Image_Recognition_using_Neural_Networks.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Image Recognition using Neural Networks",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyOs4nNhVi0DeU/F+tzc6wDj",
      "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/FowadIjaz/Portfolio/blob/main/Image_Recognition_using_Neural_Networks.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "waWG5A4s1-bP"
      },
      "source": [
        "### **Project Overview**\r\n",
        "\r\n",
        "*This project was completed by Fowad Ijaz for K723: Business Intelligence & Data Mining taught by S. Villiers and T. Francescutti at the DeGroote School of Business in Fall 2020. *\r\n",
        "\r\n",
        "The purpose of this project was to create a basic image recognition model using TensorFlow. The Keras MNIST Fashion dataset was utilized to train and test the model. More information about the dataset can be found [here](https://www.kaggle.com/zalando-research/fashionmnist)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cZdUJc4LWQuf"
      },
      "source": [
        "### **Part 1: Running the TensorFlow Script (10 epochs)**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BxVq-XdiNrzo",
        "outputId": "df4c4394-ae59-439c-d09f-4c5009d71bc4"
      },
      "source": [
        "from __future__ import absolute_import, division, print_function, unicode_literals\n",
        "\n",
        "# TensorFlow and tf.keras\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "\n",
        "# Helper libraries\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Checking TF Version to ensure it has been properly imported\n",
        "print(tf.__version__)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2.3.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZhxuJqtiOSZF",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2d0a8a64-55b8-4f2d-9aaf-127942756455"
      },
      "source": [
        "# Reading in the fashion dataset and saving as a variable, creating test and training datasets\n",
        "\n",
        "fashion_mnist = keras.datasets.fashion_mnist\n",
        "(train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-labels-idx1-ubyte.gz\n",
            "32768/29515 [=================================] - 0s 0us/step\n",
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-images-idx3-ubyte.gz\n",
            "26427392/26421880 [==============================] - 0s 0us/step\n",
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-labels-idx1-ubyte.gz\n",
            "8192/5148 [===============================================] - 0s 0us/step\n",
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-images-idx3-ubyte.gz\n",
            "4423680/4422102 [==============================] - 0s 0us/step\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "89rXLJj0OgCg"
      },
      "source": [
        "# Classify the image groups\n",
        "\n",
        "class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat',\n",
        "               'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "7XizLktUOldA",
        "outputId": "13d36c7f-0553-4176-965a-3f0575ff3f77"
      },
      "source": [
        "# Inspecting the source data (pixels with values 0 - 255)\n",
        "\n",
        "plt.figure()\n",
        "plt.imshow(train_images[0])\n",
        "plt.colorbar()\n",
        "plt.grid(False)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2UVu7rbuOnL3"
      },
      "source": [
        "# Pre-process the source data (clamp pixel values between 0 - 1)\n",
        "train_images = train_images / 255.0\n",
        "test_images = test_images / 255.0"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 589
        },
        "id": "1U2Ay6_ROpWa",
        "outputId": "8da660c3-1288-49d9-de6e-cd5f75865c39"
      },
      "source": [
        "# Inspect pre-processed data (pixel values between 0 - 1)\n",
        "plt.figure(figsize=(10,10))\n",
        "for i in range(25):\n",
        "    plt.subplot(5,5,i+1)\n",
        "    plt.xticks([])\n",
        "    plt.yticks([])\n",
        "    plt.grid(False)\n",
        "    plt.imshow(train_images[i], cmap=plt.cm.binary)\n",
        "    plt.xlabel(class_names[train_labels[i]])\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 25 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "58kjEgheOwFP"
      },
      "source": [
        "# Set up the model layers\n",
        "model = keras.Sequential([\n",
        "    keras.layers.Flatten(input_shape=(28, 28)),\n",
        "    keras.layers.Dense(128, activation='relu'),\n",
        "    keras.layers.Dense(10, activation='softmax')\n",
        "])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FTboTl4nO6Kv"
      },
      "source": [
        "# Compile the model\n",
        "model.compile(optimizer='adam',\n",
        "              loss='sparse_categorical_crossentropy',\n",
