random_state=SEED)"
      ],
      "id": "e4bdab8c-0706-48d0-aa4a-24df14ff51d1"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oGpwiJ65wkQs"
      },
      "source": [
        "# Pre-processing"
      ],
      "id": "oGpwiJ65wkQs"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "86249f42-b8e5-4cbf-a57b-ad96743ae324"
      },
      "outputs": [],
      "source": [
        "def _change_type(polyline):\n",
        "    \"\"\"\n",
        "    Convert from string to a list of longitude-latitude pairs in floats.\n",
        "    Get min and max of longitude and latitude.\n",
        "    \"\"\"\n",
        "    polyline = polyline[2:-2]\n",
        "    cords_raw = polyline.split(\"],[\")\n",
        "    cords = []\n",
        "    max_lon, min_lon, max_lat, min_lat = np.float32(\"-inf\"), np.float32(\"inf\"), np.float32(\"-inf\"), np.float32(\"inf\")\n",
        "    for cord in cords_raw:\n",
        "        cords.append([np.float32(loc) for loc in cord.split(\",\")])\n",
        "        max_lon = max(max_lon, cords[-1][0])\n",
        "        min_lon = min(min_lon, cords[-1][0])\n",
        "        max_lat = max(max_lat, cords[-1][1])\n",
        "        min_lat = min(min_lat, cords[-1][1])\n",
        "    return pd.Series({\"POLYLINE\": cords, \"max_lon\": max_lon, \"min_lon\": min_lon, \"max_lat\": max_lat, \"min_lat\": min_lat})"
      ],
      "id": "86249f42-b8e5-4cbf-a57b-ad96743ae324"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "98cb2f87"
      },
      "outputs": [],
      "source": [
        "def filter_map(train, max_lat, min_lat, max_lon, min_lon):\n",
        "    \"\"\"\n",
        "    Filter trajectories within the given longitude and latitude range.\n",
        "    \"\"\"\n",
        "    train.reset_index(drop=True, inplace=True)\n",
        "    return train[(train[\"max_lat\"] <= max_lat) & (train[\"min_lat\"] >= min_lat) &\n",
        "                 (train[\"max_lon\"] <= max_lon) & (train[\"min_lon\"] >= min_lon)]"
      ],
      "id": "98cb2f87"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "9e5f2409-e5e6-44c0-a872-bdd916a5da45"
      },
      "outputs": [],
      "source": [
        "def _normalize(polyline, max_lon, min_lon, max_lat, min_lat, m):\n",
        "    \"\"\"\n",
        "    Perform data normalization and convert to pixel coordinates.\n",
        "    Split to previous trajectories and destinations.\n",
        "    \"\"\"\n",
        "    final = []\n",
        "    for cord in polyline:\n",
        "        normalized = [(cord[0]-min_lon)/(max_lon-min_lon), (cord[1] - min_lat) / (max_lat - min_lat)]\n",
        "        normalized[0] = min(m - 1, int(normalized[0] * m))\n",
        "        normalized[1] = min(m - 1, int(normalized[1] * m))\n",
        "        final.append(normalized)\n",
        "    return pd.Series({\"POLYLINE_INIT\": final[:-1], \"POLYLINE_DEST\": final[-1]})"
      ],
      "id": "9e5f2409-e5e6-44c0-a872-bdd916a5da45"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "f68deddf-0156-43c3-9859-87945f56de18"
      },
      "outputs": [],
      "source": [
        "def _to_matrix(polyline, m, activation=lambda x: x):\n",
        "    \"\"\"\n",
        "    Create trajectory image with the given method\n",
        "    \"\"\"\n",
        "    mat = np.zeros((m, m), dtype=np.float32)\n",
        "    n = len(polyline)\n",
        "    for i in range(n):\n",
        "        x = polyline[i][0]\n",
        "        y = polyline[i][1]\n",
        "        mat[y][x] = activation((i + 1) / n)\n",
        "    return mat"
      ],
      "id": "f68deddf-0156-43c3-9859-87945f56de18"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "6e2bfcf4-5f23-452d-9b23-d1d0c02085d5"
      },
      "outputs": [],
      "source": [
        "def transform(df_train, m):\n",
        "    \"\"\"\n",
        "    Do data pre-processing using above functions\n",
        "    \"\"\"\n",
        "    # Change type\n",
        "    changed = df_train[\"POLYLINE\"].apply(_change_type)\n",
        "    # Filter map for max/min long/lat\n",
        "    changed = filter_map(changed, 41.2, 41.1, -8.6, -8.7)  #previous (41.2,41,,)\n",
        "    # Get min-max\n",
        "    max_longitude = changed[\"max_lon\"].max()\n",
        "    min_longitude = changed[\"min_lon\"].min()\n",
        "    max_latitude = changed[\"max_lat\"].max()\n",
        "    min_latitude = changed[\"min_lat\"].min()\n",
