21-a Gentle Introduction to Keras - Simple neural network(mlp).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"http://akhavanpour.ir/notebook/images/srttu.gif\" alt=\"SRTTU\" style=\"width: 150px;\"/>\n",
    "\n",
    "[![Azure Notebooks](https://notebooks.azure.com/launch.png)](https://notebooks.azure.com/import/gh/Alireza-Akhavan/class.vision)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "588ee1fa-64b5-453b-ade7-8e6b2515821c"
    }
   },
   "source": [
    "# <div style=\"direction:rtl;text-align:right;font-family:B Lotus, B Nazanin, Tahoma\">مقدمه‌ای بر شبکه‌های عصبی و چارچوب Keras (کراس)</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <div style=\"direction:rtl;text-align:right;font-family:B Lotus, B Nazanin, Tahoma\">لود کتابخانه‌های مورد استفاده</div>\n",
    "<div style=\"direction:rtl;text-align:right;font-family:Tahoma\">\n",
    "\n",
    "برای اجرای این نوت‌بوک نیاز به نصب کتابخانه کراس (Keras) دارید. برای نصب این کتابخانه میتوانید لینک زیر را مطالعه کنید.\n",
    "</div>\n",
    "\n",
    "[http://blog.class.vision/1396/12/installing-keras-with-tensorflow-backend/](http://blog.class.vision/1396/12/installing-keras-with-tensorflow-backend/)\n",
    "\n",
    "<div style=\"direction:rtl;text-align:right;font-family:Tahoma\">\n",
    "در صورتی که تمام کتابخانه‌های مورد نیاز شما نصب باشد سلول زیر باید بدون مشکل اجرا شود.\n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "nbpresent": {
     "id": "de829a92-1fb6-44ad-a2c6-fc1001e1f6e1"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import keras\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Dropout, Activation\n",
    "import numpy as np\n",
    "\n",
    "from dataset import load_hoda"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"direction:rtl;text-align:right;font-family:Tahoma\">\n",
    "برای اینکه موقع اجرای کدها دقیقا نتایج سر کلاس را بتوانید مشاهده کنید\n",
    ":\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "np.random.seed(123)  # for reproducibility"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <div style=\"direction:rtl;text-align:right;font-family:B Lotus, B Nazanin, Tahoma\">لود مجموعه داده (dataset)</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x_train_original, y_train_original, x_test_original, y_test_original = load_hoda()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <div style=\"direction:rtl;text-align:right;font-family:B Lotus, B Nazanin, Tahoma\"> پیش‌پردازش داده‌ها برای Keras</div>\n",
    "<div style=\"direction:rtl;text-align:right;font-family:Tahoma\">\n",
    "تبدیل\n",
    "x_train و x_test\n",
    "به فرمت آرایه‌های نامپای یا ndarray و تبدیل \n",
    "y_train و y_test\n",
    "به \n",
    "one-hot-encoding\n",
    ":\n",
    "</div>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"direction:rtl;text-align:right;font-family:Tahoma\">\n",
    "ابتدا تابعی ساده تعریف کردهایم که ابعاد، نوع داده ای و اطلاعات دیتاست لود شده را چاپ کند.\n",
    "<br>\n",
    "این اطلاعات را قبل و بعد از پیش‌پردازش داده ها چاپ خواهیم کرد تا متوجه تغییرات بشویم!\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def print_data_info(x_train, y_train, x_test, y_test):\n",
    "    #Check data Type\n",
    "    print (\"\\ttype(x_train): {}\".format(type(x_train)))\n",
    "    print (\"\\ttype(y_train): {}\".format(type(y_train)))\n",
    "\n",
    "    #check data Shape\n",
    "    print (\"\\tx_train.shape: {}\".format(np.shape(x_train)))\n",
    "    print (\"\\ty_train.shape: {}\".format(np.shape(y_train)))\n",
    "    print (\"\\tx_test.shape: {}\".format(np.shape(x_test)))\n",
    "    print (\"\\ty_test.shape: {}\".format(np.shape(y_test)))\n",
    "\n",
    "    #sample data\n",
