model.ipynb

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        "<a href=\"https://colab.research.google.com/github/JaredLevi18/Stock-market-predictor./blob/main/model.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "! pip install pandas_ta"
      ],
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        "id": "Ixc8z4KtMi4i",
        "outputId": "332f608f-beb3-4c77-ebf9-1997e4cb6ed5"
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      "outputs": [
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          "name": "stdout",
          "text": [
            "Collecting pandas_ta\n",
            "  Downloading pandas_ta-0.3.14b.tar.gz (115 kB)\n",
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            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (from pandas_ta) (1.5.3)\n",
            "Requirement already satisfied: python-dateutil>=2.8.1 in /usr/local/lib/python3.10/dist-packages (from pandas->pandas_ta) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas->pandas_ta) (2023.3)\n",
            "Requirement already satisfied: numpy>=1.21.0 in /usr/local/lib/python3.10/dist-packages (from pandas->pandas_ta) (1.23.5)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.1->pandas->pandas_ta) (1.16.0)\n",
            "Building wheels for collected packages: pandas_ta\n",
            "  Building wheel for pandas_ta (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for pandas_ta: filename=pandas_ta-0.3.14b0-py3-none-any.whl size=218908 sha256=4561946e6010926f1d5de7b1f77f9d0661c5bf65235390ce4f03a604906bc085\n",
            "  Stored in directory: /root/.cache/pip/wheels/69/00/ac/f7fa862c34b0e2ef320175100c233377b4c558944f12474cf0\n",
            "Successfully built pandas_ta\n",
            "Installing collected packages: pandas_ta\n",
            "Successfully installed pandas_ta-0.3.14b0\n"
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      "execution_count": 2,
      "metadata": {
        "id": "UPNxyINxLmOq"
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      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import pandas_ta as ta\n",
        "import yfinance as yf\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "import numpy as np"
      ]
    },
    {
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      "execution_count": 3,
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          "text": [
            "\r[*********************100%%**********************]  1 of 1 completed\n"
          ]
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          "data": {
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              "Open         float64\n",
              "High         float64\n",
              "Low          float64\n",
              "Close        float64\n",
              "Adj Close    float64\n",
              "Volume         int64\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ],
      "source": [
        "data = yf.download(tickers=\"^GSPC\", start=\"2012-03-11\", end = \"2023-08-09\")  # GSPC: Goldman Sachs\n",
        "data.dtypes"
      ]
    },
    {
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      "source": [
        "data.head()"
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      "execution_count": 4,
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              "                   Open         High          Low        Close    Adj Close  \\\n",
              "Date                                                                          \n",
              "2012-03-12  1370.780029  1373.040039  1366.689941  1371.089966  1371.089966   \n",
              "2012-03-13  1371.920044  1396.130005  1371.920044  1395.949951  1395.949951   \n",
              "2012-03-14  1395.949951  1399.420044  1389.969971  1394.280029  1394.280029   \n",
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              "2012-03-16  1402.550049  1405.880005  1401.469971  1404.170044  1404.170044   \n",
              "\n",
              "                Volume  \n",
              "Date                    \n",
              "2012-03-12  3081870000  \n",
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      "execution_count": 5,
      "metadata": {
        "id": "SoDUuwtbLmOu"
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      "source": [
        "# Adding indicators\n",
        "# Relative strength index: charts the current strength or weakness of the stock based on the closing prices of recent trading period.\n",
        "data['RSI'] = ta.rsi(data.Close, length=15)\n",
        "\n",
        "data['EMAF'] = ta.ema(data.Close, length=20)\n",
        "data['EMAM'] = ta.ema(data.Close, length=100)\n",
        "data['EMAS'] = ta.ema(data.Close, length=150)\n",
        "\n",
        "data['Target'] = data['Adj Close']-data.Open\n",
        "data['Target'] = data['Target'].shift(-1)\n",
        "\n",
        "data['TargetClass'] = [1 if data.Target[i]>0 else 0 for i in range(len(data))]\n",
        "data['TargetNextClose'] = data['Adj Close'].shift(-1)\n",
        "\n",
        "data.dropna(inplace=True)\n",
        "data.reset_index(inplace=True)\n",
        "data.drop(['Volume', 'Close', 'Date'], axis=1, inplace=True)"
      ]
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      "execution_count": 6,
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              "          Open         High          Low    Adj Close        RSI         EMAF  \\\n",
              "0  1432.819946  1443.900024  1432.819946  1432.839966  46.542416  1443.599870   \n",
              "1  1432.839966  1438.430054  1425.530029  1428.589966  44.420706  1442.170355   \n",
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              "\n",
              "          EMAM         EMAS     Target  TargetClass  TargetNextClose  \n",
              "0  1405.823514  1382.254066  -4.250000            0      1428.589966  \n",
