material/deep_learning/functional_api.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "functional_api.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c503DSvxvOU5"
      },
      "source": [
        "# 이전 CNN MNIST\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fYlMkT8axIoi"
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "from tensorflow.keras import optimizers\n",
        "from tensorflow.keras.layers import Dense, Flatten, Conv2D, MaxPooling2D, Input\n",
        "\n",
        "import time"
      ],
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "puN5QRLz2toU"
      },
      "source": [
        "(raw_train_x, raw_train_y), (raw_test_x, raw_test_y) = tf.keras.datasets.mnist.load_data()\n",
        "\n",
        "train_x = raw_train_x/255\n",
        "test_x = raw_test_x/255\n",
        "\n",
        "train_x = train_x.reshape((60000, 28, 28, 1))\n",
        "test_x = test_x.reshape((10000, 28, 28, 1))\n",
        "\n",
        "train_y = raw_train_y\n",
        "test_y = raw_test_y"
      ],
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kkrsyGy2Ixk2"
      },
      "source": [
        "model = keras.Sequential()\n",
        "model.add(Input((28,28,1)))\n",
        "model.add(Conv2D(32, (3, 3)))\n",
        "model.add(MaxPooling2D((2, 2)))\n",
        "model.add(Conv2D(64, (3, 3)))\n",
        "model.add(MaxPooling2D((2, 2)))\n",
        "model.add(Flatten())\n",
        "model.add(Dense(10, activation='relu'))\n",
        "model.add(Dense(10, activation='relu'))\n",
        "model.add(Dense(10, activation='softmax'))"
      ],
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cuW2ftNT2trB",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "263f8e1d-a223-4281-de62-a554147f5434"
      },
      "source": [
        "model.compile(optimizer=\"adam\", loss=\"sparse_categorical_crossentropy\", metrics=[\"accuracy\"])\n",
        "model.summary()\n",
        "\n",
        "\n",
        "model.fit(train_x, train_y, epochs=5, verbose=1, batch_size=128)\n",
        "\n",
        "\n",
        "loss, acc = model.evaluate(test_x, test_y)\n",
        "print(\"loss=\",loss)\n",
        "print(\"acc=\",acc)\n",
        "\n",
        "y_ = model.predict(test_x)\n",
        "predicted = np.argmax(y_, axis=1)\n",
        "\n",
        "print(predicted)\n"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv2d_6 (Conv2D)           (None, 26, 26, 32)        320       \n",
            "                                                                 \n",
            " max_pooling2d_6 (MaxPooling  (None, 13, 13, 32)       0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " conv2d_7 (Conv2D)           (None, 11, 11, 64)        18496     \n",
            "                                                                 \n",
            " max_pooling2d_7 (MaxPooling  (None, 5, 5, 64)         0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " flatten_3 (Flatten)         (None, 1600)              0         \n",
            "                                                                 \n",
            " dense_15 (Dense)            (None, 10)                16010     \n",
            "                                                                 \n",
            " dense_16 (Dense)            (None, 10)                110       \n",
            "                                                                 \n",
            " dense_17 (Dense)            (None, 10)                110       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 35,046\n",
            "Trainable params: 35,046\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "Epoch 1/5\n",
            "469/469 [==============================] - 2s 4ms/step - loss: 0.5685 - accuracy: 0.8154\n",
            "Epoch 2/5\n",
            "469/469 [==============================] - 2s 4ms/step - loss: 0.1575 - accuracy: 0.9522\n",
            "Epoch 3/5\n",
            "469/469 [==============================] - 2s 4ms/step - loss: 0.1120 - accuracy: 0.9664\n",
            "Epoch 4/5\n",
            "469/469 [==============================] - 2s 4ms/step - loss: 0.0895 - accuracy: 0.9733\n",
            "Epoch 5/5\n",
            "469/469 [==============================] - 2s 4ms/step - loss: 0.0767 - accuracy: 0.9768\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.0755 - accuracy: 0.9751\n",
            "loss= 0.07550355046987534\n",
            "acc= 0.9750999808311462\n",
            "WARNING:tensorflow:5 out of the last 323 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7f9447d123b0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:5 out of the last 323 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7f9447d123b0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[7 2 1 ... 4 5 6]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_6yj3_5GI064"
      },
      "source": [
        "# Functional API 타입 CNN MNIST"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nRkSjvdSkgfc"
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "from tensorflow.keras import optimizers\n",
        "from tensorflow.keras.layers import Dense, Flatten, Conv2D, MaxPooling2D, Input\n",
        "\n",
        "import time"
      ],
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H1C6Ev9hI7JD"
      },
      "source": [
        "# model = keras.Sequential()\n",
        "# model.add(Input((28,28,1)))\n",
        "# model.add(Conv2D(32, (3, 3)))\n",
        "# model.add(MaxPooling2D((2, 2)))\n",
        "# model.add(Conv2D(64, (3, 3)))\n",
        "# model.add(MaxPooling2D((2, 2)))\n",
        "# model.add(Flatten())\n",
        "# model.add(Dense(10, activation='relu'))\n",
        "# model.add(Dense(10, activation='relu'))\n",
        "# model.add(Dense(10, activation='softmax'))\n",
        "\n",
        "\n",
        "from tensorflow.keras import Model\n",
        "\n",
        "input = Input((28,28,1))\n",
        "x = Conv2D(32, (3,3))(input)\n",
        "x = MaxPooling2D((2,2))(x)\n",
        "x = Conv2D(32, (3,3))(x)\n",
        "x = MaxPooling2D((2,2))(x)\n",
        "x = Flatten()(x)\n",
        "x = Dense(10, activation='relu')(x)\n",
        "x = Dense(10, activation='relu')(x)\n",
        "output = Dense(10, activation='softmax')(x)\n",
        "\n",
        "model = Model(input, output)"
      ],
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SH90FFocI7GG",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "aa66fa63-d70a-41a3-9cc0-fe00f90e7d46"
      },
      "source": [
        "model.compile(optimizer=\"adam\", loss=\"sparse_categorical_crossentropy\", metrics=[\"accuracy\"])\n",
        "model.summary()\n",
        "\n",
        "\n",
        "model.fit(train_x, train_y, epochs=5, verbose=1, batch_size=128)\n",
        "\n",
        "\n",
        "loss, acc = model.evaluate(test_x, test_y)\n",
        "print(\"loss=\",loss)\n",
        "print(\"acc=\",acc)\n",
        "\n",
        "y_ = model.predict(test_x)\n",
        "predicted = np.argmax(y_, axis=1)\n",
        "\n",
        "print(predicted)"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model_4\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " input_7 (InputLayer)        [(None, 28, 28, 1)]       0         \n",
            "                                                                 \n",
            " conv2d_8 (Conv2D)           (None, 26, 26, 32)        320       \n",
            "                                                                 \n",
            " max_pooling2d_8 (MaxPooling  (None, 13, 13, 32)       0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " conv2d_9 (Conv2D)           (None, 11, 11, 32)        9248      \n",
            "                                                                 \n",
            " max_pooling2d_9 (MaxPooling  (None, 5, 5, 32)         0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " flatten_4 (Flatten)         (None, 800)               0         \n",
            "                                                                 \n",
            " dense_18 (Dense)            (None, 10)                8010      \n",
            "                                                                 \n",
            " dense_19 (Dense)            (None, 10)                110       \n",
            "                                                                 \n",
            " dense_20 (Dense)            (None, 10)                110       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 17,798\n",
            "Trainable params: 17,798\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "Epoch 1/5\n",
            "469/469 [==============================] - 2s 4ms/step - loss: 0.5494 - accuracy: 0.8320\n",
            "Epoch 2/5\n",
            "469/469 [==============================] - 2s 4ms/step - loss: 0.1472 - accuracy: 0.9566\n",
            "Epoch 3/5\n",
            "469/469 [==============================] - 2s 3ms/step - loss: 0.1038 - accuracy: 0.9698\n",
            "Epoch 4/5\n",
            "469/469 [==============================] - 2s 3ms/step - loss: 0.0835 - accuracy: 0.9753\n",
            "Epoch 5/5\n",
            "469/469 [==============================] - 2s 3ms/step - loss: 0.0726 - accuracy: 0.9785\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.0762 - accuracy: 0.9764\n",
            "loss= 0.07622600346803665\n",
            "acc= 0.9764000177383423\n",
            "[7 2 1 ... 4 5 6]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WRB8AVGJK_Iz"
      },
      "source": [
        "# 다중 입력\n",
        "\n",
        "copy from https://www.pyimagesearch.com/2019/02/04/keras-multiple-inputs-and-mixed-data/\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aR8fS0GoE3m_"
      },
      "source": [
        "## 데이터 준비"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "w6UWmnjzI7DS",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2d7517d4-0108-4539-aad8-e1fcec5451ef"
      },
      "source": [
        "!git clone https://github.com/emanhamed/Houses-dataset"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "fatal: destination path 'Houses-dataset' already exists and is not an empty directory.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tgLmgfnJFJkR"
      },
      "source": [
        "```\n",
        "Houses-dataset/\n",
        "  Houses Dataset/\n",
        "    100_bathroom.jpg\n",
        "    100_bedroom.jpg\n",
        "    ...\n",
        "    HousesInfo.txt\n",
        "```"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LnppMLOAFgtQ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a4ec2f6f-78e7-4300-f78e-f2d62cf3d26f"
      },
      "source": [
        "!head 'Houses-dataset/Houses Dataset/HousesInfo.txt'"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "4 4 4053 85255 869500\n",
            "4 3 3343 36372 865200\n",
            "3 4 3923 85266 889000\n",
            "5 5 4022 85262 910000\n",
            "3 4 4116 85266 971226\n",
            "4 5 4581 85266 1249000\n",
            "3 4 2544 85262 799000\n",
            "4 5 5524 85266 1698000\n",
            "3 4 4229 85255 1749000\n",
            "4 5 3550 85262 1500000\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pmpEfpGogwJb"
      },
      "source": [
        "import matplotlib\n",
        "\n",
        "from tensorflow.keras.optimizers import Adam\n",
        "from tensorflow.keras.preprocessing.image import img_to_array\n",
        "from sklearn.preprocessing import LabelBinarizer\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "from imutils import paths\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import argparse\n",
        "import random\n",
        "import pickle\n",
        "import cv2\n",
        "import os"
      ],
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FEteIEtNFwvP"
      },
      "source": [
        "# import the necessary packages\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\n",
        "\n",
        "def load_house_attributes(inputPath):\n",
        "\n",
        "\tcols = [\"bedrooms\", \"bathrooms\", \"area\", \"zipcode\", \"price\"]\n",
        "\tdf = pd.read_csv(inputPath, sep=\" \", header=None, names=cols)\n",
        " \n",
        "\t# determine (1) the unique zip codes and (2) the number of data\n",
        "\t# points with each zip code\n",
        "\tzipcodes = df[\"zipcode\"].value_counts().keys().tolist()\n",
        "\tcounts = df[\"zipcode\"].value_counts().tolist()\n",
        " \n",
        "\t# loop over each of the unique zip codes and their corresponding\n",
        "\t# count\n",
        "\tfor (zipcode, count) in zip(zipcodes, counts):\n",
        "\t\t# the zip code counts for our housing dataset is *extremely*\n",
        "\t\t# unbalanced (some only having 1 or 2 houses per zip code)\n",
        "\t\t# so let's sanitize our data by removing any houses with less\n",
        "\t\t# than 25 houses per zip code\n",
        "\t\tif count < 25:\n",
        "\t\t\tidxs = df[df[\"zipcode\"] == zipcode].index\n",
        "\t\t\tdf.drop(idxs, inplace=True)\n",
        "   \n",
        "\t# return the data frame\n",
        "\treturn df\n",
        "\n",
        "def process_house_attributes(df, train, test):\n",
        "\t# initialize the column names of the continuous data\n",
        "\tcontinuous = [\"bedrooms\", \"bathrooms\", \"area\"]\n",
        "\n",
        "\t# performin min-max scaling each continuous feature column to\n",
        "\t# the range [0, 1]\n",
        "\tcs = MinMaxScaler()\n",
        "\ttrainContinuous = cs.fit_transform(train[continuous])\n",
        "\ttestContinuous = cs.transform(test[continuous])\n",
        " \n",
        "\t# one-hot encode the zip code categorical data (by definition of\n",
        "\t# one-hot encoding, all output features are now in the range [0, 1])\n",
        "\tzipBinarizer = LabelBinarizer().fit(df[\"zipcode\"])\n",
        "\ttrainCategorical = zipBinarizer.transform(train[\"zipcode\"])\n",
        "\ttestCategorical = zipBinarizer.transform(test[\"zipcode\"])\n",
        " \n",
        "\t# construct our training and testing data points by concatenating\n",
        "\t# the categorical features with the continuous features\n",
        "\ttrainX = np.hstack([trainCategorical, trainContinuous])\n",
        "\ttestX = np.hstack([testCategorical, testContinuous])\n",
        " \n",
        "\t# return the concatenated training and testing data\n",
        "\treturn (trainX, testX)  "
      ],
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_I8GQV4mFwrk"
      },
      "source": [
        "IMAGE_SIZE = 32*4\n",
        "def load_house_images(df, inputPath):\n",
        "\n",
        "\t# initialize our images array (i.e., the house images themselves)\n",
        "\timages = []\n",
        "\n",
        "\t# loop over the indexes of the houses\n",
        "\tfor i in df.index.values:\n",
        "\t\t# find the four images for the house and sort the file paths,\n",
        "\t\t# ensuring the four are always in the *same order*\n",
        "\t\tbasePath = os.path.sep.join([inputPath, \"{}_*\".format(i + 1)])\n",
        "\t\thousePaths = sorted(list(glob.glob(basePath)))\n",
        "  \n",
        "\t\t# initialize our list of input images along with the output image\n",
        "\t\t# after *combining* the four input images\n",
        "\t\tinputImages = []\n",
        "\t\toutputImage = np.zeros((IMAGE_SIZE*2, IMAGE_SIZE*2, 3), dtype=\"uint8\")\n",
        "  \n",
        "\t\t# loop over the input house paths\n",
        "\t\tfor housePath in housePaths:\n",
        "\t\t\t# load the input image, resize it to be IMAGE_SIZExIMAGE_SIZE, and then\n",
        "\t\t\t# update the list of input images\n",
        "\t\t\timage = cv2.imread(housePath)\n",
        "\t\t\timage = cv2.resize(image, (IMAGE_SIZE, IMAGE_SIZE))\n",
        "\t\t\tinputImages.append(image)\n",
        "   \n",
        "\t\t# tile the four input images in the output image such the first\n",
        "\t\t# image goes in the top-right corner, the second image in the\n",
        "\t\t# top-left corner, the third image in the bottom-right corner,\n",
        "\t\t# and the final image in the bottom-left corner\n",
        "\t\toutputImage[0:IMAGE_SIZE, 0:IMAGE_SIZE] = inputImages[0]\n",
        "\t\toutputImage[0:IMAGE_SIZE, IMAGE_SIZE:IMAGE_SIZE*2] = inputImages[1]\n",
        "\t\toutputImage[IMAGE_SIZE:IMAGE_SIZE*2, 0:IMAGE_SIZE] = inputImages[2]\n",
        "\t\toutputImage[IMAGE_SIZE:IMAGE_SIZE*2, IMAGE_SIZE:IMAGE_SIZE*2] = inputImages[3]\n",
        "\n",
        "\t\t# add the tiled image to our set of images the network will be\n",
        "\t\t# trained on\n",
        "\t\timages.append(outputImage)\n",
        "  \n",
        "\t# return our set of images\n",
        "\treturn np.array(images)  "
      ],
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HEJ6Gp0qHVi6"
      },
      "source": [
        "### 값 정보 읽기"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ke6Xu-5PGdkr"
      },
      "source": [
        "df = load_house_attributes(\"Houses-dataset/Houses Dataset/HousesInfo.txt\")"
      ],
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ouQ27yABHR2w",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "820796eb-83c7-4cb4-c011-40df1ff94b7a"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "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"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-d019d561-926e-4fb6-98fd-da5e0c1326f5\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d019d561-926e-4fb6-98fd-da5e0c1326f5')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-d019d561-926e-4fb6-98fd-da5e0c1326f5 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-d019d561-926e-4fb6-98fd-da5e0c1326f5');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "J-xIyIR1HZiE"
      },
      "source": [
        "### 영상 데이터 읽기"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FuZ-lxe_Gde4"
      },
      "source": [
        "images = load_house_images(df, \"Houses-dataset/Houses Dataset/\")\n",
        "images = images/255."
