22_Multiple-Inputs-and-Mixed-Data.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<center><div style=\"direction:rtl;font-family:B Lotus, B Nazanin, Tahoma\">به نام خدا</div></center>\n",
    "\n",
    "<h1><center><div style=\"direction:rtl;font-family:B Lotus, B Nazanin, Tahoma\">رگرسیون تخمین قیمت خانه با چند ورودی<br>تصویر+داده های ساختار یافته\n",
    "</div></center></h1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "# import the necessary packages\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Dropout, Activation\n",
    "from sklearn.preprocessing import LabelBinarizer\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import glob\n",
    "import cv2\n",
    "import os\n",
    "import locale"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "inputPath = \"D:/dataset/Houses-dataset/Houses Dataset/HousesInfo.txt\"\n",
    "datasetPath = \"D:/dataset/Houses-dataset/Houses Dataset\"\n",
    "\n",
    "cols = [\"bedrooms\", \"bathrooms\", \"area\", \"zipcode\", \"price\"]\n",
    "df = pd.read_csv(inputPath, sep=\" \", header=None, names=cols)\n",
    "\n",
    "\n",
    "zipcodes, counts = np.unique(df[\"zipcode\"], return_counts=True)\n",
    "\n",
    "# loop over each of the unique zip codes and their corresponding\n",
    "# count\n",
    "for (zipcode, count) in zip(zipcodes, counts):\n",
    "    # the zip code counts for our housing dataset is *extremely*\n",
    "    # unbalanced (some only having 1 or 2 houses per zip code)\n",
    "    # so let's sanitize our data by removing any houses with less\n",
    "    # than 25 houses per zip code\n",
    "    if count < 25:\n",
    "        idxs = df[df[\"zipcode\"] == zipcode].index\n",
    "        df.drop(idxs, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# initialize our images array (i.e., the house images themselves)\n",
    "images = []\n",
    "\n",
    "# loop over the indexes of the houses\n",
    "for i in df.index.values:\n",
    "    # find the four images for the house and sort the file paths,\n",
    "    # ensuring the four are always in the *same order*\n",
    "    basePath = os.path.sep.join([datasetPath, \"{}_*\".format(i + 1)])\n",
    "    housePaths = sorted(list(glob.glob(basePath)))\n",
    "    # initialize our list of input images along with the output image\n",
    "    # after *combining* the four input images\n",
    "    inputImages = []\n",
    "    outputImage = np.zeros((64, 64, 3), dtype=\"uint8\")\n",
    "\n",
    "    # loop over the input house paths\n",
    "    for housePath in housePaths:\n",
    "        # load the input image, resize it to be 32 32, and then\n",
    "        # update the list of input images\n",
    "        image = cv2.imread(housePath)\n",
    "        image = cv2.resize(image, (32, 32))\n",
    "        inputImages.append(image)\n",
    "\n",
    "    # tile the four input images in the output image such the first\n",
    "    # image goes in the top-right corner, the second image in the\n",
    "    # top-left corner, the third image in the bottom-right corner,\n",
    "    # and the final image in the bottom-left corner\n",
    "    outputImage[0:32, 0:32] = inputImages[0]\n",
    "    outputImage[0:32, 32:64] = inputImages[1]\n",
    "    outputImage[32:64, 32:64] = inputImages[2]\n",
    "    outputImage[32:64, 0:32] = inputImages[3]\n",
    "\n",
    "    # add the tiled image to our set of images the network will be\n",
    "    # trained on\n",
    "    images.append(outputImage)\n",
    "images = np.array(images)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "images = images / 255.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# partition the data into training and testing splits using 75% of\n",
    "# the data for training and the remaining 25% for testing\n",
    "split = train_test_split(df, images, test_size=0.25, random_state=42)\n",
    "(trainAttrX, testAttrX, trainImagesX, testImagesX) = split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# find the largest house price in the training set and use it to\n",
    "# scale our house prices to the range [0, 1] (will lead to better\n",
    "# training and convergence)\n",
    "maxPrice = trainAttrX[\"price\"].max()\n",
    "trainY = trainAttrX[\"price\"] / maxPrice\n",
    "testY = testAttrX[\"price\"] / maxPrice"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\alire\\Miniconda3\\envs\\tensorflow\\lib\\site-packages\\sklearn\\preprocessing\\data.py:323: DataConversionWarning: Data with input dtype int64, float64 were all converted to float64 by MinMaxScaler.\n",
