KTGK_ML.ipynb

{
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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "z9yaXyXe7154",
        "outputId": "2fc73a3a-18a6-4829-ea75-cc6c0aeb6b1e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Bước 1: Import bibrary"
      ],
      "metadata": {
        "id": "-adtwlDqSRAc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# import library\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from matplotlib import pyplot as plt\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Dense # thư viện để hoàn thiện mạng ANN\n",
        "from tensorflow.keras.utils import to_categorical\n",
        "from sklearn.datasets import load_iris\n",
        "from sklearn.model_selection import train_test_split"
      ],
      "metadata": {
        "id": "viIwx4xxf-yt"
      },
      "execution_count": 96,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Bước 2: Chuẩn bị data"
      ],
      "metadata": {
        "id": "VhhxtpLqSUzB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data = load_iris()"
      ],
      "metadata": {
        "id": "CWuWwUvLITJP"
      },
      "execution_count": 97,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data.data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eB_NDi8GMQqh",
        "outputId": "d0ae86be-a937-4c2d-80d9-1007eb0d03c0"
      },
      "execution_count": 98,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[5.1, 3.5, 1.4, 0.2],\n",
              "       [4.9, 3. , 1.4, 0.2],\n",
              "       [4.7, 3.2, 1.3, 0.2],\n",
              "       [4.6, 3.1, 1.5, 0.2],\n",
              "       [5. , 3.6, 1.4, 0.2],\n",
              "       [5.4, 3.9, 1.7, 0.4],\n",
              "       [4.6, 3.4, 1.4, 0.3],\n",
              "       [5. , 3.4, 1.5, 0.2],\n",
              "       [4.4, 2.9, 1.4, 0.2],\n",
              "       [4.9, 3.1, 1.5, 0.1],\n",
              "       [5.4, 3.7, 1.5, 0.2],\n",
              "       [4.8, 3.4, 1.6, 0.2],\n",
              "       [4.8, 3. , 1.4, 0.1],\n",
              "       [4.3, 3. , 1.1, 0.1],\n",
              "       [5.8, 4. , 1.2, 0.2],\n",
              "       [5.7, 4.4, 1.5, 0.4],\n",
              "       [5.4, 3.9, 1.3, 0.4],\n",
              "       [5.1, 3.5, 1.4, 0.3],\n",
              "       [5.7, 3.8, 1.7, 0.3],\n",
              "       [5.1, 3.8, 1.5, 0.3],\n",
              "       [5.4, 3.4, 1.7, 0.2],\n",
              "       [5.1, 3.7, 1.5, 0.4],\n",
              "       [4.6, 3.6, 1. , 0.2],\n",
              "       [5.1, 3.3, 1.7, 0.5],\n",
              "       [4.8, 3.4, 1.9, 0.2],\n",
              "       [5. , 3. , 1.6, 0.2],\n",
              "       [5. , 3.4, 1.6, 0.4],\n",
              "       [5.2, 3.5, 1.5, 0.2],\n",
              "       [5.2, 3.4, 1.4, 0.2],\n",
              "       [4.7, 3.2, 1.6, 0.2],\n",
              "       [4.8, 3.1, 1.6, 0.2],\n",
              "       [5.4, 3.4, 1.5, 0.4],\n",
              "       [5.2, 4.1, 1.5, 0.1],\n",
              "       [5.5, 4.2, 1.4, 0.2],\n",
              "       [4.9, 3.1, 1.5, 0.2],\n",
              "       [5. , 3.2, 1.2, 0.2],\n",
              "       [5.5, 3.5, 1.3, 0.2],\n",
              "       [4.9, 3.6, 1.4, 0.1],\n",
              "       [4.4, 3. , 1.3, 0.2],\n",
              "       [5.1, 3.4, 1.5, 0.2],\n",
              "       [5. , 3.5, 1.3, 0.3],\n",
              "       [4.5, 2.3, 1.3, 0.3],\n",
              "       [4.4, 3.2, 1.3, 0.2],\n",
              "       [5. , 3.5, 1.6, 0.6],\n",
              "       [5.1, 3.8, 1.9, 0.4],\n",
              "       [4.8, 3. , 1.4, 0.3],\n",
              "       [5.1, 3.8, 1.6, 0.2],\n",
              "       [4.6, 3.2, 1.4, 0.2],\n",
              "       [5.3, 3.7, 1.5, 0.2],\n",
              "       [5. , 3.3, 1.4, 0.2],\n",
              "       [7. , 3.2, 4.7, 1.4],\n",
              "       [6.4, 3.2, 4.5, 1.5],\n",
              "       [6.9, 3.1, 4.9, 1.5],\n",
              "       [5.5, 2.3, 4. , 1.3],\n",
              "       [6.5, 2.8, 4.6, 1.5],\n",
              "       [5.7, 2.8, 4.5, 1.3],\n",
              "       [6.3, 3.3, 4.7, 1.6],\n",
              "       [4.9, 2.4, 3.3, 1. ],\n",
              "       [6.6, 2.9, 4.6, 1.3],\n",
              "       [5.2, 2.7, 3.9, 1.4],\n",
              "       [5. , 2. , 3.5, 1. ],\n",
              "       [5.9, 3. , 4.2, 1.5],\n",
              "       [6. , 2.2, 4. , 1. ],\n",
              "       [6.1, 2.9, 4.7, 1.4],\n",
              "       [5.6, 2.9, 3.6, 1.3],\n",
              "       [6.7, 3.1, 4.4, 1.4],\n",
              "       [5.6, 3. , 4.5, 1.5],\n",
              "       [5.8, 2.7, 4.1, 1. ],\n",
              "       [6.2, 2.2, 4.5, 1.5],\n",
              "       [5.6, 2.5, 3.9, 1.1],\n",
              "       [5.9, 3.2, 4.8, 1.8],\n",