      ],
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wZhmpoc8HNfQ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "14799e53-ce4b-4292-ac9a-9770ebb3a5f2"
      },
      "source": [
        "print(images.shape)"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(362, 256, 256, 3)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.imshow(images[0])\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "nvFxkmfXtxvO",
        "outputId": "3c8fd272-3e7f-42a1-a4f6-bc2e003818e6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        }
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZW9H5fZuHNbW"
      },
      "source": [
        "(train_attr_x, test_attr_x, train_image_x, test_image_x) = train_test_split(df, images, test_size=0.25, random_state=42)\n",
        "\n",
        "max_price = train_attr_x[\"price\"].max()\n",
        "train_y = train_attr_x[\"price\"] / max_price\n",
        "test_y = test_attr_x[\"price\"] / max_price\n",
        "(train_attr_x, test_attr_x) = process_house_attributes(df, train_attr_x, test_attr_x)"
      ],
      "execution_count": 45,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZoR7qloQHNYn",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "38884f6c-f359-44bf-ec06-160383863116"
      },
      "source": [
        "print(\"tainAttrX.shape=\", train_attr_x.shape)\n",
        "print(\"train_image_x.shape=\", train_image_x.shape)\n",
        "print(\"train_y.shape=\", train_y.shape)\n",
        "print(\"test_attr_x.shape=\", test_attr_x.shape)\n",
        "print(\"testImageX.shape=\", test_image_x.shape)\n",
        "print(\"test_y.shape=\", test_y.shape)"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tainAttrX.shape= (271, 10)\n",
            "train_image_x.shape= (271, 256, 256, 3)\n",
            "train_y.shape= (271,)\n",
            "test_attr_x.shape= (91, 10)\n",
            "testImageX.shape= (91, 256, 256, 3)\n",
            "test_y.shape= (91,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aZclMyzHFwpA"
      },
      "source": [
        "# import the necessary packages\n",
        "from tensorflow.keras import Sequential\n",
        "from tensorflow.keras.layers import BatchNormalization\n",
        "from tensorflow.keras.layers import Conv2D\n",
        "from tensorflow.keras.layers import MaxPooling2D\n",
        "from tensorflow.keras.layers import Activation\n",
        "from tensorflow.keras.layers import Dropout\n",
        "from tensorflow.keras.layers import Dense\n",
        "from tensorflow.keras.layers import Flatten\n",
        "from tensorflow.keras.layers import Input\n",
        "from tensorflow.keras import Model\n",
        "from tensorflow.keras.layers import concatenate"
      ],
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kow-UjvTIlO7"
      },
      "source": [
        "## 개별 모델로 학습 실행"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rMiM6S1aJDdi"
      },
      "source": [
        "### 속성 모델"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a7ABhyiqFwj7"
      },
      "source": [
        "input = Input(train_attr_x[0].shape)\n",
        "x = Dense(10, activation='relu')(input)\n",
        "x = Dense(10, activation='relu')(x)\n",
        "output = Dense(1)(x)\n",
        "\n",
        "attr_model = Model(input, output)"
      ],
      "execution_count": 48,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "roratdC_JdVs",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "55f1cb5c-2571-4403-cd2f-8f697f088369"
      },
      "source": [
        "attr_model.compile(optimizer=\"Adam\", loss=\"mse\")\n",
        "attr_model.summary()\n",
        "\n",
        "attr_model.fit(train_attr_x, train_y, epochs=100, verbose=1, batch_size=128, validation_split=0.1)"
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model_5\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " input_8 (InputLayer)        [(None, 10)]              0         \n",
            "                                                                 \n",
            " dense_21 (Dense)            (None, 10)                110       \n",
            "                                                                 \n",
            " dense_22 (Dense)            (None, 10)                110       \n",
            "                                                                 \n",
            " dense_23 (Dense)            (None, 1)                 11        \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 231\n",
            "Trainable params: 231\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "Epoch 1/100\n",
            "2/2 [==============================] - 0s 126ms/step - loss: 0.0714 - val_loss: 0.0581\n",
            "Epoch 2/100\n",
            "2/2 [==============================] - 0s 24ms/step - loss: 0.0619 - val_loss: 0.0502\n",
            "Epoch 3/100\n",
            "2/2 [==============================] - 0s 24ms/step - loss: 0.0531 - val_loss: 0.0431\n",
            "Epoch 4/100\n",
            "2/2 [==============================] - 0s 24ms/step - loss: 0.0454 - val_loss: 0.0369\n",
            "Epoch 5/100\n",
            "2/2 [==============================] - 0s 27ms/step - loss: 0.0385 - val_loss: 0.0315\n",
            "Epoch 6/100\n",
            "2/2 [==============================] - 0s 23ms/step - loss: 0.0326 - val_loss: 0.0269\n",
            "Epoch 7/100\n",
            "2/2 [==============================] - 0s 24ms/step - loss: 0.0277 - val_loss: 0.0230\n",
            "Epoch 8/100\n",
            "2/2 [==============================] - 0s 24ms/step - loss: 0.0238 - val_loss: 0.0198\n",
            "Epoch 9/100\n",
            "2/2 [==============================] - 0s 23ms/step - loss: 0.0203 - val_loss: 0.0173\n",
            "Epoch 10/100\n",
            "2/2 [==============================] - 0s 26ms/step - loss: 0.0177 - val_loss: 0.0152\n",
            "Epoch 11/100\n",
            "2/2 [==============================] - 0s 23ms/step - loss: 0.0157 - val_loss: 0.0136\n",
            "Epoch 12/100\n",
            "2/2 [==============================] - 0s 23ms/step - loss: 0.0143 - val_loss: 0.0125\n",
            "Epoch 13/100\n",
            "2/2 [==============================] - 0s 27ms/step - loss: 0.0132 - val_loss: 0.0117\n",
            "Epoch 14/100\n",
            "2/2 [==============================] - 0s 30ms/step - loss: 0.0126 - val_loss: 0.0111\n",
            "Epoch 15/100\n",
            "2/2 [==============================] - 0s 23ms/step - loss: 0.0122 - val_loss: 0.0107\n",
            "Epoch 16/100\n",
            "2/2 [==============================] - 0s 23ms/step - loss: 0.0119 - val_loss: 0.0105\n",
            "Epoch 17/100\n",
            "2/2 [==============================] - 0s 23ms/step - loss: 0.0118 - val_loss: 0.0103\n",
            "Epoch 18/100\n",
            "2/2 [==============================] - 0s 27ms/step - loss: 0.0118 - val_loss: 0.0102\n",
            "Epoch 19/100\n",
            "2/2 [==============================] - 0s 41ms/step - loss: 0.0117 - val_loss: 0.0100\n",
            "Epoch 20/100\n",
            "2/2 [==============================] - 0s 48ms/step - loss: 0.0116 - val_loss: 0.0098\n",
            "Epoch 21/100\n",
            "2/2 [==============================] - 0s 23ms/step - loss: 0.0115 - val_loss: 0.0096\n",
            "Epoch 22/100\n",
            "2/2 [==============================] - 0s 24ms/step - loss: 0.0113 - val_loss: 0.0094\n",
            "Epoch 23/100\n",
            "2/2 [==============================] - 0s 22ms/step - loss: 0.0111 - val_loss: 0.0091\n",
            "Epoch 24/100\n",
            "2/2 [==============================] - 0s 25ms/step - loss: 0.0109 - val_loss: 0.0089\n",
            "Epoch 25/100\n",
            "2/2 [==============================] - 0s 22ms/step - loss: 0.0106 - val_loss: 0.0087\n",
            "Epoch 26/100\n",
            "2/2 [==============================] - 0s 24ms/step - loss: 0.0104 - val_loss: 0.0084\n",
            "Epoch 27/100\n",
            "2/2 [==============================] - 0s 23ms/step - loss: 0.0102 - val_loss: 0.0082\n",
            "Epoch 28/100\n",
            "2/2 [==============================] - 0s 23ms/step - loss: 0.0100 - val_loss: 0.0080\n",
            "Epoch 29/100\n",
            "2/2 [==============================] - 0s 23ms/step - loss: 0.0098 - val_loss: 0.0078\n",
            "Epoch 30/100\n",
            "2/2 [==============================] - 0s 24ms/step - loss: 0.0097 - val_loss: 0.0077\n",
            "Epoch 31/100\n",
            "2/2 [==============================] - 0s 30ms/step - loss: 0.0095 - val_loss: 0.0075\n",
            "Epoch 32/100\n",
            "2/2 [==============================] - 0s 22ms/step - loss: 0.0094 - val_loss: 0.0073\n",
            "Epoch 33/100\n",
            "2/2 [==============================] - 0s 24ms/step - loss: 0.0093 - val_loss: 0.0072\n",
            "Epoch 34/100\n",
            "2/2 [==============================] - 0s 22ms/step - loss: 0.0092 - val_loss: 0.0070\n",
            "Epoch 35/100\n",
            "2/2 [==============================] - 0s 22ms/step - loss: 0.0091 - val_loss: 0.0069\n",
            "Epoch 36/100\n",
            "2/2 [==============================] - 0s 22ms/step - loss: 0.0090 - val_loss: 0.0067\n",
            "Epoch 37/100\n",
            "2/2 [==============================] - 0s 41ms/step - loss: 0.0089 - val_loss: 0.0066\n",
            "Epoch 38/100\n",
            "2/2 [==============================] - 0s 25ms/step - loss: 0.0088 - val_loss: 0.0064\n",
            "Epoch 39/100\n",
            "2/2 [==============================] - 0s 22ms/step - loss: 0.0087 - val_loss: 0.0063\n",
            "Epoch 40/100\n",
            "2/2 [==============================] - 0s 24ms/step - loss: 0.0087 - val_loss: 0.0061\n",
            "Epoch 41/100\n",
            "2/2 [==============================] - 0s 28ms/step - loss: 0.0086 - val_loss: 0.0060\n",
            "Epoch 42/100\n",
            "2/2 [==============================] - 0s 22ms/step - loss: 0.0085 - val_loss: 0.0059\n",
            "Epoch 43/100\n",
            "2/2 [==============================] - 0s 23ms/step - loss: 0.0084 - val_loss: 0.0057\n",
            "Epoch 44/100\n",
            "2/2 [==============================] - 0s 26ms/step - loss: 0.0084 - val_loss: 0.0056\n",
            "Epoch 45/100\n",
            "2/2 [==============================] - 0s 24ms/step - loss: 0.0083 - val_loss: 0.0055\n",
            "Epoch 46/100\n",
            "2/2 [==============================] - 0s 24ms/step - loss: 0.0082 - val_loss: 0.0054\n",
            "Epoch 47/100\n",
            "2/2 [==============================] - 0s 23ms/step - loss: 0.0082 - val_loss: 0.0053\n",
            "Epoch 48/100\n",
            "2/2 [==============================] - 0s 23ms/step - loss: 0.0081 - val_loss: 0.0052\n",
            "Epoch 49/100\n",
            "2/2 [==============================] - 0s 22ms/step - loss: 0.0081 - val_loss: 0.0051\n",
            "Epoch 50/100\n",
            "2/2 [==============================] - 0s 23ms/step - loss: 0.0080 - val_loss: 0.0050\n",
            "Epoch 51/100\n",
            "2/2 [==============================] - 0s 24ms/step - loss: 0.0080 - val_loss: 0.0049\n",
            "Epoch 52/100\n",
            "2/2 [==============================] - 0s 23ms/step - loss: 0.0079 - val_loss: 0.0049\n",
            "Epoch 53/100\n",
            "2/2 [==============================] - 0s 26ms/step - loss: 0.0079 - val_loss: 0.0048\n",
            "Epoch 54/100\n",
            "2/2 [==============================] - 0s 24ms/step - loss: 0.0078 - val_loss: 0.0047\n",
            "Epoch 55/100\n",
            "2/2 [==============================] - 0s 24ms/step - loss: 0.0078 - val_loss: 0.0047\n",
            "Epoch 56/100\n",
            "2/2 [==============================] - 0s 23ms/step - loss: 0.0077 - val_loss: 0.0046\n",
            "Epoch 57/100\n",
            "2/2 [==============================] - 0s 24ms/step - loss: 0.0077 - val_loss: 0.0046\n",
            "Epoch 58/100\n",
            "2/2 [==============================] - 0s 26ms/step - loss: 0.0076 - val_loss: 0.0045\n",
            "Epoch 59/100\n",
            "2/2 [==============================] - 0s 24ms/step - loss: 0.0076 - val_loss: 0.0045\n",
            "Epoch 60/100\n",
            "2/2 [==============================] - 0s 33ms/step - loss: 0.0076 - val_loss: 0.0044\n",
            "Epoch 61/100\n",
            "2/2 [==============================] - 0s 23ms/step - loss: 0.0075 - val_loss: 0.0044\n",
            "Epoch 62/100\n",
            "2/2 [==============================] - 0s 45ms/step - loss: 0.0075 - val_loss: 0.0044\n",
            "Epoch 63/100\n",
            "2/2 [==============================] - 0s 33ms/step - loss: 0.0075 - val_loss: 0.0043\n",
            "Epoch 64/100\n",
            "2/2 [==============================] - 0s 25ms/step - loss: 0.0074 - val_loss: 0.0043\n",
            "Epoch 65/100\n",
            "2/2 [==============================] - 0s 25ms/step - loss: 0.0074 - val_loss: 0.0043\n",
            "Epoch 66/100\n",
            "2/2 [==============================] - 0s 25ms/step - loss: 0.0074 - val_loss: 0.0042\n",
            "Epoch 67/100\n",
            "2/2 [==============================] - 0s 25ms/step - loss: 0.0073 - val_loss: 0.0042\n",
            "Epoch 68/100\n",
            "2/2 [==============================] - 0s 25ms/step - loss: 0.0073 - val_loss: 0.0042\n",
            "Epoch 69/100\n",
            "2/2 [==============================] - 0s 25ms/step - loss: 0.0073 - val_loss: 0.0042\n",
            "Epoch 70/100\n",
            "2/2 [==============================] - 0s 24ms/step - loss: 0.0073 - val_loss: 0.0042\n",
            "Epoch 71/100\n",
            "2/2 [==============================] - 0s 31ms/step - loss: 0.0072 - val_loss: 0.0041\n",
            "Epoch 72/100\n",
            "2/2 [==============================] - 0s 24ms/step - loss: 0.0072 - val_loss: 0.0041\n",
            "Epoch 73/100\n",
            "2/2 [==============================] - 0s 25ms/step - loss: 0.0072 - val_loss: 0.0041\n",
            "Epoch 74/100\n",
            "2/2 [==============================] - 0s 24ms/step - loss: 0.0071 - val_loss: 0.0041\n",
            "Epoch 75/100\n",
            "2/2 [==============================] - 0s 23ms/step - loss: 0.0071 - val_loss: 0.0041\n",
            "Epoch 76/100\n",
            "2/2 [==============================] - 0s 25ms/step - loss: 0.0071 - val_loss: 0.0041\n",
            "Epoch 77/100\n",
            "2/2 [==============================] - 0s 28ms/step - loss: 0.0071 - val_loss: 0.0040\n",
            "Epoch 78/100\n",
            "2/2 [==============================] - 0s 25ms/step - loss: 0.0070 - val_loss: 0.0040\n",
            "Epoch 79/100\n",
            "2/2 [==============================] - 0s 24ms/step - loss: 0.0070 - val_loss: 0.0040\n",
            "Epoch 80/100\n",
            "2/2 [==============================] - 0s 43ms/step - loss: 0.0070 - val_loss: 0.0040\n",
            "Epoch 81/100\n",
            "2/2 [==============================] - 0s 25ms/step - loss: 0.0070 - val_loss: 0.0040\n",
            "Epoch 82/100\n",
            "2/2 [==============================] - 0s 27ms/step - loss: 0.0069 - val_loss: 0.0040\n",
            "Epoch 83/100\n",
            "2/2 [==============================] - 0s 25ms/step - loss: 0.0069 - val_loss: 0.0040\n",
            "Epoch 84/100\n",
            "2/2 [==============================] - 0s 24ms/step - loss: 0.0069 - val_loss: 0.0039\n",
            "Epoch 85/100\n",
            "2/2 [==============================] - 0s 24ms/step - loss: 0.0069 - val_loss: 0.0039\n",
            "Epoch 86/100\n",
            "2/2 [==============================] - 0s 23ms/step - loss: 0.0068 - val_loss: 0.0039\n",
            "Epoch 87/100\n",
            "2/2 [==============================] - 0s 42ms/step - loss: 0.0068 - val_loss: 0.0039\n",
            "Epoch 88/100\n",
            "2/2 [==============================] - 0s 23ms/step - loss: 0.0067 - val_loss: 0.0039\n",
            "Epoch 89/100\n",
            "2/2 [==============================] - 0s 24ms/step - loss: 0.0067 - val_loss: 0.0038\n",
            "Epoch 90/100\n",
            "2/2 [==============================] - 0s 28ms/step - loss: 0.0067 - val_loss: 0.0038\n",
            "Epoch 91/100\n",
            "2/2 [==============================] - 0s 23ms/step - loss: 0.0066 - val_loss: 0.0037\n",
            "Epoch 92/100\n",
            "2/2 [==============================] - 0s 24ms/step - loss: 0.0065 - val_loss: 0.0037\n",
            "Epoch 93/100\n",
            "2/2 [==============================] - 0s 29ms/step - loss: 0.0065 - val_loss: 0.0037\n",
            "Epoch 94/100\n",
            "2/2 [==============================] - 0s 24ms/step - loss: 0.0064 - val_loss: 0.0036\n",
            "Epoch 95/100\n",
            "2/2 [==============================] - 0s 25ms/step - loss: 0.0064 - val_loss: 0.0036\n",
            "Epoch 96/100\n",
            "2/2 [==============================] - 0s 28ms/step - loss: 0.0063 - val_loss: 0.0035\n",
            "Epoch 97/100\n",
            "2/2 [==============================] - 0s 52ms/step - loss: 0.0063 - val_loss: 0.0035\n",
            "Epoch 98/100\n",
            "2/2 [==============================] - 0s 26ms/step - loss: 0.0062 - val_loss: 0.0034\n",
            "Epoch 99/100\n",
            "2/2 [==============================] - 0s 25ms/step - loss: 0.0062 - val_loss: 0.0034\n",
            "Epoch 100/100\n",
            "2/2 [==============================] - 0s 25ms/step - loss: 0.0061 - val_loss: 0.0033\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7f9447b3e210>"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Hk5WagwnJdTO",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1f269310-daba-48d4-ac96-b3d50bbba450"
      },
      "source": [
        "preds = attr_model.predict(test_attr_x)\n",
        "\n",
        "diff = preds.flatten() - test_y\n",
        "percentDiff = (diff / test_y) * 100\n",
        "absPercentDiff = np.abs(percentDiff)\n",
        "\n",
        "mean = np.mean(absPercentDiff)\n",
        "std = np.std(absPercentDiff)\n",
        "\n",
        "print(\"[INFO] mean: {:.2f}%, std: {:.2f}\".format(mean, std))"
      ],
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[INFO] mean: 33.75%, std: 43.40\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3L6Nr-1ZJdP_"
