00.getting_started.ipynb

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    "colab": {
      "name": "0.getting_started.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "601NM1YNGaVT",
        "colab_type": "code",
        "outputId": "3e70fda1-291b-4252-bee3-8c46138405c7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 666
        }
      },
      "source": [
        "!pip install tensorflow-gpu"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting tensorflow-gpu\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/25/44/47f0722aea081697143fbcf5d2aa60d1aee4aaacb5869aee2b568974777b/tensorflow_gpu-2.0.0-cp36-cp36m-manylinux2010_x86_64.whl (380.8MB)\n",
            "\u001b[K     |████████████████████████████████| 380.8MB 91kB/s \n",
            "\u001b[?25hRequirement already satisfied: absl-py>=0.7.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu) (0.8.0)\n",
            "Requirement already satisfied: keras-applications>=1.0.8 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu) (1.0.8)\n",
            "Requirement already satisfied: gast==0.2.2 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu) (0.2.2)\n",
            "Requirement already satisfied: six>=1.10.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu) (1.12.0)\n",
            "Requirement already satisfied: opt-einsum>=2.3.2 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu) (3.1.0)\n",
            "Requirement already satisfied: wheel>=0.26 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu) (0.33.6)\n",
            "Requirement already satisfied: google-pasta>=0.1.6 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu) (0.1.7)\n",
            "Requirement already satisfied: keras-preprocessing>=1.0.5 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu) (1.1.0)\n",
            "Collecting tensorboard<2.1.0,>=2.0.0 (from tensorflow-gpu)\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/9b/a6/e8ffa4e2ddb216449d34cfcb825ebb38206bee5c4553d69e7bc8bc2c5d64/tensorboard-2.0.0-py3-none-any.whl (3.8MB)\n",
            "\u001b[K     |████████████████████████████████| 3.8MB 29.7MB/s \n",
            "\u001b[?25hRequirement already satisfied: termcolor>=1.1.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu) (1.1.0)\n",
            "Requirement already satisfied: grpcio>=1.8.6 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu) (1.15.0)\n",
            "Collecting tensorflow-estimator<2.1.0,>=2.0.0 (from tensorflow-gpu)\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/95/00/5e6cdf86190a70d7382d320b2b04e4ff0f8191a37d90a422a2f8ff0705bb/tensorflow_estimator-2.0.0-py2.py3-none-any.whl (449kB)\n",
            "\u001b[K     |████████████████████████████████| 450kB 44.0MB/s \n",
            "\u001b[?25hRequirement already satisfied: wrapt>=1.11.1 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu) (1.11.2)\n",
            "Requirement already satisfied: protobuf>=3.6.1 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu) (3.7.1)\n",
            "Requirement already satisfied: numpy<2.0,>=1.16.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu) (1.16.5)\n",
            "Requirement already satisfied: astor>=0.6.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu) (0.8.0)\n",
            "Requirement already satisfied: h5py in /usr/local/lib/python3.6/dist-packages (from keras-applications>=1.0.8->tensorflow-gpu) (2.8.0)\n",
            "Requirement already satisfied: setuptools>=41.0.0 in /usr/local/lib/python3.6/dist-packages (from tensorboard<2.1.0,>=2.0.0->tensorflow-gpu) (41.2.0)\n",
            "Requirement already satisfied: werkzeug>=0.11.15 in /usr/local/lib/python3.6/dist-packages (from tensorboard<2.1.0,>=2.0.0->tensorflow-gpu) (0.16.0)\n",
            "Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.6/dist-packages (from tensorboard<2.1.0,>=2.0.0->tensorflow-gpu) (3.1.1)\n",
            "\u001b[31mERROR: tensorflow 1.15.0rc3 has requirement tensorboard<1.16.0,>=1.15.0, but you'll have tensorboard 2.0.0 which is incompatible.\u001b[0m\n",
            "\u001b[31mERROR: tensorflow 1.15.0rc3 has requirement tensorflow-estimator==1.15.1, but you'll have tensorflow-estimator 2.0.0 which is incompatible.\u001b[0m\n",
            "Installing collected packages: tensorboard, tensorflow-estimator, tensorflow-gpu\n",
            "  Found existing installation: tensorboard 1.15.0\n",
            "    Uninstalling tensorboard-1.15.0:\n",
            "      Successfully uninstalled tensorboard-1.15.0\n",
            "  Found existing installation: tensorflow-estimator 1.15.1\n",
            "    Uninstalling tensorflow-estimator-1.15.1:\n",
            "      Successfully uninstalled tensorflow-estimator-1.15.1\n",
            "Successfully installed tensorboard-2.0.0 tensorflow-estimator-2.0.0 tensorflow-gpu-2.0.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5zizE3IRGLdL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "from tensorflow.keras import layers\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "from pylab import rcParams\n",
        "import matplotlib.pyplot as plt\n",
        "from matplotlib import rc\n",
        "\n",
        "%matplotlib inline\n",
        "%config InlineBackend.figure_format='retina'\n",
        "\n",
        "sns.set(style='whitegrid', palette='muted', font_scale=1.5)\n",
        "\n",
        "rcParams['figure.figsize'] = 14, 8\n",
        "\n",
        "RANDOM_SEED = 42\n",
        "\n",
        "np.random.seed(RANDOM_SEED)\n",
