Copy_of_UserActivityRecognition_IoTAss4.ipynb

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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/nicoDs96/IoT-meteo_station/blob/master/Copy_of_UserActivityRecognition_IoTAss4.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NA7acFOcBS9T",
        "colab_type": "text"
      },
      "source": [
        "# User Actvity Classification\n",
        "In this notebook I will try to resample an existing dataset to adapt it to mine accelerometer data (from 50 Hz to 1 Hz) and check if results are accetable."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "loVmGc3KH6LE",
        "colab_type": "code",
        "outputId": "06de0b7a-4e17-4bcf-d6d7-26056d87c2ac",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 124
        }
      },
      "source": [
        "import pandas as pd\n",
        "import os\n",
        "from os import listdir\n",
        "from os.path import isfile, join\n",
        "from sklearn.model_selection import train_test_split\n",
        "import math\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import random\n",
        "import torch\n",
        "from torch.utils import data\n",
        "from torch import nn\n",
        "import torch.optim as optim\n",
        "import torch.nn.functional as F\n",
        "from tqdm import tqdm_notebook as tqdm\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&response_type=code&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly\n",
            "\n",
            "Enter your authorization code:\n",
            "··········\n",
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kc-4GVQMBqi_",
        "colab_type": "text"
      },
      "source": [
        "The dataset has a strange strucutre: one file with raw accelerometer data for each experiment. A separate file with labels for range of values from the raw data. Here we aggregate those data and obtain a dataset with columns:\n",
        "  x         y         z  exp_nr  activity_nr  activity_nr_2_class\n",
        "\n",
        "  were x,y,z are the raw data, exp_nr (useless), activity nr is the original label, activity_nr_2_class is the aggregated label.\n",
        "\n",
        "  Original Label are :\n",
        "  \n",
        "      \"1\": \"WALKING\",\n",
        "      \"2\": \"WALKING_UPSTAIRS\",  \n",
        "      \"3\": \"WALKING_DOWNSTAIRS\",\n",
        "      \"4\": \"SITTING\", \n",
        "      \"5\": \"STANDING\",          \n",
        "      \"6\": \"LAYING\",      \n",
        "      \"7\": \"STAND_TO_SIT\",      \n",
        "      \"8\": \"SIT_TO_STAND\",     \n",
        "      \"9\": \"SIT_TO_LIE\",        \n",
        "     \"10\": \"LIE_TO_SIT\",        \n",
        "     \"11\": \"STAND_TO_LIE\",      \n",
        "     \"12\": \"LIE_TO_STAND\"   \n",
        "\n",
        "New label maps 1-3 to 0 (Moving), 4-6 to 1 (Laying) 7-12 NaN.     "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MkZNNfZFITfB",
        "colab_type": "code",
        "outputId": "9cca4336-9298-4ed0-89fa-41c75049f9c1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 526
        }
      },
      "source": [
        "# READ DATASET or Aggregate It\n",
        "\n",
        "path = '/content/drive/My Drive/SAP/IoT/dataset/' \n",
        "mypath= '/content/drive/My Drive/SAP/IoT/dataset/acc/'\n",
        "action_dict = {\n",
        "      \"1\": \"WALKING\",\n",
        "      \"2\": \"WALKING_UPSTAIRS\",  \n",
        "      \"3\": \"WALKING_DOWNSTAIRS\",\n",
        "      \"4\": \"SITTING\", \n",
        "      \"5\": \"STANDING\",          \n",
        "      \"6\": \"LAYING\",      \n",
        "      \"7\": \"STAND_TO_SIT\",      \n",
        "      \"8\": \"SIT_TO_STAND\",     \n",
        "      \"9\": \"SIT_TO_LIE\",        \n",
        "     \"10\": \"LIE_TO_SIT\",        \n",
        "     \"11\": \"STAND_TO_LIE\",      \n",
        "     \"12\": \"LIE_TO_STAND\"      \n",
        "  }\n",
        "\n",
        "if os.path.isfile(path+'labelled_dataset.csv'):\n",
        "  df = pd.read_csv(path+'labelled_dataset.csv')\n",
        "else:\n",
        "  \n",
        "  acc_file_raw = [f for f in listdir(mypath) if isfile(join(mypath, f))]   \n",
        "  label_df = pd.read_csv(path+\"labels.txt\", sep=\" \", names=[\"exp_nr\",\"user_nr\",\"activity_nr\",\"start_point\",\"end_point\"])\n",
        "  \n",
        "  df_final = pd.DataFrame({\"x\":[],\"y\":[],\"z\":[],\"exp_nr\":[],\"activity_nr\":[],\"activity_nr_2_class\":[]})\n",
        "\n",
        "  for name in tqdm(acc_file_raw):\n",
        "    exp_nr = int(name.split(\"_\")[1][3:5:]) #extract exp number\n",
        "    experiment_df = pd.read_csv(mypath+name, sep=\" \") #read raw data\n",
        "    experiment_df.columns = ['x','y','z']\n",
        "    \n",
        "    #LABEL RAW DATA:\n",
        "    # for each row extract activity, start, stop, then filter experiment_df and \n",
        "    # append the final record to df_final\n",
        "    for index, row in label_df[label_df[\"exp_nr\"]==exp_nr].iterrows():\n",
        "      activity = row['activity_nr']\n",
        "      start_idx = row['start_point']\n",
        "      end_idx = row['end_point']\n",
        "      df_filt = pd.DataFrame(experiment_df.iloc[start_idx:end_idx])\n",
        "      df_filt['exp_nr'] = exp_nr\n",
        "      df_filt['activity_nr'] = activity\n",
        "      \n",
        "      two_class = 0\n",
        "      if(activity < 4):\n",
        "        two_class = 0 #moving\n",
        "      elif(activity < 7):\n",
        "        two_class = 1 #laying\n",
        "      else:\n",
        "        two_class = float('nan')\n",
        "\n",
