train.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b6b26ef4-ac0a-411c-a9d3-658dfbcd4aa5",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "!pip install \"tensorflow<2.11\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9ad6b844-7148-43de-bbf2-0249766aada6",
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install opencv-python matplotlib seaborn panda numpy sklearn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "5b24e80b-a3ee-4099-988b-5c6ed3322a5a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import matplotlib.pyplot as plt\n",
    "from PIL import Image\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import os\n",
    "from tensorflow.keras.utils import to_categorical\n",
    "from glob import glob\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27c74329-5628-4a1e-a749-79b3d32073c0",
   "metadata": {},
   "source": [
    "## Set up GPU for faster training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "208445f0-8a4b-4ab9-a8c3-a6a132404810",
   "metadata": {},
   "outputs": [],
   "source": [
    "gpus = tf.config.experimental.list_physical_devices('GPU')\n",
    "for gpu in gpus:\n",
    "    tf.config.experimental.set_memory_growth(gpu, True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "9f67e89d-1bb6-4225-ac9d-4c882d6ea03b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Num GPUs Available:  1\n"
     ]
    }
   ],
   "source": [
    "print(\"Num GPUs Available: \", len(tf.config.list_physical_devices('GPU')))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "663b8e32-ca81-41f2-b13d-804576169c86",
   "metadata": {},
   "source": [
    "## About the dataset\n",
    "<p>HAM10000 (\"Human Against Machine with 10000 training images\") dataset - a large collection of multi-source dermatoscopic images of pigmented lesions.</p> \n",
    "<p>The dermatoscopic images are collected from different populations, acquired and stored by different modalities. The final dataset consists of 10015 dermatoscopic images.</p>\n",
    "\n",
    "It has 7 different classes of skin cancer which are listed below:\n",
    "\n",
    "- Melanocytic nevi\n",
    "- Melanoma\n",
    "- Benign keratosis-like lesions\n",
    "- Basal cell carcinoma\n",
    "- Actinic keratoses\n",
    "- Vascular lesions\n",
    "- Dermatofibroma"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "6e893e84-b5eb-46be-b06e-255187468a50",
   "metadata": {},
   "outputs": [
    {
     "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>lesion_id</th>\n",
       "      <th>image_id</th>\n",
       "      <th>dx</th>\n",
       "      <th>dx_type</th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>localization</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>HAM_0000118</td>\n",
       "      <td>ISIC_0027419</td>\n",
       "      <td>bkl</td>\n",
       "      <td>histo</td>\n",
       "      <td>80.0</td>\n",
       "      <td>male</td>\n",
       "      <td>scalp</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>HAM_0000118</td>\n",
       "      <td>ISIC_0025030</td>\n",
       "      <td>bkl</td>\n",
       "      <td>histo</td>\n",
       "      <td>80.0</td>\n",
       "      <td>male</td>\n",
       "      <td>scalp</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>HAM_0002730</td>\n",
       "      <td>ISIC_0026769</td>\n",
       "      <td>bkl</td>\n",
       "      <td>histo</td>\n",
       "      <td>80.0</td>\n",
       "      <td>male</td>\n",
       "      <td>scalp</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>HAM_0002730</td>\n",
       "      <td>ISIC_0025661</td>\n",
       "      <td>bkl</td>\n",
       "      <td>histo</td>\n",
       "      <td>80.0</td>\n",
       "      <td>male</td>\n",
       "      <td>scalp</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>HAM_0001466</td>\n",
       "      <td>ISIC_0031633</td>\n",
       "      <td>bkl</td>\n",
       "      <td>histo</td>\n",
       "      <td>75.0</td>\n",
       "      <td>male</td>\n",
       "      <td>ear</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     lesion_id      image_id   dx dx_type   age   sex localization\n",
       "0  HAM_0000118  ISIC_0027419  bkl   histo  80.0  male        scalp\n",
       "1  HAM_0000118  ISIC_0025030  bkl   histo  80.0  male        scalp\n",
       "2  HAM_0002730  ISIC_0026769  bkl   histo  80.0  male        scalp\n",
       "3  HAM_0002730  ISIC_0025661  bkl   histo  80.0  male        scalp\n",
       "4  HAM_0001466  ISIC_0031633  bkl   histo  75.0  male          ear"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read metadata\n",
    "df = pd.read_csv('./skin-cancer-mnist/HAM10000_metadata.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "06fca974-720b-46da-8660-314e0f6a9329",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fill null age in csv file with mean value of age\n",
    "df['age'].fillna(int(df['age'].mean()),inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "6499d072-1c17-4658-9738-d481dde05062",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "lesion_id       0\n",
