FASHIONMNIST WITH CNN.ipynb

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  "metadata": {
    "colab": {
      "name": "PHÂN LOẠI QUẦN ÁO.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "TPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1Q_tGbOZOxPi",
        "colab_type": "text"
      },
      "source": [
        "**IMPORT THƯ VIỆN**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EfeMRV3RMDG7",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "%tensorflow_version 1.x\n",
        "from keras.utils import to_categorical\n",
        "import tensorflow as tf\n",
        "import sklearn\n",
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
        "from sklearn.model_selection import train_test_split\n",
        "import seaborn as sns\n",
        "import math\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "from matplotlib import*\n",
        "import plotly.graph_objs as go\n",
        "from plotly import tools\n",
        "from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot\n",
        "from keras.applications import VGG16;\n",
        "from keras.applications.vgg16 import preprocess_input\n",
        "import tensorflow_hub as tfHub\n",
        "from tensorflow.keras import layers as tfLayers\n",
        "from sklearn.metrics import confusion_matrix, classification_report\n",
        "import itertools"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cQUp1f0PO7nS",
        "colab_type": "text"
      },
      "source": [
        "**DOWLOAD DỮ LIỆU TỪ KAGGLE**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6hOvPzSOOxiw",
        "colab_type": "code",
        "outputId": "facb4b4a-9cb5-4fe7-bbc4-94f271c19f9a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 202
        }
      },
      "source": [
        "!pip install kaggle"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: kaggle in /usr/local/lib/python3.6/dist-packages (1.5.6)\n",
            "Requirement already satisfied: certifi in /usr/local/lib/python3.6/dist-packages (from kaggle) (2019.11.28)\n",
            "Requirement already satisfied: urllib3<1.25,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from kaggle) (1.24.3)\n",
            "Requirement already satisfied: python-slugify in /usr/local/lib/python3.6/dist-packages (from kaggle) (4.0.0)\n",
            "Requirement already satisfied: python-dateutil in /usr/local/lib/python3.6/dist-packages (from kaggle) (2.6.1)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.6/dist-packages (from kaggle) (4.28.1)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.6/dist-packages (from kaggle) (2.21.0)\n",
            "Requirement already satisfied: six>=1.10 in /usr/local/lib/python3.6/dist-packages (from kaggle) (1.12.0)\n",
            "Requirement already satisfied: text-unidecode>=1.3 in /usr/local/lib/python3.6/dist-packages (from python-slugify->kaggle) (1.3)\n",
            "Requirement already satisfied: idna<2.9,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests->kaggle) (2.8)\n",
            "Requirement already satisfied: chardet<3.1.0,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests->kaggle) (3.0.4)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZS3rr9CQOxod",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "! mkdir ~/.kaggle"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "csZ4FK54Oxrq",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!cp './kaggle.json' '/root/.kaggle/'"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XAisnKZSOxug",
        "colab_type": "code",
        "outputId": "c0b55c50-c395-44bc-9591-81c5f72c8fbe",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 84
        }
      },
      "source": [
        "!kaggle datasets download --force -d 'zalando-research/fashionmnist'\n",
        "!rm '/root/.kaggle/kaggle.json'"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Warning: Your Kaggle API key is readable by other users on this system! To fix this, you can run 'chmod 600 /root/.kaggle/kaggle.json'\n",
            "Downloading fashionmnist.zip to /content\n",
            " 93% 64.0M/68.8M [00:00<00:00, 50.2MB/s]\n",
            "100% 68.8M/68.8M [00:00<00:00, 83.2MB/s]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YwVdgwTnOxwz",
        "colab_type": "code",
        "outputId": "3b42b71f-1704-4364-d553-5a12d338496b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 134
        }
      },
      "source": [
        "!unzip 'fashionmnist.zip' -d 'my_data'"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Archive:  fashionmnist.zip\n",
            "  inflating: my_data/fashion-mnist_test.csv  \n",
            "  inflating: my_data/fashion-mnist_train.csv  \n",
            "  inflating: my_data/t10k-images-idx3-ubyte  \n",
            "  inflating: my_data/t10k-labels-idx1-ubyte  \n",
            "  inflating: my_data/train-images-idx3-ubyte  \n",
            "  inflating: my_data/train-labels-idx1-ubyte  \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xuCt0QmFPMIe",
        "colab_type": "text"
      },
      "source": [
        "**ĐỌC DỮ LIỆU**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OREr6Y3BOxzu",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_dataset = pd.read_csv('./my_data/fashion-mnist_train.csv').values\n",
        "test_dataset = pd.read_csv('./my_data/fashion-mnist_test.csv').values\n",
        "train_data = pd.read_csv('./my_data/fashion-mnist_train.csv')\n",
        "test_data = pd.read_csv('./my_data/fashion-mnist_test.csv')\n",
        "num_train_examples = len(train_dataset)\n",
        "num_test_examples = len(test_dataset)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZTVr0WzhbY_H",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_df = pd.read_csv('./my_data/fashion-mnist_train.csv',sep=',')\n",
        "test_df = pd.read_csv('./my_data/fashion-mnist_test.csv', sep = ',')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QhCc0rcTbHVz",
        "colab_type": "code",
        "outputId": "33a2a309-e448-4347-c24b-a761f13705d8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 244
        }
      },
      "source": [
        "train_df.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>114</td>\n",
              "      <td>183</td>\n",
              "      <td>112</td>\n",
              "      <td>55</td>\n",
              "      <td>23</td>\n",
              "      <td>72</td>\n",
              "      <td>102</td>\n",
              "      <td>165</td>\n",
              "      <td>160</td>\n",
              "      <td>28</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>24</td>\n",
              "      <td>188</td>\n",
              "      <td>163</td>\n",
              "      <td>93</td>\n",
              "      <td>...</td>\n",
              "      <td>171</td>\n",
              "      <td>249</td>\n",
              "      <td>207</td>\n",
              "      <td>197</td>\n",
              "      <td>202</td>\n",
              "      <td>45</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>22</td>\n",
              "      <td>21</td>\n",
              "      <td>25</td>\n",
              "      <td>69</td>\n",
              "      <td>52</td>\n",
              "      <td>45</td>\n",
              "      <td>74</td>\n",
              "      <td>39</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>46</td>\n",
              "      <td>0</td>\n",
              "      <td>21</td>\n",
              "      <td>68</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>25</td>\n",
              "      <td>187</td>\n",
              "      <td>189</td>\n",
              "      <td>...</td>\n",
              "      <td>230</td>\n",
              "      <td>237</td>\n",
              "      <td>229</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>68</td>\n",
              "      <td>116</td>\n",
              "      <td>112</td>\n",
              "      <td>136</td>\n",
              "      <td>147</td>\n",
              "      <td>144</td>\n",
              "      <td>121</td>\n",
              "      <td>102</td>\n",
              "      <td>63</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 785 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "   label  pixel1  pixel2  pixel3  ...  pixel781  pixel782  pixel783  pixel784\n",
              "0      2       0       0       0  ...         0         0         0         0\n",
              "1      9       0       0       0  ...         0         0         0         0\n",
              "2      6       0       0       0  ...         0         0         0         0\n",
              "3      0       0       0       0  ...         0         0         0         0\n",
              "4      3       0       0       0  ...         0         0         0         0\n",
              "\n",
              "[5 rows x 785 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_W9LctInbOrP",
        "colab_type": "code",
        "outputId": "27f3bf51-2621-445d-979d-e6fa67dcfdd5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 244
        }
      },
      "source": [
        "test_df.head()"
      ],
      "execution_count": 0,
      "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>label</th>\n",
              "      <th>pixel1</th>\n",
              "      <th>pixel2</th>\n",
              "      <th>pixel3</th>\n",
              "      <th>pixel4</th>\n",
              "      <th>pixel5</th>\n",
              "      <th>pixel6</th>\n",
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              "      <th>pixel8</th>\n",
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              "      <th>pixel13</th>\n",
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              "      <th>pixel15</th>\n",
              "      <th>pixel16</th>\n",
              "      <th>pixel17</th>\n",
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              "      <th>pixel19</th>\n",
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              "      <th>pixel21</th>\n",
