old_Approach_5.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Approach 5.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "CZ_luYEyhNva",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "eb28a1c4-8551-4567-c678-bf50738487b0"
      },
      "source": [
        "import torch\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from torch.utils.data import DataLoader\n",
        "from torch.utils.data import TensorDataset\n",
        "from torch import optim, nn\n",
        "import torch.nn.functional as F\n",
        "from numpy import genfromtxt\n",
        "from google.colab import drive\n",
        "\n",
        "drive.mount('/content/gdrive')\n",
        "%cd /content/gdrive/My\\ Drive/Colab\\ Notebooks"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/gdrive\n",
            "[Errno 2] No such file or directory: '/content/gdrive/My Drive/Colab Notebooks'\n",
            "/content\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dlNUu77bQ6XO",
        "outputId": "2bffb35a-79ee-4bee-ad18-7779f2a7c3c4"
      },
      "source": [
        "!apt-get install -y -qq software-properties-common python-software-properties module-init-tools\n",
        "!add-apt-repository -y ppa:alessandro-strada/ppa 2>&1 > /dev/null\n",
        "!apt-get update -qq 2>&1 > /dev/null\n",
        "!apt-get -y install -qq google-drive-ocamlfuse fuse\n",
        "from google.colab import auth\n",
        "auth.authenticate_user()\n",
        "from oauth2client.client import GoogleCredentials\n",
        "creds = GoogleCredentials.get_application_default()\n",
        "import getpass\n",
        "!google-drive-ocamlfuse -headless -id={creds.client_id} -secret={creds.client_secret} < /dev/null 2>&1 | grep URL\n",
        "vcode = getpass.getpass()\n",
        "!echo {vcode} | google-drive-ocamlfuse -headless -id={creds.client_id} -secret={creds.client_secret}\n",
        "\n",
        "!mkdir -p drive\n",
        "!google-drive-ocamlfuse drive\n",
        "import os\n",
        "os.chdir('drive/4242')\n",
        "!ls"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "E: Package 'python-software-properties' has no installation candidate\n",
            "Selecting previously unselected package google-drive-ocamlfuse.\n",
            "(Reading database ... 155219 files and directories currently installed.)\n",
            "Preparing to unpack .../google-drive-ocamlfuse_0.7.27-0ubuntu1~ubuntu18.04.1_amd64.deb ...\n",
            "Unpacking google-drive-ocamlfuse (0.7.27-0ubuntu1~ubuntu18.04.1) ...\n",
            "Setting up google-drive-ocamlfuse (0.7.27-0ubuntu1~ubuntu18.04.1) ...\n",
            "Processing triggers for man-db (2.8.3-2ubuntu0.1) ...\n",
            "Please, open the following URL in a web browser: https://accounts.google.com/o/oauth2/auth?client_id=32555940559.apps.googleusercontent.com&redirect_uri=urn%3Aietf%3Awg%3Aoauth%3A2.0%3Aoob&scope=https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive&response_type=code&access_type=offline&approval_prompt=force\n",
            "··········\n",
            "Please, open the following URL in a web browser: https://accounts.google.com/o/oauth2/auth?client_id=32555940559.apps.googleusercontent.com&redirect_uri=urn%3Aietf%3Awg%3Aoauth%3A2.0%3Aoob&scope=https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive&response_type=code&access_type=offline&approval_prompt=force\n",
            "Please enter the verification code: Access token retrieved correctly.\n",
            "dengue_features_test.csv  dengue_features_train.csv  dengue_labels_train.csv\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sPqAHLfJSymz",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 417
        },
        "outputId": "3cc2907b-9a77-47dd-ffd5-30d4443dca16"
      },
      "source": [
        "train_features = pd.read_csv(\"./dengue_features_train.csv\")\n",
        "train_labels = pd.read_csv(\"./dengue_labels_train.csv\")\n",
        "\n",
        "# train_features = pd.read_csv(\"./content/drive/Shared drives/Colab\\ Notebooks\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "FileNotFoundError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-31-6873d551dd39>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mtrain_labels\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[0;34m\"./dengue_labels_train.csv\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mtrain_features\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[0;34m\"./content/drive/Shared drives/Colab\\ Notebooks\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/io/parsers.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, dialect, error_bad_lines, warn_bad_lines, delim_whitespace, low_memory, memory_map, float_precision)\u001b[0m\n\u001b[1;32m    686\u001b[0m     )\n\u001b[1;32m    687\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 688\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    689\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    690\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m_read\u001b[0;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[1;32m    452\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    453\u001b[0m     \u001b[0;31m# Create the parser.