              "       [6.1, 2.8, 4. , 1.3],\n",
              "       [6.3, 2.5, 4.9, 1.5],\n",
              "       [6.1, 2.8, 4.7, 1.2],\n",
              "       [6.4, 2.9, 4.3, 1.3],\n",
              "       [6.6, 3. , 4.4, 1.4],\n",
              "       [6.8, 2.8, 4.8, 1.4],\n",
              "       [6.7, 3. , 5. , 1.7],\n",
              "       [6. , 2.9, 4.5, 1.5],\n",
              "       [5.7, 2.6, 3.5, 1. ],\n",
              "       [5.5, 2.4, 3.8, 1.1],\n",
              "       [5.5, 2.4, 3.7, 1. ],\n",
              "       [5.8, 2.7, 3.9, 1.2],\n",
              "       [6. , 2.7, 5.1, 1.6],\n",
              "       [5.4, 3. , 4.5, 1.5],\n",
              "       [6. , 3.4, 4.5, 1.6],\n",
              "       [6.7, 3.1, 4.7, 1.5],\n",
              "       [6.3, 2.3, 4.4, 1.3],\n",
              "       [5.6, 3. , 4.1, 1.3],\n",
              "       [5.5, 2.5, 4. , 1.3],\n",
              "       [5.5, 2.6, 4.4, 1.2],\n",
              "       [6.1, 3. , 4.6, 1.4],\n",
              "       [5.8, 2.6, 4. , 1.2],\n",
              "       [5. , 2.3, 3.3, 1. ],\n",
              "       [5.6, 2.7, 4.2, 1.3],\n",
              "       [5.7, 3. , 4.2, 1.2],\n",
              "       [5.7, 2.9, 4.2, 1.3],\n",
              "       [6.2, 2.9, 4.3, 1.3],\n",
              "       [5.1, 2.5, 3. , 1.1],\n",
              "       [5.7, 2.8, 4.1, 1.3],\n",
              "       [6.3, 3.3, 6. , 2.5],\n",
              "       [5.8, 2.7, 5.1, 1.9],\n",
              "       [7.1, 3. , 5.9, 2.1],\n",
              "       [6.3, 2.9, 5.6, 1.8],\n",
              "       [6.5, 3. , 5.8, 2.2],\n",
              "       [7.6, 3. , 6.6, 2.1],\n",
              "       [4.9, 2.5, 4.5, 1.7],\n",
              "       [7.3, 2.9, 6.3, 1.8],\n",
              "       [6.7, 2.5, 5.8, 1.8],\n",
              "       [7.2, 3.6, 6.1, 2.5],\n",
              "       [6.5, 3.2, 5.1, 2. ],\n",
              "       [6.4, 2.7, 5.3, 1.9],\n",
              "       [6.8, 3. , 5.5, 2.1],\n",
              "       [5.7, 2.5, 5. , 2. ],\n",
              "       [5.8, 2.8, 5.1, 2.4],\n",
              "       [6.4, 3.2, 5.3, 2.3],\n",
              "       [6.5, 3. , 5.5, 1.8],\n",
              "       [7.7, 3.8, 6.7, 2.2],\n",
              "       [7.7, 2.6, 6.9, 2.3],\n",
              "       [6. , 2.2, 5. , 1.5],\n",
              "       [6.9, 3.2, 5.7, 2.3],\n",
              "       [5.6, 2.8, 4.9, 2. ],\n",
              "       [7.7, 2.8, 6.7, 2. ],\n",
              "       [6.3, 2.7, 4.9, 1.8],\n",
              "       [6.7, 3.3, 5.7, 2.1],\n",
              "       [7.2, 3.2, 6. , 1.8],\n",
              "       [6.2, 2.8, 4.8, 1.8],\n",
              "       [6.1, 3. , 4.9, 1.8],\n",
              "       [6.4, 2.8, 5.6, 2.1],\n",
              "       [7.2, 3. , 5.8, 1.6],\n",
              "       [7.4, 2.8, 6.1, 1.9],\n",
              "       [7.9, 3.8, 6.4, 2. ],\n",
              "       [6.4, 2.8, 5.6, 2.2],\n",
              "       [6.3, 2.8, 5.1, 1.5],\n",
              "       [6.1, 2.6, 5.6, 1.4],\n",
              "       [7.7, 3. , 6.1, 2.3],\n",
              "       [6.3, 3.4, 5.6, 2.4],\n",
              "       [6.4, 3.1, 5.5, 1.8],\n",
              "       [6. , 3. , 4.8, 1.8],\n",
              "       [6.9, 3.1, 5.4, 2.1],\n",
              "       [6.7, 3.1, 5.6, 2.4],\n",
              "       [6.9, 3.1, 5.1, 2.3],\n",
              "       [5.8, 2.7, 5.1, 1.9],\n",
              "       [6.8, 3.2, 5.9, 2.3],\n",
              "       [6.7, 3.3, 5.7, 2.5],\n",
              "       [6.7, 3. , 5.2, 2.3],\n",
              "       [6.3, 2.5, 5. , 1.9],\n",
              "       [6.5, 3. , 5.2, 2. ],\n",
              "       [6.2, 3.4, 5.4, 2.3],\n",
              "       [5.9, 3. , 5.1, 1.8]])"
            ]
          },
          "metadata": {},
          "execution_count": 98
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.target"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AlwFBU5eMVfu",
        "outputId": "ba20b0ba-e420-42fd-a597-90f29b3f872c"
      },
      "execution_count": 99,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
              "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
              "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2])"
            ]
          },
          "metadata": {},
          "execution_count": 99
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(data.data, data.target, test_size=0.3)"
      ],
      "metadata": {
        "id": "qlxDdwkVMOW2"
      },
      "execution_count": 100,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train.shape, X_test.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HqBIdQ_bIpH-",
        "outputId": "5a8f6964-138e-467f-9ed9-058c3c739700"
      },