      },
      "source": [
        ""
      ],
      "execution_count": 50,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EY4cMiohJFKc"
      },
      "source": [
        "### 영상 모델"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JEbOBK0ULRlT"
      },
      "source": [
        "input = Input(train_image_x[0].shape)\n",
        "x = Conv2D(32, (3,3), padding='same')(input)\n",
        "x = MaxPooling2D((2,2))(x)\n",
        "x = Conv2D(32, (3,3), padding='same')(x)\n",
        "x = MaxPooling2D((2,2))(x)\n",
        "x = Flatten()(x)\n",
        "x = Dense(64, activation='relu')(x)\n",
        "x = Dense(10, activation='relu')(x)\n",
        "output = Dense(1)(x)\n",
        "\n",
        "image_model = Model(input, output)"
      ],
      "execution_count": 51,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jprcg41pLRhL",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5094074c-f0f6-44b2-80bd-6da2c9836e1e"
      },
      "source": [
        "image_model.compile(optimizer=\"Adam\", loss=\"mse\")\n",
        "image_model.summary()\n",
        "\n",
        "image_model.fit(train_image_x, train_y, epochs=100, verbose=1, batch_size=128, validation_split=0.1)"
      ],
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model_6\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " input_9 (InputLayer)        [(None, 256, 256, 3)]     0         \n",
            "                                                                 \n",
            " conv2d_10 (Conv2D)          (None, 256, 256, 32)      896       \n",
            "                                                                 \n",
            " max_pooling2d_10 (MaxPoolin  (None, 128, 128, 32)     0         \n",
            " g2D)                                                            \n",
            "                                                                 \n",
            " conv2d_11 (Conv2D)          (None, 128, 128, 32)      9248      \n",
            "                                                                 \n",
            " max_pooling2d_11 (MaxPoolin  (None, 64, 64, 32)       0         \n",
            " g2D)                                                            \n",
            "                                                                 \n",
            " flatten_5 (Flatten)         (None, 131072)            0         \n",
            "                                                                 \n",
            " dense_24 (Dense)            (None, 64)                8388672   \n",
            "                                                                 \n",
            " dense_25 (Dense)            (None, 10)                650       \n",
            "                                                                 \n",
            " dense_26 (Dense)            (None, 1)                 11        \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 8,399,477\n",
            "Trainable params: 8,399,477\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "Epoch 1/100\n",
            "2/2 [==============================] - 1s 224ms/step - loss: 809.4283 - val_loss: 25.7913\n",
            "Epoch 2/100\n",
            "2/2 [==============================] - 0s 123ms/step - loss: 20.1137 - val_loss: 1.6277\n",
            "Epoch 3/100\n",
            "2/2 [==============================] - 0s 111ms/step - loss: 1.7490 - val_loss: 2.5650\n",
            "Epoch 4/100\n",
            "2/2 [==============================] - 0s 118ms/step - loss: 1.5160 - val_loss: 0.4211\n",
            "Epoch 5/100\n",
            "2/2 [==============================] - 0s 108ms/step - loss: 48.7922 - val_loss: 0.8729\n",
            "Epoch 6/100\n",
            "2/2 [==============================] - 0s 112ms/step - loss: 0.9201 - val_loss: 0.8008\n",
            "Epoch 7/100\n",
            "2/2 [==============================] - 0s 107ms/step - loss: 0.7504 - val_loss: 0.2754\n",
            "Epoch 8/100\n",
            "2/2 [==============================] - 0s 111ms/step - loss: 0.1781 - val_loss: 0.0079\n",
            "Epoch 9/100\n",
            "2/2 [==============================] - 0s 114ms/step - loss: 0.0100 - val_loss: 0.0081\n",
            "Epoch 10/100\n",
            "2/2 [==============================] - 0s 105ms/step - loss: 0.0101 - val_loss: 0.0080\n",
            "Epoch 11/100\n",
            "2/2 [==============================] - 0s 109ms/step - loss: 0.0103 - val_loss: 0.0088\n",
            "Epoch 12/100\n",
            "2/2 [==============================] - 0s 113ms/step - loss: 0.0118 - val_loss: 0.0103\n",
            "Epoch 13/100\n",
            "2/2 [==============================] - 0s 107ms/step - loss: 0.0134 - val_loss: 0.0113\n",
            "Epoch 14/100\n",
            "2/2 [==============================] - 0s 110ms/step - loss: 0.0140 - val_loss: 0.0109\n",
            "Epoch 15/100\n",
            "2/2 [==============================] - 0s 104ms/step - loss: 0.0132 - val_loss: 0.0098\n",
            "Epoch 16/100\n",
            "2/2 [==============================] - 0s 106ms/step - loss: 0.0118 - val_loss: 0.0093\n",
            "Epoch 17/100\n",
            "2/2 [==============================] - 0s 110ms/step - loss: 0.0111 - val_loss: 0.0098\n",
            "Epoch 18/100\n",
            "2/2 [==============================] - 0s 113ms/step - loss: 0.0114 - val_loss: 0.0103\n",
            "Epoch 19/100\n",
            "2/2 [==============================] - 0s 106ms/step - loss: 0.0119 - val_loss: 0.0106\n",
            "Epoch 20/100\n",
            "2/2 [==============================] - 0s 104ms/step - loss: 0.0121 - val_loss: 0.0103\n",
            "Epoch 21/100\n",
            "2/2 [==============================] - 0s 105ms/step - loss: 0.0117 - val_loss: 0.0099\n",
            "Epoch 22/100\n",
            "2/2 [==============================] - 0s 105ms/step - loss: 0.0112 - val_loss: 0.0097\n",
            "Epoch 23/100\n",
            "2/2 [==============================] - 0s 113ms/step - loss: 0.0111 - val_loss: 0.0096\n",
            "Epoch 24/100\n",
            "2/2 [==============================] - 0s 110ms/step - loss: 0.0112 - val_loss: 0.0097\n",
            "Epoch 25/100\n",
            "2/2 [==============================] - 0s 111ms/step - loss: 0.0114 - val_loss: 0.0097\n",
            "Epoch 26/100\n",
            "2/2 [==============================] - 0s 111ms/step - loss: 0.0112 - val_loss: 0.0096\n",
            "Epoch 27/100\n",
            "2/2 [==============================] - 0s 113ms/step - loss: 0.0110 - val_loss: 0.0096\n",
            "Epoch 28/100\n",
            "2/2 [==============================] - 0s 108ms/step - loss: 0.0109 - val_loss: 0.0096\n",
            "Epoch 29/100\n",
            "2/2 [==============================] - 0s 108ms/step - loss: 0.0109 - val_loss: 0.0096\n",
            "Epoch 30/100\n",
            "2/2 [==============================] - 0s 109ms/step - loss: 0.0109 - val_loss: 0.0095\n",
            "Epoch 31/100\n",
            "2/2 [==============================] - 0s 106ms/step - loss: 0.0108 - val_loss: 0.0094\n",
            "Epoch 32/100\n",
            "2/2 [==============================] - 0s 112ms/step - loss: 0.0107 - val_loss: 0.0093\n",
            "Epoch 33/100\n",
            "2/2 [==============================] - 0s 108ms/step - loss: 0.0106 - val_loss: 0.0093\n",
            "Epoch 34/100\n",
            "2/2 [==============================] - 0s 108ms/step - loss: 0.0106 - val_loss: 0.0092\n",
            "Epoch 35/100\n",
            "2/2 [==============================] - 0s 109ms/step - loss: 0.0105 - val_loss: 0.0092\n",
            "Epoch 36/100\n",
            "2/2 [==============================] - 0s 104ms/step - loss: 0.0104 - val_loss: 0.0091\n",
            "Epoch 37/100\n",
            "2/2 [==============================] - 0s 107ms/step - loss: 0.0103 - val_loss: 0.0091\n",
            "Epoch 38/100\n",
            "2/2 [==============================] - 0s 109ms/step - loss: 0.0103 - val_loss: 0.0091\n",
            "Epoch 39/100\n",
            "2/2 [==============================] - 0s 113ms/step - loss: 0.0102 - val_loss: 0.0090\n",
            "Epoch 40/100\n",
            "2/2 [==============================] - 0s 108ms/step - loss: 0.0101 - val_loss: 0.0089\n",
            "Epoch 41/100\n",
            "2/2 [==============================] - 0s 113ms/step - loss: 0.0100 - val_loss: 0.0088\n",
            "Epoch 42/100\n",
            "2/2 [==============================] - 0s 113ms/step - loss: 0.0100 - val_loss: 0.0088\n",
            "Epoch 43/100\n",
            "2/2 [==============================] - 0s 107ms/step - loss: 0.0099 - val_loss: 0.0087\n",
            "Epoch 44/100\n",
            "2/2 [==============================] - 0s 108ms/step - loss: 0.0099 - val_loss: 0.0087\n",
            "Epoch 45/100\n",
            "2/2 [==============================] - 0s 106ms/step - loss: 0.0098 - val_loss: 0.0086\n",
            "Epoch 46/100\n",
            "2/2 [==============================] - 0s 106ms/step - loss: 0.0097 - val_loss: 0.0086\n",
            "Epoch 47/100\n",
            "2/2 [==============================] - 0s 110ms/step - loss: 0.0096 - val_loss: 0.0085\n",
            "Epoch 48/100\n",
            "2/2 [==============================] - 0s 113ms/step - loss: 0.0096 - val_loss: 0.0085\n",
            "Epoch 49/100\n",
            "2/2 [==============================] - 0s 124ms/step - loss: 0.0095 - val_loss: 0.0084\n",
            "Epoch 50/100\n",
            "2/2 [==============================] - 0s 132ms/step - loss: 0.0094 - val_loss: 0.0084\n",
            "Epoch 51/100\n",
            "2/2 [==============================] - 0s 137ms/step - loss: 0.0094 - val_loss: 0.0083\n",
            "Epoch 52/100\n",
            "2/2 [==============================] - 0s 120ms/step - loss: 0.0093 - val_loss: 0.0083\n",
            "Epoch 53/100\n",
            "2/2 [==============================] - 0s 132ms/step - loss: 0.0092 - val_loss: 0.0082\n",
            "Epoch 54/100\n",
            "2/2 [==============================] - 0s 123ms/step - loss: 0.0092 - val_loss: 0.0082\n",
            "Epoch 55/100\n",
            "2/2 [==============================] - 0s 121ms/step - loss: 0.0091 - val_loss: 0.0082\n",
            "Epoch 56/100\n",
            "2/2 [==============================] - 0s 111ms/step - loss: 0.0090 - val_loss: 0.0081\n",
            "Epoch 57/100\n",
            "2/2 [==============================] - 0s 108ms/step - loss: 0.0090 - val_loss: 0.0080\n",
            "Epoch 58/100\n",
            "2/2 [==============================] - 0s 109ms/step - loss: 0.0089 - val_loss: 0.0080\n",
            "Epoch 59/100\n",
            "2/2 [==============================] - 0s 115ms/step - loss: 0.0088 - val_loss: 0.0080\n",
            "Epoch 60/100\n",
            "2/2 [==============================] - 0s 106ms/step - loss: 0.0088 - val_loss: 0.0079\n",
            "Epoch 61/100\n",
            "2/2 [==============================] - 0s 107ms/step - loss: 0.0087 - val_loss: 0.0079\n",
            "Epoch 62/100\n",
            "2/2 [==============================] - 0s 109ms/step - loss: 0.0087 - val_loss: 0.0079\n",
            "Epoch 63/100\n",
            "2/2 [==============================] - 0s 111ms/step - loss: 0.0086 - val_loss: 0.0078\n",
            "Epoch 64/100\n",
            "2/2 [==============================] - 0s 110ms/step - loss: 0.0085 - val_loss: 0.0078\n",
            "Epoch 65/100\n",
            "2/2 [==============================] - 0s 109ms/step - loss: 0.0085 - val_loss: 0.0077\n",
            "Epoch 66/100\n",
            "2/2 [==============================] - 0s 110ms/step - loss: 0.0084 - val_loss: 0.0077\n",
            "Epoch 67/100\n",
            "2/2 [==============================] - 0s 108ms/step - loss: 0.0084 - val_loss: 0.0076\n",
            "Epoch 68/100\n",
            "2/2 [==============================] - 0s 113ms/step - loss: 0.0083 - val_loss: 0.0076\n",
            "Epoch 69/100\n",
            "2/2 [==============================] - 0s 109ms/step - loss: 0.0083 - val_loss: 0.0076\n",
            "Epoch 70/100\n",
            "2/2 [==============================] - 0s 108ms/step - loss: 0.0082 - val_loss: 0.0076\n",
            "Epoch 71/100\n",
            "2/2 [==============================] - 0s 112ms/step - loss: 0.0081 - val_loss: 0.0075\n",
            "Epoch 72/100\n",
            "2/2 [==============================] - 0s 100ms/step - loss: 0.0081 - val_loss: 0.0075\n",
            "Epoch 73/100\n",
            "2/2 [==============================] - 0s 107ms/step - loss: 0.0080 - val_loss: 0.0074\n",
            "Epoch 74/100\n",
            "2/2 [==============================] - 0s 109ms/step - loss: 0.0079 - val_loss: 0.0074\n",
            "Epoch 75/100\n",
            "2/2 [==============================] - 0s 112ms/step - loss: 0.0079 - val_loss: 0.0074\n",
            "Epoch 76/100\n",
            "2/2 [==============================] - 0s 108ms/step - loss: 0.0078 - val_loss: 0.0074\n",
            "Epoch 77/100\n",
            "2/2 [==============================] - 0s 117ms/step - loss: 0.0078 - val_loss: 0.0073\n",
            "Epoch 78/100\n",
            "2/2 [==============================] - 0s 110ms/step - loss: 0.0077 - val_loss: 0.0073\n",
            "Epoch 79/100\n",
            "2/2 [==============================] - 0s 106ms/step - loss: 0.0077 - val_loss: 0.0072\n",
            "Epoch 80/100\n",
            "2/2 [==============================] - 0s 113ms/step - loss: 0.0076 - val_loss: 0.0072\n",
            "Epoch 81/100\n",
            "2/2 [==============================] - 0s 109ms/step - loss: 0.0076 - val_loss: 0.0072\n",
            "Epoch 82/100\n",
            "2/2 [==============================] - 0s 108ms/step - loss: 0.0075 - val_loss: 0.0072\n",
            "Epoch 83/100\n",
            "2/2 [==============================] - 0s 108ms/step - loss: 0.0074 - val_loss: 0.0071\n",
            "Epoch 84/100\n",
            "2/2 [==============================] - 0s 108ms/step - loss: 0.0074 - val_loss: 0.0071\n",
            "Epoch 85/100\n",
            "2/2 [==============================] - 0s 106ms/step - loss: 0.0074 - val_loss: 0.0071\n",
            "Epoch 86/100\n",
            "2/2 [==============================] - 0s 108ms/step - loss: 0.0073 - val_loss: 0.0071\n",
            "Epoch 87/100\n",
            "2/2 [==============================] - 0s 112ms/step - loss: 0.0073 - val_loss: 0.0070\n",
            "Epoch 88/100\n",
            "2/2 [==============================] - 0s 107ms/step - loss: 0.0073 - val_loss: 0.0070\n",
            "Epoch 89/100\n",
            "2/2 [==============================] - 0s 111ms/step - loss: 0.0071 - val_loss: 0.0070\n",
            "Epoch 90/100\n",
            "2/2 [==============================] - 0s 109ms/step - loss: 0.0071 - val_loss: 0.0070\n",
            "Epoch 91/100\n",
            "2/2 [==============================] - 0s 109ms/step - loss: 0.0070 - val_loss: 0.0069\n",
            "Epoch 92/100\n",
            "2/2 [==============================] - 0s 111ms/step - loss: 0.0070 - val_loss: 0.0069\n",
            "Epoch 93/100\n",
            "2/2 [==============================] - 0s 116ms/step - loss: 0.0069 - val_loss: 0.0069\n",
            "Epoch 94/100\n",
            "2/2 [==============================] - 0s 107ms/step - loss: 0.0069 - val_loss: 0.0069\n",
            "Epoch 95/100\n",
            "2/2 [==============================] - 0s 111ms/step - loss: 0.0069 - val_loss: 0.0069\n",
            "Epoch 96/100\n",
            "2/2 [==============================] - 0s 117ms/step - loss: 0.0068 - val_loss: 0.0068\n",
            "Epoch 97/100\n",
            "2/2 [==============================] - 0s 111ms/step - loss: 0.0067 - val_loss: 0.0068\n",
            "Epoch 98/100\n",
            "2/2 [==============================] - 0s 116ms/step - loss: 0.0068 - val_loss: 0.0068\n",
            "Epoch 99/100\n",
            "2/2 [==============================] - 0s 109ms/step - loss: 0.0066 - val_loss: 0.0068\n",
            "Epoch 100/100\n",
            "2/2 [==============================] - 0s 96ms/step - loss: 0.0066 - val_loss: 0.0068\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7f944789b1d0>"
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OROOOvr9LRet",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "13de007e-a1da-4f4c-889f-f27aef2c7a52"
      },
      "source": [
        "preds = image_model.predict(test_image_x)\n",
        "\n",
        "diff = preds.flatten() - test_y\n",
        "percentDiff = (diff / test_y) * 100\n",
        "absPercentDiff = np.abs(percentDiff)\n",
        "\n",
        "mean = np.mean(absPercentDiff)\n",
        "std = np.std(absPercentDiff)\n",
        "\n",
        "print(\"[INFO] mean: {:.2f}%, std: {:.2f}\".format(mean, std))"
      ],
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[INFO] mean: 128.51%, std: 204.23\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "k7DX0yusIn78"
      },
      "source": [
        "## 다중 입력 모델로 학습 실행"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P7iDDfb-LRb6"
      },
      "source": [
        "# 속성 브랜치\n",
        "attr_input = Input(train_attr_x[0].shape)\n",
        "x = Dense(10, activation='relu')(attr_input)\n",
        "attr_out = Dense(10, activation='relu')(x)\n",
        "\n",
        "# 영상 브랜치\n",
        "image_input = Input(train_image_x[0].shape)\n",
        "x = Conv2D(32, (3,3), padding='same')(image_input)\n",
        "x = MaxPooling2D((2,2))(x)\n",
        "x = Conv2D(32, (3,3), padding='same')(x)\n",
        "x = MaxPooling2D((2,2))(x)\n",
        "x = Flatten()(x)\n",
        "x = Dense(64, activation='relu')(x)\n",
        "image_output = Dense(10, activation='relu')(x)\n",
        "\n",
        "# 2개의 브랜치를 합치고\n",
        "combined_input = concatenate([attr_out, image_output])\n",
        "\n",
        "# 1개의 출력\n",
        "x = Dense(10, activation=\"relu\")(combined_input)\n",
        "output = Dense(1)(x)\n",
        "\n",
        "# 모델 정의\n",
        "combined_model = Model([attr_input, image_input], output)"
      ],
      "execution_count": 55,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2fVpru2CLRYu",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "aeff12ef-bc9f-469c-a0b9-de779889f7e1"
      },
      "source": [
        "combined_model.compile(optimizer=\"Adam\", loss=\"mse\")\n",
