housing_prices_images_regression.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "housing_prices_images_regression.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "5KVqR8jN8jUn",
        "colab_type": "code",
        "outputId": "4d82c2c4-fd10-439b-9590-643f704026f2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 139
        }
      },
      "source": [
        "!git clone https://github.com/emanhamed/Houses-dataset"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Cloning into 'Houses-dataset'...\n",
            "remote: Enumerating objects: 5, done.\u001b[K\n",
            "remote: Counting objects: 100% (5/5), done.\u001b[K\n",
            "remote: Compressing objects: 100% (5/5), done.\u001b[K\n",
            "remote: Total 2166 (delta 0), reused 0 (delta 0), pack-reused 2161\u001b[K\n",
            "Receiving objects: 100% (2166/2166), 176.31 MiB | 42.61 MiB/s, done.\n",
            "Resolving deltas: 100% (18/18), done.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1uGaPUcq8u0S",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import LabelBinarizer\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import glob\n",
        "import cv2\n",
        "import os"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6aiViltLLK8a",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from keras.models import Sequential\n",
        "from keras.optimizers import Adam\n",
        "from keras.layers.normalization import BatchNormalization\n",
        "from keras.layers.convolutional import Conv2D\n",
        "from keras.layers.convolutional import MaxPooling2D\n",
        "from keras.layers.core import Activation\n",
        "from keras.layers.core import Dropout\n",
        "from keras.layers.core import Dense\n",
        "from keras.layers import Flatten\n",
        "from keras.layers import Input\n",
        "from keras.models import Model"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "F11iFHEdIdVz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def load_house_attributes(inputPath):\n",
        "    cols = [\"bedrooms\", \"bathrooms\", \"area\", \"zipcode\", \"price\"]\n",
        "    df = pd.read_csv(inputPath, sep=' ', header=None, names=cols)\n",
        "    \n",
        "    zipcodes = df['zipcode'].value_counts().keys().tolist()\n",
        "    counts   = df['zipcode'].value_counts().tolist()\n",
        "    #df.head()\n",
        "    \n",
        "    for (zipcode, count) in zip(zipcodes, counts):\n",
        "      \n",
        "        if count < 25:\n",
        "            idxs = df[df['zipcode'] == zipcode].index\n",
        "            df.drop(idxs, inplace=True)\n",
        "            \n",
        "    return df\n",
        "  \n",
        "def process_house_attributes(df, train, test):\n",
        "    continuous = ['bedrooms', 'bathrooms', 'area']\n",
        "    \n",
        "    cs               = MinMaxScaler()\n",
        "    train_continuous = cs.fit_transform(train[continuous])\n",
        "    test_continuous  = cs.transform(test[continuous])\n",
        "    \n",
        "    zip_binarizer     = LabelBinarizer().fit(df['zipcode'])\n",
        "    train_categorical = zip_binarizer.transform(train['zipcode'])\n",
        "    test_categorical  = zip_binarizer.transform(test['zipcode'])\n",
        "    \n",
        "    train_X = np.hstack([train_categorical, train_continuous])\n",
        "    test_X  = np.hstack([test_categorical, test_continuous])\n",
        "    \n",
        "    return (train_X, test_X)\n",
        "  \n",
        "def load_house_images(df, inputPath):\n",
        "    images = []\n",
        "    \n",
        "    for i in df.index.values:\n",
        "        base_path   = os.path.sep.join([inputPath, \"{}_*\".format(i + 1)])\n",
        "        house_paths = sorted(list(glob.glob(base_path)))\n",
        "        \n",
        "        input_images = []\n",
        "        output_image = np.zeros((64, 64, 3), dtype=\"uint8\")\n",
        "        \n",
        "        for house_path in house_paths:\n",
        "            image = cv2.imread(house_path)\n",
        "            image = cv2.resize(image, (32, 32))\n",
        "            input_images.append(image)\n",
        "            \n",
        "        output_image[0:32, 0:32]   = input_images[0]\n",
        "        output_image[0:32, 32:64]  = input_images[1]\n",
        "        output_image[32:64, 32:64] = input_images[2]\n",
        "        output_image[32:64, 0:32]  = input_images[3]\n",
        "        \n",
        "        images.append(output_image)\n",
        "        \n",
        "    return np.array(images)\n",
        "        "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NEfGxUzVIjpp",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def create_cnn(width, height, depth, filters=(16, 32, 64), regress=False):\n",
        "  \n",
        "    input_shape = (height, width, depth)\n",
        "    chan_dim = -1\n",
        "    \n",
        "    inputs = Input(shape=input_shape)\n",
        "    \n",
        "    for (i, f) in enumerate(filters):\n",