        "      df_filt['activity_nr_2_class'] = two_class\n",
        "      \n",
        "      df_final = df_final.append(df_filt, ignore_index=True)\n",
        "      \n",
        "  #print(df_final.head())\n",
        "  df_final.to_csv(path+'labelled_dataset.csv', index=False)\n",
        "  df = df_final\n",
        "  del df_final  \n",
        "\n",
        "print(df.head())\n",
        "print(\"\")\n",
        "print(df.tail())\n",
        "df.describe()    "
      ],
      "execution_count": 0,
      "outputs": [
        {
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          "text": [
            "          x         y         z  exp_nr  activity_nr  activity_nr_2_class\n",
            "0  1.025000 -0.125000  0.101389     1.0          5.0                  1.0\n",
            "1  1.020833 -0.125000  0.104167     1.0          5.0                  1.0\n",
            "2  1.016667 -0.125000  0.108333     1.0          5.0                  1.0\n",
            "3  1.018056 -0.127778  0.108333     1.0          5.0                  1.0\n",
            "4  1.018056 -0.129167  0.104167     1.0          5.0                  1.0\n",
            "\n",
            "               x         y         z  exp_nr  activity_nr  activity_nr_2_class\n",
            "814395  0.880556 -0.390278 -0.156944    61.0          2.0                  0.0\n",
            "814396  0.834722 -0.358333 -0.098611    61.0          2.0                  0.0\n",
            "814397  0.802778 -0.329167 -0.104167    61.0          2.0                  0.0\n",
            "814398  0.770833 -0.287500 -0.098611    61.0          2.0                  0.0\n",
            "814399  0.718056 -0.268056 -0.055556    61.0          2.0                  0.0\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>x</th>\n",
              "      <th>y</th>\n",
              "      <th>z</th>\n",
              "      <th>exp_nr</th>\n",
              "      <th>activity_nr</th>\n",
              "      <th>activity_nr_2_class</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>814400.000000</td>\n",
              "      <td>814400.000000</td>\n",
              "      <td>814400.000000</td>\n",
              "      <td>814400.000000</td>\n",
              "      <td>814400.000000</td>\n",
              "      <td>747550.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>0.806492</td>\n",
              "      <td>0.032874</td>\n",
              "      <td>0.087617</td>\n",
              "      <td>32.256451</td>\n",
              "      <td>4.101631</td>\n",
              "      <td>0.536803</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.409179</td>\n",
              "      <td>0.405612</td>\n",
              "      <td>0.345857</td>\n",
              "      <td>17.728574</td>\n",
              "      <td>2.395082</td>\n",
              "      <td>0.498644</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>-0.647222</td>\n",
              "      <td>-1.698611</td>\n",
              "      <td>-1.837500</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>0.675000</td>\n",
              "      <td>-0.223611</td>\n",
              "      <td>-0.119444</td>\n",
              "      <td>17.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>0.950000</td>\n",
              "      <td>-0.072222</td>\n",
              "      <td>0.045833</td>\n",
              "      <td>34.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>1.019445</td>\n",
              "      <td>0.213889</td>\n",
              "      <td>0.238889</td>\n",
              "      <td>48.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>2.004167</td>\n",
              "      <td>1.716667</td>\n",
              "      <td>1.502778</td>\n",
              "      <td>61.000000</td>\n",
              "      <td>12.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                   x              y  ...    activity_nr  activity_nr_2_class\n",
              "count  814400.000000  814400.000000  ...  814400.000000        747550.000000\n",
              "mean        0.806492       0.032874  ...       4.101631             0.536803\n",
              "std         0.409179       0.405612  ...       2.395082             0.498644\n",
              "min        -0.647222      -1.698611  ...       1.000000             0.000000\n",
              "25%         0.675000      -0.223611  ...       2.000000             0.000000\n",
              "50%         0.950000      -0.072222  ...       4.000000             1.000000\n",
              "75%         1.019445       0.213889  ...       5.000000             1.000000\n",
              "max         2.004167       1.716667  ...      12.000000             1.000000\n",
              "\n",
              "[8 rows x 6 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dRmSLnrxCzGT",
        "colab_type": "text"
      },
      "source": [
        "Here we discard data with activity_2_class == NaN and then we aggregate data: 50 sample windows (1sec at 50 Hz) is built and averaged to reduce at 1 Hz (1 sample)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N9kp4XojqoSG",
        "colab_type": "code",
        "outputId": "3639badf-406c-4b3d-be96-77ce4364ba57",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        }
      },
      "source": [
        "_df=pd.DataFrame()\n",
        "moving_reocrds = df[df['activity_nr_2_class']==0]\n",
        "laying_record = df[df['activity_nr_2_class']==1]\n",
        "sample_size = min(len(moving_reocrds),len(laying_record))\n",
        "#make class number uniform\n",
        "_df = _df.append(moving_reocrds.sample(sample_size), ignore_index=True).append(laying_record.sample(sample_size), ignore_index=True)\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "df_second_aggregated = pd.DataFrame()\n",
