tytanci1.ipynb

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  "metadata": {
    "colab": {
      "name": "tytanci1.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyOf2w+jdDikZJlIQkxU6I8X",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/sarthakpant772/MLnow_2.0/blob/main/tytanci1.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ovyP4BaRkgKi",
        "outputId": "8d949946-d0ce-4c34-8006-429fdf27ccd0"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QN1skWjDk3Uw",
        "outputId": "2e12eb0c-ca62-4f1c-ba74-688cdcb55975"
      },
      "source": [
        "cd '/content/drive/MyDrive/tytanic/tytanic'"
      ],
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/drive/MyDrive/tytanic/tytanic\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UjtM5JCplWxK",
        "outputId": "f367090a-c6ce-4b4a-d8f1-533d30067529"
      },
      "source": [
        "ls"
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "cleandata.csv  train.csv  unitytanic,csv\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mfOUCqnnlddv"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "import os"
      ],
      "execution_count": 50,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qza-60kelqGv"
      },
      "source": [
        "tytanic_data=pd.read_csv('/content/drive/MyDrive/tytanic/tytanic/train.csv')"
      ],
      "execution_count": 51,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 921
        },
        "id": "rg4o4W1_may1",
        "outputId": "dbb5e71f-9003-48f0-f619-2d539a88a59e"
      },
      "source": [
        "tytanic_data.head(10)"
      ],
      "execution_count": 52,
      "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>PassengerId</th>\n",
              "      <th>Survived</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Name</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Ticket</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Cabin</th>\n",
              "      <th>Embarked</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Braund, Mr. Owen Harris</td>\n",
              "      <td>male</td>\n",
              "      <td>22.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>A/5 21171</td>\n",
              "      <td>7.2500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
              "      <td>female</td>\n",
              "      <td>38.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>PC 17599</td>\n",
              "      <td>71.2833</td>\n",
              "      <td>C85</td>\n",
              "      <td>C</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>Heikkinen, Miss. Laina</td>\n",
              "      <td>female</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>STON/O2. 3101282</td>\n",
              "      <td>7.9250</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
              "      <td>female</td>\n",
              "      <td>35.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>113803</td>\n",
              "      <td>53.1000</td>\n",
              "      <td>C123</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Allen, Mr. William Henry</td>\n",
              "      <td>male</td>\n",
              "      <td>35.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>373450</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Moran, Mr. James</td>\n",
              "      <td>male</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>330877</td>\n",
              "      <td>8.4583</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Q</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>7</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>McCarthy, Mr. Timothy J</td>\n",
              "      <td>male</td>\n",
              "      <td>54.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>17463</td>\n",
              "      <td>51.8625</td>\n",
              "      <td>E46</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>8</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Palsson, Master. Gosta Leonard</td>\n",
              "      <td>male</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>349909</td>\n",
              "      <td>21.0750</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>9</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)</td>\n",
              "      <td>female</td>\n",
              "      <td>27.0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>347742</td>\n",
              "      <td>11.1333</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>10</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>Nasser, Mrs. Nicholas (Adele Achem)</td>\n",
              "      <td>female</td>\n",
              "      <td>14.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>237736</td>\n",
              "      <td>30.0708</td>\n",
              "      <td>NaN</td>\n",
              "      <td>C</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   PassengerId  Survived  Pclass  ...     Fare Cabin  Embarked\n",
              "0            1         0       3  ...   7.2500   NaN         S\n",
              "1            2         1       1  ...  71.2833   C85         C\n",
              "2            3         1       3  ...   7.9250   NaN         S\n",
              "3            4         1       1  ...  53.1000  C123         S\n",
              "4            5         0       3  ...   8.0500   NaN         S\n",
              "5            6         0       3  ...   8.4583   NaN         Q\n",
              "6            7         0       1  ...  51.8625   E46         S\n",
              "7            8         0       3  ...  21.0750   NaN         S\n",
              "8            9         1       3  ...  11.1333   NaN         S\n",
              "9           10         1       2  ...  30.0708   NaN         C\n",
              "\n",
              "[10 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Vpj-eJQSn9U7"