    "    print (\"\\ty_train[0]: {}\".format(y_train[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Preprocess input data for Keras. \n",
    "x_train = np.array(x_train_original)\n",
    "y_train = keras.utils.to_categorical(y_train_original, num_classes=10)\n",
    "x_test = np.array(x_test_original)\n",
    "y_test = keras.utils.to_categorical(y_test_original, num_classes=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Before Preprocessing:\n",
      "\ttype(x_train): <class 'list'>\n",
      "\ttype(y_train): <class 'numpy.ndarray'>\n",
      "\tx_train.shape: (1000, 25)\n",
      "\ty_train.shape: (1000,)\n",
      "\tx_test.shape: (200, 25)\n",
      "\ty_test.shape: (200,)\n",
      "\ty_train[0]: 6\n",
      "After Preprocessing:\n",
      "\ttype(x_train): <class 'numpy.ndarray'>\n",
      "\ttype(y_train): <class 'numpy.ndarray'>\n",
      "\tx_train.shape: (1000, 25)\n",
      "\ty_train.shape: (1000, 10)\n",
      "\tx_test.shape: (200, 25)\n",
      "\ty_test.shape: (200, 10)\n",
      "\ty_train[0]: [ 0.  0.  0.  0.  0.  0.  1.  0.  0.  0.]\n"
     ]
    }
   ],
   "source": [
    "print(\"Before Preprocessing:\")\n",
    "print_data_info(x_train_original, y_train_original, x_test_original, y_test_original)\n",
    "print(\"After Preprocessing:\")\n",
    "print_data_info(x_train, y_train, x_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"direction:rtl;text-align:right;font-family:Tahoma\">\n",
    "آخرین گام پیش‌پردازش تبدیل داده‌ها به \n",
    "**float32**\n",
    "و نرمال سازی مقادیر به مقدار بین 0 و 1 است.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x_train = x_train.astype('float32')\n",
    "x_test = x_test.astype('float32')\n",
    "x_train /= 255\n",
    "x_test /= 255"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <div style=\"direction:rtl;text-align:right;font-family:B Lotus, B Nazanin, Tahoma\">تعریف معماری مدل (model architecture)</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Sequential()\n",
    "model.add(Dense(64, activation='relu', input_dim=25))\n",
    "model.add(Dense(10, activation='softmax'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <div style=\"direction:rtl;text-align:right;font-family:B Lotus, B Nazanin, Tahoma\">Compile model</div>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model.compile(loss='categorical_crossentropy',\n",
    "              optimizer='rmsprop',\n",
    "              metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <div style=\"direction:rtl;text-align:right;font-family:B Lotus, B Nazanin, Tahoma\">آموش مدل با داده‌های آموزشی</div>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/30\n",
      "1000/1000 [==============================] - 0s 137us/step - loss: 2.0820 - acc: 0.2990\n",
      "Epoch 2/30\n",
      "1000/1000 [==============================] - 0s 38us/step - loss: 1.8258 - acc: 0.5030\n",
      "Epoch 3/30\n",
      "1000/1000 [==============================] - 0s 48us/step - loss: 1.6154 - acc: 0.6720\n",
      "Epoch 4/30\n",
      "1000/1000 [==============================] - 0s 38us/step - loss: 1.4207 - acc: 0.7540\n",
      "Epoch 5/30\n",
      "1000/1000 [==============================] - 0s 52us/step - loss: 1.2467 - acc: 0.7940\n",
      "Epoch 6/30\n",
      "1000/1000 [==============================] - 0s 36us/step - loss: 1.0966 - acc: 0.8070\n",
      "Epoch 7/30\n",
      "1000/1000 [==============================] - 0s 36us/step - loss: 0.9711 - acc: 0.8120\n",
      "Epoch 8/30\n",
      "1000/1000 [==============================] - 0s 45us/step - loss: 0.8647 - acc: 0.8330\n",
      "Epoch 9/30\n",
      "1000/1000 [==============================] - 0s 41us/step - loss: 0.7746 - acc: 0.8440\n",