      "  return self.partial_fit(X, y)\n"
     ]
    }
   ],
   "source": [
    "# initialize the column names of the continuous data\n",
    "continuous = [\"bedrooms\", \"bathrooms\", \"area\"]\n",
    "\n",
    "# performin min-max scaling each continuous feature column to\n",
    "# the range [0, 1]\n",
    "cs = MinMaxScaler()\n",
    "trainContinuous = cs.fit_transform(trainAttrX[continuous])\n",
    "testContinuous = cs.transform(testAttrX[continuous])\n",
    "\n",
    "# one-hot encode the zip code categorical data (by definition of\n",
    "# one-hot encoing, all output features are now in the range [0, 1])\n",
    "zipBinarizer = LabelBinarizer().fit(df[\"zipcode\"])\n",
    "trainCategorical = zipBinarizer.transform(trainAttrX[\"zipcode\"])\n",
    "testCategorical = zipBinarizer.transform(testAttrX[\"zipcode\"])\n",
    "\n",
    "# construct our training and testing data points by concatenating\n",
    "# the categorical features with the continuous features\n",
    "trainAttrX = np.hstack([trainCategorical, trainContinuous])\n",
    "testAttrX = np.hstack([testCategorical, testContinuous])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "dim = trainAttrX.shape[1] #10\n",
    "\n",
    "# define our MLP network\n",
    "mlp = Sequential()\n",
    "mlp.add(Dense(8, input_dim=dim, activation=\"relu\"))\n",
    "mlp.add(Dense(4, activation=\"relu\"))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.models import Model, Input\n",
    "from keras.layers.convolutional import Conv2D, MaxPooling2D\n",
    "from keras.layers import Activation, Dense, Flatten, Dropout\n",
    "from keras.layers.normalization import BatchNormalization\n",
    "\n",
    "width, height, depth = 64, 64, 3\n",
    "filters=(16, 32, 64)\n",
    "# initialize the input shape and channel dimension, assuming\n",
    "# TensorFlow/channels-last ordering\n",
    "inputShape = (height, width, depth)\n",
    "\n",
    "# define the model input\n",
    "inputs = Input(shape=inputShape)\n",
    "\n",
    "# loop over the number of filters\n",
    "for (i, f) in enumerate(filters):\n",
    "    # if this is the first CONV layer then set the input\n",
    "    # appropriately\n",
    "    if i == 0:\n",
    "        x = inputs\n",
    "\n",
    "    # CONV => RELU => BN => POOL\n",
    "    x = Conv2D(f, (3, 3), padding=\"same\")(x)\n",
    "    x = Activation(\"relu\")(x)\n",
    "    x = BatchNormalization()(x)\n",
    "    x = MaxPooling2D(pool_size=(2, 2))(x)\n",
    "    \n",
    "# flatten the volume, then FC => RELU => BN => DROPOUT\n",
    "x = Flatten()(x)\n",
    "x = Dense(16)(x)\n",
    "x = Activation(\"relu\")(x)\n",
    "x = BatchNormalization()(x)\n",
    "x = Dropout(0.5)(x)\n",
    "\n",
    "# apply another FC layer, this one to match the number of nodes\n",
    "# coming out of the MLP\n",
    "x = Dense(4)(x)\n",
    "x = Activation(\"relu\")(x)\n",
    "\n",
    "# construct the CNN\n",
    "cnn = Model(inputs, x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.layers import concatenate\n",
    "# create the input to our final set of layers as the *output* of both\n",
    "# the MLP and CNN\n",
    "combinedInput = concatenate([mlp.output, cnn.output])\n",
    "\n",
    "# our final FC layer head will have two dense layers, the final one\n",
    "# being our regression head\n",
    "x = Dense(4, activation=\"relu\")(combinedInput)\n",
    "x = Dense(1, activation=\"linear\")(x)\n",
    "\n",
    "model = Model(inputs=[mlp.input, cnn.input], outputs=x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(loss=\"mean_absolute_percentage_error\", optimizer='adam')\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 271 samples, validate on 91 samples\n",
      "Epoch 1/200\n",
      "271/271 [==============================] - 4s 13ms/step - loss: 648.9047 - val_loss: 1557.0419\n",
      "Epoch 2/200\n",
      "271/271 [==============================] - 1s 2ms/step - loss: 295.0318 - val_loss: 435.3303\n",
      "Epoch 3/200\n",
      "271/271 [==============================] - 1s 2ms/step - loss: 263.3781 - val_loss: 89.4173\n",
      "Epoch 4/200\n",
      "271/271 [==============================] - 1s 2ms/step - loss: 148.8689 - val_loss: 71.4119\n",
      "Epoch 5/200\n",
      "271/271 [==============================] - 1s 2ms/step - loss: 125.9273 - val_loss: 56.9353\n",