       "image_id        0\n",
       "dx              0\n",
       "dx_type         0\n",
       "age             0\n",
       "sex             0\n",
       "localization    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "854eb411-5ff0-452b-8149-24b67969c1fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "lesion_type_dict = {\n",
    "    'nv': 'Melanocytic nevi',\n",
    "    'mel': 'Melanoma',\n",
    "    'bkl': 'Benign keratosis-like lesions',\n",
    "    'bcc': 'Basal cell carcinoma',\n",
    "    'akiec': 'Actinic keratoses',\n",
    "    'vasc': 'Vascular lesions',\n",
    "    'df': 'Dermatofibroma'\n",
    "}\n",
    "base_dir = '.\\skin-cancer-mnist'\n",
    "\n",
    "# Merge images from both folders into one dictionary\n",
    "imageid_path_dict = {os.path.splitext(os.path.basename(x))[0]: x\n",
    "                     for x in glob(os.path.join(base_dir, '*', '*.jpg'))}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "85d3427e-fb2d-438f-bf64-12f72f9acbcd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>lesion_id</th>\n",
       "      <th>image_id</th>\n",
       "      <th>dx</th>\n",
       "      <th>dx_type</th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>localization</th>\n",
       "      <th>path</th>\n",
       "      <th>cell_type</th>\n",
       "      <th>cell_type_idx</th>\n",
       "      <th>image</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>HAM_0000118</td>\n",
       "      <td>ISIC_0027419</td>\n",
       "      <td>bkl</td>\n",
       "      <td>histo</td>\n",
       "      <td>80.0</td>\n",
       "      <td>male</td>\n",
       "      <td>scalp</td>\n",
       "      <td>.\\skin-cancer-mnist\\HAM10000_images_part_1\\ISI...</td>\n",
       "      <td>Benign keratosis-like lesions</td>\n",
       "      <td>2</td>\n",
       "      <td>[[[189, 152, 194], [192, 156, 198], [191, 154,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>HAM_0000118</td>\n",
       "      <td>ISIC_0025030</td>\n",
       "      <td>bkl</td>\n",
       "      <td>histo</td>\n",
       "      <td>80.0</td>\n",
       "      <td>male</td>\n",
       "      <td>scalp</td>\n",
       "      <td>.\\skin-cancer-mnist\\HAM10000_images_part_1\\ISI...</td>\n",
       "      <td>Benign keratosis-like lesions</td>\n",
       "      <td>2</td>\n",
       "      <td>[[[24, 13, 22], [24, 14, 22], [24, 14, 26], [2...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>HAM_0002730</td>\n",
       "      <td>ISIC_0026769</td>\n",
       "      <td>bkl</td>\n",
       "      <td>histo</td>\n",
       "      <td>80.0</td>\n",
       "      <td>male</td>\n",
       "      <td>scalp</td>\n",
       "      <td>.\\skin-cancer-mnist\\HAM10000_images_part_1\\ISI...</td>\n",
       "      <td>Benign keratosis-like lesions</td>\n",
       "      <td>2</td>\n",
       "      <td>[[[186, 127, 135], [189, 133, 145], [192, 135,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>HAM_0002730</td>\n",
       "      <td>ISIC_0025661</td>\n",
       "      <td>bkl</td>\n",
       "      <td>histo</td>\n",
       "      <td>80.0</td>\n",
       "      <td>male</td>\n",
       "      <td>scalp</td>\n",
       "      <td>.\\skin-cancer-mnist\\HAM10000_images_part_1\\ISI...</td>\n",
       "      <td>Benign keratosis-like lesions</td>\n",
       "      <td>2</td>\n",
       "      <td>[[[24, 11, 17], [24, 11, 20], [30, 15, 25], [4...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>HAM_0001466</td>\n",
       "      <td>ISIC_0031633</td>\n",
       "      <td>bkl</td>\n",
       "      <td>histo</td>\n",
       "      <td>75.0</td>\n",
       "      <td>male</td>\n",
       "      <td>ear</td>\n",
       "      <td>.\\skin-cancer-mnist\\HAM10000_images_part_2\\ISI...</td>\n",
       "      <td>Benign keratosis-like lesions</td>\n",
       "      <td>2</td>\n",
       "      <td>[[[131, 88, 110], [142, 97, 120], [152, 107, 1...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     lesion_id      image_id   dx dx_type   age   sex localization  \\\n",
       "0  HAM_0000118  ISIC_0027419  bkl   histo  80.0  male        scalp   \n",
       "1  HAM_0000118  ISIC_0025030  bkl   histo  80.0  male        scalp   \n",
       "2  HAM_0002730  ISIC_0026769  bkl   histo  80.0  male        scalp   \n",
       "3  HAM_0002730  ISIC_0025661  bkl   histo  80.0  male        scalp   \n",
       "4  HAM_0001466  ISIC_0031633  bkl   histo  75.0  male          ear   \n",
       "\n",
       "                                                path  \\\n",
       "0  .\\skin-cancer-mnist\\HAM10000_images_part_1\\ISI...   \n",
       "1  .\\skin-cancer-mnist\\HAM10000_images_part_1\\ISI...   \n",
       "2  .\\skin-cancer-mnist\\HAM10000_images_part_1\\ISI...   \n",
       "3  .\\skin-cancer-mnist\\HAM10000_images_part_1\\ISI...   \n",
       "4  .\\skin-cancer-mnist\\HAM10000_images_part_2\\ISI...   \n",
       "\n",
       "                       cell_type  cell_type_idx  \\\n",
       "0  Benign keratosis-like lesions              2   \n",
       "1  Benign keratosis-like lesions              2   \n",