        "    # Normalize min-max and split\n",
        "    cleaned = changed[\"POLYLINE\"].apply(_normalize, args=(max_longitude, min_longitude, max_latitude, min_latitude, m))\n",
        "    # Transform to matrices\n",
        "    cleaned[\"MATRIX_LIN\"] = cleaned[\"POLYLINE_INIT\"].apply(_to_matrix, args=(m,))\n",
        "    cleaned[\"MATRIX_QUA\"] = cleaned[\"POLYLINE_INIT\"].apply(_to_matrix, args=(m, lambda x: x ** 2))\n",
        "    cleaned[\"MATRIX_CON\"] = cleaned[\"POLYLINE_INIT\"].apply(_to_matrix, args=(m, lambda x: 1))\n",
        "    return cleaned"
      ],
      "id": "6e2bfcf4-5f23-452d-9b23-d1d0c02085d5"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7e561371-815f-461a-9722-399e7be64a06",
        "outputId": "2a0ac861-dff4-4285-f601-874e95711057"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "5784"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ],
      "source": [
        "transformed = transform(train_1, 40)\n",
        "len(transformed)"
      ],
      "id": "7e561371-815f-461a-9722-399e7be64a06"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Z01-7MPxwwI8"
      },
      "source": [
        "\n",
        "# Hyperparameters Tuning"
      ],
      "id": "Z01-7MPxwwI8"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ee19dc38-f35a-4672-9705-659aa6900364"
      },
      "outputs": [],
      "source": [
        "# Convert to tensors\n",
        "def sequence2tensor(sequence, seqlen=200):\n",
        "    \"\"\"\n",
        "    Converted to a seqlen * 3 tensor.\n",
        "    Rows represent a sequence with fixed length seqlen:\n",
        "    - for a list with length L greater than seqlen, only take the last seqlen coordinates;\n",
        "    - for L less than seqlen, the last L rows are filled with coordinates while the rest are padded with zeros.\n",
        "    \"\"\"\n",
        "    if len(sequence) > seqlen:\n",
        "        sequence = sequence[-seqlen:]  # take last seqlen coordinates\n",
        "    tensor = torch.zeros(seqlen, 3, dtype=torch.float32)\n",
        "    for i, point in enumerate(sequence, start=seqlen-len(sequence)):\n",
        "        # \n",
        "        tensor[i, :2] = torch.Tensor(point)\n",
        "        tensor[i, 2] = 1  # 1 for in the sequence, 0 for out of the sequence\n",
        "    return tensor\n",
        "\n",
        "def matrix2tensor(matrix):\n",
        "    return torch.Tensor(matrix)\n",
        "\n",
        "def output2tensor(output, classification=False):\n",
        "    if classification:\n",
        "        return output[0] * 40 + output[1]\n",
        "    return torch.Tensor(output)"
      ],
      "id": "ee19dc38-f35a-4672-9705-659aa6900364"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "VH0Ofq0EM8CV"
      },
      "outputs": [],
      "source": [
        "# Split to train, validation sets\n",
        "idx_train, idx_valid = train_test_split(range(len(transformed)), test_size=0.2, random_state=SEED)"
      ],
      "id": "VH0Ofq0EM8CV"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "JBRo85htQFxA"
      },
      "outputs": [],
      "source": [
        "def train_model(model, train_data, valid_data, lr=0.001, epochs=20, device=DEVICE):\n",
        "    \"\"\"\n",
        "    Main training loop.\n",
        "    \"\"\"\n",
        "    train_loader = DataLoader(train_data, batch_size=64, shuffle=True, worker_init_fn=seed_worker)\n",
        "    valid_loader = DataLoader(valid_data, batch_size=64, shuffle=True, worker_init_fn=seed_worker)\n",
        "    \n",
        "    model.to(device)\n",
        "    optimizer = torch.optim.Adam(model.parameters(), lr=lr)\n",
        "    criterion = nn.MSELoss()\n",
        "    criterion2 = nn.L1Loss()\n",
        "\n",
        "    train_losses, valid_losses = [], []\n",
        "    for epoch in tqdm(range(epochs)):\n",
        "        model.train()\n",
        "        train_loss = []\n",
        "        for in_, out in train_loader:\n",
        "            in_, out = in_.to(device), out.to(device)\n",
        "            optimizer.zero_grad()\n",
        "            pred = model(in_)\n",
        "            loss = criterion(pred, out)\n",
        "            loss.backward()\n",
        "            optimizer.step()\n",
        "            train_loss.append(loss.item())\n",
        "        train_losses.append(np.mean(train_loss))\n",
        "        model.eval()\n",