              "1  1406.274334  1382.867786  11.380005            1      1440.130005  \n",
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              "      <td>1382.867786</td>\n",
              "      <td>11.380005</td>\n",
              "      <td>1</td>\n",
              "      <td>1440.130005</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1428.750000</td>\n",
              "      <td>1441.310059</td>\n",
              "      <td>1427.239990</td>\n",
              "      <td>1440.130005</td>\n",
              "      <td>50.928689</td>\n",
              "      <td>1441.976036</td>\n",
              "      <td>1406.944744</td>\n",
              "      <td>1383.626226</td>\n",
              "      <td>14.609985</td>\n",
              "      <td>1</td>\n",
              "      <td>1454.920044</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1440.310059</td>\n",
              "      <td>1455.510010</td>\n",
              "      <td>1440.310059</td>\n",
              "      <td>1454.920044</td>\n",
              "      <td>57.725930</td>\n",
              "      <td>1443.208799</td>\n",
              "      <td>1407.894750</td>\n",
              "      <td>1384.570515</td>\n",
              "      <td>6.690063</td>\n",
              "      <td>1</td>\n",
              "      <td>1460.910034</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1454.219971</td>\n",
              "      <td>1462.199951</td>\n",
              "      <td>1453.349976</td>\n",
              "      <td>1460.910034</td>\n",
              "      <td>60.122828</td>\n",
              "      <td>1444.894631</td>\n",
              "      <td>1408.944557</td>\n",
              "      <td>1385.581635</td>\n",
              "      <td>-3.599976</td>\n",
              "      <td>0</td>\n",
              "      <td>1457.339966</td>\n",
              "    </tr>\n",
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          "metadata": {},
          "execution_count": 6
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    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 677
        },
        "id": "7irP5ovnLmOv",
        "outputId": "547cb055-6062-4870-bf63-a6e063e44412"
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      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           Open         High          Low    Adj Close        RSI  \\\n",
              "0   1432.819946  1443.900024  1432.819946  1432.839966  46.542416   \n",
              "1   1432.839966  1438.430054  1425.530029  1428.589966  44.420706   \n",
              "2   1428.750000  1441.310059  1427.239990  1440.130005  50.928689   \n",
              "3   1440.310059  1455.510010  1440.310059  1454.920044  57.725930   \n",
              "4   1454.219971  1462.199951  1453.349976  1460.910034  60.122828   \n",
              "5   1460.939941  1464.020020  1452.630005  1457.339966  58.022041   \n",
              "6   1457.339966  1457.339966  1429.849976  1433.189941  46.297315   \n",
              "7   1433.209961  1435.459961  1422.060059  1433.819946  46.598928   \n",
              "8   1433.739990  1433.739990  1407.560059  1413.109985  38.903386   \n",
              "9   1413.199951  1420.040039  1407.099976  1408.750000  37.506259   \n",
              "10  1409.739990  1421.119995  1405.140015  1412.969971  39.750102   \n",
              "11  1412.969971  1417.089966  1403.280029  1411.939941  39.380326   \n",
              "12  1410.989990  1418.760010  1405.949951  1412.160034  39.509154   \n",
              "13  1412.199951  1428.349976  1412.199951  1427.589966  47.836128   \n",
              "14  1427.589966  1434.270020  1412.910034  1414.199951  42.408271   \n",
              "15  1414.020020  1419.900024  1408.130005  1417.260010  43.965107   \n",
              "16  1417.260010  1433.380005  1417.260010  1428.390015  49.305491   \n",
              "17  1428.270020  1428.270020  1388.140015  1394.530029  37.619171   \n",
              "18  1394.530029  1401.229980  1377.510010  1377.510010  33.360696   \n",
              "19  1377.550049  1391.390015  1373.030029  1379.849976  34.453660   \n",
              "\n",
              "           EMAF         EMAM         EMAS     Target  TargetClass  \\\n",
              "0   1443.599870  1405.823514  1382.254066  -4.250000            0   \n",
              "1   1442.170355  1406.274334  1382.867786  11.380005            1   \n",
              "2   1441.976036  1406.944744  1383.626226  14.609985            1   \n",
              "3   1443.208799  1407.894750  1384.570515   6.690063            1   \n",
              "4   1444.894631  1408.944557  1385.581635  -3.599976            0   \n",
              "5   1446.079901  1409.902882  1386.532076 -24.150024            0   \n",
              "6   1444.852286  1410.364012  1387.150061   0.609985            1   \n",
              "7   1443.801587  1410.828486  1387.768205 -20.630005            0   \n",
              "8   1440.878577  1410.873664  1388.103858  -4.449951            0   \n",
              "9   1437.818713  1410.831612  1388.377317   3.229980            1   \n",
              "10  1435.452166  1410.873955  1388.703047  -1.030029            0   \n",
              "11  1433.212906  1410.895064  1389.010821   1.170044            1   \n",
              "12  1431.207871  1410.920113  1389.317433  15.390015            1   \n",
              "13  1430.863308  1411.250209  1389.824354 -13.390015            0   \n",
              "14  1429.276322  1411.308620  1390.147209   3.239990            1   \n",
              "15  1428.131911  1411.426469  1390.506319  11.130005            1   \n",
              "16  1428.156493  1411.762381  1391.008090 -33.739990            0   \n",
              "17  1424.953972  1411.421146  1391.054738 -17.020020            0   \n",
              "18  1420.435500  1410.749638  1390.875338   2.299927            1   \n",
