TF2_0_Demo.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "TF2.0 Demo.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "WxbuABLT4BcD"
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ag20USa3ImDp"
      },
      "source": [
        "x = np.linspace(0, 10*np.pi, 1000)\n",
        "y = np.sin(x)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GjmssZiLJCge",
        "outputId": "ee72fc70-db88-4455-f178-7679b65a4817",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        }
      },
      "source": [
        "plt.plot(x,y)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f4aa6de3208>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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e+1JxsitllkxShh4ISZNiz6tLQ7jkCYN56Zvz2RWKZeWa9y9iz58ZNM5us38V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\nK1zrAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LpFcO-ycG6su"
      },
      "source": [
        "# This is my title\n",
        "\n",
        "Here is some regular text.\n",
        "\n",
        "\n",
        "\n",
        "```\n",
        "import numpy as np\n",
        "```\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B9ZnLMvRJ-J-",
        "outputId": "71a33c36-6673-4740-f95c-1523e7a3d70a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        }
      },
      "source": [
        "import sklearn\n",
        "print(sklearn.__version__)\n",
        "import numpy\n",
        "print(numpy.__version__)\n",
        "import scipy\n",
        "print(scipy.__version__)\n",
        "import matplotlib\n",
        "print(matplotlib.__version__)\n",
        "import pandas\n",
        "print(pandas.__version__)\n",
        "import torch\n",
        "print(torch.__version__)\n",
        "import seaborn\n",
        "print(seaborn.__version__)\n",
        "import wordcloud\n",
        "print(wordcloud.__version__)\n",
        "import bs4\n",
        "print(bs4.__version__)\n",
        "import requests\n",
        "print(requests.__version__)\n",
        "import theano\n",
        "print(theano.__version__)\n",
        "import networkx\n",
        "print(networkx.__version__)\n",
        "import cv2\n",
        "print(cv2.__version__)\n",
        "import gym\n",
        "print(gym.__version__)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.21.2\n",
            "1.16.4\n",
            "1.3.0\n",
            "3.0.3\n",
            "0.24.2\n",
            "1.1.0\n",
            "0.9.0\n",
            "1.5.0\n",
            "4.6.3\n",
            "2.21.0\n",
            "1.0.4\n",
            "2.3\n",
            "3.4.3\n",
            "0.10.11\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mtuj76Q0b6pC",
        "outputId": "851146c6-d970-4815-b6c7-7711b9ee95e0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "!ls"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "sample_data\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3sAJhjynb9Mf",
        "outputId": "b6759862-d885-4829-9ded-fdeee2cdd3aa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        }
      },
      "source": [
        "!ls sample_data"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "anscombe.json\t\t      mnist_test.csv\n",
            "california_housing_test.csv   mnist_train_small.csv\n",
            "california_housing_train.csv  README.md\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "izGOV3s3cJI-",
        "outputId": "4bb1e822-9081-4aef-9de5-617a8b0693db",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 765
        }
      },
      "source": [
        "import json\n",
        "json.loads(open('sample_data/anscombe.json').read())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[{'Series': 'I', 'X': 10.0, 'Y': 8.04},\n",
              " {'Series': 'I', 'X': 8.0, 'Y': 6.95},\n",
              " {'Series': 'I', 'X': 13.0, 'Y': 7.58},\n",
              " {'Series': 'I', 'X': 9.0, 'Y': 8.81},\n",
              " {'Series': 'I', 'X': 11.0, 'Y': 8.33},\n",
              " {'Series': 'I', 'X': 14.0, 'Y': 9.96},\n",
              " {'Series': 'I', 'X': 6.0, 'Y': 7.24},\n",
              " {'Series': 'I', 'X': 4.0, 'Y': 4.26},\n",
              " {'Series': 'I', 'X': 12.0, 'Y': 10.84},\n",
              " {'Series': 'I', 'X': 7.0, 'Y': 4.81},\n",
              " {'Series': 'I', 'X': 5.0, 'Y': 5.68},\n",
              " {'Series': 'II', 'X': 10.0, 'Y': 9.14},\n",
              " {'Series': 'II', 'X': 8.0, 'Y': 8.14},\n",
              " {'Series': 'II', 'X': 13.0, 'Y': 8.74},\n",
              " {'Series': 'II', 'X': 9.0, 'Y': 8.77},\n",
              " {'Series': 'II', 'X': 11.0, 'Y': 9.26},\n",
              " {'Series': 'II', 'X': 14.0, 'Y': 8.1},\n",
              " {'Series': 'II', 'X': 6.0, 'Y': 6.13},\n",
              " {'Series': 'II', 'X': 4.0, 'Y': 3.1},\n",
              " {'Series': 'II', 'X': 12.0, 'Y': 9.13},\n",
              " {'Series': 'II', 'X': 7.0, 'Y': 7.26},\n",
              " {'Series': 'II', 'X': 5.0, 'Y': 4.74},\n",
              " {'Series': 'III', 'X': 10.0, 'Y': 7.46},\n",
              " {'Series': 'III', 'X': 8.0, 'Y': 6.77},\n",
              " {'Series': 'III', 'X': 13.0, 'Y': 12.74},\n",
              " {'Series': 'III', 'X': 9.0, 'Y': 7.11},\n",
              " {'Series': 'III', 'X': 11.0, 'Y': 7.81},\n",
              " {'Series': 'III', 'X': 14.0, 'Y': 8.84},\n",
              " {'Series': 'III', 'X': 6.0, 'Y': 6.08},\n",
              " {'Series': 'III', 'X': 4.0, 'Y': 5.39},\n",
              " {'Series': 'III', 'X': 12.0, 'Y': 8.15},\n",
              " {'Series': 'III', 'X': 7.0, 'Y': 6.42},\n",
              " {'Series': 'III', 'X': 5.0, 'Y': 5.73},\n",
              " {'Series': 'IV', 'X': 8.0, 'Y': 6.58},\n",
              " {'Series': 'IV', 'X': 8.0, 'Y': 5.76},\n",
              " {'Series': 'IV', 'X': 8.0, 'Y': 7.71},\n",
              " {'Series': 'IV', 'X': 8.0, 'Y': 8.84},\n",
              " {'Series': 'IV', 'X': 8.0, 'Y': 8.47},\n",
              " {'Series': 'IV', 'X': 8.0, 'Y': 7.04},\n",
              " {'Series': 'IV', 'X': 8.0, 'Y': 5.25},\n",
              " {'Series': 'IV', 'X': 19.0, 'Y': 12.5},\n",
              " {'Series': 'IV', 'X': 8.0, 'Y': 5.56},\n",
              " {'Series': 'IV', 'X': 8.0, 'Y': 7.91},\n",
              " {'Series': 'IV', 'X': 8.0, 'Y': 6.89}]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "anDA80FI3aFv"
      },
      "source": [
        "a = 5"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "69QKJdGL3cEM",
        "outputId": "fca0c6f9-4510-4e56-a6c7-1ce5f675009f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 164
        }
      },
      "source": [
        "print(a)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-1-bca0e2660b9f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m: name 'a' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xdD1rY5R0gMk"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}