        "        valid_loss = []\n",
        "        for in_, out in valid_loader:\n",
        "            in_, out = in_.to(device), out.to(device)\n",
        "            pred = model(in_)\n",
        "            loss = criterion(pred, out)\n",
        "            valid_loss.append(loss.item())\n",
        "        valid_losses.append(np.mean(valid_loss))\n",
        "    print(\"Last epoch train L2 loss: {}\".format(train_losses[-1]))\n",
        "    print(\"Last epoch validation L2 loss: {}\".format(valid_losses[-1]))"
      ],
      "id": "JBRo85htQFxA"
    },
    {
      "cell_type": "code",
      "source": [
        "# Get output tensors\n",
        "output_tensor = transformed[\"POLYLINE_DEST\"].apply(output2tensor).values\n",
        "out_train, out_valid = output_tensor[idx_train], output_tensor[idx_valid]"
      ],
      "metadata": {
        "id": "BVcVWNM1yB7j"
      },
      "id": "BVcVWNM1yB7j",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## LSTM"
      ],
      "metadata": {
        "id": "-jHSyjJ3yBmB"
      },
      "id": "-jHSyjJ3yBmB"
    },
    {
      "cell_type": "code",
      "source": [
        "# Get input sequence tensors\n",
        "sequence_tensor = transformed[\"POLYLINE_INIT\"].apply(sequence2tensor).values\n",
        "seq_train, seq_valid = sequence_tensor[idx_train], sequence_tensor[idx_valid]"
      ],
      "metadata": {
        "id": "VkELugI2tYTC"
      },
      "id": "VkELugI2tYTC",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "162b4630-cc93-47d1-ae2f-def8df92bec4"
      },
      "outputs": [],
      "source": [
        "class SequenceDataset(Dataset):\n",
        "    \"\"\"\n",
        "    Customized dataset for sequences\n",
        "    \"\"\"\n",
        "    def __init__(self, sequence, output):\n",
        "        self.sequence = sequence\n",
        "        self.output = output\n",
        "\n",
        "    def __len__(self):\n",
        "        return len(self.sequence)\n",
        "\n",
        "    def __getitem__(self, idx):\n",
        "        return self.sequence[idx], self.output[idx]"
      ],
      "id": "162b4630-cc93-47d1-ae2f-def8df92bec4"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "SmRWcZ7wxuMX"
      },
      "outputs": [],
      "source": [
        "train_data = SequenceDataset(seq_train, out_train)\n",
        "valid_data = SequenceDataset(seq_valid, out_valid)"
      ],
      "id": "SmRWcZ7wxuMX"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "dbbd66cc-4461-44b7-9aee-18a4f2f301a3"
      },
      "outputs": [],
      "source": [
        "class LSTM(nn.Module):\n",
        "    \"\"\"\n",
        "    Implementation of LSTM\n",
        "    \"\"\"\n",
        "    def __init__(self, i_size=3, h_size=4, n_layers=1, o_size=2):\n",
        "        super(LSTM, self).__init__()\n",
        "        self.lstm = nn.LSTM(\n",
        "            input_size = i_size,\n",
        "            hidden_size = h_size,\n",
        "            num_layers = n_layers,\n",
        "            bias = False,\n",
        "            batch_first = True\n",
        "        )\n",
        "        self.out = nn.Linear(h_size, o_size)\n",
        "        \n",
        "    def forward(self, x):\n",
        "        out, (h, c) = self.lstm(x, None)  # None represents zero initial hidden state\n",
        "        return self.out(out[:, -1, :]) # choose last time step of output"
      ],
      "id": "dbbd66cc-4461-44b7-9aee-18a4f2f301a3"
    },
    {
      "cell_type": "code",
      "source": [
        "for h in range(2, 6):\n",
        "    print(\"Hidden size: {}\".format(h))\n",
        "    set_seed(SEED)\n",
        "    lstm = LSTM(h_size=h)\n",
        "    train_model(lstm, train_data, valid_data, lr=0.005, epochs=40)\n",
        "    print(\"--------------------\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 492,
          "referenced_widgets": [
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        },
        "id": "gEqChDmsJ9p7",
        "outputId": "27649be7-b6a7-40e9-8bdb-4a767b267057"
      },
      "id": "gEqChDmsJ9p7",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Hidden size: 2\n",
            "Random seed 2023 has been set.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/40 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "784fe5d472524f91b73e78d580c2ac86"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Last epoch train L2 loss: 51.05022401679052\n",