        "tf.random.set_seed(RANDOM_SEED)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AxaTEc2qGU92",
        "colab_type": "code",
        "outputId": "925a8df3-86eb-47b2-b330-b151d5505699",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "tf.__version__"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'2.0.0'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NARe9dG-OB7o",
        "colab_type": "text"
      },
      "source": [
        "# Tensors"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cEXlDWIoGWNl",
        "colab_type": "code",
        "outputId": "2491b4eb-e5a9-432f-d3ee-1f619b196c18",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "x = tf.constant(1)\n",
        "print(x)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "tf.Tensor(1, shape=(), dtype=int32)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X3oQO-1DR3CY",
        "colab_type": "code",
        "outputId": "6f6e5043-47cb-460a-9bc1-91aaec25f813",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "x.numpy()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FLda630KR5Jf",
        "colab_type": "code",
        "outputId": "f67630be-d900-496b-b60a-ddefb66583e1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "x.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TensorShape([])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_ab4zU1hX6Xi",
        "colab_type": "code",
        "outputId": "43d4bd7a-f941-40b7-e940-0f5a81a11dda",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "tf.rank(x).numpy()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g-KsjDhsO8TB",
        "colab_type": "code",
        "outputId": "a3e86c55-2106-417d-bc31-0fd970f66163",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        }
      },
      "source": [
        "m = tf.constant([[1, 2, 1], [3, 4, 2]])\n",
        "print(m)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "tf.Tensor(\n",
            "[[1 2 1]\n",
            " [3 4 2]], shape=(2, 3), dtype=int32)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ew_GiInPR88I",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "st = tf.constant([\"Hello\", \"World\"])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OG8kOjylSIQ6",
        "colab_type": "code",
        "outputId": "8565b021-f3a4-44d1-e3eb-65accbce734c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "print(st)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "tf.Tensor([b'Hello' b'World'], shape=(2,), dtype=string)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nYAsRk8JX-rh",
        "colab_type": "code",
        "outputId": "f84835d7-3f5d-4a4d-b02a-e7977ff04930",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "tf.rank(st).numpy()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OaZHD6r1SZIq",
        "colab_type": "text"
      },
      "source": [
        "## Helpers"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d0BPI2x-SIz5",
        "colab_type": "code",
        "outputId": "8ad9356a-d577-4f98-9a8a-78e67acc32d8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        }
      },
      "source": [
        "ones = tf.ones([3, 3])\n",
        "print(ones)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "tf.Tensor(\n",
            "[[1. 1. 1.]\n",
            " [1. 1. 1.]\n",
            " [1. 1. 1.]], shape=(3, 3), dtype=float32)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zZik0WwfTH5h",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "zeros = tf.zeros([2, 3])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K5CclppGTTgW",
        "colab_type": "code",
        "outputId": "1d59cf70-b7da-4ecd-c225-b9afe82fc344",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        }
      },
      "source": [
        "print(zeros)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "tf.Tensor(\n",
            "[[0. 0. 0.]\n",
            " [0. 0. 0.]], shape=(2, 3), dtype=float32)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h1_qjxV2TT6D",
        "colab_type": "code",
        "outputId": "1faa9c64-a44c-4e4e-f7dd-f4e23f24bd0f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        }
      },
      "source": [
        "print(tf.reshape(zeros, [3, 2]))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "tf.Tensor(\n",
            "[[0. 0.]\n",
            " [0. 0.]\n",
            " [0. 0.]], shape=(3, 2), dtype=float32)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_RqSXxUjUPdB",
        "colab_type": "code",
        "outputId": "f954ea8b-8e78-41f8-d6c6-c82942e4924b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        }
      },
      "source": [
        "tf.transpose(zeros)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: id=14, shape=(3, 2), dtype=float32, numpy=\n",
              "array([[0., 0.],\n",
              "       [0., 0.],\n",
              "       [0., 0.]], dtype=float32)>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wlSbmG0yVKYV",
        "colab_type": "text"
      },
      "source": [
        "# Tensor Math"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "w4v0E9GPUegj",
        "colab_type": "code",
        "outputId": "b0d25237-0454-460d-de16-b7549eb2fc01",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "a = tf.constant(1)\n",
        "b = tf.constant(1)\n",
        "\n",
        "tf.add(a, b).numpy()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4SoNQTg6VW1o",
        "colab_type": "code",