      },
      "source": [
        "we are going to consider `survived` and `age`"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "1RfuOWVjmi_e",
        "outputId": "e9964b3b-57df-4236-f48e-6d39f4f844be"
      },
      "source": [
        "unitytanic_data=tytanic_data[['Survived','Age']]\n",
        "unitytanic_data.head(10)"
      ],
      "execution_count": 53,
      "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>Survived</th>\n",
              "      <th>Age</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>22.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>38.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>26.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>35.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>35.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>0</td>\n",
              "      <td>54.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>0</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>1</td>\n",
              "      <td>27.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>1</td>\n",
              "      <td>14.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Survived   Age\n",
              "0         0  22.0\n",
              "1         1  38.0\n",
              "2         1  26.0\n",
              "3         1  35.0\n",
              "4         0  35.0\n",
              "5         0   NaN\n",
              "6         0  54.0\n",
              "7         0   2.0\n",
              "8         1  27.0\n",
              "9         1  14.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yYZnvdAJorXJ"
      },
      "source": [
        "unitytanic_data.to_csv('unitytanic,csv')"
      ],
      "execution_count": 54,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Oi6fKsplng7U"
      },
      "source": [
        "## Data visulization"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 153
        },
        "id": "wtnorVvZnnoX",
        "outputId": "7e75d327-7c51-4d57-ae19-4bfee2029c68"
      },
      "source": [
        "import seaborn as sns\n",
        "sns.palplot(sns.color_palette('PuOr'))\n",
        "pal=sns.color_palette('PuOr',6)\n",
        "pal.as_hex()\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "plt.style.use(['seaborn'])\n",
        "sns_colors = ['#c6690c', '#664697'] \n",
        "sns.set_palette(sns_colors)  \n",
        "sns.palplot(sns.color_palette(sns_colors)) "
      ],
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x72 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 144x72 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DKhaZgnordKq",
        "outputId": "b24ebd5a-338b-451e-c57c-d3d0668ea03f"
      },
      "source": [
        "tytanic_data.columns"
      ],
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['PassengerId', 'Survived', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp',\n",
              "       'Parch', 'Ticket', 'Fare', 'Cabin', 'Embarked'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "D4gZiTzqpfSo",
        "outputId": "cea7dab7-72dd-4e24-d771-6f20a4f5cdbb"
      },
      "source": [
        "unitytanic_data.columns"
      ],
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Survived', 'Age'], dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 391
        },
        "id": "H4E9NbbiqAXb",
        "outputId": "b65da937-874b-4a0b-a900-feb7da691a0c"
      },
      "source": [
        "unitytanic_data=unitytanic_data.dropna()\n",
        "sns.pairplot(unitytanic_data,\n",
        "            x_vars=['Survived','Age'],\n",
        "            y_vars=['Survived','Age'],\n",
        "             diag_kind='kde'\n",
        "             )"
      ],
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.PairGrid at 0x7fd5b51421d0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 69
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x360 with 6 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "p5dyz9ySqqDq"
      },
      "source": [
        "##Data Splits"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OMevJDTnqaUg",
        "outputId": "9886b550-7dfe-4d30-c61d-81d2443ecf8b"
      },
      "source": [
        "trainDataset=unitytanic_data.sample(frac=0.8,random_state=0)\n",
        "testDataset=unitytanic_data.drop(trainDataset.index)\n",
        "print(trainDataset.head())\n",
        "print(testDataset.head())"
      ],
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "     Survived    Age\n",
            "423         0  28.00\n",
            "177         0  50.00\n",
            "305         1   0.92\n",
            "292         0  36.00\n",
            "889         1  26.00\n",
            "    Survived   Age\n",
            "10         1   4.0\n",
            "12         0  20.0\n",
            "22         1  15.0\n",
            "27         0  19.0\n",
            "37         0  21.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1otXyDAxsXs4",
        "outputId": "8c9205d6-8ca9-4146-99a8-9ef26e35cda0"
      },
      "source": [
        "print(trainDataset.shape)\n",
        "print(testDataset.shape)"
      ],
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(571, 2)\n",
            "(143, 2)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-uY2k0T8uSYN",
        "outputId": "3886a37a-9cda-4469-bc3e-04960f9b3c4b"
      },
      "source": [
        "trainFeatures=trainDataset.copy()\n",
        "testFeatures=testDataset.copy()\n",
        "\n",
        "print(trainFeatures.head())\n",
        "print(testFeatures.head())"
      ],
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "     Survived    Age\n",
            "423         0  28.00\n",
            "177         0  50.00\n",
            "305         1   0.92\n",
            "292         0  36.00\n",
            "889         1  26.00\n",
            "    Survived   Age\n",
