iris_classification.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "iris_classification.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyOi3nx0ML+zF+zfFVSBhMKC",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/abhirukth/Data-Sci.-File/blob/main/iris_classification.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mXVvaNiBTpBS"
      },
      "source": [
        "# Imporing Libraries"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ew8LTbY9T0xq"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "\n",
        "import tensorflow as tf\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import LabelEncoder"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BOr5pWwNe5Qp"
      },
      "source": [
        "# Loading Data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QLoW1j9FVE7A"
      },
      "source": [
        "data = pd.read_csv('/content/Iris.csv')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 417
        },
        "id": "xYyzSfknV1nB",
        "outputId": "b6010f03-4aff-46b7-cb7d-ee7acc243c4c"
      },
      "source": [
        "data"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Id</th>\n",
              "      <th>SepalLengthCm</th>\n",
              "      <th>SepalWidthCm</th>\n",
              "      <th>PetalLengthCm</th>\n",
              "      <th>PetalWidthCm</th>\n",
              "      <th>Species</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>5.1</td>\n",
              "      <td>3.5</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>4.9</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>4.7</td>\n",
              "      <td>3.2</td>\n",
              "      <td>1.3</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>4.6</td>\n",
              "      <td>3.1</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>5.0</td>\n",
              "      <td>3.6</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>145</th>\n",
              "      <td>146</td>\n",
              "      <td>6.7</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.2</td>\n",
              "      <td>2.3</td>\n",
              "      <td>Iris-virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>146</th>\n",
              "      <td>147</td>\n",
              "      <td>6.3</td>\n",
              "      <td>2.5</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1.9</td>\n",
              "      <td>Iris-virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>147</th>\n",
              "      <td>148</td>\n",
              "      <td>6.5</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.2</td>\n",
              "      <td>2.0</td>\n",
              "      <td>Iris-virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>148</th>\n",
              "      <td>149</td>\n",
              "      <td>6.2</td>\n",
              "      <td>3.4</td>\n",
              "      <td>5.4</td>\n",
              "      <td>2.3</td>\n",
              "      <td>Iris-virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>149</th>\n",
              "      <td>150</td>\n",
              "      <td>5.9</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.1</td>\n",
              "      <td>1.8</td>\n",
              "      <td>Iris-virginica</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>150 rows × 6 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      Id  SepalLengthCm  ...  PetalWidthCm         Species\n",
              "0      1            5.1  ...           0.2     Iris-setosa\n",
              "1      2            4.9  ...           0.2     Iris-setosa\n",
              "2      3            4.7  ...           0.2     Iris-setosa\n",
              "3      4            4.6  ...           0.2     Iris-setosa\n",
              "4      5            5.0  ...           0.2     Iris-setosa\n",
              "..   ...            ...  ...           ...             ...\n",
              "145  146            6.7  ...           2.3  Iris-virginica\n",
              "146  147            6.3  ...           1.9  Iris-virginica\n",
              "147  148            6.5  ...           2.0  Iris-virginica\n",
              "148  149            6.2  ...           2.3  Iris-virginica\n",
              "149  150            5.9  ...           1.8  Iris-virginica\n",
              "\n",