        "outputId": "23c38b68-5518-48bc-d27a-73fcfb871cfc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "(a + b).numpy()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kYIXobGKV8lD",
        "colab_type": "code",
        "outputId": "c243e6a7-8ad6-47ba-fa2f-7493ec84087c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "c = a + b\n",
        "tf.square(c)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: id=20, shape=(), dtype=int32, numpy=4>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e-ioFyN_Wxx6",
        "colab_type": "code",
        "outputId": "6e36b923-eadd-492e-e123-7eb2d32301fc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "c * c"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: id=21, shape=(), dtype=int32, numpy=4>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JpYEU0oSWzlf",
        "colab_type": "code",
        "outputId": "28c1ab70-3acc-43c9-a053-2cd916cbc6fc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "d1 = tf.constant([[1, 2], [1, 2]]);\n",
        "d2 = tf.constant([[3, 4], [3, 4]]);\n",
        "\n",
        "tf.tensordot(d1, d2, axes=1).numpy()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 9, 12],\n",
              "       [ 9, 12]], dtype=int32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GGRdSkWBXmQD",
        "colab_type": "text"
      },
      "source": [
        "# Sampling"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Qr3WL7zXW9-C",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "norm = tf.random.normal(shape=(1000, 1), mean=0., stddev=1.)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6hOhcsXBenDb",
        "colab_type": "code",
        "outputId": "66f2a39e-acab-458a-840c-4ef9fe1f172a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 495
        }
      },
      "source": [
        "sns.distplot(norm);"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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tun5qV3+BaQAAoDYUQgAAwLz01z8+mtOD40mSlUta8v63LSs4EbPJtZsndwk9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\nniafJMlhs6iyxKkJJ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            "text/plain": [
              "<Figure size 1008x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "image/png": {
              "width": 834,
              "height": 478
            }
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "22yBFunneuL8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "unif = tf.random.uniform(shape=(1000, 1), minval=0, maxval=100)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EsIO11GBgBtW",
        "colab_type": "code",
        "outputId": "ab408f4e-bbb6-4f11-d4ca-e33dba42f1d1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 495
        }
      },
      "source": [
        "sns.distplot(unif);"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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6naqsrNSNN96oe+65R7Nnz77gbZ04cUL79u3Tvn37tHfvXjU3NyscTg0zUVVV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231K5XkmT1WXQEzj+AKgClh5+GRp8SWXNeU5ka/ogAAAOBCqzoaqttbtQsEZiB3BQGYEl44VJxv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\nFzbXuCIAAABgOimVS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+MAAAhDSURB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\nXvgfwiPkXVT+ZFoAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 1008x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "image/png": {
              "width": 855,
              "height": 478
            }
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Kx49h7NNgCnk",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "pois = tf.random.poisson(shape=(1000, 1), lam=0.8)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z-57Vy-Ugnfo",
        "colab_type": "code",
        "outputId": "668c59e4-b7a7-47fd-e64f-a00444515fe5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 495
        }
      },
      "source": [
        "sns.distplot(pois);"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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CIWhgp9eMi+uKTCZTt2v1dueip6v8kbC8UoqZ2ZW6XQsAAAAAgM0l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\nRK5CYII=\n",
            "text/plain": [
              "<Figure size 1008x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "image/png": {
              "width": 834,
              "height": 478
            }
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j_6D65TIgqXv",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "gam = tf.random.gamma(shape=(1000, 1), alpha=0.8)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N0c0LoXHhhLu",
        "colab_type": "code",
        "outputId": "c64f9997-b4d7-4961-c757-a4d4b328ccaa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 495
        }
      },
      "source": [
        "sns.distplot(gam);"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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OO+7Irr/85S9HT0/Pae/bsWNHbNy48bTPna8rrrgi2trastdf/OIX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\ndBw7Ph4RERWpiDkt1QlXNHvdcHkuEHpm+4lIpzM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            "text/plain": [
              "<Figure size 1008x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "image/png": {
              "width": 834,
              "height": 478
            }
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "No7RCrN1iB4Y",
        "colab_type": "text"
      },
      "source": [
        "# Linear Regression\n",
        "\n",
