csv.ipynb

csv.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "csv.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true,
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/gist/srfrnk/1ea0fa0ac30cc91cae69043b09822fd8/csv.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hEtPeWfJznuN",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!pip3 install --user tensorflow==2.0.0-rc1 numpy==1.17.1 gast==0.2.2 matplotlib pandas pathlib"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZjATphMazTAV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from __future__ import absolute_import, division, print_function\n",
        "\n",
        "import tensorflow as tf\n",
        "import numpy as np\n",
        "import pathlib\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "from tensorflow import keras\n",
        "from tensorflow.keras import layers\n",
        "import functools\n",
        "from collections import OrderedDict\n",
        "\n",
        "print(tf.version.VERSION)\n",
        "print(tf.keras.__version__)\n",
        "print(np.__version__)\n",
        "\n",
        "pd.set_option('display.max_rows', 1000)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4m4KZGlNzCXb",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def foo(f):  \n",
        "    print(f)\n",
        "    return f\n",
        "\n",
        "print('Load with single epoch:')\n",
        "ds1 = tf.data.experimental.make_csv_dataset(\n",
        "    './test.csv',\n",
        "    batch_size=1,\n",
        "    select_columns=['PassengerId', 'Pclass', 'Sex', 'Age', 'SibSp', 'Parch', 'Fare', 'Embarked'],\n",
        "    num_epochs=1\n",
        ").map(foo).take(1)\n",
        "\n",
        "print('Load with endless epochs:')\n",
        "ds2 = tf.data.experimental.make_csv_dataset(\n",
        "    './test.csv',\n",
        "    batch_size=1,\n",
        "    select_columns=['PassengerId', 'Pclass', 'Sex', 'Age', 'SibSp', 'Parch', 'Fare', 'Embarked'],\n",
        "    num_epochs=None\n",
        ").map(foo).take(1)\n"
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}