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              "      <th>pixel37</th>\n",
              "      <th>pixel38</th>\n",
              "      <th>pixel39</th>\n",
              "      <th>...</th>\n",
              "      <th>pixel745</th>\n",
              "      <th>pixel746</th>\n",
              "      <th>pixel747</th>\n",
              "      <th>pixel748</th>\n",
              "      <th>pixel749</th>\n",
              "      <th>pixel750</th>\n",
              "      <th>pixel751</th>\n",
              "      <th>pixel752</th>\n",
              "      <th>pixel753</th>\n",
              "      <th>pixel754</th>\n",
              "      <th>pixel755</th>\n",
              "      <th>pixel756</th>\n",
              "      <th>pixel757</th>\n",
              "      <th>pixel758</th>\n",
              "      <th>pixel759</th>\n",
              "      <th>pixel760</th>\n",
              "      <th>pixel761</th>\n",
              "      <th>pixel762</th>\n",
              "      <th>pixel763</th>\n",
              "      <th>pixel764</th>\n",
              "      <th>pixel765</th>\n",
              "      <th>pixel766</th>\n",
              "      <th>pixel767</th>\n",
              "      <th>pixel768</th>\n",
              "      <th>pixel769</th>\n",
              "      <th>pixel770</th>\n",
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              "      <th>pixel772</th>\n",
              "      <th>pixel773</th>\n",
              "      <th>pixel774</th>\n",
              "      <th>pixel775</th>\n",
              "      <th>pixel776</th>\n",
              "      <th>pixel777</th>\n",
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              "      <th>pixel779</th>\n",
              "      <th>pixel780</th>\n",
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              "      <th>pixel782</th>\n",
              "      <th>pixel783</th>\n",
              "      <th>pixel784</th>\n",
              "    </tr>\n",
              "  </thead>\n",
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              "    <tr>\n",
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              "      <td>1</td>\n",
              "      <td>0</td>\n",
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              "      <td>24</td>\n",
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              "      <td>53</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>38</td>\n",
              "      <td>106</td>\n",
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              "      <td>89</td>\n",
              "      <td>94</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>87</td>\n",
              "      <td>12</td>\n",
              "      <td>26</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>46</td>\n",
              "      <td>50</td>\n",
              "      <td>29</td>\n",
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              "      <td>2</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>63</td>\n",
              "      <td>53</td>\n",
              "      <td>31</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
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              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>161</td>\n",
              "      <td>212</td>\n",
              "      <td>138</td>\n",
              "      <td>150</td>\n",
              "      <td>169</td>\n",
              "      <td>164</td>\n",
              "      <td>176</td>\n",
              "      <td>202</td>\n",
              "      <td>255</td>\n",
              "      <td>183</td>\n",
              "      <td>26</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>129</td>\n",
              "      <td>221</td>\n",
              "      <td>255</td>\n",
              "      <td>255</td>\n",
              "      <td>215</td>\n",
              "      <td>...</td>\n",
              "      <td>255</td>\n",
              "      <td>255</td>\n",
              "      <td>254</td>\n",
              "      <td>252</td>\n",
              "      <td>255</td>\n",
              "      <td>101</td>\n",
              "      <td>196</td>\n",
              "      <td>254</td>\n",
              "      <td>252</td>\n",
              "      <td>100</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>168</td>\n",
              "      <td>232</td>\n",
              "      <td>225</td>\n",
              "      <td>0</td>\n",
              "      <td>41</td>\n",
              "      <td>172</td>\n",
              "      <td>147</td>\n",
              "      <td>148</td>\n",
              "      <td>153</td>\n",
              "      <td>155</td>\n",
              "      <td>146</td>\n",
              "      <td>137</td>\n",
              "      <td>141</td>\n",
              "      <td>143</td>\n",
              "      <td>137</td>\n",
              "      <td>137</td>\n",
              "      <td>126</td>\n",
              "      <td>140</td>\n",
              "      <td>0</td>\n",
              "      <td>133</td>\n",
              "      <td>224</td>\n",
              "      <td>222</td>\n",
              "      <td>56</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>37</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>17</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>21</td>\n",
              "      <td>204</td>\n",
              "      <td>235</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>69</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 785 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "   label  pixel1  pixel2  pixel3  ...  pixel781  pixel782  pixel783  pixel784\n",
              "0      0       0       0       0  ...         0         0         0         0\n",
              "1      1       0       0       0  ...         0         0         0         0\n",
              "2      2       0       0       0  ...        31         0         0         0\n",
              "3      2       0       0       0  ...       222        56         0         0\n",
              "4      3       0       0       0  ...         0         0         0         0\n",
              "\n",
              "[5 rows x 785 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qTaBYgEROx2E",
        "colab_type": "code",
        "outputId": "380e0a8d-9a0c-4fec-fac9-0cc7a302274a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 536
        }
      },
      "source": [
        "class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat',\n",
        "               'Sandal',      'Shirt',   'Sneaker',  'Bag',   'Ankle boot']\n",
        "num_classes = len(class_names)\n",
        "\n",
        "plt.figure(figsize=(12,9))\n",
        "for i in range(0,12):\n",
        "    plt.subplot(3,4,i+1)\n",
        "    image_resized = np.resize(train_data.iloc[i,1:].values,(28,28))\n",
        "    plt.title(class_names[train_data.iloc[i,0]])\n",
        "    plt.imshow(image_resized, cmap='gray', interpolation='none')\n",
        "    plt.axis('off')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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75557sszNsQkTJmSZ+6Yet1/Jz4mPfexjWeaq7D/5yU9m2ZNPPpllr7/+epZNnz7djse9\nPtzjnjx5cpbtvffeWebaIHfv3j3LRo8ebcfjvlWhLKr+AQAA0OGwUAUAAEAlsVAFAABAJbFQBQAA\nQCVVophq5MiRWfbMM89k2fz587PMXYC/aNGiLGtOKz/XOu/SSy/NMteqzF2Y3alTpyxzFzwX3dYV\nD7iigBEjRmTZSy+9ZPfTFtbXgpT25No0OmujdePRRx+dZa4l8Jw5c7KsbHvJdb0NpdR2c/ezn/1s\nll144YVZ9vbbb2eZa1nqikDcsVSSevTokWWuEGOLLbbIMlcY69qlutttsskmdjzuuOtu695b3GO8\n7bbbssy9h6wN61Ix1eGHH55lrpD45ptvzrJhw4Zl2TbbbJNl7nfviqYk6Wc/+1mWnXnmmVnmiqJd\nUaKbt+54tu+++9rxuDWR89vf/jbLdt999ywbPHhwlrnX0Q9+8AO7n7Fjx5Yaj0MxFQAAADocFqoA\nAACoJBaqAAAAqCQWqgAAAKikvBJpDbS0wMFdgOs6OyxZsiTLNtxwwyxzF/oXFS+5C5xd4dUVV1yR\nZa4oxF1s7zqkvPPOO3Y87nmbOnVqlrkigxdeeKHUvp2iIpy1UaiC1tFWhUWNFRXIfPOb38yyN998\nM8vc66bsuJmPrWePPfbIMnc8dQWrXbp0KZUVHedWrlyZZZtuummWud+3K3QZOHBgqdu5Y6kkzZw5\nM8tcYYnjXofDhw8vdV807Ywzzsgy9953yimnZJnr5rTbbrtlmStoKno/PP7447PslVdeyTLXeXLr\nrbfOMtcZ083Fxx9/3I5n/PjxWeaKEl3Buus45bbnXoNujGsTZ1QBAABQSSxUAQAAUEksVAEAAFBJ\nLFQBAABQSU0WU5Ut1mhOgYPrfOIuMnbFVD179iy1D9dJZbPNNrO3dV0XXAGAK1RyhQeuaMtdWO0K\nviRp2bJlWda1a9csmzJlSpb169cvy6666qoscxeoU6TS9lyh29r4PbR20dVPfvITmy9YsCDLvvGN\nb2TZ1772tSybO3dulpXtslVUMOju7wps1te5744X7nhYtsDT3dcduyRp6dKlWeYK71zhXt++fbPM\nHQ/duF1HP8kXjLmCL5e5Y/aWW25p94Pmcb+vefPmZdl2222XZa6zmOt4udVWW2XZX//6VzueL37x\ni1nm5v2tt96aZW6dc8cdd2SZK/iaOHGiHc/kyZOzzK2dRo0alWWuANFtz3Xzcp2uJP8YWwNnVAEA\nAFBJLFQBAABQSSxUAQAAUEksVAEAAFBJTRZTuYvRXeGCu5i4yFlnnZVlCxcuzDLXpcQVSZXtpOKK\nKCRfOFW2Q4or1nCdrtxztmrVKjset01XaOLG7S4yP/3007PMdRByBV9Sy3/fKNbS59HNlbK/r7K3\nO++887KsqFDEXcT/kY98JMtcByM3x93r2HEFLvhwZbs5ueOhO+664hXXJUcqX4DkCl3csdMVbZV9\nvyjajyvkcc+P22aPHj3sfuDtsMMONnevbfd7dYWc7j3S3ff555/PskmTJtnxPPfcc1nmjnuuaMt1\nhzr66KOzzL1nH3HEEXY88+fPz7JZs2Zl2YMPPphl7vW/9957Z5l7X3Bdv9YmzqgCAACgklioAgAA\noJJYqAIAAKCSWKgCAACgkspVKzRQtgjj+OOPt/d3F9EvWbIky9wF/O7iaNfhyV3w7oqcJF/Y4S7g\nLtu9xhW4uIvti4qp3H6Kulg15sY9c+bMLLvhhhuyzF3ULVE4VWVurrnfl5t/7jXyyU9+Msu++tWv\nZtntt99ux+Nex674oKhQobGWFkn90z/9U5a98sorWeaKD9YHruDHHd9doZIrvnRd+VyhbNFt3e/b\nHbfde4MrvHPdCJcvX27H44q+yhZJudsVdUKEd+SRR9p8xowZWeaOM67wbfvtt88y18Hs73//e5a5\ntYYkXXTRRVnmXjN77LFHlrn3Xddt80tf+lKW7b///nY87nGfffbZWebWFTvttFOWvfHGG1nmXoNF\n6yHXAcu9BzQXZ1QBAABQSSxUAQAAUEksVAEAAFBJLFQBAABQSdFUkVBElKsgMsaPH2/zTp06ZZm7\nwN1lbqwucxf/u0zyFwq7C6ndxfEucxfluwueiwpFXJGL66bhigdc5oprXEHAxz72MTueyZMnl9qP\nKw5LKeXVPm2kJXO3rbhiqLId0IpuW9aee+6ZZTfffHOWPfTQQ1nmOqBJ0uzZs7Ps2GOPzbJtttkm\ny37zm99k2SWXXJJlrtNVUReg0047Lctch5eCYoF1fu5OnDgxy9yxzxWcuiJNV+TkCl8kP3fdvt1x\ncvjw4VnmCvTcMcl1EZJ8UYsbo+ue5fbj5qQrGHLFPS3VXnO3JfO2V69eNnedLF3h7xZbbJFlrnDS\nFQEefPDBWeaKSCVpu+22y7Jjjjkmy15++eUsW7p0aZZ9/OMfzzJ3vO/evbsdz6JFi7Ls1VdfzTLX\nFct1tXIdtVyxadH65ZZbbsmyorVgY03NW86oAgAAoJJYqAIAAKCSWKgCAACgklioAgAAoJJYqAIA\nAKCSmqz632CDDbIflq2EfPrpp+02XZWaU7Z1Xrdu3bLMVYA++eSTdj+uyt5V4rk2YK7q31Xou2q/\nbbfd1o7HVRX269cvyxYsWJBl7psNXCWtq5B84okn7HiOOuoom5exPlROt7ay3wTQHK5V3t13351l\n9913X5a5KllX3S9JO+64Y5a5b5NwbTXd3O3bt2+WuRZ/EyZMsONx1aqnn366vW1j68Pcdb/bqVOn\nZlnv3r2zzFWrN6fq332byKBBg7LM/Q7dvHCPxR133RiLcjf2rbfeutR+XNvrk046KcteeuklO56W\n6IhV/y01YMCALHNz+cUXX8wy9w0N48aNs/txrVHde7F7zbhWq2694NYvQ4cOteP57ne/m2XuuTjl\nlFOybOedd7bbbC9U/QMAAKDDYaEKAACASmKhCgAAgEpioQoAAIBKynthNlC2iONf//Vfs6yo7aNr\nN+dacm6yySZZ5tqTuguUXcHFs88+a8fjirF23333LHMtXZ9//vksc4VlrhjKPQ+SLzYbOHBglrnn\n1z0/bj/u4u9Pf/rTdjyuyMUVLhT9vjuiosfi5porxitbELXhhhuW2p4r2pP8nHTFHvfee2+Wudao\nZYtrdtllFzue/fbbL8vmzJmTZW6eunZ+ruhq7ty5WeZe75Jv1eoKvlzLwfXBsmXLsswdd928dy1U\n+/Tpk2WufXTRbd1rxB2f3THWFT6515d7zJIv3nXtvt3rwbVfdceAola/aDn3e3Hce98999yTZbfe\nequ9/3nnnZdlrjDaHV/d/HZFre746uaYJB1yyCFZNnbs2CxzBVpluddR0drQzXv3ntZcnFEFAABA\nJbFQBQAAQCWxUAUAAEAlsVAFAABAJTVZTFXWl7/85SxzHZEkaeXKlVnmLsB9//33S+3bdQVxxUsH\nHXSQvb8r7HAX0bvCDNchxRUPuC4VRY/PFQ+459J1UnEXLbtCNfc7KOo29P3vfz/LzjrrrCxrafek\nKil6LGUvCm/Jc+F+X65oSvLFGa4wwHVjcZ2FXOHT/vvvn2UjRoyw43EFUW6eb7755lnmnjPXQe1v\nf/tblhV1P3L3P/TQQ7NsfSimcnPFZW6uuUIj9zt0RX/uvpIv0Ch7zHfFXS5zY3QFg5J/bbs56Ypa\n3DHf7bt///5232ielnTvc8fC3XbbLcueeuope/+LL744y04++eRS23RFn2ULpbt06WLHc9hhh2WZ\nK8h2RYTuvcYVX7vXZVu/33NGFQAAAJXEQhUAAACVxEIVAAAAlcRCFQAAAJXU7GIq1zXBXfzruspI\n/qJgdyG7uxjdXfzrLup1xR677rqrHY+72N9lgwcPzjJXPOAKn1zBVtHFyK4owBUeuOfC7adsIZbr\n+CNJX/3qV7PMFVNVjZuT7rl1v4fm/G7cbct25yhbnFVUCHjllVdm2bRp07LshRdeyDLXyWXMmDFZ\ntsMOO2TZ9OnT7XhcUYmbp+7YMGDAgCybMGFClo0bN67UfSVfvLAudVBrjqFDh2aZmyvu+XHdqlzH\nOvf7L3ofcF399txzT3vbxlzxnBujm+NFnanc8+MKdV3nN3fsLHtcQPO1pJBn++23z7JBgwZl2Ukn\nnWTvf8UVV2SZe991r4UhQ4Zk2eGHH55lbo4VFYy64jBXTN6rV68sa0nxYmt0m2oOzqgCAACgklio\nAgAAoJJYqAIAAKCSWKgCAACgkppdTHXOOedkWXMKUlxxT9luPO6CeXdx/KxZs7LMFT5J/gJ3N0Z3\nMbPr4uDG6C5GdhdbS/4iZbfNshfml+1M5TLJFwq4Aquf/vSnpcbTVtz8a+kF4K19AfmoUaOyzL2+\n9t57b3t/V5Ayc+bMLHOvhyOOOCLLXKHkxIkTs8wV40m+qMDNU9fRzRXdPPTQQ1nmChJcd5eifR95\n5JFZdvnll9v7r0tcgUbZY4g7fpUtEHWFoJK00047ZZkr7nD3L3uM3WqrrbLs7bfftuNxBbTz5s3L\nMteh0B1jXZe2Lbfc0u4bbcfNUVeQ5OaY5H+Hffr0yTJXfH3ddddl2fjx47PMFfEVvT8/9thjWXbA\nAQdkmSt+LFtMVYWuk5xRBQAAQCWxUAUAAEAlsVAFAABAJbFQBQAAQCU1u5jq05/+dJbNnj07y4ou\n1C/bXcpxF/+6ApfmFNJ07tw5y1zhlSuccuNx+3b3LXp+3G3dc9apU6csK/tcuAuzi8bjisjOP//8\nLKtaMZXjuqK559YV00n+eevevXuWjR49Osv+5V/+JcuGDx+eZa7w6a677rLjcWN3evfunWWuQ4sr\nNHFFBUWvV/f8utfS5MmTs+ypp57KMjfuHj16ZJkr7JGk1157Lct23HHHLHNdidY1bq70798/y1wx\nnpsD7jjlCp+KCjZccYebV2ULpxYsWJBlbu65jodF43SvRVfo4sboCmzcuNG2fv3rX2fZnDlzsuyw\nww6z9y/qcNnYf/3Xf2WZW2u47nvuOOxel5J/D3HvU48++miWuWNCc9YqbYlXDgAAACqJhSoAAAAq\niYUqAAAAKomFKgAAACqpyWqMj3zkI1nmChymTp2aZe6iXMlfwOu6NLnCoLJdm9z2ijo7LFq0KMtc\n5x1XvFTUdaUxN8aiIgO3b1cs4p5H91j69euXZa7jSlGnLFc84H63rttQexo5cmSWPffcc1l23333\nZVlRsZCbQ65TiZsX7mL4Bx98MMtcIZebe1L5ziLudi+//HKWDRs2LMu6deuWZUVFXK4owV3E74ov\n3fxz43b3Leo25MbjfjdFxVjrEndcKVv46Y67rvPOm2++mWVFz22vXr2yzBVtuA6FZd9D3Ou16LXt\ncncMKVvA6Iq7KKZqf3vttVeWuQ5md955p73/tGnTsuyss87KMncsdV3xnnzyySx76623sqyo4NPN\nx8cffzzLjjnmmCy75ZZbsswVoLp529pdGj8MrxwAAABUEgtVAAAAVBILVQAAAFQSC1UAAABUEgtV\nAAAAVFKTJYwHHnhglrmqMFfZ6Srdm6Nsu1RXKeoq4YqqNV1V6bJly0qNx+27bFZUNeeqkl1F66BB\ng7LsqquuyrK5c+dm2WWXXZZlroWl5MfuKvxPOOEEe//24lrLPf3001k2Y8aMLCuaK+534yrL3bcv\nOK7Fo2tDWlQ57SqV3Rhd9uKLL2aZ+yaAnj17ZtmKFSvseNxrybWYHThwYJa5Oe6qTV1VumvHKflv\nS3DPpXuNrGtc1b/jfg/uOXffTuG+EcZVSUv+eOra47q55qr5BwwYkGXumzZcS2jJv+Zdu1X3TR2u\notq1Xy37O8AH3PtP2ZbrzogRI7LMHaP23HNPe/8f//jHWeZ+r64Fq/t2EvftF257O++8sx3P4sWL\ns+zQQw/NsokTJ2ZZ2W+NoYUqAAAAUICFKgAAACqJhSoAAAAqiYUqAAAAKqnJYqqPfvSjWeZaRroC\nhaKCC9eS0V2s7y5ud9t0FwS7i+2LWoS6ghZ3f1f85Io9XOGKuxi56MJ6t2/3/LhCgS9/+ctZ5p7v\n008/Pcu22WYbOx637yeeeCLLbr755iz70Y9+ZLfZFtzF527+uIvRi1pyurnWuXPnLHMXyLviQvfc\nuoKkouIBN6/KvpZcQZNr++j27R6fJO20005Z5l4P7sJ+91jKFoYVtaZ0r1n3+nLP+brGFQaVLUpx\nBUjudePmnjv+SP736Fpxtw2YtKoAACAASURBVKTNq5tTRXPFvbZdUW3Z+7rje1FbcbSdV155Jct2\n2WWXLBs/fry9/3777Zdl06dPzzJ3LHUFf0OGDMmyCRMmZNn8+fPteFzhrtu3Kyx1hbtlFRVYtaTQ\nrSmcUQUAAEAlsVAFAABAJbFQBQAAQCWxUAUAAEAlNVlMdcEFF2SZu3B4r732yrLRo0fbbf7qV7/K\nMneB86WXXpplzz77bJa5i4Sb0wnKFbm4wgN3Yb27cNjtp2zBl+SLu4rG3ljZi/Vd4dS9995rb/vz\nn/88y2699dZS+2lPffr0yTL3uF1xhetWJfkioHnz5mWZ61bluAI/V2RS1OXNFUm5bbqCFpe5AquR\nI0eW2q8kPfDAA1nm5r6b427uusftCuLKPt+Sfy2trQKAKnHzwhXBut+X69zj7usKO4qeW/f7dp2t\nXBGJe40UFW01VjR33XuGO+YfcMABWebmpMuK9o1irf3a7NevX5a5Of/GG2/Y+5900klZ5tYLv/71\nr7PMFU797ne/K3W7osInN29dp7299947y4q6tDXmfgdtfczkjCoAAAAqiYUqAAAAKomFKgAAACqJ\nhSoAAAAqqcmru113hq997WulNjx48GCbT548OcsuuuiiLHMXzLuL210xVVH3EadsJ6iizlaNuQuz\nm8Pt2xUuuPHcdddda7zfgw8+eI3vW0WnnXZalo0ZMybLzj333Czr37+/3eawYcOyzP2+XYGVKzRx\nv1fX0aY5XdXKdhtx437sscey7Pzzz8+yxx9/3G7TFSoddNBBWXbVVVdl2ZtvvpllZbu8bbHFFnY8\n7vl1r6+uXbva+69LXAc1d+x0c8odT8t2aXOFHZKf0+7+bk65rljudVO2WKxoPG6bPXr0KDWesu8X\naD53DChb3DNlypQs+/Of/1zqdpJf17jOdk8//XSWDRo0KMtccZYrJHeFrpL01ltvZdlTTz2VZe64\n596TnJauaVoDZ1QBAABQSSxUAQAAUEksVAEAAFBJLFQBAABQSU0WU5W9iN5xRVNFXn311SxzF0y7\nC/1dB5B33nkny1xhRlHu9u2eC3e7sllzOju427qLo12xmVP0XLRkPFW44PrD/OlPfyqVFT0/o0aN\nyjLXge2II47IsuHDh2dZr169sszNZ1ecJfl5fvfdd2fZHXfckWWucGpteOaZZ7LMFQCU7QbnCoCK\nOre5jkrjxo3LMlcMs65xHXncvHLPryu6cK93d1wo6qpWtuuYm+PufcDNH9dJrqi4y3Wyc8fYpUuX\nZpmbP248HeEY2RG0pCuS67Q3YMCALLv22mvt/S+77LIs69mzZ5a5473r3OcKU93xzI1bko499tgs\nu/rqq7PsiSeeyLKhQ4dm2ezZs7PMrV/aGmdUAQAAUEksVAEAAFBJLFQBAABQSSxUAQAAUElNFlOV\nvfjbXWy70UZ+064T1NixY7PspptuyjLXgcZ1D3EX6rv9Sv7CZfe4yxYPOO6+Rc+P26brfNGtW7cs\ne+SRR0qNp6MWQ7WUK5Jyv/+i4hxXGOSyn/3sZ2swuvbX0m467jW2YMGCLDvkkENatB80X9HxpjF3\nHHCFpG577jhV9FpyBVpDhgzJskmTJmWZK/hyc9cVXfXt29eOxz3Gsl26XNGeKwJzzw+aryVF3m6e\nuMIn18VQkqZPn55ln/jEJ7LMHQtfeumlLHPdDl2RU1GBrzu+7rvvvlnmHuP3vve9LHNFts3p9Lm2\ntP8IAAAAAIOFKgAAACqJhSoAAAAqiYUqAAAAKqncFfYfwhXnFBUvlfXLX/4yy9yFx+7i5rJdpIpu\n67gCrbJFV66goKgQy3VDeffdd7PMdTW6/vrr7TbL7ttpaVetKikq7EBNS1+zqC7XmeYzn/lMlrnj\njytYdR1sXAFRUYGeu63r8LRkyZIsc4Vc7pjkiqFcEZckLVy4MMu6du2aZQ899FCW7bzzzlnmjs/u\nvQrN15JiKvf7f/bZZ7PMdTWTpK985StZ5jqqufF86lOfyjL3OnjttdeyzBUQStK0adOy7NJLL82y\nr3/966XGWFWcUQUAAEAlsVAFAABAJbFQBQAAQCWxUAUAAEAlsVAFAABAJbVK1f/acNppp7X3ENZZ\nzana76gV/gA+4KqLHVeZfsstt2TZ97///SxzFfHLli2z+9l8882zzH3jwOjRo7PMtY10Vfvu21KK\nKu/79OmTZdtvv32WuXaZ99xzT5b17Nkzy9w3AaD5yr4nuW8HcHPCue+++2w+ZcqULDv++OOzzH3j\nxBtvvJFljz/+eJa5b6c57LDD7HjcbY877rgsc61RJ0yYYLfZWBW+HYAzqgAAAKgkFqoAAACoJBaq\nAAAAqCQWqgAAAKikyhZTAQBaxxZbbJFlrr3piBEjsmy33XbLsuXLl2fZj3/84yxz7SAlX9TSo0eP\nLHMFSK6gxbXGdAVN++yzT+nxnHHGGfa2jR1yyCFZ9uabb2ZZv379Sm0PTStbTLXhhhtmWZcuXbJs\n+PDhWfbzn//cbtO1dneFSt/61reybObMmVnWv3//Urc7+OCD7XheeeWVUtt0r/UHH3zQbrOxKhRU\nc0YVAAAAlcRCFQAAAJXEQhUAAACVxEIVAAAAlRRNXSgbEe1/FS06rJRStNe+mbtoifVh7u6yyy5Z\nNmnSpCxbsmRJqe3tuOOOWXbSSSfZ2w4YMCDLBg4cmGVbb711lj3zzDNZ9vbbb2eZKyBzXbYk6b//\n+79tXsagQYOyrGvXrln28ssvr/E+mqO95m57HnMj8ofckiKgrbbayuZumxttlNeku9eWm6MrVqzI\nMvc6mDhxoh3P66+/nmWuW1VLtPZzW6SpecsZVQAAAFQSC1UAAABUEgtVAAAAVBILVQAAAFRSk8VU\nAAAAQHvhjCoAAAAqiYUqAAAAKomFKgAAACqJhSoAAAAqiYUqAAAAKomFKgAAACqJhSoAAAAqiYUq\nAAAAKomFKgAAACqJhSoAAAAqiYUqAAAAKomFKgAAACqJhSoAAAAqiYUqAABAO4mIFBFDS9xum/pt\nN2qLcVXFer1QjYhJEbE8IhZHxIKIeCwivhIR6/Xzgo4pIj4XEU9HxJKImBERd0XEPi3c5oMRcWpr\njRFojHmLqoqIferrgoURMT8iHo2Ij7b3uNY3LMikT6WUukoaLOkySd+UdI27YURs2JYDA8qKiK9L\nulzS9yVtLWmQpKskHdWe4wKawrxFVUVEN0m3S7pSUi9J/SVdJOmd9hzX+oiFal1KaWFK6c+S/lnS\nyRGxS0RcFxE/i4g7I2KppH+KiE4R8X8i4q2ImBURV0fEZpIUEb0j4vb62dn5EfHw6rOzEfHNiJhW\nP3s7PiIOaseHi3VIRHSXdLGkr6aU/pBSWppSWplSui2l9I36nL08IqbX/1weEZ3q9+1Zn7NzIuLt\n+t8H1H92iaR9Jf2kfrbrJ+33KLGuYd6i4naQpJTS2JTSeyml5Smle1JKL0TEdhFxf0TMi4i5EXFj\nRPRYfcf6p7X/FhEv1M/G3hwRmzb4+Tfqnx5Mj4gvNtxpRBwZEX+LiEURMSUiLmyzR1xRLFQbSSk9\nKWmqagc6SfqcpEskdZX0iGpnXXeQNErSUNX+lfXt+m3Prd93S9XODvynpBQRwySdKemj9bO3h0qa\n1AYPB+uHvSVtKumPBT8/X9Jeqs3ZkZJGS7qg/rMNJF2r2icKgyQtl/QTSUopnS/pYUlnppS6pJTO\nXFsPAOsl5i2q7DVJ70XE9RFxeET0bPCzkHSppH6ShksaKOnCRvf/jKTDJA2RNELSKZIUEYdJ+jdJ\nh0jaXtLBje63VNJJknpIOlLS6RExptUeVQfEQtWbrtqpfkn675TSoyml91U75f+vks5JKc1PKS1W\n7SOrE+q3XSmpr6TB9TMDD6eUkqT3JHWStFNEbJxSmpRSeqNNHxHWZVtImptSWlXw8xMlXZxSmp1S\nmqPax1dfkKSU0ryU0u9TSsvq8/kSSfu3yaixvmPeorJSSosk7SMpSfqFpDkR8eeI2Dql9HpK6a8p\npXfqc/NHyuffFSml6Sml+ZJuU+0fXFJtAXttSumllNJSNVrgppQeTCm9mFJ6P6X0gqSxZtvrFRaq\nXn9J8+t/n9Ig31LS5pKeqX+8v0DSX+q5JP2XpNcl3RMREyPiPElKKb0u6WzVJuTsiPhtRPRb+w8D\n64l5kno3UQnaT9LkBv8/uZ4pIjaPiJ9HxOSIWCTpIUk9uB4bbYB5i0pLKf09pXRKSmmApF1Um3+X\nR8TW9ffxafX59xtJvRvdfWaDvy+T1KX+9376x3VFwzmuiNgzIh6oX9ayUNJXzLbXKyxUG6lX9PVX\n7WN+qfavqdXmqvYR084ppR71P91TSl0kKaW0OKV0bkppW0mflvT11deippRuSinto9pHVUnSD9ro\nIWHdN061s/1FHw9NV23erTaonkm1y1WGSdozpdRN0n71POr/bTj/gdbEvEWHkVJ6VdJ1qi1Yv6/a\nHNu1Pv8+rw/m3oeZodqlAqsNavTzmyT9WdLAlFJ3SVc3Y9vrJBaqdRHRLSI+Kem3kn6TUnqx8W3q\nH///QtKPI2Kr+v36R8Sh9b9/MiKGRkRIWqjaR/7vR8SwiDiwXgiwQrXF7vtt88iwrkspLVTtOumf\nRsSY+tmmjevXVf1v1T46uiAitoyI3vXb/qZ+966qzccFEdFL0ncabX6WpG3b5pFgfcK8RZVFxI4R\ncW6DIr2Bkj4r6XHV5t8SSQsjor+kbzRj07dIOiUidoqIzZXP3a6S5qeUVkTEaNXqZNZrLFSl2yJi\nsWqn4s9X7VqTf2ni9t9U7eP9x+un/O9V7V/2Uu3C6HtVm8DjJF2VUnpAtetTL1PtjOxMSVtJ+o/W\nfyhYX6WUfijp66oVm8xRbT6fKelPkr4n6WlJL0h6UdKz9UyqfTXQZqrNzcdVu5Slof8r6bh6ZfUV\na/lhYD3DvEWFLZa0p6QnovatP49Lekm1s/kXSdpdtRNSd0j6Q9mNppTuUm3+3q/aWuL+Rjc5Q9LF\n9XXJt1Vb2K7XolbrAwAAAFQLZ1QBAABQSSxUAQAAUEksVAEAAFBJLFQBAABQSUVftCxJioj1stJq\nxIgRWfbCCy9k2ZZbbpllI0eOzLJ77723dQbWwaSU2u2731oydzfeeGObu8LD2jeRfbj338+/jWyD\nDfJ/J7rtrVrlG/e4bbanso9no43yw857771Xah9ln29JWrlyZenbNtZR525Hdu2112aZm1Pz58/P\nsqFDh2bZhAkTsuzSSy+1+54zZ06ZIXYI7TV319d5i9bR1LzljCoAAAAqiYUqAAAAKomFKgAAACqJ\nhSoAAAAqqcnOVB3h4ui+fftm2Q033JBl7qJ8STrooIOy7Jvf/GaWjR49OssOPPDALLvooouy7PLL\nL8+y++67z47nwQcfzLLvfve79rZV11ELUlyxT32bWVa2wGqTTTbJsuXLl2dZcwqkXDGfK/q74447\nsmz27NlZtvPOO2dZz549s2z//fe342lJl7vNN988y8oWQxXtt6gIreQ2O+TcbSvueNqcuet+t+7+\ny5Yty7Lu3buX2t7SpUuzzM1nSRoyZEiWTZo0Kcs23HDDLCtbCNhWKKZCR0QxFQAAADocFqoAAACo\nJBaqAAAAqCQWqgAAAKikJjtTVU23bt2y7KGHHsoy11lo8eLFdpuf//zns8wVOp155plZtnDhwixz\nF/q7fbjiGkk6++yzs6xz585Zdt5559n7o225Qh5XcOEKO5x+/fpl2Wc+8xl727322ivL/vrXv2bZ\nnnvumWVunk6bNi3LnnrqqSwbO3asHc9LL72UZddcc02WzZgxI8tc0YzjCt2a060KraNs4dSFF15Y\nepuvv/56lrmCQ3fc7d27d5a5Aqvtt9/e7vtXv/pVlrli2aoVTgHrA86oAgAAoJJYqAIAAKCSWKgC\nAACgklioAgAAoJI6VGeq008/PcsuueSSLHMX5bsCF0naYostsswVZ6xYsaLU7VyxhyvucgUlRffv\n0qVLlu2www72/lXSUbv7NKczlZtXbq644qUxY8Zk2ciRI7NswIABdjzTp0/Psjlz5mSZ67rTo0eP\nLHOdeFwxS9Hz47rEzZo1q9T9XYHW008/bffTWNFruyWFLx117rYV153MddBzxYGSNGjQoCxzBYeu\nA5brquYKbZ2iueu2uWDBgiw79dRTs+zll18ute+2QmcqdER0pgIAAECHw0IVAAAAlcRCFQAAAJXE\nQhUAAACVxEIVAAAAldShqv5vuummLDv88MOzzFUaF7X8c9Wervq0T58+WeaeO1fNv+mmm2ZZr169\n7HhcBfNWW22VZZ/4xCeyzLWwbE8dtXLafUtDfZtZ9u6775ba5o033phlb7zxRpa5VqKbb7653ead\nd96ZZQcffHCWuep31xr1yCOPzDL3TQCLFi2y49l2222zzD0/7v4f+chHsuznP/95lj3yyCNZVtSO\nuOzvxumoc7etPP/881m25ZZbZtnbb79t79+1a9csc9/AsmrVqixz36rhfteuwr9oPG5Obr311lnm\n3i9ci+L2RNU/OiKq/gEAANDhsFAFAABAJbFQBQAAQCWxUAUAAEAl+X5yFTV69Ogscxfb9+zZM8vm\nzp1rt+ku6t9ss81K3d8VU7mCAne7omIqV7Dj2gjuvffeWVa1YqqOqqjwrqj9YmNf+tKXSt3OFW25\ngqRx48bZ+7t56gqLOnfunGW77rprmSHauedat0q+6GvHHXfMMlcM416HZ599dpa5YqqmCkLRcvvt\nt1+WDRw4MMtmzpyZZUWvJVfw6u7vWqO6182SJUuyzBVIufks+eOua0e8zTbbZJkr6L3rrrvsfgA0\nH2dUAQAAUEksVAEAAFBJLFQBAABQSSxUAQAAUEkdqpjKXcjuLphfvnx5lrliDcl3jXLdoVznE3c7\n183EFbi4wgHJdxEaMmRIlrliql/84hd2m2geV1ghSe+8806p+3/xi1/MMjcnXXGW63yz22672f24\nwitXAFJ2PrvCRFf054prJP9acgVf/fr1KzWeHj162P00tnLlylK3w5o56qijssz9Xl1WVEzlCuAW\nL16cZe743qVLlyxzc6Bs8aPki6zc/HO3GzNmTJZRTAW0Hs6oAgAAoJJYqAIAAKCSWKgCAACgklio\nAgAAoJIqW0zlLsyfMWNGlrnOJa6o6JxzzrH7Wbp0aZa5AgB3Yb27WL9st6qHH37YjmfatGlZNnz4\n8CwbNmyYvT9arqh7TVkDBgzIsuuvvz7LXCcn1/HHFVhJvnDq/vvvzzLXOWfQoEFrvL2irla77757\nlrkiMtcpa8KECVl2yCGHZJkr7po/f74dD1rHEUcckWXu9+qKRosK3VxBlCskdZnbj7udU1Qo6QoB\nXQHusmXLsuywww4rtW8Aa4YzqgAAAKgkFqoAAACoJBaqAAAAqCQWqgAAAKikyhZT7bTTTlm28cYb\nZ5m7sP7GG2/MslNPPdXux13U74ozXAGIuzDfdS9yxVmuCEySXnrppSxznYVchtZRVHDhjBo1Kstc\nNx03dxcsWFBqH+PGjbP5kUcemWXbbbddlnXv3j3LJk6cmGWuA9pxxx2XZb1797bjefnll7PMFbm4\nrmru9eDG7R7zr3/9azsetA7XEdB11nPFgUWdqdxrzB1jXdGWO/a511fZ4qyifbv9uNfsiBEj7DYB\ntA7OqAIAAKCSWKgCAACgklioAgAAoJJYqAIAAKCSKltM5TrQuE5QrqOIKxRxRU6SL9hwXMcpxxUP\nuHFPmTLF3v+pp57KMtcpqey40Xxlf9eStNdee2WZm5OuO9Qrr7ySZa54ZOTIkXbfrhDQdZdyj8d1\nwHIFKe6+bj5LvmPVa6+9lmX9+/fPsn79+mWZe826YjG0HjcH3Hx286JTp05ZtmLFCrsfd5x0xzl3\nu1WrVmWZ64D17rvvZpnreCj5QjB3W1e867judFOnTi11XwD/iDOqAAAAqCQWqgAAAKgkFqoAAACo\nJBaqAAAAqKTKFlO5QhGXFV2s31jXrl1t7i7ML8sVFLjOJ64Li+tAJUl33313qf24C/232GKLLJs3\nb57dD1qHK0pyRRyugMgVikyYMCHLDjjgALvvJ554IssefvjhLNt5552z7OMf/3iWjR8/PsteffXV\nLNtyyy3teI466qgsc/PUveZckcqyZcuyrE+fPnbfaB1ln9+yxyTXpU3yXZ+KCp0ac68bV8jlsqJO\nWa7jlLu/e207u+yyS5ZRTFVN7v25qDuhm/dli29PPvnkLPvb3/6WZS+88EKp7VWRe96aU5xchDOq\nAAAAqCQWqgAAAKgkFqoAAACoJBaqAAAAqCQWqgAAAKikylb9u+pT17rxySefLLU9VxEvSXPnzs0y\nV7lWVC1ahqt66927d+n7uxaGLttxxx2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eBQsW2Hz+/PlZtmjRolLbrBqKqdARUUwFAACA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\nre5dAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 864x648 with 12 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qu9eP8C8p1r_",
        "colab_type": "text"
      },
      "source": [
        "Các class sẽ phân bố đều vào 60000 hình ảnh\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SH3-IvGcSyHs",
        "colab_type": "code",
        "outputId": "01906b03-691c-4b67-f0f9-1a6d226cc7b0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67
        }
      },
      "source": [
        "print('num_train: ',num_train_examples)\n",
        "print('num_test: ',num_test_examples)\n",
        "print('num_class: ',num_classes)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "num_train:  60000\n",
            "num_test:  10000\n",
            "num_class:  10\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Iu2XnWSBP9Yx",
        "colab_type": "text"
      },
      "source": [
        "**PREROCESS DATA**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5Q6lDqgxsvsR",
        "colab_type": "text"
      },
      "source": [
        " Lưu ý rằng chúng ta có các hình ảnh dưới dạng vector 1 chiều mỗi hình chứa 784 pixel.\n",
        "\n",
        "Trước khi đưa vào model CNN,  phải định hình lại dữ liệu thành ma trận 3 chiều (28x28x1). \n",
        "\n",
        "Nếu đây là hình ảnh RGB, sẽ có 3 kênh, nhưng vì MNIST có thang màu xám nên nó chỉ sử dụng một kênh."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ms05Lt7HQAbt",
        "colab_type": "text"
      },
      "source": [
        "Giá trị của mỗi pixel trong dữ liệu ảnh là một số nguyên trong phạm vi [0,255]. Để mô hình hoạt động chính xác, các giá trị này cần được chuẩn hóa thành phạm vi [0,1]. \n",
        "\n",
        "x = (x - min) / (max - min) ;  min=0 and max=255"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0_tjfxV2Ox7m",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_dataset_x = train_dataset[:,1:] / 255\n",
        "train_dataset_x = train_dataset_x.reshape((num_train_examples, 28, 28, 1))\n",
        "train_dataset_y = train_dataset[:,0]\n",
        "\n",
        "\n",
        "test_dataset_x = test_dataset[:,1:] / 255\n",
        "test_dataset_x = test_dataset_x.reshape((num_test_examples, 28, 28, 1))\n",
        "test_dataset_y = test_dataset[:,0]\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4DB0wSN5UDRe",
        "colab_type": "code",
        "outputId": "570de28e-a518-4028-8fa4-ee7b42ba528f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "train_dataset.shape,test_dataset.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((60000, 785), (10000, 785))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 71
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XCl3WktRtXbn",
        "colab_type": "text"
      },
      "source": [
        "Các nhãn được đưa ra dưới dạng số nguyên trong khoảng 0-9. Chúng ta cần one-hot encode \n",
        "\n",
        "ví dụ:  8 được biểu diễn thành [0, 0, 0, 0, 0, 0, 0, 0, 1, 0].\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b5k3jYaStqoE",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_Y_one_hot = to_categorical(train_dataset_y)\n",
        "test_Y_one_hot = to_categorical(test_dataset_y)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KUqZ1zQAu7JQ",
        "colab_type": "code",
        "outputId": "2483a9e6-740e-414f-fbca-35ad87f710c5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "train_Y_one_hot.shape,test_Y_one_hot.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((60000, 10), (10000, 10))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 75
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FynrcVhdt5Y8",
        "colab_type": "text"
      },
      "source": [
        "Chia tập train và validation "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_nh-ii69t4k-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X_train, X_val, y_train, y_val = train_test_split(train_dataset_x, train_Y_one_hot, test_size=0.2, random_state=13)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lurna2OeutEb",
        "colab_type": "code",
        "outputId": "3abb84cf-ec50-4b61-990d-709166967c92",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "X_train.shape,y_train.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((48000, 28, 28, 1), (48000, 10))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 74
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4liT7DvE9z6n",
        "colab_type": "text"
      },
      "source": [
        "**Hàm vẽ**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-s278hCU9y2C",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def create_trace(x,y,ylabel,color):\n",
        "        trace = go.Scatter(\n",
        "            x = x,y = y,\n",
        "            name=ylabel,\n",
        "            marker=dict(color=color),\n",
        "            mode = \"markers+lines\",\n",
        "            text=x\n",
        "        )\n",
        "        return trace\n",
        "    \n",
        "def plot_accuracy_and_loss(train_model):\n",
        "    hist = train_model.history\n",
        "    acc = hist['acc']\n",
        "    val_acc = hist['val_acc']\n",
        "    loss = hist['loss']\n",
        "    val_loss = hist['val_loss']\n",
        "    epochs = list(range(1,len(acc)+1))\n",
        "    \n",
        "    trace_ta = create_trace(epochs,acc,\"Training accuracy\", \"Green\")\n",
        "    trace_va = create_trace(epochs,val_acc,\"Validation accuracy\", \"Red\")\n",
        "    trace_tl = create_trace(epochs,loss,\"Training loss\", \"Blue\")\n",
        "    trace_vl = create_trace(epochs,val_loss,\"Validation loss\", \"Magenta\")\n",
        "   \n",
        "    fig = tools.make_subplots(rows=1,cols=2, subplot_titles=('Training and validation accuracy',\n",
        "                                                             'Training and validation loss'))\n",
        "    fig.append_trace(trace_ta,1,1)\n",
        "    fig.append_trace(trace_va,1,1)\n",
        "    fig.append_trace(trace_tl,1,2)\n",
        "    fig.append_trace(trace_vl,1,2)\n",
        "    fig['layout']['xaxis'].update(title = 'Epoch')\n",
        "    fig['layout']['xaxis2'].update(title = 'Epoch')\n",
        "    fig['layout']['yaxis'].update(title = 'Accuracy', range=[0,1])\n",
        "    fig['layout']['yaxis2'].update(title = 'Loss', range=[0,1])\n",
        "\n",
        "    \n",
        "    iplot(fig, filename='accuracy-loss')\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bcJt1mCPQPNi",
        "colab_type": "text"
      },
      "source": [
        "**BUILD CNN MODEL**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uVeEDRvGOx4g",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model = tf.keras.Sequential([\n",
        "    tf.keras.layers.Conv2D(32, (3,3), padding='same', activation=tf.nn.relu,\n",
        "                           input_shape=(28, 28, 1)),\n",
        "    tf.keras.layers.MaxPooling2D((2, 2), strides=2),\n",
        "    tf.keras.layers.Conv2D(64, (3,3), padding='same', activation=tf.nn.relu),\n",
        "    tf.keras.layers.MaxPooling2D((2, 2), strides=2),\n",
        "    tf.keras.layers.Conv2D(128, (3, 3), padding='same', activation=tf.nn.relu),\n",
        "    tf.keras.layers.Flatten(),\n",
        "    tf.keras.layers.Dense(128, activation=tf.nn.relu),\n",
        "    tf.keras.layers.Dense(10,  activation=tf.nn.softmax)\n",
        "\n",
        "])\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4cL3mjcvy7BJ",
        "colab_type": "code",
        "outputId": "0b4187ef-c066-4563-9e26-2c7b9186561e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 420
        }
      },
      "source": [