        "combined_model.summary()\n",
        "\n",
        "combined_model.fit([train_attr_x, train_image_x], train_y, epochs=100, verbose=1, batch_size=128, validation_split=0.1)"
      ],
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model_7\"\n",
            "__________________________________________________________________________________________________\n",
            " Layer (type)                   Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            " input_11 (InputLayer)          [(None, 256, 256, 3  0           []                               \n",
            "                                )]                                                                \n",
            "                                                                                                  \n",
            " conv2d_12 (Conv2D)             (None, 256, 256, 32  896         ['input_11[0][0]']               \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " max_pooling2d_12 (MaxPooling2D  (None, 128, 128, 32  0          ['conv2d_12[0][0]']              \n",
            " )                              )                                                                 \n",
            "                                                                                                  \n",
            " conv2d_13 (Conv2D)             (None, 128, 128, 32  9248        ['max_pooling2d_12[0][0]']       \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " max_pooling2d_13 (MaxPooling2D  (None, 64, 64, 32)  0           ['conv2d_13[0][0]']              \n",
            " )                                                                                                \n",
            "                                                                                                  \n",
            " input_10 (InputLayer)          [(None, 10)]         0           []                               \n",
            "                                                                                                  \n",
            " flatten_6 (Flatten)            (None, 131072)       0           ['max_pooling2d_13[0][0]']       \n",
            "                                                                                                  \n",
            " dense_27 (Dense)               (None, 10)           110         ['input_10[0][0]']               \n",
            "                                                                                                  \n",
            " dense_29 (Dense)               (None, 64)           8388672     ['flatten_6[0][0]']              \n",
            "                                                                                                  \n",
            " dense_28 (Dense)               (None, 10)           110         ['dense_27[0][0]']               \n",
            "                                                                                                  \n",
            " dense_30 (Dense)               (None, 10)           650         ['dense_29[0][0]']               \n",
            "                                                                                                  \n",
            " concatenate_1 (Concatenate)    (None, 20)           0           ['dense_28[0][0]',               \n",
            "                                                                  'dense_30[0][0]']               \n",
            "                                                                                                  \n",
            " dense_31 (Dense)               (None, 10)           210         ['concatenate_1[0][0]']          \n",
            "                                                                                                  \n",
            " dense_32 (Dense)               (None, 1)            11          ['dense_31[0][0]']               \n",
            "                                                                                                  \n",
            "==================================================================================================\n",
            "Total params: 8,399,907\n",
            "Trainable params: 8,399,907\n",
            "Non-trainable params: 0\n",
            "__________________________________________________________________________________________________\n",
            "Epoch 1/100\n",
            "2/2 [==============================] - 1s 244ms/step - loss: 20.9972 - val_loss: 28.4456\n",
            "Epoch 2/100\n",
            "2/2 [==============================] - 0s 110ms/step - loss: 17.4449 - val_loss: 2.2748\n",
            "Epoch 3/100\n",
            "2/2 [==============================] - 0s 111ms/step - loss: 2.8711 - val_loss: 36.4997\n",
            "Epoch 4/100\n",
            "2/2 [==============================] - 0s 111ms/step - loss: 19.5936 - val_loss: 1.1534\n",
            "Epoch 5/100\n",
            "2/2 [==============================] - 0s 106ms/step - loss: 1.4800 - val_loss: 2.3957\n",
            "Epoch 6/100\n",
            "2/2 [==============================] - 0s 114ms/step - loss: 2.5088 - val_loss: 2.2965\n",
            "Epoch 7/100\n",
            "2/2 [==============================] - 0s 116ms/step - loss: 2.1223 - val_loss: 1.3777\n",
            "Epoch 8/100\n",
            "2/2 [==============================] - 0s 108ms/step - loss: 1.0204 - val_loss: 0.2473\n",
            "Epoch 9/100\n",
            "2/2 [==============================] - 0s 118ms/step - loss: 0.1339 - val_loss: 0.1110\n",
            "Epoch 10/100\n",
            "2/2 [==============================] - 0s 112ms/step - loss: 0.2923 - val_loss: 0.7629\n",
            "Epoch 11/100\n",
            "2/2 [==============================] - 0s 113ms/step - loss: 0.8753 - val_loss: 0.6413\n",
            "Epoch 12/100\n",
            "2/2 [==============================] - 0s 113ms/step - loss: 0.4909 - val_loss: 0.6290\n",
            "Epoch 13/100\n",
            "2/2 [==============================] - 0s 112ms/step - loss: 0.8001 - val_loss: 0.0349\n",
            "Epoch 14/100\n",
            "2/2 [==============================] - 0s 111ms/step - loss: 0.1827 - val_loss: 0.3634\n",
            "Epoch 15/100\n",
            "2/2 [==============================] - 0s 113ms/step - loss: 0.3580 - val_loss: 0.1314\n",
            "Epoch 16/100\n",
            "2/2 [==============================] - 0s 112ms/step - loss: 0.0898 - val_loss: 0.0371\n",
            "Epoch 17/100\n",
            "2/2 [==============================] - 0s 118ms/step - loss: 0.0671 - val_loss: 0.2212\n",
            "Epoch 18/100\n",
            "2/2 [==============================] - 0s 111ms/step - loss: 0.2096 - val_loss: 0.1464\n",
            "Epoch 19/100\n",
            "2/2 [==============================] - 0s 112ms/step - loss: 0.0927 - val_loss: 0.0173\n",
            "Epoch 20/100\n",
            "2/2 [==============================] - 0s 114ms/step - loss: 0.0357 - val_loss: 0.0996\n",
            "Epoch 21/100\n",
            "2/2 [==============================] - 0s 116ms/step - loss: 0.1189 - val_loss: 0.0780\n",
            "Epoch 22/100\n",
            "2/2 [==============================] - 0s 113ms/step - loss: 0.0625 - val_loss: 0.0177\n",
            "Epoch 23/100\n",
            "2/2 [==============================] - 0s 112ms/step - loss: 0.0239 - val_loss: 0.0790\n",
            "Epoch 24/100\n",
            "2/2 [==============================] - 0s 109ms/step - loss: 0.0727 - val_loss: 0.0636\n",
            "Epoch 25/100\n",
            "2/2 [==============================] - 0s 112ms/step - loss: 0.0397 - val_loss: 0.0168\n",
            "Epoch 26/100\n",
            "2/2 [==============================] - 0s 110ms/step - loss: 0.0212 - val_loss: 0.0418\n",
            "Epoch 27/100\n",
            "2/2 [==============================] - 0s 112ms/step - loss: 0.0471 - val_loss: 0.0287\n",
            "Epoch 28/100\n",
            "2/2 [==============================] - 0s 110ms/step - loss: 0.0242 - val_loss: 0.0203\n",
            "Epoch 29/100\n",
            "2/2 [==============================] - 0s 113ms/step - loss: 0.0201 - val_loss: 0.0412\n",
            "Epoch 30/100\n",
            "2/2 [==============================] - 0s 111ms/step - loss: 0.0312 - val_loss: 0.0248\n",
            "Epoch 31/100\n",
            "2/2 [==============================] - 0s 108ms/step - loss: 0.0164 - val_loss: 0.0177\n",
            "Epoch 32/100\n",
            "2/2 [==============================] - 0s 111ms/step - loss: 0.0191 - val_loss: 0.0228\n",
            "Epoch 33/100\n",
            "2/2 [==============================] - 0s 106ms/step - loss: 0.0216 - val_loss: 0.0160\n",
            "Epoch 34/100\n",
            "2/2 [==============================] - 0s 114ms/step - loss: 0.0133 - val_loss: 0.0223\n",
            "Epoch 35/100\n",
            "2/2 [==============================] - 0s 110ms/step - loss: 0.0183 - val_loss: 0.0235\n",
            "Epoch 36/100\n",
            "2/2 [==============================] - 0s 108ms/step - loss: 0.0159 - val_loss: 0.0158\n",
            "Epoch 37/100\n",
            "2/2 [==============================] - 0s 111ms/step - loss: 0.0143 - val_loss: 0.0174\n",
            "Epoch 38/100\n",
            "2/2 [==============================] - 0s 113ms/step - loss: 0.0163 - val_loss: 0.0155\n",
            "Epoch 39/100\n",
            "2/2 [==============================] - 0s 110ms/step - loss: 0.0128 - val_loss: 0.0175\n",
            "Epoch 40/100\n",
            "2/2 [==============================] - 0s 105ms/step - loss: 0.0142 - val_loss: 0.0196\n",
            "Epoch 41/100\n",
            "2/2 [==============================] - 0s 107ms/step - loss: 0.0140 - val_loss: 0.0156\n",
            "Epoch 42/100\n",
            "2/2 [==============================] - 0s 110ms/step - loss: 0.0125 - val_loss: 0.0153\n",
            "Epoch 43/100\n",
            "2/2 [==============================] - 0s 105ms/step - loss: 0.0138 - val_loss: 0.0149\n",
            "Epoch 44/100\n",
            "2/2 [==============================] - 0s 110ms/step - loss: 0.0123 - val_loss: 0.0161\n",
            "Epoch 45/100\n",
            "2/2 [==============================] - 0s 110ms/step - loss: 0.0127 - val_loss: 0.0174\n",
            "Epoch 46/100\n",
            "2/2 [==============================] - 0s 111ms/step - loss: 0.0127 - val_loss: 0.0149\n",
            "Epoch 47/100\n",
            "2/2 [==============================] - 0s 112ms/step - loss: 0.0121 - val_loss: 0.0144\n",
            "Epoch 48/100\n",
            "2/2 [==============================] - 0s 108ms/step - loss: 0.0124 - val_loss: 0.0142\n",
            "Epoch 49/100\n",
            "2/2 [==============================] - 0s 111ms/step - loss: 0.0116 - val_loss: 0.0151\n",
            "Epoch 50/100\n",
            "2/2 [==============================] - 0s 111ms/step - loss: 0.0117 - val_loss: 0.0153\n",
            "Epoch 51/100\n",
            "2/2 [==============================] - 0s 108ms/step - loss: 0.0117 - val_loss: 0.0141\n",
            "Epoch 52/100\n",
            "2/2 [==============================] - 0s 110ms/step - loss: 0.0113 - val_loss: 0.0138\n",
            "Epoch 53/100\n",
            "2/2 [==============================] - 0s 115ms/step - loss: 0.0112 - val_loss: 0.0139\n",
            "Epoch 54/100\n",
            "2/2 [==============================] - 0s 110ms/step - loss: 0.0110 - val_loss: 0.0143\n",
            "Epoch 55/100\n",
            "2/2 [==============================] - 0s 110ms/step - loss: 0.0111 - val_loss: 0.0141\n",
            "Epoch 56/100\n",
            "2/2 [==============================] - 0s 111ms/step - loss: 0.0108 - val_loss: 0.0134\n",
            "Epoch 57/100\n",
            "2/2 [==============================] - 0s 110ms/step - loss: 0.0110 - val_loss: 0.0132\n",
            "Epoch 58/100\n",
            "2/2 [==============================] - 0s 109ms/step - loss: 0.0109 - val_loss: 0.0135\n",
            "Epoch 59/100\n",
            "2/2 [==============================] - 0s 111ms/step - loss: 0.0106 - val_loss: 0.0137\n",
            "Epoch 60/100\n",
            "2/2 [==============================] - 0s 116ms/step - loss: 0.0106 - val_loss: 0.0131\n",
            "Epoch 61/100\n",
            "2/2 [==============================] - 0s 111ms/step - loss: 0.0104 - val_loss: 0.0129\n",
            "Epoch 62/100\n",
            "2/2 [==============================] - 0s 113ms/step - loss: 0.0103 - val_loss: 0.0129\n",
            "Epoch 63/100\n",
            "2/2 [==============================] - 0s 113ms/step - loss: 0.0103 - val_loss: 0.0130\n",
            "Epoch 64/100\n",
            "2/2 [==============================] - 0s 110ms/step - loss: 0.0102 - val_loss: 0.0127\n",
            "Epoch 65/100\n",
            "2/2 [==============================] - 0s 115ms/step - loss: 0.0101 - val_loss: 0.0124\n",
            "Epoch 66/100\n",
            "2/2 [==============================] - 0s 111ms/step - loss: 0.0100 - val_loss: 0.0125\n",
            "Epoch 67/100\n",
            "2/2 [==============================] - 0s 114ms/step - loss: 0.0099 - val_loss: 0.0126\n",
            "Epoch 68/100\n",
            "2/2 [==============================] - 0s 112ms/step - loss: 0.0098 - val_loss: 0.0124\n",
            "Epoch 69/100\n",
            "2/2 [==============================] - 0s 110ms/step - loss: 0.0097 - val_loss: 0.0121\n",
            "Epoch 70/100\n",
            "2/2 [==============================] - 0s 112ms/step - loss: 0.0097 - val_loss: 0.0120\n",
            "Epoch 71/100\n",
            "2/2 [==============================] - 0s 114ms/step - loss: 0.0096 - val_loss: 0.0121\n",
            "Epoch 72/100\n",
            "2/2 [==============================] - 0s 111ms/step - loss: 0.0095 - val_loss: 0.0121\n",
            "Epoch 73/100\n",
            "2/2 [==============================] - 0s 110ms/step - loss: 0.0094 - val_loss: 0.0119\n",
            "Epoch 74/100\n",
            "2/2 [==============================] - 0s 111ms/step - loss: 0.0093 - val_loss: 0.0117\n",
            "Epoch 75/100\n",
            "2/2 [==============================] - 0s 109ms/step - loss: 0.0093 - val_loss: 0.0116\n",
            "Epoch 76/100\n",
            "2/2 [==============================] - 0s 111ms/step - loss: 0.0092 - val_loss: 0.0116\n",
            "Epoch 77/100\n",
            "2/2 [==============================] - 0s 114ms/step - loss: 0.0091 - val_loss: 0.0114\n",
            "Epoch 78/100\n",
            "2/2 [==============================] - 0s 108ms/step - loss: 0.0092 - val_loss: 0.0115\n",
            "Epoch 79/100\n",
            "2/2 [==============================] - 0s 121ms/step - loss: 0.0090 - val_loss: 0.0111\n",
            "Epoch 80/100\n",
            "2/2 [==============================] - 0s 110ms/step - loss: 0.0090 - val_loss: 0.0111\n",
            "Epoch 81/100\n",
            "2/2 [==============================] - 0s 108ms/step - loss: 0.0089 - val_loss: 0.0114\n",
            "Epoch 82/100\n",
            "2/2 [==============================] - 0s 103ms/step - loss: 0.0088 - val_loss: 0.0111\n",
            "Epoch 83/100\n",
            "2/2 [==============================] - 0s 111ms/step - loss: 0.0088 - val_loss: 0.0107\n",
            "Epoch 84/100\n",
            "2/2 [==============================] - 0s 109ms/step - loss: 0.0087 - val_loss: 0.0108\n",
            "Epoch 85/100\n",
            "2/2 [==============================] - 0s 109ms/step - loss: 0.0086 - val_loss: 0.0111\n",
            "Epoch 86/100\n",
            "2/2 [==============================] - 0s 110ms/step - loss: 0.0085 - val_loss: 0.0107\n",
            "Epoch 87/100\n",
            "2/2 [==============================] - 0s 112ms/step - loss: 0.0085 - val_loss: 0.0104\n",
            "Epoch 88/100\n",
            "2/2 [==============================] - 0s 111ms/step - loss: 0.0084 - val_loss: 0.0106\n",
            "Epoch 89/100\n",
            "2/2 [==============================] - 0s 121ms/step - loss: 0.0083 - val_loss: 0.0107\n",
            "Epoch 90/100\n",
            "2/2 [==============================] - 0s 111ms/step - loss: 0.0083 - val_loss: 0.0103\n",
            "Epoch 91/100\n",
            "2/2 [==============================] - 0s 112ms/step - loss: 0.0082 - val_loss: 0.0102\n",
            "Epoch 92/100\n",
            "2/2 [==============================] - 0s 113ms/step - loss: 0.0082 - val_loss: 0.0103\n",
            "Epoch 93/100\n",
            "2/2 [==============================] - 0s 111ms/step - loss: 0.0081 - val_loss: 0.0103\n",
            "Epoch 94/100\n",
            "2/2 [==============================] - 0s 109ms/step - loss: 0.0080 - val_loss: 0.0100\n",
            "Epoch 95/100\n",
            "2/2 [==============================] - 0s 114ms/step - loss: 0.0080 - val_loss: 0.0099\n",
            "Epoch 96/100\n",
            "2/2 [==============================] - 0s 110ms/step - loss: 0.0079 - val_loss: 0.0100\n",
            "Epoch 97/100\n",
            "2/2 [==============================] - 0s 115ms/step - loss: 0.0079 - val_loss: 0.0102\n",
            "Epoch 98/100\n",
            "2/2 [==============================] - 0s 112ms/step - loss: 0.0078 - val_loss: 0.0097\n",
            "Epoch 99/100\n",
            "2/2 [==============================] - 0s 107ms/step - loss: 0.0077 - val_loss: 0.0097\n",
            "Epoch 100/100\n",
            "2/2 [==============================] - 0s 95ms/step - loss: 0.0077 - val_loss: 0.0097\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7f944149d410>"
            ]
          },
          "metadata": {},
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cdJXO5maFwh0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "cb3543e9-d995-477a-fe48-53fa1e95ed66"
      },
      "source": [
        "preds = combined_model.predict([test_attr_x, test_image_x])\n",
        "\n",
        "diff = preds.flatten() - test_y\n",
        "percentDiff = (diff / test_y) * 100\n",
        "absPercentDiff = np.abs(percentDiff)\n",
        "\n",
        "mean = np.mean(absPercentDiff)\n",
        "std = np.std(absPercentDiff)\n",
        "\n",
        "print(\"[INFO] mean: {:.2f}%, std: {:.2f}\".format(mean, std))"
      ],
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[INFO] mean: 117.06%, std: 149.54\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZAh3boxuFwez",