        "        if i == 0:\n",
        "            x = inputs\n",
        "            \n",
        "        x = Conv2D(f, (3, 3), padding=\"same\")(x)\n",
        "        x = Activation(\"relu\")(x)\n",
        "        x = BatchNormalization(axis=chan_dim)(x)\n",
        "        x = MaxPooling2D(pool_size=(2, 2))(x)\n",
        "        \n",
        "    x = Flatten()(x)\n",
        "    x = Dense(16)(x)\n",
        "    x = Activation(\"relu\")(x)\n",
        "    x = BatchNormalization(axis=chan_dim)(x)\n",
        "    x = Dropout(0.5)(x)\n",
        "    \n",
        "    x = Dense(4)(x)\n",
        "    x = Activation(\"relu\")(x)\n",
        "    \n",
        "    if regress:\n",
        "        x = Dense(1, activation=\"linear\")(x)\n",
        "        \n",
        "    model = Model(inputs, x)\n",
        "    \n",
        "    return model"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q4hvSupNNTwC",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "cwd = os.getcwd()\n",
        "house_jpg_dir = os.path.join(cwd, 'Houses-dataset', 'Houses Dataset')\n",
        "houses_info   = os.path.join(house_jpg_dir, 'HousesInfo.txt')\n",
        "\n",
        "df = load_house_attributes(houses_info)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "edpCRf-qNotv",
        "colab_type": "code",
        "outputId": "4ed0afee-2cd5-446c-85b9-80d63a1ad40f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        }
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>bedrooms</th>\n",
              "      <th>bathrooms</th>\n",
              "      <th>area</th>\n",
              "      <th>zipcode</th>\n",
              "      <th>price</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>5</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2520</td>\n",
              "      <td>93446</td>\n",
              "      <td>789000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>3</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1802</td>\n",
              "      <td>93446</td>\n",
              "      <td>365000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39</th>\n",
              "      <td>3</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2146</td>\n",
              "      <td>93446</td>\n",
              "      <td>455000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>80</th>\n",
              "      <td>4</td>\n",
              "      <td>2.5</td>\n",
              "      <td>2464</td>\n",
              "      <td>91901</td>\n",
              "      <td>599000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>81</th>\n",
              "      <td>2</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1845</td>\n",
              "      <td>91901</td>\n",
              "      <td>529800</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    bedrooms  bathrooms  area  zipcode   price\n",
              "30         5        3.0  2520    93446  789000\n",
              "32         3        2.0  1802    93446  365000\n",
              "39         3        3.0  2146    93446  455000\n",
              "80         4        2.5  2464    91901  599000\n",
              "81         2        2.0  1845    91901  529800"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6iuQ4RxSOMe1",
        "colab_type": "code",
        "outputId": "a3fc2f96-251d-4fcc-9693-21bf0bae3156",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "images = load_house_images(df, house_jpg_dir)\n",
        "print(f\"{len(images)} images.\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "362 images.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DbG10i3nOlFh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Scaling pixel intensities to the range [0, 1].\n",
        "images = images / 255.0"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NY2UbFi6PP3q",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "split = train_test_split(df, images, test_size=0.25, random_state=42)\n",
        "(trainAttrX, testAttrX, trainImagesX, testImagesX) = split"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WHpcjolrPi7G",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "maxPrice = trainAttrX[\"price\"].max()\n",
        "trainY = trainAttrX[\"price\"] / maxPrice\n",
        "testY = testAttrX[\"price\"] / maxPrice"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aZxHs47UP22B",
        "colab_type": "code",
        "outputId": "1ccc3ccd-b11c-42c5-b665-00e9083b2d84",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "model = create_cnn(64, 64, 3, regress=True)\n",
        "opt = Adam(lr=1e-3, decay=1e-3 / 200)\n",
        "model.compile(loss=\"mean_absolute_percentage_error\", optimizer=opt)\n",
        "model.summary()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "W0726 21:47:28.497643 139817191442304 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:3976: The name tf.nn.max_pool is deprecated. Please use tf.nn.max_pool2d instead.\n",
            "\n",
            "W0726 21:47:28.823161 139817191442304 deprecation.py:506] From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:3445: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.\n",
