linear_regression_keras.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "linear_regression_keras.ipynb",
      "provenance": [],
      "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/github/schneider128k/machine_learning_course/blob/master/linear_regression_keras.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "l5rq92V7ObHs",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "%matplotlib inline\n",
        "import matplotlib as mpl\n",
        "import matplotlib.pyplot as plt\n",
        "mpl.rc('axes', labelsize=14)\n",
        "mpl.rc('xtick', labelsize=12)\n",
        "mpl.rc('ytick', labelsize=12)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B4VTPpXkOhds",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "np.random.seed(42)\n",
        "\n",
        "xs = 2 * np.random.rand(100, 1)\n",
        "ys = 4 + 3 * xs + np.random.rand(100, 1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PlhVE5hJSbsy",
        "colab_type": "code",
        "outputId": "e0f492e6-34cf-4140-efbb-a14716130f6e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 290
        }
      },
      "source": [
        "plt.plot(xs, ys, \"b.\")\n",
        "plt.xlabel(\"$x_1$\", fontsize=18)\n",
        "plt.ylabel(\"$y$\", rotation=0, fontsize=18)\n",
        "plt.axis([0, 2, 0, 15])\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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4pMlhsNCiPY46951//oFlQ+taUKgzD/K61xWBaJLmjJKOdCa4tWiPo859cCDhvHIlnHba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\nIvk5wCdlZwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9WIBehqmUXeM",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# split the data into training and test sets\n",
        "# train set\n",
        "train_xs = xs[:80]\n",
        "train_ys = ys[:80]\n",
        "# test set\n",
        "test_xs = xs[80:]\n",
        "test_ys = ys[80:]\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wq5R9tfxOlK6",
        "colab_type": "text"
      },
      "source": [
        "# Linear regression using Keras\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VmYytw5LSeki",
        "colab_type": "code",
        "outputId": "7700254f-1993-4acc-cb5c-0c9e674d4bdf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "%tensorflow_version 2.x\n",
        "from tensorflow.keras import models\n",
        "from tensorflow.keras import layers"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "TensorFlow 2.x selected.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jVT8Z0VLXXbg",
        "colab_type": "text"
      },
      "source": [
        "## Build the Keras model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z5z0s6p2SeV0",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "network = models.Sequential()\n",
        "network.add(layers.Dense(1, input_shape=(1,)))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ruI7_rT2ayse",
        "colab_type": "text"
      },
      "source": [
        "## Compile the model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CqvgS-KsS99D",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "network.compile(optimizer='sgd', loss='mse', metrics=['mse'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qAK6EAMTa2-r",
        "colab_type": "text"
      },
      "source": [
        "## Train the model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kt4lOhcFTOJz",
        "colab_type": "code",
        "outputId": "e73be42c-2c3d-47bf-b439-7a0c3bfe53b4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 399
        }
      },
      "source": [
        "network.fit(train_xs, train_ys, epochs=10, batch_size=8)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 80 samples\n",
            "Epoch 1/10\n",
            "80/80 [==============================] - 0s 6ms/sample - loss: 58.7943 - mse: 58.7943\n",
            "Epoch 2/10\n",
            "80/80 [==============================] - 0s 189us/sample - loss: 25.3486 - mse: 25.3486\n",
            "Epoch 3/10\n",
            "80/80 [==============================] - 0s 226us/sample - loss: 10.9852 - mse: 10.9852\n",
            "Epoch 4/10\n",
            "80/80 [==============================] - 0s 184us/sample - loss: 4.7903 - mse: 4.7903\n",
            "Epoch 5/10\n",
            "80/80 [==============================] - 0s 165us/sample - loss: 2.1217 - mse: 2.1217\n",
            "Epoch 6/10\n",
            "80/80 [==============================] - 0s 184us/sample - loss: 0.9687 - mse: 0.9687\n",
            "Epoch 7/10\n",
            "80/80 [==============================] - 0s 186us/sample - loss: 0.4722 - mse: 0.4722\n",
            "Epoch 8/10\n",
            "80/80 [==============================] - 0s 179us/sample - loss: 0.2607 - mse: 0.2607\n",
            "Epoch 9/10\n",
            "80/80 [==============================] - 0s 181us/sample - loss: 0.1686 - mse: 0.1686\n",
            "Epoch 10/10\n",
            "80/80 [==============================] - 0s 187us/sample - loss: 0.1280 - mse: 0.1280\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7fd013abaac8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "s9Iv2S5UXSnt",
        "colab_type": "text"
      },
      "source": [
        "## Evaluate the model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ouGCs5h0Vb7j",
        "colab_type": "code",
        "outputId": "7a3fb9ad-454b-44b4-8824-1e3e36be7a4a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "test_loss, test_acc = network.evaluate(test_xs, test_ys)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\r20/20 [==============================] - 0s 3ms/sample - loss: 0.1624 - mse: 0.1624\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ceyzFn1sVtb8",
        "colab_type": "code",
        "outputId": "085a23bb-1e88-45a1-e4be-3f62392bf079",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "test_acc"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.16240528"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OagpMGGUVw5k",
        "colab_type": "code",
        "outputId": "7137fa95-ecc6-4097-e9fd-bbe1937b4a60",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "test_loss"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.16240528225898743"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pSWeUAxoV2Xk",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "pred_ys = network.predict(test_xs)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QS4IdviyXBac",
        "colab_type": "code",
        "outputId": "1f13d676-a0fb-416b-bf03-c220d9dc7f73",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 290
        }
      },
      "source": [
        "plt.plot(test_xs, test_ys, \"b.\")\n",
        "plt.plot(test_xs, pred_ys, \"r.\") # predicted values\n",
        "plt.xlabel(\"$x_1$\", fontsize=18)\n",
        "plt.ylabel(\"$y$\", rotation=0, fontsize=18)\n",
        "plt.axis([0, 2, 0, 15])\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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GpR0WjV+VJ0kr09I+DeVpJUlqxNLuWXhaSZIa0ZewiIgjgLuAv8zMdy5oZ55WkqSe69dp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\nDAtJUqX/DxRSgQ9vuLQsAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bG_ZQa2_X4o9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}