              "19  1416.570212  1410.137764  1390.729307   0.170044            1   \n",
              "\n",
              "    TargetNextClose  \n",
              "0       1428.589966  \n",
              "1       1440.130005  \n",
              "2       1454.920044  \n",
              "3       1460.910034  \n",
              "4       1457.339966  \n",
              "5       1433.189941  \n",
              "6       1433.819946  \n",
              "7       1413.109985  \n",
              "8       1408.750000  \n",
              "9       1412.969971  \n",
              "10      1411.939941  \n",
              "11      1412.160034  \n",
              "12      1427.589966  \n",
              "13      1414.199951  \n",
              "14      1417.260010  \n",
              "15      1428.390015  \n",
              "16      1394.530029  \n",
              "17      1377.510010  \n",
              "18      1379.849976  \n",
              "19      1380.030029  "
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Open</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Adj Close</th>\n",
              "      <th>RSI</th>\n",
              "      <th>EMAF</th>\n",
              "      <th>EMAM</th>\n",
              "      <th>EMAS</th>\n",
              "      <th>Target</th>\n",
              "      <th>TargetClass</th>\n",
              "      <th>TargetNextClose</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1432.819946</td>\n",
              "      <td>1443.900024</td>\n",
              "      <td>1432.819946</td>\n",
              "      <td>1432.839966</td>\n",
              "      <td>46.542416</td>\n",
              "      <td>1443.599870</td>\n",
              "      <td>1405.823514</td>\n",
              "      <td>1382.254066</td>\n",
              "      <td>-4.250000</td>\n",
              "      <td>0</td>\n",
              "      <td>1428.589966</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1432.839966</td>\n",
              "      <td>1438.430054</td>\n",
              "      <td>1425.530029</td>\n",
              "      <td>1428.589966</td>\n",
              "      <td>44.420706</td>\n",
              "      <td>1442.170355</td>\n",
              "      <td>1406.274334</td>\n",
              "      <td>1382.867786</td>\n",
              "      <td>11.380005</td>\n",
              "      <td>1</td>\n",
              "      <td>1440.130005</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1428.750000</td>\n",
              "      <td>1441.310059</td>\n",
              "      <td>1427.239990</td>\n",
              "      <td>1440.130005</td>\n",
              "      <td>50.928689</td>\n",
              "      <td>1441.976036</td>\n",
              "      <td>1406.944744</td>\n",
              "      <td>1383.626226</td>\n",
              "      <td>14.609985</td>\n",
              "      <td>1</td>\n",
              "      <td>1454.920044</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1440.310059</td>\n",
              "      <td>1455.510010</td>\n",
              "      <td>1440.310059</td>\n",
              "      <td>1454.920044</td>\n",
              "      <td>57.725930</td>\n",
              "      <td>1443.208799</td>\n",
              "      <td>1407.894750</td>\n",
              "      <td>1384.570515</td>\n",
              "      <td>6.690063</td>\n",
              "      <td>1</td>\n",
              "      <td>1460.910034</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1454.219971</td>\n",
              "      <td>1462.199951</td>\n",
              "      <td>1453.349976</td>\n",
              "      <td>1460.910034</td>\n",
              "      <td>60.122828</td>\n",
              "      <td>1444.894631</td>\n",
              "      <td>1408.944557</td>\n",
              "      <td>1385.581635</td>\n",
              "      <td>-3.599976</td>\n",
              "      <td>0</td>\n",
              "      <td>1457.339966</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>1460.939941</td>\n",
              "      <td>1464.020020</td>\n",
              "      <td>1452.630005</td>\n",
              "      <td>1457.339966</td>\n",
              "      <td>58.022041</td>\n",
              "      <td>1446.079901</td>\n",
              "      <td>1409.902882</td>\n",
              "      <td>1386.532076</td>\n",
              "      <td>-24.150024</td>\n",
              "      <td>0</td>\n",
              "      <td>1433.189941</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>1457.339966</td>\n",
              "      <td>1457.339966</td>\n",
              "      <td>1429.849976</td>\n",
              "      <td>1433.189941</td>\n",
              "      <td>46.297315</td>\n",
              "      <td>1444.852286</td>\n",
              "      <td>1410.364012</td>\n",
              "      <td>1387.150061</td>\n",
              "      <td>0.609985</td>\n",
              "      <td>1</td>\n",
              "      <td>1433.819946</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>1433.209961</td>\n",
              "      <td>1435.459961</td>\n",
              "      <td>1422.060059</td>\n",
              "      <td>1433.819946</td>\n",
              "      <td>46.598928</td>\n",
              "      <td>1443.801587</td>\n",
              "      <td>1410.828486</td>\n",
              "      <td>1387.768205</td>\n",
              "      <td>-20.630005</td>\n",
              "      <td>0</td>\n",
              "      <td>1413.109985</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>1433.739990</td>\n",
              "      <td>1433.739990</td>\n",
              "      <td>1407.560059</td>\n",
              "      <td>1413.109985</td>\n",
              "      <td>38.903386</td>\n",
              "      <td>1440.878577</td>\n",
              "      <td>1410.873664</td>\n",
              "      <td>1388.103858</td>\n",