       "2  Benign keratosis-like lesions              2   \n",
       "3  Benign keratosis-like lesions              2   \n",
       "4  Benign keratosis-like lesions              2   \n",
       "\n",
       "                                               image  \n",
       "0  [[[189, 152, 194], [192, 156, 198], [191, 154,...  \n",
       "1  [[[24, 13, 22], [24, 14, 22], [24, 14, 26], [2...  \n",
       "2  [[[186, 127, 135], [189, 133, 145], [192, 135,...  \n",
       "3  [[[24, 11, 17], [24, 11, 20], [30, 15, 25], [4...  \n",
       "4  [[[131, 88, 110], [142, 97, 120], [152, 107, 1...  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Cell type index\n",
    "# 0 - Actinic keratoses\t\n",
    "# 1 - Basal cell carcinoma\n",
    "# 2 - Benign keratosis-like lesions\n",
    "# 3 - Dermatofibroma\t\n",
    "# 4 - Melanocytic nevi\n",
    "# 5 - Melanoma\n",
    "# 6 - Vascular lesions\n",
    "df['path'] = df['image_id'].map(imageid_path_dict.get)\n",
    "df['cell_type'] = df['dx'].map(lesion_type_dict.get) \n",
    "df['cell_type_idx'] = pd.Categorical(df['cell_type']).codes\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "df025b78-9098-44fd-ac46-c36bb14e3040",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Resize Image\n",
    "df['image'] = df['path'].map(lambda x: np.asarray(Image.open(x).resize((125,100))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6181d404-8650-4659-a194-91b296cd28b7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Code to show some sample images and save it\n",
    "n_samples = 5\n",
    "fig, m_axs = plt.subplots(7, n_samples, figsize = (4*n_samples, 3*7))\n",
    "for n_axs, (type_name, type_rows) in zip(m_axs, \n",
    "                                         df.sort_values(['cell_type']).groupby('cell_type')):\n",
    "    n_axs[0].set_title(type_name)\n",
    "    for c_ax, (_, c_row) in zip(n_axs, type_rows.sample(n_samples, random_state=2018).iterrows()):\n",
    "        c_ax.imshow(c_row['image'])\n",
    "        c_ax.axis('off')\n",
    "fig.savefig('category_samples.png', dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "ed6307b8-0b0e-4675-b6b2-788790f2806d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "image\n",
       "(100, 125, 3)    10015\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# See the image size distribution - should just return one row (all images are uniform)\n",
    "df['image'].map(lambda x: x.shape).value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "19291d08-7e7b-4e7f-937e-c85b483ef289",
   "metadata": {},
   "source": [
    "## Data Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "6ab940e3-f808-4dab-a30d-2ddf328d403d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: title={'center': 'CELL TYPE'}, xlabel='cell_type', ylabel='Count'>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x1000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,10))\n",
    "plt.subplots_adjust(left=0.125, bottom=1, right=0.9, top=2, hspace=0.2)\n",
    "plt.subplot(2,4,1)\n",
    "plt.title(\"AGE\",fontsize=15)\n",
    "plt.ylabel(\"Count\")\n",
    "df['age'].value_counts().plot.bar()\n",
    "\n",
    "plt.subplot(2,4,2)\n",
    "plt.title(\"GENDER\",fontsize=15)\n",
    "plt.ylabel(\"Count\")\n",
    "df['sex'].value_counts().plot.bar()\n",
    "\n",
    "plt.subplot(2,4,3)\n",
    "plt.title(\"localization\",fontsize=15)\n",
    "plt.ylabel(\"Count\")\n",
    "plt.xticks(rotation=45)\n",
    "df['localization'].value_counts().plot.bar()\n",
    "\n",
    "plt.subplot(2,4,4)\n",
    "plt.title(\"CELL TYPE\",fontsize=15)\n",
    "plt.ylabel(\"Count\")\n",
    "df['cell_type'].value_counts().plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "da8ab97a-ff23-431a-9883-874f95813094",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Data feature and labels\n",
    "features=df.drop(columns=['cell_type_idx'],axis=1)\n",
    "target=df['cell_type_idx']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "951a7a45-13d8-4e5b-8e32-f751eac731c0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    2\n",
       "1    2\n",
       "2    2\n",
       "3    2\n",
       "4    2\n",
       "Name: cell_type_idx, dtype: int8"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "target.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "e85fe743-2e16-48e3-a614-fe02a7debc8c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Unique(y=<tf.Tensor: shape=(7,), dtype=string, numpy=\n",
       "array([b'Melanocytic nevi', b'Basal cell carcinoma', b'Melanoma',\n",
       "       b'Vascular lesions', b'Benign keratosis-like lesions',\n",
       "       b'Actinic keratoses', b'Dermatofibroma'], dtype=object)>, idx=<tf.Tensor: shape=(7511,), dtype=int32, numpy=array([0, 1, 0, ..., 1, 0, 0])>)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train_o, x_test_o, y_train_o, y_test_o = train_test_split(features, target, test_size=0.25,random_state=666)\n",
    "tf.unique(x_train_o.cell_type.values)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2953b3f1-8604-4e4f-b70f-56c12c6931a4",