        "              metrics=['accuracy'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ReIdywK7O8EQ",
        "outputId": "fa12a912-68d2-47f1-9680-34c8ebb3713b"
      },
      "source": [
        "# Train the model\n",
        "model.fit(train_images, train_labels, epochs=10)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/10\n",
            "1875/1875 [==============================] - 3s 2ms/step - loss: 0.4991 - accuracy: 0.8251\n",
            "Epoch 2/10\n",
            "1875/1875 [==============================] - 3s 2ms/step - loss: 0.3752 - accuracy: 0.8654\n",
            "Epoch 3/10\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.3367 - accuracy: 0.8783\n",
            "Epoch 4/10\n",
            "1875/1875 [==============================] - 3s 2ms/step - loss: 0.3128 - accuracy: 0.8854\n",
            "Epoch 5/10\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.2961 - accuracy: 0.8891\n",
            "Epoch 6/10\n",
            "1875/1875 [==============================] - 3s 2ms/step - loss: 0.2808 - accuracy: 0.8965\n",
            "Epoch 7/10\n",
            "1875/1875 [==============================] - 3s 2ms/step - loss: 0.2678 - accuracy: 0.9003\n",
            "Epoch 8/10\n",
            "1875/1875 [==============================] - 3s 2ms/step - loss: 0.2573 - accuracy: 0.9058\n",
            "Epoch 9/10\n",
            "1875/1875 [==============================] - 3s 2ms/step - loss: 0.2474 - accuracy: 0.9069\n",
            "Epoch 10/10\n",
            "1875/1875 [==============================] - 3s 2ms/step - loss: 0.2398 - accuracy: 0.9101\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7f31dc91fba8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "L70hIcrjPJLZ",
        "outputId": "4f3995a7-ecd5-4243-ccb7-83a42430aa7c"
      },
      "source": [
        "# Evaluate the model\n",
        "test_acc = model.evaluate(test_images,  test_labels, verbose=2)\n",
        "print('\\nTest accuracy:', test_acc)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "313/313 - 0s - loss: 0.3355 - accuracy: 0.8818\n",
            "\n",
            "Test accuracy: [0.33546099066734314, 0.8817999958992004]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BIejizBmPPgQ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "76caacc0-c786-41db-cece-3d774288199e"
      },
      "source": [
        "# Predict labels for all images, evaluate the first item\n",
        "predictions = model.predict(test_images)\n",
        "print(predictions[0])\n",
        "image_index = np.argmax(predictions[0])\n",
        "print(\"Predicted: \", image_index, \"Actual: \", test_labels[0])\n",
        "print(class_names[image_index])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[2.8021920e-09 1.7371674e-10 4.0278551e-11 8.7800291e-14 1.1796256e-11\n",
            " 1.0053774e-03 1.7629109e-08 5.0042360e-03 1.8352923e-08 9.9399042e-01]\n",
            "Predicted:  9 Actual:  9\n",
            "Ankle boot\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7S1FnIrquKlK"
      },
      "source": [
        "**Discussion**: \n",
        "The accuracy on the *training* dataset after 10 epochs was 91%, with 23% loss. \n",
        "\n",
        "The accuracy on the *test* dataset was a little lower, at 88% and 33% loss. Loss of accuracy is expected from training to test data. \n",
        "\n",
        "The first entry of the test dataset was correctly predicted with 99.61% confidence"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NZ6cVdUjWE6Y"
      },
      "source": [
        "### **Part 2: Running the Script with 11 Epochs**\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "D6bwIf1IU0FZ",
        "outputId": "adc2fb07-79a9-4c8a-a241-ee1a3d7001d8"
      },
      "source": [
        "# Train the model\n",
        "model.fit(train_images, train_labels, epochs=11)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/11\n",
            "1875/1875 [==============================] - 3s 2ms/step - loss: 0.2315 - accuracy: 0.9141\n",
            "Epoch 2/11\n",
            "1875/1875 [==============================] - 3s 2ms/step - loss: 0.2239 - accuracy: 0.9157\n",
            "Epoch 3/11\n",
            "1875/1875 [==============================] - 3s 2ms/step - loss: 0.2161 - accuracy: 0.9186\n",
            "Epoch 4/11\n",
            "1875/1875 [==============================] - 3s 2ms/step - loss: 0.2109 - accuracy: 0.9210\n",
            "Epoch 5/11\n",
            "1875/1875 [==============================] - 3s 2ms/step - loss: 0.2044 - accuracy: 0.9241\n",
            "Epoch 6/11\n",
            "1875/1875 [==============================] - 3s 2ms/step - loss: 0.1965 - accuracy: 0.9276\n",
            "Epoch 7/11\n",
            "1875/1875 [==============================] - 3s 2ms/step - loss: 0.1943 - accuracy: 0.9258\n",
            "Epoch 8/11\n",
            "1875/1875 [==============================] - 3s 2ms/step - loss: 0.1896 - accuracy: 0.9276\n",