            "10         1   4.0\n",
            "12         0  20.0\n",
            "22         1  15.0\n",
            "27         0  19.0\n",
            "37         0  21.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FJYweSVGvkZm",
        "outputId": "839d1f6c-17de-4f42-8dbb-d48f921e45b8"
      },
      "source": [
        "trainLabels = trainFeatures.pop('Age')\n",
        "testLabels = testFeatures.pop('Age')\n",
        "\n",
        "\n",
        "print(trainLabels.head())\n",
        "print(testLabels.head())"
      ],
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "423    28.00\n",
            "177    50.00\n",
            "305     0.92\n",
            "292    36.00\n",
            "889    26.00\n",
            "Name: Age, dtype: float64\n",
            "10     4.0\n",
            "12    20.0\n",
            "22    15.0\n",
            "27    19.0\n",
            "37    21.0\n",
            "Name: Age, dtype: float64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "t1nvUKrQ3_Fv",
        "outputId": "2bc75150-39de-44d8-eb5e-ce67b623876d"
      },
      "source": [
        "print(trainFeatures.head())\n",
        "print(testFeatures.head())\n"
      ],
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "     Survived\n",
            "423         0\n",
            "177         0\n",
            "305         1\n",
            "292         0\n",
            "889         1\n",
            "    Survived\n",
            "10         1\n",
            "12         0\n",
            "22         1\n",
            "27         0\n",
            "37         0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TZBi8Ld_ydpv"
      },
      "source": [
        "model = tf.keras.Sequential([tf.keras.layers.Dense(1)])"
      ],
      "execution_count": 74,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VXhcwFYn0fkv"
      },
      "source": [
        "model.compile(\n",
        "    loss='mean_abslute_error',\n",
        "    optimizer= tf.keras.optimizers.Adam(0.001),\n",
        "    metrics=['mae','mse']\n",
        ")"
      ],
      "execution_count": 75,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 382
        },
        "id": "VAhCZMmC0_Af",
        "outputId": "4a49965b-fd12-4bd0-853b-fd54491ec126"
      },
      "source": [
        "numEpochs=300\n",
        "history= model.fit(x=trainLabels,y=trainFeatures,validation_data=(testLabels,testFeatures),\n",
        "                  epochs=numEpochs)"
      ],
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/300\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "error",
          "ename": "TypeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-79-f9011ffd1342>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mnumEpochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m300\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m history= model.fit(x=trainLabels,y=trainFeatures,validation_data=(testLabels,testFeatures),\n\u001b[0;32m----> 3\u001b[0;31m                   epochs=numEpochs)\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/training.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)\u001b[0m\n\u001b[1;32m   1181\u001b[0m                 _r=1):\n\u001b[1;32m   1182\u001b[0m               \u001b[0mcallbacks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_train_batch_begin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1183\u001b[0;31m               \u001b[0mtmp_logs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miterator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1184\u001b[0m               \u001b[0;32mif\u001b[0m \u001b[0mdata_handler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshould_sync\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1185\u001b[0m                 \u001b[0mcontext\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masync_wait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/def_function.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m    887\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    888\u001b[0m       \u001b[0;32mwith\u001b[0m \u001b[0mOptionalXlaContext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_jit_compile\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--> 889\u001b[0;31m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_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[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    890\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    891\u001b[0m       \u001b[0mnew_tracing_count\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexperimental_get_tracing_count\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/def_function.py\u001b[0m in \u001b[0;36m_call\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m    915\u001b[0m       \u001b[0;31m# In this case we have created variables on the first call, so we run the\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    916\u001b[0m       \u001b[0;31m# defunned version which is guaranteed to never create variables.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 917\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_stateless_fn\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[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# pylint: disable=not-callable\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    918\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_stateful_fn\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\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    919\u001b[0m       \u001b[0;31m# Release the lock early so that multiple threads can perform the call\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mTypeError\u001b[0m: 'NoneType' object is not callable"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GrlNqS134pZi"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}