   "metadata": {},
   "source": [
    "## Data Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "c2c9c51e-b659-46e4-8d5c-fb6713925745",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train = np.asarray(x_train_o['image'].tolist())\n",
    "x_test = np.asarray(x_test_o['image'].tolist())\n",
    "\n",
    "x_train_mean = np.mean(x_train)\n",
    "x_train_std = np.std(x_train)\n",
    "\n",
    "x_test_mean = np.mean(x_test)\n",
    "x_test_std = np.std(x_test)\n",
    "\n",
    "x_train = (x_train - x_train_mean)/x_train_std\n",
    "x_test = (x_test - x_test_mean)/x_test_std"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "29cfea55-38a3-4011-b99e-d458d596a5a6",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train = to_categorical(y_train_o, num_classes = 7)\n",
    "y_test = to_categorical(y_test_o, num_classes = 7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "93c3466a-155a-42f8-83d4-19a4d0cdc2bf",
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
    "\n",
    "x_train, x_validate, y_train, y_validate = train_test_split(x_train, y_train, test_size = 0.1, random_state = 999)\n",
    "# Reshape image in 3 dimensions (height = 100, width = 125 , canal = 3)\n",
    "x_train = x_train.reshape(x_train.shape[0], *(100, 125, 3))\n",
    "x_test = x_test.reshape(x_test.shape[0], *(100, 125, 3))\n",
    "x_validate = x_validate.reshape(x_validate.shape[0], *(100, 125, 3))\n",
    "\n",
    "# Data augmentation to prevent overfitting \n",
    "datagen = ImageDataGenerator(\n",
    "        featurewise_center=False,  # set input mean to 0 over the dataset\n",
    "        samplewise_center=False,  # set each sample mean to 0\n",
    "        featurewise_std_normalization=False,  # divide inputs by std of the dataset\n",
    "        samplewise_std_normalization=False,  # divide each input by its std\n",
    "        zca_whitening=False,  # apply ZCA whitening\n",
    "        rotation_range=10,  # randomly rotate images in the range (degrees, 0 to 180)\n",
    "        zoom_range = 0.1, # Randomly zoom image \n",
    "        width_shift_range=0.12,  # randomly shift images horizontally (fraction of total width)\n",
    "        height_shift_range=0.12,  # randomly shift images vertically (fraction of total height)\n",
    "        horizontal_flip=True,  # randomly flip images\n",
    "        vertical_flip=True)  # randomly flip images\n",
    "\n",
    "datagen.fit(x_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f652afed-1a44-477d-b0be-3da193b43c82",
   "metadata": {},
   "source": [
    "# Training step\n",
    "## CNN\n",
    "I choose CNN for image classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "48b5bfc5-6ab5-41ba-b1b0-92091bd30f60",
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.layers import Flatten,Dense,Dropout,BatchNormalization,Conv2D, MaxPool2D\n",
    "from keras.optimizers import Adam\n",
    "from keras.callbacks import ReduceLROnPlateau\n",
    "from tensorflow.keras.models import Sequential"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "f4d2a49d-0d5d-4576-a833-31f7e3f41c49",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_11\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " conv2d_77 (Conv2D)          (None, 100, 125, 64)      1792      \n",
      "                                                                 \n",
      " conv2d_78 (Conv2D)          (None, 100, 125, 64)      36928     \n",
      "                                                                 \n",
      " max_pooling2d_34 (MaxPoolin  (None, 50, 62, 64)       0         \n",
      " g2D)                                                            \n",
      "                                                                 \n",
      " conv2d_79 (Conv2D)          (None, 50, 62, 64)        36928     \n",
      "                                                                 \n",
      " conv2d_80 (Conv2D)          (None, 50, 62, 64)        36928     \n",
      "                                                                 \n",
      " max_pooling2d_35 (MaxPoolin  (None, 25, 31, 64)       0         \n",
      " g2D)                                                            \n",
      "                                                                 \n",
      " conv2d_81 (Conv2D)          (None, 25, 31, 64)        36928     \n",
      "                                                                 \n",
      " conv2d_82 (Conv2D)          (None, 25, 31, 64)        36928     \n",
      "                                                                 \n",
      " max_pooling2d_36 (MaxPoolin  (None, 12, 15, 64)       0         \n",
      " g2D)                                                            \n",
      "                                                                 \n",
      " dropout_22 (Dropout)        (None, 12, 15, 64)        0         \n",
      "                                                                 \n",
      " flatten_11 (Flatten)        (None, 11520)             0         \n",
