Training and Validation.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d726754a-9cb6-44d2-b37d-8824a0b53dfa",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import datetime\n",
    "import requests\n",
    "from requests.auth import HTTPBasicAuth\n",
    "import tarfile\n",
    "import os\n",
    "import shutil\n",
    "import warnings\n",
    "from sklearn.metrics import classification_report, confusion_matrix\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "#keras\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras.layers import Dense,Dropout,Conv2D, Flatten, MaxPooling2D, BatchNormalization\n",
    "from tensorflow.keras.models import Model,Sequential \n",
    "from tensorflow.keras import optimizers, regularizers \n",
    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
    "from keras.callbacks import ModelCheckpoint"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ed43f813-eec8-4ea4-84fd-773f2bb3c7f6",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_organize_files(): \n",
    "    #download files with images\n",
    "    #https://talhassner.github.io/home/projects/Adience/Adience-data.html\n",
    "    print('starting download')\n",
    "    BASE_URL = \"http://www.cslab.openu.ac.il/download/adiencedb/AdienceBenchmarkOfUnfilteredFacesForGenderAndAgeClassification\"\n",
    "    DOWNLOAD_FILES = {\n",
    "              \"http://www.cslab.openu.ac.il/download/adiencedb/AdienceBenchmarkOfUnfilteredFacesForGenderAndAgeClassification/aligned.tar.gz\": \"aligned.tar.gz\",\n",
    "              \"http://www.cslab.openu.ac.il/download/adiencedb/AdienceBenchmarkOfUnfilteredFacesForGenderAndAgeClassification/fold_0_data.txt\": \"fold_0_data.txt\",\n",
    "              \"http://www.cslab.openu.ac.il/download/adiencedb/AdienceBenchmarkOfUnfilteredFacesForGenderAndAgeClassification/fold_1_data.txt\": \"fold_1_data.txt\",\n",
    "              \"http://www.cslab.openu.ac.il/download/adiencedb/AdienceBenchmarkOfUnfilteredFacesForGenderAndAgeClassification/fold_2_data.txt\": \"fold_2_data.txt\",\n",
    "              \"http://www.cslab.openu.ac.il/download/adiencedb/AdienceBenchmarkOfUnfilteredFacesForGenderAndAgeClassification/fold_3_data.txt\": \"fold_3_data.txt\",\n",
    "              \"http://www.cslab.openu.ac.il/download/adiencedb/AdienceBenchmarkOfUnfilteredFacesForGenderAndAgeClassification/fold_4_data.txt\": \"fold_4_data.txt\"\n",
    "                    }\n",
    "\n",
    "    for DOWNLOAD_FILE, FILE_NAME in DOWNLOAD_FILES.items():\n",
    "        if not os.path.exists(FILE_NAME):\n",
    "            with open(FILE_NAME, 'wb') as file:\n",
    "                r = requests.get(DOWNLOAD_FILE, auth = HTTPBasicAuth('adiencedb', 'adience'))\n",
    "                file.write(r.content)\n",
    "                print('downloaded {}'.format(FILE_NAME))\n",
    "    \n",
    "    print('unziping images...')\n",
    "    \n",
    "    if not os.path.exists('aligned'):\n",
    "        with tarfile.open('aligned.tar.gz') as file:\n",
    "            file.extractall()\n",
    "        \n",
    "    print('images unziped')\n",
    "    \n",
    "    #compile all files catalog\n",
    "    fold = pd.read_csv('fold_0_data.txt',sep='\\t')\n",
    "    fold.rename(columns={'user_id':'user_id'},inplace=True)\n",
    "    fold['fold'] = 0\n",
    "    for i in range(1,5):\n",
    "        temp = pd.read_csv('fold_'+str(i)+'_data.txt',sep='\\t')\n",
    "        temp['fold'] = i\n",
    "        fold = fold.append(temp,ignore_index=True)\n",
    "\n",
    "    fold.dropna(subset=['gender'],inplace=True)\n",
    "    fold = fold[['user_id','original_image','face_id','age','gender','fold']]\n",
    "     \n",
    "    train = fold.groupby('gender',as_index=False,group_keys=False).apply(lambda x: x.sample(frac=.7))\n",
    "    test = fold.drop(train.index).groupby('gender',as_index=False,group_keys=False).apply(lambda x: x.sample(frac=.5))\n",
    "    validation = fold.drop(train.index).drop(test.index)\n",
    "    \n",
    "    print('Train and test split done')\n",
    "    \n",
    "    #creates directories for training, test and validation files\n",
    "    cwd = os.getcwd()\n",
    "    \n",
    "    train_faces = os.path.join(cwd,'train')\n",
    "    if not os.path.exists(train_faces):\n",
    "        os.mkdir(train_faces)\n",
    "\n",
    "    test_faces = os.path.join(cwd,'test')\n",
    "    if not os.path.exists(test_faces):\n",
    "        os.mkdir(test_faces)\n",
    "    \n",
    "    validation_faces = os.path.join(cwd,'validation')\n",
    "    if not os.path.exists(validation_faces):\n",
    "        os.mkdir(validation_faces)\n",
    "\n",
    "    #copy images to each directory\n",
    "    for index, row in train.iterrows():\n",
    "        src = os.path.join(cwd,'aligned',row['user_id'],'landmark_aligned_face.'+str(row['face_id'])+'.'+row['original_image'])\n",
    "        dst = os.path.join(train_faces,row['original_image'])\n",
    "        shutil.copyfile(src,dst)\n",
    "\n",
    "    print('train images copied')\n",
    "\n",
    "    for index, row in test.iterrows():\n",
    "        src = os.path.join(cwd,'aligned',row['user_id'],'landmark_aligned_face.'+str(row['face_id'])+'.'+row['original_image'])\n",
    "        dst = os.path.join(test_faces,row['original_image'])\n",
    "        shutil.copyfile(src,dst)\n",
    "        \n",
    "    for index, row in validation.iterrows():\n",
    "        src = os.path.join(cwd,'aligned',row['user_id'],'landmark_aligned_face.'+str(row['face_id'])+'.'+row['original_image'])\n",
    "        dst = os.path.join(validation_faces,row['original_image'])\n",
    "        shutil.copyfile(src,dst)\n",
    "\n",
    "    print('train images copied')\n",