        "\n",
        "for count in range(0,len(_df)-50,50):\n",
        "  sec_interval = pd.DataFrame(_df.iloc[count:count+50])\n",
        "  df_second_aggregated = df_second_aggregated.append(pd.DataFrame([[ sec_interval['x'].mean(), sec_interval['y'].mean(), sec_interval['z'].mean(), sec_interval['activity_nr'].iloc[0], sec_interval['activity_nr_2_class'].iloc[0]     ]]))\n",
        "\n",
        "df_second_aggregated.columns=[\"x\",\"y\",\"z\",\"activity_nr\",\"activity_nr_2_class\"]\n",
        "df_second_aggregated.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>x</th>\n",
              "      <th>y</th>\n",
              "      <th>z</th>\n",
              "      <th>activity_nr</th>\n",
              "      <th>activity_nr_2_class</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.990806</td>\n",
              "      <td>-0.191778</td>\n",
              "      <td>-0.055722</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1.050278</td>\n",
              "      <td>-0.277500</td>\n",
              "      <td>-0.086528</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.963861</td>\n",
              "      <td>-0.173639</td>\n",
              "      <td>-0.136917</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1.091194</td>\n",
              "      <td>-0.226167</td>\n",
              "      <td>-0.090472</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.987417</td>\n",
              "      <td>-0.180694</td>\n",
              "      <td>-0.070333</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          x         y         z  activity_nr  activity_nr_2_class\n",
              "0  0.990806 -0.191778 -0.055722          1.0                  0.0\n",
              "0  1.050278 -0.277500 -0.086528          3.0                  0.0\n",
              "0  0.963861 -0.173639 -0.136917          1.0                  0.0\n",
              "0  1.091194 -0.226167 -0.090472          1.0                  0.0\n",
              "0  0.987417 -0.180694 -0.070333          1.0                  0.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 89
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pNBN04h2DYb9",
        "colab_type": "text"
      },
      "source": [
        "we can see how data size reduces drastically"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4axqpZVpQpWY",
        "colab_type": "code",
        "outputId": "b0712994-d1c0-4dcb-c322-96103431451b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        }
      },
      "source": [
        "print(df_second_aggregated[df_second_aggregated['activity_nr_2_class']==0].describe())"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "                 x            y            z  activity_nr  activity_nr_2_class\n",
            "count  6926.000000  6926.000000  6926.000000  6926.000000               6926.0\n",
            "mean      0.982050    -0.201356    -0.088072     1.952209                  0.0\n",
            "std       0.042196     0.031043     0.033032     0.813353                  0.0\n",
            "min       0.764028    -0.333583    -0.207722     1.000000                  0.0\n",
            "25%       0.953646    -0.221944    -0.110556     1.000000                  0.0\n",
            "50%       0.982056    -0.200889    -0.087486     2.000000                  0.0\n",
            "75%       1.010028    -0.180417    -0.065500     3.000000                  0.0\n",
            "max       1.128111     0.121194     0.148444     3.000000                  0.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ydq-1j2JREhB",
        "colab_type": "code",
        "outputId": "0cb35354-bb22-4738-bdb7-b241ca7619d6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        }
      },
      "source": [
        "print(df_second_aggregated[df_second_aggregated['activity_nr_2_class']==1].describe())"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "                 x            y            z  activity_nr  activity_nr_2_class\n",
            "count  6924.000000  6924.000000  6924.000000  6924.000000               6924.0\n",
            "mean      0.669180     0.201141     0.223764     5.026863                  1.0\n",
            "std       0.062571     0.059172     0.050744     0.809181                  0.0\n",
            "min       0.430278    -0.031944     0.044889     4.000000                  1.0\n",
            "25%       0.626361     0.162167     0.189083     4.000000                  1.0\n",
            "50%       0.670556     0.200306     0.223917     5.000000                  1.0\n",
            "75%       0.711590     0.241056     0.257674     6.000000                  1.0\n",
            "max       0.876111     0.433944     0.413528     6.000000                  1.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_y3YCpOERrVJ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df_second_aggregated.to_csv(path+'second_agg_lab_dataset.csv', index=False)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "stZjx5A9DdRk",
        "colab_type": "text"
      },
      "source": [
        "Create train and test subsets"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eRA9Vob3Lrr_",
        "colab_type": "code",
        "outputId": "54b5e131-1416-4143-f01b-9c987043e3fb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 86
        }
      },
      "source": [
        "# train test split\n",
        "X_train, X_test, y_train, y_test = train_test_split( df_second_aggregated[[\"x\",\"y\",\"z\"]], df_second_aggregated[['activity_nr_2_class']], test_size=0.3, random_state=42)\n",