      "execution_count": 101,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((105, 4), (45, 4))"
            ]
          },
          "metadata": {},
          "execution_count": 101
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_train.shape, y_test.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KcXDHsUXI3EO",
        "outputId": "a0f4a583-7ca8-44bd-8417-23471734d47a"
      },
      "execution_count": 102,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((105,), (45,))"
            ]
          },
          "metadata": {},
          "execution_count": 102
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Bước 3: Khai báo cấu trúc ANN"
      ],
      "metadata": {
        "id": "Kx1YW_SCSciE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Build the model.\n",
        "model = Sequential([\n",
        "  Dense(16, activation='relu', input_shape=(4,)),\n",
        "  Dense(16, activation='relu'),\n",
        "  Dense(16, activation='relu'),\n",
        "  Dense(16, activation='relu'),\n",
        "  Dense(16, activation='relu'),\n",
        "  Dense(3, activation='softmax'),\n",
        "])"
      ],
      "metadata": {
        "id": "aHNHBzAWJCJn"
      },
      "execution_count": 125,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WowtqhpVKIwr",
        "outputId": "f0167f30-ba40-49e5-c9c6-e7dc5a6051d7"
      },
      "execution_count": 111,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_8\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " dense_48 (Dense)            (None, 24)                120       \n",
            "                                                                 \n",
            " dense_49 (Dense)            (None, 24)                600       \n",
            "                                                                 \n",
            " dense_50 (Dense)            (None, 24)                600       \n",
            "                                                                 \n",
            " dense_51 (Dense)            (None, 24)                600       \n",
            "                                                                 \n",
            " dense_52 (Dense)            (None, 24)                600       \n",
            "                                                                 \n",
            " dense_53 (Dense)            (None, 3)                 75        \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 2,595\n",
            "Trainable params: 2,595\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Bước 4: Compile the model and Train ANN"
      ],
      "metadata": {
        "id": "e8Q6PpDqShH7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Compile the model \n",
        "model.compile(\n",
        "    optimizer='adam',\n",
        "    loss='categorical_crossentropy', \n",
        "    metrics=['accuracy'],\n",
        ")"
      ],
      "metadata": {
        "id": "kb5SX108KK7a"
      },
      "execution_count": 126,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "history=model.fit(\n",
        "    X_train, \n",
        "    to_categorical(y_train),\n",
        "    epochs=120,\n",
        "    batch_size=32\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9md3muJHLexb",
        "outputId": "5cdfcdff-526f-4415-85b6-3e7943eb4772"
      },
      "execution_count": 127,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/120\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 1.4685 - accuracy: 0.3333\n",
            "Epoch 2/120\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 1.2915 - accuracy: 0.4571\n",
            "Epoch 3/120\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 1.1939 - accuracy: 0.3619\n",
            "Epoch 4/120\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 1.1440 - accuracy: 0.3619\n",
            "Epoch 5/120\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 1.1213 - accuracy: 0.3619\n",
            "Epoch 6/120\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 1.1159 - accuracy: 0.3619\n",
            "Epoch 7/120\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 1.1067 - accuracy: 0.3619\n",
            "Epoch 8/120\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 1.0919 - accuracy: 0.3619\n",
            "Epoch 9/120\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 1.0746 - accuracy: 0.6095\n",
            "Epoch 10/120\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 1.0650 - accuracy: 0.6952\n",
            "Epoch 11/120\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 1.0593 - accuracy: 0.6952\n",
            "Epoch 12/120\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 1.0541 - accuracy: 0.6952\n",
            "Epoch 13/120\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 1.0460 - accuracy: 0.6952\n",