      "Epoch 6/200\n",
      "271/271 [==============================] - 1s 2ms/step - loss: 117.0790 - val_loss: 52.3655\n",
      "Epoch 7/200\n",
      "271/271 [==============================] - 1s 2ms/step - loss: 86.9748 - val_loss: 92.0126\n",
      "Epoch 8/200\n",
      "271/271 [==============================] - 1s 2ms/step - loss: 74.0905 - val_loss: 84.1273\n",
      "Epoch 9/200\n",
      "271/271 [==============================] - 1s 2ms/step - loss: 70.3482 - val_loss: 68.4371\n",
      "Epoch 10/200\n",
      "271/271 [==============================] - 1s 2ms/step - loss: 62.8653 - val_loss: 61.1865\n",
      "Epoch 11/200\n",
      "271/271 [==============================] - 1s 2ms/step - loss: 68.4560 - val_loss: 98.4405\n",
      "Epoch 12/200\n",
      "271/271 [==============================] - 1s 2ms/step - loss: 61.4715 - val_loss: 107.6340\n",
      "Epoch 13/200\n",
      "271/271 [==============================] - 1s 3ms/step - loss: 63.7499 - val_loss: 78.6291\n",
      "Epoch 14/200\n",
      "271/271 [==============================] - 1s 2ms/step - loss: 59.4053 - val_loss: 62.9307\n",
      "Epoch 15/200\n",
      "271/271 [==============================] - 1s 2ms/step - loss: 62.8789 - val_loss: 61.8369\n",
      "Epoch 16/200\n",
      "271/271 [==============================] - 1s 3ms/step - loss: 58.4935 - val_loss: 52.9061\n",
      "Epoch 17/200\n",
      "271/271 [==============================] - 1s 2ms/step - loss: 55.2341 - val_loss: 52.2281\n",
      "Epoch 18/200\n",
      "271/271 [==============================] - 1s 2ms/step - loss: 57.4398 - val_loss: 49.2343\n",
      "Epoch 19/200\n",
      "271/271 [==============================] - 1s 2ms/step - loss: 49.1358 - val_loss: 49.0960\n",
      "Epoch 20/200\n",
      "271/271 [==============================] - 1s 3ms/step - loss: 48.4470 - val_loss: 38.7266\n",
      "Epoch 21/200\n",
      "271/271 [==============================] - 1s 2ms/step - loss: 45.8759 - val_loss: 40.5615\n",
      "Epoch 22/200\n",
      "271/271 [==============================] - 1s 2ms/step - loss: 45.1791 - val_loss: 35.9699\n",
      "Epoch 23/200\n",
      "271/271 [==============================] - 1s 2ms/step - loss: 47.2793 - val_loss: 34.6656\n",
      "Epoch 24/200\n",
      "271/271 [==============================] - 1s 2ms/step - loss: 41.3985 - val_loss: 32.6100\n",
      "Epoch 25/200\n",
      "271/271 [==============================] - 1s 2ms/step - loss: 43.0263 - val_loss: 29.8411\n",
      "Epoch 26/200\n",
      "271/271 [==============================] - 1s 2ms/step - loss: 38.7130 - val_loss: 30.4016\n",
      "Epoch 27/200\n",
      "271/271 [==============================] - 1s 2ms/step - loss: 39.3143 - val_loss: 30.3668\n",
      "Epoch 28/200\n",
      "271/271 [==============================] - 1s 2ms/step - loss: 35.6419 - val_loss: 28.1644\n",
      "Epoch 29/200\n",
      "271/271 [==============================] - 1s 2ms/step - loss: 36.1231 - val_loss: 26.3628\n",
      "Epoch 30/200\n",
      "271/271 [==============================] - 1s 2ms/step - loss: 37.1179 - val_loss: 27.8835\n",
      "Epoch 31/200\n",
      "271/271 [==============================] - 1s 2ms/step - loss: 34.4293 - val_loss: 27.0757\n",
      "Epoch 32/200\n",
      "271/271 [==============================] - 1s 3ms/step - loss: 33.9238 - val_loss: 26.0894\n",
      "Epoch 33/200\n",
      "271/271 [==============================] - 1s 2ms/step - loss: 32.0280 - val_loss: 24.7928\n",
      "Epoch 34/200\n",
      "271/271 [==============================] - 1s 2ms/step - loss: 33.4151 - val_loss: 23.2257\n",
      "Epoch 35/200\n",
      "271/271 [==============================] - 1s 3ms/step - loss: 30.6855 - val_loss: 23.1927\n",
      "Epoch 36/200\n",
      "271/271 [==============================] - 1s 3ms/step - loss: 31.0782 - val_loss: 24.5160\n",
      "Epoch 37/200\n",
      "271/271 [==============================] - 1s 2ms/step - loss: 29.6105 - val_loss: 23.6624\n",
      "Epoch 38/200\n",
      "271/271 [==============================] - 1s 3ms/step - loss: 29.1123 - val_loss: 24.6812\n",
      "Epoch 39/200\n",
      "271/271 [==============================] - 1s 2ms/step - loss: 29.5348 - val_loss: 23.9998\n",
      "Epoch 40/200\n",
      "271/271 [==============================] - 1s 2ms/step - loss: 28.0747 - val_loss: 24.2892\n",
      "Epoch 41/200\n",