            "Last epoch validation L2 loss: 51.52779006958008\n",
            "--------------------\n",
            "Hidden size: 3\n",
            "Random seed 2023 has been set.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/40 [00:00<?, ?it/s]"
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            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Last epoch train L2 loss: 50.399993896484375\n",
            "Last epoch validation L2 loss: 48.784828989129316\n",
            "--------------------\n",
            "Hidden size: 4\n",
            "Random seed 2023 has been set.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/40 [00:00<?, ?it/s]"
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              "version_minor": 0,
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            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Last epoch train L2 loss: 29.954251655160565\n",
            "Last epoch validation L2 loss: 28.658432408383018\n",
            "--------------------\n",
            "Hidden size: 5\n",
            "Random seed 2023 has been set.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/40 [00:00<?, ?it/s]"
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            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "094bd1dd1051478ca1336f6e1f7e6af8"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Last epoch train L2 loss: 30.770522261319094\n",
            "Last epoch validation L2 loss: 29.37538548519737\n",
            "--------------------\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0vtsWSXIMZzF"
      },
      "source": [
        "## Matrix input with linear activation"
      ],
      "id": "0vtsWSXIMZzF"
    },
    {
      "cell_type": "code",
      "source": [
        "matrix_tensor = transformed[\"MATRIX_LIN\"].apply(matrix2tensor).values\n",
        "mat_train, mat_valid = matrix_tensor[idx_train], matrix_tensor[idx_valid]"
      ],
      "metadata": {
        "id": "mMO27Wcs0EC3"
      },
      "id": "mMO27Wcs0EC3",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "fjpIojbC5E2h"
      },
      "outputs": [],
      "source": [
        "class MatrixDataset(Dataset):\n",
        "    \"\"\"\n",
        "    Customized dataset for images\n",
        "    \"\"\"\n",
        "    def __init__(self, matrix, output):\n",
        "        self.matrix = matrix\n",
        "        self.output = output\n",
        "\n",
        "    def __len__(self):\n",
        "        return len(self.matrix)\n",
        "\n",
        "    def __getitem__(self, idx):\n",
        "        return self.matrix[idx], self.output[idx]"
      ],
      "id": "fjpIojbC5E2h"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "hZAbbjWeOby-"
      },
      "outputs": [],
      "source": [
        "train_data = MatrixDataset(mat_train, out_train)\n",
        "valid_data = MatrixDataset(mat_valid, out_valid)"
      ],
      "id": "hZAbbjWeOby-"
    },
    {
      "cell_type": "markdown",
      "source": [
        "### MLP"
      ],
      "metadata": {
        "id": "m-SBA1pS0WAU"
      },
      "id": "m-SBA1pS0WAU"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "gs4moJE9Oj9p"
      },
      "outputs": [],
      "source": [
        "class MLP(nn.Module):\n",
        "    \"\"\"\n",
        "    Implementation of MLP\n",
        "    \"\"\"\n",
        "    def __init__(self, i_size=40*40, h_size=150, h_layers=4, o_size=2):\n",
        "        super(MLP, self).__init__()\n",
        "        self.hiddens = nn.Sequential()\n",
        "        self.hiddens.append(nn.Sequential(nn.Linear(i_size, h_size), nn.Dropout(), nn.ReLU()))\n",
        "        for i in range(1, h_layers):\n",
        "            self.hiddens.append(nn.Sequential(nn.Linear(h_size, h_size), nn.Dropout(), nn.ReLU()))\n",
        "        self.out = nn.Linear(h_size, o_size)\n",
        "        \n",
        "    def forward(self, x):\n",
        "        x = x.flatten(1)\n",
        "        for hidden in self.hiddens:\n",
        "            x = hidden(x)\n",
        "        return self.out(x)"
      ],
      "id": "gs4moJE9Oj9p"
    },
    {
      "cell_type": "code",
      "source": [
        "for l in range(1, 5):\n",
        "    for h in (50, 100, 150, 200):\n",
        "        print(\"Hidden layers: {}, hidden size: {}\".format(l, h))\n",
        "        set_seed(SEED)\n",
        "        mlp = MLP(h_size=h, h_layers=l)\n",