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 454\u001b[0;31m     \u001b[0mparser\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mTextFileReader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfp_or_buf\u001b[0m\u001b[0;34m,\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    455\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    456\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mchunksize\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0miterator\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.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[1;32m    946\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"has_index_names\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"has_index_names\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    947\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 948\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_make_engine\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mengine\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    949\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    950\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mclose\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[0;32m/usr/local/lib/python3.7/dist-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m_make_engine\u001b[0;34m(self, engine)\u001b[0m\n\u001b[1;32m   1178\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_make_engine\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mengine\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"c\"\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   1179\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mengine\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"c\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1180\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mCParserWrapper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptions\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   1181\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   1182\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mengine\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"python\"\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.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, src, **kwds)\u001b[0m\n\u001b[1;32m   2008\u001b[0m         \u001b[0mkwds\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"usecols\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0musecols\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2009\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2010\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_reader\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mparsers\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTextReader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msrc\u001b[0m\u001b[0;34m,\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   2011\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munnamed_cols\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[0munnamed_cols\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2012\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32mpandas/_libs/parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader.__cinit__\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32mpandas/_libs/parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader._setup_parser_source\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: './content/drive/Shared drives/Colab\\\\ Notebooks'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gCXSv4QFTGmf"
      },
      "source": [
        "sj_train_features=train_features[train_features.city=='sj']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "o7Q4j5UiTjY7",
        "outputId": "9b6d04dd-5e0d-417a-8af9-064087352b68"
      },
      "source": [
        "sj_train_features.drop('city',axis=1, inplace=True)\n",
        "#sj_train_features.drop('week_start_date',axis=1,inplace=True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/pandas/core/frame.py:4174: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  errors=errors,\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XU_b4zuZTyr4",
        "outputId": "98764b20-c11b-4cab-d88e-81935f2d8176"
      },
      "source": [
        "sj_train_features.info()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 936 entries, 0 to 935\n",
            "Data columns (total 23 columns):\n",
            " #   Column                                 Non-Null Count  Dtype  \n",
            "---  ------                                 --------------  -----  \n",
            " 0   year                                   936 non-null    int64  \n",
            " 1   weekofyear                             936 non-null    int64  \n",
            " 2   week_start_date                        936 non-null    object \n",
            " 3   ndvi_ne                                745 non-null    float64\n",
            " 4   ndvi_nw                                887 non-null    float64\n",
            " 5   ndvi_se                                917 non-null    float64\n",
            " 6   ndvi_sw                                917 non-null    float64\n",
            " 7   precipitation_amt_mm                   927 non-null    float64\n",
            " 8   reanalysis_air_temp_k                  930 non-null    float64\n",