      "                                                                 \n",
      " dense_31 (Dense)            (None, 256)               2949376   \n",
      "                                                                 \n",
      " dense_32 (Dense)            (None, 128)               32896     \n",
      "                                                                 \n",
      " dropout_23 (Dropout)        (None, 128)               0         \n",
      "                                                                 \n",
      " dense_33 (Dense)            (None, 7)                 903       \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 3,169,607\n",
      "Trainable params: 3,169,607\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "input_shape = (100, 125, 3)\n",
    "num_classes = 7\n",
    "\n",
    "model = Sequential()\n",
    "model.add(Conv2D(64, (3, 3), 1, activation='relu', padding = 'Same', input_shape=input_shape))\n",
    "model.add(Conv2D(64, (3, 3), 1, activation='relu', padding = 'Same'))\n",
    "model.add(MaxPool2D())\n",
    "\n",
    "model.add(Conv2D(64, (3, 3), 1, activation='relu',padding = 'Same'))\n",
    "model.add(Conv2D(64, (3, 3), 1, activation='relu', padding = 'Same'))\n",
    "model.add(MaxPool2D())\n",
    "\n",
    "model.add(Conv2D(64, (3, 3), 1, activation='relu',padding = 'Same'))\n",
    "model.add(Conv2D(64, (3, 3), 1, activation='relu',padding = 'Same'))\n",
    "model.add(MaxPool2D())\n",
    "model.add(Dropout(0.25))\n",
    "\n",
    "model.add(Flatten())\n",
    "\n",
    "model.add(Dense(256, activation='relu'))\n",
    "model.add(Dense(128, activation='relu'))\n",
    "model.add(Dropout(0.4))\n",
    "model.add(Dense(num_classes, activation='softmax'))\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "3ba352ea-f847-4fe5-a022-f038f4f99c41",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the optimizer\n",
    "optimizer = Adam()\n",
    "# Compile the model\n",
    "model.compile(optimizer = optimizer, loss = \"categorical_crossentropy\", metrics=[\"accuracy\"])\n",
    "# Set a learning rate annealer\n",
    "learning_rate_reduction = ReduceLROnPlateau(monitor='val_accuracy', \n",
    "                                            patience=4, \n",
    "                                            verbose=1, \n",
    "                                            factor=0.5, \n",
    "                                            min_lr=0.00001)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "2fdf5040-6d75-41e4-b2d1-986deb57bbc9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/60\n",
      "423/423 [==============================] - 11s 24ms/step - loss: 1.0291 - accuracy: 0.6655 - val_loss: 1.0298 - val_accuracy: 0.6410 - lr: 0.0010\n",
      "Epoch 2/60\n",
      "423/423 [==============================] - 10s 23ms/step - loss: 0.9261 - accuracy: 0.6727 - val_loss: 0.9508 - val_accuracy: 0.6543 - lr: 0.0010\n",
      "Epoch 3/60\n",
      "423/423 [==============================] - 10s 23ms/step - loss: 0.8962 - accuracy: 0.6821 - val_loss: 0.9053 - val_accuracy: 0.6822 - lr: 0.0010\n",
      "Epoch 4/60\n",
      "423/423 [==============================] - 10s 23ms/step - loss: 0.8717 - accuracy: 0.6911 - val_loss: 0.8592 - val_accuracy: 0.6715 - lr: 0.0010\n",
      "Epoch 5/60\n",
      "423/423 [==============================] - 10s 24ms/step - loss: 0.8395 - accuracy: 0.6970 - val_loss: 0.8343 - val_accuracy: 0.7021 - lr: 0.0010\n",
      "Epoch 6/60\n",
      "423/423 [==============================] - 10s 24ms/step - loss: 0.8336 - accuracy: 0.7097 - val_loss: 0.8779 - val_accuracy: 0.6702 - lr: 0.0010\n",
      "Epoch 7/60\n",
      "423/423 [==============================] - 10s 23ms/step - loss: 0.8077 - accuracy: 0.7127 - val_loss: 0.8247 - val_accuracy: 0.7048 - lr: 0.0010\n",
      "Epoch 8/60\n",
      "423/423 [==============================] - 10s 23ms/step - loss: 0.7770 - accuracy: 0.7174 - val_loss: 0.7574 - val_accuracy: 0.7021 - lr: 0.0010\n",
      "Epoch 9/60\n",
      "423/423 [==============================] - 10s 23ms/step - loss: 0.7577 - accuracy: 0.7213 - val_loss: 0.7901 - val_accuracy: 0.7008 - lr: 0.0010\n",
      "Epoch 10/60\n",
      "423/423 [==============================] - 10s 23ms/step - loss: 0.7570 - accuracy: 0.7306 - val_loss: 0.7700 - val_accuracy: 0.7008 - lr: 0.0010\n",
      "Epoch 11/60\n",
      "423/423 [==============================] - 10s 24ms/step - loss: 0.7405 - accuracy: 0.7335 - val_loss: 0.7489 - val_accuracy: 0.7221 - lr: 0.0010\n",
      "Epoch 12/60\n",
      "423/423 [==============================] - 10s 24ms/step - loss: 0.7285 - accuracy: 0.7417 - val_loss: 0.7370 - val_accuracy: 0.7314 - lr: 0.0010\n",
      "Epoch 13/60\n",
      "423/423 [==============================] - 10s 23ms/step - loss: 0.7170 - accuracy: 0.7393 - val_loss: 0.7434 - val_accuracy: 0.7194 - lr: 0.0010\n",
      "Epoch 14/60\n",
      "423/423 [==============================] - 10s 23ms/step - loss: 0.7161 - accuracy: 0.7440 - val_loss: 0.7221 - val_accuracy: 0.7261 - lr: 0.0010\n",