            "Epoch 14/120\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 1.0381 - accuracy: 0.6952\n",
            "Epoch 15/120\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 1.0300 - accuracy: 0.6952\n",
            "Epoch 16/120\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 1.0227 - accuracy: 0.6952\n",
            "Epoch 17/120\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 1.0116 - accuracy: 0.6952\n",
            "Epoch 18/120\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 1.0001 - accuracy: 0.6952\n",
            "Epoch 19/120\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.9893 - accuracy: 0.6952\n",
            "Epoch 20/120\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.9752 - accuracy: 0.6952\n",
            "Epoch 21/120\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.9607 - accuracy: 0.6952\n",
            "Epoch 22/120\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.9422 - accuracy: 0.6952\n",
            "Epoch 23/120\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.9144 - accuracy: 0.6952\n",
            "Epoch 24/120\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.8897 - accuracy: 0.6952\n",
            "Epoch 25/120\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.8562 - accuracy: 0.6952\n",
            "Epoch 26/120\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.8291 - accuracy: 0.6952\n",
            "Epoch 27/120\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.7988 - accuracy: 0.6952\n",
            "Epoch 28/120\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.7659 - accuracy: 0.6952\n",
            "Epoch 29/120\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.7321 - accuracy: 0.6952\n",
            "Epoch 30/120\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.7052 - accuracy: 0.6952\n",
            "Epoch 31/120\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.6785 - accuracy: 0.6952\n",
            "Epoch 32/120\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.6549 - accuracy: 0.6952\n",
            "Epoch 33/120\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.6351 - accuracy: 0.6857\n",
            "Epoch 34/120\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.6178 - accuracy: 0.6857\n",
            "Epoch 35/120\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.6012 - accuracy: 0.6952\n",
            "Epoch 36/120\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.5857 - accuracy: 0.6952\n",
            "Epoch 37/120\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.5724 - accuracy: 0.6952\n",
            "Epoch 38/120\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.5569 - accuracy: 0.6952\n",
            "Epoch 39/120\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.5426 - accuracy: 0.6952\n",
            "Epoch 40/120\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.5286 - accuracy: 0.6952\n",
            "Epoch 41/120\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.5163 - accuracy: 0.6952\n",
            "Epoch 42/120\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.5010 - accuracy: 0.6952\n",
            "Epoch 43/120\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.4879 - accuracy: 0.7143\n",
            "Epoch 44/120\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.4749 - accuracy: 0.8095\n",
            "Epoch 45/120\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.4607 - accuracy: 0.8857\n",
            "Epoch 46/120\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.4476 - accuracy: 0.8952\n",
            "Epoch 47/120\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.4347 - accuracy: 0.8762\n",
            "Epoch 48/120\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.4278 - accuracy: 0.9238\n",
            "Epoch 49/120\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.4055 - accuracy: 0.9619\n",
            "Epoch 50/120\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.3926 - accuracy: 0.9333\n",
            "Epoch 51/120\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.3785 - accuracy: 0.9619\n",
            "Epoch 52/120\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.3613 - accuracy: 0.9333\n",
            "Epoch 53/120\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.3446 - accuracy: 0.9524\n",
            "Epoch 54/120\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.3332 - accuracy: 0.9810\n",
            "Epoch 55/120\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.3184 - accuracy: 0.9714\n",
            "Epoch 56/120\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.3088 - accuracy: 0.9714\n",
            "Epoch 57/120\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.2861 - accuracy: 0.9714\n",
            "Epoch 58/120\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.2889 - accuracy: 0.9333\n",