        "https://vincentarelbundock.github.io/Rdatasets/datasets.html"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vwiGDIgskLLu",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "data = tf.constant([\n",
        "  [4,2],\n",
        "  [4,10],\n",
        "  [7,4],\n",
        "  [7,22],\n",
        "  [8,16],\n",
        "  [9,10],\n",
        "  [10,18],\n",
        "  [10,26],\n",
        "  [10,34],\n",
        "  [11,17],\n",
        "  [11,28],\n",
        "  [12,14],\n",
        "  [12,20],\n",
        "  [12,24],\n",
        "  [12,28],\n",
        "  [13,26],\n",
        "  [13,34],\n",
        "  [13,34],\n",
        "  [13,46],\n",
        "  [14,26],\n",
        "  [14,36],\n",
        "  [14,60],\n",
        "  [14,80],\n",
        "  [15,20],\n",
        "  [15,26],\n",
        "  [15,54],\n",
        "  [16,32],\n",
        "  [16,40],\n",
        "  [17,32],\n",
        "  [17,40],\n",
        "  [17,50],\n",
        "  [18,42],\n",
        "  [18,56],\n",
        "  [18,76],\n",
        "  [18,84],\n",
        "  [19,36],\n",
        "  [19,46],\n",
        "  [19,68],\n",
        "  [20,32],\n",
        "  [20,48],\n",
        "  [20,52],\n",
        "  [20,56],\n",
        "  [20,64],\n",
        "  [22,66],\n",
        "  [23,54],\n",
        "  [24,70],\n",
        "  [24,92],\n",
        "  [24,93],\n",
        "  [24,120],\n",
        "  [25,85]\n",
        "])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Hycczf3AlYVO",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "speed = data[:, 0]\n",
        "stopping_distance = data[:, 1]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qE9noO1ZlvX-",
        "colab_type": "code",
        "outputId": "2781a5df-4ac5-45eb-8cc4-ce899944255e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 516
        }
      },
      "source": [
        "sns.scatterplot(speed, stopping_distance);\n",
        "plt.xlabel(\"speed\")\n",
        "plt.ylabel(\"stopping distance\");"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 1008x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "image/png": {
              "width": 860,
              "height": 499
            }
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2bgTfH77nEpF",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "lin_reg = keras.Sequential([\n",
        "  layers.Dense(1, activation='linear', input_shape=[1]),\n",
        "])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SPNUQvXfnRQJ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "optimizer = tf.keras.optimizers.RMSprop(0.001)\n",
        "\n",
        "lin_reg.compile(loss='mse',\n",
        "              optimizer=optimizer,\n",
        "              metrics=['mse'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aiCVkGiAnYSn",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "history = lin_reg.fit(\n",
        "  x=speed,\n",
        "  y=stopping_distance,\n",
        "  shuffle=True,\n",
        "  epochs=1000,\n",
        "  validation_split=0.2,\n",
        "  verbose=0\n",
        ")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_pjY0jBHvi3K",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def plot_error(history):\n",
        "  hist = pd.DataFrame(history.history)\n",
        "  hist['epoch'] = history.epoch\n",
        "\n",
        "  plt.figure()\n",
        "  plt.xlabel('Epoch')\n",
        "  plt.ylabel('Mean Square Error')\n",
        "  plt.plot(hist['epoch'], hist['mse'],\n",
        "            label='Train Error')\n",
        "  plt.plot(hist['epoch'], hist['val_mse'],\n",
        "            label = 'Val Error')\n",
        "\n",
        "  plt.legend()\n",
        "  plt.show()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UK5kkmornvPd",
        "colab_type": "code",
        "outputId": "833cfbc5-e8c7-4034-bbf1-13d6f1eca544",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 516
        }
      },
      "source": [
        "plot_error(history)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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dZ8bR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\nZcs0c+ZMff3rX1e/fv161JgAA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            "text/plain": [
              "<Figure size 1008x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "image/png": {
              "width": 871,
              "height": 499
            }
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zuNE0Uq4calR",
        "colab_type": "code",
        "outputId": "91d450ae-374e-4221-b916-1823f555598f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 187
        }
      },
      "source": [
        "lin_reg.summary()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "dense (Dense)                (None, 1)                 2         \n",
            "=================================================================\n",
            "Total params: 2\n",
            "Trainable params: 2\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gmfy7I6Dcffh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "weights = lin_reg.get_layer(\"dense\").get_weights()\n",
        "intercept = weights[0][0][0]\n",
        "slope = weights[1][0]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wJGROyipospK",
        "colab_type": "code",
        "outputId": "8d67751c-0f8b-4411-e064-8ca819a0d50d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "slope"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.3159121"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Er7Hgz8yiDrd",
        "colab_type": "text"