    "\n",
    "    print('test images copied')\n",
    "    \n",
    "    print('validation images copied')\n",
    "    \n",
    "    return cwd,fold,train_faces,test_faces,validation_faces,train,test,validation\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f483a38a-b71d-4c8c-b315-8aaa6a06b40d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "starting download\n",
      "downloaded fold_0_data.txt\n",
      "downloaded fold_1_data.txt\n",
      "downloaded fold_2_data.txt\n",
      "downloaded fold_3_data.txt\n",
      "downloaded fold_4_data.txt\n",
      "unziping images...\n",
      "images unziped\n",
      "Train and test split done\n",
      "train images copied\n",
      "train images copied\n",
      "test images copied\n",
      "validation images copied\n"
     ]
    }
   ],
   "source": [
    "cwd,fold,train_faces,test_faces,validation_faces,train_images_ids,test_images_ids,validation_images_ids = get_organize_files()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f7416533",
   "metadata": {},
   "outputs": [],
   "source": [
    "def Model_gender_mx():\n",
    "    model = Sequential()\n",
    "    model.add(Conv2D(32, (3, 3), input_shape = (150, 150, 3), activation = 'relu'))\n",
    "    model.add(MaxPooling2D(pool_size = (3, 3)))\n",
    "    model.add(Conv2D(32, (3, 3), activation = 'relu'))\n",
    "    model.add(MaxPooling2D(pool_size = (3, 3))) \n",
    "    model.add(Conv2D(64, (3, 3), activation = 'relu'))\n",
    "    model.add(MaxPooling2D(pool_size = (3, 3))) \n",
    "    model.add(BatchNormalization())\n",
    "    model.add(Flatten())\n",
    "    model.add(Dropout(0.5))\n",
    "    model.add(Dense(units = 64, activation = 'relu'))\n",
    "    model.add(Dropout(0.5))\n",
    "    model.add(Dense(units = 32, activation = 'relu'))\n",
    "    model.add(Dense(units = 32, activation = 'relu'))\n",
    "    model.add(Dense(units = 3, activation = 'softmax'))\n",
    "    model.summary()\n",
    "    model.compile(loss='categorical_crossentropy', optimizer = 'adam', metrics=['acc'])\n",
    "    \n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c0d4a7b2-ac6e-41f6-8915-e35481b5066c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 13013 validated image filenames belonging to 3 classes.\n",
      "Found 2789 validated image filenames belonging to 3 classes.\n",
      "Found 2789 validated image filenames belonging to 3 classes.\n"
     ]
    }
   ],
   "source": [
    "#vectorizing images \n",
    "train_datagen = ImageDataGenerator(rescale=1./255)\n",
    "train_generator = train_datagen.flow_from_dataframe(\n",
    "                    train_images_ids,\n",
    "                    x_col='original_image',\n",
    "                    y_col='gender',\n",
    "                    directory=train_faces,\n",
    "                    target_size=(150,150),\n",
    "                    batch_size=100,\n",
    "                    class_mode='categorical',\n",
    "                   # rotation_range = 40,\n",
    "                   # width_shift_range = 0.2,\n",
    "                   # height_shift_range = 0.2,\n",
    "                   # shear_range = 0.2,\n",
    "                   # zoom_range = 0.2,\n",
    "                   # horizontal_flip = True,\n",
    "                   # fill_mode = 'nearest'\n",
    "                    ) \n",
    "\n",
    "\n",
    "validation_datagen = ImageDataGenerator(rescale=1./255)\n",
    "validation_generator = validation_datagen.flow_from_dataframe(\n",
    "                    validation_images_ids,\n",
    "                    x_col='original_image',\n",
    "                    y_col='gender',\n",
    "                    directory=validation_faces,\n",
    "                    target_size=(150,150),\n",
    "                    batch_size=100,\n",
    "                    class_mode='categorical',\n",
    "                    )\n",
    "\n",
    "test_datagen = ImageDataGenerator(rescale=1./255)\n",
    "test_generator = test_datagen.flow_from_dataframe(\n",
    "                    test_images_ids,\n",
    "                    x_col='original_image',\n",
    "                    y_col='gender',\n",
    "                    directory=test_faces,\n",
    "                    target_size=(150,150),\n",
    "                    batch_size=100,\n",
    "                    class_mode='categorical',\n",
    "                    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "cf03ce2f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:`period` argument is deprecated. Please use `save_freq` to specify the frequency in number of batches seen.\n"
     ]
    }
   ],
   "source": [
    "checkpoint = ModelCheckpoint(\"best_model_mx.hdf5\", monitor='val_acc', verbose=1,\n",
    "    save_best_only=True, mode='auto', period=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "22da8a6d-bd01-43de-9c6f-6b1bd62676f5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " conv2d (Conv2D)             (None, 148, 148, 32)      896       \n",
      "                                                                 \n",
      " max_pooling2d (MaxPooling2D  (None, 49, 49, 32)       0         \n",
      " )                                                               \n",
      "                                                                 \n",
      " conv2d_1 (Conv2D)           (None, 47, 47, 32)        9248      \n",
      "                                                                 \n",
      " max_pooling2d_1 (MaxPooling  (None, 15, 15, 32)       0         \n",
      " 2D)                                                             \n",
      "                                                                 \n",
      " conv2d_2 (Conv2D)           (None, 13, 13, 64)        18496     \n",
      "                                                                 \n",
      " max_pooling2d_2 (MaxPooling  (None, 4, 4, 64)         0         \n",
      " 2D)                                                             \n",
      "                                                                 \n",
      " batch_normalization (BatchN  (None, 4, 4, 64)         256       \n",
      " ormalization)                                                   \n",
      "                                                                 \n",
      " flatten (Flatten)           (None, 1024)              0         \n",
      "                                                                 \n",
      " dropout (Dropout)           (None, 1024)              0         \n",
      "                                                                 \n",
      " dense (Dense)               (None, 64)                65600     \n",
      "                                                                 \n",
      " dropout_1 (Dropout)         (None, 64)                0         \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 32)                2080      \n",
      "                                                                 \n",
      " dense_2 (Dense)             (None, 32)                1056      \n",
      "                                                                 \n",
      " dense_3 (Dense)             (None, 3)                 99        \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 97,731\n",
      "Trainable params: 97,603\n",
      "Non-trainable params: 128\n",
      "_________________________________________________________________\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-10-2fee13cf7a6d>:3: UserWarning: `Model.fit_generator` is deprecated and will be removed in a future version. Please use `Model.fit`, which supports generators.\n",
      "  history = model.fit_generator(train_generator,\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/30\n",
      "130/130 [==============================] - ETA: 0s - loss: 0.8759 - acc: 0.5134\n",
      "Epoch 1: val_acc improved from -inf to 0.50519, saving model to best_model_mx.hdf5\n",
      "130/130 [==============================] - 273s 2s/step - loss: 0.8759 - acc: 0.5134 - val_loss: 0.9507 - val_acc: 0.5052\n",
      "Epoch 2/30\n",
      "130/130 [==============================] - ETA: 0s - loss: 0.8373 - acc: 0.5342\n",
      "Epoch 2: val_acc improved from 0.50519 to 0.53444, saving model to best_model_mx.hdf5\n",
      "130/130 [==============================] - 325s 2s/step - loss: 0.8373 - acc: 0.5342 - val_loss: 0.8805 - val_acc: 0.5344\n",
      "Epoch 3/30\n",
      "130/130 [==============================] - ETA: 0s - loss: 0.8129 - acc: 0.5541\n",
      "Epoch 3: val_acc did not improve from 0.53444\n",
      "130/130 [==============================] - 327s 3s/step - loss: 0.8129 - acc: 0.5541 - val_loss: 0.8407 - val_acc: 0.5226\n",
      "Epoch 4/30\n",
      "130/130 [==============================] - ETA: 0s - loss: 0.7884 - acc: 0.5751\n",
      "Epoch 4: val_acc improved from 0.53444 to 0.63630, saving model to best_model_mx.hdf5\n",
      "130/130 [==============================] - 289s 2s/step - loss: 0.7884 - acc: 0.5751 - val_loss: 0.7400 - val_acc: 0.6363\n",
      "Epoch 5/30\n",
      "130/130 [==============================] - ETA: 0s - loss: 0.7711 - acc: 0.5905\n",
      "Epoch 5: val_acc improved from 0.63630 to 0.64000, saving model to best_model_mx.hdf5\n",
      "130/130 [==============================] - 374s 3s/step - loss: 0.7711 - acc: 0.5905 - val_loss: 0.7163 - val_acc: 0.6400\n",
      "Epoch 6/30\n",
      "130/130 [==============================] - ETA: 0s - loss: 0.7536 - acc: 0.6037\n",
      "Epoch 6: val_acc improved from 0.64000 to 0.67222, saving model to best_model_mx.hdf5\n",
      "130/130 [==============================] - 410s 3s/step - loss: 0.7536 - acc: 0.6037 - val_loss: 0.7262 - val_acc: 0.6722\n",
      "Epoch 7/30\n",
      "130/130 [==============================] - ETA: 0s - loss: 0.7358 - acc: 0.6200\n",
      "Epoch 7: val_acc did not improve from 0.67222\n",
      "130/130 [==============================] - 338s 3s/step - loss: 0.7358 - acc: 0.6200 - val_loss: 0.7831 - val_acc: 0.5781\n",
      "Epoch 8/30\n",
      "130/130 [==============================] - ETA: 0s - loss: 0.7192 - acc: 0.6342\n",
      "Epoch 8: val_acc did not improve from 0.67222\n",
      "130/130 [==============================] - 323s 2s/step - loss: 0.7192 - acc: 0.6342 - val_loss: 0.7291 - val_acc: 0.6281\n",
      "Epoch 9/30\n",
      "130/130 [==============================] - ETA: 0s - loss: 0.6952 - acc: 0.6443\n",
      "Epoch 9: val_acc improved from 0.67222 to 0.70111, saving model to best_model_mx.hdf5\n",
      "130/130 [==============================] - 341s 3s/step - loss: 0.6952 - acc: 0.6443 - val_loss: 0.6703 - val_acc: 0.7011\n",
      "Epoch 10/30\n",
      "130/130 [==============================] - ETA: 0s - loss: 0.6909 - acc: 0.6477\n",
      "Epoch 10: val_acc improved from 0.70111 to 0.73444, saving model to best_model_mx.hdf5\n",
      "130/130 [==============================] - 339s 3s/step - loss: 0.6909 - acc: 0.6477 - val_loss: 0.6144 - val_acc: 0.7344\n",
      "Epoch 11/30\n",
      "130/130 [==============================] - ETA: 0s - loss: 0.6718 - acc: 0.6612\n",
      "Epoch 11: val_acc did not improve from 0.73444\n",
      "130/130 [==============================] - 339s 3s/step - loss: 0.6718 - acc: 0.6612 - val_loss: 0.8800 - val_acc: 0.5907\n",
      "Epoch 12/30\n",
      "130/130 [==============================] - ETA: 0s - loss: 0.6646 - acc: 0.6635\n",