            "W0726 21:47:28.889328 139817191442304 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/keras/optimizers.py:790: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "input_2 (InputLayer)         (None, 64, 64, 3)         0         \n",
            "_________________________________________________________________\n",
            "conv2d_2 (Conv2D)            (None, 64, 64, 16)        448       \n",
            "_________________________________________________________________\n",
            "activation_2 (Activation)    (None, 64, 64, 16)        0         \n",
            "_________________________________________________________________\n",
            "batch_normalization_2 (Batch (None, 64, 64, 16)        64        \n",
            "_________________________________________________________________\n",
            "max_pooling2d_1 (MaxPooling2 (None, 32, 32, 16)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_3 (Conv2D)            (None, 32, 32, 32)        4640      \n",
            "_________________________________________________________________\n",
            "activation_3 (Activation)    (None, 32, 32, 32)        0         \n",
            "_________________________________________________________________\n",
            "batch_normalization_3 (Batch (None, 32, 32, 32)        128       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_2 (MaxPooling2 (None, 16, 16, 32)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_4 (Conv2D)            (None, 16, 16, 64)        18496     \n",
            "_________________________________________________________________\n",
            "activation_4 (Activation)    (None, 16, 16, 64)        0         \n",
            "_________________________________________________________________\n",
            "batch_normalization_4 (Batch (None, 16, 16, 64)        256       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_3 (MaxPooling2 (None, 8, 8, 64)          0         \n",
            "_________________________________________________________________\n",
            "flatten_1 (Flatten)          (None, 4096)              0         \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 16)                65552     \n",
            "_________________________________________________________________\n",
            "activation_5 (Activation)    (None, 16)                0         \n",
            "_________________________________________________________________\n",
            "batch_normalization_5 (Batch (None, 16)                64        \n",
            "_________________________________________________________________\n",
            "dropout_1 (Dropout)          (None, 16)                0         \n",
            "_________________________________________________________________\n",
            "dense_2 (Dense)              (None, 4)                 68        \n",
            "_________________________________________________________________\n",
            "activation_6 (Activation)    (None, 4)                 0         \n",
            "_________________________________________________________________\n",
            "dense_3 (Dense)              (None, 1)                 5         \n",
            "=================================================================\n",
            "Total params: 89,721\n",
            "Trainable params: 89,465\n",
            "Non-trainable params: 256\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nopH_aKRP_v9",
        "colab_type": "code",
        "outputId": "233b02d8-f6a8-422f-8e22-34e354787d43",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "model.fit(trainImagesX, \n",
        "          trainY, \n",
        "          validation_data=(testImagesX, testY),\t\n",
        "          epochs=200, \n",
        "          batch_size=8)\n",
        "\n",
        "model.save(\"housing_prices_images_model.h5\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 271 samples, validate on 91 samples\n",
            "Epoch 1/200\n",
            "271/271 [==============================] - 4s 16ms/step - loss: 2757.7543 - val_loss: 5332.6830\n",
            "Epoch 2/200\n",
            "271/271 [==============================] - 3s 10ms/step - loss: 1852.7459 - val_loss: 1232.7159\n",
            "Epoch 3/200\n",
            "271/271 [==============================] - 3s 10ms/step - loss: 1226.1698 - val_loss: 488.2959\n",
            "Epoch 4/200\n",
            "271/271 [==============================] - 3s 9ms/step - loss: 1043.9225 - val_loss: 604.7963\n",
            "Epoch 5/200\n",
            "271/271 [==============================] - 2s 9ms/step - loss: 1023.1950 - val_loss: 485.1415\n",
            "Epoch 6/200\n",
            "271/271 [==============================] - 3s 9ms/step - loss: 897.8002 - val_loss: 741.5215\n",
            "Epoch 7/200\n",
            "271/271 [==============================] - 3s 10ms/step - loss: 609.2281 - val_loss: 361.3148\n",
            "Epoch 8/200\n",
            "271/271 [==============================] - 3s 10ms/step - loss: 526.9116 - val_loss: 243.1528\n",