        "        train_model(mlp, train_data, valid_data, lr=0.001, epochs=50)\n",
        "        print(\"--------------------\")"
      ],
      "metadata": {
        "colab": {
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          "height": 1000,
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        "id": "XscgTQoNLF8U",
        "outputId": "5bf9db4c-a4f1-495c-f05e-5df0c0216581"
      },
      "id": "XscgTQoNLF8U",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Hidden layers: 1, hidden size: 50\n",
            "Random seed 2023 has been set.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/50 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "af8ff7990fff42fcb8bb0c09edf6002b"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Last epoch train L2 loss: 29.54943069039959\n",
            "Last epoch validation L2 loss: 12.082071555288215\n",
            "--------------------\n",
            "Hidden layers: 1, hidden size: 100\n",
            "Random seed 2023 has been set.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/50 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "1f4ecc8908584f3bb7ec8a9656b3aa91"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Last epoch train L2 loss: 19.339705663184596\n",
            "Last epoch validation L2 loss: 11.213913515994424\n",
            "--------------------\n",
            "Hidden layers: 1, hidden size: 150\n",
            "Random seed 2023 has been set.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/50 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "62e337e6ef1c46afade62e955cf9ab5a"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Last epoch train L2 loss: 13.645598489944248\n",
            "Last epoch validation L2 loss: 9.37280747764989\n",
            "--------------------\n",
            "Hidden layers: 1, hidden size: 200\n",
            "Random seed 2023 has been set.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "01cc0e2182f747a9b3dfcb7a7576d5b2"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Last epoch train L2 loss: 11.660718943974743\n",
            "Last epoch validation L2 loss: 9.365546213953118\n",
            "--------------------\n",
            "Hidden layers: 2, hidden size: 50\n",
            "Random seed 2023 has been set.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/50 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "52a7ff8d208a4454ac264b756813fb58"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Last epoch train L2 loss: 31.09068225181266\n",
            "Last epoch validation L2 loss: 11.510492073862176\n",
            "--------------------\n",
            "Hidden layers: 2, hidden size: 100\n",
            "Random seed 2023 has been set.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/50 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "1c7fff671e3a4699bdd4a923d36eb0d6"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Last epoch train L2 loss: 18.27569382811246\n",
            "Last epoch validation L2 loss: 9.26976826316432\n",
            "--------------------\n",
            "Hidden layers: 2, hidden size: 150\n",
            "Random seed 2023 has been set.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/50 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "2f6bb481474d45b4948cb57ff7a2a15a"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Last epoch train L2 loss: 14.542109411056728\n",
            "Last epoch validation L2 loss: 8.794815013283177\n",
            "--------------------\n",
            "Hidden layers: 2, hidden size: 200\n",
            "Random seed 2023 has been set.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/50 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "474740155ae64c5aaeedf608dd35c1f3"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Last epoch train L2 loss: 12.162331770544183\n",
            "Last epoch validation L2 loss: 8.704546652342144\n",
            "--------------------\n",
            "Hidden layers: 3, hidden size: 50\n",
            "Random seed 2023 has been set.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/50 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "2cc13218657f437bb8674fb1dba57ff8"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Last epoch train L2 loss: 38.952083430878105\n",
            "Last epoch validation L2 loss: 13.531556330229106\n",
            "--------------------\n",