              "      <td>-4.449951</td>\n",
              "      <td>0</td>\n",
              "      <td>1408.750000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>1413.199951</td>\n",
              "      <td>1420.040039</td>\n",
              "      <td>1407.099976</td>\n",
              "      <td>1408.750000</td>\n",
              "      <td>37.506259</td>\n",
              "      <td>1437.818713</td>\n",
              "      <td>1410.831612</td>\n",
              "      <td>1388.377317</td>\n",
              "      <td>3.229980</td>\n",
              "      <td>1</td>\n",
              "      <td>1412.969971</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>1409.739990</td>\n",
              "      <td>1421.119995</td>\n",
              "      <td>1405.140015</td>\n",
              "      <td>1412.969971</td>\n",
              "      <td>39.750102</td>\n",
              "      <td>1435.452166</td>\n",
              "      <td>1410.873955</td>\n",
              "      <td>1388.703047</td>\n",
              "      <td>-1.030029</td>\n",
              "      <td>0</td>\n",
              "      <td>1411.939941</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>1412.969971</td>\n",
              "      <td>1417.089966</td>\n",
              "      <td>1403.280029</td>\n",
              "      <td>1411.939941</td>\n",
              "      <td>39.380326</td>\n",
              "      <td>1433.212906</td>\n",
              "      <td>1410.895064</td>\n",
              "      <td>1389.010821</td>\n",
              "      <td>1.170044</td>\n",
              "      <td>1</td>\n",
              "      <td>1412.160034</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>1410.989990</td>\n",
              "      <td>1418.760010</td>\n",
              "      <td>1405.949951</td>\n",
              "      <td>1412.160034</td>\n",
              "      <td>39.509154</td>\n",
              "      <td>1431.207871</td>\n",
              "      <td>1410.920113</td>\n",
              "      <td>1389.317433</td>\n",
              "      <td>15.390015</td>\n",
              "      <td>1</td>\n",
              "      <td>1427.589966</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>1412.199951</td>\n",
              "      <td>1428.349976</td>\n",
              "      <td>1412.199951</td>\n",
              "      <td>1427.589966</td>\n",
              "      <td>47.836128</td>\n",
              "      <td>1430.863308</td>\n",
              "      <td>1411.250209</td>\n",
              "      <td>1389.824354</td>\n",
              "      <td>-13.390015</td>\n",
              "      <td>0</td>\n",
              "      <td>1414.199951</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>1427.589966</td>\n",
              "      <td>1434.270020</td>\n",
              "      <td>1412.910034</td>\n",
              "      <td>1414.199951</td>\n",
              "      <td>42.408271</td>\n",
              "      <td>1429.276322</td>\n",
              "      <td>1411.308620</td>\n",
              "      <td>1390.147209</td>\n",
              "      <td>3.239990</td>\n",
              "      <td>1</td>\n",
              "      <td>1417.260010</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>1414.020020</td>\n",
              "      <td>1419.900024</td>\n",
              "      <td>1408.130005</td>\n",
              "      <td>1417.260010</td>\n",
              "      <td>43.965107</td>\n",
              "      <td>1428.131911</td>\n",
              "      <td>1411.426469</td>\n",
              "      <td>1390.506319</td>\n",
              "      <td>11.130005</td>\n",
              "      <td>1</td>\n",
              "      <td>1428.390015</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>1417.260010</td>\n",
              "      <td>1433.380005</td>\n",
              "      <td>1417.260010</td>\n",
              "      <td>1428.390015</td>\n",
              "      <td>49.305491</td>\n",
              "      <td>1428.156493</td>\n",
              "      <td>1411.762381</td>\n",
              "      <td>1391.008090</td>\n",
              "      <td>-33.739990</td>\n",
              "      <td>0</td>\n",
              "      <td>1394.530029</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>1428.270020</td>\n",
              "      <td>1428.270020</td>\n",
              "      <td>1388.140015</td>\n",
              "      <td>1394.530029</td>\n",
              "      <td>37.619171</td>\n",
              "      <td>1424.953972</td>\n",
              "      <td>1411.421146</td>\n",
              "      <td>1391.054738</td>\n",
              "      <td>-17.020020</td>\n",
              "      <td>0</td>\n",
              "      <td>1377.510010</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>1394.530029</td>\n",
              "      <td>1401.229980</td>\n",
              "      <td>1377.510010</td>\n",
              "      <td>1377.510010</td>\n",
              "      <td>33.360696</td>\n",
              "      <td>1420.435500</td>\n",
              "      <td>1410.749638</td>\n",
              "      <td>1390.875338</td>\n",
              "      <td>2.299927</td>\n",
              "      <td>1</td>\n",
              "      <td>1379.849976</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>1377.550049</td>\n",
              "      <td>1391.390015</td>\n",
              "      <td>1373.030029</td>\n",
              "      <td>1379.849976</td>\n",
              "      <td>34.453660</td>\n",
              "      <td>1416.570212</td>\n",
              "      <td>1410.137764</td>\n",
              "      <td>1390.729307</td>\n",
              "      <td>0.170044</td>\n",
              "      <td>1</td>\n",
              "      <td>1380.030029</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "\n",
              "  <div class=\"colab-df-container\">\n",