        "model.summary();"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_6\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d_18 (Conv2D)           (None, 28, 28, 32)        320       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_12 (MaxPooling (None, 14, 14, 32)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_19 (Conv2D)           (None, 14, 14, 64)        18496     \n",
            "_________________________________________________________________\n",
            "max_pooling2d_13 (MaxPooling (None, 7, 7, 64)          0         \n",
            "_________________________________________________________________\n",
            "conv2d_20 (Conv2D)           (None, 7, 7, 128)         73856     \n",
            "_________________________________________________________________\n",
            "flatten_6 (Flatten)          (None, 6272)              0         \n",
            "_________________________________________________________________\n",
            "dense_12 (Dense)             (None, 128)               802944    \n",
            "_________________________________________________________________\n",
            "dense_13 (Dense)             (None, 10)                1290      \n",
            "=================================================================\n",
            "Total params: 896,906\n",
            "Trainable params: 896,906\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hictyWGTy9LO",
        "colab_type": "code",
        "outputId": "e9a9a7e6-f74d-4d7b-c4ee-75737e3e1022",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 976
        }
      },
      "source": [
        "from tensorflow.keras.utils import plot_model\n",
        "plot_model(model , 'classyfier.png' , show_shapes=True)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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q1Kni7u5uEDc6OloAyJ07d/RtL730knTv3t2g36FDh8TJyUkiIyNrPZaqxjRY0rGImDam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\nAElFTkSuQmCC\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 83
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-gmhhL9ZQZ74",
        "colab_type": "text"
      },
      "source": [
        "Loss function : An algorithm for measuring how far the model's outputs are from the desired output. The goal of training is this measures loss.\n",
        "\n",
        "Optimizer :An algorithm for adjusting the inner parameters of the model in order to minimize loss.\n",
        "\n",
        "Metrics :Used to monitor the training and testing steps. The following example uses accuracy, the fraction of the images that are correctly classified."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "noxrHE2OQjlk",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model.compile(optimizer='adam',\n",
        "              loss='categorical_crossentropy',\n",
        "              metrics=['accuracy'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zHBvc4t1Qo3V",
        "colab_type": "text"
      },
      "source": [
        "**train model**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wb0UpeYUVbUi",
        "colab_type": "code",
        "outputId": "723925da-3d8e-44fe-ad57-ba1a50140bcb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "\n",
        "history = model.fit(X_train, y_train,\n",
        "          batch_size=256,\n",
        "          epochs=30,\n",
        "          verbose=1,\n",
        "          validation_data=(X_val, y_val))\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 48000 samples, validate on 12000 samples\n",
            "Epoch 1/30\n",
            "48000/48000 [==============================] - 77s 2ms/sample - loss: 0.6061 - acc: 0.7784 - val_loss: 0.3992 - val_acc: 0.8537\n",
            "Epoch 2/30\n",
            "48000/48000 [==============================] - 77s 2ms/sample - loss: 0.3454 - acc: 0.8764 - val_loss: 0.3328 - val_acc: 0.8754\n",
            "Epoch 3/30\n",
            "48000/48000 [==============================] - 77s 2ms/sample - loss: 0.2934 - acc: 0.8932 - val_loss: 0.3278 - val_acc: 0.8813\n",
            "Epoch 4/30\n",
            "48000/48000 [==============================] - 77s 2ms/sample - loss: 0.2600 - acc: 0.9063 - val_loss: 0.2630 - val_acc: 0.9034\n",
            "Epoch 5/30\n",
            "48000/48000 [==============================] - 77s 2ms/sample - loss: 0.2363 - acc: 0.9144 - val_loss: 0.2813 - val_acc: 0.9001\n",
            "Epoch 6/30\n",
            "48000/48000 [==============================] - 76s 2ms/sample - loss: 0.2132 - acc: 0.9228 - val_loss: 0.2611 - val_acc: 0.9014\n",
            "Epoch 7/30\n",
            "48000/48000 [==============================] - 76s 2ms/sample - loss: 0.2002 - acc: 0.9267 - val_loss: 0.2298 - val_acc: 0.9145\n",
            "Epoch 8/30\n",
            "48000/48000 [==============================] - 77s 2ms/sample - loss: 0.1831 - acc: 0.9338 - val_loss: 0.2440 - val_acc: 0.9139\n",
            "Epoch 9/30\n",
            "48000/48000 [==============================] - 78s 2ms/sample - loss: 0.1673 - acc: 0.9384 - val_loss: 0.2346 - val_acc: 0.9151\n",
            "Epoch 10/30\n",
            "48000/48000 [==============================] - 76s 2ms/sample - loss: 0.1524 - acc: 0.9439 - val_loss: 0.2380 - val_acc: 0.9162\n",
            "Epoch 11/30\n",
            "48000/48000 [==============================] - 76s 2ms/sample - loss: 0.1398 - acc: 0.9496 - val_loss: 0.2250 - val_acc: 0.9229\n",
            "Epoch 12/30\n",
            "48000/48000 [==============================] - 76s 2ms/sample - loss: 0.1253 - acc: 0.9555 - val_loss: 0.2418 - val_acc: 0.9185\n",
            "Epoch 13/30\n",
            "48000/48000 [==============================] - 76s 2ms/sample - loss: 0.1093 - acc: 0.9603 - val_loss: 0.2207 - val_acc: 0.9258\n",
            "Epoch 14/30\n",
            "48000/48000 [==============================] - 76s 2ms/sample - loss: 0.1007 - acc: 0.9637 - val_loss: 0.2384 - val_acc: 0.9204\n",
            "Epoch 15/30\n",
            "48000/48000 [==============================] - 76s 2ms/sample - loss: 0.0885 - acc: 0.9678 - val_loss: 0.2593 - val_acc: 0.9199\n",
            "Epoch 16/30\n",
            "48000/48000 [==============================] - 77s 2ms/sample - loss: 0.0760 - acc: 0.9724 - val_loss: 0.2557 - val_acc: 0.9177\n",
            "Epoch 17/30\n",
            "48000/48000 [==============================] - 76s 2ms/sample - loss: 0.0680 - acc: 0.9763 - val_loss: 0.2552 - val_acc: 0.9217\n",
            "Epoch 18/30\n",
            "48000/48000 [==============================] - 77s 2ms/sample - loss: 0.0587 - acc: 0.9790 - val_loss: 0.3179 - val_acc: 0.9146\n",
            "Epoch 19/30\n",
            "48000/48000 [==============================] - 78s 2ms/sample - loss: 0.0524 - acc: 0.9817 - val_loss: 0.2730 - val_acc: 0.9222\n",
            "Epoch 20/30\n",
            "48000/48000 [==============================] - 77s 2ms/sample - loss: 0.0430 - acc: 0.9849 - val_loss: 0.3071 - val_acc: 0.9203\n",
            "Epoch 21/30\n",
            "48000/48000 [==============================] - 76s 2ms/sample - loss: 0.0389 - acc: 0.9863 - val_loss: 0.3181 - val_acc: 0.9233\n",
            "Epoch 22/30\n",
            "48000/48000 [==============================] - 77s 2ms/sample - loss: 0.0334 - acc: 0.9880 - val_loss: 0.3656 - val_acc: 0.9148\n",
            "Epoch 23/30\n",
            "48000/48000 [==============================] - 76s 2ms/sample - loss: 0.0328 - acc: 0.9887 - val_loss: 0.3481 - val_acc: 0.9217\n",
            "Epoch 24/30\n",
            "48000/48000 [==============================] - 76s 2ms/sample - loss: 0.0261 - acc: 0.9905 - val_loss: 0.3465 - val_acc: 0.9202\n",
            "Epoch 25/30\n",
            "48000/48000 [==============================] - 77s 2ms/sample - loss: 0.0247 - acc: 0.9911 - val_loss: 0.3516 - val_acc: 0.9227\n",
            "Epoch 26/30\n",
            "48000/48000 [==============================] - 76s 2ms/sample - loss: 0.0169 - acc: 0.9944 - val_loss: 0.3834 - val_acc: 0.9219\n",
            "Epoch 27/30\n",
            "48000/48000 [==============================] - 77s 2ms/sample - loss: 0.0236 - acc: 0.9918 - val_loss: 0.3747 - val_acc: 0.9207\n",
            "Epoch 28/30\n",
            "48000/48000 [==============================] - 77s 2ms/sample - loss: 0.0225 - acc: 0.9920 - val_loss: 0.3814 - val_acc: 0.9212\n",
            "Epoch 29/30\n",
            "48000/48000 [==============================] - 77s 2ms/sample - loss: 0.0239 - acc: 0.9917 - val_loss: 0.4243 - val_acc: 0.9200\n",
            "Epoch 30/30\n",
            "48000/48000 [==============================] - 77s 2ms/sample - loss: 0.0177 - acc: 0.9944 - val_loss: 0.4217 - val_acc: 0.9222\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "krgnaGWUW65W",
        "colab_type": "code",
        "outputId": "66ed54f6-35c0-42a9-8060-82a1193b17a2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "print(history.history.keys())"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "dict_keys(['loss', 'acc', 'val_loss', 'val_acc'])\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KMfHh2RgdkPx",
        "colab_type": "code",
        "outputId": "92d7c8e8-f519-4597-a34d-94b2d5cbcc82",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 610
        }
      },
      "source": [
        "plot_accuracy_and_loss(history)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/plotly/tools.py:465: DeprecationWarning:\n",
            "\n",
            "plotly.tools.make_subplots is deprecated, please use plotly.subplots.make_subplots instead\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
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              "// Listen for the removal of the full notebook cells\n",
              "var notebookContainer = gd.closest('#notebook-container');\n",
              "if (notebookContainer) {{\n",
              "    x.observe(notebookContainer, {childList: true});\n",
              "}}\n",
              "\n",