      "Epoch 12: val_acc did not improve from 0.73444\n",
      "130/130 [==============================] - 359s 3s/step - loss: 0.6646 - acc: 0.6635 - val_loss: 0.6305 - val_acc: 0.6904\n",
      "Epoch 13/30\n",
      "130/130 [==============================] - ETA: 0s - loss: 0.6579 - acc: 0.6727\n",
      "Epoch 13: val_acc improved from 0.73444 to 0.74333, saving model to best_model_mx.hdf5\n",
      "130/130 [==============================] - 333s 3s/step - loss: 0.6579 - acc: 0.6727 - val_loss: 0.6118 - val_acc: 0.7433\n",
      "Epoch 14/30\n",
      "130/130 [==============================] - ETA: 0s - loss: 0.6490 - acc: 0.6743\n",
      "Epoch 14: val_acc did not improve from 0.74333\n",
      "130/130 [==============================] - 335s 3s/step - loss: 0.6490 - acc: 0.6743 - val_loss: 0.6137 - val_acc: 0.7281\n",
      "Epoch 15/30\n",
      "130/130 [==============================] - ETA: 0s - loss: 0.6436 - acc: 0.6781\n",
      "Epoch 15: val_acc improved from 0.74333 to 0.76074, saving model to best_model_mx.hdf5\n",
      "130/130 [==============================] - 331s 3s/step - loss: 0.6436 - acc: 0.6781 - val_loss: 0.5860 - val_acc: 0.7607\n",
      "Epoch 16/30\n",
      "130/130 [==============================] - ETA: 0s - loss: 0.6331 - acc: 0.6867\n",
      "Epoch 16: val_acc did not improve from 0.76074\n",
      "130/130 [==============================] - 331s 3s/step - loss: 0.6331 - acc: 0.6867 - val_loss: 0.5785 - val_acc: 0.7607\n",
      "Epoch 17/30\n",
      "130/130 [==============================] - ETA: 0s - loss: 0.6212 - acc: 0.6899\n",
      "Epoch 17: val_acc did not improve from 0.76074\n",
      "130/130 [==============================] - 335s 3s/step - loss: 0.6212 - acc: 0.6899 - val_loss: 0.5984 - val_acc: 0.7456\n",
      "Epoch 18/30\n",
      "130/130 [==============================] - ETA: 0s - loss: 0.6061 - acc: 0.7030\n",
      "Epoch 18: val_acc improved from 0.76074 to 0.76815, saving model to best_model_mx.hdf5\n",
      "130/130 [==============================] - 331s 3s/step - loss: 0.6061 - acc: 0.7030 - val_loss: 0.5723 - val_acc: 0.7681\n",
      "Epoch 19/30\n",
      "130/130 [==============================] - ETA: 0s - loss: 0.6097 - acc: 0.7015\n",
      "Epoch 19: val_acc improved from 0.76815 to 0.78333, saving model to best_model_mx.hdf5\n",
      "130/130 [==============================] - 332s 3s/step - loss: 0.6097 - acc: 0.7015 - val_loss: 0.5489 - val_acc: 0.7833\n",
      "Epoch 20/30\n",
      "130/130 [==============================] - ETA: 0s - loss: 0.6014 - acc: 0.7070\n",
      "Epoch 20: val_acc improved from 0.78333 to 0.78370, saving model to best_model_mx.hdf5\n",
      "130/130 [==============================] - 331s 3s/step - loss: 0.6014 - acc: 0.7070 - val_loss: 0.5402 - val_acc: 0.7837\n",
      "Epoch 21/30\n",
      "130/130 [==============================] - ETA: 0s - loss: 0.5970 - acc: 0.7116\n",
      "Epoch 21: val_acc did not improve from 0.78370\n",
      "130/130 [==============================] - 281s 2s/step - loss: 0.5970 - acc: 0.7116 - val_loss: 0.5448 - val_acc: 0.7811\n",
      "Epoch 22/30\n",
      "130/130 [==============================] - ETA: 0s - loss: 0.5890 - acc: 0.7138\n",
      "Epoch 22: val_acc did not improve from 0.78370\n",
      "130/130 [==============================] - 172s 1s/step - loss: 0.5890 - acc: 0.7138 - val_loss: 0.5950 - val_acc: 0.7693\n",
      "Epoch 23/30\n",
      "130/130 [==============================] - ETA: 0s - loss: 0.5813 - acc: 0.7216\n",
      "Epoch 23: val_acc did not improve from 0.78370\n",
      "130/130 [==============================] - 170s 1s/step - loss: 0.5813 - acc: 0.7216 - val_loss: 0.6508 - val_acc: 0.7456\n",
      "Epoch 24/30\n",
      "130/130 [==============================] - ETA: 0s - loss: 0.5720 - acc: 0.7245\n",
      "Epoch 24: val_acc improved from 0.78370 to 0.79000, saving model to best_model_mx.hdf5\n",
      "130/130 [==============================] - 182s 1s/step - loss: 0.5720 - acc: 0.7245 - val_loss: 0.5315 - val_acc: 0.7900\n",
      "Epoch 25/30\n",
      "130/130 [==============================] - ETA: 0s - loss: 0.5648 - acc: 0.7303\n",
      "Epoch 25: val_acc did not improve from 0.79000\n",
      "130/130 [==============================] - 171s 1s/step - loss: 0.5648 - acc: 0.7303 - val_loss: 0.5575 - val_acc: 0.7770\n",
      "Epoch 26/30\n",
      "130/130 [==============================] - ETA: 0s - loss: 0.5690 - acc: 0.7273\n",
      "Epoch 26: val_acc improved from 0.79000 to 0.79519, saving model to best_model_mx.hdf5\n",
      "130/130 [==============================] - 170s 1s/step - loss: 0.5690 - acc: 0.7273 - val_loss: 0.5415 - val_acc: 0.7952\n",
      "Epoch 27/30\n",
      "130/130 [==============================] - ETA: 0s - loss: 0.5622 - acc: 0.7321\n",
      "Epoch 27: val_acc did not improve from 0.79519\n",
      "130/130 [==============================] - 173s 1s/step - loss: 0.5622 - acc: 0.7321 - val_loss: 0.5369 - val_acc: 0.7874\n",
      "Epoch 28/30\n",
      "130/130 [==============================] - ETA: 0s - loss: 0.5574 - acc: 0.7298\n",
      "Epoch 28: val_acc did not improve from 0.79519\n",
      "130/130 [==============================] - 181s 1s/step - loss: 0.5574 - acc: 0.7298 - val_loss: 0.5368 - val_acc: 0.7948\n",
      "Epoch 29/30\n",
      "130/130 [==============================] - ETA: 0s - loss: 0.5557 - acc: 0.7314\n",
      "Epoch 29: val_acc did not improve from 0.79519\n",
      "130/130 [==============================] - 173s 1s/step - loss: 0.5557 - acc: 0.7314 - val_loss: 0.5371 - val_acc: 0.7893\n",
      "Epoch 30/30\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "130/130 [==============================] - ETA: 0s - loss: 0.5495 - acc: 0.7362\n",