      "271/271 [==============================] - 1s 3ms/step - loss: 28.3978 - val_loss: 24.3075\n",
      "Epoch 42/200\n",
      "271/271 [==============================] - 1s 2ms/step - loss: 28.5522 - val_loss: 24.4653\n",
      "Epoch 43/200\n",
      "271/271 [==============================] - 1s 2ms/step - loss: 28.5345 - val_loss: 23.2335\n",
      "Epoch 44/200\n",
      "271/271 [==============================] - 1s 2ms/step - loss: 30.2791 - val_loss: 23.6889\n",
      "Epoch 45/200\n",
      "271/271 [==============================] - 1s 3ms/step - loss: 26.7179 - val_loss: 23.5281\n",
      "Epoch 46/200\n",
      "271/271 [==============================] - 1s 2ms/step - loss: 26.7942 - val_loss: 22.6142\n",
      "Epoch 47/200\n",
      "271/271 [==============================] - 1s 2ms/step - loss: 26.1200 - val_loss: 24.0474\n",
      "Epoch 48/200\n",
      "271/271 [==============================] - 1s 2ms/step - loss: 27.1239 - val_loss: 24.5988\n",
      "Epoch 49/200\n",
      "271/271 [==============================] - 1s 2ms/step - loss: 26.3363 - val_loss: 23.5277\n",
      "Epoch 50/200\n",
      "271/271 [==============================] - 1s 2ms/step - loss: 26.9117 - val_loss: 23.7065\n",
      "Epoch 51/200\n",
      "271/271 [==============================] - 1s 3ms/step - loss: 26.0222 - val_loss: 22.4367\n",
      "Epoch 52/200\n",
      "271/271 [==============================] - 1s 2ms/step - loss: 26.5655 - val_loss: 23.2711\n",
      "Epoch 53/200\n",
      "271/271 [==============================] - 1s 2ms/step - loss: 25.3407 - val_loss: 23.7255\n",
      "Epoch 54/200\n",
      "271/271 [==============================] - 1s 3ms/step - loss: 26.0835 - val_loss: 23.9367\n",
      "Epoch 55/200\n",
      "271/271 [==============================] - 1s 3ms/step - loss: 25.8226 - val_loss: 24.7970\n",
      "Epoch 56/200\n",
      "271/271 [==============================] - 1s 3ms/step - loss: 25.0464 - val_loss: 24.6682\n",
      "Epoch 57/200\n",
      "271/271 [==============================] - 1s 3ms/step - loss: 25.4406 - val_loss: 25.9341\n",
      "Epoch 58/200\n",
      "271/271 [==============================] - 1s 3ms/step - loss: 26.6027 - val_loss: 25.4297\n",
      "Epoch 59/200\n",
      "271/271 [==============================] - 1s 3ms/step - loss: 25.3199 - val_loss: 23.6805\n",
      "Epoch 60/200\n",
      "271/271 [==============================] - 1s 3ms/step - loss: 24.9309 - val_loss: 25.1149\n",
      "Epoch 61/200\n",
      "271/271 [==============================] - 1s 3ms/step - loss: 24.7824 - val_loss: 22.6854\n",
      "Epoch 62/200\n",
      "271/271 [==============================] - 1s 3ms/step - loss: 24.6862 - val_loss: 23.8381\n",
      "Epoch 63/200\n",
      "271/271 [==============================] - 1s 3ms/step - loss: 24.9382 - val_loss: 24.3762\n",
      "Epoch 64/200\n",
      "271/271 [==============================] - 1s 3ms/step - loss: 25.6479 - val_loss: 23.7328\n",
      "Epoch 65/200\n",
      "271/271 [==============================] - 1s 3ms/step - loss: 25.2829 - val_loss: 23.5561\n",
      "Epoch 66/200\n",
      "271/271 [==============================] - 1s 3ms/step - loss: 25.3596 - val_loss: 24.2026\n",
      "Epoch 67/200\n",
      "271/271 [==============================] - 1s 3ms/step - loss: 25.1306 - val_loss: 24.8977\n",
      "Epoch 68/200\n",
      "271/271 [==============================] - 1s 3ms/step - loss: 24.1819 - val_loss: 23.8329\n",
      "Epoch 69/200\n",
      "271/271 [==============================] - 1s 3ms/step - loss: 24.2964 - val_loss: 24.2676\n",
      "Epoch 70/200\n",
      "271/271 [==============================] - 1s 3ms/step - loss: 24.4155 - val_loss: 24.6608\n",
      "Epoch 71/200\n",
      "271/271 [==============================] - 1s 3ms/step - loss: 24.0031 - val_loss: 25.8975\n",
      "Epoch 72/200\n",
      "271/271 [==============================] - 1s 3ms/step - loss: 24.6550 - val_loss: 24.9884\n",
      "Epoch 73/200\n",
      "271/271 [==============================] - 1s 3ms/step - loss: 24.9320 - val_loss: 24.0555\n",
      "Epoch 74/200\n",
      "271/271 [==============================] - 1s 3ms/step - loss: 24.6098 - val_loss: 22.7259\n",
      "Epoch 75/200\n",
      "271/271 [==============================] - 1s 3ms/step - loss: 24.7021 - val_loss: 23.7776\n",
      "Epoch 76/200\n",
      "271/271 [==============================] - 1s 3ms/step - loss: 24.5322 - val_loss: 23.1760\n",