        "outputId": "27dc4eef-0c87-417d-f703-019dc05b5fe3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 902
        }
      },
      "source": [
        "import tensorflow as tf\n",
        "tf.keras.utils.plot_model(model=combined_model, rankdir=\"TD\", dpi=72, show_shapes=True)"
      ],
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<IPython.core.display.Image object>"
            ],
            "image/png": "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\n"
          },
          "metadata": {},
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ejOg9i5KMU28"
      },
      "source": [
        "# 다중 출력\n",
        "\n",
        "copy from https://www.pyimagesearch.com/2018/06/04/keras-multiple-outputs-and-multiple-losses/"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oLnb63o5ottv"
      },
      "source": [
        "## 데이터 준비"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FBXMpbDn76uf",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4a9127b7-5523-4974-a803-f362246d5163"
      },
      "source": [
        "!wget https://github.com/dhrim/2022_WISET/raw/main/material/deep_learning/fashion_dataset.zip"
      ],
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2022-08-09 01:57:18--  https://github.com/dhrim/2022_WISET/raw/main/material/deep_learning/fashion_dataset.zip\n",
            "Resolving github.com (github.com)... 140.82.121.3\n",
            "Connecting to github.com (github.com)|140.82.121.3|:443... connected.\n",
            "HTTP request sent, awaiting response... 302 Found\n",
            "Location: https://raw.githubusercontent.com/dhrim/2022_WISET/main/material/deep_learning/fashion_dataset.zip [following]\n",
            "--2022-08-09 01:57:18--  https://raw.githubusercontent.com/dhrim/2022_WISET/main/material/deep_learning/fashion_dataset.zip\n",
            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.110.133, 185.199.109.133, ...\n",
            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 26991216 (26M) [application/zip]\n",
            "Saving to: ‘fashion_dataset.zip’\n",
            "\n",
            "fashion_dataset.zip 100%[===================>]  25.74M  --.-KB/s    in 0.08s   \n",
            "\n",
            "2022-08-09 01:57:20 (313 MB/s) - ‘fashion_dataset.zip’ saved [26991216/26991216]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mFt96FMqvjah",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6f46e3a5-45e8-4190-840e-7cc4fd3a5d6e"
      },
      "source": [
        "!ls -alh"
      ],
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "total 26M\n",
            "drwxr-xr-x 1 root root 4.0K Aug  9 01:57 .\n",
            "drwxr-xr-x 1 root root 4.0K Aug  9 01:09 ..\n",
            "drwxr-xr-x 4 root root 4.0K Aug  3 20:20 .config\n",
            "-rw-r--r-- 1 root root  26M Aug  9 01:57 fashion_dataset.zip\n",
            "drwxr-xr-x 4 root root 4.0K Aug  9 01:27 Houses-dataset\n",
            "-rw-r--r-- 1 root root  68K Aug  9 01:57 model.png\n",
            "drwxr-xr-x 1 root root 4.0K Aug  3 20:21 sample_data\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zYCavDIjh5RK",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d32dcfad-df89-4250-8f9c-338c84a92107"
      },
      "source": [
        "!unzip fashion_dataset.zip"
      ],
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Archive:  fashion_dataset.zip\n",
            "   creating: fashion_dataset/\n",
            "   creating: fashion_dataset/black_jeans/\n",
            "  inflating: fashion_dataset/black_jeans/00000000.jpg  \n",
            "   creating: __MACOSX/\n",
            "   creating: __MACOSX/fashion_dataset/\n",
            "   creating: __MACOSX/fashion_dataset/black_jeans/\n",
            "  inflating: __MACOSX/fashion_dataset/black_jeans/._00000000.jpg  \n",
            "  inflating: fashion_dataset/black_jeans/00000004.jpg  \n",
            "  inflating: __MACOSX/fashion_dataset/black_jeans/._00000004.jpg  \n",
            "  inflating: fashion_dataset/black_jeans/00000006.jpg  \n",
            "  inflating: __MACOSX/fashion_dataset/black_jeans/._00000006.jpg  \n",
            "  inflating: fashion_dataset/black_jeans/00000007.jpg  \n",
            "  inflating: __MACOSX/fashion_dataset/black_jeans/._00000007.jpg  \n",
            "  inflating: fashion_dataset/black_jeans/00000009.jpg  \n",
            "  inflating: __MACOSX/fashion_dataset/black_jeans/._00000009.jpg  \n",
            "  inflating: fashion_dataset/black_jeans/00000010.jpg  \n",
            "  inflating: __MACOSX/fashion_dataset/black_jeans/._00000010.jpg  \n",
            "  inflating: fashion_dataset/black_jeans/00000011.jpg  \n",
            "  inflating: __MACOSX/fashion_dataset/black_jeans/._00000011.jpg  \n",
            "  inflating: fashion_dataset/black_jeans/00000014.jpg  \n",
            "  inflating: __MACOSX/fashion_dataset/black_jeans/._00000014.jpg  \n",
            "  inflating: fashion_dataset/black_jeans/00000015.jpg  \n",
            "  inflating: __MACOSX/fashion_dataset/black_jeans/._00000015.jpg  \n",
            "  inflating: fashion_dataset/black_jeans/00000016.jpg  \n",
            "  inflating: __MACOSX/fashion_dataset/black_jeans/._00000016.jpg  \n",
            "  inflating: fashion_dataset/black_jeans/00000017.jpg  \n",
            "  inflating: __MACOSX/fashion_dataset/black_jeans/._00000017.jpg  \n",
            "  inflating: fashion_dataset/black_jeans/00000018.jpg  \n",
            "  inflating: __MACOSX/fashion_dataset/black_jeans/._00000018.jpg  \n",
            "  inflating: fashion_dataset/black_jeans/00000019.jpg  \n",
            "  inflating: __MACOSX/fashion_dataset/black_jeans/._00000019.jpg  \n",
            "  inflating: fashion_dataset/black_jeans/00000040.jpg  \n",
            "  inflating: __MACOSX/fashion_dataset/black_jeans/._00000040.jpg  \n",
            "  inflating: fashion_dataset/black_jeans/00000041.jpg  \n",
            "  inflating: __MACOSX/fashion_dataset/black_jeans/._00000041.jpg  \n",
            "  inflating: fashion_dataset/black_jeans/00000047.jpg  \n",
            "  inflating: __MACOSX/fashion_dataset/black_jeans/._00000047.jpg  \n",
            "  inflating: fashion_dataset/black_jeans/00000048.jpg  \n",
            "  inflating: __MACOSX/fashion_dataset/black_jeans/._00000048.jpg  \n",
            "  inflating: fashion_dataset/black_jeans/00000049.jpg  \n",
            "  inflating: __MACOSX/fashion_dataset/black_jeans/._00000049.jpg  \n",
            "  inflating: fashion_dataset/black_jeans/00000051.jpg  \n",
            "  inflating: __MACOSX/fashion_dataset/black_jeans/._00000051.jpg  \n",
            "  inflating: fashion_dataset/black_jeans/00000054.jpg  \n",
            "  inflating: __MACOSX/fashion_dataset/black_jeans/._00000054.jpg  \n",
            "  inflating: fashion_dataset/black_jeans/00000055.jpg  \n",
            "  inflating: __MACOSX/fashion_dataset/black_jeans/._00000055.jpg  \n",
            "  inflating: fashion_dataset/black_jeans/00000056.jpg  \n",
            "  inflating: __MACOSX/fashion_dataset/black_jeans/._00000056.jpg  \n",
            "  inflating: fashion_dataset/black_jeans/00000057.jpg  \n",
            "  inflating: __MACOSX/fashion_dataset/black_jeans/._00000057.jpg  \n",
            "  inflating: fashion_dataset/black_jeans/00000060.jpg  \n",
            "  inflating: __MACOSX/fashion_dataset/black_jeans/._00000060.jpg  \n",
            "  inflating: fashion_dataset/black_jeans/00000061.jpg  \n",
            "  inflating: __MACOSX/fashion_dataset/black_jeans/._00000061.jpg  \n",
            "  inflating: fashion_dataset/black_jeans/00000064.jpg  \n",
            "  inflating: __MACOSX/fashion_dataset/black_jeans/._00000064.jpg  \n",
            "  inflating: fashion_dataset/black_jeans/00000065.jpg  \n",
            "  inflating: __MACOSX/fashion_dataset/black_jeans/._00000065.jpg  \n",
            "  inflating: fashion_dataset/black_jeans/00000066.jpg  \n",
            "  inflating: __MACOSX/fashion_dataset/black_jeans/._00000066.jpg  \n",
            "  inflating: fashion_dataset/black_jeans/00000067.jpg  \n",
            "  inflating: __MACOSX/fashion_dataset/black_jeans/._00000067.jpg  \n",
            "  inflating: fashion_dataset/black_jeans/00000068.jpg  \n",
            "  inflating: __MACOSX/fashion_dataset/black_jeans/._00000068.jpg  \n",
            "  inflating: fashion_dataset/black_jeans/00000071.jpg  \n",
            "  inflating: __MACOSX/fashion_dataset/black_jeans/._00000071.jpg  \n",
            "  inflating: fashion_dataset/black_jeans/00000074.jpg  \n",
            "  inflating: __MACOSX/fashion_dataset/black_jeans/._00000074.jpg  \n",
            "  inflating: fashion_dataset/black_jeans/00000075.jpg  \n",
            "  inflating: __MACOSX/fashion_dataset/black_jeans/._00000075.jpg  \n",
            "  inflating: fashion_dataset/black_jeans/00000076.jpg  \n",
            "  inflating: __MACOSX/fashion_dataset/black_jeans/._00000076.jpg  \n",
            "  inflating: fashion_dataset/black_jeans/00000078.jpg  \n",
            "  inflating: __MACOSX/fashion_dataset/black_jeans/._00000078.jpg  \n",
            "  inflating: fashion_dataset/black_jeans/00000079.png  \n",
            "  inflating: __MACOSX/fashion_dataset/black_jeans/._00000079.png  \n",
            "  inflating: fashion_dataset/black_jeans/00000080.jpg  \n",
            "  inflating: __MACOSX/fashion_dataset/black_jeans/._00000080.jpg  \n",
            "  inflating: fashion_dataset/black_jeans/00000081.jpg  \n",
            "  inflating: __MACOSX/fashion_dataset/black_jeans/._00000081.jpg  \n",
            "  inflating: fashion_dataset/black_jeans/00000084.jpg  \n",
            "  inflating: __MACOSX/fashion_dataset/black_jeans/._00000084.jpg  \n",
            "  inflating: fashion_dataset/black_jeans/00000085.jpg  \n",
            "  inflating: __MACOSX/fashion_dataset/black_jeans/._00000085.jpg  \n",
            "  inflating: fashion_dataset/black_jeans/00000086.jpg  \n",
            "  inflating: __MACOSX/fashion_dataset/black_jeans/._00000086.jpg  \n",
            "  inflating: fashion_dataset/black_jeans/00000088.jpg  \n",
            "  inflating: __MACOSX/fashion_dataset/black_jeans/._00000088.jpg  \n",
            "  inflating: fashion_dataset/black_jeans/00000089.jpg  \n",
            "  inflating: __MACOSX/fashion_dataset/black_jeans/._00000089.jpg  \n",
            "  inflating: fashion_dataset/black_jeans/00000090.jpg  \n",
            "  inflating: __MACOSX/fashion_dataset/black_jeans/._00000090.jpg  \n",
            "  inflating: fashion_dataset/black_jeans/00000094.jpg  \n",
            "  inflating: __MACOSX/fashion_dataset/black_jeans/._00000094.jpg  \n",
            "  inflating: fashion_dataset/black_jeans/00000095.jpg  \n",
            "  inflating: __MACOSX/fashion_dataset/black_jeans/._00000095.jpg  \n",
            "  inflating: fashion_dataset/black_jeans/00000096.jpg  \n",
            "  inflating: __MACOSX/fashion_dataset/black_jeans/._00000096.jpg  \n",
            "  inflating: fashion_dataset/black_jeans/00000097.jpg  \n",
            "  inflating: __MACOSX/fashion_dataset/black_jeans/._00000097.jpg  \n",
            "  inflating: fashion_dataset/black_jeans/00000098.jpg  \n",
            "  inflating: __MACOSX/fashion_dataset/black_jeans/._00000098.jpg  \n",
            "  inflating: fashion_dataset/black_jeans/00000100.jpg  \n",
            "  inflating: __MACOSX/fashion_dataset/black_jeans/._00000100.jpg  \n",
            "  inflating: fashion_dataset/black_jeans/00000101.jpg  \n",
            "  inflating: __MACOSX/fashion_dataset/black_jeans/._00000101.jpg  \n",
            "  inflating: fashion_dataset/black_jeans/00000104.jpg  \n",
            "  inflating: __MACOSX/fashion_dataset/black_jeans/._00000104.jpg  \n",
            "  inflating: fashion_dataset/black_jeans/00000105.jpg  \n",
            "  inflating: __MACOSX/fashion_dataset/black_jeans/._00000105.jpg  \n",
            "  inflating: fashion_dataset/black_jeans/00000107.jpg  \n",
            "  inflating: __MACOSX/fashion_dataset/black_jeans/._00000107.jpg  \n",
            "  inflating: fashion_dataset/black_jeans/00000108.jpg  \n",
            "  inflating: __MACOSX/fashion_dataset/black_jeans/._00000108.jpg  \n",
            "  inflating: fashion_dataset/black_jeans/00000110.jpg  \n",
            "  inflating: __MACOSX/fashion_dataset/black_jeans/._00000110.jpg  \n",
            "  inflating: fashion_dataset/black_jeans/00000111.jpg  \n",
            "  inflating: __MACOSX/fashion_dataset/black_jeans/._00000111.jpg  \n",
            "  inflating: fashion_dataset/black_jeans/00000117.jpg  \n",
            "  inflating: __MACOSX/fashion_dataset/black_jeans/._00000117.jpg  \n",
            "  inflating: fashion_dataset/black_jeans/00000118.jpg  \n",
            "  inflating: __MACOSX/fashion_dataset/black_jeans/._00000118.jpg  \n",
            "  inflating: fashion_dataset/black_jeans/00000140.jpg  \n",
            "  inflating: __MACOSX/fashion_dataset/black_jeans/._00000140.jpg  \n",
            "  inflating: fashion_dataset/black_jeans/00000141.jpg  \n",
            "  inflating: __MACOSX/fashion_dataset/black_jeans/._00000141.jpg  \n",
            "  inflating: fashion_dataset/black_jeans/00000144.jpg  \n",
            "  inflating: __MACOSX/fashion_dataset/black_jeans/._00000144.jpg  \n",
            "  inflating: fashion_dataset/black_jeans/00000145.jpg  \n",
            "  inflating: __MACOSX/fashion_dataset/black_jeans/._00000145.jpg  \n",
            "  inflating: fashion_dataset/black_jeans/00000146.jpg  \n",
            "  inflating: __MACOSX/fashion_dataset/black_jeans/._00000146.jpg  \n",
            "  inflating: fashion_dataset/black_jeans/00000148.jpg  \n",
            "  inflating: __MACOSX/fashion_dataset/black_jeans/._00000148.jpg  \n",
            "  inflating: fashion_dataset/black_jeans/00000149.jpg  \n",
            "  inflating: __MACOSX/fashion_dataset/black_jeans/._00000149.jpg  \n",
            "  inflating: fashion_dataset/black_jeans/00000150.jpg  \n",
            "  inflating: __MACOSX/fashion_dataset/black_jeans/._00000150.jpg  \n",
            "  inflating: fashion_dataset/black_jeans/00000151.jpg  \n",
            "  inflating: __MACOSX/fashion_dataset/black_jeans/._00000151.jpg  \n",
            "  inflating: fashion_dataset/black_jeans/00000155.jpg  \n",
            "  inflating: __MACOSX/fashion_dataset/black_jeans/._00000155.jpg  \n",
            "  inflating: fashion_dataset/black_jeans/00000156.jpg  \n",
            "  inflating: __MACOSX/fashion_dataset/black_jeans/._00000156.jpg  \n",
            "  inflating: fashion_dataset/black_jeans/00000157.jpg  \n",
            "  inflating: __MACOSX/fashion_dataset/black_jeans/._00000157.jpg  \n",
            "  inflating: fashion_dataset/black_jeans/00000159.jpg  \n",
            "  inflating: __MACOSX/fashion_dataset/black_jeans/._00000159.jpg  \n",
            "  inflating: fashion_dataset/black_jeans/00000160.jpg  \n",
            "  inflating: __MACOSX/fashion_dataset/black_jeans/._00000160.jpg  \n",
            "  inflating: fashion_dataset/black_jeans/00000164.JPG  \n",
            "  inflating: __MACOSX/fashion_dataset/black_jeans/._00000164.JPG  \n",
            "  inflating: fashion_dataset/black_jeans/00000166.jpg  \n",
            "  inflating: __MACOSX/fashion_dataset/black_jeans/._00000166.jpg  \n",
            "  inflating: fashion_dataset/black_jeans/00000167.jpg  \n",
            "  inflating: __MACOSX/fashion_dataset/black_jeans/._00000167.jpg  \n",
            "  inflating: fashion_dataset/black_jeans/00000168.jpg  \n",
            "  inflating: __MACOSX/fashion_dataset/black_jeans/._00000168.jpg  \n",
            "  inflating: fashion_dataset/black_jeans/00000169.png  \n",
            "  inflating: __MACOSX/fashion_dataset/black_jeans/._00000169.png  \n",
            "  inflating: fashion_dataset/black_jeans/00000171.jpg  \n",
            "  inflating: __MACOSX/fashion_dataset/black_jeans/._00000171.jpg  \n",
            "  inflating: fashion_dataset/black_jeans/00000176.jpg  \n",
            "  inflating: __MACOSX/fashion_dataset/black_jeans/._00000176.jpg  \n",
            "  inflating: fashion_dataset/black_jeans/00000177.jpg  \n",
            "  inflating: __MACOSX/fashion_dataset/black_jeans/._00000177.jpg  \n",
            "  inflating: fashion_dataset/black_jeans/00000184.jpg  \n",
            "  inflating: __MACOSX/fashion_dataset/black_jeans/._00000184.jpg  \n",
            "  inflating: fashion_dataset/black_jeans/00000185.jpg  \n",
            "  inflating: __MACOSX/fashion_dataset/black_jeans/._00000185.jpg  \n",
            "  inflating: fashion_dataset/black_jeans/00000186.jpg  \n",
            "  inflating: __MACOSX/fashion_dataset/black_jeans/._00000186.jpg  \n",
            "  inflating: fashion_dataset/black_jeans/00000189.jpg  \n",
            "  inflating: __MACOSX/fashion_dataset/black_jeans/._00000189.jpg  \n",
            "  inflating: fashion_dataset/black_jeans/00000190.jpg  \n",
            "  inflating: __MACOSX/fashion_dataset/black_jeans/._00000190.jpg  \n",
            "  inflating: fashion_dataset/black_jeans/00000191.jpg  \n",
            "  inflating: __MACOSX/fashion_dataset/black_jeans/._00000191.jpg  \n",
            "  inflating: fashion_dataset/black_jeans/00000194.jpg  \n",
            "  inflating: __MACOSX/fashion_dataset/black_jeans/._00000194.jpg  \n",
            "  inflating: fashion_dataset/black_jeans/00000195.jpg  \n",
            "  inflating: __MACOSX/fashion_dataset/black_jeans/._00000195.jpg  \n",
            "  inflating: fashion_dataset/black_jeans/00000196.jpg  \n",
            "  inflating: __MACOSX/fashion_dataset/black_jeans/._00000196.jpg  \n",
            "  inflating: fashion_dataset/black_jeans/00000197.jpg  \n",
            "  inflating: __MACOSX/fashion_dataset/black_jeans/._00000197.jpg  \n",
            "  inflating: fashion_dataset/black_jeans/00000198.jpg  \n",
            "  inflating: __MACOSX/fashion_dataset/black_jeans/._00000198.jpg  \n",
            "  inflating: fashion_dataset/black_jeans/00000199.jpg  \n",
            "  inflating: __MACOSX/fashion_dataset/black_jeans/._00000199.jpg  \n",
            "   creating: fashion_dataset/black_shoes/\n",
            "  inflating: fashion_dataset/black_shoes/00000000.jpg  \n",
            "  inflating: fashion_dataset/black_shoes/00000001.jpg  \n",
            "  inflating: fashion_dataset/black_shoes/00000004.jpg  \n",
            "  inflating: fashion_dataset/black_shoes/00000005.jpg  \n",
            "  inflating: fashion_dataset/black_shoes/00000006.jpg  \n",