      "Epoch 30: val_acc improved from 0.79519 to 0.79741, saving model to best_model_mx.hdf5\n",
      "130/130 [==============================] - 171s 1s/step - loss: 0.5495 - acc: 0.7362 - val_loss: 0.5533 - val_acc: 0.7974\n"
     ]
    }
   ],
   "source": [
    "#training model\n",
    "model = Model_gender_mx()\n",
    "history = model.fit_generator(train_generator,\n",
    "                              steps_per_epoch=130,\n",
    "                              epochs=30,\n",
    "                              validation_data = validation_generator,\n",
    "                              validation_steps=27,\n",
    "                              callbacks=[checkpoint])\n",
    "model.save('face_gender_detection.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "c93543f3-b265-4996-9dd1-9bb992a65077",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-11-8f47c2426443>:1: UserWarning: `Model.predict_generator` is deprecated and will be removed in a future version. Please use `Model.predict`, which supports generators.\n",
      "  Y_pred = model.predict_generator(validation_generator, (2789  / 100)) # ceil(num_of_test_samples / batch_size)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "         Men       0.50      0.49      0.50      1406\n",
      "       Woman       0.41      0.42      0.42      1218\n",
      "        Baby       0.08      0.07      0.08       165\n",
      "\n",
      "    accuracy                           0.44      2789\n",
      "   macro avg       0.33      0.33      0.33      2789\n",
      "weighted avg       0.44      0.44      0.44      2789\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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mTcleX6Cnqw6qSecSCglxpYJPPoHTpyGHdAk2xq8GDfL9XF2RkW4FwaQUK1aMxo0bM2fOHDp16sTEiRO55557EBFeeeUVihYtSmxsLDfeeCNr166lbt26iR5nxYoVTJw4kVWrVhETE0ODBg1o2LAhAF27duWBBx4A4LnnnuPjjz/m0UcfpWPHjtx+++3ceeedlx3r7Nmz9O7dm/nz51OtWjV69erF+++/z6BBgwAoXrw4K1eu5L333mPYsGF89NFHSV5foKerDuoSAbhEcOYMfPttoCMxxiQnfvVQ/GqhL774ggYNGlC/fn3Wr19/WTVOQosXL6ZLly5ERERQsGBBOnbsePG13377jeuuu446deowYcIE1q9fn2w8mzdvpnLlylSrVg2A++67j0WLFl18vauna2LDhg0vTlSXlCVLltCzZ08g8emqR4wYwdGjRwkLC6NRo0aMGTOGoUOHsm7dOgoUKJDssVMjqEsEAK1aQZEibglL74hjY0zSkvvl7k+dO3dm8ODBrFy5kjNnztCgQQO2b9/OsGHDWLZsGUWKFKF3796cPXs22eNIEvPP9+7dm+nTp1OvXj0+/fRTFixYkOxxUpqnzTuVdVJTXad0rMycrtqvJQIRaScim0Vkq4g8k8jrRURkmoisFZGlIlLbn/EkJlcu6NABZsyAJNaxMMZkAfnz56d169b07dv3Ymng+PHj5MuXj0KFCrF//35mz56d7DFatWrFtGnTOHPmDCdOnGDGjBkXXztx4gSlS5fmwoULF6eOBihQoAAnTpy44lg1atRgx44dbN26FYBx48Zx/fXXp+vaAj1dtd8SgYiEAiOB9sC1QDcRuTbBZv8AVqtqXaAX8La/4klO165w9CgsXBiIsxtjUqtbt26sWbOGe++9F4B69epRv359atWqRd++fWnRokWy+zdo0IB77rmHyMhI7rjjDq6Lt1Thv//9b5o0aULbtm2pUaPGxefvvfde3njjDerXr8+2bdsuPh8eHs6YMWO46667qFOnDiEhITz00EPpuq5AT1ftt2moRaQZMFRVb/E8fhZAVV+Nt80s4FVVXeJ5vA1orqr7kzquL6ahTujMGSheHHr3hpEjfXpoY3IEm4Y6e8lK01CXBf6K93iX57n41gBdAUSkMVARKJdgG0Skn4gsF5HlBw8e9HmgefNCu3auG2lcnM8Pb4wxWZo/E0FiLTIJix+vAUVEZDXwKLAKuKJVRVVHqWqUqkaVKFHC54GC6z20dy8sXeqXwxtjTJblz15Du4Dy8R6XAy6b61NVjwN9AMQ15W/33DLdbbdBWJgrFTRtGogIjMnaVDXJHjcm60hPdb8/SwTLgGtEpLKI5AbuBb6Ov4GIFPa8BnA/sMiTHDJdkSLQpo1LBNls9U5j/C48PJzo6Oh0fcmYzKOqREdHEx4enqb9/FYiUNUYEXkEmAuEAp+o6noRecjz+gdATeAzEYkFNgB/91c8qdGlC/TvDxs2QK1agYzEmKylXLly7Nq1C3+00RnfCg8Pp1y5K5pakxWUi9cnZe9eKFsWXnwRnn/eL6cwxpiAsMXrU6l0adc+YJPQGWOCiSWCBLp2hVWrIIWpQYwxJsewRJBAly7u7/TpAQ0jKKnC4cOBjsKY4GOJIIGqVaFBA3jvPTh/PtDRBJcZM9z6ENYeaUzmCppEcCH2AtM2pq7y/6WXYMsW+OADPwdlLrNpE5w7Z9VyxmS2oEkEY9eMpesXXflmyzcpbnvrrXDTTa73kK1nnHkOHHB/9yc505Qxxh+CJhH0qteLasWqMXjuYM7HJl/nIwLDhrkk8PLLmRSguVgl5E0IxpjMETSJIHdobv57y3/ZHL2ZkUtTnmK0Xj3o0wfeeQfizTxr/MibCKxEYEzmCppEAHDrNbfS/ur2vLjwRQ6cSvln57//7RaueeaKJXWMP1jVkDGBEVSJAODNW97k1IVTPP99ykOHy5SBp5+GyZPhxx8zIbggZyUCYwIj6BJBjeI1eKTRI4xeOZrV+1anuP0TT7iEMHiwrVXgT6pWIjAmUIIuEQD86/p/USyiGIPmDEpxNsV8+eCVV9w6BZMmZVKAQejUKfCuOW6JwJjMFZSJoEjeIrx8w8ss3LmQKRunpLh9r14QGQnPPnvpy8r4lrc0EBFhvYaMyWxBmQgA7m9wP3VL1eXJb5/kzIUzyW4bEgLDh8POnfD225kUYJDxtg/UqgXR0RBzxTp1xhh/CdpEEBoSytvt3mbnsZ0M/3l4itu3aQMdOrhqIvvF6nve97ROHddeYNNMGJN5gjYRALSu1Jo7at7Bq0teZffx3Slu//rrcPo0DB3q/9iCjfeLv3Zt99faCYzJPEGdCADeaPsGsXGxPDM/5cECNWrAQw/BqFGwcWMmBBdEvCUC78pwlgiMyTxBnwgqF6nMk82fZPza8fz8188pbv/CC64n0ZAhmRBcEDl40DUUV67sHlsiMCbzBH0iAHim5TOUzl+ax+Y8RpwmP1igRAl47jmYNQvmzcukAIPAwYNQsiSUKuUeWzuMMZnHEgGQP3d+/nPTf1i2Zxnj145PcftHH4VKldxgs9hY/8cXDA4ccEm2QAEID7cSgTGZyRKBR/e63WlStgnPzHuGE+dOJLtteDi89hqsXQtjx2ZSgDmct0Qg4koFlgiMyTyWCDxCJIS3273N3pN7eXXJqyluf/fdbqH7555zPYlMxnhLBGCJwJjMZokgniblmtCzbk+G/zycP478key2Ii4J7N1rE9JllHfcQMmS7rElAmMylyWCBF698VVyheSi71d9ORuT/HwSLVu6hPDLL5kUXA514oRbotJbIihZ0hKBMZnJEkECZQuW5cPbP2ThzoX0nNaT2LikW4MLFYJrr4WfU+51apLhHUwWv2ro4EGb7dWYzGKJIBHd63Zn+M3DmbxhMo/OfjTZGUqbNnUlghQmMTXJ8HYVjV81FBsLhw8HLiZjgoklgiQMbjaYp5o/xfvL3+elhS8luV2zZm5t499/z8TgcpjESgRg1UPGZBZLBMl47abX6B3Zm6ELh/LB8g8S3aZpU/fXqofSL7ESAVgiMCazWCJIhogwusNobq92OwNmDWDyhslXbFOzpmsrsAbj9LMSgTGBZYkgBWEhYUy6cxLNyjej+9Tu/LD9h8teDwmBJk2sRJARBw9C/vyQN697bInAmMxliSAVInJFMKPbDK4peg2dJnZi1d5Vl73etCn89pvrBmnSLv5gMoDChSEszOYbMiazWCJIpaJ5izKnxxwKhxem/YT2bDu87eJrzZq5ro7LlgUwwGws/mAycKUsG0tgTOaxRJAG5QqW49ue3xITF8PN429m/0n3TdWkiXvd2gnSJ2GJAGx0sTGZyRJBGtUoXoNZf5vFvpP7aD+hPcfPHadIEbdojbUTpE/CEgFYIjAmM1kiSIcm5Zow5e4prDuwjs4TO3M25qwNLEsnVSsRGBNolgjSqd3V7fi006f8sOMHek7rSZMmcRw6BNu2pbyvueT4cbhwIelEYInVGP+zRJAB3et2582b32Tyhsn8EPt/gFUPpVXCwWReJUvC+fMuURhj/MsSQQY93uxxnmr+FF8ceIE8EeeswTiNEg4m87KxBMZkHr8mAhFpJyKbRWSriDyTyOuFRGSGiKwRkfUi0sef8fjLaze9xn31e3Ku1GJmzD8Y6HCylaRKBJYIjMk8fksEIhIKjATaA9cC3UTk2gSbPQxsUNV6QGtguIjk9ldM/uKdiuLquof4a0sRPl/xVaBDyjasRGBM4PmzRNAY2Kqqf6jqeWAi0CnBNgoUEBEB8gOHgRg/xuQ3uUJz8Z8+nSEujPvee4cFOxYEOqRswVsisERgTOD4MxGUBf6K93iX57n43gVqAnuAdcBjqnrFciQi0k9ElovI8oMHs27VS6sW4QAUOXQrnSZ2Ys2+NQGOKOs7eBAKFIDw8MufL17crf5micAY//NnIpBEnkvYGfAWYDVQBogE3hWRglfspDpKVaNUNapEwp+OWUjx