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              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const charts = await google.colab.kernel.invokeFunction(\n",
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              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ],
      "source": [
        "data_set = data.iloc[:, 0:11] #.values\n",
        "pd.set_option('display.max_columns', None)\n",
        "data_set.head(20)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JBXEb5kKLmOx",
        "outputId": "7257cf08-2338-4eb6-b322-40d7b5958870"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.0230242 , 0.02408329, 0.02603376, ..., 0.48695693, 0.        ,\n",
              "        0.02185739],\n",
              "       [0.02303   , 0.02250146, 0.02391256, ..., 0.53909866, 1.        ,\n",
              "        0.0252089 ],\n",
              "       [0.0218449 , 0.02333431, 0.02441012, ..., 0.54987388, 1.        ,\n",
              "        0.0295043 ],\n",
              "       ...,\n",
              "       [0.9101054 , 0.91349628, 0.91430708, ..., 0.3812718 , 0.        ,\n",
              "        0.90749085],\n",
              "       [0.91581072, 0.91952566, 0.91110917, ..., 0.5907395 , 1.        ,\n",
              "        0.91922697],\n",
              "       [0.90932596, 0.91359738, 0.91593943, ..., 0.50563887, 1.        ,\n",
              "        0.91369146]])"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ],
      "source": [
        "# normalizing the data\n",
        "sc = MinMaxScaler(feature_range=(0,1))\n",
        "data_set_scaled = sc.fit_transform(data_set)\n",
        "data_set_scaled   # this is a numpy array now, no longer a Dataframe"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_set_scaled.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SHFUwhJN0d83",
        "outputId": "821cc886-b77c-4da3-e419-22b10f8064f2"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2721, 11)"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hE9bj08fLmOy",
        "outputId": "bf5dbc64-32eb-4c13-94dc-f6b2de8383a9"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(2691, 30, 8)\n",
            "(2691, 1)\n"
          ]
        }
      ],
      "source": [
        "# multiple features from data provided to the model\n",
        "X = []\n",
        "\n",
        "backcandles = 30\n",
        "for j in range(8):\n",
        "    X.append([])\n",
        "    for i in range(backcandles, data_set_scaled.shape[0]):\n",
        "        X[j].append(data_set_scaled[i-backcandles:i, j])\n",
        "\n",
        "# move axis from 0 to position 2\n",
        "X = np.moveaxis(X, [0], [2])\n",
        "\n",
        "# Erase first elements of y because of backcandles to match X length\n",
        "X, yi = np.array(X), np.array(data_set_scaled[backcandles:,-1])\n",
        "y = np.reshape(yi, (len(yi),1))\n",
        "print(X.shape)\n",
        "print(y.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZHjVmhlaLmOz",
        "outputId": "c7a85403-6e45-4dd9-e673-f7908d410aa8"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2152\n",
            "(539, 30, 8)\n",
            "(2152, 30, 8)\n",
            "(2152, 1)\n",
            "(539, 1)\n"
          ]
        }
      ],
      "source": [
        "# split data into train and test sets\n",
        "splitlimits = int(len(X) * 0.8)\n",
        "print(splitlimits)\n",
        "X_train, X_test = X[:splitlimits], X[splitlimits:]\n",
        "y_train, y_test = y[:splitlimits], y[splitlimits:]\n",
        "print(X_test.shape)\n",
        "print(X_train.shape)\n",
        "print(y_train.shape)\n",
        "print(y_test.shape)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train.shape[-1]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NSh3yP2J8bIr",
        "outputId": "728b1d16-7bf8-41be-9566-85b24f682446"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "8"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ruDhvimMLmO0",
        "outputId": "894ff16f-4d8d-414a-cdb4-edb0b96c1754"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<generator object <genexpr> at 0x7e13386a9230>\n"
          ]
        }
      ],
      "source": [
        "import torch\n",
        "import torch.nn.functional as F\n",
        "import torch.nn as nn\n",
        "import torch.optim as op\n",
        "\n",
        "X_train, X_test = torch.from_numpy(X_train), torch.from_numpy(y_train)\n",
        "y_train, y_test = torch.from_numpy(y_train), torch.from_numpy(y_test)\n",
        "\n",
        "# LSTM() returns tuple of (tensor, (recurrent state))\n",
        "class extract_tensor(nn.Module):\n",
        "    def forward(self,x):\n",
        "        # Output shape (batch, features, hidden)\n",
        "        tensor, _ = x\n",
        "        # Reshape shape (batch, hidden)\n",
        "        return tensor[:, -1, :]\n",
        "\n",
        "model = nn.Sequential(\n",
        "    #nn.Flatten(),\n",
        "    nn.LSTM(input_size = X_train.shape[-1], hidden_size=542, num_layers=1),\n",
        "    extract_tensor(),\n",
        "    nn.Linear(in_features=542, out_features=8, bias=False),\n",
        "    nn.BatchNorm1d(8),\n",
        "    nn.ReLU(),\n",
        "    nn.Linear(8,1, bias=False),\n",
        "    nn.ReLU()\n",
        ")\n",
        "\n",
        "parameters = model.parameters()\n",
        "print(p.nelement() for p in parameters) # print the number of parameters in total.\n",
        "for p in parameters:\n",
        "    p.requires_grad = True"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZJIhMzClLmO1",
        "outputId": "c3ab89e8-520b-42fb-807e-34ffa9163d61"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "      0/     10: -0.000000\n",
            "      1/     10: -0.000000\n",
            "      2/     10: -0.000000\n",
            "      3/     10: -0.000000\n",
            "      4/     10: -0.000000\n",
            "      5/     10: -0.000000\n",