            "  inflating: fashion_dataset/black_shoes/00000007.jpg  \n",
            "  inflating: fashion_dataset/black_shoes/00000008.jpg  \n",
            "  inflating: fashion_dataset/black_shoes/00000009.jpg  \n",
            "  inflating: fashion_dataset/black_shoes/00000010.jpg  \n",
            "  inflating: fashion_dataset/black_shoes/00000011.jpg  \n",
            "  inflating: fashion_dataset/black_shoes/00000015.jpg  \n",
            "  inflating: fashion_dataset/black_shoes/00000016.jpg  \n",
            "  inflating: fashion_dataset/black_shoes/00000017.jpg  \n",
            "  inflating: fashion_dataset/black_shoes/00000018.jpg  \n",
            "  inflating: fashion_dataset/black_shoes/00000019.jpg  \n",
            "  inflating: fashion_dataset/black_shoes/00000040.jpg  \n",
            "  inflating: fashion_dataset/black_shoes/00000041.jpg  \n",
            "  inflating: fashion_dataset/black_shoes/00000044.jpg  \n",
            "  inflating: fashion_dataset/black_shoes/00000045.jpg  \n",
            "  inflating: fashion_dataset/black_shoes/00000048.jpg  \n",
            "  inflating: fashion_dataset/black_shoes/00000049.jpg  \n",
            "  inflating: fashion_dataset/black_shoes/00000051.jpg  \n",
            "  inflating: fashion_dataset/black_shoes/00000054.jpg  \n",
            "  inflating: fashion_dataset/black_shoes/00000055.jpg  \n",
            "  inflating: fashion_dataset/black_shoes/00000056.jpg  \n",
            "  inflating: fashion_dataset/black_shoes/00000057.jpg  \n",
            "  inflating: fashion_dataset/black_shoes/00000059.jpg  \n",
            "  inflating: fashion_dataset/black_shoes/00000060.jpg  \n",
            "  inflating: fashion_dataset/black_shoes/00000061.jpg  \n",
            "  inflating: fashion_dataset/black_shoes/00000064.jpg  \n",
            "  inflating: fashion_dataset/black_shoes/00000065.jpg  \n",
            "  inflating: fashion_dataset/black_shoes/00000066.jpg  \n",
            "  inflating: fashion_dataset/black_shoes/00000067.png  \n",
            "  inflating: fashion_dataset/black_shoes/00000068.jpg  \n",
            "  inflating: fashion_dataset/black_shoes/00000069.jpg  \n",
            "  inflating: fashion_dataset/black_shoes/00000070.jpg  \n",
            "  inflating: fashion_dataset/black_shoes/00000071.jpg  \n",
            "  inflating: fashion_dataset/black_shoes/00000075.jpg  \n",
            "  inflating: fashion_dataset/black_shoes/00000076.jpg  \n",
            "  inflating: fashion_dataset/black_shoes/00000078.jpg  \n",
            "  inflating: fashion_dataset/black_shoes/00000079.jpg  \n",
            "  inflating: fashion_dataset/black_shoes/00000080.jpg  \n",
            "  inflating: fashion_dataset/black_shoes/00000081.jpg  \n",
            "  inflating: fashion_dataset/black_shoes/00000084.JPG  \n",
            "  inflating: fashion_dataset/black_shoes/00000086.jpg  \n",
            "  inflating: fashion_dataset/black_shoes/00000087.jpg  \n",
            "  inflating: fashion_dataset/black_shoes/00000088.jpg  \n",
            "  inflating: fashion_dataset/black_shoes/00000089.jpg  \n",
            "  inflating: fashion_dataset/black_shoes/00000090.jpg  \n",
            "  inflating: fashion_dataset/black_shoes/00000091.jpg  \n",
            "  inflating: fashion_dataset/black_shoes/00000094.jpg  \n",
            "  inflating: fashion_dataset/black_shoes/00000095.jpg  \n",
            "  inflating: fashion_dataset/black_shoes/00000096.jpg  \n",
            "  inflating: fashion_dataset/black_shoes/00000097.jpg  \n",
            "  inflating: fashion_dataset/black_shoes/00000098.jpg  \n",
            "  inflating: fashion_dataset/black_shoes/00000099.jpg  \n",
            "  inflating: fashion_dataset/black_shoes/00000100.jpg  \n",
            "  inflating: fashion_dataset/black_shoes/00000104.jpg  \n",
            "  inflating: fashion_dataset/black_shoes/00000105.jpg  \n",
            "  inflating: fashion_dataset/black_shoes/00000106.jpg  \n",
            "  inflating: fashion_dataset/black_shoes/00000107.jpg  \n",
            "  inflating: fashion_dataset/black_shoes/00000108.jpg  \n",
            "  inflating: fashion_dataset/black_shoes/00000109.jpg  \n",
            "  inflating: fashion_dataset/black_shoes/00000110.jpg  \n",
            "  inflating: fashion_dataset/black_shoes/00000111.jpg  \n",
            "  inflating: fashion_dataset/black_shoes/00000114.jpg  \n",
            "  inflating: fashion_dataset/black_shoes/00000115.jpg  \n",
            "  inflating: fashion_dataset/black_shoes/00000116.png  \n",
            "  inflating: fashion_dataset/black_shoes/00000117.jpg  \n",
            "  inflating: fashion_dataset/black_shoes/00000118.jpg  \n",
            "  inflating: fashion_dataset/black_shoes/00000119.jpg  \n",
            "  inflating: fashion_dataset/black_shoes/00000140.jpg  \n",
            "  inflating: fashion_dataset/black_shoes/00000141.jpg  \n",
            "  inflating: fashion_dataset/black_shoes/00000144.jpg  \n",
            "  inflating: fashion_dataset/black_shoes/00000145.JPG  \n",
            "  inflating: fashion_dataset/black_shoes/00000146.jpg  \n",
            "  inflating: fashion_dataset/black_shoes/00000147.jpg  \n",
            "  inflating: fashion_dataset/black_shoes/00000148.jpg  \n",
            "  inflating: fashion_dataset/black_shoes/00000149.jpg  \n",
            "  inflating: fashion_dataset/black_shoes/00000150.jpg  \n",
            "  inflating: fashion_dataset/black_shoes/00000151.jpg  \n",
            "  inflating: fashion_dataset/black_shoes/00000154.jpg  \n",
            "  inflating: fashion_dataset/black_shoes/00000156.JPG  \n",
            "  inflating: fashion_dataset/black_shoes/00000157.jpg  \n",
            "  inflating: fashion_dataset/black_shoes/00000158.jpg  \n",
            "  inflating: fashion_dataset/black_shoes/00000159.jpg  \n",
            "  inflating: fashion_dataset/black_shoes/00000160.jpg  \n",
            "  inflating: fashion_dataset/black_shoes/00000161.jpg  \n",
            "  inflating: fashion_dataset/black_shoes/00000165.jpg  \n",
            "  inflating: fashion_dataset/black_shoes/00000166.jpg  \n",
            "  inflating: fashion_dataset/black_shoes/00000168.jpg  \n",
            "  inflating: fashion_dataset/black_shoes/00000169.jpg  \n",
            "  inflating: fashion_dataset/black_shoes/00000170.jpg  \n",
            "  inflating: fashion_dataset/black_shoes/00000171.jpg  \n",
            "  inflating: fashion_dataset/black_shoes/00000174.jpg  \n",
            "  inflating: fashion_dataset/black_shoes/00000175.jpg  \n",
            "  inflating: fashion_dataset/black_shoes/00000176.jpg  \n",
            "  inflating: fashion_dataset/black_shoes/00000177.jpg  \n",
            "  inflating: fashion_dataset/black_shoes/00000179.jpg  \n",
            "  inflating: fashion_dataset/black_shoes/00000180.jpg  \n",
            "  inflating: fashion_dataset/black_shoes/00000181.jpg  \n",
            "  inflating: fashion_dataset/black_shoes/00000185.jpg  \n",
            "  inflating: fashion_dataset/black_shoes/00000186.jpg  \n",
            "  inflating: fashion_dataset/black_shoes/00000187.jpg  \n",
            "  inflating: fashion_dataset/black_shoes/00000188.jpg  \n",
            "  inflating: fashion_dataset/black_shoes/00000189.jpg  \n",
            "  inflating: fashion_dataset/black_shoes/00000190.jpg  \n",
            "  inflating: fashion_dataset/black_shoes/00000191.jpg  \n",
            "  inflating: fashion_dataset/black_shoes/00000194.jpg  \n",
            "  inflating: fashion_dataset/black_shoes/00000195.jpg  \n",
            "  inflating: fashion_dataset/black_shoes/00000196.jpg  \n",
            "  inflating: fashion_dataset/black_shoes/00000198.jpg  \n",
            "  inflating: fashion_dataset/black_shoes/00000199.gif  \n",
            "   creating: fashion_dataset/blue_dress/\n",
            "  inflating: fashion_dataset/blue_dress/00000000.jpg  \n",
            "  inflating: fashion_dataset/blue_dress/00000001.jpg  \n",
            "  inflating: fashion_dataset/blue_dress/00000004.jpg  \n",
            "  inflating: fashion_dataset/blue_dress/00000005.jpg  \n",
            "  inflating: fashion_dataset/blue_dress/00000007.jpg  \n",
            "  inflating: fashion_dataset/blue_dress/00000008.jpg  \n",
            "  inflating: fashion_dataset/blue_dress/00000009.jpg  \n",
            "  inflating: fashion_dataset/blue_dress/00000010.jpg  \n",
            "  inflating: fashion_dataset/blue_dress/00000011.jpg  \n",
            "  inflating: fashion_dataset/blue_dress/00000014.jpg  \n",
            "  inflating: fashion_dataset/blue_dress/00000015.jpg  \n",
            "  inflating: fashion_dataset/blue_dress/00000016.jpg  \n",
            "  inflating: fashion_dataset/blue_dress/00000017.jpg  \n",
            "  inflating: fashion_dataset/blue_dress/00000018.jpg  \n",
            "  inflating: fashion_dataset/blue_dress/00000019.jpg  \n",
            "  inflating: fashion_dataset/blue_dress/00000040.jpg  \n",
            "  inflating: fashion_dataset/blue_dress/00000044.jpg  \n",
            "  inflating: fashion_dataset/blue_dress/00000045.jpg  \n",
            "  inflating: fashion_dataset/blue_dress/00000046.jpg  \n",
            "  inflating: fashion_dataset/blue_dress/00000047.jpg  \n",
            "  inflating: fashion_dataset/blue_dress/00000048.jpg  \n",
            "  inflating: fashion_dataset/blue_dress/00000049.jpg  \n",
            "  inflating: fashion_dataset/blue_dress/00000050.jpg  \n",
            "  inflating: fashion_dataset/blue_dress/00000051.jpg  \n",
            "  inflating: fashion_dataset/blue_dress/00000054.jpg  \n",
            "  inflating: fashion_dataset/blue_dress/00000056.jpg  \n",
            "  inflating: fashion_dataset/blue_dress/00000057.jpg  \n",
            "  inflating: fashion_dataset/blue_dress/00000058.jpg  \n",
            "  inflating: fashion_dataset/blue_dress/00000059.jpg  \n",
            "  inflating: fashion_dataset/blue_dress/00000060.jpg  \n",
            "  inflating: fashion_dataset/blue_dress/00000061.jpg  \n",
            "  inflating: fashion_dataset/blue_dress/00000064.jpg  \n",
            "  inflating: fashion_dataset/blue_dress/00000065.jpg  \n",
            "  inflating: fashion_dataset/blue_dress/00000066.jpg  \n",
            "  inflating: fashion_dataset/blue_dress/00000067.jpg  \n",
            "  inflating: fashion_dataset/blue_dress/00000068.jpg  \n",
            "  inflating: fashion_dataset/blue_dress/00000069.jpg  \n",
            "  inflating: fashion_dataset/blue_dress/00000071.jpg  \n",
            "  inflating: fashion_dataset/blue_dress/00000074.jpg  \n",
            "  inflating: fashion_dataset/blue_dress/00000075.jpg  \n",
            "  inflating: fashion_dataset/blue_dress/00000076.jpg  \n",
            "  inflating: fashion_dataset/blue_dress/00000077.jpg  \n",
            "  inflating: fashion_dataset/blue_dress/00000079.jpg  \n",
            "  inflating: fashion_dataset/blue_dress/00000080.jpg  \n",
            "  inflating: fashion_dataset/blue_dress/00000081.jpg  \n",
            "  inflating: fashion_dataset/blue_dress/00000084.jpg  \n",
            "  inflating: fashion_dataset/blue_dress/00000085.jpg  \n",
            "  inflating: fashion_dataset/blue_dress/00000086.jpg  \n",
            "  inflating: fashion_dataset/blue_dress/00000087.jpg  \n",
            "  inflating: fashion_dataset/blue_dress/00000088.jpg  \n",
            "  inflating: fashion_dataset/blue_dress/00000089.jpg  \n",
            "  inflating: fashion_dataset/blue_dress/00000090.jpg  \n",
            "  inflating: fashion_dataset/blue_dress/00000091.jpg  \n",
            "  inflating: fashion_dataset/blue_dress/00000094.jpg  \n",
            "  inflating: fashion_dataset/blue_dress/00000095.jpg  \n",
            "  inflating: fashion_dataset/blue_dress/00000097.jpg  \n",
            "  inflating: fashion_dataset/blue_dress/00000100.jpg  \n",
            "  inflating: fashion_dataset/blue_dress/00000101.jpg  \n",
            "  inflating: fashion_dataset/blue_dress/00000104.jpg  \n",
            "  inflating: fashion_dataset/blue_dress/00000105.jpg  \n",
            "  inflating: fashion_dataset/blue_dress/00000106.jpg  \n",
            "  inflating: fashion_dataset/blue_dress/00000107.jpg  \n",
            "  inflating: fashion_dataset/blue_dress/00000108.jpg  \n",
            "  inflating: fashion_dataset/blue_dress/00000109.jpg  \n",
            "  inflating: fashion_dataset/blue_dress/00000110.jpg  \n",
            "  inflating: fashion_dataset/blue_dress/00000111.jpg  \n",
            "  inflating: fashion_dataset/blue_dress/00000114.jpg  \n",
            "  inflating: fashion_dataset/blue_dress/00000115.jpg  \n",
            "  inflating: fashion_dataset/blue_dress/00000116.jpg  \n",
            "  inflating: fashion_dataset/blue_dress/00000117.jpg  \n",
            "  inflating: fashion_dataset/blue_dress/00000118.jpg  \n",
            "  inflating: fashion_dataset/blue_dress/00000119.jpg  \n",
            "  inflating: fashion_dataset/blue_dress/00000141.jpg  \n",
            "  inflating: fashion_dataset/blue_dress/00000144.jpg  \n",
            "  inflating: fashion_dataset/blue_dress/00000146.jpg  \n",
            "  inflating: fashion_dataset/blue_dress/00000147.jpg  \n",
            "  inflating: fashion_dataset/blue_dress/00000148.jpg  \n",
            "  inflating: fashion_dataset/blue_dress/00000149.jpg  \n",
            "  inflating: fashion_dataset/blue_dress/00000150.jpg  \n",
            "  inflating: fashion_dataset/blue_dress/00000155.jpg  \n",
            "  inflating: fashion_dataset/blue_dress/00000156.jpg  \n",
            "  inflating: fashion_dataset/blue_dress/00000157.jpg  \n",
            "  inflating: fashion_dataset/blue_dress/00000158.jpg  \n",
            "  inflating: fashion_dataset/blue_dress/00000159.jpg  \n",
            "  inflating: fashion_dataset/blue_dress/00000160.jpg  \n",
            "  inflating: fashion_dataset/blue_dress/00000161.jpg  \n",
            "  inflating: fashion_dataset/blue_dress/00000164.jpg  \n",
            "  inflating: fashion_dataset/blue_dress/00000166.jpg  \n",
            "  inflating: fashion_dataset/blue_dress/00000167.jpg  \n",
            "  inflating: fashion_dataset/blue_dress/00000168.jpg  \n",
            "  inflating: fashion_dataset/blue_dress/00000169.jpg  \n",
            "  inflating: fashion_dataset/blue_dress/00000170.jpg  \n",
            "  inflating: fashion_dataset/blue_dress/00000171.jpg  \n",
            "  inflating: fashion_dataset/blue_dress/00000174.jpg  \n",
            "  inflating: fashion_dataset/blue_dress/00000176.jpg  \n",
            "  inflating: fashion_dataset/blue_dress/00000177.jpg  \n",
            "  inflating: fashion_dataset/blue_dress/00000178.jpg  \n",
            "  inflating: fashion_dataset/blue_dress/00000179.jpg  \n",
            "  inflating: fashion_dataset/blue_dress/00000180.jpg  \n",
            "  inflating: fashion_dataset/blue_dress/00000181.jpg  \n",
            "  inflating: fashion_dataset/blue_dress/00000184.jpg  \n",
            "  inflating: fashion_dataset/blue_dress/00000185.jpg  \n",
            "  inflating: fashion_dataset/blue_dress/00000186.jpg  \n",
            "  inflating: fashion_dataset/blue_dress/00000187.jpg  \n",
            "  inflating: fashion_dataset/blue_dress/00000188.jpg  \n",
            "  inflating: fashion_dataset/blue_dress/00000189.jpg  \n",
            "  inflating: fashion_dataset/blue_dress/00000190.jpg  \n",
            "  inflating: fashion_dataset/blue_dress/00000191.jpg  \n",
            "  inflating: fashion_dataset/blue_dress/00000194.jpg  \n",
            "  inflating: fashion_dataset/blue_dress/00000195.jpg  \n",
            "  inflating: fashion_dataset/blue_dress/00000196.jpg  \n",
            "  inflating: fashion_dataset/blue_dress/00000197.jpg  \n",
            "  inflating: fashion_dataset/blue_dress/00000198.jpg  \n",
            "  inflating: fashion_dataset/blue_dress/00000199.jpg  \n",
            "   creating: fashion_dataset/blue_jeans/\n",
            "  inflating: fashion_dataset/blue_jeans/00000000.jpg  \n",
            "  inflating: fashion_dataset/blue_jeans/00000004.jpg  \n",
            "  inflating: fashion_dataset/blue_jeans/00000005.jpg  \n",
            "  inflating: fashion_dataset/blue_jeans/00000006.jpg  \n",
            "  inflating: fashion_dataset/blue_jeans/00000007.jpg  \n",
            "  inflating: fashion_dataset/blue_jeans/00000009.jpg  \n",
            "  inflating: fashion_dataset/blue_jeans/00000010.jpg  \n",
            "  inflating: fashion_dataset/blue_jeans/00000011.jpg  \n",
            "  inflating: fashion_dataset/blue_jeans/00000014.jpg  \n",
            "  inflating: fashion_dataset/blue_jeans/00000015.jpg  \n",
            "  inflating: fashion_dataset/blue_jeans/00000016.jpg  \n",
            "  inflating: fashion_dataset/blue_jeans/00000017.jpg  \n",
            "  inflating: fashion_dataset/blue_jeans/00000019.jpg  \n",
            "  inflating: fashion_dataset/blue_jeans/00000040.jpg  \n",
            "  inflating: fashion_dataset/blue_jeans/00000041.jpg  \n",
            "  inflating: fashion_dataset/blue_jeans/00000044.jpg  \n",
            "  inflating: fashion_dataset/blue_jeans/00000045.jpg  \n",
            "  inflating: fashion_dataset/blue_jeans/00000046.jpg  \n",
            "  inflating: fashion_dataset/blue_jeans/00000047.jpg  \n",
            "  inflating: fashion_dataset/blue_jeans/00000049.jpg  \n",
            "  inflating: fashion_dataset/blue_jeans/00000050.jpg  \n",
            "  inflating: fashion_dataset/blue_jeans/00000051.jpg  \n",
            "  inflating: fashion_dataset/blue_jeans/00000054.jpg  \n",
            "  inflating: fashion_dataset/blue_jeans/00000055.jpg  \n",
            "  inflating: fashion_dataset/blue_jeans/00000056.jpg  \n",
            "  inflating: fashion_dataset/blue_jeans/00000057.jpg  \n",
            "  inflating: fashion_dataset/blue_jeans/00000058.jpg  \n",
            "  inflating: fashion_dataset/blue_jeans/00000059.jpg  \n",
            "  inflating: fashion_dataset/blue_jeans/00000061.jpg  \n",
            "  inflating: fashion_dataset/blue_jeans/00000064.jpg  \n",
            "  inflating: fashion_dataset/blue_jeans/00000065.jpg  \n",
            "  inflating: fashion_dataset/blue_jeans/00000067.jpg  \n",
            "  inflating: fashion_dataset/blue_jeans/00000069.jpg  \n",
            "  inflating: fashion_dataset/blue_jeans/00000071.jpg  \n",