              "// Listen for the clearing of the current output cell\n",
              "var outputEl = gd.closest('.output');\n",
              "if (outputEl) {{\n",
              "    x.observe(outputEl, {childList: true});\n",
              "}}\n",
              "\n",
              "                        })\n",
              "                };\n",
              "                \n",
              "            </script>\n",
              "        </div>\n",
              "</body>\n",
              "</html>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JsocRoP4B7CG",
        "colab_type": "text"
      },
      "source": [
        "predict trên test"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DRP5eQkBXcDN",
        "colab_type": "code",
        "outputId": "23615cf8-a1eb-4665-8f0b-745f245d3d7e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67
        }
      },
      "source": [
        "score = model.evaluate(test_dataset_x, test_Y_one_hot)\n",
        "print('Test loss:', score[0])\n",
        "print('Test accuracy:', score[1])\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "10000/10000 [==============================] - 5s 535us/sample - loss: 0.4231 - acc: 0.9253\n",
            "Test loss: 0.4231032437230111\n",
            "Test accuracy: 0.9253\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "biq_2l58ClxJ",
        "colab_type": "text"
      },
      "source": [
        "**=> overfitting**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8sjj39o4ER-b",
        "colab_type": "text"
      },
      "source": [
        "**thêm DropOut vào mô hình**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nCmQFtRwEYcy",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model_2 = tf.keras.Sequential([\n",
        "    tf.keras.layers.Conv2D(32, (3,3), padding='same', activation=tf.nn.relu,\n",
        "                           input_shape=(28, 28, 1)),\n",
        "    tf.keras.layers.MaxPooling2D((2, 2), strides=2),\n",
        "    tf.keras.layers.Dropout(0.25), \n",
        "    tf.keras.layers.Conv2D(64, (3,3), padding='same', activation=tf.nn.relu),\n",
        "    tf.keras.layers.MaxPooling2D((2, 2), strides=2),\n",
        "    tf.keras.layers.Dropout(0.25), \n",
        "    tf.keras.layers.Conv2D(128, (3, 3), padding='same', activation=tf.nn.relu),\n",
        "    tf.keras.layers.Dropout(0.4), \n",
        "    tf.keras.layers.Flatten(),\n",
        "    tf.keras.layers.Dense(128, activation=tf.nn.relu),\n",
        "    tf.keras.layers.Dense(10,  activation=tf.nn.softmax)\n",
        "\n",
        "])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OoM9596vE1sR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model_2.compile(optimizer='adam',\n",
        "              loss='categorical_crossentropy',\n",
        "              metrics=['accuracy'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TzTQ4gldPBqi",
        "colab_type": "code",
        "outputId": "2ba1d0e3-67d4-42b7-d60f-941455a36457",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 521
        }
      },
      "source": [
        "model_2.summary();"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_9\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d_27 (Conv2D)           (None, 28, 28, 32)        320       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_18 (MaxPooling (None, 14, 14, 32)        0         \n",
            "_________________________________________________________________\n",
            "dropout_15 (Dropout)         (None, 14, 14, 32)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_28 (Conv2D)           (None, 14, 14, 64)        18496     \n",
            "_________________________________________________________________\n",
            "max_pooling2d_19 (MaxPooling (None, 7, 7, 64)          0         \n",
            "_________________________________________________________________\n",
            "dropout_16 (Dropout)         (None, 7, 7, 64)          0         \n",
            "_________________________________________________________________\n",
            "conv2d_29 (Conv2D)           (None, 7, 7, 128)         73856     \n",
            "_________________________________________________________________\n",
            "dropout_17 (Dropout)         (None, 7, 7, 128)         0         \n",
            "_________________________________________________________________\n",
            "flatten_9 (Flatten)          (None, 6272)              0         \n",
            "_________________________________________________________________\n",
            "dense_18 (Dense)             (None, 128)               802944    \n",
            "_________________________________________________________________\n",
            "dense_19 (Dense)             (None, 10)                1290      \n",
            "=================================================================\n",
            "Total params: 896,906\n",
            "Trainable params: 896,906\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sEaueGn-PFSR",
        "colab_type": "code",
        "outputId": "2b8ee15f-ed04-4407-b5d0-3fdde60f1127",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "from tensorflow.keras.utils import plot_model\n",
        "plot_model(model_2 , 'classyfier.png' , show_shapes=True)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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5LNWNabCkYxExbUzD/v37ZeXKlVJQUCD379+XwYMHGzz7/eqrr4qVlZXcunXL4HWvv/66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\npIGIiIisIgbwr7ODICIiItf3HxyT3ueCCF+RAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 112
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FqjKGyt8-Bgs",
        "colab_type": "text"
      },
      "source": [
        "**Data Augmentation**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KnWMLHfT-IXt",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from keras.preprocessing.image import ImageDataGenerator\n",
        "datagen = ImageDataGenerator(\n",
        "        rotation_range = 8,  # randomly rotate images in the range (degrees, 0 to 180)\n",
        "        zoom_range = 0.1, # Randomly zoom image \n",
        "        shear_range = 0.3,# shear angle in counter-clockwise direction in degrees  \n",
        "        width_shift_range=0.08,  # randomly shift images horizontally (fraction of total width)\n",
        "        height_shift_range=0.08,  # randomly shift images vertically (fraction of total height)\n",
        "        vertical_flip=True)  # randomly flip images\n",
        "batches = datagen.flow(X_train, y_train, batch_size=256)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cBzKdODxBv2I",
        "colab_type": "code",
        "outputId": "920f708a-b9e8-4f36-a256-a728e82b7f58",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "print(len(batches)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "188 47\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N3tcBRVbiraK",
        "colab_type": "code",
        "outputId": "1ed4643b-264e-4463-efca-d0abc43e01f5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "print(len(batches))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "188\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_vfmKvMv-NtZ",
        "colab_type": "code",
        "outputId": "1c2d8588-e434-4413-e292-9a50c1cccef5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "\n",
        "\n",
        "history3 = model_2.fit_generator(batches, steps_per_epoch=48000//256, epochs=30,\n",
        "                    validation_data=(X_val, y_val), verbose=1)\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/30\n",
            "186/187 [============================>.] - ETA: 0s - loss: 0.9167 - acc: 0.6543Epoch 1/30\n",
            "12000/187 [=====================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================] - 5s 440us/sample - loss: 0.5606 - acc: 0.7874\n",
            "187/187 [==============================] - 95s 509ms/step - loss: 0.9159 - acc: 0.6547 - val_loss: 0.5685 - val_acc: 0.7874\n",
            "Epoch 2/30\n",
            "186/187 [============================>.] - ETA: 0s - loss: 0.6268 - acc: 0.7622Epoch 1/30\n",
            "12000/187 [=====================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================] - 5s 420us/sample - loss: 0.5315 - acc: 0.8049\n",
            "187/187 [==============================] - 94s 503ms/step - loss: 0.6266 - acc: 0.7622 - val_loss: 0.5177 - val_acc: 0.8049\n",
            "Epoch 3/30\n",
            "186/187 [============================>.] - ETA: 0s - loss: 0.5566 - acc: 0.7891Epoch 1/30\n",
            "12000/187 [=====================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================] - 5s 422us/sample - loss: 0.4964 - acc: 0.8282\n",
            "187/187 [==============================] - 93s 499ms/step - loss: 0.5565 - acc: 0.7892 - val_loss: 0.4667 - val_acc: 0.8282\n",
            "Epoch 4/30\n",
            "186/187 [============================>.] - ETA: 0s - loss: 0.5178 - acc: 0.8055Epoch 1/30\n",
            "12000/187 [=====================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================] - 5s 419us/sample - loss: 0.4424 - acc: 0.8499\n",
            "187/187 [==============================] - 93s 500ms/step - loss: 0.5175 - acc: 0.8057 - val_loss: 0.4295 - val_acc: 0.8499\n",
            "Epoch 5/30\n",
            "186/187 [============================>.] - ETA: 0s - loss: 0.4898 - acc: 0.8163Epoch 1/30\n",
            "12000/187 [=====================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================] - 5s 415us/sample - loss: 0.4226 - acc: 0.8528\n",
            "187/187 [==============================] - 93s 499ms/step - loss: 0.4896 - acc: 0.8165 - val_loss: 0.4035 - val_acc: 0.8528\n",
            "Epoch 6/30\n",
            "186/187 [============================>.] - ETA: 0s - loss: 0.4607 - acc: 0.8282Epoch 1/30\n",