            " 9   reanalysis_avg_temp_k                  930 non-null    float64\n",
            " 10  reanalysis_dew_point_temp_k            930 non-null    float64\n",
            " 11  reanalysis_max_air_temp_k              930 non-null    float64\n",
            " 12  reanalysis_min_air_temp_k              930 non-null    float64\n",
            " 13  reanalysis_precip_amt_kg_per_m2        930 non-null    float64\n",
            " 14  reanalysis_relative_humidity_percent   930 non-null    float64\n",
            " 15  reanalysis_sat_precip_amt_mm           927 non-null    float64\n",
            " 16  reanalysis_specific_humidity_g_per_kg  930 non-null    float64\n",
            " 17  reanalysis_tdtr_k                      930 non-null    float64\n",
            " 18  station_avg_temp_c                     930 non-null    float64\n",
            " 19  station_diur_temp_rng_c                930 non-null    float64\n",
            " 20  station_max_temp_c                     930 non-null    float64\n",
            " 21  station_min_temp_c                     930 non-null    float64\n",
            " 22  station_precip_mm                      930 non-null    float64\n",
            "dtypes: float64(20), int64(2), object(1)\n",
            "memory usage: 175.5+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5rvcMhvDTOa8"
      },
      "source": [
        "sj_train_labels=train_labels[train_labels.city=='sj']\n",
        "sj_train_labels.drop('city',axis=1, inplace=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V0ciUQqAfO7U"
      },
      "source": [
        "train_features = pd.read_csv(\"./dengue_features_train.csv\")\n",
        "train_labels = pd.read_csv(\"./dengue_labels_train.csv\")\n",
        "\n",
        "sj_train_features=train_features[train_features.city=='sj']\n",
        "sj_train_features.drop(['city','week_start_date'],axis=1, inplace=True)\n",
        "\n",
        "sj_train_labels=train_labels[train_labels.city=='sj']\n",
        "sj_train_labels.drop('city',axis=1, inplace=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vEnaf9gzfTac"
      },
      "source": [
        "#After preprocessing,transform to tensor\n",
        "sj_train_features_t=torch.tensor(sj_train_features.values,dtype=torch.float)\n",
        "sj_train_labels_t=torch.tensor(sj_train_labels.total_cases.values,dtype=torch.float).view(-1,1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lGOqZFlBhQi7"
      },
      "source": [
        "Loss function. log rsme is used as as rsme is too large.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_qPMhqpjfPDB"
      },
      "source": [
        "loss=nn.MSELoss()\n",
        "\n",
        "def log_rmse(net,features,labels):\n",
        "    with torch.no_grad():\n",
        "        #rmse always too high,and the scale is large, so use log \n",
        "        clipped_preds=torch.max(net(features),torch.tensor(1.0))\n",
        "        rmse=torch.sqrt(2*loss(clipped_preds.float().log(),labels.float().log()).mean())\n",
        "    return rmse.item() "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gAfLQ2pRoRsv"
      },
      "source": [
        "Initialise neural network linearly."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xixhldDSfXGP"
      },
      "source": [
        "#initialize net,linear~\n",
        "def get_net(feature_num):\n",
        "    net=nn.Linear(feature_num,1)\n",
        "    \n",
        "    #parameters() generate tensor data：\n",
        "    for param in net.parameters():\n",
        "        nn.init.normal_(param,mean=0,std=0.01)\n",
        "    \n",
        "    return net"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lmkJrMI0gmeR"
      },
      "source": [
        "Training codes.\n",
        "\n",
        " ADAM optimizer is used for it's stochastic gradient descent method to update biases\n",
        "\n",
        "Backpropagation is also used.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lfH0m8T0fY_u"
      },
      "source": [
        "#Train model\n",
        "def train(net,train_features,train_labels,test_features,test_labels,\n",
        "          num_epochs,learning_rate,weight_decay,batch_size):\n",
        "    #Loss\n",
        "    train_ls=[] \n",
        "    dataset=utils.data.TensorDataset(train_features,train_labels)\n",
        "    train_iter=utils.data.DataLoader(dataset,batch_size,shuffle=True)\n",
        "    #optimizer：Adam\n",
        "    optimizer=optim.Adam(params=net.parameters(),lr=learning_rate,weight_decay=weight_decay)\n",
        "    net=net.float()\n",
        "    for epoch in range(num_epochs):\n",
        "        for x , y in train_iter:\n",
        "            #Forward：\n",
        "            output=net(x.float())\n",
        "            #Loss：\n",
        "            l=loss(output,y.float())\n",
        "            #zero\n",
        "            optimizer.zero_grad()\n",
        "            #backward\n",
        "            l.backward()\n",
        "            #optimizer\n",
        "            optimizer.step()\n",
        "        train_ls.append(log_rmse(net,train_features,train_labels))  #train\n",
        "    return train_ls"
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}