              "[150 rows x 6 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QzigXE3kV2Th",
        "outputId": "3a055163-5f59-4da8-fd90-8070a2273c45"
      },
      "source": [
        "data.isnull().any()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Id               False\n",
              "SepalLengthCm    False\n",
              "SepalWidthCm     False\n",
              "PetalLengthCm    False\n",
              "PetalWidthCm     False\n",
              "Species          False\n",
              "dtype: bool"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "df2aYWwBe-Vz"
      },
      "source": [
        "> Data is already clean with no NULL values."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "T_hvzG-QWCl1",
        "outputId": "c45cda9c-f690-40ce-c379-916bbbd80c62"
      },
      "source": [
        "data.columns"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Id', 'SepalLengthCm', 'SepalWidthCm', 'PetalLengthCm', 'PetalWidthCm',\n",
              "       'Species'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "etiT26G0WGvf",
        "outputId": "32bc0f96-9081-46c1-85e3-01ca15c7f66e"
      },
      "source": [
        "data['Species'].unique()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Iris-setosa', 'Iris-versicolor', 'Iris-virginica'], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yaq0JofqfRQO"
      },
      "source": [
        "# Model Training"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TvVYNJyAWOVR"
      },
      "source": [
        "X = data.drop(['Id', 'Species'], axis = 1)\n",
        "y = data['Species']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 417
        },
        "id": "3QVpZjFRWm8O",
        "outputId": "4d6a496e-d87c-4673-ae5c-762363bfc827"
      },
      "source": [
        "X"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>SepalLengthCm</th>\n",
              "      <th>SepalWidthCm</th>\n",
              "      <th>PetalLengthCm</th>\n",
              "      <th>PetalWidthCm</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>5.1</td>\n",
              "      <td>3.5</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4.9</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>4.7</td>\n",
              "      <td>3.2</td>\n",
              "      <td>1.3</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4.6</td>\n",
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              "      <td>1.5</td>\n",
              "      <td>0.2</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
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              "      <td>3.6</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
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              "    <tr>\n",
              "      <th>145</th>\n",
              "      <td>6.7</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.2</td>\n",
              "      <td>2.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>146</th>\n",
              "      <td>6.3</td>\n",
              "      <td>2.5</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>147</th>\n",
              "      <td>6.5</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.2</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>148</th>\n",
              "      <td>6.2</td>\n",
              "      <td>3.4</td>\n",
              "      <td>5.4</td>\n",
              "      <td>2.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>149</th>\n",
              "      <td>5.9</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.1</td>\n",
              "      <td>1.8</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>150 rows × 4 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm\n",
              "0              5.1           3.5            1.4           0.2\n",
              "1              4.9           3.0            1.4           0.2\n",
              "2              4.7           3.2            1.3           0.2\n",
              "3              4.6           3.1            1.5           0.2\n",
              "4              5.0           3.6            1.4           0.2\n",
              "..             ...           ...            ...           ...\n",
              "145            6.7           3.0            5.2           2.3\n",
