Assignment2.ipynb

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  "metadata": {
    "colab": {
      "name": "Assignment2.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "KxcGuLyVi268"
      },
      "source": [
        "import pandas as pd\n",
        "df = pd.read_csv(\"Iris.csv\")\n",
        "df.drop(\"Id\", axis=1, inplace=True)"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 417
        },
        "id": "nGddphVji8Ds",
        "outputId": "293fd4b9-b276-48a1-93df-4d8a3f206c67"
      },
      "source": [
        "df"
      ],
      "execution_count": 4,
      "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>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>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>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>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>4.6</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>5.0</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",
              "    </tr>\n",
              "    <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",
              "      <td>Iris-virginica</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",
              "      <td>Iris-virginica</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",
              "      <td>Iris-virginica</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",
              "      <td>Iris-virginica</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",
              "      <td>Iris-virginica</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>150 rows × 5 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm         Species\n",
              "0              5.1           3.5            1.4           0.2     Iris-setosa\n",
              "1              4.9           3.0            1.4           0.2     Iris-setosa\n",
              "2              4.7           3.2            1.3           0.2     Iris-setosa\n",
              "3              4.6           3.1            1.5           0.2     Iris-setosa\n",
              "4              5.0           3.6            1.4           0.2     Iris-setosa\n",
              "..             ...           ...            ...           ...             ...\n",
              "145            6.7           3.0            5.2           2.3  Iris-virginica\n",
              "146            6.3           2.5            5.0           1.9  Iris-virginica\n",
              "147            6.5           3.0            5.2           2.0  Iris-virginica\n",
              "148            6.2           3.4            5.4           2.3  Iris-virginica\n",
              "149            5.9           3.0            5.1           1.8  Iris-virginica\n",
              "\n",
              "[150 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oEdhxkFUi8r1",
        "outputId": "298588ee-467d-47e9-c9f8-e6d36b26587c"
      },
      "source": [
        "df[\"Species\"].unique()"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Iris-setosa', 'Iris-versicolor', 'Iris-virginica'], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8qvr13Iqi_EY"
      },
      "source": [
        "class_names = list(df[\"Species\"].unique())\n",
        "\n",
        "df[\"Species\"] = df[\"Species\"].map(lambda label: class_names.index(label))"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UqknFIdSjBbe"
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "train_df, val_df = train_test_split(df, test_size=0.3, stratify=df[\"Species\"], random_state=42)"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S5I5LHrajDnc"
      },
      "source": [
        "import tensorflow as tf\n",
        "\n",
        "model = tf.keras.Sequential([\n",
        "    tf.keras.layers.Dense(64, activation=\"relu\"),\n",
        "    tf.keras.layers.Dense(64, activation=\"relu\"),\n",
        "    tf.keras.layers.Dense(3, activation=\"softmax\")\n",
        "])"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CINInz6WjFzC"
      },
      "source": [
        "model.compile(\n",
        "    loss=\"sparse_categorical_crossentropy\",\n",
        "    metrics=[\"accuracy\"]\n",
        ")"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Bx68omxDjIDv"
      },
      "source": [
        "tf.random.set_seed(12345)"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "psbMfXoYjJ7m"
      },
      "source": [
        "earlystopping = tf.keras.callbacks.EarlyStopping(patience=10, restore_best_weights=True)"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BIjeHW5BjMUo",
        "outputId": "79dfa0fe-c749-48c5-eb37-7827b3e3898a"
      },
      "source": [
        "model.fit(\n",
        "    train_df.drop(\"Species\", axis=1),\n",
        "    train_df[\"Species\"],\n",
        "    epochs=250,\n",
        "    validation_data=(val_df.drop(\"Species\", axis=1), val_df[\"Species\"]),\n",
        "    callbacks=[earlystopping],\n",
        "    batch_size=64,\n",
        "    verbose=0,\n",
        ")"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7f8086f6eb90>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nUwFOvdDjO5m",
        "outputId": "1b90e410-52ed-4e94-d8ff-e2c26b2f9016"
      },
      "source": [
        "val_loss, val_acc = model.evaluate(val_df.drop(\"Species\", axis=1), val_df[\"Species\"])\n",
        "train_loss, train_acc = model.evaluate(train_df.drop(\"Species\", axis=1), train_df[\"Species\"])\n",
        "\n",
        "print(f\"Training loss: {train_loss}\")\n",
        "print(f\"Training accuracy: {train_acc}\")\n",
        "print(f\"Validation loss: {val_loss}\")\n",
        "print(f\"Validation accuracy: {val_acc}\")"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2/2 [==============================] - 0s 8ms/step - loss: 0.0584 - accuracy: 1.0000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0647 - accuracy: 0.9714\n",
            "Training loss: 0.06471424549818039\n",
            "Training accuracy: 0.9714285731315613\n",
            "Validation loss: 0.058361269533634186\n",
            "Validation accuracy: 1.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CZpqQkq7jQ86",
        "outputId": "0f198aac-04ae-4e00-ce17-d38f215f9124"
      },
      "source": [
        "# Saving our model\n",
        "model.save(\"./model\")"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:Assets written to: ./model/assets\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}