      },
      "source": [
        "# Simple Neural Network"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3sk9PKFB0yhc",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def build_neural_net():\n",
        "  net = keras.Sequential([\n",
        "    layers.Dense(32, activation='relu', input_shape=[1]),\n",
        "    layers.Dense(16, activation='relu'),\n",
        "    layers.Dense(1),\n",
        "  ])\n",
        "   \n",
        "  optimizer = tf.keras.optimizers.RMSprop(0.001)\n",
        "\n",
        "  net.compile(loss='mse',\n",
        "                optimizer=optimizer,\n",
        "                metrics=['mse', 'accuracy'])\n",
        "  \n",
        "  return net"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z4PJe5Cy09--",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "net = build_neural_net()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DfYOc5PArJ7C",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "history = net.fit(\n",
        "  x=speed,\n",
        "  y=stopping_distance,\n",
        "  shuffle=True,\n",
        "  epochs=1000,\n",
        "  validation_split=0.2,\n",
        "  verbose=0\n",
        ")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gOGQz5z5rN1Q",
        "colab_type": "code",
        "outputId": "f6909c86-bf8e-4e84-9660-6a96d96684c1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 516
        }
      },
      "source": [
        "plot_error(history)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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emSR55plnNrrfkiVLerS/7rrrrlW7JwOvMLZbx1m7O84AAAAAYCSZMWNGdthh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\nQRWoaRhfHpdaV6RUKlWwGgA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Ot+QEAAAAAABeTXBWkeOPPz633357br311tx1111ZtGhR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\ntbFT6usOwRkAAAAAAAjOYKjqGpw55wwAAAAAAAR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BfdBYZhoOyIFB+59PZgnMlmkSCeStQKNrU5GGcySLAKJORH1Qf3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\n53lyW8lSaMW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            "text/plain": [
              "<Figure size 1008x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "image/png": {
              "width": 871,
              "height": 499
            }
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_k7_35uW3TOC",
        "colab_type": "text"
      },
      "source": [
        "## Stop training early"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RzP_YuZx8-X0",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "early_stop = keras.callbacks.EarlyStopping(\n",
        "  monitor='val_loss',\n",
        "  patience=10\n",
        ")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-d3xYvcMv1ls",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "net = build_neural_net()\n",
        "\n",
        "history = net.fit(\n",
        "  x=speed,\n",
        "  y=stopping_distance,\n",
        "  shuffle=True,\n",
        "  epochs=1000,\n",
        "  validation_split=0.2,\n",
        "  verbose=0,\n",
        "  callbacks=[early_stop]\n",
        ")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wIdjFoMJ1cSV",
        "colab_type": "code",
        "outputId": "66ba5e0c-9520-48b0-c941-e11a113da369",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 516
        }
      },
      "source": [
        "plot_error(history)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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bfuUWznJgeTAQAAAAAqsjKdTvl8Pg0ePFh33XWXvv/+e/3888/69ddflZ+fr2g0Kp/Pp+rVq6tV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\nUaWef6tS2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c2Vyfd55s43XNdDZuYz932vU3t7uyTp+eefV3l5uZ544gm5XK6PHXfFihW6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            "text/plain": [
              "<Figure size 1008x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "image/png": {
              "width": 871,
              "height": 499
            }
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ziq6mTc2iFag",
        "colab_type": "text"
      },
      "source": [
        "# Save/Restore Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1itOlR6kiEsB",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "net.save('simple_net.h5')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0t_jb2co4VHR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "simple_net = keras.models.load_model('simple_net.h5')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P2NOs0xV4bRe",
        "colab_type": "code",
        "outputId": "49220ba3-7e38-41cc-f29e-6f84379c78aa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        }
      },
      "source": [
        "simple_net.summary()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_2\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "dense_4 (Dense)              (None, 32)                64        \n",
            "_________________________________________________________________\n",
            "dense_5 (Dense)              (None, 16)                528       \n",
            "_________________________________________________________________\n",
            "dense_6 (Dense)              (None, 1)                 17        \n",
            "=================================================================\n",
            "Total params: 609\n",
            "Trainable params: 609\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}