              "146            6.3           2.5            5.0           1.9\n",
              "147            6.5           3.0            5.2           2.0\n",
              "148            6.2           3.4            5.4           2.3\n",
              "149            5.9           3.0            5.1           1.8\n",
              "\n",
              "[150 rows x 4 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "h4dkMis9Wpyk",
        "outputId": "1adadf87-317d-4d81-a9c0-c1c65066f0e5"
      },
      "source": [
        "y"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0         Iris-setosa\n",
              "1         Iris-setosa\n",
              "2         Iris-setosa\n",
              "3         Iris-setosa\n",
              "4         Iris-setosa\n",
              "            ...      \n",
              "145    Iris-virginica\n",
              "146    Iris-virginica\n",
              "147    Iris-virginica\n",
              "148    Iris-virginica\n",
              "149    Iris-virginica\n",
              "Name: Species, Length: 150, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "t-tT84vLWqSH",
        "outputId": "29eaaf9a-b175-4a2a-bdc9-467116bab460"
      },
      "source": [
        "encoder = LabelEncoder()\n",
        "y = pd.DataFrame(encoder.fit_transform(y))\n",
        "\n",
        "encoded_iris_types = {index : label for index, label in enumerate(encoder.classes_)}\n",
        "encoded_iris_types"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{0: 'Iris-setosa', 1: 'Iris-versicolor', 2: 'Iris-virginica'}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 417
        },
        "id": "kV1hznNbXD1s",
        "outputId": "b9a656c3-71ee-4c37-e9e4-df946b86ad17"
      },
      "source": [
        "y"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th>3</th>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <th>...</th>\n",
              "      <td>...</td>\n",
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              "      <th>145</th>\n",
              "      <td>2</td>\n",
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              "    <tr>\n",
              "      <th>146</th>\n",
              "      <td>2</td>\n",
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              "    <tr>\n",
              "      <th>147</th>\n",
              "      <td>2</td>\n",
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              "    <tr>\n",
              "      <th>148</th>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>149</th>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>150 rows × 1 columns</p>\n",
              "</div>"
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            "text/plain": [
              "     0\n",
              "0    0\n",
              "1    0\n",
              "2    0\n",
              "3    0\n",
              "4    0\n",
              "..  ..\n",
              "145  2\n",
              "146  2\n",
              "147  2\n",
              "148  2\n",
              "149  2\n",
              "\n",
              "[150 rows x 1 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ldP18S6dZtQX"
      },
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, y, train_size = 0.7, random_state = 40)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "t4RKACgqfhh4"
      },
      "source": [
        "## Implementing neural networks"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qczJl-BWZ-AX",
        "outputId": "5a9e347f-6bef-4799-908d-7af141b1bac6"
      },
      "source": [
        "inputs = tf.keras.Input(shape = (4, ))\n",
        "x = tf.keras.layers.Dense(256, activation = 'relu')(inputs)\n",
        "x = tf.keras.layers.Dense(256, activation = 'relu')(x)\n",
        "outputs = tf.keras.layers.Dense(3, activation = 'softmax')(x)\n",
        "\n",
        "model = tf.keras.Model(inputs, outputs)\n",
        "\n",
        "model.compile(\n",
        "    optimizer = 'adam',\n",
        "    loss = 'sparse_categorical_crossentropy',\n",
        "    metrics = ['accuracy']\n",
        ")\n",
        "\n",
        "batch_size = 32\n",
        "epochs = 40\n",
        "\n",
        "history = model.fit(\n",
        "    X_train,\n",
        "    y_train,\n",
        "    validation_split = 0.2,\n",