            "Epoch 59/120\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.2667 - accuracy: 0.9619\n",
            "Epoch 60/120\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.2620 - accuracy: 0.9619\n",
            "Epoch 61/120\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.2471 - accuracy: 0.9524\n",
            "Epoch 62/120\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.2384 - accuracy: 0.9619\n",
            "Epoch 63/120\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.2374 - accuracy: 0.9619\n",
            "Epoch 64/120\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.2092 - accuracy: 0.9714\n",
            "Epoch 65/120\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.2043 - accuracy: 0.9714\n",
            "Epoch 66/120\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.1907 - accuracy: 0.9714\n",
            "Epoch 67/120\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.1812 - accuracy: 0.9810\n",
            "Epoch 68/120\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.1766 - accuracy: 0.9810\n",
            "Epoch 69/120\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.1841 - accuracy: 0.9524\n",
            "Epoch 70/120\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.1853 - accuracy: 0.9714\n",
            "Epoch 71/120\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.1587 - accuracy: 0.9714\n",
            "Epoch 72/120\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.1564 - accuracy: 0.9524\n",
            "Epoch 73/120\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.1715 - accuracy: 0.9524\n",
            "Epoch 74/120\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.1325 - accuracy: 0.9714\n",
            "Epoch 75/120\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.1573 - accuracy: 0.9429\n",
            "Epoch 76/120\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.1366 - accuracy: 0.9810\n",
            "Epoch 77/120\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.1276 - accuracy: 0.9810\n",
            "Epoch 78/120\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.1208 - accuracy: 0.9714\n",
            "Epoch 79/120\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.1170 - accuracy: 0.9714\n",
            "Epoch 80/120\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.1151 - accuracy: 0.9810\n",
            "Epoch 81/120\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.1089 - accuracy: 0.9810\n",
            "Epoch 82/120\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.1174 - accuracy: 0.9524\n",
            "Epoch 83/120\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.1029 - accuracy: 0.9905\n",
            "Epoch 84/120\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.1020 - accuracy: 0.9810\n",
            "Epoch 85/120\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.1073 - accuracy: 0.9619\n",
            "Epoch 86/120\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.0921 - accuracy: 0.9810\n",
            "Epoch 87/120\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.1003 - accuracy: 0.9810\n",
            "Epoch 88/120\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0908 - accuracy: 0.9810\n",
            "Epoch 89/120\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.0850 - accuracy: 0.9905\n",
            "Epoch 90/120\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.0956 - accuracy: 0.9810\n",
            "Epoch 91/120\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.0878 - accuracy: 0.9619\n",
            "Epoch 92/120\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.0972 - accuracy: 0.9619\n",
            "Epoch 93/120\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.0911 - accuracy: 0.9810\n",
            "Epoch 94/120\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.0840 - accuracy: 0.9810\n",
            "Epoch 95/120\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.0808 - accuracy: 0.9619\n",
            "Epoch 96/120\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.1057 - accuracy: 0.9619\n",
            "Epoch 97/120\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.0866 - accuracy: 0.9714\n",
            "Epoch 98/120\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.0879 - accuracy: 0.9810\n",
            "Epoch 99/120\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0892 - accuracy: 0.9810\n",
            "Epoch 100/120\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.0860 - accuracy: 0.9810\n",
            "Epoch 101/120\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.0712 - accuracy: 0.9810\n",
            "Epoch 102/120\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0678 - accuracy: 0.9905\n",