            "Epoch 9/200\n",
            "271/271 [==============================] - 3s 10ms/step - loss: 456.6049 - val_loss: 208.5166\n",
            "Epoch 10/200\n",
            "271/271 [==============================] - 3s 10ms/step - loss: 393.6756 - val_loss: 171.2853\n",
            "Epoch 11/200\n",
            "271/271 [==============================] - 3s 9ms/step - loss: 359.9907 - val_loss: 109.4122\n",
            "Epoch 12/200\n",
            "271/271 [==============================] - 3s 9ms/step - loss: 281.5299 - val_loss: 107.4854\n",
            "Epoch 13/200\n",
            "271/271 [==============================] - 3s 10ms/step - loss: 214.0434 - val_loss: 94.1081\n",
            "Epoch 14/200\n",
            "271/271 [==============================] - 3s 9ms/step - loss: 209.5046 - val_loss: 97.0448\n",
            "Epoch 15/200\n",
            "271/271 [==============================] - 3s 9ms/step - loss: 180.3884 - val_loss: 90.2799\n",
            "Epoch 16/200\n",
            "271/271 [==============================] - 3s 10ms/step - loss: 146.5299 - val_loss: 81.3527\n",
            "Epoch 17/200\n",
            "271/271 [==============================] - 3s 9ms/step - loss: 172.7540 - val_loss: 80.4516\n",
            "Epoch 18/200\n",
            "271/271 [==============================] - 3s 9ms/step - loss: 158.5630 - val_loss: 85.6137\n",
            "Epoch 19/200\n",
            "271/271 [==============================] - 3s 9ms/step - loss: 120.6897 - val_loss: 79.6945\n",
            "Epoch 20/200\n",
            "271/271 [==============================] - 3s 9ms/step - loss: 106.9109 - val_loss: 78.6765\n",
            "Epoch 21/200\n",
            "271/271 [==============================] - 3s 9ms/step - loss: 148.3130 - val_loss: 75.8331\n",
            "Epoch 22/200\n",
            "271/271 [==============================] - 3s 10ms/step - loss: 85.1024 - val_loss: 73.4279\n",
            "Epoch 23/200\n",
            "271/271 [==============================] - 3s 9ms/step - loss: 105.4823 - val_loss: 71.9218\n",
            "Epoch 24/200\n",
            "271/271 [==============================] - 3s 9ms/step - loss: 106.5207 - val_loss: 71.4004\n",
            "Epoch 25/200\n",
            "271/271 [==============================] - 3s 9ms/step - loss: 105.0232 - val_loss: 71.3888\n",
            "Epoch 26/200\n",
            "271/271 [==============================] - 3s 9ms/step - loss: 107.0867 - val_loss: 71.5766\n",
            "Epoch 27/200\n",
            "271/271 [==============================] - 3s 9ms/step - loss: 81.7035 - val_loss: 71.2625\n",
            "Epoch 28/200\n",
            "271/271 [==============================] - 3s 9ms/step - loss: 94.9850 - val_loss: 71.3087\n",
            "Epoch 29/200\n",
            "271/271 [==============================] - 3s 9ms/step - loss: 83.7655 - val_loss: 71.4286\n",
            "Epoch 30/200\n",
            "271/271 [==============================] - 3s 10ms/step - loss: 88.7962 - val_loss: 71.1362\n",
            "Epoch 31/200\n",
            "271/271 [==============================] - 3s 9ms/step - loss: 76.7114 - val_loss: 71.2747\n",
            "Epoch 32/200\n",
            "271/271 [==============================] - 3s 10ms/step - loss: 76.5840 - val_loss: 70.9470\n",
            "Epoch 33/200\n",
            "271/271 [==============================] - 3s 10ms/step - loss: 74.7684 - val_loss: 71.0643\n",
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          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0pL0YBTuQgBJ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "preds = model.predict(testImagesX)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2NCxeh4kSkwM",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "diff = preds.flatten() - testY\n",
        "percentDiff = (diff / testY) * 100\n",
        "absPercentDiff = np.abs(percentDiff)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RdOZB6nbSqmI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "mean = np.mean(absPercentDiff)\n",
        "std = np.std(absPercentDiff)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UHwGb_LWSrgw",
        "colab_type": "code",
        "outputId": "2b433e07-696b-421f-fdd2-0d394c5f89d3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        }
      },
      "source": [
        "print(\"avg. house price: ${:.2f}, std house price: ${:.2f}\".format(\n",
        "    df[\"price\"].mean(), df[\"price\"].std()))\n",
        "\n",
        "print(\"mean: {:.2f}%, std: {:.2f}%\".format(mean, std))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "avg. house price: $533388.27, std house price: $493403.08\n",
            "mean: 56.89%, std: 31.98%\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZVOQbb2qTROm",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from google.colab import files\n",
        "files.download(\"housing_prices_images_model.h5\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L3h52Ig2URSd",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}