        "    batch_size = batch_size,\n",
        "    epochs = epochs\n",
        ")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/40\n",
            "3/3 [==============================] - 1s 84ms/step - loss: 1.0340 - accuracy: 0.3571 - val_loss: 0.9340 - val_accuracy: 0.5714\n",
            "Epoch 2/40\n",
            "3/3 [==============================] - 0s 14ms/step - loss: 0.8421 - accuracy: 0.6905 - val_loss: 0.7859 - val_accuracy: 0.5714\n",
            "Epoch 3/40\n",
            "3/3 [==============================] - 0s 14ms/step - loss: 0.7351 - accuracy: 0.7024 - val_loss: 0.7121 - val_accuracy: 0.5714\n",
            "Epoch 4/40\n",
            "3/3 [==============================] - 0s 14ms/step - loss: 0.6484 - accuracy: 0.7262 - val_loss: 0.5974 - val_accuracy: 0.8095\n",
            "Epoch 5/40\n",
            "3/3 [==============================] - 0s 15ms/step - loss: 0.5682 - accuracy: 0.7381 - val_loss: 0.5616 - val_accuracy: 0.5714\n",
            "Epoch 6/40\n",
            "3/3 [==============================] - 0s 15ms/step - loss: 0.5003 - accuracy: 0.7500 - val_loss: 0.4766 - val_accuracy: 0.9524\n",
            "Epoch 7/40\n",
            "3/3 [==============================] - 0s 13ms/step - loss: 0.4569 - accuracy: 0.9643 - val_loss: 0.4388 - val_accuracy: 0.9524\n",
            "Epoch 8/40\n",
            "3/3 [==============================] - 0s 13ms/step - loss: 0.4142 - accuracy: 0.9286 - val_loss: 0.4310 - val_accuracy: 0.8095\n",
            "Epoch 9/40\n",
            "3/3 [==============================] - 0s 13ms/step - loss: 0.3867 - accuracy: 0.9048 - val_loss: 0.3886 - val_accuracy: 0.9048\n",
            "Epoch 10/40\n",
            "3/3 [==============================] - 0s 17ms/step - loss: 0.3605 - accuracy: 0.9167 - val_loss: 0.3625 - val_accuracy: 0.9524\n",
            "Epoch 11/40\n",
            "3/3 [==============================] - 0s 18ms/step - loss: 0.3407 - accuracy: 0.9405 - val_loss: 0.3222 - val_accuracy: 0.9524\n",
            "Epoch 12/40\n",
            "3/3 [==============================] - 0s 14ms/step - loss: 0.3089 - accuracy: 0.9762 - val_loss: 0.3372 - val_accuracy: 0.9048\n",
            "Epoch 13/40\n",
            "3/3 [==============================] - 0s 14ms/step - loss: 0.3117 - accuracy: 0.8929 - val_loss: 0.3468 - val_accuracy: 0.8095\n",
            "Epoch 14/40\n",
            "3/3 [==============================] - 0s 14ms/step - loss: 0.2907 - accuracy: 0.9643 - val_loss: 0.2707 - val_accuracy: 0.9048\n",
            "Epoch 15/40\n",
            "3/3 [==============================] - 0s 16ms/step - loss: 0.2738 - accuracy: 0.9405 - val_loss: 0.2649 - val_accuracy: 0.9524\n",
            "Epoch 16/40\n",
            "3/3 [==============================] - 0s 15ms/step - loss: 0.2470 - accuracy: 0.9762 - val_loss: 0.2705 - val_accuracy: 0.9524\n",
            "Epoch 17/40\n",
            "3/3 [==============================] - 0s 17ms/step - loss: 0.2322 - accuracy: 0.9762 - val_loss: 0.2338 - val_accuracy: 1.0000\n",
            "Epoch 18/40\n",
            "3/3 [==============================] - 0s 27ms/step - loss: 0.2165 - accuracy: 0.9762 - val_loss: 0.2285 - val_accuracy: 0.9524\n",
            "Epoch 19/40\n",
            "3/3 [==============================] - 0s 15ms/step - loss: 0.2065 - accuracy: 0.9762 - val_loss: 0.2246 - val_accuracy: 0.9524\n",
            "Epoch 20/40\n",
            "3/3 [==============================] - 0s 13ms/step - loss: 0.1939 - accuracy: 0.9762 - val_loss: 0.2125 - val_accuracy: 0.9524\n",
            "Epoch 21/40\n",
            "3/3 [==============================] - 0s 13ms/step - loss: 0.1853 - accuracy: 0.9762 - val_loss: 0.2003 - val_accuracy: 0.9524\n",
            "Epoch 22/40\n",
            "3/3 [==============================] - 0s 13ms/step - loss: 0.1732 - accuracy: 0.9762 - val_loss: 0.1858 - val_accuracy: 1.0000\n",
            "Epoch 23/40\n",
            "3/3 [==============================] - 0s 16ms/step - loss: 0.1636 - accuracy: 0.9762 - val_loss: 0.1850 - val_accuracy: 0.9524\n",
            "Epoch 24/40\n",
            "3/3 [==============================] - 0s 13ms/step - loss: 0.1562 - accuracy: 0.9762 - val_loss: 0.1815 - val_accuracy: 0.9524\n",
            "Epoch 25/40\n",