      "Epoch 77/200\n",
      "271/271 [==============================] - 1s 3ms/step - loss: 24.9114 - val_loss: 23.4618\n",
      "Epoch 78/200\n",
      "271/271 [==============================] - 1s 3ms/step - loss: 24.4171 - val_loss: 22.4097\n",
      "Epoch 79/200\n",
      "271/271 [==============================] - 1s 3ms/step - loss: 24.1813 - val_loss: 23.5668\n",
      "Epoch 80/200\n",
      "271/271 [==============================] - 1s 2ms/step - loss: 24.0919 - val_loss: 24.1634\n",
      "Epoch 81/200\n",
      "271/271 [==============================] - 1s 2ms/step - loss: 24.2783 - val_loss: 23.2844\n",
      "Epoch 82/200\n",
      "271/271 [==============================] - 1s 2ms/step - loss: 23.1378 - val_loss: 23.1739\n",
      "Epoch 83/200\n",
      "271/271 [==============================] - 1s 2ms/step - loss: 23.6621 - val_loss: 22.0500\n",
      "Epoch 84/200\n",
      "271/271 [==============================] - 1s 2ms/step - loss: 24.1445 - val_loss: 24.2814\n",
      "Epoch 85/200\n",
      "271/271 [==============================] - 1s 2ms/step - loss: 23.9020 - val_loss: 23.6259\n",
      "Epoch 86/200\n",
      "271/271 [==============================] - 1s 2ms/step - loss: 23.0769 - val_loss: 25.0544\n",
      "Epoch 87/200\n",
      "271/271 [==============================] - 1s 2ms/step - loss: 23.6500 - val_loss: 24.7339\n",
      "Epoch 88/200\n",
      "271/271 [==============================] - 1s 2ms/step - loss: 23.5629 - val_loss: 24.6911\n",
      "Epoch 89/200\n",
      "271/271 [==============================] - 1s 2ms/step - loss: 23.0431 - val_loss: 22.8248\n",
      "Epoch 90/200\n",
      "271/271 [==============================] - 1s 2ms/step - loss: 22.3786 - val_loss: 23.5871\n",
      "Epoch 91/200\n",
      "271/271 [==============================] - 1s 2ms/step - loss: 23.3300 - val_loss: 23.8678\n",
      "Epoch 92/200\n",
      "271/271 [==============================] - 1s 2ms/step - loss: 22.8454 - val_loss: 24.8007\n",
      "Epoch 93/200\n",
      "271/271 [==============================] - 1s 2ms/step - loss: 23.5883 - val_loss: 23.9744\n",
      "Epoch 94/200\n",
      "271/271 [==============================] - 1s 2ms/step - loss: 23.9266 - val_loss: 22.5172\n",
      "Epoch 95/200\n",
      "271/271 [==============================] - 1s 2ms/step - loss: 23.7154 - val_loss: 24.2703\n",
      "Epoch 96/200\n",
      "271/271 [==============================] - 1s 2ms/step - loss: 23.2175 - val_loss: 25.0560\n",
      "Epoch 97/200\n",
      "271/271 [==============================] - 1s 2ms/step - loss: 22.7850 - val_loss: 22.8530\n",
      "Epoch 98/200\n",
      "271/271 [==============================] - 1s 2ms/step - loss: 22.8096 - val_loss: 23.0487\n",
      "Epoch 99/200\n",
      "271/271 [==============================] - 1s 2ms/step - loss: 23.3764 - val_loss: 22.9279\n",
      "Epoch 100/200\n",
      "271/271 [==============================] - 1s 2ms/step - loss: 22.8511 - val_loss: 24.4953\n",
      "Epoch 101/200\n",
      "271/271 [==============================] - 1s 2ms/step - loss: 22.7844 - val_loss: 24.6558\n",
      "Epoch 102/200\n",
      "271/271 [==============================] - 1s 2ms/step - loss: 23.9293 - val_loss: 23.1721\n",
      "Epoch 103/200\n",
      "271/271 [==============================] - 1s 2ms/step - loss: 22.3994 - val_loss: 22.6509\n",
      "Epoch 104/200\n",
      "271/271 [==============================] - 1s 2ms/step - loss: 22.0766 - val_loss: 23.9287\n",
      "Epoch 105/200\n",
      "271/271 [==============================] - 1s 2ms/step - loss: 21.9605 - val_loss: 23.3830\n",
      "Epoch 106/200\n",
      "271/271 [==============================] - 1s 2ms/step - loss: 22.1403 - val_loss: 22.9586\n",
      "Epoch 107/200\n",
      "271/271 [==============================] - 1s 2ms/step - loss: 22.9115 - val_loss: 23.6826\n",
      "Epoch 108/200\n",
      "271/271 [==============================] - 1s 2ms/step - loss: 21.6992 - val_loss: 24.7995\n",
      "Epoch 109/200\n",
      "271/271 [==============================] - 1s 2ms/step - loss: 22.3458 - val_loss: 23.7422\n",
      "Epoch 110/200\n",
      "271/271 [==============================] - 1s 2ms/step - loss: 21.6766 - val_loss: 23.1388\n",
      "Epoch 111/200\n",
      "271/271 [==============================] - 1s 2ms/step - loss: 21.9769 - val_loss: 24.3957\n",