            "      6/     10: -0.000000\n",
            "      7/     10: -0.000000\n",
            "      8/     10: -0.000000\n",
            "      9/     10: -0.000000\n"
          ]
        }
      ],
      "source": [
        "max_steps = 10\n",
        "batch_size = 2152\n",
        "lossi = []\n",
        "model.double()\n",
        "\n",
        "# training loop\n",
        "for i in range(max_steps):\n",
        "\n",
        "    # mini-batch construct\n",
        "    ix = torch.randint(0, X_train.shape[0], (batch_size,))\n",
        "    Xb, Yb = X_train[ix], y_train[ix]\n",
        "\n",
        "    # forward pass\n",
        "    logits = model(X_train)\n",
        "    loss = F.cross_entropy(logits, Yb) # loss function\n",
        "\n",
        "    # backward pass\n",
        "    for p in parameters:\n",
        "        p.grad = None\n",
        "    loss.backward()\n",
        "\n",
        "    # update: simple SGD\n",
        "    lr = 0.3 if i < (max_steps / 3) else 0.05 # step learning rate decay\n",
        "    for p in parameters:\n",
        "        p.data += -lr * p.grad\n",
        "    op.AdamW(params=model.parameters())\n",
        "\n",
        "    # track of stats\n",
        "    if i % 1 == 0:\n",
        "        print(f\"{i:7d}/{max_steps:7d}: {loss.item():4f}\")\n",
        "    lossi.append(loss.log10().item())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "bDNxKciaLmO3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "outputId": "67990b60-40c1-43b0-a7b4-779a40eba5b5"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7e13387d2770>]"
            ]
          },
          "metadata": {},
          "execution_count": 35
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.plot(torch.tensor(lossi).view(-1, 10).mean(1))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "WGudHYy6LmO3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 373
        },
        "outputId": "e3c89a94-36f6-4f14-fe4d-fd0cbd560a76"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "train -0.0\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "RuntimeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-36-bf4cbb3522aa>\u001b[0m in \u001b[0;36m<cell line: 13>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     11\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     12\u001b[0m \u001b[0msplit_loss\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'train'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 13\u001b[0;31m \u001b[0msplit_loss\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'val'\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/python3.10/dist-packages/torch/utils/_contextlib.py\u001b[0m in \u001b[0;36mdecorate_context\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    113\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mdecorate_context\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    114\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mctx_factory\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 115\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    116\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    117\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mdecorate_context\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<ipython-input-36-bf4cbb3522aa>\u001b[0m in \u001b[0;36msplit_loss\u001b[0;34m(split)\u001b[0m\n\u001b[1;32m      6\u001b[0m         \u001b[0;34m'val'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mX_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_test\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      7\u001b[0m     }[split]\n\u001b[0;32m----> 8\u001b[0;31m     \u001b[0mlogits\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      9\u001b[0m     \u001b[0mloss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mF\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcross_entropy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlogits\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msplit\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mloss\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1499\u001b[0m                 \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_pre_hooks\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_hooks\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1500\u001b[0m                 or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1501\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1502\u001b[0m         \u001b[0;31m# Do not call functions when jit is used\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1503\u001b[0m         \u001b[0mfull_backward_hooks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnon_full_backward_hooks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/container.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, input)\u001b[0m\n\u001b[1;32m    215\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\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    216\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mmodule\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 217\u001b[0;31m             \u001b[0minput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodule\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\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    218\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    219\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1499\u001b[0m                 \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_pre_hooks\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_hooks\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1500\u001b[0m                 or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1501\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1502\u001b[0m         \u001b[0;31m# Do not call functions when jit is used\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1503\u001b[0m         \u001b[0mfull_backward_hooks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnon_full_backward_hooks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/rnn.