        "\n",
        "print(\"X_train: %s\\nX_test: %s\\ny_train: %s\\ny_test: %s\"%(X_train.shape, X_test.shape, y_train.shape, y_test.shape))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "X_train: (9695, 3)\n",
            "X_test: (4155, 3)\n",
            "y_train: (9695, 1)\n",
            "y_test: (4155, 1)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "P8hnrg7eIcxJ",
        "colab_type": "text"
      },
      "source": [
        "# Pytorch"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iTDBlBfIDgv5",
        "colab_type": "text"
      },
      "source": [
        "set training using CUDA"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fU_r6ComK6AY",
        "colab_type": "code",
        "outputId": "b4cdd927-e866-46e8-afdd-c9039fff0762",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# Set CUDA training if CUDA is availabe\n",
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "print(\"Using Device:\\t\" + str(device))\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using Device:\tcuda\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PAMsNr9gDlha",
        "colab_type": "text"
      },
      "source": [
        "Our CNN will take as input data of shape (BATCH_SIZE,CHANNEL_IN,VALUE) and I have engineered feature st. shape is (BATCH_SIZE,3,1), where each channel is an axes of the accelerator. Then we use datautils of pytorch to split data into batches."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_QD8cJ4xrk1R",
        "colab_type": "code",
        "outputId": "08f7df74-b26b-4bb6-8bac-c21bdc77a18f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 156
        }
      },
      "source": [
        "CHANNELS_IN = 3\n",
        "SEQUENCE_SIZE = 1\n",
        "\n",
        "# Reshape data to match conv1d input shape\n",
        "X_train = np.array(X_train).reshape(X_train.shape[0],CHANNELS_IN,SEQUENCE_SIZE)\n",
        "X_test = np.array(X_test).reshape(X_test.shape[0],CHANNELS_IN,SEQUENCE_SIZE)\n",
        "y_train = np.array(y_train)\n",
        "y_test = np.array(y_test)\n",
        "# transorm array into tensor\n",
        "X_train = torch.from_numpy(X_train).float()\n",
        "X_test = torch.from_numpy(X_test).float()\n",
        "y_train = torch.from_numpy(y_train).float()\n",
        "y_test = torch.from_numpy(y_test).float()\n",
        "\n",
        "#create a 3 second window\n",
        "X_train = X_train[0:int(X_train.shape[0]/3)*3]\n",
        "X_train = X_train.view(-1,3,3)\n",
        "\n",
        "#sample the labels according to the window\n",
        "y_train = y_train[0:int(y_train.shape[0]/3)*3:3]\n",
        "\n",
        "\n",
        "X_test = X_test[0:int(X_test.shape[0]/3)*3]\n",
        "X_test = X_test.view(-1,3,3)\n",
        "y_test = y_test[0:int(y_test.shape[0]/3)*3:3]\n",
        "\n",
        "print(X_train.shape)\n",
        "print(y_train.shape)\n",
        "\n",
        "print(X_test.shape)\n",
        "print(y_test.shape)\n",
        "# get a data loader and divide data into batches\n",
        "train_data = data.TensorDataset(X_train, y_train) \n",
        "test_data = data.TensorDataset(X_test, y_test)\n",
        "\n",
        "print(train_data[0]) \n",
        "print(len(train_data))\n",
        "\n",
        "train_data_loader = data.DataLoader(train_data, batch_size=16, shuffle=True)\n",
        "test_data_loader = data.DataLoader(test_data, batch_size=16, shuffle=True)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "torch.Size([3231, 3, 3])\n",
            "torch.Size([3231, 1])\n",
            "torch.Size([1385, 3, 3])\n",
            "torch.Size([1385, 1])\n",
            "(tensor([[ 1.0493, -0.2107, -0.0848],\n",
            "        [ 1.0318, -0.2260, -0.0497],\n",
            "        [ 0.9278, -0.1947, -0.0853]]), tensor([0.]))\n",
            "3231\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uNIht9j7D9m3",
        "colab_type": "text"
      },
      "source": [
        "Here we have defined two different models"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WHa6mvSqIaJx",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Define the NN model\n",
        "\n",
        "class ConvNet(nn.Module):\n",
        "    def __init__(self, seq_len_in, out_dim, kernel_size=8):\n",
        "        super(ConvNet, self).__init__()\n",
        "        \n",
        "        n_filters=64\n",
        "        max_pool_kernel=2\n",
        "        \n",
        "        self.conv1 = nn.Conv1d(CHANNELS_IN, n_filters, kernel_size, padding=5)\n",
        "        self.maxPool = nn.MaxPool1d(max_pool_kernel, stride= 1 )\n",
        "        self.drop_layer = nn.Dropout(0.6)\n",
        "        self.LRelu = nn.LeakyReLU()\n",
        "        self.sigmoid = nn.Sigmoid()\n",
        "        \n",
        "        self.conv2 = nn.Conv1d(n_filters, n_filters, kernel_size//4 )\n",
        "\n",
        "        self.hidden_dim = n_filters *((((seq_len_in +2*5 - kernel_size +1) - max_pool_kernel +1) -kernel_size//4 + 1) - max_pool_kernel +1)\n",
        "        #self.hidden_dim = 64;\n",
        "\n",
        "        self.lin_layer = nn.Linear(self.hidden_dim, self.hidden_dim)\n",
        "        self.out_layer = nn.Linear(self.hidden_dim, out_dim)\n",
        "        \n",
        "        \n",
        "\n",
        "    def forward(self,x):\n",
        "        \n",
        "        c1_out =  self.conv1(x)  \n",
        "        max_pool_1 = self.maxPool( self.LRelu(c1_out) )       \n",
        "        c2_out = self.drop_layer(self.conv2( max_pool_1 ))     \n",
        "        max_pool_2 =  self.maxPool( self.LRelu(c2_out) )\n",
        "        \n",
        "        # flatten data    \n",