      "Epoch 10/30\n",
      "1000/1000 [==============================] - 0s 48us/step - loss: 0.6997 - acc: 0.8490\n",
      "Epoch 11/30\n",
      "1000/1000 [==============================] - 0s 51us/step - loss: 0.6390 - acc: 0.8560\n",
      "Epoch 12/30\n",
      "1000/1000 [==============================] - 0s 48us/step - loss: 0.5892 - acc: 0.8710\n",
      "Epoch 13/30\n",
      "1000/1000 [==============================] - 0s 40us/step - loss: 0.5474 - acc: 0.8760\n",
      "Epoch 14/30\n",
      "1000/1000 [==============================] - 0s 44us/step - loss: 0.5130 - acc: 0.8770\n",
      "Epoch 15/30\n",
      "1000/1000 [==============================] - 0s 52us/step - loss: 0.4820 - acc: 0.8850\n",
      "Epoch 16/30\n",
      "1000/1000 [==============================] - 0s 51us/step - loss: 0.4581 - acc: 0.8930\n",
      "Epoch 17/30\n",
      "1000/1000 [==============================] - 0s 49us/step - loss: 0.4352 - acc: 0.8910\n",
      "Epoch 18/30\n",
      "1000/1000 [==============================] - 0s 48us/step - loss: 0.4160 - acc: 0.8940\n",
      "Epoch 19/30\n",
      "1000/1000 [==============================] - 0s 45us/step - loss: 0.3995 - acc: 0.8960\n",
      "Epoch 20/30\n",
      "1000/1000 [==============================] - 0s 45us/step - loss: 0.3848 - acc: 0.8960\n",
      "Epoch 21/30\n",
      "1000/1000 [==============================] - 0s 47us/step - loss: 0.3707 - acc: 0.8970\n",
      "Epoch 22/30\n",
      "1000/1000 [==============================] - 0s 53us/step - loss: 0.3595 - acc: 0.9050\n",
      "Epoch 23/30\n",
      "1000/1000 [==============================] - 0s 48us/step - loss: 0.3481 - acc: 0.9040\n",
      "Epoch 24/30\n",
      "1000/1000 [==============================] - 0s 70us/step - loss: 0.3387 - acc: 0.9060\n",
      "Epoch 25/30\n",
      "1000/1000 [==============================] - 0s 43us/step - loss: 0.3302 - acc: 0.9120\n",
      "Epoch 26/30\n",
      "1000/1000 [==============================] - 0s 44us/step - loss: 0.3220 - acc: 0.9110\n",
      "Epoch 27/30\n",
      "1000/1000 [==============================] - 0s 42us/step - loss: 0.3141 - acc: 0.9110\n",
      "Epoch 28/30\n",
      "1000/1000 [==============================] - 0s 43us/step - loss: 0.3073 - acc: 0.9100\n",
      "Epoch 29/30\n",
      "1000/1000 [==============================] - 0s 34us/step - loss: 0.3009 - acc: 0.9080\n",
      "Epoch 30/30\n",
      "1000/1000 [==============================] - 0s 39us/step - loss: 0.2920 - acc: 0.9130\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x67f7c2aef0>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(x_train, y_train,\n",
    "          epochs=30,\n",
    "          batch_size=64)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <div style=\"direction:rtl;text-align:right;font-family:B Lotus, B Nazanin, Tahoma\">ارزیابی مدل روی داده های آزمون</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "200/200 [==============================] - 0s 195us/step\n",
      "\n",
      "Testing loss: 0.33, acc: 0.91%\n"
     ]
    }
   ],
   "source": [
    "loss, acc = model.evaluate(x_test, y_test)\n",
    "print('\\nTesting loss: %.2f, acc: %.2f%%'%(loss, acc))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <div style=\"direction:rtl;text-align:right;font-family:B Lotus, B Nazanin, Tahoma\">پیش‌بینی داده‌های آموزشی</div>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "predicted:\n",
      "[7 2 3 8 5 5 4 7 3 2 0 8 8 0 2 9 3 6 7 4 0 3 6 3 9 2 7 5 2 9 2 5 5 8 9 2 5\n",
      " 1 4 8 8 4 2 2 1 2 7 9 0 3 7 2 7 5 2 9 8 2 9 8 8 6 6 6 7 6 2 4 2 4 4 5 9 1\n",
      " 8 4 0 5 6 2 4 3 2 7 7 7 7 1 8 1 7 8 7 7 8 9 7 2 3 1 0 2 9 6 3 5 5 0 0 9 6\n",