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, input, hx)\u001b[0m\n\u001b[1;32m    808\u001b[0m             \u001b[0mhx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpermute_hidden\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msorted_indices\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    809\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 810\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcheck_forward_args\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_sizes\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    811\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mbatch_sizes\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    812\u001b[0m             result = _VF.lstm(input, hx, self._flat_weights, self.bias, self.num_layers,\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/rnn.py\u001b[0m in \u001b[0;36mcheck_forward_args\u001b[0;34m(self, input, hidden, batch_sizes)\u001b[0m\n\u001b[1;32m    728\u001b[0m                            \u001b[0mbatch_sizes\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mOptional\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mTensor\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    729\u001b[0m                            ):\n\u001b[0;32m--> 730\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcheck_input\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_sizes\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    731\u001b[0m         self.check_hidden_size(hidden[0], self.get_expected_hidden_size(input, batch_sizes),\n\u001b[1;32m    732\u001b[0m                                'Expected hidden[0] size {}, got {}')\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/rnn.py\u001b[0m in \u001b[0;36mcheck_input\u001b[0;34m(self, input, batch_sizes)\u001b[0m\n\u001b[1;32m    216\u001b[0m                     expected_input_dim, input.dim()))\n\u001b[1;32m    217\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minput_size\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 218\u001b[0;31m             raise RuntimeError(\n\u001b[0m\u001b[1;32m    219\u001b[0m                 'input.size(-1) must be equal to input_size. Expected {}, got {}'.format(\n\u001b[1;32m    220\u001b[0m                     self.input_size, input.size(-1)))\n",
            "\u001b[0;31mRuntimeError\u001b[0m: input.size(-1) must be equal to input_size. Expected 8, got 1"
          ]
        }
      ],
      "source": [
        "# evaluate the loss\n",
        "@torch.no_grad() # this decorator disables gradient tracking inside pytorch\n",
        "def split_loss(split):\n",
        "    x,y = {\n",
        "        'train': (X_train, y_train),\n",
        "        'val': (X_test, y_test),\n",
        "    }[split]\n",
        "    logits = model(x)\n",
        "    loss = F.cross_entropy(logits,y)\n",
        "    print(split, loss.item())\n",
        "\n",
        "split_loss('train')\n",
        "split_loss('val')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "OQAC7x0eLmO5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 794
        },
        "outputId": "e0a3edf4-e1ea-4fd3-f27f-424983d030b4"
      },
      "outputs": [
        {
          "output_type": "error",
          "ename": "RuntimeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-38-22182dad9c7a>\u001b[0m in \u001b[0;36m<cell line: 4>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolor\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'black'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'Test'\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[0mlogits\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdetach\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnumpy\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----> 4\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlogits\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolor\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'green'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'Target'\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/python3.10/dist-packages/matplotlib/pyplot.py\u001b[0m in \u001b[0;36mplot\u001b[0;34m(scalex, scaley, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m   2810\u001b[0m \u001b[0;34m@\u001b[0m\u001b[0m_copy_docstring_and_deprecators\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mAxes\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2811\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mscalex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mscaley\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2812\u001b[0;31m     return gca().plot(\n\u001b[0m\u001b[1;32m   2813\u001b[0m         \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mscalex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mscalex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mscaley\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mscaley\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2814\u001b[0m         **({\"data\": data} if data is not None else {}), **kwargs)\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/axes/_axes.py\u001b[0m in \u001b[0;36mplot\u001b[0;34m(self, scalex, scaley, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1688\u001b[0m         \u001b[0mlines\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_lines\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1689\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mline\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mlines\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1690\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_line\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mline\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   