            "12000/187 [=====================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================] - 5s 414us/sample - loss: 0.3670 - acc: 0.8693\n",
            "187/187 [==============================] - 93s 498ms/step - loss: 0.4608 - acc: 0.8281 - val_loss: 0.3598 - val_acc: 0.8693\n",
            "Epoch 7/30\n",
            "186/187 [============================>.] - ETA: 0s - loss: 0.4475 - acc: 0.8317Epoch 1/30\n",
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            "187/187 [==============================] - 93s 497ms/step - loss: 0.4474 - acc: 0.8320 - val_loss: 0.3637 - val_acc: 0.8685\n",
            "Epoch 8/30\n",
            "186/187 [============================>.] - ETA: 0s - loss: 0.4340 - acc: 0.8365Epoch 1/30\n",
            "12000/187 [=====================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================] - 5s 418us/sample - loss: 0.3630 - acc: 0.8754\n",
            "187/187 [==============================] - 94s 503ms/step - loss: 0.4340 - acc: 0.8365 - val_loss: 0.3383 - val_acc: 0.8754\n",
            "Epoch 9/30\n",
            "186/187 [============================>.] - ETA: 0s - loss: 0.4212 - acc: 0.8433Epoch 1/30\n",
            "12000/187 [=====================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================] - 5s 420us/sample - loss: 0.3455 - acc: 0.8849\n",
            "187/187 [==============================] - 93s 498ms/step - loss: 0.4212 - acc: 0.8434 - val_loss: 0.3185 - val_acc: 0.8849\n",
            "Epoch 10/30\n",
            "186/187 [============================>.] - ETA: 0s - loss: 0.4137 - acc: 0.8447Epoch 1/30\n",
            "12000/187 [=====================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================] - 5s 432us/sample - loss: 0.3725 - acc: 0.8755\n",
            "187/187 [==============================] - 94s 505ms/step - loss: 0.4135 - acc: 0.8448 - val_loss: 0.3301 - val_acc: 0.8755\n",
            "Epoch 11/30\n",
            "186/187 [============================>.] - ETA: 0s - loss: 0.4031 - acc: 0.8486Epoch 1/30\n",
            "12000/187 [=====================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================] - 5s 428us/sample - loss: 0.3354 - acc: 0.8846\n",
            "187/187 [==============================] - 97s 519ms/step - loss: 0.4031 - acc: 0.8486 - val_loss: 0.3074 - val_acc: 0.8846\n",
            "Epoch 12/30\n",
            "186/187 [============================>.] - ETA: 0s - loss: 0.3913 - acc: 0.8552Epoch 1/30\n",
            "12000/187 [=====================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================] - 5s 416us/sample - loss: 0.3428 - acc: 0.8782\n",
            "187/187 [==============================] - 94s 503ms/step - loss: 0.3912 - acc: 0.8551 - val_loss: 0.3281 - val_acc: 0.8782\n",
            "Epoch 13/30\n",
            "186/187 [============================>.] - ETA: 0s - loss: 0.3840 - acc: 0.8572Epoch 1/30\n",
            "12000/187 [=====================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================] - 5s 423us/sample - loss: 0.3464 - acc: 0.8882\n",
            "187/187 [==============================] - 97s 521ms/step - loss: 0.3838 - acc: 0.8573 - val_loss: 0.3003 - val_acc: 0.8882\n",
            "Epoch 14/30\n",
            "186/187 [============================>.] - ETA: 0s - loss: 0.3752 - acc: 0.8616Epoch 1/30\n",
            "12000/187 [=====================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================] - 5s 417us/sample - loss: 0.3355 - acc: 0.8866\n",
            "187/187 [==============================] - 93s 498ms/step - loss: 0.3749 - acc: 0.8618 - val_loss: 0.2991 - val_acc: 0.8866\n",
            "Epoch 15/30\n",
            "186/187 [============================>.] - ETA: 0s - loss: 0.3696 - acc: 0.8612Epoch 1/30\n",
            "12000/187 [=====================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================] - 5s 422us/sample - loss: 0.3175 - acc: 0.8949\n",
            "187/187 [==============================] - 93s 499ms/step - loss: 0.3696 - acc: 0.8611 - val_loss: 0.2786 - val_acc: 0.8949\n",
            "Epoch 16/30\n",
            "186/187 [============================>.] - ETA: 0s - loss: 0.3644 - acc: 0.8628Epoch 1/30\n",
            "12000/187 [=====================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================] - 5s 416us/sample - loss: 0.3138 - acc: 0.8966\n",
            "187/187 [==============================] - 96s 515ms/step - loss: 0.3643 - acc: 0.8628 - val_loss: 0.2770 - val_acc: 0.8966\n",
            "Epoch 17/30\n",
            "186/187 [============================>.] - ETA: 0s - loss: 0.3568 - acc: 0.8654Epoch 1/30\n",
            "12000/187 [=====================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================] - 5s 419us/sample - loss: 0.3026 - acc: 0.8982\n",
            "187/187 [==============================] - 95s 506ms/step - loss: 0.3567 - acc: 0.8655 - val_loss: 0.2724 - val_acc: 0.8982\n",
            "Epoch 18/30\n",
            "186/187 [============================>.] - ETA: 0s - loss: 0.3528 - acc: 0.8672Epoch 1/30\n",
            "12000/187 [=====================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================] - 5s 417us/sample - loss: 0.3135 - acc: 0.8993\n",
            "187/187 [==============================] - 93s 496ms/step - loss: 0.3527 - acc: 0.8674 - val_loss: 0.2757 - val_acc: 0.8993\n",
            "Epoch 19/30\n",
            "186/187 [============================>.] - ETA: 0s - loss: 0.3500 - acc: 0.8694Epoch 1/30\n",
            "12000/187 [=====================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================] - 5s 415us/sample - loss: 0.3205 - acc: 0.8930\n",
            "187/187 [==============================] - 93s 496ms/step - loss: 0.3497 - acc: 0.8696 - val_loss: 0.2783 - val_acc: 0.8930\n",
            "Epoch 20/30\n",
            "186/187 [============================>.] - ETA: 0s - loss: 0.3430 - acc: 0.8730Epoch 1/30\n",
            "12000/187 [=====================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================] - 5s 423us/sample - loss: 0.3085 - acc: 0.8988\n",
            "187/187 [==============================] - 96s 512ms/step - loss: 0.3426 - acc: 0.8732 - val_loss: 0.2649 - val_acc: 0.8988\n",
            "Epoch 21/30\n",
            "186/187 [============================>.] - ETA: 0s - loss: 0.3341 - acc: 0.8744Epoch 1/30\n",
            "12000/187 [=====================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================] - 5s 424us/sample - loss: 0.2986 - acc: 0.9047\n",
            "187/187 [==============================] - 94s 501ms/step - loss: 0.3343 - acc: 0.8745 - val_loss: 0.2506 - val_acc: 0.9047\n",
            "Epoch 22/30\n",
            "186/187 [============================>.] - ETA: 0s - loss: 0.3399 - acc: 0.8739Epoch 1/30\n",
            "12000/187 [=====================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================] - 5s 419us/sample - loss: 0.3065 - acc: 0.9009\n",
            "187/187 [==============================] - 95s 506ms/step - loss: 0.3401 - acc: 0.8738 - val_loss: 0.2610 - val_acc: 0.9009\n",
            "Epoch 23/30\n",
            "186/187 [============================>.] - ETA: 0s - loss: 0.3314 - acc: 0.8753Epoch 1/30\n",
            "12000/187 [=====================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================] - 5s 433us/sample - loss: 0.3034 - acc: 0.9013\n",
            "187/187 [==============================] - 97s 517ms/step - loss: 0.3317 - acc: 0.8751 - val_loss: 0.2594 - val_acc: 0.9013\n",
            "Epoch 24/30\n",
            "186/187 [============================>.] - ETA: 0s - loss: 0.3237 - acc: 0.8794Epoch 1/30\n",
            "12000/187 [=====================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================] - 5s 452us/sample - loss: 0.3144 - acc: 0.8942\n",
            "187/187 [==============================] - 99s 531ms/step - loss: 0.3239 - acc: 0.8794 - val_loss: 0.2769 - val_acc: 0.8942\n",
            "Epoch 25/30\n",
            "186/187 [============================>.] - ETA: 0s - loss: 0.3291 - acc: 0.8769Epoch 1/30\n",
            "12000/187 [=====================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================] - 5s 419us/sample - loss: 0.2896 - acc: 0.9083\n",
            "187/187 [==============================] - 98s 526ms/step - loss: 0.3287 - acc: 0.8771 - val_loss: 0.2461 - val_acc: 0.9083\n",
            "Epoch 26/30\n",
            "186/187 [============================>.] - ETA: 0s - loss: 0.3226 - acc: 0.8799Epoch 1/30\n",
            "12000/187 [=====================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================] - 5s 417us/sample - loss: 0.2831 - acc: 0.9042\n",
            "187/187 [==============================] - 94s 502ms/step - loss: 0.3223 - acc: 0.8801 - val_loss: 0.2537 - val_acc: 0.9042\n",
            "Epoch 27/30\n",
            "186/187 [============================>.] - ETA: 0s - loss: 0.3226 - acc: 0.8802Epoch 1/30\n",
            "12000/187 [=====================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================] - 5s 434us/sample - loss: 0.2844 - acc: 0.9068\n",
            "187/187 [==============================] - 97s 521ms/step - loss: 0.3223 - acc: 0.8803 - val_loss: 0.2460 - val_acc: 0.9068\n",
            "Epoch 28/30\n",
            "186/187 [============================>.] - ETA: 0s - loss: 0.3194 - acc: 0.8798Epoch 1/30\n",
            "12000/187 [=====================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================] - 5s 439us/sample - loss: 0.2991 - acc: 0.9048\n",
            "187/187 [==============================] - 99s 528ms/step - loss: 0.3192 - acc: 0.8798 - val_loss: 0.2510 - val_acc: 0.9048\n",
            "Epoch 29/30\n",
            "186/187 [============================>.] - ETA: 0s - loss: 0.3186 - acc: 0.8808Epoch 1/30\n",