            "  inflating: fashion_dataset/blue_jeans/00000074.jpg  \n",
            "  inflating: fashion_dataset/blue_jeans/00000077.jpg  \n",
            "  inflating: fashion_dataset/blue_jeans/00000080.jpg  \n",
            "  inflating: fashion_dataset/blue_jeans/00000081.jpg  \n",
            "  inflating: fashion_dataset/blue_jeans/00000084.jpg  \n",
            "  inflating: fashion_dataset/blue_jeans/00000086.jpg  \n",
            "  inflating: fashion_dataset/blue_jeans/00000087.jpg  \n",
            "  inflating: fashion_dataset/blue_jeans/00000088.jpg  \n",
            "  inflating: fashion_dataset/blue_jeans/00000089.jpg  \n",
            "  inflating: fashion_dataset/blue_jeans/00000090.jpg  \n",
            "  inflating: fashion_dataset/blue_jeans/00000091.jpg  \n",
            "  inflating: fashion_dataset/blue_jeans/00000094.jpg  \n",
            "  inflating: fashion_dataset/blue_jeans/00000095.jpg  \n",
            "  inflating: fashion_dataset/blue_jeans/00000096.jpg  \n",
            "  inflating: fashion_dataset/blue_jeans/00000097.jpg  \n",
            "  inflating: fashion_dataset/blue_jeans/00000098.jpg  \n",
            "  inflating: fashion_dataset/blue_jeans/00000099.jpg  \n",
            "  inflating: fashion_dataset/blue_jeans/00000100.jpg  \n",
            "  inflating: fashion_dataset/blue_jeans/00000104.jpg  \n",
            "  inflating: fashion_dataset/blue_jeans/00000105.jpg  \n",
            "  inflating: fashion_dataset/blue_jeans/00000106.jpg  \n",
            "  inflating: fashion_dataset/blue_jeans/00000108.jpg  \n",
            "  inflating: fashion_dataset/blue_jeans/00000109.jpg  \n",
            "  inflating: fashion_dataset/blue_jeans/00000110.jpg  \n",
            "  inflating: fashion_dataset/blue_jeans/00000111.jpg  \n",
            "  inflating: fashion_dataset/blue_jeans/00000117.jpg  \n",
            "  inflating: fashion_dataset/blue_jeans/00000118.jpg  \n",
            "  inflating: fashion_dataset/blue_jeans/00000119.jpg  \n",
            "  inflating: fashion_dataset/blue_jeans/00000140.jpg  \n",
            "  inflating: fashion_dataset/blue_jeans/00000141.jpg  \n",
            "  inflating: fashion_dataset/blue_jeans/00000144.jpg  \n",
            "  inflating: fashion_dataset/blue_jeans/00000146.jpg  \n",
            "  inflating: fashion_dataset/blue_jeans/00000148.jpg  \n",
            "  inflating: fashion_dataset/blue_jeans/00000149.jpg  \n",
            "  inflating: fashion_dataset/blue_jeans/00000150.jpg  \n",
            "  inflating: fashion_dataset/blue_jeans/00000151.jpg  \n",
            "  inflating: fashion_dataset/blue_jeans/00000154.jpg  \n",
            "  inflating: fashion_dataset/blue_jeans/00000155.jpg  \n",
            "  inflating: fashion_dataset/blue_jeans/00000156.jpg  \n",
            "  inflating: fashion_dataset/blue_jeans/00000157.jpg  \n",
            "  inflating: fashion_dataset/blue_jeans/00000158.jpg  \n",
            "  inflating: fashion_dataset/blue_jeans/00000159.jpg  \n",
            "  inflating: fashion_dataset/blue_jeans/00000160.jpg  \n",
            "  inflating: fashion_dataset/blue_jeans/00000161.jpg  \n",
            "  inflating: fashion_dataset/blue_jeans/00000164.jpg  \n",
            "  inflating: fashion_dataset/blue_jeans/00000165.png  \n",
            "  inflating: fashion_dataset/blue_jeans/00000167.jpg  \n",
            "  inflating: fashion_dataset/blue_jeans/00000168.jpg  \n",
            "  inflating: fashion_dataset/blue_jeans/00000169.jpg  \n",
            "  inflating: fashion_dataset/blue_jeans/00000170.jpg  \n",
            "  inflating: fashion_dataset/blue_jeans/00000171.png  \n",
            "  inflating: fashion_dataset/blue_jeans/00000174.jpg  \n",
            "  inflating: fashion_dataset/blue_jeans/00000175.jpg  \n",
            "  inflating: fashion_dataset/blue_jeans/00000177.jpg  \n",
            "  inflating: fashion_dataset/blue_jeans/00000180.jpg  \n",
            "  inflating: fashion_dataset/blue_jeans/00000186.jpg  \n",
            "  inflating: fashion_dataset/blue_jeans/00000187.jpg  \n",
            "  inflating: fashion_dataset/blue_jeans/00000188.jpg  \n",
            "  inflating: fashion_dataset/blue_jeans/00000189.jpg  \n",
            "  inflating: fashion_dataset/blue_jeans/00000190.jpg  \n",
            "  inflating: fashion_dataset/blue_jeans/00000194.jpg  \n",
            "  inflating: fashion_dataset/blue_jeans/00000195.png  \n",
            "  inflating: fashion_dataset/blue_jeans/00000196.jpg  \n",
            "  inflating: fashion_dataset/blue_jeans/00000198.jpg  \n",
            "   creating: fashion_dataset/blue_shirt/\n",
            "  inflating: fashion_dataset/blue_shirt/00000000.jpg  \n",
            "  inflating: fashion_dataset/blue_shirt/00000001.jpg  \n",
            "  inflating: fashion_dataset/blue_shirt/00000004.jpg  \n",
            "  inflating: fashion_dataset/blue_shirt/00000005.jpg  \n",
            "  inflating: fashion_dataset/blue_shirt/00000006.jpg  \n",
            "  inflating: fashion_dataset/blue_shirt/00000007.jpg  \n",
            "  inflating: fashion_dataset/blue_shirt/00000008.jpg  \n",
            "  inflating: fashion_dataset/blue_shirt/00000009.JPG  \n",
            "  inflating: fashion_dataset/blue_shirt/00000010.jpg  \n",
            "  inflating: fashion_dataset/blue_shirt/00000011.jpg  \n",
            "  inflating: fashion_dataset/blue_shirt/00000014.jpg  \n",
            "  inflating: fashion_dataset/blue_shirt/00000015.jpg  \n",
            "  inflating: fashion_dataset/blue_shirt/00000016.jpg  \n",
            "  inflating: fashion_dataset/blue_shirt/00000017.jpg  \n",
            "  inflating: fashion_dataset/blue_shirt/00000018.jpg  \n",
            "  inflating: fashion_dataset/blue_shirt/00000019.jpg  \n",
            "  inflating: fashion_dataset/blue_shirt/00000040.png  \n",
            "  inflating: fashion_dataset/blue_shirt/00000041.jpg  \n",
            "  inflating: fashion_dataset/blue_shirt/00000044.jpg  \n",
            "  inflating: fashion_dataset/blue_shirt/00000045.jpg  \n",
            "  inflating: fashion_dataset/blue_shirt/00000046.jpg  \n",
            "  inflating: fashion_dataset/blue_shirt/00000049.jpg  \n",
            "  inflating: fashion_dataset/blue_shirt/00000050.jpg  \n",
            "  inflating: fashion_dataset/blue_shirt/00000051.jpg  \n",
            "  inflating: fashion_dataset/blue_shirt/00000054.jpg  \n",
            "  inflating: fashion_dataset/blue_shirt/00000055.jpg  \n",
            "  inflating: fashion_dataset/blue_shirt/00000056.png  \n",
            "  inflating: fashion_dataset/blue_shirt/00000057.jpg  \n",
            "  inflating: fashion_dataset/blue_shirt/00000058.jpg  \n",
            "  inflating: fashion_dataset/blue_shirt/00000060.jpg  \n",
            "  inflating: fashion_dataset/blue_shirt/00000061.jpg  \n",
            "  inflating: fashion_dataset/blue_shirt/00000064.jpg  \n",
            "  inflating: fashion_dataset/blue_shirt/00000065.jpg  \n",
            "  inflating: fashion_dataset/blue_shirt/00000067.jpg  \n",
            "  inflating: fashion_dataset/blue_shirt/00000068.jpg  \n",
            "  inflating: fashion_dataset/blue_shirt/00000069.jpg  \n",
            "  inflating: fashion_dataset/blue_shirt/00000070.jpg  \n",
            "  inflating: fashion_dataset/blue_shirt/00000071.jpg  \n",
            "  inflating: fashion_dataset/blue_shirt/00000074.jpg  \n",
            "  inflating: fashion_dataset/blue_shirt/00000075.jpg  \n",
            "  inflating: fashion_dataset/blue_shirt/00000078.jpg  \n",
            "  inflating: fashion_dataset/blue_shirt/00000079.jpg  \n",
            "  inflating: fashion_dataset/blue_shirt/00000080.jpg  \n",
            "  inflating: fashion_dataset/blue_shirt/00000081.jpg  \n",
            "  inflating: fashion_dataset/blue_shirt/00000084.jpg  \n",
            "  inflating: fashion_dataset/blue_shirt/00000085.jpg  \n",
            "  inflating: fashion_dataset/blue_shirt/00000086.jpg  \n",
            "  inflating: fashion_dataset/blue_shirt/00000088.jpg  \n",
            "  inflating: fashion_dataset/blue_shirt/00000089.jpg  \n",
            "  inflating: fashion_dataset/blue_shirt/00000090.jpg  \n",
            "  inflating: fashion_dataset/blue_shirt/00000091.png  \n",
            "  inflating: fashion_dataset/blue_shirt/00000094.jpg  \n",
            "  inflating: fashion_dataset/blue_shirt/00000095.jpg  \n",
            "  inflating: fashion_dataset/blue_shirt/00000096.jpg  \n",
            "  inflating: fashion_dataset/blue_shirt/00000097.jpg  \n",
            "  inflating: fashion_dataset/blue_shirt/00000098.jpg  \n",
            "  inflating: fashion_dataset/blue_shirt/00000099.png  \n",
            "  inflating: fashion_dataset/blue_shirt/00000100.png  \n",
            "  inflating: fashion_dataset/blue_shirt/00000101.jpg  \n",
            "  inflating: fashion_dataset/blue_shirt/00000104.jpg  \n",
            "  inflating: fashion_dataset/blue_shirt/00000105.jpg  \n",
            "  inflating: fashion_dataset/blue_shirt/00000106.png  \n",
            "  inflating: fashion_dataset/blue_shirt/00000107.jpg  \n",
            "  inflating: fashion_dataset/blue_shirt/00000108.jpg  \n",
            "  inflating: fashion_dataset/blue_shirt/00000109.jpg  \n",
            "  inflating: fashion_dataset/blue_shirt/00000110.jpg  \n",
            "  inflating: fashion_dataset/blue_shirt/00000111.jpg  \n",
            "  inflating: fashion_dataset/blue_shirt/00000114.jpg  \n",
            "  inflating: fashion_dataset/blue_shirt/00000115.jpg  \n",
            "  inflating: fashion_dataset/blue_shirt/00000116.jpg  \n",
            "  inflating: fashion_dataset/blue_shirt/00000117.jpg  \n",
            "  inflating: fashion_dataset/blue_shirt/00000118.jpg  \n",
            "  inflating: fashion_dataset/blue_shirt/00000119.jpg  \n",
            "  inflating: fashion_dataset/blue_shirt/00000140.jpg  \n",
            "  inflating: fashion_dataset/blue_shirt/00000141.jpg  \n",
            "  inflating: fashion_dataset/blue_shirt/00000144.JPG  \n",
            "  inflating: fashion_dataset/blue_shirt/00000146.jpg  \n",
            "  inflating: fashion_dataset/blue_shirt/00000147.jpg  \n",
            "  inflating: fashion_dataset/blue_shirt/00000148.jpg  \n",
            "  inflating: fashion_dataset/blue_shirt/00000149.jpg  \n",
            "  inflating: fashion_dataset/blue_shirt/00000151.jpg  \n",
            "  inflating: fashion_dataset/blue_shirt/00000154.jpg  \n",
            "  inflating: fashion_dataset/blue_shirt/00000155.jpg  \n",
            "  inflating: fashion_dataset/blue_shirt/00000156.JPG  \n",
            "  inflating: fashion_dataset/blue_shirt/00000157.jpg  \n",
            "  inflating: fashion_dataset/blue_shirt/00000158.jpg  \n",
            "  inflating: fashion_dataset/blue_shirt/00000159.jpg  \n",
            "  inflating: fashion_dataset/blue_shirt/00000160.jpg  \n",
            "  inflating: fashion_dataset/blue_shirt/00000161.jpg  \n",
            "  inflating: fashion_dataset/blue_shirt/00000164.jpg  \n",
            "  inflating: fashion_dataset/blue_shirt/00000165.jpg  \n",
            "  inflating: fashion_dataset/blue_shirt/00000166.jpg  \n",
            "  inflating: fashion_dataset/blue_shirt/00000167.jpg  \n",
            "  inflating: fashion_dataset/blue_shirt/00000168.jpg  \n",
            "  inflating: fashion_dataset/blue_shirt/00000169.png  \n",
            "  inflating: fashion_dataset/blue_shirt/00000171.jpg  \n",
            "  inflating: fashion_dataset/blue_shirt/00000174.jpg  \n",
            "  inflating: fashion_dataset/blue_shirt/00000175.jpg  \n",
            "  inflating: fashion_dataset/blue_shirt/00000178.jpg  \n",
            "  inflating: fashion_dataset/blue_shirt/00000179.jpg  \n",
            "  inflating: fashion_dataset/blue_shirt/00000180.jpg  \n",
            "  inflating: fashion_dataset/blue_shirt/00000181.png  \n",
            "  inflating: fashion_dataset/blue_shirt/00000184.jpg  \n",
            "  inflating: fashion_dataset/blue_shirt/00000185.jpg  \n",
            "  inflating: fashion_dataset/blue_shirt/00000186.jpg  \n",
            "  inflating: fashion_dataset/blue_shirt/00000187.jpg  \n",
            "  inflating: fashion_dataset/blue_shirt/00000190.jpg  \n",
            "  inflating: fashion_dataset/blue_shirt/00000191.jpg  \n",
            "  inflating: fashion_dataset/blue_shirt/00000194.jpg  \n",
            "  inflating: fashion_dataset/blue_shirt/00000195.jpg  \n",
            "  inflating: fashion_dataset/blue_shirt/00000196.jpg  \n",
            "  inflating: fashion_dataset/blue_shirt/00000197.jpg  \n",
            "  inflating: fashion_dataset/blue_shirt/00000198.jpg  \n",
            "   creating: fashion_dataset/red_dress/\n",
            "  inflating: fashion_dataset/red_dress/00000000.jpg  \n",
            "  inflating: fashion_dataset/red_dress/00000001.jpg  \n",
            "  inflating: fashion_dataset/red_dress/00000004.jpg  \n",
            "  inflating: fashion_dataset/red_dress/00000005.jpg  \n",
            "  inflating: fashion_dataset/red_dress/00000006.jpg  \n",
            "  inflating: fashion_dataset/red_dress/00000007.jpg  \n",
            "  inflating: fashion_dataset/red_dress/00000008.jpg  \n",
            "  inflating: fashion_dataset/red_dress/00000009.JPG  \n",
            "  inflating: fashion_dataset/red_dress/00000010.jpg  \n",
            "  inflating: fashion_dataset/red_dress/00000011.jpg  \n",
            "  inflating: fashion_dataset/red_dress/00000014.jpg  \n",
            "  inflating: fashion_dataset/red_dress/00000015.jpg  \n",
            "  inflating: fashion_dataset/red_dress/00000016.jpg  \n",
            "  inflating: fashion_dataset/red_dress/00000017.jpg  \n",
            "  inflating: fashion_dataset/red_dress/00000018.jpg  \n",
            "  inflating: fashion_dataset/red_dress/00000019.jpg  \n",
            "  inflating: fashion_dataset/red_dress/00000040.jpg  \n",
            "  inflating: fashion_dataset/red_dress/00000041.jpg  \n",
            "  inflating: fashion_dataset/red_dress/00000044.jpg  \n",
            "  inflating: fashion_dataset/red_dress/00000045.jpg  \n",
            "  inflating: fashion_dataset/red_dress/00000046.jpg  \n",
            "  inflating: fashion_dataset/red_dress/00000047.png  \n",
            "  inflating: fashion_dataset/red_dress/00000048.jpg  \n",
            "  inflating: fashion_dataset/red_dress/00000049.png  \n",
            "  inflating: fashion_dataset/red_dress/00000050.jpg  \n",
            "  inflating: fashion_dataset/red_dress/00000051.jpg  \n",
            "  inflating: fashion_dataset/red_dress/00000054.jpg  \n",
            "  inflating: fashion_dataset/red_dress/00000055.jpg  \n",
            "  inflating: fashion_dataset/red_dress/00000056.png  \n",
            "  inflating: fashion_dataset/red_dress/00000057.jpg  \n",
            "  inflating: fashion_dataset/red_dress/00000058.jpg  \n",
            "  inflating: fashion_dataset/red_dress/00000059.jpg  \n",
            "  inflating: fashion_dataset/red_dress/00000060.jpg  \n",
            "  inflating: fashion_dataset/red_dress/00000061.jpg  \n",
            "  inflating: fashion_dataset/red_dress/00000064.jpg  \n",
            "  inflating: fashion_dataset/red_dress/00000065.jpg  \n",
            "  inflating: fashion_dataset/red_dress/00000066.jpg  \n",
            "  inflating: fashion_dataset/red_dress/00000067.jpg  \n",
            "  inflating: fashion_dataset/red_dress/00000069.jpg  \n",
            "  inflating: fashion_dataset/red_dress/00000071.jpg  \n",
            "  inflating: fashion_dataset/red_dress/00000074.jpg  \n",
            "  inflating: fashion_dataset/red_dress/00000075.jpg  \n",
            "  inflating: fashion_dataset/red_dress/00000076.jpg  \n",
            "  inflating: fashion_dataset/red_dress/00000077.jpg  \n",
            "  inflating: fashion_dataset/red_dress/00000078.jpg  \n",
            "  inflating: fashion_dataset/red_dress/00000079.jpg  \n",
            "  inflating: fashion_dataset/red_dress/00000080.JPG  \n",
            "  inflating: fashion_dataset/red_dress/00000081.jpg  \n",
            "  inflating: fashion_dataset/red_dress/00000085.jpg  \n",
            "  inflating: fashion_dataset/red_dress/00000086.jpg  \n",
            "  inflating: fashion_dataset/red_dress/00000087.jpg  \n",
            "  inflating: fashion_dataset/red_dress/00000088.jpg  \n",
            "  inflating: fashion_dataset/red_dress/00000089.jpg  \n",
            "  inflating: fashion_dataset/red_dress/00000090.jpg  \n",
            "  inflating: fashion_dataset/red_dress/00000094.jpg  \n",
            "  inflating: fashion_dataset/red_dress/00000095.jpg  \n",
            "  inflating: fashion_dataset/red_dress/00000096.jpg  \n",
            "  inflating: fashion_dataset/red_dress/00000097.jpg  \n",
            "  inflating: fashion_dataset/red_dress/00000098.jpg  \n",
            "  inflating: fashion_dataset/red_dress/00000099.jpg  \n",
            "  inflating: fashion_dataset/red_dress/00000100.jpg  \n",
            "  inflating: fashion_dataset/red_dress/00000101.jpg  \n",
            "  inflating: fashion_dataset/red_dress/00000104.jpg  \n",
            "  inflating: fashion_dataset/red_dress/00000105.jpg  \n",
            "  inflating: fashion_dataset/red_dress/00000106.jpg  \n",
            "  inflating: fashion_dataset/red_dress/00000107.jpg  \n",
            "  inflating: fashion_dataset/red_dress/00000108.jpg  \n",
            "  inflating: fashion_dataset/red_dress/00000109.jpg  \n",
            "  inflating: fashion_dataset/red_dress/00000110.jpg  \n",
            "  inflating: fashion_dataset/red_dress/00000111.jpg  \n",
            "  inflating: fashion_dataset/red_dress/00000114.jpg  \n",
            "  inflating: fashion_dataset/red_dress/00000115.jpg  \n",
            "  inflating: fashion_dataset/red_dress/00000116.gif  \n",
            "  inflating: fashion_dataset/red_dress/00000117.jpg  \n",
            "  inflating: fashion_dataset/red_dress/00000118.jpg  \n",
            "  inflating: fashion_dataset/red_dress/00000140.jpg  \n",
            "  inflating: fashion_dataset/red_dress/00000141.jpg  \n",
            "  inflating: fashion_dataset/red_dress/00000146.jpg  \n",
            "  inflating: fashion_dataset/red_dress/00000147.jpg  \n",
            "  inflating: fashion_dataset/red_dress/00000148.jpg  \n",
            "  inflating: fashion_dataset/red_dress/00000149.jpg  \n",