      "Epoch 112/200\n",
      "271/271 [==============================] - 1s 2ms/step - loss: 23.5219 - val_loss: 24.9222\n",
      "Epoch 113/200\n",
      "271/271 [==============================] - 1s 2ms/step - loss: 23.9933 - val_loss: 22.9760\n",
      "Epoch 114/200\n",
      "271/271 [==============================] - 1s 2ms/step - loss: 23.3419 - val_loss: 23.9621\n",
      "Epoch 115/200\n",
      "271/271 [==============================] - 1s 3ms/step - loss: 22.9877 - val_loss: 24.5436\n",
      "Epoch 116/200\n",
      "271/271 [==============================] - 1s 2ms/step - loss: 22.5001 - val_loss: 24.2677\n",
      "Epoch 117/200\n",
      "271/271 [==============================] - 1s 2ms/step - loss: 22.2790 - val_loss: 25.4166\n",
      "Epoch 118/200\n",
      "271/271 [==============================] - 1s 3ms/step - loss: 21.5446 - val_loss: 23.1773\n",
      "Epoch 119/200\n",
      "271/271 [==============================] - 1s 3ms/step - loss: 21.0119 - val_loss: 22.5232\n",
      "Epoch 120/200\n",
      "271/271 [==============================] - 1s 3ms/step - loss: 23.0591 - val_loss: 23.4623\n",
      "Epoch 121/200\n",
      "271/271 [==============================] - 1s 3ms/step - loss: 21.3468 - val_loss: 24.3521\n",
      "Epoch 122/200\n",
      "271/271 [==============================] - 1s 3ms/step - loss: 22.3401 - val_loss: 25.6655\n",
      "Epoch 123/200\n",
      "271/271 [==============================] - 1s 3ms/step - loss: 21.1024 - val_loss: 25.0159\n",
      "Epoch 124/200\n",
      "271/271 [==============================] - 1s 3ms/step - loss: 22.4840 - val_loss: 23.6090\n",
      "Epoch 125/200\n",
      "271/271 [==============================] - 1s 3ms/step - loss: 23.0498 - val_loss: 24.6601\n",
      "Epoch 126/200\n",
      "271/271 [==============================] - 1s 3ms/step - loss: 22.8579 - val_loss: 23.0653\n",
      "Epoch 127/200\n",
      "271/271 [==============================] - 1s 3ms/step - loss: 21.1758 - val_loss: 23.6464\n",
      "Epoch 128/200\n",
      "271/271 [==============================] - 1s 3ms/step - loss: 21.7066 - val_loss: 24.2441\n",
      "Epoch 129/200\n",
      "271/271 [==============================] - 1s 3ms/step - loss: 21.3897 - val_loss: 23.8289\n",
      "Epoch 130/200\n",
      "271/271 [==============================] - 1s 3ms/step - loss: 21.1459 - val_loss: 24.2874\n",
      "Epoch 131/200\n",
      "271/271 [==============================] - 1s 3ms/step - loss: 22.2433 - val_loss: 26.3437\n",
      "Epoch 132/200\n",
      "271/271 [==============================] - 1s 3ms/step - loss: 21.6145 - val_loss: 24.2066\n",
      "Epoch 133/200\n",
      "271/271 [==============================] - 1s 3ms/step - loss: 21.7898 - val_loss: 24.2229\n",
      "Epoch 134/200\n",
      "271/271 [==============================] - 1s 2ms/step - loss: 21.0470 - val_loss: 25.2485\n",
      "Epoch 135/200\n",
      "271/271 [==============================] - 1s 2ms/step - loss: 21.4547 - val_loss: 24.8701\n",
      "Epoch 136/200\n",
      "271/271 [==============================] - 1s 3ms/step - loss: 21.3248 - val_loss: 24.6392\n",
      "Epoch 137/200\n",
      "271/271 [==============================] - 1s 3ms/step - loss: 21.4210 - val_loss: 24.5334\n",
      "Epoch 138/200\n",
      "271/271 [==============================] - 1s 3ms/step - loss: 20.5434 - val_loss: 24.1110\n",
      "Epoch 139/200\n",
      "271/271 [==============================] - 1s 3ms/step - loss: 20.1904 - val_loss: 24.9667\n",
      "Epoch 140/200\n",
      "271/271 [==============================] - 1s 2ms/step - loss: 22.0516 - val_loss: 23.1555\n",
      "Epoch 141/200\n",
      "271/271 [==============================] - 1s 2ms/step - loss: 20.9837 - val_loss: 25.2765\n",
      "Epoch 142/200\n",
      "271/271 [==============================] - 1s 3ms/step - loss: 20.0432 - val_loss: 24.0895\n",
      "Epoch 143/200\n",
      "271/271 [==============================] - 1s 3ms/step - loss: 21.1797 - val_loss: 25.6608\n",
      "Epoch 144/200\n",
      "271/271 [==============================] - 1s 3ms/step - loss: 21.1109 - val_loss: 23.1126\n",
      "Epoch 145/200\n",
      "271/271 [==============================] - 1s 2ms/step - loss: 20.9540 - val_loss: 24.0595\n",
      "Epoch 146/200\n",
      "271/271 [==============================] - 1s 3ms/step - loss: 20.9492 - val_loss: 25.6181\n",
      "Epoch 147/200\n",