1691\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mscalex\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1692\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_request_autoscale_view\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"x\"\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/python3.10/dist-packages/matplotlib/axes/_base.py\u001b[0m in \u001b[0;36madd_line\u001b[0;34m(self, line)\u001b[0m\n\u001b[1;32m   2302\u001b[0m             \u001b[0mline\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_clip_path\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpatch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2303\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2304\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_update_line_limits\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mline\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   2305\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mline\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_label\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2306\u001b[0m             \u001b[0mline\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_label\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf'_child{len(self._children)}'\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/python3.10/dist-packages/matplotlib/axes/_base.py\u001b[0m in \u001b[0;36m_update_line_limits\u001b[0;34m(self, line)\u001b[0m\n\u001b[1;32m   2325\u001b[0m         \u001b[0mFigures\u001b[0m \u001b[0mout\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mdata\u001b[0m \u001b[0mlimit\u001b[0m \u001b[0mof\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mgiven\u001b[0m \u001b[0mline\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mupdating\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdataLim\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2326\u001b[0m         \"\"\"\n\u001b[0;32m-> 2327\u001b[0;31m         \u001b[0mpath\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mline\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_path\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2328\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvertices\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2329\u001b[0m             \u001b[0;32mreturn\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/lines.py\u001b[0m in \u001b[0;36mget_path\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1027\u001b[0m         \u001b[0;34m\"\"\"Return the `~matplotlib.path.Path` associated with this line.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1028\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_invalidy\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_invalidx\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1029\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrecache\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1030\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_path\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1031\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/lines.py\u001b[0m in \u001b[0;36mrecache\u001b[0;34m(self, always)\u001b[0m\n\u001b[1;32m    660\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0malways\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_invalidy\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    661\u001b[0m             \u001b[0myconv\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconvert_yunits\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_yorig\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 662\u001b[0;31m             \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_to_unmasked_float_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0myconv\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mravel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    663\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    664\u001b[0m             \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_y\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/cbook/__init__.py\u001b[0m in \u001b[0;36m_to_unmasked_float_array\u001b[0;34m(x)\u001b[0m\n\u001b[1;32m   1333\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfloat\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfilled\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnan\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1334\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[0;32m-> 1335\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfloat\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   1336\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1337\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/_tensor.py\u001b[0m in \u001b[0;36m__array__\u001b[0;34m(self, dtype)\u001b[0m\n\u001b[1;32m    970\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnumpy\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    971\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[0;32m--> 972\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnumpy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\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    973\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    974\u001b[0m     \u001b[0;31m# Wrap Numpy array again in a suitable tensor when done, to support e.g.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mRuntimeError\u001b[0m: Can't call numpy() on Tensor that requires grad. Use tensor.detach().numpy() instead."
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x800 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.figure(figsize=(16,8))\n",
        "plt.plot(y_test, color = 'black', label = 'Test')\n",
        "plt.plot(logits, color = 'green', label = 'Target')"
      ]
    }
  ],
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