            "Epoch 103/120\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0703 - accuracy: 0.9905\n",
            "Epoch 104/120\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0737 - accuracy: 0.9810\n",
            "Epoch 105/120\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0638 - accuracy: 0.9810\n",
            "Epoch 106/120\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.0902 - accuracy: 0.9524\n",
            "Epoch 107/120\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.0581 - accuracy: 0.9905\n",
            "Epoch 108/120\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0835 - accuracy: 0.9714\n",
            "Epoch 109/120\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.0780 - accuracy: 0.9619\n",
            "Epoch 110/120\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0810 - accuracy: 0.9524\n",
            "Epoch 111/120\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.0704 - accuracy: 0.9905\n",
            "Epoch 112/120\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0643 - accuracy: 0.9714\n",
            "Epoch 113/120\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.0681 - accuracy: 0.9619\n",
            "Epoch 114/120\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0563 - accuracy: 0.9905\n",
            "Epoch 115/120\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.0572 - accuracy: 0.9905\n",
            "Epoch 116/120\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.0562 - accuracy: 0.9905\n",
            "Epoch 117/120\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0552 - accuracy: 0.9905\n",
            "Epoch 118/120\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.0554 - accuracy: 0.9905\n",
            "Epoch 119/120\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.0511 - accuracy: 0.9905\n",
            "Epoch 120/120\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.0598 - accuracy: 0.9810\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(history.history.keys())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dlqiWhDxJP6X",
        "outputId": "9aa6b030-1a4e-48ac-d658-26d30c663481"
      },
      "execution_count": 120,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "dict_keys(['loss', 'accuracy'])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(history.history['accuracy'])\n",
        "plt.title('model accuracy')\n",
        "plt.ylabel('accuracy')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train', 'test'], loc='upper left')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "SMcap0FpN4Ho",
        "outputId": "62f74009-e8f7-4916-e2f6-e8a1cb4ca7aa"
      },
      "execution_count": 121,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(history.history['loss'])\n",
        "plt.title('model loss')\n",
        "plt.ylabel('loss')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train', 'test'], loc='upper left')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "Du_LUPfoN77a",
        "outputId": "351c7f28-1aae-49e4-bdc0-cbdd0ecf690e"
      },
      "execution_count": 122,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# độ chính xác hàm loss.\n",
        "model.evaluate(\n",
        "    X_test,\n",
        "    to_categorical(y_test)\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OBFIMF9ETZsw",
        "outputId": "177a4a1b-e313-41bd-bc71-be7b40f96dcf"
      },
      "execution_count": 123,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2/2 [==============================] - 0s 3ms/step - loss: 0.1945 - accuracy: 0.9556\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.19454528391361237, 0.9555555582046509]"
            ]
          },
          "metadata": {},
          "execution_count": 123
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Predict on the first 5 test images.\n",
        "predictions = model.predict([(2,3,4,5)])\n",
        "\n",
        "# Print our model's predictions.\n",
        "print('Hoa này thuộc lớp số: ', np.argmax(predictions, axis=1)) "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "q5BeIHBdQBGS",
        "outputId": "2eb859da-66f8-4db7-a651-86b685a8e8d4"
      },
      "execution_count": 124,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 59ms/step\n",
            "Hoa này thuộc lớp số:  [2]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Save model "
      ],
      "metadata": {
        "id": "wKg1uyQPSqMb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import keras\n",
        "model.save('/content/drive/MyDrive/Colab Notebooks/model.gk')"
      ],
      "metadata": {
        "id": "myUIbf1lQmM7"
      },
      "execution_count": 89,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "mxDV-njPSw70"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "E7fJ7yM9R-Tu"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}