      " 7 9 3 9 9 8 7 9 2 5 2 5 5 9 6 9 2 0 3 7 9 5 2 9 0 4 1 8 2 2 3 5 2 9 3 8 2\n",
      " 7 0 9 9 0 7 6 7 4 0 9 3 7 0 7 4 9 4 7 3 4 1 5 6 7 9 1 3 5 4 5 7 4 1 3 3 1\n",
      " 1 4 3 8 9 6 7 7 2 3 0 1 4 9 5]\n",
      "True Label:\n",
      "[7 2 3 1 5 5 4 7 3 2 0 8 8 0 2 9 3 6 7 4 0 3 6 3 9 2 7 5 2 9 7 5 5 8 9 6 5\n",
      " 1 4 8 8 4 7 7 1 2 7 9 0 3 7 4 7 5 2 9 8 2 9 8 8 6 6 6 6 6 2 4 3 4 4 5 9 1\n",
      " 8 2 0 5 6 2 4 3 2 7 7 7 7 1 8 1 7 8 7 7 8 9 3 2 3 1 0 2 9 6 3 5 5 0 0 3 6\n",
      " 7 9 3 9 9 8 7 9 2 5 2 5 5 9 6 9 2 0 3 7 6 5 2 9 0 4 1 8 2 2 3 0 2 9 3 8 6\n",
      " 7 0 9 9 0 7 6 5 4 7 9 3 7 0 7 1 9 4 7 3 4 1 5 6 7 9 1 3 5 4 5 7 4 1 3 3 1\n",
      " 2 3 3 8 9 6 7 7 2 3 0 1 4 9 5]\n"
     ]
    }
   ],
   "source": [
    "# The predict_classes function outputs the highest probability class\n",
    "# according to the trained classifier for each input example.\n",
    "predicted_classes = model.predict_classes(x_test)\n",
    "print(\"predicted:\")\n",
    "print(predicted_classes)\n",
    "print(\"True Label:\")\n",
    "print(y_test_original)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <div style=\"direction:rtl;text-align:right;font-family:B Lotus, B Nazanin, Tahoma\">کد کامل، از ابتدا تا انتها</div>\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/30\n",
      "1000/1000 [==============================] - 0s 148us/step - loss: 2.0820 - acc: 0.2990\n",
      "Epoch 2/30\n",
      "1000/1000 [==============================] - 0s 28us/step - loss: 1.8258 - acc: 0.5030\n",
      "Epoch 3/30\n",
      "1000/1000 [==============================] - 0s 31us/step - loss: 1.6154 - acc: 0.6720\n",
      "Epoch 4/30\n",
      "1000/1000 [==============================] - 0s 28us/step - loss: 1.4207 - acc: 0.7540\n",
      "Epoch 5/30\n",
      "1000/1000 [==============================] - 0s 31us/step - loss: 1.2467 - acc: 0.7940\n",
      "Epoch 6/30\n",
      "1000/1000 [==============================] - 0s 42us/step - loss: 1.0966 - acc: 0.8070\n",
      "Epoch 7/30\n",
      "1000/1000 [==============================] - 0s 36us/step - loss: 0.9711 - acc: 0.8120\n",
      "Epoch 8/30\n",
      "1000/1000 [==============================] - 0s 38us/step - loss: 0.8647 - acc: 0.8330\n",
      "Epoch 9/30\n",
      "1000/1000 [==============================] - 0s 37us/step - loss: 0.7746 - acc: 0.8440\n",
      "Epoch 10/30\n",
      "1000/1000 [==============================] - 0s 35us/step - loss: 0.6997 - acc: 0.8490\n",
      "Epoch 11/30\n",
      "1000/1000 [==============================] - 0s 39us/step - loss: 0.6390 - acc: 0.8560\n",
      "Epoch 12/30\n",
      "1000/1000 [==============================] - 0s 31us/step - loss: 0.5892 - acc: 0.8710\n",
      "Epoch 13/30\n",
      "1000/1000 [==============================] - 0s 31us/step - loss: 0.5474 - acc: 0.8760\n",
      "Epoch 14/30\n",
      "1000/1000 [==============================] - 0s 28us/step - loss: 0.5130 - acc: 0.8770\n",
      "Epoch 15/30\n",
      "1000/1000 [==============================] - 0s 35us/step - loss: 0.4820 - acc: 0.8850\n",
      "Epoch 16/30\n",
      "1000/1000 [==============================] - 0s 31us/step - loss: 0.4581 - acc: 0.8930\n",
      "Epoch 17/30\n",
      "1000/1000 [==============================] - 0s 29us/step - loss: 0.4352 - acc: 0.8910\n",
      "Epoch 18/30\n",
      "1000/1000 [==============================] - 0s 30us/step - loss: 0.4160 - acc: 0.8940\n",
      "Epoch 19/30\n",
      "1000/1000 [==============================] - 0s 36us/step - loss: 0.3995 - acc: 0.8960\n",
      "Epoch 20/30\n",
      "1000/1000 [==============================] - 0s 31us/step - loss: 0.3848 - acc: 0.8960\n",