            "Hidden layers: 3, hidden size: 100\n",
            "Random seed 2023 has been set.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/50 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "638cbc0c3b97480099ae78f002534294"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Last epoch train L2 loss: 20.05815044821125\n",
            "Last epoch validation L2 loss: 9.977286966223465\n",
            "--------------------\n",
            "Hidden layers: 3, hidden size: 150\n",
            "Random seed 2023 has been set.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/50 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d537e08fbe5d4897abb09d1e0045abf2"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Last epoch train L2 loss: 16.015170646040406\n",
            "Last epoch validation L2 loss: 7.7208192724930615\n",
            "--------------------\n",
            "Hidden layers: 3, hidden size: 200\n",
            "Random seed 2023 has been set.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/50 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "19f8226390e740e691a4cdb36c08700c"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Last epoch train L2 loss: 13.436548076263845\n",
            "Last epoch validation L2 loss: 7.3026734904239055\n",
            "--------------------\n",
            "Hidden layers: 4, hidden size: 50\n",
            "Random seed 2023 has been set.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/50 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "2895da4ba79a4e05af88fde06b320f7c"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Last epoch train L2 loss: 41.74470648047042\n",
            "Last epoch validation L2 loss: 13.70777325881155\n",
            "--------------------\n",
            "Hidden layers: 4, hidden size: 100\n",
            "Random seed 2023 has been set.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/50 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "2b40ffebba6545f685245e42001fd3ab"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Last epoch train L2 loss: 20.974197466079502\n",
            "Last epoch validation L2 loss: 7.838876561114662\n",
            "--------------------\n",
            "Hidden layers: 4, hidden size: 150\n",
            "Random seed 2023 has been set.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/50 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "71de23ee3b3142d991494290b34b8a64"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Last epoch train L2 loss: 15.798835571498087\n",
            "Last epoch validation L2 loss: 6.852387378090306\n",
            "--------------------\n",
            "Hidden layers: 4, hidden size: 200\n",
            "Random seed 2023 has been set.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/50 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "64662acb673240c4b2502441e5af8e6d"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Last epoch train L2 loss: 12.959819793701172\n",
            "Last epoch validation L2 loss: 7.3046588144804305\n",
            "--------------------\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1Wg5C-XPNkim"
      },
      "source": [
        "### CNN"
      ],
      "id": "1Wg5C-XPNkim"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "fW3Wv8sfxuMT"
      },
      "outputs": [],
      "source": [
        "class CNN(nn.Module):\n",
        "    \"\"\"\n",
        "    Implementation of CNN\n",
        "    \"\"\"\n",
        "    def __init__(self, conv_channels=128, kernel_size=7):\n",
        "        super(CNN, self).__init__()\n",
        "        self.conv1 = nn.Conv2d(1, out_channels=conv_channels, kernel_size=kernel_size)\n",
        "        self.fc1 = nn.Linear((40 - kernel_size + 1) ** 2 * conv_channels, 128)\n",
        "        self.fc2 = nn.Linear(128, 2)\n",
        "\n",
        "    def forward(self, x):\n",
        "        x = x.unsqueeze(1)\n",
        "        x = self.conv1(x)\n",
        "        x = nn.functional.relu(x)\n",
        "        x = torch.flatten(x, 1)\n",
        "        x = self.fc1(x)\n",
        "        x = nn.functional.relu(x)\n",
        "        x = self.fc2(x)\n",
        "        return x"
      ],
      "id": "fW3Wv8sfxuMT"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
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            "0c3147cbd28141848c41b3f95f2eea59",
            "b523008257744cceb66987c311569e18",