            "Epoch 9/11\n",
            "1875/1875 [==============================] - 3s 2ms/step - loss: 0.1834 - accuracy: 0.9305\n",
            "Epoch 10/11\n",
            "1875/1875 [==============================] - 3s 2ms/step - loss: 0.1789 - accuracy: 0.9322\n",
            "Epoch 11/11\n",
            "1875/1875 [==============================] - 3s 2ms/step - loss: 0.1758 - accuracy: 0.9337\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7f31da298898>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3AXs4QE9VPjG",
        "outputId": "b665471a-9dfe-4970-9f45-b645be1c7a16"
      },
      "source": [
        "# Evaluate the model\n",
        "test_acc = model.evaluate(test_images,  test_labels, verbose=2)\n",
        "print('\\nTest accuracy:', test_acc)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "313/313 - 0s - loss: 0.3498 - accuracy: 0.8878\n",
            "\n",
            "Test accuracy: [0.34975650906562805, 0.8877999782562256]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "itEi__plWiIv",
        "outputId": "f37931f3-bbcb-4b0b-ddae-dcad20928248"
      },
      "source": [
        "# Predict labels for all images, evaluate the first item\n",
        "predictions = model.predict(test_images)\n",
        "print(predictions[0])\n",
        "image_index = np.argmax(predictions[0])\n",
        "print(\"Predicted: \", image_index, \"Actual: \", test_labels[0])\n",
        "print(class_names[image_index])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[1.03642745e-11 3.18435091e-15 1.20790927e-17 2.48206167e-19\n",
            " 1.60350732e-17 3.78111836e-05 1.04271442e-12 6.94469200e-05\n",
            " 1.05977130e-12 9.99892712e-01]\n",
            "Predicted:  9 Actual:  9\n",
            "Ankle boot\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KFvz1rv5SCdg",
        "outputId": "c7dfc3c7-89e5-4be2-f401-3c688483da38"
      },
      "source": [
        "estimates = [np.argmax(predictions[i]) for i in range(len(predictions))]\n",
        "\n",
        "my_count = pd.Series(estimates).value_counts()\n",
        "my_count"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "6    1117\n",
              "4    1028\n",
              "3    1026\n",
              "9    1013\n",
              "7    1003\n",
              "8    1001\n",
              "5     994\n",
              "1     989\n",
              "2     937\n",
              "0     892\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 359
        },
        "id": "_GygSRIJczRe",
        "outputId": "12ffbae9-fcd2-468c-9fa5-16355c740c85"
      },
      "source": [
        "df1 = pd.DataFrame(class_names,\n",
        "                   columns = ['Class Names'])\n",
        "\n",
        "df2 = pd.DataFrame(my_count, \n",
        "                  columns = ['Estimates'])\n",
        "\n",
        "df3 = pd.merge(df1, \n",
        "               df2,\n",
        "               left_index =True, \n",
        "               right_index=True)\n",
        "df3.sort_index(axis=0, ascending=True)"
      ],
      "execution_count": null,
      "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>Class Names</th>\n",
              "      <th>Estimates</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>T-shirt/top</td>\n",
              "      <td>892</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Trouser</td>\n",
              "      <td>989</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Pullover</td>\n",
              "      <td>937</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Dress</td>\n",
              "      <td>1026</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Coat</td>\n",
              "      <td>1028</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Sandal</td>\n",
              "      <td>994</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Shirt</td>\n",
              "      <td>1117</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Sneaker</td>\n",
              "      <td>1003</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Bag</td>\n",
              "      <td>1001</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Ankle boot</td>\n",
              "      <td>1013</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Class Names  Estimates\n",
              "0  T-shirt/top        892\n",
              "1      Trouser        989\n",
              "2     Pullover        937\n",
              "3        Dress       1026\n",
              "4         Coat       1028\n",
              "5       Sandal        994\n",
              "6        Shirt       1117\n",
              "7      Sneaker       1003\n",
              "8          Bag       1001\n",
              "9   Ankle boot       1013"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bWLXdKAYuw4R"
      },
      "source": [