4nDNNVA/5iEK5ilIuwntUlz7ONglNpgMIDTUvZ8235Ax/ufPRLALKB/vcTncL//4+gBT1dkKbAdq+DEmv2vWDFYvj2Bu9285H3ueW8bfwoFT9m2WlMQGk3nZoDJjMoc/E8Ey4BoRqexpAL4X+DrBNn8CNwKISCmgOpCtf0I3beq+3PKeqsnMbjPZfXw3t064lRPnbGrSxCRVIgBLBMZkFr8lAlWNAR4B5gIbgS9Udb2IPCQiD3k2+zfQXETWAfOBp1X1kL9iygzNmrm/P/8Mzco348u7vmT1vtV0/aIr52LOBTa4LMhKBMYEnl/HEajqN6paTVWrquornuc+UNUPPPf3qOrNqlpHVWur6nh/xpMZateGfPkuzUR6W7Xb+KTTJ8z7Yx6dJ3XmyJkjgQ0wC1F1JQJLBMYElo0s9rGwMGjU6PKpJnrV68Wo20cx/4/5NBzV8IqFbYLV0aMQE5N81dCpU+5mjPEfSwR+0LQprF4NZ85ceu6Bhg+wqM8iLsRdoNnHzRizakymxXPsGHz8sVs8JytJajCZlzdBWM8hY/zLEoEfNGvmfumuWHH5803LNWVlv5W0rNCSvl/3pd+MfpyNOev3eN54A+6/H2bO9Pup0iSp6SW8bFCZMZnDEoEfNG3q/iY2E2mJfCWY22Mu/2j5D0avHE3LT1qy4+gOv8USEwNjPIWP99/322nSJaUSgSUCYzKHJQI/KFkSqlRJeunK0JBQXrnxFb669yu2Ht5Kgw8bMHvLbL/EMmcO7NkDjRvD3LlZa70EKxEYkzVYIvCTZs1ciSC5hVU6Vu/Iin4rqFCoArd9fhtDFwwl7soZNjJk9Gj3hfrFF25R+A8/9OnhM8RbIihePPHXvQnCEoEx/mWJwE+aNoW9e+Gvv5LfrmrRqvz095/oVa8XLy58kds+v43o09E+iWHPHpg1C3r3hooVoVMn+OQTOOv/ZolUOXgQChWCPHkSfz13bihSxBKBMf5micBP4g8sS0lErgjGdBrDB7d9wPfbv6fhqIas2LMi5R1TMHYsxMbC3//uHg8YANHR8OWXGT60TyQ3mMyrZEnrNWSMv1ki8JO6dSFv3tQvXSkiPBj1IEv6LCFO42jxSQs+WfVJus8fF+e6jLZu7SbCA2jTBqpVyzqNxskNJvOyQWXG+J8lAj/JlQuiopJuME5Ko7KNWNFvBS0rtOTvX/+dB2c8mK6pKRYudA3D999/6TkReOghl5xWr07zIX3uwIGkG4q9LBEY43+WCPyoaVNYuTLtdfIl8pVgTo85PN3iaUatHEWrT1vx17EUGhsS+OgjKFwYuna9/PnevV1JJSuUCqxEYEzWYInAj5o1gwsXYFU6ZpQICwnjtZteY8rdU9hwcAMNRzXkh+0/pGrfw4dhyhTo0cN96cdXpAjcey9MmADHj6c9Ll+Ji0t+5lGvUqXcVBTnbL4+Y/zGEoEfJTewLLW61uzKsgeWUSyiGG3HtWXYT8PQ5Pqk4r7kz527vFoovv793fw948alP66MOnrUNWSnpkQA1mBsjD9ZIvCj0qVdt820thMkVKN4DZbev5TONToz5Lsh3DP5niTXN1B1YweioqBevcSP16iRe/2995If5+BPKQ0m87KxBMb4X6oSgYjkE5EQz/1qItJRRHL5N7ScwTuwLKMK5CnAl3d9yes3vc6UjVNo8lETNh/afMV2y5fDunVJlwa8+veHDRtg8eKMx5YeKU0v4WUlAmP8L7UlgkVAuIiUxS0g0wf41F9B5SRNm8KuXe6WUSLCkBZD+K7ndxw8fZBGoxsxfdP0y7b56COIiIBu3ZI/1r33usbk997LeFzpkdoSgU0zYYz/pTYRiKqeBroC76hqF+Ba/4WVc3gHlmW0eii+NpXbsLLfSmoUr0GXSV341w//Ik7jOHkSPv8c7r4bChZM/hgREa4H0dSpgfmSTWuJwBKBMf6T6kQgIs2A7sAsz3Nh/gkpZ4mMdFMo+KJ6KL7yhcqzqM8i+kT24d+L/k2niZ34dMJpTp5MuVrI66GHXK+mjz/2bWypkdI8Q14REZA/vyUCY/wptYlgEPAsMM2z7nAVIHV9GYNc7tzQsKFvSwRe4WHhfNzxY0beOpI5W+cw5LVNVLnmHM2bp27/6tXhxhvdRHSxsb6PLzkHDriqqdy5U97WxhIY41+pSgSqulBVO6rqfzyNxodUdaCfY8sxmjVzi9ScP+/7Y4sIAxoN4OOmv3B2RwN2Xf0iX22enur9+/eHP/+Eb77xfWzJSc1gMi9LBMb4V2p7DX0uIgVFJB+wAdgsIkP8G1rO0bSp69fvz2kdVs9uSK5cyrU3Lb+s3SAlHTtCmTKZP9I4NdNLeNnEc8b4V2qrhq5V1eNAZ+AboALQ019B5TRpmYk0Pc6dg88+g86dhZ8Hfn2x3aDj/zpy9OzRZPfNlQseeMAtYPPHH/6JLzFWIjAm60htIsjlGTfQGfhKVS8AARqKlP2ULesGln38sVtI3te++spNL33//Ze3G8zdNpfGoxuz4eCGZPd/4IHMX7QmLSWCUqXg0CG37KYxxvdSmwg+BHYA+YBFIlIRCOBMNdnP++/Dxo3QoQOcPu3bY3/0kUs0N93kHnvbDb7v9T3Hzh2jyUdNmLZxWpL7ly3rFq35+OPMWbQmLs59saelRKDq9jHG+F5qG4tHqGpZVb1VnZ3ADX6OLUdp397NAbRkCdxxh+8ajrdvh+++g7593a/6+K6reB0r+q2gZvGadP2iK09++yQXYi8kepz+/V2pYvJk38SVnMOHXTJIS4kArHrIGH9JbWNxIRF5U0SWe27DcaUDkwZ33w2jRrn6+B49fNNlc8wYt85Anz6Jv16uYDkW9VnEgKgBDP95ONeNuY6dR3desV1mLlqT2sFkXpYIjPGv1FYNfQKcAO723I4DY/wVVE52//0wfLhbLrJfv4xN+hYb69YgbtcOypdPervwsHBG3jaSL+78go2HNhL5YSRfbfrqsm1CQtwAs59+gjVr0h9TaqR2egkv73bWc8gY/0htIqiqqi+o6h+e24tAFX8GlpMNHgzPP+++xJ94Iv3JYO5c2L079SOJ76p1Fyv7raRqkap0ntSZQXMGcT72Uh3VffdBeDh88EH64kktKxEYk7WkNhGcEZGW3gci0gI445+QgsOLL8LAgfDf/8JLL6V9/+3b4dVX3a/l229P/X5Vi1blx74/MrDxQN7+9W1aftKS7Ue2A1C0KNx1F/zvf3DGj5+u95d9ahNBwYJumg5LBMb4R2oTwUPASBHZISI7gHeBB/0WVRAQcUmgd28YOhTeeivlfVRd1c2dd8LVV7tpK154IXXTNMSXJywPb7d/m6l3T2XL4S3U/7A+UzdOBVxbw7FjMC3pTkYZltp5hrxEbCyBMf6U2l5Da1S1HlAXqKuq9YE2fo0sCISEuEVkunaFxx93VUWJiYmBSZPcwLQWLWD+fBgyxJUKBgxI//m71OzCqgdXUb14de744g4e/eZRmrY4R6VKrhHaXw4edEtm5krDihaWCIzxnzStUKaqxz0jjAEG+yGeoBMW5qaOvvlmN7Dryy8vvXb0KAwbBlWruvUDoqPh3Xfhr7/gtdegXLmMn79S4Uos7rOYwU0H8+6yd2n5aXM63nOY+fPdHET+kJbBZF6WCIzxn4wsVSk+iyLI5cnj1gVo2hS6d4dPP4VBg1xPoCFDoHJlN3p40yZ4+GE3LbMv5Q7NzfBbhvP1vV+z/ch2vsh1G6owdqxvz+OVluklvGy+IWP8JyOJwKaY8KF8+WDWLKhVy9XTjxwJnTu7WUsXLHCTw4WG+jeGDtU7sKD3As7m30Teaj/z8ZhY4lKety7N0lsiOHAAv8RjTLBLNhGIyAkROZ7I7QRQJpNiDBqFC8O338KIEbBjB4wbBw0aZG4MdUvVZWa3mcTUHc3O7aHMnn/S5+dIT4mgVCnXVnLkiM/DMSboJZsIVLWAqhZM5FZAVW2FMj8oUQIefdTN/xMoLSq04It/3Qt5jtH3xcWcueC7vqSxsW7OoPSUCMDaCYzxh4xUDZkcrHOdm2lz+yEOLG1F13H3JTlHUVodPuy6waanRACWCIzxB0sEJkkvP1EVLuRjzlf56ft131QtdJOStE4v4WWJwBj/8WsiEJF2IrJZRLaKyDOJvD5ERFZ7br+JSKyIFPVnTCb1mjZ16xpX3PEi49eOZ9CcQWhGJkci7dNLeNl8Q8b4j98SgYiEAiOB9sC1QDcRuTb+Nqr6hqpGqmok8CywUFUP+ysmkzbeWU13ri1Pn/L/xztL3+GlhemYDyOetE4v4VW0qOs1ZSUCY3zPnyWCxsBWzyR154GJQKdktu8G/M+P8Zh06NnTjYC+ausz9Insw9CFQxnx64h0H89bIkhr1VBIiNvHEoExvufPnj9lgb/iPd4FNElsQxGJANoBjyTxej+gH0CFChV8G6VJVpkybprrzz4Ttr04iiNnj/DYnMcomrcoPer2SPPxvImgWLG0x2Kji43xD3+WCBIbeZxUBXMH4MekqoVUdZSqRqlqVIm01imYDOvTx013veD7MP53x/9oU7kNvaf3ZsbmGWk+1oEDLgmEpeMniCUCY/zDnyWCXUD85VLKAXuS2PZerFooy+rQwdXRjxkDt9wSzvR7pnPjZzfSaWInahSvQf3S9WlwVQPql65P/avqUyRvkSSPlZ7BZF6lSrl1n40xvuXPRLAMuEZEKgO7cV/2f0u4kYgUAq4H0l7PYDJFnjxuDqRRo9zI3iJFCjCnxxxGLh3J8r3LWbRzEZ+v+/zi9pULV74sOTQo3YCr8l8FpG96CS/vfEOqriHbGOMbfksEqhojIo8Ac4FQ4BNVXS8iD3le966D1QX4VlVP+SsWk3F9+sA777hFawYMgKJ5i/L89c9ffP3gqYOs2reKVXtXsXLfSlbtXXVxjQOATtU7Mb7reA4ezM+11yZ2hpSVKgVnz8KJE26xGmOMb0hG+4VntqioKF2+fHmgwwhKkZFuDYFly1K3/fFzx1mzbw3z/pjHy4tfpm6puuz8x3LuvSeU995L+/nHjYNeveD33+Gaa9K+vzHBTERWqGpUYq/ZyGKTan36wPLl8Ntvqdu+YJ6CXFfxOl684UVmdpvJloPbOXJEiM27L13nt9HFxviHJQKTat27uxJBelYva39Ne2Z0/gk0hE9/f4OZv89M8zEsERjjH5YITKoVL+56EI0fDxfSMQddCVzjQNnSueg0sRPvLn03TftbIjDGPywRmDTp08f13Pnmm7Tv651e4t07X6BDtQ48OvtRHpv9GLFxsanav3hx11vI5hsyxrcsEZg0adcOrroqfdVD3lHFFcvmZcrdU3i86eOMWDqCLpO6cPJ8ygvghIW5wWhWIjDGtywRmDQJC3PzD82alfZf5vFnHg0NCeXNW95k5K0jmbVlFq3GtGL38d0pHsNGFxvje5YITJr16eOWjRw/Pm37HTjgqnbizzM0oNEA16Po8BaafNSENfvWJHsMSwTG+J4lApNmNWtCkyaueigtw1AOHnRJIDT08ufbX9OeJX2WICI0+agJXSZ1Yfza8Rw9e/SKY1giMMb3LBGYdOnTx40nSO3gMkh+eol6V9Vj6f1L6dewH8t2L6PntJ6UfKMk7Se0Z/SK0Rw85eqVLBEY43s2stiky/HjULo0/O1vMHp06vZp1cqtK7BgQfLbxWkcy3YvY8rGKUzZOIU/jvxBiIRwXYXrKLjs/5jxXnNOnYKIiAxfhjFBw0YWG58rWBC6dXNzDx0/nrp9