            "5b7c0221b6cc419d82fbf8841cf54102"
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        },
        "id": "KZXeDHX8O3rv",
        "outputId": "9eb9c889-54ce-4fa0-8273-424b23c1cd5c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Kernal size: 3, conv channels: 16\n",
            "Random seed 2023 has been set.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/20 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "5dc94c13fe8f452ca12c0cd56d4d4ed8"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Last epoch train L2 loss: 4.449866781496022\n",
            "Last epoch validation L2 loss: 6.633932741064775\n",
            "--------------------\n",
            "Kernal size: 3, conv channels: 32\n",
            "Random seed 2023 has been set.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/20 [00:00<?, ?it/s]"
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            "application/vnd.jupyter.widget-view+json": {
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Last epoch train L2 loss: 4.420960473687681\n",
            "Last epoch validation L2 loss: 6.784470620908235\n",
            "--------------------\n",
            "Kernal size: 3, conv channels: 64\n",
            "Random seed 2023 has been set.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/20 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "615e5efff69240538b42603c576e32b9"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Last epoch train L2 loss: 3.3983645079887075\n",
            "Last epoch validation L2 loss: 5.700418396999962\n",
            "--------------------\n",
            "Kernal size: 3, conv channels: 128\n",
            "Random seed 2023 has been set.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/20 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "72587212967c4ed18d3922cecbdb1d40"
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        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Last epoch train L2 loss: 2.983460733335312\n",
            "Last epoch validation L2 loss: 4.911886208935788\n",
            "--------------------\n",
            "Kernal size: 5, conv channels: 16\n",
            "Random seed 2023 has been set.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/20 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "04479e765fe7489e9e5d0e2e931e7be5"
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          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Last epoch train L2 loss: 3.5709776388455743\n",
            "Last epoch validation L2 loss: 6.004807622809159\n",
            "--------------------\n",
            "Kernal size: 5, conv channels: 32\n",
            "Random seed 2023 has been set.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/20 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e02b63c99bf1437382eedbe92e54076e"
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Last epoch train L2 loss: 2.7603287247762287\n",
            "Last epoch validation L2 loss: 4.940412326862938\n",
            "--------------------\n",
            "Kernal size: 5, conv channels: 64\n",
            "Random seed 2023 has been set.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/20 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "0f4fcadf85324ad59bfa2f29006a8b99"
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Last epoch train L2 loss: 2.6436816494758815\n",
            "Last epoch validation L2 loss: 4.629060582110756\n",
            "--------------------\n",
            "Kernal size: 5, conv channels: 128\n",
            "Random seed 2023 has been set.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/20 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
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              "model_id": "3d7806edd51c4a3f9532e765f91d93ac"
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          },
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        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Last epoch train L2 loss: 2.2215644962167085\n",
            "Last epoch validation L2 loss: 4.362302234298305\n",
            "--------------------\n",
            "Kernal size: 7, conv channels: 16\n",
            "Random seed 2023 has been set.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/20 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "89c6c0bf8d5242b1a9d5ed687dbe437e"