        "        max_pool_2 = max_pool_2.view(-1,self.hidden_dim)\n",
        "        \n",
        "        return self.LRelu( self.drop_layer(self.out_layer( \n",
        "            self.LRelu(self.drop_layer(self.lin_layer(max_pool_2))) \n",
        "            )))\n",
        "        \n",
        "\n",
        "class ConvNet2(nn.Module):\n",
        "    def __init__(self, seq_len_in, out_dim, kernel_size=3):\n",
        "        super(ConvNet2, self).__init__()\n",
        "        \n",
        "        n_filters=64\n",
        "        max_pool_kernel=2\n",
        "        \n",
        "        self.conv1 = nn.Conv1d(CHANNELS_IN, n_filters, kernel_size, padding=1)\n",
        "        self.maxPool = nn.MaxPool1d(max_pool_kernel, stride= 1 )\n",
        "        self.drop_layer = nn.Dropout(0.6)\n",
        "        self.LRelu = nn.LeakyReLU()\n",
        "        self.sigmoid = nn.Sigmoid()\n",
        "        \n",
        "      \n",
        "        self.hidden_dim = n_filters *((seq_len_in +2*1 - kernel_size +1) - max_pool_kernel +1)\n",
        "        \n",
        "        #self.hidden_dim = n_filters *((((seq_len_in +2*1 - kernel_size +1) - max_pool_kernel +1) -kernel_size//4 + 1) - max_pool_kernel +1)\n",
        "        #self.hidden_dim = 64;\n",
        "\n",
        "        self.lin_layer = nn.Linear(self.hidden_dim, self.hidden_dim)\n",
        "        self.out_layer = nn.Linear(self.hidden_dim, out_dim)\n",
        "        \n",
        "        \n",
        "\n",
        "    def forward(self,x):\n",
        "        \n",
        "        c1_out =  self.conv1(x)  \n",
        "        max_pool_1 = self.maxPool( self.LRelu(c1_out) )       \n",
        "        \n",
        "        # flatten data    \n",
        "        max_pool_1 = max_pool_1.view(-1,self.hidden_dim)\n",
        "      \n",
        "        \n",
        "        return self.sigmoid( self.drop_layer(self.out_layer( \n",
        "            self.LRelu(self.drop_layer(self.lin_layer(max_pool_1))) \n",
        "            )))\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_9mdqzrIEGDb",
        "colab_type": "text"
      },
      "source": [
        "here we define the optimizer, learning rate and loss funciton"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5P512jWqLG46",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "seq_len_in, out_dim = 3,1\n",
        "\n",
        "#net = ConvNet(seq_len_in,out_dim).to(device)\n",
        "net = ConvNet2(seq_len_in,out_dim).to(device)\n",
        "\n",
        "\n",
        "criterion = nn.MSELoss()\n",
        "#criterion =  nn.BCEWithLogitsLoss\n",
        "#opt = optim.Adam(net.parameters(), lr=0.001)\n",
        "opt = optim.SGD(net.parameters(), lr=0.01, momentum=0.9)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eblbHMXVEKeT",
        "colab_type": "text"
      },
      "source": [
        "Train the network"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5W2R-SQupwcl",
        "colab_type": "code",
        "outputId": "9c896bec-501a-413c-bd47-b8d79be42d62",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 118,
          "referenced_widgets": [
            "b6f018fab43e47d0a9cbc8620ce4685f",
            "c6a5ec1fdac140408614e3cade3e1e5d",
            "f942fd82753b4ff2b9b5cf0a6b364a1d",
            "6374ad207a1e4e6ab361722c9fe5c4d5",
            "0bd5e61a099f416fbb59ad331f11512a",
            "4c8956fef62548f39f453ab4c0d07c5e",
            "7e0f813c2beb43faa43b05a9250b2ef3",
            "8669d9ef037246f183b92f2b8e9aa47a"
          ]
        }
      },
      "source": [
        "# TRAINING LOOP\n",
        "net.to(device)\n",
        "EPOCH_NR = 150\n",
        "losses = []\n",
        "epoch_losses = []\n",
        "for epoch in tqdm(range(EPOCH_NR)):\n",
        "  net.train()\n",
        "  running_loss = 0.0\n",
        "  i = 0\n",
        "  for Xb, yb in train_data_loader: #extracts batches from the dataset\n",
        "    Xb, yb = Xb.to(device), yb.to(device) \n",
        "    \n",
        "    # TRAIN STEP\n",
        "    net.train()\n",
        "\n",
        "    y_pred = net(Xb)\n",
        "\n",
        "    #loss_batch = criterion(y_pred, torch.max(yb, 1)[1])\n",
        "   \n",
        "    loss_batch = criterion(y_pred, yb)\n",
        "\n",
        "    loss_batch.backward()\n",
        "\n",
        "    opt.step()\n",
        "\n",
        "    opt.zero_grad()\n",
        "    \n",
        "    \n",
        "\n",
        "    i = i+1\n",
        "    # SAVE STATS\n",
        "    losses.append(loss_batch.cpu().data.numpy())\n",
        "    \n",
        "    running_loss += loss_batch.item()\n",
        "    if i % 1000 == 999:    # print every 1000 mini-batches\n",
        "        print('[%d, %5d] loss: %.3f' %\n",
        "              (epoch + 1, i + 1, running_loss / 2000))\n",
        "        running_loss = 0.0\n",
        "\n",
        "  epoch_losses.append(running_loss)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:6: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0\n",
            "Please use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook`\n",
            "  \n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "b6f018fab43e47d0a9cbc8620ce4685f",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=150.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "T3z0t0YxENmW",
        "colab_type": "text"
      },
      "source": [
        "Plot result: convergence after 20 Epoch and bad performances   :`(   "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5NEhxDF9Efwc",
        "colab_type": "code",
        "outputId": "390fdcba-8e50-450c-8fc5-4c78ccab91ee",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        }