        "**Discussion:** The accuracy on the test dataset after 11 epochs appears to be lower or higher than 10 epochs, changing every time we run the model. \n",
        "\n",
        "If the acccuracy was consistently lower we could say that the model was overfitting the data, but since it fluctuates, we can say that 10 vs 11 epochs yield roughly similar results."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "I4-NsqEGlo46"
      },
      "source": [
        "### **Part 3: Calculating Delta**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "M-zNnlEOjCG7",
        "outputId": "658533a8-ee0c-45b7-b986-5e8ad63c8782"
      },
      "source": [
        "true_count = pd.Series(test_labels).value_counts()\n",
        "true_count\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "7    1000\n",
              "6    1000\n",
              "5    1000\n",
              "4    1000\n",
              "3    1000\n",
              "2    1000\n",
              "9    1000\n",
              "1    1000\n",
              "8    1000\n",
              "0    1000\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_beNoR-N__FN",
        "outputId": "e3d795f2-bc1c-4c95-d8f5-f18a8a6b9ef9"
      },
      "source": [
        "my_count - true_count"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0   -108\n",
              "1    -11\n",
              "2    -63\n",
              "3     26\n",
              "4     28\n",
              "5     -6\n",
              "6    117\n",
              "7      3\n",
              "8      1\n",
              "9     13\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FAWVuD0TADhz",
        "outputId": "f0a81578-7d94-48a8-978e-cc568284609b"
      },
      "source": [
        "abs(my_count - true_count)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    108\n",
              "1     11\n",
              "2     63\n",
              "3     26\n",
              "4     28\n",
              "5      6\n",
              "6    117\n",
              "7      3\n",
              "8      1\n",
              "9     13\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "D-OrtY6hAIlk",
        "outputId": "917b760b-3ed2-4c77-bc55-8c0557879300"
      },
      "source": [
        "sum(abs(my_count - true_count))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "376"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "BKyZCGp6k4lD",
        "outputId": "ddec3e35-5e5e-4ae8-deee-3e3f990ef5e6"
      },
      "source": [
        "total_delta = sum(abs(my_count - true_count))\n",
        "\"Total delta: \" + str(total_delta)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'Total delta: 376'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZJp40PZ8wFo3"
      },
      "source": [
        "**Discussion**: When this code was run previously, we got a total delta result of roughly 300. Again this shows there is quite a bit of variation in the model, however, 300 or even 650 incorrect guesses out of a set of 10,000 is not a bad result. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vq6q8bply15P"
      },
      "source": [
        "**Bonus**: Confusion matrix of actual vs predicted results."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 459
        },
        "id": "XJwSn9Y4xAdK",
        "outputId": "e2d215e4-b5bc-43b0-a2fa-17582b1863ae"
      },
      "source": [
        "cm= tf.math.confusion_matrix(labels = test_labels, predictions = estimates)\n",
        "\n",
        "import seaborn as sn\n",
        "plt.figure(figsize=(10,7))\n",
        "sn.heatmap(cm, annot=True, fmt ='d')\n",
        "plt.xlabel('Predicted')\n",
        "plt.ylabel('Actual')\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(69.0, 0.5, 'Actual')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x504 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vN4p1fSWARJh"
      },
      "source": [
        "test_labels\n",
        "\n",
        "est = np.array(estimates)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "O4Mcb4sGOjCF",
        "outputId": "c38575aa-6dfc-45d4-f0b4-705bf0e0198e"
      },
      "source": [
        "pd.Series(test_labels == est).value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True     8878\n",
              "False    1122\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "06ZRKrwBRM3o"
      },
      "source": [
        "### Bokeh Graphics"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dOxaI-byRRaR"
      },
      "source": [
        "from bokeh.plotting import figure\n",
        "from bokeh.layouts import layout, widgetbox\n",
        "from bokeh.models import ColumnDataSource, HoverTool, BoxZoomTool, ResetTool, PanTool\n",
        "from bokeh.models.widgets import Slider, Select, TextInput, Div\n",
        "from bokeh.models import WheelZoomTool, SaveTool, LassoSelectTool\n",
        "from bokeh.io import curdoc\n",
        "from functools import lru_cache"
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}