      "Epoch 21/30\n",
      "1000/1000 [==============================] - 0s 29us/step - loss: 0.3707 - acc: 0.8970\n",
      "Epoch 22/30\n",
      "1000/1000 [==============================] - 0s 34us/step - loss: 0.3595 - acc: 0.9050\n",
      "Epoch 23/30\n",
      "1000/1000 [==============================] - 0s 32us/step - loss: 0.3481 - acc: 0.9040\n",
      "Epoch 24/30\n",
      "1000/1000 [==============================] - 0s 31us/step - loss: 0.3387 - acc: 0.9060\n",
      "Epoch 25/30\n",
      "1000/1000 [==============================] - 0s 28us/step - loss: 0.3302 - acc: 0.9120\n",
      "Epoch 26/30\n",
      "1000/1000 [==============================] - 0s 28us/step - loss: 0.3220 - acc: 0.9110\n",
      "Epoch 27/30\n",
      "1000/1000 [==============================] - 0s 29us/step - loss: 0.3141 - acc: 0.9110\n",
      "Epoch 28/30\n",
      "1000/1000 [==============================] - 0s 34us/step - loss: 0.3073 - acc: 0.9100\n",
      "Epoch 29/30\n",
      "1000/1000 [==============================] - 0s 37us/step - loss: 0.3009 - acc: 0.9080\n",
      "Epoch 30/30\n",
      "1000/1000 [==============================] - 0s 33us/step - loss: 0.2920 - acc: 0.9130\n",
      "200/200 [==============================] - 0s 188us/step\n",
      "\n",
      "Testing loss: 0.33, acc: 0.91%\n"
     ]
    }
   ],
   "source": [
    "# 1. Import libraries and modules\n",
    "import keras\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Dropout, Activation\n",
    "import numpy as np\n",
    "from dataset import load_hoda\n",
    "\n",
    "np.random.seed(123)  # for reproducibility\n",
    "\n",
    "# 2. Load pre-shuffled HODA data into train and test sets\n",
    "x_train_original, y_train_original, x_test_original, y_test_original = load_hoda()\n",
    "\n",
    "# 3. Preprocess input data\n",
    "''' 3.1: input data in numpy array format'''\n",
    "x_train = np.array(x_train_original)\n",
    "x_test = np.array(x_test_original)\n",
    "'''3.2 normalize our data values to the range [0, 1]'''\n",
    "x_train = x_train.astype('float32')\n",
    "x_test = x_test.astype('float32')\n",
    "x_train /= 255\n",
    "x_test /= 255\n",
    "\n",
    "# 4. Preprocess class labels\n",
    "y_train = keras.utils.to_categorical(y_train_original, num_classes=10)\n",
    "y_test = keras.utils.to_categorical(y_test_original, num_classes=10)\n",
    "\n",
    "# 5. Define model architecture\n",
    "model = Sequential()\n",
    "model.add(Dense(64, activation='relu', input_dim=25))\n",
    "model.add(Dense(10, activation='softmax'))\n",
    "\n",
    "# 6. Compile model\n",
    "model.compile(loss='categorical_crossentropy',\n",
    "              optimizer='rmsprop',\n",
    "              metrics=['accuracy'])\n",
    "\n",
    "# 7. Fit model on training data\n",
    "model.fit(x_train, y_train,\n",
    "          epochs=30,\n",
    "          batch_size=64)\n",
    "\n",
    "# 8. Evaluate model on test data\n",
    "loss, acc = model.evaluate(x_test, y_test)\n",
    "print('\\nTesting loss: %.2f, acc: %.2f%%'%(loss, acc))"
   ]
  },
  {
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   "source": [
    "<div class=\"alert alert-block alert-info\">\n",
    "<div style=\"direction:rtl;text-align:right;font-family:B Lotus, B Nazanin, Tahoma\"> دانشگاه تربیت دبیر شهید رجایی<br>مباحث ویژه - آشنایی با بینایی کامپیوتر<br>علیرضا اخوان پور<br>96-97<br>\n",
    "</div>\n",
    "<a href=\"https://www.srttu.edu/\">SRTTU.edu</a> - <a href=\"http://class.vision\">Class.Vision</a> - <a href=\"http://AkhavanPour.ir\">AkhavanPour.ir</a>\n",
    "</div>"
   ]
  }
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