      "Epoch 15/60\n",
      "423/423 [==============================] - 10s 23ms/step - loss: 0.6988 - accuracy: 0.7503 - val_loss: 0.7361 - val_accuracy: 0.7194 - lr: 0.0010\n",
      "Epoch 16/60\n",
      "423/423 [==============================] - 10s 24ms/step - loss: 0.7014 - accuracy: 0.7412 - val_loss: 0.7009 - val_accuracy: 0.7340 - lr: 0.0010\n",
      "Epoch 17/60\n",
      "423/423 [==============================] - 10s 24ms/step - loss: 0.7067 - accuracy: 0.7520 - val_loss: 0.7141 - val_accuracy: 0.7194 - lr: 0.0010\n",
      "Epoch 18/60\n",
      "423/423 [==============================] - 10s 24ms/step - loss: 0.6933 - accuracy: 0.7439 - val_loss: 0.7266 - val_accuracy: 0.7088 - lr: 0.0010\n",
      "Epoch 19/60\n",
      "423/423 [==============================] - 10s 24ms/step - loss: 0.6848 - accuracy: 0.7525 - val_loss: 0.6845 - val_accuracy: 0.7274 - lr: 0.0010\n",
      "Epoch 20/60\n",
      "423/423 [==============================] - ETA: 0s - loss: 0.6851 - accuracy: 0.7565\n",
      "Epoch 20: ReduceLROnPlateau reducing learning rate to 0.0005000000237487257.\n",
      "423/423 [==============================] - 10s 23ms/step - loss: 0.6851 - accuracy: 0.7565 - val_loss: 0.7030 - val_accuracy: 0.7314 - lr: 0.0010\n",
      "Epoch 21/60\n",
      "423/423 [==============================] - 10s 24ms/step - loss: 0.6396 - accuracy: 0.7698 - val_loss: 0.6802 - val_accuracy: 0.7434 - lr: 5.0000e-04\n",
      "Epoch 22/60\n",
      "423/423 [==============================] - 10s 24ms/step - loss: 0.6246 - accuracy: 0.7717 - val_loss: 0.6828 - val_accuracy: 0.7407 - lr: 5.0000e-04\n",
      "Epoch 23/60\n",
      "423/423 [==============================] - 10s 24ms/step - loss: 0.6232 - accuracy: 0.7692 - val_loss: 0.6636 - val_accuracy: 0.7513 - lr: 5.0000e-04\n",
      "Epoch 24/60\n",
      "423/423 [==============================] - 10s 24ms/step - loss: 0.6201 - accuracy: 0.7686 - val_loss: 0.6681 - val_accuracy: 0.7553 - lr: 5.0000e-04\n",
      "Epoch 25/60\n",
      "423/423 [==============================] - 10s 24ms/step - loss: 0.6127 - accuracy: 0.7766 - val_loss: 0.6705 - val_accuracy: 0.7513 - lr: 5.0000e-04\n",
      "Epoch 26/60\n",
      "423/423 [==============================] - 10s 23ms/step - loss: 0.6113 - accuracy: 0.7686 - val_loss: 0.6523 - val_accuracy: 0.7500 - lr: 5.0000e-04\n",
      "Epoch 27/60\n",
      "423/423 [==============================] - 10s 23ms/step - loss: 0.5982 - accuracy: 0.7794 - val_loss: 0.6566 - val_accuracy: 0.7447 - lr: 5.0000e-04\n",
      "Epoch 28/60\n",
      "421/423 [============================>.] - ETA: 0s - loss: 0.5990 - accuracy: 0.7818\n",
      "Epoch 28: ReduceLROnPlateau reducing learning rate to 0.0002500000118743628.\n",
      "423/423 [==============================] - 10s 24ms/step - loss: 0.6000 - accuracy: 0.7813 - val_loss: 0.6887 - val_accuracy: 0.7447 - lr: 5.0000e-04\n",
      "Epoch 29/60\n",
      "423/423 [==============================] - 10s 24ms/step - loss: 0.5822 - accuracy: 0.7830 - val_loss: 0.6582 - val_accuracy: 0.7620 - lr: 2.5000e-04\n",
      "Epoch 30/60\n",
      "423/423 [==============================] - 10s 24ms/step - loss: 0.5676 - accuracy: 0.7904 - val_loss: 0.6406 - val_accuracy: 0.7593 - lr: 2.5000e-04\n",
      "Epoch 31/60\n",
      "423/423 [==============================] - 10s 24ms/step - loss: 0.5660 - accuracy: 0.7874 - val_loss: 0.6347 - val_accuracy: 0.7633 - lr: 2.5000e-04\n",
      "Epoch 32/60\n",
      "423/423 [==============================] - 10s 23ms/step - loss: 0.5652 - accuracy: 0.7883 - val_loss: 0.6520 - val_accuracy: 0.7633 - lr: 2.5000e-04\n",
      "Epoch 33/60\n",
      "423/423 [==============================] - 10s 24ms/step - loss: 0.5534 - accuracy: 0.7914 - val_loss: 0.6327 - val_accuracy: 0.7673 - lr: 2.5000e-04\n",
      "Epoch 34/60\n",
      "423/423 [==============================] - 10s 24ms/step - loss: 0.5510 - accuracy: 0.7926 - val_loss: 0.6399 - val_accuracy: 0.7646 - lr: 2.5000e-04\n",
      "Epoch 35/60\n",
      "423/423 [==============================] - 10s 24ms/step - loss: 0.5620 - accuracy: 0.7887 - val_loss: 0.6396 - val_accuracy: 0.7620 - lr: 2.5000e-04\n",
      "Epoch 36/60\n",
      "423/423 [==============================] - 10s 24ms/step - loss: 0.5447 - accuracy: 0.7982 - val_loss: 0.6529 - val_accuracy: 0.7646 - lr: 2.5000e-04\n",
      "Epoch 37/60\n",
      "421/423 [============================>.] - ETA: 0s - loss: 0.5367 - accuracy: 0.8002\n",
      "Epoch 37: ReduceLROnPlateau reducing learning rate to 0.0001250000059371814.\n",
      "423/423 [==============================] - 10s 24ms/step - loss: 0.5382 - accuracy: 0.7997 - val_loss: 0.6324 - val_accuracy: 0.7580 - lr: 2.5000e-04\n",
      "Epoch 38/60\n",
      "423/423 [==============================] - 10s 24ms/step - loss: 0.5335 - accuracy: 0.7973 - val_loss: 0.6286 - val_accuracy: 0.7620 - lr: 1.2500e-04\n",
      "Epoch 39/60\n",
      "423/423 [==============================] - 10s 24ms/step - loss: 0.5229 - accuracy: 0.8028 - val_loss: 0.6460 - val_accuracy: 0.7673 - lr: 1.2500e-04\n",