UjvhXIiE0KRcE15v+zpbH93K6gdX89x1z3Ho9CFm7PoIgBYjuvDaktdYf2B9hpfPNCbYWSIw6davH5w65ZJBaqRnCmoRod5V9Xjxhhf5bcBvfHCPWyrz7LFCPDv/WWq/X5uqI6ry2OzHmPfHPM7Hnk/jVRhjLBGYdGvUCOrVc9NTp+TCBTh8OP1rEXg1rFYOgP80+5Rdj+/iw9s/pFbJWoxaOYq249pS4o0S3P3l3YxbM47o09EZO5kxQcKf6xGYHE7ElQoefhhWrICGDZPeNtrznZzetQi84k8zUbZgWfo17Ee/hv04feE08/6Yx4zNM5i5ZSZfbviSEAmhRfkWdKreiR51e1Aqf6mMndyYHMpKBCZDuneHvHlTLhV4B59ltETgTSQJew5F5IqgY/WOjO44mt2Dd7P0/qX887p/cvzccZ787knK/bccd315F/P+mEecxmUsCGNyGEsEJkMKFYJ77oHPP3cLxiTFO6o4oyWCPHmgcOHku5CGSAiNyjbipRteYvVDq9n48EYGNh7I99u/p+24tlR7pxr/WfIf9p+0fqjGgCUC4wP9+sHJkzBxYtLbxJ9eIqO8S1amVo3iNRh+y3B2D97N510/p1zBcjwz/xkrJWQRqnD6dKCjCG6WCEyGNW0KtWsnXz3k/eLOaIkA0j+oLDwsnG51urGg9wI2PbyJgY0H8sP2H2g7ri3XvHMNry15jT+O/GHdUTPZBx+4MSmp7YZsfM8Sgckwb6Px8uWwcmXi2xw86AaTFS2a8fP5YnRx9eLVGX7LcHYN3sXnXT+nfMHyPDv/WaqOqEqFtyrQY2oPRq0YxeZDmy0x+JEqvP22SwJrkp9myviRJQLjEz16QHh40qOMDxxw6wmE+OBfnC+nmYhfStjy6BZG3jqSFuVbMH/7fB6c+SA1RtagzJtluGfyPYxcOpLfDvxm1Ug+tHgxbN7s7q9eHdBQgpp1HzU+UaQI3H03TJgAb7wB+fNf/np6BpMlpVQpOHIEzp+H3Ll9c0yAq4tezdVFr2ZAowGoKlsOb2HhjoUs3OluX6z/AoBieYvRpnIbBjYZSMsKLX0XQBAaNcp1OAgNtUQQSFYiMD7Tr5/rOfTFF1e+duCAbxOB95j+IiJUK1aNBxo+wPiu4/lz0J9sG7iNMZ3G0KF6B77f/j3XjbmOlp+0ZMbmGVZKSIfoaJg82a1v0aCBVQ0FkiUC4zPNm8O11ybeaHzwoG8aiuHScTJzyUoRoUqRKvSO7M2YTmP48/E/eaf9O+w6vouOEztS9/26jFszjgux6VjMOUiNGwfnzrkfEJGRbjbb9KyFbTLOEoHxGW+j8a+/Xvnrzh8lgkBORx2RK4JHGj/Clke3MK7LOESEXtN7cfU7VzPi1xGcOn8qcMFlA6ruB0PTplCnjksE585dai8wmcsSgfGpnj3doK/4jcbnz8PRo74rEWSFROCVKzQXPer2YO1Da5nZbSYVClXgsTmPUfGtiry08CWb7ygJP/4IGze6Hw7g5qwCqx4KFEsExqeKFoW77nLFfu8goUOH3N+cVCJISES4rdptLO6zmMV9FtOsfDNeWPACFd+qyNPfPc2RM0cCHWKWMmqUm8r87rvd4+rV3Q8IazAODEsExuf69XP9wr2Nxr6aXsIrXz5327PHN8fztZYVWjKj2wzW9V9HpxqdeOOnN6gyogqv//g6Zy6cCXR4AXf4sPu30aOH+xwBcuVygxItEQSGJQLjcy1bQo0alxqNfTXhXHyNGsH777t1k7Oq2iVrM6HrBFY/tJrm5Zvz9Lynueada/h45cfExMUEOryAGT/+UiNxfJGRLhHY+L3MZ4nA+Jy30fjnn2HdOt+XCACmTYPWraFvXxgyBGJjfXdsX6tbqi6z/jaLH+77gbIFy3L/jPup+35dpm+aHnSjlr2NxI0bX2oX8KpXz1Uj7t0bmNiCmSUC4xc9e7rBXqNH+3bCOa/CheGbb9xaCMOGQdeuyc9+mhW0rtSaX/7+C1PunkKsxtJlUhdafNKCxTsXBzq0TPPzz7B+/ZWlAXAlArDqoUCwRGD8onhxuOMO12i8c6cbOVqkiG/PERYG777rbrNmuSqpnTt9ew5fExG61uzK+gHrGXX7KHYc3UGrT1tx++e3s27/ukCH53ejRkGBAm7q8oTq1nV/LRFkPksExm/69XPdRseO9d08Q4l5+GGYPdslgcaN3a/OrC4sJIwHGj7A1oFbefXGV1ny5xLqf1if575/Lseuu3zkCEya5BYzSjgFCbipJqpUsUQQCJYIjN9cfz1cc41v1ipOSdu28Msv7tdm69auQTI7iMgVwTMtn2HbwG30rNeTVxa/QqPRjVizL+d1qJ8wAc6eTbxayKtePRtLEAiWCIzfeBuNwbcNxUmpUcONam7e3LVR/POfEJdNpgAqFlGMMZ3G8PW9X7P/5H4ajW7EK4teyTG9i7yNxFFRUL9+0ttFRsKWLW6hI5N5LBEYv7rvPtdHPDMSAUCxYjB3LjzwAPzf/7nBbafizfYQE+MGoq1bB/Pnw//+5+bDf+456N8fFi7MnDiT0qF6B9YPWM8d197Bcz88R/OPm7Px4MbABuUDv/7q3vPkSgPgEoGq29ZkHslu3deioqJ0+fLlgQ7DpMG0aVC58qVeIZnBu+DJE09AuXJu4NKBA66aKrF/8iEhbmRrSIirYqpdO/NiTcqX67+k/6z+nDx/klfavMKgpoMIDQkNdFjp0rcvfPmlGwRYoEDS2/35J1Ss6MaIPPRQ5sUXDERkhapGJfqaJQKTk82e7RJCgQKunaJkSXeLf79kSdejad8+aNgQIiJg2TLfrKaWUftP7ufBmQ/y1eavaFmhJZ92+pSqRasmuq2qsuv4LjYe2sjGgxvZeGgjx84do2uNrnSo3oHwsPBMjt45dswtRdmzJ3z4YfLbqrr3/Z573BKWxncsERiTSj//7Bq5b7jBjVMIzQI/wFWV8WvH8+jsR7kQd4HXb3qdtlXbXvyy937xbzq0iRPnLw2mKBJehFyhuThw6gCFwwvTrXY3ekf2plGZRohIpsX/3nuuZ9fy5S7RpuSGG+DMGVcyM74TsEQgIu2At4FQ4CNVfS2RbVoDbwG5gEOqen1yx7REYPzto49cG8NTT8F//hPoaC7ZdXwX9399P3O3zb3s+bIFylKzRE1qFvfcPPdL5itJnMYxf/t8xq4Zy9SNUzkbc5YaxWvQu15vetTtQdmCZf0as6qrEgwLgxUrUrfP44+7ksOJE1kjEecUAUkEIhIK/A60BXYBy4Buqroh3jaFgZ+Adqr6p4iUVNVklxuxRGAyQ//+rmpi4sTEBz8FiqoybdM0jp87Ts3iNalRvAaFwgulat9jZ4/x5YYv+XT1p/z414+ESAhtq7Sld2RvOlXvRN5ceX0e79Kl0KSJey8ffDB1+4wdC717w6ZNblZS4xuBSgTNgKGqeovn8bMAqvpqvG0GAGVU9bnUHtcSgckM589DmzawcqWrLko4L052tyV6C5+t+Yyxa8by1/G/KJSnEG0qt6FB6QYXb1flvyrD57n/fpdM9+xx006nxurVrotpVkvC2V2gEsGduF/693se9wSaqOoj8bZ5C1clVAsoALytqp8ld1xLBCaz7Nvn+r3nyuUaj4sX99+5jh1z57jxRjf+IrPEaRwLdixg/Nrx/PjXj/we/fvF10rnL02D0g1oWLrhxeRQrmC5VLcvHD/uGon/9rfLFypKyfnzbuTxE0/Aq6+mvL1JneQSQZg/z5vIcwmzThjQELgRyAv8LCK/qOrv8TcSkX5AP4AKFSr4IVRjrnTVVa7r63XXuV+mc+e6um5f++svaN/eTcb25puujjyzhEgIbSq3oU3lNgAcP3ecNfvWsHLvSlbuW8mKPSuYvXU2cepG5hWPKE6Tsk24u9bddKnRhQJ5ku4L+vnnbnGilMYOJJQ7t1v72qaayDyBrhp6BghX1aGexx8Dc1T1y6SOayUCk9k+/RT69HFf0G++6dtj//YbtGvnGkYbNHAD2qZNg06dfHuejDh94TRr9691yWHvSr7f/j3bj24nb1heOtfoTPc63bm56s3kCs11cR9Vdz3gqtfSWsrp3Ru+/TbrLj6UHSVXIkBV/XLD/dr/A6gM5AbWALUSbFMTmO/ZNgL4Daid3HEbNmyoxmS2gQNVQfWzz3x3zIULVQsXVi1dWnXNGtVTp1QbN1aNiFBdscJ35/G1uLg4/fHPH7X/zP5a7D/FlKFo8deL6yOzHtGf//pZ4+LidOFC93598EH6zvHmm27/fft8G3swA5ZrEt+r/u4+eiuua2go8ImqviIiD3kS0AeebYYAfYA4XBfTt5I7ppUITCBcuAA33+z6ti9Zkrr+8MmZPNnNwlm1qhv0VrGie37fPtfLJibGTctQrlzGY/en87Hnmbt1LuPXjefrzV9zNuYsVYtUJdeXM9i3oRq7/wolIiLtx/3hB9dYP3eue99NxgWkROCvm5UITKAcOKBaoYJq+fKq+/en/zgjRqiKqDZvrhodfeXra9eqFiigWq+e6okT6T9PZjt29piOWTVGm73eUyFWue7fGjUqSof/NFx3HduVpmNFR7sSweuv+ynYIEQyJQKbdM6YVCpRAqZPd8spdu4MP/6YttlNVeGZZ2DgQNcGMG9e4tNY1KnjFnf/7Tfo1i1rL8MZX8E8Bekd2Zuovz4jVy7h+SeLEadxPPHtE5T/b3laf9qaD5Z/wKHTh1I8VtGiUKGCNRhnmqQyRFa9WYnABNqkSarh4e4Xa7lyqoMHq/76q2pcXNL7nDun2rOn26d/f9WYmJTP8957bvvHHvNZ6H535IhqvnyqvXpdem7zoc364oIXtca7NZShaOiLodpufDsdu3qsHjt7LMljdeigeu21/o85WJBMiSDgX+xpvVkiMFnBsWOq48ap3n67aq5c7n9S5cqqTz+tunLl5Unh+HHVm29227z8cvIJI6FBg9x+777r+2vwh9dfd/GuWnXla3Fxcbp672p9+runteJ/KypD0Tz/zqNdJ3XVKRum6PmY85dt//zzqiEhqqdPZ07sOV1yicAmnTMmg44ccVVGkya56p7YWLcy2z33uIbOQYPcqlujR7tuqGkRGwtdurg1mWfNcl1Ns6oLF1zj99VXw/ffJ7+tqvLLrl/432//44v1X7D/1H5K5y9Nv4b96NewH2UKlGHqVLfu9bJlbmCfyRibfdSYTHLokBsHMGmS6/kSF+emtf7yS7j11vQd8+RJaNUKtm517RJ16vg2Zl+ZONG1aXz9NXTokPr9YuNimb11Nu8te4/ZW2cTFhJGlxpd6FLqCf52fRNGj3ZTVZiMsURgTADs3w9z5rhfs7VqZexYu3dD48ZuNs6lS92o56xE1XV7PXrUTRYXks5uKNsOb+P95e/zyapPOHL6KCGvH+e6DtuZMa5SsqOYTcqSSwTWa8gYPylVyi3VmdEkAFC2LMycCdHR0LGjm7ohK/npJ1eFM2hQ+pMAQNWiVRl28zB2Dd7Fx50/Im+5LSz85Rhl3yzLI988kiOW7cyKLBEYk03Ur+/WWF6+3LU/XLgQ6Igu+e9/3Spv993nm+NF5Iqgb/2+9L4lkogjzehUrQujV47m2veupelHTRn20zC2H9num5MZSwTGZCcdO7oVv2bOhB49ssYYg+3bXbvIgw+6taF9qX594fTJUIZGjmXX47v4z03/ISYuhiHfDaHKiCpEjYri1cWvsiV6S5qOq+qW9Zz5+0xeXfwqn6/7nCNnjvg2+GzE2