      "271/271 [==============================] - 1s 2ms/step - loss: 21.0759 - val_loss: 24.4433\n",
      "Epoch 148/200\n",
      "271/271 [==============================] - 1s 3ms/step - loss: 21.0449 - val_loss: 24.2352\n",
      "Epoch 149/200\n",
      "271/271 [==============================] - 1s 3ms/step - loss: 20.7127 - val_loss: 25.9453\n",
      "Epoch 150/200\n",
      "271/271 [==============================] - 1s 3ms/step - loss: 20.9094 - val_loss: 24.5455\n",
      "Epoch 151/200\n",
      "271/271 [==============================] - 1s 2ms/step - loss: 21.0932 - val_loss: 24.7296\n",
      "Epoch 152/200\n",
      "271/271 [==============================] - 1s 3ms/step - loss: 19.6064 - val_loss: 25.7715\n",
      "Epoch 153/200\n",
      "271/271 [==============================] - 1s 3ms/step - loss: 21.9080 - val_loss: 26.7118\n",
      "Epoch 154/200\n",
      "271/271 [==============================] - 1s 3ms/step - loss: 21.3151 - val_loss: 24.6854\n",
      "Epoch 155/200\n",
      "271/271 [==============================] - 1s 3ms/step - loss: 19.3823 - val_loss: 24.8574\n",
      "Epoch 156/200\n",
      "271/271 [==============================] - 1s 2ms/step - loss: 20.7068 - val_loss: 24.8640\n",
      "Epoch 157/200\n",
      "271/271 [==============================] - 1s 2ms/step - loss: 19.4891 - val_loss: 24.4119\n",
      "Epoch 158/200\n",
      "271/271 [==============================] - 1s 2ms/step - loss: 19.5401 - val_loss: 24.1409\n",
      "Epoch 159/200\n",
      "271/271 [==============================] - 1s 2ms/step - loss: 18.7161 - val_loss: 24.9310\n",
      "Epoch 160/200\n",
      "271/271 [==============================] - 1s 2ms/step - loss: 19.6882 - val_loss: 25.2773\n",
      "Epoch 161/200\n",
      "271/271 [==============================] - 1s 2ms/step - loss: 19.6932 - val_loss: 23.6790\n",
      "Epoch 162/200\n",
      "271/271 [==============================] - 1s 2ms/step - loss: 19.6560 - val_loss: 24.7376\n",
      "Epoch 163/200\n",
      "271/271 [==============================] - 1s 2ms/step - loss: 19.7813 - val_loss: 23.6806\n",
      "Epoch 164/200\n",
      "271/271 [==============================] - 1s 2ms/step - loss: 20.4543 - val_loss: 23.7171\n",
      "Epoch 165/200\n",
      "271/271 [==============================] - 1s 2ms/step - loss: 20.6330 - val_loss: 23.0373\n",
      "Epoch 166/200\n",
      "271/271 [==============================] - 1s 2ms/step - loss: 19.7199 - val_loss: 23.2605\n",
      "Epoch 167/200\n",
      "271/271 [==============================] - 1s 2ms/step - loss: 20.5046 - val_loss: 24.3895\n",
      "Epoch 168/200\n",
      "271/271 [==============================] - 1s 2ms/step - loss: 20.2475 - val_loss: 23.7202\n",
      "Epoch 169/200\n",
      "271/271 [==============================] - 1s 2ms/step - loss: 20.3394 - val_loss: 25.8319\n",
      "Epoch 170/200\n",
      "271/271 [==============================] - 1s 2ms/step - loss: 20.7570 - val_loss: 24.1905\n",
      "Epoch 171/200\n",
      "271/271 [==============================] - 1s 2ms/step - loss: 19.7351 - val_loss: 24.5459\n",
      "Epoch 172/200\n",
      "271/271 [==============================] - 1s 2ms/step - loss: 19.6232 - val_loss: 25.1260\n",
      "Epoch 173/200\n",
      "271/271 [==============================] - 1s 2ms/step - loss: 19.4657 - val_loss: 24.6098\n",
      "Epoch 174/200\n",
      "271/271 [==============================] - 1s 2ms/step - loss: 19.4516 - val_loss: 23.7137\n",
      "Epoch 175/200\n",
      "271/271 [==============================] - 1s 2ms/step - loss: 19.9536 - val_loss: 24.7625\n",
      "Epoch 176/200\n",
      "271/271 [==============================] - 1s 2ms/step - loss: 20.5934 - val_loss: 24.1400\n",
      "Epoch 177/200\n",
      "271/271 [==============================] - 1s 2ms/step - loss: 19.2723 - val_loss: 24.1578\n",
      "Epoch 178/200\n",
      "271/271 [==============================] - 1s 2ms/step - loss: 18.9053 - val_loss: 23.1135\n",
      "Epoch 179/200\n",
      "271/271 [==============================] - 1s 2ms/step - loss: 18.6973 - val_loss: 22.2897\n",
      "Epoch 180/200\n",
      "271/271 [==============================] - 1s 2ms/step - loss: 18.4351 - val_loss: 23.7637\n",
      "Epoch 181/200\n",
      "271/271 [==============================] - 1s 2ms/step - loss: 19.4901 - val_loss: 25.4164\n",
      "Epoch 182/200\n",