      "Epoch 40/60\n",
      "423/423 [==============================] - 10s 24ms/step - loss: 0.5275 - accuracy: 0.8028 - val_loss: 0.6334 - val_accuracy: 0.7660 - lr: 1.2500e-04\n",
      "Epoch 41/60\n",
      "422/423 [============================>.] - ETA: 0s - loss: 0.5181 - accuracy: 0.8026\n",
      "Epoch 41: ReduceLROnPlateau reducing learning rate to 6.25000029685907e-05.\n",
      "423/423 [==============================] - 10s 24ms/step - loss: 0.5189 - accuracy: 0.8023 - val_loss: 0.6296 - val_accuracy: 0.7580 - lr: 1.2500e-04\n",
      "Epoch 42/60\n",
      "423/423 [==============================] - 10s 24ms/step - loss: 0.5183 - accuracy: 0.8040 - val_loss: 0.6299 - val_accuracy: 0.7646 - lr: 6.2500e-05\n",
      "Epoch 43/60\n",
      "423/423 [==============================] - 10s 24ms/step - loss: 0.5082 - accuracy: 0.8050 - val_loss: 0.6344 - val_accuracy: 0.7660 - lr: 6.2500e-05\n",
      "Epoch 44/60\n",
      "423/423 [==============================] - 10s 24ms/step - loss: 0.5117 - accuracy: 0.8041 - val_loss: 0.6346 - val_accuracy: 0.7620 - lr: 6.2500e-05\n",
      "Epoch 45/60\n",
      "422/423 [============================>.] - ETA: 0s - loss: 0.5021 - accuracy: 0.8063\n",
      "Epoch 45: ReduceLROnPlateau reducing learning rate to 3.125000148429535e-05.\n",
      "423/423 [==============================] - 10s 25ms/step - loss: 0.5021 - accuracy: 0.8062 - val_loss: 0.6317 - val_accuracy: 0.7580 - lr: 6.2500e-05\n",
      "Epoch 46/60\n",
      "423/423 [==============================] - 10s 24ms/step - loss: 0.5041 - accuracy: 0.8102 - val_loss: 0.6359 - val_accuracy: 0.7620 - lr: 3.1250e-05\n",
      "Epoch 47/60\n",
      "423/423 [==============================] - 10s 24ms/step - loss: 0.5068 - accuracy: 0.8093 - val_loss: 0.6343 - val_accuracy: 0.7606 - lr: 3.1250e-05\n",
      "Epoch 48/60\n",
      "423/423 [==============================] - 10s 24ms/step - loss: 0.4950 - accuracy: 0.8103 - val_loss: 0.6360 - val_accuracy: 0.7580 - lr: 3.1250e-05\n",
      "Epoch 49/60\n",
      "421/423 [============================>.] - ETA: 0s - loss: 0.4961 - accuracy: 0.8122\n",
      "Epoch 49: ReduceLROnPlateau reducing learning rate to 1.5625000742147677e-05.\n",
      "423/423 [==============================] - 10s 24ms/step - loss: 0.4977 - accuracy: 0.8117 - val_loss: 0.6349 - val_accuracy: 0.7646 - lr: 3.1250e-05\n",
      "Epoch 50/60\n",
      "423/423 [==============================] - 10s 24ms/step - loss: 0.4943 - accuracy: 0.8120 - val_loss: 0.6347 - val_accuracy: 0.7606 - lr: 1.5625e-05\n",
      "Epoch 51/60\n",
      "423/423 [==============================] - 10s 24ms/step - loss: 0.4952 - accuracy: 0.8109 - val_loss: 0.6353 - val_accuracy: 0.7593 - lr: 1.5625e-05\n",
      "Epoch 52/60\n",
      "423/423 [==============================] - 10s 24ms/step - loss: 0.4994 - accuracy: 0.8099 - val_loss: 0.6343 - val_accuracy: 0.7606 - lr: 1.5625e-05\n",
      "Epoch 53/60\n",
      "421/423 [============================>.] - ETA: 0s - loss: 0.5010 - accuracy: 0.8114\n",
      "Epoch 53: ReduceLROnPlateau reducing learning rate to 1e-05.\n",
      "423/423 [==============================] - 10s 24ms/step - loss: 0.5001 - accuracy: 0.8117 - val_loss: 0.6339 - val_accuracy: 0.7593 - lr: 1.5625e-05\n",
      "Epoch 54/60\n",
      "423/423 [==============================] - 10s 24ms/step - loss: 0.5036 - accuracy: 0.8111 - val_loss: 0.6332 - val_accuracy: 0.7606 - lr: 1.0000e-05\n",
      "Epoch 55/60\n",
      "423/423 [==============================] - 10s 24ms/step - loss: 0.4974 - accuracy: 0.8130 - val_loss: 0.6340 - val_accuracy: 0.7606 - lr: 1.0000e-05\n",
      "Epoch 56/60\n",
      "423/423 [==============================] - 10s 24ms/step - loss: 0.4945 - accuracy: 0.8121 - val_loss: 0.6345 - val_accuracy: 0.7606 - lr: 1.0000e-05\n",
      "Epoch 57/60\n",
      "423/423 [==============================] - 10s 24ms/step - loss: 0.4900 - accuracy: 0.8167 - val_loss: 0.6347 - val_accuracy: 0.7606 - lr: 1.0000e-05\n",
      "Epoch 58/60\n",
      "423/423 [==============================] - 10s 24ms/step - loss: 0.4925 - accuracy: 0.8142 - val_loss: 0.6334 - val_accuracy: 0.7580 - lr: 1.0000e-05\n",
      "Epoch 59/60\n",
      "423/423 [==============================] - 10s 24ms/step - loss: 0.4925 - accuracy: 0.8083 - val_loss: 0.6351 - val_accuracy: 0.7580 - lr: 1.0000e-05\n",
      "Epoch 60/60\n",
      "423/423 [==============================] - 10s 24ms/step - loss: 0.4955 - accuracy: 0.8078 - val_loss: 0.6372 - val_accuracy: 0.7606 - lr: 1.0000e-05\n"
     ]
    }
   ],
   "source": [
    "# Train model\n",
    "epochs = 60\n",
    "batch_size = 16\n",
    "history = model.fit(datagen.flow(x_train,y_train, batch_size=batch_size),\n",
    "                              epochs = epochs, validation_data = (x_validate, y_validate),\n",
    "                              verbose = 1, callbacks=[learning_rate_reduction])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6eee78d3-2e5c-4533-96a2-29739df2a39c",