            "12000/187 [=====================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================] - 5s 415us/sample - loss: 0.2805 - acc: 0.9077\n",
            "187/187 [==============================] - 97s 519ms/step - loss: 0.3189 - acc: 0.8806 - val_loss: 0.2514 - val_acc: 0.9077\n",
            "Epoch 30/30\n",
            "186/187 [============================>.] - ETA: 0s - loss: 0.3142 - acc: 0.8839Epoch 1/30\n",
            "12000/187 [=====================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================] - 5s 423us/sample - loss: 0.2773 - acc: 0.9062\n",
            "187/187 [==============================] - 96s 515ms/step - loss: 0.3142 - acc: 0.8838 - val_loss: 0.2489 - val_acc: 0.9062\n"
          ],
          "name": "stdout"
        }
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    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pqUfnMgEZlqs",
        "colab_type": "code",
        "outputId": "790cc169-f98b-4286-8f0e-c5de4a0e0644",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 610
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      "source": [
        "plot_accuracy_and_loss(history3)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/plotly/tools.py:465: DeprecationWarning:\n",
            "\n",
            "plotly.tools.make_subplots is deprecated, please use plotly.subplots.make_subplots instead\n",
            "\n"
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          "name": "stderr"
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    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c0GXNfqHRmmC",
        "colab_type": "text"
      },
      "source": [
        "**ĐÁNH GIÁ**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ngcg_Ur8Z2XZ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def plot_confusion_matrix(cm, classes,\n",
        "                          normalize=False,\n",
        "                          title='Confusion matrix',\n",
        "                          cmap=plt.cm.Blues):\n",
        "    \n",
        "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
        "    plt.title(title)\n",
        "    plt.colorbar()\n",
        "    tick_marks = np.arange(len(classes))\n",
        "    plt.xticks(tick_marks, classes, rotation=90)\n",
        "    plt.yticks(tick_marks, classes)\n",
        "\n",
        "    if normalize:\n",
        "        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
        "\n",
        "    thresh = cm.max() / 2.\n",
        "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
        "        plt.text(j, i, cm[i, j],\n",
        "                 horizontalalignment=\"center\",\n",
        "                 color=\"white\" if cm[i, j] > thresh else \"black\")\n",
        "\n",
        "    plt.tight_layout()\n",
        "    plt.ylabel('True label')\n",
        "    plt.xlabel('Predicted label')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rNmAEaNtZ7Cb",
        "colab_type": "code",
        "outputId": "344cc41b-099a-4810-bc61-815bd85e5c48",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 311
        }
      },
      "source": [
        "\n",
        "x_test=test_dataset_x\n",
        "y_test=test_Y_one_hot\n",
        "# Predict the values from the validation dataset\n",
        "Y_pred = model.predict(x_test)\n",
        "# Convert predictions classes to one hot vectors \n",
        "Y_pred_classes = np.argmax(Y_pred,axis = 1) \n",
        "# Convert validation observations to one hot vectors\n",
        "Y_true = np.argmax(y_test,axis = 1) \n",
        "# compute the confusion matrix\n",
        "confusion_mtx = confusion_matrix(Y_true, Y_pred_classes) \n",
        "# plot the confusion matrix\n",
        "plot_confusion_matrix(confusion_mtx, \n",
        "            classes = ['T-shirt/Top','Trouser','Pullover','Dress','Coat','Sandal','Shirt','Sneaker','Bag','Ankle Boot'])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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wJIcQEOD+8Af8scX5H+xxx1exAuIBrNaiu/qHROQZrL2pAsxS1Wn2uVO2VEZF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\nAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iHzJGdPU-m9G",
        "colab_type": "text"
      },
      "source": [
        "**Classification Report**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wjOxWO_e-ufo",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "predicted_classes = model_2.predict_classes(x_test)\n",
        "# get the indices to be plotted\n",
        "y_true = test_dataset_y\n",
        "correct = np.nonzero(predicted_classes==y_true)[0]\n",
        "incorrect = np.nonzero(predicted_classes!=y_true)[0]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IZcuCEhQjpPR",
        "colab_type": "code",
        "outputId": "b0a8e928-4693-45a2-fe15-8507854d23cb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        }
      },
      "source": [
        "print(\"Correct predicted classes:\",correct.shape[0])\n",
        "print(\"Incorrect predicted classes:\",incorrect.shape[0])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Correct predicted classes: 9151\n",
            "Incorrect predicted classes: 849\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pFRVkf-EAQ3S",
        "colab_type": "code",
        "outputId": "cc5a50f8-c490-433e-c374-8760135ba19d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 302
        }
      },
      "source": [
        "from sklearn.metrics import classification_report\n",
        "target_names = [\"Class {}\".format(i) for i in range(10)]\n",
        "print(classification_report(y_true, predicted_classes, target_names=target_names))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "     Class 0       0.90      0.84      0.87      1000\n",
            "     Class 1       0.98      0.99      0.99      1000\n",
            "     Class 2       0.92      0.83      0.87      1000\n",
            "     Class 3       0.88      0.96      0.92      1000\n",
            "     Class 4       0.89      0.83      0.86      1000\n",
            "     Class 5       0.99      0.97      0.98      1000\n",
            "     Class 6       0.71      0.79      0.75      1000\n",
            "     Class 7       0.93      0.98      0.96      1000\n",
            "     Class 8       0.98      0.99      0.99      1000\n",
            "     Class 9       0.98      0.96      0.97      1000\n",
            "\n",
            "    accuracy                           0.92     10000\n",
            "   macro avg       0.92      0.92      0.92     10000\n",
            "weighted avg       0.92      0.92      0.92     10000\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DD9QBxdnRq10",
        "colab_type": "text"
      },
      "source": [
        "**đánh giá trên tập test**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dyaM_BVZRyt7",
        "colab_type": "code",
        "outputId": "45c6569c-ca0e-426c-9613-7372b41ea831",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67
        }
      },
      "source": [
        "score = model_2.evaluate(test_dataset_x, test_Y_one_hot)\n",
        "print('Test loss:', score[0])\n",
        "print('Test accuracy:', score[1])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "10000/10000 [==============================] - 6s 565us/sample - loss: 0.2373 - acc: 0.9151\n",
            "Test loss: 0.2372579971909523\n",
            "Test accuracy: 0.9151\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UAC_W8SWiPXE",
        "colab_type": "code",
        "outputId": "2934fca6-04a8-4747-d02a-91b2175c839b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 589
        }
      },
      "source": [
        "predictions = model.predict(x_test)\n",
        "def plot_image(i, predictions_array, true_labels, images):\n",
        "  predictions_array, true_label, img = predictions_array[i], true_labels[i], images[i]\n",
        "  plt.grid(False)\n",
        "  plt.xticks([])\n",
        "  plt.yticks([])\n",
        "  \n",
        "  plt.imshow(img[...,0], cmap=plt.cm.binary)\n",
        "\n",
        "  predicted_label = np.argmax(predictions_array)\n",
        "  if predicted_label == true_label:\n",
        "    color = 'blue'\n",
        "  else:\n",
        "    color = 'red'\n",
        "  \n",
        "  plt.xlabel(\"{} {:2.0f}% ({})\".format(class_names[predicted_label],\n",
        "                                100*np.max(predictions_array),\n",
        "                                class_names[true_label]),\n",
        "                                color=color)\n",
        "\n",
        "def plot_value_array(i, predictions_array, true_label):\n",
        "  predictions_array, true_label = predictions_array[i], true_label[i]\n",
        "  plt.grid(False)\n",
        "  plt.xticks([])\n",
        "  plt.yticks([])\n",
        "  thisplot = plt.bar(range(10), predictions_array, color=\"#777777\")\n",
        "  plt.ylim([0, 1]) \n",
        "  predicted_label = np.argmax(predictions_array)\n",
        "  \n",
        "  thisplot[predicted_label].set_color('red')\n",
        "  thisplot[true_label].set_color('blue')\n",
        "\n",
        "num_rows = 5\n",
        "num_cols = 3\n",
        "num_images = num_rows*num_cols\n",
        "plt.figure(figsize=(2*2*num_cols, 2*num_rows))\n",
        "for i in range(num_images):\n",
        "  plt.subplot(num_rows, 2*num_cols, 2*i+1)\n",
        "  plot_image(i, predictions, test_dataset_y, test_dataset_x)\n",
        "  plt.subplot(num_rows, 2*num_cols, 2*i+2)\n",
        "  plot_value_array(i, predictions, test_dataset_y)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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i9hX+bHd6Vju9z/JTJOJmjNOiKij5fXD+/Pk21ukOfsqgfl6/yoTu80Qaj5aIiIiIKNF4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\nYII=\n",
            "text/plain": [
              "<Figure size 864x720 with 30 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}