      },
      "source": [
        "plt.plot(epoch_losses)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f5cfaf95dd8>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 176
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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wsEaiNHZoBa6UCi4hkcD1cnqlVDAKkQQeqTMSKqWCTkgk8BSdkVApFYRCIoGnuSI42tHLgHvEKcuVUioghUQCT42LxG305sZKqeASEgk8JVbHgiulgk9IJPD0eCuB17ZqAldKBY+QSOB5KTEAHKxv93MkSik1cUIigae5IomLDKesocPfoSil1ITxKYGLyCER2Ski20Sk2F72bRGptJdtE5ErJjfUkyciFKTFUlavCVwpFTx8vScmwAXGmIZhy35mjLlnIgOaLAVpLjaXNfo7DKWUmjAh0UIBKEiNpaqlm87efn+HopRSE8LXBG6AF0Rki4jcOWj5v4jIDhF5UESSJiG+CVOQ5gLgoPbBlVJBwtcEfrYxZhlwOfB5ETkXuA+YCSwBqoGfjPRGEblTRIpFpLi+vn4iYj4pBWmxANoHV0oFDZ8SuDGm0v67DngSWGmMqTXGDBhj3MBvgZWjvHedMabIGFOUlpY2UXGPW37K+BO4MYbLfv46D755cLLCUkqpkzZmAheRWBGJ8/wMXALsEpGsQS+7Htg1OSFOjOiIMHISoylr8H0seH1bD/tq2thR0TyJkSml1MnxZRRKBvCkiHhe/ydjzHMi8oiILMHqjx8CPj1pUU6Q8Q4l3FvTBkBdm17BqZSaesZM4MaYMmDxCMs/PikRTaKC1FjWb6nAGIO9Qzqh/TWtgCZwpdTUFDLDCMEaidLROzBiQu7td2PM0Olm93kq8Nbu0xKfUkqNR4glcOtE5vu1bUOWVzV3sfx7L/LU9qohy/dVW69r7e6nu2/g9ASplFI+CqkEvmR6IrERYTy5tXLI8nWvl9HW089bpceu1OwfcHOgvp2U2AgA6nQmQ6WmjNbuPtb88GU2lQ6/ODy0hFQCj4tycsPyafxjRxV1bVZbpK6tm0ffOQzA7qpW72sPNXbQ2+/mnMJU7+uUCibGGB7edCgg58kvqW2jsrmL4kNNPr+nqaOXrt7gOpIOqQQO8Imz8ukbMPxps5W0H3jjIH0Dbi6el8H+2jb6BtwA7LXbJ+cUWmPXx3sic3g/Xamp5vDRTr711G6e2Frh71DG7fDRTgAqmjp9fs9Hfvs2X318x2SF5Bchl8Bnprk4f3Yaf3j7MD9+fh+/f6ucqxdnc+XCLHr73ZTac4bvr2kjzCGsnpkCjP9E5k9eeJ8L7nmVvdWtY79YKT+oabG+055kGEgON3YBcORol0+v7xtwU1LXznO7a2jr7pvM0E6rkEvgAJ9cM4OG9h7ue7WUlTOS+eplc5ifHQ/A7kor4e6raaMgNZbM+CjCHTLuCvztskYONnRww32beGV/3YT/H07FQxsP8tyuan+Hofys1v5OlzcGYAL3VODNvsVe2dTFgNvQ2+/mhd21kxnaaRWSCfycwlT+cPuZvH33Wh7+5EqyE6MpSHMR5XSwp9qTwFuZnRmHwyGkuiLHncDLj3Zy/uw0piXF8JX1U+ewzRjDT198n4c3lfs7FOVnnqPKIxNQgW880MDL+05fYvTEXNXcTb/d9jyRcvv1YQ45brRZIAvJBC4inF2YSnp8lHdZmEOYkxnP7qoWth1ppqKpi2W51gSLGfHjS+Cdvf3Ut/VQlJfE9ctyqG/roXWKHLZVNHXR2t3PoUbfr0jt7hsIyBNd6sQ8LZSKpi6fkuCJfPup3fzwn/smIiyfHD7aiTNMGHAbanxob5bb3/drl2Tz5oEGjnb0sqOimcMBePQxWEgm8NHMz45nT1Urv9xQQmKMkxtXTAcgLS5qXD1wz+FdbkosecnW/TinyhfFc4RR3dLt8xn5e57fz2U/f4Pe/lPbyE9GT/8AA249ITwZPC2UfrehuuXkR1nVtnZTUtd+Sv+GL/ZWt2KMobtvgJrWbpZOtwosX/rg5Y2dRDkd3H72DAbchg/cu5Fr/m9jwJ/U1AQ+yLzseFq7+3l5Xx23r5mBK9KaaSB9nBW4p6eYlxxDrn1D5bH6jG63obLZtxMyp2LwUElfT169VdZIQ3sPb5/mOxoZY7j0Z6/zkxf2n9bPnSyv7KubUie1a1u7cYZZU0qcyolMz1jstu5+2nsm54Yp2440c/kv3uD53bVUNFnbiWeAgS8jUcobO8lLjmVeVjzzs+Np6uzjjAwX+2paA3rEmCbwQeZnJwAQFxXOLWvyvcvT4yI52tF7wgr0r8VHeOStQ8CxajsvJYY8exrb8qMnblk8tb2K83/8CrWTfNn+nqpW70bry80tunoHvFMKPL+7ZlJjA3jt/XqOdvQC0NDey6HGTv5afOSUD/H9zRjDv/75PX764vun9TN3VbaMmqBqW7tZPC0ROLUTmW+WHNux10xSFe7ZSbz2fr23/72qIAURONI0tPB5blcNVcOKofLGDnJTYhAR/vLp1bx991puWpFLU2cfjfb3bbye3lHFpT973a9XaWsCH2ROZhwJ0U4+c95M4qOc3uXpcVav/ER94HWvl/Hzl0owxlB+tIP4qHASYyJwRYaTEhsxZguluPwofQPG2+KYLHuqWrxj28t96IPvqmphwG1Ijo3ghT21uE+xnbGnqnXUf6Olq49bf/cOv32jDICSOmvH0dDey5sHhl5x92ZJw4RM81tW3873n94z6W2aw0c7ae3u50Dd6NMZt3X30XSSyWQkm0obuep/3+SHzx3fmzbGUNvazaJpiUSEOU66AjfGsKm0gfS4SGDyErjngp2NBxo4YlfcM9OtUWKDK/CG9h4+84ct3P/GsTn83W7D4aOd5NtHw67IcKIjwijMsO7SVVLr+xTTHh09/XznH3vYX9vG/pq2E762p3+AJ9+roLlz4n63HprAB4lyhrHprgv53Pkzhyz3fDlHa6N09w1QVt9OY0cvBxs6rMM1u/IGyE2JGbPC8Vw4VFJ74i9DbWs3D755kI6TOFRt6uilqqWbM2ckkxIb4dOJzO1HrCT5+QtmUd/Ww3tHjl359nZZI7c8+A49/b5VIH97r5IrfvkGL+wZebRCWX07xsCuyhbg2IYV5XTwt/eOTX9QUtvGbQ+9w2ce2eK98OpkPb2jmvvfPDjmRniqdtr/p/LGjlErtq+s38EHf73J553JptIG/uOx7aMmhj12u+w3r5Wx7vXSIc+1dvXT3ecmOzGKaUnRHB7jCNGjsb1nSJvkYEMH1S3dXL8sB4DqlolvA7rdhuJDR4mNCOPw0U7eLGkgyukgzRXJ9KQYKgb1wD1tPs/1HAC1bd309LvJHbRNAhSmxwFwoG78v/vfvFZKvZ0P9tWcuOjaXHaUL/1lO1sP+37VqK80gQ8TGxl+3FSz6fF2Ah+lvbG/pg3PNvfuoaMcPtrp7X2D1QsfXuH0Dbi9LRm327DPrrxHqwaMMfz0hf2c9+NX+O7Te3jSTmit3X2s+sEGXtk39lhzT/91fnYCeSkxPrVQ3jvSTE5iNB8qmoYzTHhu17E2ylPbq3jt/Xo2lx0d89852tHLd5/eA8A7B0d+vWeu9j1VVl+ypK6NuKhwrl86jed319LR08+A2/CVx3cgCFUt3Ty788Tj2e9/o4yb1r01alL0VG+7qlrG/D+cCk8Cd5uRW1fGGN491ERZfQcvjrKDG2zr4SZuf6iY9Vsq+NgDm2npPH6UU0ldG6muCK5clMUPnt3Hzopj/8dae2qI9PgoclOO/36OpLtvgKv+902++Oh73mUb7SOjDy6bBpx6Bd7Y3sNvXisdsmN+v66N1u5+bjkrH4AN++rITbbaIdOSoodU4JtKj0/gnuIpf9A2CdbosrjIcEpOcFQ0kqrmLta9UcaVi7KIiQjzFl8e7x46ytLvvuD9Pb+yv47IcAerC1LH9Tm+0ATugwx7uOFoFbin7RER5uCt0kYqm7q8o08AcpNjqGrpoqd/gKd3VLH8ey9S+PV/svq/N9DZ28+Rpk467BEh74/yZdpS3sQvXz7AeWekkRjj9FbGW8qbqGnt9mlSH88JzLlZceSnxvrU99x2uJkl0xOJj3KyZlYqz+2u8fZUPS2Ml/aOnXC+/8weWrv6mJ4cPWol4rlbUmNHL7WtPbxf284ZGXF8YFkOXX0D/PCf+/j2U7t573AzP7phITPTYln3etmQHu/3nt7Dp35f7H386DuHebvsKM+Mkug9Ixh2V05uAt9V2eI9KT5Swqht7fG26O63W0ij2Vvdyicfepf0+Eh+fMMi3q9p5xMPbj7uaORAXTuz0l1866p5AGwpP7bj9JxryYyPIjfZOkL0rMfefjfn//gVrr93I6+9X+9d/vu3DlHd0s3L++q8raBX9teTkxhNYbqLlNgIqsd5DqeurZvfvFZKd98Axhj+/bHt/Pc/9w3Zib1rt08+vGI6mfFRDLgN05Os7WtacgzVrd3eYsgzIV1lc5f3SMd7Tip5aAUuIszKcI27hfLHzeX0DRjuumwOszPjjqvAH9p0iKbOPtZvOQJYJ69Xz0whOiJsXJ/jC03gPkiJjUDkBAm8qpW4yHDOm53Gi3tq6Xcb8gbt7XNTYjHGGm/7+7fKiQx3cNOK6TR29LLpQKP3UHdpbiIHattGPOnk6ft95bI5LJ2eyDY7gW8tt77cvlQRe6pbyYyPIsUVyYyU2DGHEta39VDZ3MWS6daJrrVz0jlytIvyxk66+wa80+2+tKf2hGfyd1e18MTWSj5z3kyuWJDF7qqWEdsIZfUdOOTYew7UtVOY7mJ5bhKF6S4eebucR94u57L5mVy3JIdPnVPA7qpW70bb3TfAX949wot7ajlytJPDjZ2U1ncgAv+7oWTE3rvnSr5d9u9gwG2GHCEYY9hf08ar++t4fnfNkJOplc1dPp0TsE4mtnLJ/Awcwoh9cE+FftWiLIrLm0bdyW3YW8sN920iMtzBI588kw8VTed7181ne0WL9zvh+cySunYK0+NIi4skKcbpPRkNxyrljPhIcpNjaOvup6XLquJf3V/HocZOyuo7uOXBd/jMH7ZQ29rNva+WsjwviYgwBw9vOsSm0gZe3lfHDcunISJkJkRRbZ88bO3uG7Ea7+kfGFIxP/DmQf77n/u44+FiHnjzIK/uryfMId4jTIDiQ0dJj7PiXDPLqmKn2wXStKRojLFaN1XNXRxs6GBpbiJm0JHOocYOwh1CdmIUwxWmu7znWnz1ZkkDS6cnMj05hjmZ8eyrObbNNnX08qJ9pefft1VRWt/OocZOLpyTPq7P8JUmcB+EhznIT4ll3eul3P3ETu5/o4zv/GO39xL5PdWtzM2KZ2V+sreSzh20t/ck822Hmyk+dJQbiqbz3WsX4IoMZ4M9tMwhcOXCLDp6B0YcT1vVbC3LTohmyfQkDtS309bd593Qx6oitpQ38UZJA/PsKQPyUsceHeNJCEtyrQR+lr3xbCxtYG91K/1uw9o56