giMyYaGD4cnn3STs338ccaqYzLq8cfh3Xdhxw5XheVLy5dDo0ZuSo477rj0/PYj25mycQqTN0zm192/AlC3VF3uqHkHd157J9eWuPbitrFxsWw5vIVVe1exat8qVu9bzap9q64Y2BYWEkariq3oWK0jHat3pHKRyr69mACzxmJjcqAXX4ShQ+GRR2DEiMxdx8Dr2DEoX96VVMaP9/3xz5yBAgXgH/+Al15KfJu/jv3F1I1TmbxxMj/++SOKUrN4TZqWa8rGQxtZu38tpy+4RpXcobmpXbI29a+qT+RVkdS/qj61S9Zm46GNfL35a77a/BUbDrpFFOuUrEPH6h3pVL0TDcs0JESydwWKJQJjciBVt67ysGHw9NNuEZfMTgZvvukWkEntwvTpUbs2VKniuqWmZO+JvUzbNI3JGyaz7sA6apWodfELv37p+tQoXoPcobmTPcbWw1uZsXkGX23+isV/LiZO4yidvzQdqnXg9mq307RcU0rkK+Gjq8s8lgiMyaFUYcAAtybwyy/DP/+ZeeeOiXGDxypWdOso+Ev37rB4Mfz5p//OkZTo09HM3jqbrzZ/xZytczh5/iQAlQtXpkm5JjQp24TGZRtT/6r6flnz2ZcCtUKZMcbPRGDkSDh1Cp57zjXWDhqUOeeeNg127oS33vLveSIj3Wpnhw8nPklfUs6cgbwZ/G4uFlGMHnV70KNuD87FnOPX3b/y665f+XX3ryz5cwkTf5sIuPaFeqXq0bhs44vJoXKRyoSHhWcsgExiJQJjcoCYGLj3XpgyBUaNggce8P85mzeHAwdg82Y30M1fvvvOTdXx/fdwww2p22fwYNe76vXX4dFH/VdltufEHpbuXnoxOSzbs+xiqQEgX658FIsoRvGI4hTLe+lv/OfKFypP47KNU6yyyigrERiTw4WFuV/NnTu7bpwREa5KxV9++QV+/tk1UvszCQDUq+f+rl6dukTw7rtuXEOVKvDYY25xmzFjoGRJ38dWpkAZOtfoTOcanQHXQ2nToU0s27OM3cd3E30mmugz0Rw6fYjo09H8ceQPos9Ec/Ts0cuOkz93ftpUbkO7qu1od3W7RHssbdvmRpT7uosuWCIwJsfInduVCG691Q3siohwE9b5w3//C4UKpX0SvfQoWRJKl07d2gSzZrkv/44dYepU13byxBNQt65be9rfk/aFhoRSq2QtapVMfjh5TFwMh88cJvp0NL9H/87cbXOZvXU2X292LeLVilXjlqq30O7qdrSu1JqpkyLo3x/69oW33/ZD4ElNS5pVbzYNtTHJO35ctWlTNz327Nm+P/6OHW566CFDfH/spLRvr1q3bvLbrF6tmj+/aoMGqidPXnp+7VrVWrXc9NiPP6569qx/Y02vuLg43Xxos779y9vafnx7zftyXuXZ/BpSb7yCapV6f+mC1X+k+/jYegTGBJfDh1UjI1XDwlS7dFH95pvULYaTGk88oRoaqvrnn745Xmo8+6xLbOfOJf767t1ukaBy5dz9hE6fVn3kEfeNV6+e6oYNfg3XJ5b8fFbLVDylEhKrJW59R3k+VJ/69ql0Hy+5RJC9R0gYYxJVpIhbG2HQIFiyxFUXVa7sBqBlpBvmiRNuXYW77nIDyTJLZKSbW2nDhitfO3XKTXt99KibeqNMmSu3yZsX3nkHZsxwM7k2bOga1bNiX5m4ODdy/IZWeQiNi2DhghAOzHqEnU/8wcAmA/1z0qQyRFa9WYnAmLQ5d071yy/dKmki7tauneqUKarnzye9X1yc6tatqhMnqj75pOr116sWKOB+Vf/6a6aFr6qqmza5844Zc/nzMTGqHTu6qqpZs1J3rD17VNu2dcfr0kX10CGfh5tu+/ap3nLLpdiio313bKxqyBijqrp9u1sCsmxZ97+/ZEnVp55S/f13V9UzdarqP/7hviiLFHHbgGru3KqNG6sOGKA6Y0bmxx0ToxoR4ZbujG/wYBffO++k7XixsarDhrnqprJl3f2FC1VPnPBdzGk1Z477PMLDVd9/P21LmqZGconAxhEYE4RiY92iOR995KpL4s9iGhrqVkGLinITvkVFuWkecvu3m3uKmjWD8HC38hu4HkH9+7txAiNGpO+YK1e6nk9r17rHIlCz5qXrbtTIdV8N9+O4sPPn3YjwYcPc2hUTJ7r329dsigljTJL27nVfPrlyuS+/evUyPiLXH/r3d3EePgzffgu33ea6g371VcbHMuzf7+ZLWrbs0t8DB9xrYWGXEmOtWu59Erl0CwlJ/HFcnEuw3r8J73sfT53qztm/v2sb8Nd7b4nAGJPteUsAM2e6tZGrVHFzEBXwwwqWqrBr16XE4L0d8cOSBcWLw4cfQteuvj92fDay2BiT7UVGur9du7ovz5kz/ZMEwP2iL1/e3bxf0KpuqdC4OHe71IKS+OOQEFdS8f5N6n7u3IFdTwIsERhjsok6ddwXdFiYa9coVy5zzy/iElBOZInAGJMt5Mvn1lyIioIGDQIdTc5iicAYk208/XSgI8iZbGSxMcYEOUsExhgT5CwRGGNMkLNEYIwxQc4SgTHGBDlLBMYYE+QsERhjTJCzRGCMMUEu2006JyIHgZ0Jni4OHApAOP6S064Hct415bTrgZx3TTnteiBj11RRVUsk9kK2SwSJEZHlSc2qlx3ltOuBnHdNOe16IOddU067HvDfNVnVkDHGBDlLBMYYE+RySiIYFegAfCynXQ/kvGvKadcDOe+actr1gJ+uKUe0ERhjjEm/nFIiMMYYk06WCIwxJshl60QgIu1EZLOIbBWRZwIdjy+IyA4RWSciq0VkeaDjSQ8R+UREDojIb/GeKyoi34nIFs/fIoGMMS2SuJ6hIrLb8zmtFpFbAxljWohIeRH5QUQ2ish6EXnM83x2/oySuqZs+TmJSLiILBWRNZ7redHzvF8+o2zbRiAiocDvQFtgF7AM6KaqGwIaWAaJyA4gSlWz7UAYEWkFnAQ+U9XanudeBw6r6muepF1EVbPFelNJXM9Q4KSqDgtkbOkhIqWB0qq6UkQKACuAzkBvsu9nlNQ13U02/JxERIB8qnpSRHIBS4DHgK744TPKziWCxsBWVf1DVc8DE4FOAY7JAKq6CDic4OlOwFjP/bG4/6TZQhLXk22p6l5VXem5fwLYCJQle39GSV1TtqTOSc/DXJ6b4qfPKDsngrLAX/Ee7yIbf/DxKPCtiKwQkX6BDsaHSqnqXnD/aYGSAY7HFx4RkbWeqqNsU40Sn4hUAuoDv5JDPqME1wTZ9HMSkVARWQ0cAL5TVb99Rtk5EUgiz2XPeq7LtVDVBkB74GFPtYTJet4HqgKRwF5geECjSQcRyQ9MAQap6vFAx+MLiVxTtv2cVDVWVSOBckBjEantr3Nl50SwCygf73E5YE+AYvEZVd3j+XsAmIarAssJ9nvqcb31uQcCHE+GqOp+z3/UOGA02exz8tQ7TwEmqOpUz9PZ+jNK7Jqy++cEoKpHgQVAO/z0GWXnRLAMuEZEKotIbuBe4OsAx5QhIpLP09CFiOQDbgZ+S36vbONr4D7P/fuArwIYS4Z5/zN6dCEbfU6ehsiPgY2q+ma8l7LtZ5TUNWXXz0lESohIYc/9vMBNwCb89Bll215DAJ6uYG8BocAnqvpKYCPKGBGpgisFAIQBn2fHaxKR/wGtcVPm7gdeAKYDXwAVgD+Bu1Q1WzTAJnE9rXHVDQrsAB701t1mdSLSElgMrAPiPE//A1ennl0/o6SuqRvZ8HMSkbq4xuBQ3A/2L1T1JREphh8+o2ydCIwxxmRcdq4aMsYY4wOWCIwxJshZIjDGmCBnicAYY4KcJQJjjAlylgiM8RCR2HizVK725Yy2IlIp/uylxmQlYYEOwJgs5IxnSL8xQcVKBMakwLNGxH8888MvFZGrPc9XFJH5ngnN5otIBc/zpURkmmcu+TUi0txzqFARGe2ZX/5bz4hRRGSgiGzwHGdigC7TBDFLBMZckjdB1dA98V47rqqNgXdxo9nx3P9MVesCE4ARnudHAAtVtR7QAFjvef4aYKSq1gKOAnd4nn8GqO85zkP+uTRjkmYji43xEJGTqpo/ked3AG1U9Q/PxGb7VLWYiBzCLYZywfP8XlUtLiIHgXKqei7eMSrhphK+xvP4aSCXqr4sInNwC99MB6bHm4femExhJQJjUkeTuJ/UNok5F+9+LJfa6G4DRgINgRUiYm13JlNZIjAmde6J9/dnz/2fcLPeAnTHLScIMB/oDxcXFymY1EFFJAQor6o/AE8BhYErSiXG+JP98jDmkryeFaG85qiqtwtpHhH5FffjqZvnuYHAJyIyBDgI9PE8/xgwSkT+jvvl3x+3KEpiQoHxIlIIt9jSfz3zzxuTaayNwJgUeNoIolT1UKBjMcYfrGrIGGOCnJUIjDEmyFmJwBhjgpwlAmOMCXKWCIwxJshZIjDGmCBnicAYY4Lc/wPVjhK+YVQhYQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Y_pred = model.predict_generator(validation_generator, (2789  / 100)) # ceil(num_of_test_samples / batch_size)\n",
    "target_names = ['Men', 'Woman','Baby']\n",
    "print(classification_report(validation_generator.classes[:Y_pred.shape[0]],\n",
    "                            list(np.argmax(Y_pred,axis=1)), \n",
    "                            target_names=target_names))\n",
    "\n",
    "\n",
    "acc_train = history.history['acc']\n",
    "acc_val = history.history['val_acc']\n",
    "epochs = range(1,31)\n",
    "plt.plot(epochs, acc_train, 'g', label='Training accuracy')\n",
    "plt.plot(epochs, acc_val, 'b', label='Validation accuracy')\n",
    "plt.title('Training and Validation accuracy (g1)')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "loss_train = history.history['loss']\n",
    "loss_val = history.history['val_loss']\n",
    "epochs = range(1,31)\n",
    "plt.plot(epochs, loss_train, 'g', label='Training loss')\n",
    "plt.plot(epochs, loss_val, 'b', label='Validation loss')\n",
    "plt.title('Training and Validation loss(g1)')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Loss')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d1b56d2d",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}