            "3/3 [==============================] - 0s 14ms/step - loss: 0.1551 - accuracy: 0.9762 - val_loss: 0.1654 - val_accuracy: 0.9524\n",
            "Epoch 26/40\n",
            "3/3 [==============================] - 0s 13ms/step - loss: 0.1474 - accuracy: 0.9643 - val_loss: 0.1609 - val_accuracy: 0.9524\n",
            "Epoch 27/40\n",
            "3/3 [==============================] - 0s 13ms/step - loss: 0.1365 - accuracy: 0.9643 - val_loss: 0.1699 - val_accuracy: 0.9524\n",
            "Epoch 28/40\n",
            "3/3 [==============================] - 0s 15ms/step - loss: 0.1432 - accuracy: 0.9643 - val_loss: 0.1576 - val_accuracy: 0.9524\n",
            "Epoch 29/40\n",
            "3/3 [==============================] - 0s 16ms/step - loss: 0.1214 - accuracy: 0.9762 - val_loss: 0.1587 - val_accuracy: 0.9524\n",
            "Epoch 30/40\n",
            "3/3 [==============================] - 0s 13ms/step - loss: 0.1386 - accuracy: 0.9643 - val_loss: 0.1392 - val_accuracy: 0.9524\n",
            "Epoch 31/40\n",
            "3/3 [==============================] - 0s 16ms/step - loss: 0.1182 - accuracy: 0.9762 - val_loss: 0.1488 - val_accuracy: 0.9524\n",
            "Epoch 32/40\n",
            "3/3 [==============================] - 0s 17ms/step - loss: 0.1196 - accuracy: 0.9762 - val_loss: 0.1327 - val_accuracy: 0.9524\n",
            "Epoch 33/40\n",
            "3/3 [==============================] - 0s 16ms/step - loss: 0.1131 - accuracy: 0.9762 - val_loss: 0.1285 - val_accuracy: 0.9524\n",
            "Epoch 34/40\n",
            "3/3 [==============================] - 0s 13ms/step - loss: 0.1139 - accuracy: 0.9643 - val_loss: 0.1260 - val_accuracy: 0.9524\n",
            "Epoch 35/40\n",
            "3/3 [==============================] - 0s 13ms/step - loss: 0.1072 - accuracy: 0.9762 - val_loss: 0.1353 - val_accuracy: 0.9524\n",
            "Epoch 36/40\n",
            "3/3 [==============================] - 0s 14ms/step - loss: 0.1134 - accuracy: 0.9762 - val_loss: 0.1204 - val_accuracy: 0.9524\n",
            "Epoch 37/40\n",
            "3/3 [==============================] - 0s 15ms/step - loss: 0.1035 - accuracy: 0.9643 - val_loss: 0.1277 - val_accuracy: 0.9524\n",
            "Epoch 38/40\n",
            "3/3 [==============================] - 0s 15ms/step - loss: 0.1069 - accuracy: 0.9524 - val_loss: 0.1169 - val_accuracy: 0.9524\n",
            "Epoch 39/40\n",
            "3/3 [==============================] - 0s 15ms/step - loss: 0.0973 - accuracy: 0.9762 - val_loss: 0.1159 - val_accuracy: 0.9524\n",
            "Epoch 40/40\n",
            "3/3 [==============================] - 0s 15ms/step - loss: 0.0968 - accuracy: 0.9762 - val_loss: 0.1120 - val_accuracy: 0.9524\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "35HEusuNfnwV"
      },
      "source": [
        "# Graph comparing training and validation loss"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Xi-2cwm0bBhg",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 606
        },
        "outputId": "eff530e6-aed7-4145-979a-0ac6f0fd2ab8"
      },
      "source": [
        "plt.figure(figsize = (14, 10))\n",
        "\n",
        "epoch_range = range(1, epochs + 1)\n",
        "train_loss = history.history['loss']\n",
        "val_loss = history.history['val_loss']\n",
        "\n",
        "plt.plot(epoch_range, train_loss, label = \"Training Loss\")\n",
        "plt.plot(epoch_range, val_loss, label = \"Validation Loss\")\n",
        "\n",
        "plt.xlabel(\"Epochs\")\n",
        "plt.ylabel(\"Loss\")\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1008x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "J-L90noA1Es9"
      },
      "source": [
        "# Score"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FdDgmyjP0cxK",
        "outputId": "62686d06-6cb2-4631-9463-916d071f5161"
      },
      "source": [
        "model.evaluate(X_test, y_test)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2/2 [==============================] - 0s 4ms/step - loss: 0.0972 - accuracy: 1.0000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.09715938568115234, 1.0]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pMgtsZE22M7k"
      },
      "source": [
        "### A model with an accuracy of 100%"
      ]
    }
  ]
}