VS1dA8Znjjc6+9bRwe3nJXP0twk+gYMu0doWZTVd7ByRjIi8EaJdbFFYYZ1Neyz/3oOW/7fRbx991ru+9gyRITrluaQ6orkvtdK7c+p9/Znn95R7b0y8N8vPsM7D8Zg/QNuqpu7EbF2wgNuw6PvHObG37zljW9LeROX/vx1bv3du3z6kS3exHKgro1z/+cVnh6jhQPWjrulq4/leUnkpcSO2HPdWdmCQ+Db18wnPiqcda8NrcKbOnr5/tN7uOP3xcxIi+Xvnz/b26a7dH4mIvD2oOmQ69p6aOvupzDDhYjYlWLbkOfBOrr0jpSyK9UntlaS6ork7bvXctflc3hxTy0X3vMqzZ19fPvq+Vy9OJvHt1bw/57cRW5yDJ+1zxllJUR5v7vfeWoPH7h345AdXGdvPx/97WYu/unr3pbPW6WNpLoi2VTawPef2cvZs1K57ax8Xt1f5z2hW3yoiRX5yfYFeNbQwVw7gXsq8SNHu7w78o+vygOOtVHKj3YyLSma8LDj011hehwN7b3ekU8Af3n3MPc8P/LQ1ZbOPnZWtnh3JHOz4mju7KPWnm76yfcq6R1w8+lzC6ho6vIOgT3/DD8mcBE5JCI7RWSbiBTby5JF5EURKbH/TpqUCKeI+28p4trFOTyxtYLvP7OXhzYd4u7Hd9Lb72ZfdStzs+JYMSPZ+/q8YT1wgAc3HsRt4JJ5GUSEOzinMJWX99Wyp7qVgjQXC3OsYYzDbzgBVrWXHBtBdEQYi6cnYIyVYC0FrI0AABfmSURBVLcdbibcIVQ2d414YrOrd4CvPbmTD963CWeY8PkLZgEww95oDzWM3kbZUt5EmENYYA+v9MwNs+lAIzvsfuqXLj4DkRO3UTYfbGRWuou0uEiW5Vk7g63lQ0eQDLgNBxs7WJiTQH5KLP+wL3cuTLdGCjjDHKS4IslMiPKeo4hyhnHHOTN4o6SB9w438czOahJjnCyalsA/tlexYV8dBWmxfPb8WRSkxfLLDSVDjhRqWrvpdxuK8pLo6hvgYEO79zLrrYeb7ditavxPd5xJfkoMf9tmJfD1WyoZcBt2DhoJs6ty5CMLT3W9MCeBWekjH7LvrmxhVrqLVFcknzx7Bs/truHNEmvH9/dtlZz7P6/wwMaD3Lh8Oo99+iwyE45Vk4kxEczJjOftg8cSuKfKn5Vmrb85mfG8X9vmTai1rd0kRDuJcoZ5k2H50U6aO3vZsK+Wa5dkEx0RxmfOm8lDt60kzCFcuySbhdMSuG1NPp29A5Q1dPCda+cT5bRaA5kJUd6rIjcfbKSqpZu9dnuht9/NZ/+wlS3lTXT1DfDK/jpvMrz5zFx+cdNSVuQn8eMPLeIDy6bRN2B4emc1pfXtVDZ3UZRvpZfzz0hnWW7ioEo8GrAS5yv760iOjeDyBVmIQGmdVZwcbuw87gSmxyx7JIpnfW070szXntzF/71yYMRzFW+VNeI2cLY9zfScTKsg2muPJ/9r8REWTUvgC2sLiXI6eHZnDTPTYoecE5tI46nALzDGLDHGFNmP7wI2GGMKgQ3246A1M83Fj25YxJZvXMz2b17Cuo8XUdPazf1vltHRO8C8bOsCgWhnGBHhDjIHXaafFhdJtDOM3VWt5CRGeyfOunBOOrWtPbxe0sDcrHjOyPCcFT9+A69q7iLL3mg9LY2/vHuEjt4BLpqbAXDcDZvL6tu5/t6NPPrOYT51zgxe+vJ5LM+zNoS8VOsLNdpIlPLGDn7/1iHOLUz19u5EhLNmpbCptIHtR5pJdUUyPzue5blJoybw/gE3xYeaONPeuaXHWaMehrcIqpq76O13U5DmYl52vHdsrmedjObjq/JIinFyzwv7eWlPLZfNz+TaJTnsqW5lU2kja+ekE+YQPnf+LPbVtPF6ybFzBZ4LQi5bkAXAhr11vHvIStiecwzbjjQzIzWWs2alct3SHDaVNlLZ3MWT71lTsL5vJ+Pmzl6u+9VGvvG3XcfFuKuyhXCHcEZGHIXpLg42dBzXr95Z2eLdUX7mvJnMSI3la0/u5J87q/nyX7czJyuO5//tXH50w6IRe6mrCpLZUt7kHRHkGc3kSVCzM+Po7B3w/p9rWrq939G8lBgSop385IX9/PaNMvoGDB+wR5UAnHtGGm9/bS33fMi6Ne6CnAQunpfBB5dN44LZxyrLrIRomjv7OHK00/s5r71fD8AvN5Tw2vv1/OD6haTHRfLCnhreKmvEGFgzK5WrF2fz2GfOIishmrlZcczOiOOhjQf56G/fJiYizNuCSIqN4InPrWF2pvW9yEmM5hOr83h8awVP76hmdYHVa85JjKasoZ2Onn7er23z7siG8xQIJXVtdPUO8OW/biPVFUG4Q7z3Cdhb3cr6Ldbve+OBBmIjwrzboCeOfdVtvHekmX01bdxYNB1XZDgXz8sEmLT2CZxaC+Va4GH754eB6049nKnPFRlOQoyTC+ekMy0pml9uKAFgblY8zjAHRflJFKTG4nAcG8kiIt4q55L5Gd4K8vzZ6YhY1cncrDiSYiNIdUWMWIFXN3eTnWhVG4kxEeSnxPBPe0TIh+1L/gf38lo6+7jh129R29rNQ7et5OtXziM28tg9rOOjnNZQwhGqjP4BN1/6yzbCHMJ/Xb9wyHNrZqbS1NnHc7trWDwtARHhonkZ7Kps9V5gUdncxVfWb6elq4891a209/RzZkGK999YlpvEe4eHVuCew92C1FjvDi4uMpwMewTQaGIjw7njnAI2Hmiko3eAKxdlceVCqwIbcBsusDeeaxZnkxEfOWQ4nSfe885II8rp4L7XSjHGuvXe9iPNGGPYdqTZu7FetyQHY+Cux3dQ29pDcmyEN1HuqrRaSuu3VgwZ7QFWcj4jI44opzX2uN9thpxArmvtpq6thwX2EViUM4wfXL+Qw0c7+ewftzI3K47f3bbyhDuzVQUpdPe5vUdGJXXtJEQ7SXNZ68+baOyKuLatxzu6KsoZxkO3reBoRy+/eqWU2RlxzMuKH/Lvx0SE4xzUgvjtJ4r4yY1D73XuKTA8I4NiIsJ4dX89Pf0D/Omdw1wyL4OPnpnLJfMzeHV/Pa/sqyPaeSwZenjaY6X1HYQ7HDz+2bOGDMsd/trvXruAR25fyaJpCdyw3BoNMzPNRWl9Oy/vq6On380l8zNGfH92QjQxEWG8tKeWOx8ppqy+g5/euISL52XwWPERalq6ufV37/Afj23nuV3VbCxtYOWMZO+6SIh2kpMYza6qFr71992kuiK4dkk2AB+yY7l0fuaInz0RfE3gBnhBRLaIyJ32sgxjjKcBWAOMuIZE5E4RKRaR4vr6+lMMd+oIcwgfX5VHd5+bMLu6AvifGxZx783Ljnu95xDqknnHfplpcZEssq+E82wwhelxI56QrGruIsdO4GBV4QNuQ1pcJGcXphLukCHvu++1Upo6e3nk9jM574y0Ef8PhRku7wYP8I2/7WLeN5/jvB+/ytbDzXz/ugXenYaH59C1s3fAG/s1i7MRwVul/Oa1Uv5aXMG610u9QwxXDWovLctNpKa1m5f31fL87hp7HL21IylIc3mviJ1l92/H8onVeSREO0mKcbK6IIXMhChW5icTFxnOinzrcyPCHdy2ZgYbDzR6x5lXNHUhYvVT52bF09zZx5zMOK5dnMOB+nYO1LVT39bjTTD5qbEszU3kjZIG4qPC+diqPKpaumnr7vO2SRKjnXznH7u9rZqth5t45+BR73mEWWnHjz32tlimJXiXrZ6Zwq1n5VOY7uLBW1d4R7CM5kz73IGnD+4ZgeJZf57v5+AbdWcMOkpcmpvEn+5YRVZCFLefPcOn9T6cp63z7K4anGHCTSty2VrexONbKjna0cvNdm/6knmZdPYO8PjWClbMSCYi/Pg0dPOqXL588Rn87fNrmDtsZzKScwrTeOpfzvbusGemuSit6+Af26tIi4v0fg+GcziEWekuXtlfz46KFr551TzWzErl5jPzaOrs4wP3bqSpo4+ZabH85/odlNV3eLcBjzmZcTy7s5qdlS1855oFxNkXAZ57Rhpv372WolE+eyL4msDPNsYsAy4HPi8i5w5+0ljf1hFPxxtj1hljiowxRWlpIyeSQHVj0XQiwx3MTIv19gGzEqypaYdbMj2RaUnRrMgfeqrgknnWyATPycXCDBcHatuH9Gpbu/to6+kfchbdk1SW5SbiDHOQnxrrbb1Ut3Txu40HuW5JjreqG8l5Z6Szp7qV6hZryNXjWyuYmeZifnY8nzt/Jtcszj7uPZkJUd6bQy+abv3b2YnRnD0rlfVbKmjt7uOJrZWEOYQH3zzEs7uqKUiNHTLzo+cL/cmHivn0I1v4/jN7KGtoJy4qnFRXhLcCPyP9xO0Tj7goJz+/aQk/+uAi74mq//7AQh64dcWQqvGjZ+biigz3Xul5pKmTzPgoIsId3vbFlQuzvOcYHnnbmnJ38aAK8fqlVmvh6sXZLLDjPFDXzq6qFqYlRfPVy+ZQXN7E157cybM7q7n1wXfISoji3y4qBKyrB2HoSeedlS2IcFzV++1r5vPCl871Xgl8IsP74J4RPB6uyHByk2PYX9PGgNtQ19YzpM0H1g5k010XeidxG6+sBGtnv/1IM/OyE7hkfgb9bsMPnt3L9ORozrET36qCFOIiw+l3G9bMTBnx34qPcvLFtYWkxZ34CGw0BWmxdPUN8NLeWq5cmEWYY/Qd0reunsePb1jE23ev5ZNnzwDgrJkp5KXEUNXSzV2Xz+E3H1/uHaro6X97zMmKwxi4dH4GVywcWm0PPlcxGU68W7cZYyrtv+tE5ElgJVArIlnGmGoRyQKm1l0LToOk2Ai+dfV8oiPG3g9+7vyZfOqcguPOhN9xzgzOLUzzbqSFGXG09fSzo6LFmziq7REong0EjiUVz5S3hekub3X18xdLMAa+fPEZJ4zpornp/Oi5fWzYW0d6XCSdvQN85bLZ3kvtR7NmZipl9R0sGrRzuLFoOl949D2+8tgO2nv6+dmHF/Mfj+3gvcPNfGTl0IQwPzueX39sGeEOBxv21fLHzYfJtnd8Itb865+/YOa4eoeDe7FgVfIFw/4b8VFOPrxiOg9tOsQ3rppHRVOXdxTDihnJ/Pndw1y1OJvEaKuCenxLBRFhDuZmHduRXLs4h1f313PbmhmE20mhpK6dXXYP+0NF0ym2q85H3zlCVkIUf7jjTO/vNyYinGlJ0UNG7Ww/0kxBauyQFpfHeCrhVQXJPPrOYYoPHaWxo5dZ6UMLCc+Y5YqmTgbcZsT21MlU3h6DdwhLpyeyPC8JV2Q47T39fP6CWd62YkS4gwvmpPPU9qrjqtmJMtMuotwGrlyUdcLXLs9LZnne0CrZ4RC+c818ig81cduafESE7123gGd2VDN7WCvrwjkZbCpt5HvXLjil9Xcyxsw8IhIrInGen4FLgF3AU8At9stuAf4+WUFOZR89M5frl04b83UiMuKhYmR42JBD50vnZ5AZH8Udvy/2XoDgmZhncDtj8bREvnPNfG5akQtYCby8sYM3Sup5bMsRPrYqzztWdjSz0l3kJsewYW8tz+2qISHayaqCkSuiwT5/wSzuu3kZKa5jCeDieRnER4Xz3O4aFuYkcN2SHG4sstbLmTOG/psiwmULsrhoXgZfv3Ie6XGRVDZ3MTP1WJ/zPy+dc9xGNRE+VDSNAbfhmR3VVNjDywCuXpTFxq9eyIzUWJJiI