   "metadata": {},
   "source": [
    "## Evaluate Performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "2fab9e6d-2ed7-4551-a726-6c254af0d91d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "plt.plot(history.history['loss'], color='teal', label='loss')\n",
    "plt.plot(history.history['val_loss'], color='orange', label='val_loss')\n",
    "fig.suptitle('Loss', fontsize=20)\n",
    "plt.legend(loc=\"upper left\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "538cc82a-3b17-40af-b17a-26aef08e744d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "plt.plot(history.history['accuracy'], color='teal', label='accuracy')\n",
    "plt.plot(history.history['val_accuracy'], color='orange', label='val_accuracy')\n",
    "fig.suptitle('Accuracy', fontsize=20)\n",
    "plt.legend(loc=\"upper left\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "bf07b433-f797-4f82-88d8-d6acfa0cd9d3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 0., 0., ..., 0., 1., 0.],\n",
       "       [0., 0., 0., ..., 1., 0., 0.],\n",
       "       [1., 0., 0., ..., 0., 0., 0.],\n",
       "       ...,\n",
       "       [0., 0., 0., ..., 1., 0., 0.],\n",
       "       [0., 0., 0., ..., 1., 0., 0.],\n",
       "       [0., 0., 0., ..., 1., 0., 0.]], dtype=float32)"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from tensorflow.keras.metrics import Precision, Recall, BinaryAccuracy\n",
    "precision = Precision()\n",
    "recall = Recall()\n",
    "accuracy = BinaryAccuracy\n",
    "y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "4ae887e3-dd63-45b0-b94b-65f5ec1a5836",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "79/79 [==============================] - 1s 7ms/step - loss: 0.6413 - accuracy: 0.7668\n",
      "24/24 [==============================] - 0s 7ms/step - loss: 0.6372 - accuracy: 0.7606\n",
      "Validation: accuracy = 0.760638  ;  loss_v = 0.637224\n",
      "Test: accuracy = 0.766773  ;  loss = 0.641329\n"
     ]
    }
   ],
   "source": [
    "# Show metrics and save model\n",
    "loss, accuracy = model.evaluate(x_test, y_test, verbose=1)\n",
    "loss_v, accuracy_v = model.evaluate(x_validate, y_validate, verbose=1)\n",
    "print(\"Validation: accuracy = %f  ;  loss_v = %f\" % (accuracy_v, loss_v))\n",
    "print(\"Test: accuracy = %f  ;  loss = %f\" % (accuracy, loss))\n",
    "model.save(\"model.h5\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "96810a1b-a69c-4e87-b5fd-35a1418649db",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "24/24 [==============================] - 0s 6ms/step\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report,confusion_matrix\n",
    "import itertools\n",
    "def plot_confusion_matrix(cm, classes,\n",
    "                          normalize=False,\n",
    "                          title='Confusion matrix',\n",
    "                          cmap=plt.cm.Blues):\n",
    "    \"\"\"\n",
    "    This function prints and plots the confusion matrix.\n",
    "    Normalization can be applied by setting `normalize=True`.\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=45)\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')\n",
    "# Predict the values from the validation dataset\n",
    "Y_pred = model.predict(x_validate)\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_validate,axis = 1) \n",
    "# compute the confusion matrix\n",
    "confusion_mtx = confusion_matrix(Y_true, Y_pred_classes)\n",
    "\n",
    "# plot the confusion matrix\n",
    "plot_confusion_matrix(confusion_mtx, classes = range(7)) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "a0757762-6ea0-4b8a-b755-5e154c798e5b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "79/79 [==============================] - 1s 9ms/step\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 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",
    "\n",
    " \n",
    "\n",
    "# plot the confusion matrix\n",
    "plot_confusion_matrix(confusion_mtx, classes = range(7)) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "5356b5e6-af51-4630-b6bb-7ca893cbc0ab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Fraction classified incorrectly')"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "label_frac_error = 1 - np.diag(confusion_mtx) / np.sum(confusion_mtx, axis=1)\n",
    "plt.bar(np.arange(7),label_frac_error)\n",
    "plt.xlabel('True Label')\n",
    "plt.ylabel('Fraction classified incorrectly')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "68e0ceb2-d778-4e71-b255-28af7ecdde7b",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'y_test' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[1], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[43my_test\u001b[49m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'y_test' is not defined"
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1959b4c5-4b92-4527-a94d-e6f3c7568d67",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "cnn",
   "language": "python",
   "name": "cnn"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}