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Last epoch train L2 loss: 2.983143010368086\n",
            "Last epoch validation L2 loss: 5.482414584410818\n",
            "--------------------\n",
            "Kernal size: 7, conv channels: 32\n",
            "Random seed 2023 has been set.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/20 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "20cc0c9f4cb4405aa288fa9f28e25367"
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        {
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          "name": "stdout",
          "text": [
            "Last epoch train L2 loss: 2.807702024505563\n",
            "Last epoch validation L2 loss: 4.365839738594858\n",
            "--------------------\n",
            "Kernal size: 7, conv channels: 64\n",
            "Random seed 2023 has been set.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/20 [00:00<?, ?it/s]"
            ],
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              "model_id": "20eb66aa01464a679a89bd2cb2ddd504"
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        {
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          "name": "stdout",
          "text": [
            "Last epoch train L2 loss: 2.1790730259189868\n",
            "Last epoch validation L2 loss: 4.258225566462467\n",
            "--------------------\n",
            "Kernal size: 7, conv channels: 128\n",
            "Random seed 2023 has been set.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/20 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
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              "model_id": "0630768d7ea347c5a0d0281c90440f76"
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        {
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          "text": [
            "Last epoch train L2 loss: 1.9976773180373728\n",
            "Last epoch validation L2 loss: 4.077375957840367\n",
            "--------------------\n",
            "Kernal size: 9, conv channels: 16\n",
            "Random seed 2023 has been set.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/20 [00:00<?, ?it/s]"
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              "model_id": "a9971bc5b8b34a1d9dcd07140c95eae1"
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        },
        {
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          "name": "stdout",
          "text": [
            "Last epoch train L2 loss: 3.01835338873406\n",
            "Last epoch validation L2 loss: 5.63180084604966\n",
            "--------------------\n",
            "Kernal size: 9, conv channels: 32\n",
            "Random seed 2023 has been set.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/20 [00:00<?, ?it/s]"
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        {
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          "name": "stdout",
          "text": [
            "Last epoch train L2 loss: 2.7844245458302432\n",
            "Last epoch validation L2 loss: 4.4874857287657886\n",
            "--------------------\n",
            "Kernal size: 9, conv channels: 64\n",
            "Random seed 2023 has been set.\n"
          ]
        },
        {
          "output_type": "display_data",
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          "text": [
            "Last epoch train L2 loss: 2.0113538720019877\n",
            "Last epoch validation L2 loss: 4.5062839859410335\n",
            "--------------------\n",
            "Kernal size: 9, conv channels: 128\n",
            "Random seed 2023 has been set.\n"
          ]
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        {
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            "Last epoch train L2 loss: 2.105982685334062\n",
            "Last epoch validation L2 loss: 5.840756196724741\n",
            "--------------------\n"
          ]
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      "source": [
        "for k in (3, 5, 7, 9):\n",
        "    for c in (16, 32, 64, 128):\n",
        "        print(\"Kernal size: {}, conv channels: {}\".format(k, c))\n",
        "        set_seed(SEED)\n",
        "        cnn = CNN(conv_channels=c, kernel_size=k)\n",
        "        train_model(cnn, train_data, valid_data, lr=0.001)\n",
        "        print(\"--------------------\")"
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