8hNjqGjd4C52fFEhh87aZgQ4+TBW1cwK93F9OQYIsMdbC1voryxk4XTEghzCPd8aDHvfv0ifv7hJfz106uZljT093DhnHRetkdh1Lf18EZJw3E7oJNx1aJs+gcMN/z6LYDjEviczDgONnTwuT9uJdoZ5h0WOlGiI8JIjLF2fsvyknCGOVgzKwVnmPChoqHbyKfOKeBjq3J9ao+cDM+RTmZ8FMtzT26A3Pmz0/mPS2d7k/KNRdN5+JMrj0vSy/OSePJza4YcZZ4uvrRQMoA3RWQ78A7wjDHmOeCHwMUiUgJcZD9Wpyg9LopHbl9J34Cbjz2wmdbuPu80s4N74A6HcMtZ+STYG8zMdBduA5/9w1Zyk2P490tOXH2DlUjXzk1nY2kjL+6t5aK5GUNaDqPJTIji8oVDq5ooZxjX2e2Fj6/OQ0T48sWz+fiqPNbOHT05uSLD+dbV873/h8k2JzOeOZlxrN9SQU1rtzeBi8iQDdBzhLN02Am2wcIcwsw0l/cqT0/rB6xEf93SnBF3ojcWTae3383ftlWyfksF/W7DTStPrm0x2PK8JDbdfSFfu2IOVy/OPq73OjszDrex+uD33rzMW6VOJE8V7llvX79iHg/euoJU19Bqf+G0BL5/3cITtjZORZorkpzEaD64PGfIgIJgM2YLxRhTBiweYXkjsHYyggp1hRlx/Oqjy7j5/s28tKeW6pYuwh1ywn6gZ2Ke7r4BfnHT0hEPx0dy0dwMfrfxEL39bi5bcGpny+88t4Awh3h752lxkXzvugVjvu/yBZn86qPLvPM7T7brluZ47x4zbZSjlMX2WPLhIySGK8xweadSONH5hsEW5CQwPzueP797hK7eflbmJzPLx37/WNLjorjz3JkjPrcsN4mEaCdfv3Ku92TfRMtJjKahvde7Y8xNiZm0MdAnIiK8+OVzhxw9BSPftnJ12q0uSCEtLpIN++qICHOQER91wmqlIC2WnMRobluTP+Sk21hW2KM1BozhnMJTO6SelhTjrabHQ0TG7FNOpGsWZ/Oj5/ZhDN5EM9zF8zJ4YU/tcSeshvOM7shKiDquyjyRm1ZM5xt/3w3AF9cW+vy+U5GdGM22b148qX3aL19yBs2dfae9FzySmIjgT2/B/z8MUA6HsHZOOs/sqKYwwzWkfTKSKGcYb371gnFvOBHhDj5z/kyMMd6RNMEuOzGalfnJbD541HsSc7i8lFj++unVY/5bnpEenqGPvrpmSQ7ff2YvkeEOrlh4+nZek51Yx7se1KnRBD6FXTgnnT+/e4T3jjSPOKRvuJPdOD2X14eSO88tQOTYxScnq9CuwBf62D7xSIh28rUr5hIdERYyO0418TSBT2FrZqUSEe6gt9993AU16tSsnZvB2rkjX503HvkpMfzX9QtO6mo7z/zWSp0snY1wCouNDGe1PaxPE/jUJCLcfGbeuPrfSk0UTeBTnGcIXvYkX9GllAo82kKZ4q5dkkN5Y6dPF9gopUKLJvApLiHayTfsW2IppdRg2kJRSqkApQlcKaUClCZwpZQKUJrAlVIqQGkCV0qpAKUJXCmlApQmcKWUClCawJVSKkDJ4JvnTvqHidQD5Sf59lSgYQLDmQwa48TQGE/dVI8PNMbxyDPGHHez2tOawE+FiBQbY4r8HceJaIwTQ2M8dVM9PtAYJ4K2UJRSKkBpAldKqQAVSAl8nb8D8IHGODE0xlM31eMDjfGUBUwPXCml1FCBVIErpZQaRBO4UkoFqIBI4CJymYjsF5EDInLXFIhnuoi8IiJ7RGS3iPyrvTxZRF4UkRL776QpEGuYiLwnIk/bj2eIyGZ7Xf5FRCL8HF+iiKwXkX0isldEVk+19SgiX7J/z7tE5FERifL3ehSRB0WkTkR2DVo24noTyy/tWHeIyDI/xvhj+3e9Q0SeFJHEQc/dbce4X0Qu9VeMg577dxExIpJqP/bLejyRKZ/ARSQM+BVwOTAP+IiI+PsWNf3Avxtj5gGrgM/bMd0FbDDGFAIb7Mf+9q/A3kGPfwT8zBgzC2gCbvdLVMf8AnjOGDMHWIwV65RZjyKSA3wRKDLGLADCgJvw/3p8CLhs2LLR1tvlQKH9507gPj/G+CKwwBizCHgfuBvA3n5uAubb77nX3vb9ESMiMh24BDg8aLG/1uPojDFT+g+wGnh+0OO7gbv9HdewGP8OXAzsB7LsZVnAfj/HNQ1rQ74QeBoQrKvKwkdat36ILwE4iH0yfdDyKbMegRzgCJCMdQvCp4FLp8J6BPKBXWOtN+A3wEdGet3pjnHYc9cDf7R/HrJdA88Dq/0VI7Aeq6A4BKT6ez2O9mfKV+Ac24A8KuxlU4KI5ANLgc1AhjGm2n6qBsjwU1gePwe+ArjtxylAszGm337s73U5A6gHfme3ee4XkVim0Ho0xlQC92BVYtVAC7CFqbUePUZbb1N1G/ok8E/75ykTo4hcC1QaY7YPe2rKxOgRCAl8yhIRF/A48G/GmNbBzxlrF+23MZoichVQZ4zZ4q8YfBAOLAPuM8YsBToY1i6ZAusxCbgWa2eTDcQywiH3VOPv9TYWEfk6Vivyj/6OZTARiQG+BnzT37H4IhASeCUwfdDjafYyvxIRJ1by/qMx5gl7ca2IZNnPZwF1/ooPWANcIyKHgD9jtVF+ASSKSLj9Gn+vywqgwhiz2X68HiuhT6X1eBFw0BhTb4zpA57AWrdTaT16jLbeptQ2JCK3AlcBN9s7Gpg6Mc7E2llvt7edacBWEclk6sToFQgJ/F2g0D7rH4F1ouMpfwYkIgI8AOw1xvx00FNPAbfYP9+C1Rv3C2PM3caYacaYfKx19rIx5mbgFeAG+2X+jrEGOCIis+1Fa4E9TKH1iNU6WSUiMfbv3RPjlFmPg4y23p4CPmGPolgFtAxqtZxWInIZVlvvGmNM56CnngJuEpFIEZmBdaLwndMdnzFmpzEm3RiTb287FcAy+7s6Zdajlz8b8OM4yXAF1hnrUuDrUyCes7EOT3cA2+w/V2D1mDcAJcBLQLK/Y7XjPR942v65AGvDOAA8BkT6ObYlQLG9Lv8GJE219Qh8B9gH7AIeASL9vR6BR7F68n1YSeb20dYb1snrX9nbz06sETX+ivEAVh/Zs938etDrv27HuB+43F8xDnv+EMdOYvplPZ7oj15Kr5RSASoQWihKKaVGoAlcKaUClCZwpZQKUJrAlVIqQGkCV0qpAKUJXCmlApQmcKWUClD/H6R6F7WM/IsjAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5v7bAjn7EfU5",
        "colab_type": "text"
      },
      "source": [
        "Train Test Accurcay "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xDrgTZ4Hp4Fl",
        "colab_type": "code",
        "outputId": "8f2bf903-4852-448d-e794-fbbe0dcf65a7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 364
        }
      },
      "source": [
        "with torch.no_grad():\n",
        "\n",
        "  net.eval()\n",
        "  y_pred = net(X_train.to(device))\n",
        "  y_train = y_train.to(device)\n",
        "\n",
        "  print(y_pred.shape)\n",
        "  print(y_pred)\n",
        "  print(y_train)\n",
        "\n",
        "\n",
        "  correct = (y_pred.max(dim=1)[1] == y_train) \n",
        "  print('Train Acc')\n",
        "  print(torch.mean(correct.float()).item())\n",
        "\n",
        "with torch.no_grad():\n",
        "\n",
        "  net.eval()\n",
        "  y_pred = net(X_test.to(device))\n",
        "  y_test = y_test.to(device)\n",
        "\n",
        "  print(y_pred.shape)\n",
        "  correct = (y_pred.max(dim=1)[1] == y_test) \n",
        "  print('Test Acc')\n",
        "  print(torch.mean(correct.float()).item())\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "torch.Size([3231, 1])\n",
            "tensor([[0.0185],\n",
            "        [0.9954],\n",
            "        [0.9727],\n",
            "        ...,\n",
            "        [0.9888],\n",
            "        [0.0379],\n",
            "        [0.0219]], device='cuda:0')\n",
            "tensor([[0.],\n",
            "        [1.],\n",
            "        [1.],\n",
            "        ...,\n",
            "        [1.],\n",
            "        [0.],\n",
            "        [0.]], device='cuda:0')\n",
            "Train Acc\n",
            "0.5032497644424438\n",
            "torch.Size([1385, 1])\n",
            "Test Acc\n",
            "0.471480131149292\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GbrhLBQ-EmCq",
        "colab_type": "text"
      },
      "source": [
        "Eventually export with ONNX (not working do do not export anything)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gGLmol06Myz5",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Save model (next export it)\n",
        "\n",
        "PATH = path+'net.pth'\n",
        "torch.save(net.state_dict(), PATH)"
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}