      "271/271 [==============================] - 1s 2ms/step - loss: 19.5306 - val_loss: 22.9352\n",
      "Epoch 183/200\n",
      "271/271 [==============================] - 1s 2ms/step - loss: 18.6304 - val_loss: 22.2590\n",
      "Epoch 184/200\n",
      "271/271 [==============================] - 1s 2ms/step - loss: 17.5532 - val_loss: 23.7157\n",
      "Epoch 185/200\n",
      "271/271 [==============================] - 1s 2ms/step - loss: 17.5140 - val_loss: 22.3475\n",
      "Epoch 186/200\n",
      "271/271 [==============================] - 1s 3ms/step - loss: 18.5302 - val_loss: 23.5306\n",
      "Epoch 187/200\n",
      "271/271 [==============================] - 1s 2ms/step - loss: 18.8947 - val_loss: 22.6086\n",
      "Epoch 188/200\n",
      "271/271 [==============================] - 1s 2ms/step - loss: 17.2308 - val_loss: 22.4057\n",
      "Epoch 189/200\n",
      "271/271 [==============================] - 1s 2ms/step - loss: 18.7233 - val_loss: 23.2331\n",
      "Epoch 190/200\n",
      "271/271 [==============================] - 1s 2ms/step - loss: 18.6952 - val_loss: 21.4894\n",
      "Epoch 191/200\n",
      "271/271 [==============================] - 1s 2ms/step - loss: 17.2299 - val_loss: 22.7292\n",
      "Epoch 192/200\n",
      "271/271 [==============================] - 1s 2ms/step - loss: 18.1840 - val_loss: 21.0965\n",
      "Epoch 193/200\n",
      "271/271 [==============================] - 1s 2ms/step - loss: 18.3752 - val_loss: 23.2270\n",
      "Epoch 194/200\n",
      "271/271 [==============================] - 1s 2ms/step - loss: 17.4234 - val_loss: 22.1689\n",
      "Epoch 195/200\n",
      "271/271 [==============================] - 1s 2ms/step - loss: 17.1990 - val_loss: 21.6774\n",
      "Epoch 196/200\n",
      "271/271 [==============================] - 1s 2ms/step - loss: 18.0884 - val_loss: 21.8389\n",
      "Epoch 197/200\n",
      "271/271 [==============================] - 1s 2ms/step - loss: 16.6357 - val_loss: 21.4126\n",
      "Epoch 198/200\n",
      "271/271 [==============================] - 1s 2ms/step - loss: 19.2526 - val_loss: 24.5718\n",
      "Epoch 199/200\n",
      "271/271 [==============================] - 1s 2ms/step - loss: 19.4204 - val_loss: 25.7963\n",
      "Epoch 200/200\n",
      "271/271 [==============================] - 1s 2ms/step - loss: 19.6354 - val_loss: 21.8603\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x294eac8bda0>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# train the model\n",
    "model.fit(\n",
    "    [trainAttrX, trainImagesX], trainY,\n",
    "    validation_data=([testAttrX, testImagesX], testY),\n",
    "    epochs=200, batch_size=8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# make predictions on the testing data\n",
    "preds = model.predict([testAttrX, testImagesX])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[INFO] avg. house price: $533,388.27, std house price: $493,403.08\n",
      "[INFO] mean: 21.86%, std: 18.29%\n"
     ]
    }
   ],
   "source": [
    "# compute the difference between the *predicted* house prices and the\n",
    "# *actual* house prices, then compute the percentage difference and\n",
    "# the absolute percentage difference\n",
    "diff = preds.flatten() - testY\n",
    "percentDiff = (diff / testY) * 100\n",
    "absPercentDiff = np.abs(percentDiff)\n",
    " \n",
    "# compute the mean and standard deviation of the absolute percentage\n",
    "# difference\n",
    "mean = np.mean(absPercentDiff)\n",
    "std = np.std(absPercentDiff)\n",
    " \n",
    "# finally, show some statistics on our model\n",
    "locale.setlocale(locale.LC_ALL, \"en_US.UTF-8\")\n",
    "print(\"[INFO] avg. house price: {}, std house price: {}\".format(\n",
    "    locale.currency(df[\"price\"].mean(), grouping=True),\n",
    "    locale.currency(df[\"price\"].std(), grouping=True)))\n",
    "print(\"[INFO] mean: {:.2f}%, std: {:.2f}%\".format(mean, std))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-info\">\n",
    "<div style=\"direction:rtl;text-align:right;font-family:B Lotus, B Nazanin, Tahoma\"> دوره مقدماتی یادگیری عمیق<br>علیرضا اخوان پور<br>پنج شنبه، ۲۵ بهمن ۱۳۹۷<br>\n",
    "</div>\n",
    "<a href=\"http://class.vision\">Class.Vision</a> - <a href=\"http://AkhavanPour.ir\">AkhavanPour.ir</a> - <a href=\"https://github.com/Alireza-Akhavan/\">GitHub</a>\n",
    "\n",
    "</div>"
   ]
  }
 ],
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