            "  inflating: fashion_dataset/red_dress/00000151.jpg  \n",
            "  inflating: fashion_dataset/red_dress/00000154.jpg  \n",
            "  inflating: fashion_dataset/red_dress/00000155.jpg  \n",
            "  inflating: fashion_dataset/red_dress/00000156.jpg  \n",
            "  inflating: fashion_dataset/red_dress/00000158.jpg  \n",
            "  inflating: fashion_dataset/red_dress/00000159.jpg  \n",
            "  inflating: fashion_dataset/red_dress/00000160.jpg  \n",
            "  inflating: fashion_dataset/red_dress/00000161.jpg  \n",
            "  inflating: fashion_dataset/red_dress/00000164.jpg  \n",
            "  inflating: fashion_dataset/red_dress/00000165.jpg  \n",
            "  inflating: fashion_dataset/red_dress/00000166.jpg  \n",
            "  inflating: fashion_dataset/red_dress/00000167.jpg  \n",
            "  inflating: fashion_dataset/red_dress/00000168.jpg  \n",
            "  inflating: fashion_dataset/red_dress/00000169.jpg  \n",
            "  inflating: fashion_dataset/red_dress/00000170.jpg  \n",
            "  inflating: fashion_dataset/red_dress/00000174.jpg  \n",
            "  inflating: fashion_dataset/red_dress/00000175.jpg  \n",
            "  inflating: fashion_dataset/red_dress/00000176.jpg  \n",
            "  inflating: fashion_dataset/red_dress/00000177.jpg  \n",
            "  inflating: fashion_dataset/red_dress/00000178.jpg  \n",
            "  inflating: fashion_dataset/red_dress/00000179.jpg  \n",
            "  inflating: fashion_dataset/red_dress/00000181.jpg  \n",
            "  inflating: fashion_dataset/red_dress/00000184.jpg  \n",
            "  inflating: fashion_dataset/red_dress/00000185.jpg  \n",
            "  inflating: fashion_dataset/red_dress/00000186.jpg  \n",
            "  inflating: fashion_dataset/red_dress/00000187.jpg  \n",
            "  inflating: fashion_dataset/red_dress/00000188.jpg  \n",
            "  inflating: fashion_dataset/red_dress/00000189.jpg  \n",
            "  inflating: fashion_dataset/red_dress/00000190.jpg  \n",
            "  inflating: fashion_dataset/red_dress/00000191.jpg  \n",
            "  inflating: fashion_dataset/red_dress/00000194.jpg  \n",
            "  inflating: fashion_dataset/red_dress/00000195.jpg  \n",
            "  inflating: fashion_dataset/red_dress/00000196.jpg  \n",
            "  inflating: fashion_dataset/red_dress/00000197.jpg  \n",
            "  inflating: fashion_dataset/red_dress/00000198.jpg  \n",
            "  inflating: fashion_dataset/red_dress/00000199.jpg  \n",
            "   creating: fashion_dataset/red_shirt/\n",
            "  inflating: fashion_dataset/red_shirt/00000000.jpg  \n",
            "  inflating: fashion_dataset/red_shirt/00000001.png  \n",
            "  inflating: fashion_dataset/red_shirt/00000004.jpg  \n",
            "  inflating: fashion_dataset/red_shirt/00000005.png  \n",
            "  inflating: fashion_dataset/red_shirt/00000006.png  \n",
            "  inflating: fashion_dataset/red_shirt/00000007.png  \n",
            "  inflating: fashion_dataset/red_shirt/00000008.png  \n",
            "  inflating: fashion_dataset/red_shirt/00000011.jpg  \n",
            "  inflating: fashion_dataset/red_shirt/00000014.jpg  \n",
            "  inflating: fashion_dataset/red_shirt/00000015.jpg  \n",
            "  inflating: fashion_dataset/red_shirt/00000016.jpg  \n",
            "  inflating: fashion_dataset/red_shirt/00000018.jpg  \n",
            "  inflating: fashion_dataset/red_shirt/00000019.png  \n",
            "  inflating: fashion_dataset/red_shirt/00000040.png  \n",
            "  inflating: fashion_dataset/red_shirt/00000041.jpg  \n",
            "  inflating: fashion_dataset/red_shirt/00000044.png  \n",
            "  inflating: fashion_dataset/red_shirt/00000045.jpg  \n",
            "  inflating: fashion_dataset/red_shirt/00000046.jpg  \n",
            "  inflating: fashion_dataset/red_shirt/00000047.jpg  \n",
            "  inflating: fashion_dataset/red_shirt/00000048.jpg  \n",
            "  inflating: fashion_dataset/red_shirt/00000049.jpg  \n",
            "  inflating: fashion_dataset/red_shirt/00000050.jpg  \n",
            "  inflating: fashion_dataset/red_shirt/00000051.jpg  \n",
            "  inflating: fashion_dataset/red_shirt/00000054.jpg  \n",
            "  inflating: fashion_dataset/red_shirt/00000055.jpg  \n",
            "  inflating: fashion_dataset/red_shirt/00000056.jpg  \n",
            "  inflating: fashion_dataset/red_shirt/00000057.jpg  \n",
            "  inflating: fashion_dataset/red_shirt/00000058.png  \n",
            "  inflating: fashion_dataset/red_shirt/00000059.jpg  \n",
            "  inflating: fashion_dataset/red_shirt/00000060.jpg  \n",
            "  inflating: fashion_dataset/red_shirt/00000064.jpg  \n",
            "  inflating: fashion_dataset/red_shirt/00000065.jpg  \n",
            "  inflating: fashion_dataset/red_shirt/00000066.jpg  \n",
            "  inflating: fashion_dataset/red_shirt/00000067.jpg  \n",
            "  inflating: fashion_dataset/red_shirt/00000068.jpg  \n",
            "  inflating: fashion_dataset/red_shirt/00000069.jpg  \n",
            "  inflating: fashion_dataset/red_shirt/00000070.jpg  \n",
            "  inflating: fashion_dataset/red_shirt/00000071.jpg  \n",
            "  inflating: fashion_dataset/red_shirt/00000074.jpg  \n",
            "  inflating: fashion_dataset/red_shirt/00000075.jpg  \n",
            "  inflating: fashion_dataset/red_shirt/00000076.png  \n",
            "  inflating: fashion_dataset/red_shirt/00000077.jpg  \n",
            "  inflating: fashion_dataset/red_shirt/00000078.jpg  \n",
            "  inflating: fashion_dataset/red_shirt/00000079.jpg  \n",
            "  inflating: fashion_dataset/red_shirt/00000080.jpg  \n",
            "  inflating: fashion_dataset/red_shirt/00000081.png  \n",
            "  inflating: fashion_dataset/red_shirt/00000084.jpg  \n",
            "  inflating: fashion_dataset/red_shirt/00000085.jpg  \n",
            "  inflating: fashion_dataset/red_shirt/00000089.jpg  \n",
            "  inflating: fashion_dataset/red_shirt/00000091.jpg  \n",
            "  inflating: fashion_dataset/red_shirt/00000094.jpg  \n",
            "  inflating: fashion_dataset/red_shirt/00000095.jpg  \n",
            "  inflating: fashion_dataset/red_shirt/00000096.jpg  \n",
            "  inflating: fashion_dataset/red_shirt/00000097.jpg  \n",
            "  inflating: fashion_dataset/red_shirt/00000098.jpg  \n",
            "  inflating: fashion_dataset/red_shirt/00000099.jpg  \n",
            "  inflating: fashion_dataset/red_shirt/00000100.jpg  \n",
            "  inflating: fashion_dataset/red_shirt/00000101.jpg  \n",
            "  inflating: fashion_dataset/red_shirt/00000105.jpg  \n",
            "  inflating: fashion_dataset/red_shirt/00000106.jpg  \n",
            "  inflating: fashion_dataset/red_shirt/00000107.jpg  \n",
            "  inflating: fashion_dataset/red_shirt/00000108.jpg  \n",
            "  inflating: fashion_dataset/red_shirt/00000110.jpg  \n",
            "  inflating: fashion_dataset/red_shirt/00000111.jpg  \n",
            "  inflating: fashion_dataset/red_shirt/00000114.jpg  \n",
            "  inflating: fashion_dataset/red_shirt/00000115.jpg  \n",
            "  inflating: fashion_dataset/red_shirt/00000116.jpg  \n",
            "  inflating: fashion_dataset/red_shirt/00000117.jpg  \n",
            "  inflating: fashion_dataset/red_shirt/00000140.jpg  \n",
            "  inflating: fashion_dataset/red_shirt/00000141.jpg  \n",
            "  inflating: fashion_dataset/red_shirt/00000144.jpg  \n",
            "  inflating: fashion_dataset/red_shirt/00000145.jpg  \n",
            "  inflating: fashion_dataset/red_shirt/00000146.jpg  \n",
            "  inflating: fashion_dataset/red_shirt/00000147.jpg  \n",
            "  inflating: fashion_dataset/red_shirt/00000149.jpg  \n",
            "  inflating: fashion_dataset/red_shirt/00000150.jpg  \n",
            "  inflating: fashion_dataset/red_shirt/00000151.jpg  \n",
            "  inflating: fashion_dataset/red_shirt/00000154.jpg  \n",
            "  inflating: fashion_dataset/red_shirt/00000155.jpg  \n",
            "  inflating: fashion_dataset/red_shirt/00000156.jpg  \n",
            "  inflating: fashion_dataset/red_shirt/00000157.jpg  \n",
            "  inflating: fashion_dataset/red_shirt/00000158.jpg  \n",
            "  inflating: fashion_dataset/red_shirt/00000159.jpg  \n",
            "  inflating: fashion_dataset/red_shirt/00000160.jpg  \n",
            "  inflating: fashion_dataset/red_shirt/00000161.jpg  \n",
            "  inflating: fashion_dataset/red_shirt/00000164.jpg  \n",
            "  inflating: fashion_dataset/red_shirt/00000165.jpg  \n",
            "  inflating: fashion_dataset/red_shirt/00000167.jpg  \n",
            "  inflating: fashion_dataset/red_shirt/00000168.jpg  \n",
            "  inflating: fashion_dataset/red_shirt/00000169.jpg  \n",
            "  inflating: fashion_dataset/red_shirt/00000170.jpg  \n",
            "  inflating: fashion_dataset/red_shirt/00000171.jpg  \n",
            "  inflating: fashion_dataset/red_shirt/00000174.jpg  \n",
            "  inflating: fashion_dataset/red_shirt/00000175.jpg  \n",
            "  inflating: fashion_dataset/red_shirt/00000176.jpg  \n",
            "  inflating: fashion_dataset/red_shirt/00000177.jpg  \n",
            "  inflating: fashion_dataset/red_shirt/00000178.jpg  \n",
            "  inflating: fashion_dataset/red_shirt/00000180.jpg  \n",
            "  inflating: fashion_dataset/red_shirt/00000181.jpg  \n",
            "  inflating: fashion_dataset/red_shirt/00000184.jpg  \n",
            "  inflating: fashion_dataset/red_shirt/00000187.jpg  \n",
            "  inflating: fashion_dataset/red_shirt/00000188.jpg  \n",
            "  inflating: fashion_dataset/red_shirt/00000190.jpg  \n",
            "  inflating: fashion_dataset/red_shirt/00000191.jpg  \n",
            "  inflating: fashion_dataset/red_shirt/00000195.jpg  \n",
            "  inflating: fashion_dataset/red_shirt/00000196.jpg  \n",
            "  inflating: fashion_dataset/red_shirt/00000197.jpg  \n",
            "  inflating: fashion_dataset/red_shirt/00000198.jpg  \n",
            "  inflating: fashion_dataset/red_shirt/00000199.jpg  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c48Y7utfiKHQ"
      },
      "source": [
        "```\n",
        "fashion_dataset/\n",
        "  black_jeans\n",
        "  black_shoes/\n",
        "  blue_dress/\n",
        "  blue_jeans\n",
        "  blue_shirt/\n",
        "  red_dress/\n",
        "  red_shirt/\n",
        "```\n",
        "\n",
        "```\n",
        "category : jeans, shoes, dress, shirt\n",
        "color : black, blue, red\n",
        "```"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8dodBjvth_d-"
      },
      "source": [
        "import matplotlib\n",
        "\n",
        "from tensorflow.keras.optimizers import Adam\n",
        "from tensorflow.keras.preprocessing.image import img_to_array\n",
        "from sklearn.preprocessing import LabelBinarizer\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "from imutils import paths\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import argparse\n",
        "import random\n",
        "import pickle\n",
        "import cv2\n",
        "import os"
      ],
      "execution_count": 62,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pD_ONY_4jtzK"
      },
      "source": [
        "IMAGE_DIMS = (96, 96, 3)"
      ],
      "execution_count": 63,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XTw_iOmOizug"
      },
      "source": [
        "### 영상 파일이름 읽기"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "w_f5QIPziT6R",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "66c65651-a081-4064-8091-6a44a633b60a"
      },
      "source": [
        "imagePaths = sorted(list(paths.list_images('fashion_dataset')))\n",
        "random.shuffle(imagePaths)\n",
        "print(len(imagePaths))\n",
        "print(imagePaths[:3])"
      ],
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "755\n",
            "['fashion_dataset/blue_shirt/00000061.jpg', 'fashion_dataset/red_dress/00000179.jpg', 'fashion_dataset/blue_jeans/00000090.jpg']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4gIJx0rTjdMj"
      },
      "source": [
        "### 영상 읽기, 카테고리와 컬러 읽기"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0cKf7OqHjXq-"
      },
      "source": [
        "data = []\n",
        "categoryLabels = []\n",
        "colorLabels = []\n",
        "\n",
        "for imagePath in imagePaths:\n",
        "\timage = cv2.imread(imagePath)\n",
        "\timage = cv2.resize(image, (IMAGE_DIMS[1], IMAGE_DIMS[0]))\n",
        "\timage = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n",
        "\timage = img_to_array(image)\n",
        "\tdata.append(image)\n",
        "\n",
        "\t(color, cat) = imagePath.split(os.path.sep)[-2].split(\"_\")\n",
        "  # imagePath = 'fashion_dataset/black_shoes/00000048.jpg'\n",
        "  # color = 'black'\n",
        "  # cat = 'shoes'\n",
        "\tcategoryLabels.append(cat)\n",
        "\tcolorLabels.append(color)"
      ],
      "execution_count": 65,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IME5dUwqilgV",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c2fd6875-861c-404a-dd90-c9ebaa271fa6"
      },
      "source": [
        "print(categoryLabels[:10])\n",
        "print(colorLabels[:10])"
      ],
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['shirt', 'dress', 'jeans', 'shirt', 'shoes', 'dress', 'shirt', 'shoes', 'shirt', 'shirt']\n",
            "['blue', 'red', 'blue', 'blue', 'black', 'red', 'blue', 'black', 'red', 'red']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FHIVMXWyjxov",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 601
        },
        "outputId": "10ec163e-9cb8-4ba7-faef-daa84e053202"
      },
      "source": [
        "from IPython.display import Image, display\n",
        "\n",
        "print(len(data), len(categoryLabels), len(colorLabels))\n",
        "print(\"iamgePaths[0]=\", imagePaths[0])\n",
        "display(Image(imagePaths[0]))\n",
        "\n",
        "print(\"data[0]=\", data[0].shape)\n",
        "plt.imshow(data[0]/255)\n",
        "print(\"categoryLabels[0]=\", categoryLabels[0])\n",
        "print(\"colorLabels[0]=\", colorLabels[0])"
      ],
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "755 755 755\n",
            "iamgePaths[0]= fashion_dataset/blue_shirt/00000061.jpg\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Image object>"
            ],
            "image/jpeg": "/9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAAMCAgMCAgMDAwMEAwMEBQgFBQQEBQoHBwYIDAoMDAsKCwsNDhIQDQ4RDgsLEBYQERMUFRUVDA8XGBYUGBIUFRT/2wBDAQMEBAUEBQkFBQkUDQsNFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBT/wAARCAD0APQDASIAAhEBAxEB/8QAHwAAAQUBAQEBAQEAAAAAAAAAAAECAwQFBgcICQoL/8QAtRAAAgEDAwIEAwUFBAQAAAF9AQIDAAQRBRIhMUEGE1FhByJxFDKBkaEII0KxwRVS0fAkM2JyggkKFhcYGRolJicoKSo0NTY3ODk6Q0RFRkdISUpTVFVWV1hZWmNkZWZnaGlqc3R1dnd4eXqDhIWGh4iJipKTlJWWl5iZmqKjpKWmp6ipqrKztLW2t7i5usLDxMXGx8jJytLT1NXW19jZ2uHi4+Tl5ufo6erx8vP09fb3+Pn6/8QAHwEAAwEBAQEBAQEBAQAAAAAAAAECAwQFBgcICQoL/8QAtREAAgECBAQDBAcFBAQAAQJ3AAECAxEEBSExBhJBUQdhcRMiMoEIFEKRobHBCSMzUvAVYnLRChYkNOEl8RcYGRomJygpKjU2Nzg5OkNERUZHSElKU1RVVldYWVpjZGVmZ2hpanN0dXZ3eHl6goOEhYaHiImKkpOUlZaXmJmaoqOkpaanqKmqsrO0tba3uLm6wsPExcbHyMnK0tPU1dbX2Nna4uPk5ebn6Onq8vP09fb3+Pn6/9oADAMBAAIRAxEAPwD9U6KKKAEPSm4p9FADcGgDBp1FABRRRQAUmabJIsaM7MFUDJYnAFcf4i+KWkaMrxwOdRuRwEgPyg+7dPyzWlOlOq+WCuzGrWp0I81SVkdlnivOfib8f/BfwokittZ1VH1Wf/U6ZaESXD+mVzhAfViBXB+IfiFrHiB2R7g2tsf+WFuSo/E9T+NfMn7Tnw4n1zSYvFGmRlr/AE5Nt0ifeeAHIf3KHJ+hPpXu0cpbs6svkv8AM+Zr57G/LQj83/keo3H/AAUfi0O+lGvfCXxTZaYjELfafPb3oK54ZlUjH0ya6HQf+CmfwL1hgl1reqaHIeqalpUqkfXYGFfJXgnxUNf01GLhb+HCzBTgn0Yex/nXn/7UHivxTpWkeHm8O6BbayHlm/tBpNFS+wgC7A3yEr1bkEdK66+V4eEHUi3ZfM48LnWKqVVQnGN312R+mWlfts/A3WQpt/iZoS5HS5maA/8Aj6itwftT/B9k3D4meFcf9hWH/wCKr8ND8VopGEepeAPCryHBYPaXNmw9sLMMflSXXxj8M6fYmdvhf4aeRgCgbU7vn6L5nNeJ7LC3t7Rr/t1n0ft8Zb+En6SR+3uofth/BPS4y9x8T/DQUf8APO/WQ/kuTXEar/wUV+CFpcrbaZ4hv/E1wxwI9E0m4uD+ZVR+tfHXwx8E+Add+HPhvxLL4H0ixvdRso7t4FQyqhbJwC5ORjB59a6yKytLE7NPs7ayjI+SO3iWMD8gK9qnk0JJSc3ZnzdbiOcJOCparTc+5Phz+0X4I+Js1va6fqT6fqs6eZHpWrwm0umX1VG+9/wEk16bmvyD1EyeLPjBoel2ZMhhuYYiyHowffIc+wzz7V966T8Rtf0iQbL57iIH/VXP7wY+p5H4GuWtlLTfsZfedtDPVyx+sQs32/yZ9EUVwHh34u6dqQSLUU/s6c8byd0RP16j8fzrvIpUmRXjZXRhlWU5BHqDXiVKNSi7VFY+ko4iliI81KVx9FJnmisToA0gxQ1JmgBeM0opM0A5NADqKKKACiiigAooooAKKKKACiiqmqanBo9hNd3LbIolyfU+gHuaaTbshNpK7Jri5itIWlnkSKNeS7sAB+JrgvE/xbs9PRotMVbybp5rZEa/1b+VedeLPE1/r901xcSs0O75IAfkiHbA7n3rAKNKMmvosNladpVX8j4/G5243hQVvM1NZ8W6nrrE3d5JMCciPOEH0UcVkhWfrk1NFbfLz2p4j29q+ip0Y01aKsfIVa9Sq7zdyARHNNaHcpDAEHgg1aCgDpTtgxnArXlMbnyr8Xfgdf8Ag3VZfFXg2JmswTJc2EQy0H94qv8AFGe69V+nTD8H/EC11qWONGNrfgcw54b1Knv/ADr7IWMeleB/Hb9nCHxBDL4i8JQiy1+E+dJawHYt0RzuTH3ZPpw315qleOxbcZrll955B4h0T4z3/ivVbzRdU0nUPD9xIv2Owuxbu0CBQDkTQkZzk/e712vw38Ealc+F7s/Efwz4al1r7W/lfZtNtCDBgbSxRcFs59KxPhh8UPtxbS9WH2bV4cqQw2ibHXAPRh3X/I9BvfE8ZhbMgGBnOeBSp0IKftOZvybuvuCtiavIqLjFWtqlZ6eaepk6u8FuIrSCOO2towESKJQqIo6AAcAe1cN428eW+gxyW1nKJdQ2lF2nIh/2j7+grC8dfEk31w1horGSRjsa5jGeTxtT1Pv+Vew/Aj9msaStv4k8XQeZqJxLa6bLyIO4eUd37heg789NZyvpEzp0lH36n3Df2bPhRc6HDJ4q1mFo9Qu022kUo+eOM8s5B6M3b2+te8gH04rQMSjtzUZjB7VhyGkqjbuyuox3rb0Lxfq3hdgbS5JgzzC/zRn8O34YrLEeKciEfSonRhUXLJXLpV6lGXNB2Z7X4T+KWmeItkE7CwvTgeXIfkc/7Lf0NdrkV8rS2pD5TP4V3XgH4kXWjXKWOpu0+nkYDt8zRe47lfb8q+axeV8ic6P3f5H2WBzvnap4j7/8/wDNHtxpKbFPHcQpJE6yRuAyupyCPUGnV88fXBSr1pKVetADqKKKACiiigAooooAKKKKACvFPih4wbVtabTLd/8ARLNtr4PDydz+HT869X8T6yvh/QL/AFB/+WETMPduij88V8z2TPcSNJIxaR2LMx6knmvbyugqlR1JdPzPms7xTo0lSi9ZfkaoQSKQeQR0psMe5SQOnFMklNsvmEExD7xHO33PtVq1AZpAOQfmGPcV9glyn585cxGvGQRilKA1JjcSOhFMIIPrVIkaYsUzZirAII96CnFAEIB570r528U/bT1j9qAPAPj98A4vGqP4h8OxraeJYf3kkcZ2C7x3B7SDse/Q9jXzJBceKPG2oaf4ejikkvbphGkSfIZiect6ADk9uCa/RCeLZI2BwRXxT+zPcrF8c7eGT5yY72Jd/O07WPHp0/WsJc11Z2XXzO6k4uEuZXaWnke7/Br9nnSfhuIdTv2j1fxDjInxmK2PpED3/wBs8+mK9jXO3FJHGC2cdOeKeVx9K1Rxybb1I2Tim4qUjjFJiqJI9n5UbSM8U/b14pdpAoAgPB6UIimUEdgc1MVx2pm394fpSdrFJnTeD/Hk/ha5SGdmm0xj88Z5Mf8AtL/h3r223njuoI5onWSKRQyOp4YHoa+Zrgh0wDnPcV6Z8GfFfnQTaFcSfvYcyW249UP3lH0PP0PtXy+Z4RRXtoL1/wAz7XJcfKT+r1Hft/keo0q9aSlXrXzZ9iOooooAKKKKACiiigApKWkPQ0AeV/HzWjBo9hpUbYe7m3uB/cX/AOv/ACrymzVkt1dRlh8wHrXRfFnU/wC2fH7Qqd0dnG0S/VV5/Vj+VYOmH/QYmwcglT9Qa+3y6j7OhHu9fvPzPN8R7bEytstPu/4NzUjKuqunKMMj0qppD+Rd3Fmf+WONv+4eV/LkfhRC32K4MDf6mTLxH0/vL+HX8TVe6mFnrttIxwJomiz7g5H9a9fdHz19TQmYpJntUkbhxTJxuBI5FV0YhuKk2SuXjHz15pQMjFLC29ATT8ZOMUXEMCFiKlVAB0pyjkDFP24FS2MqXNuH2+hODj618NfCqeHSP2idKtYbZEWLVbmz8xSd0gPmIpbtke2M192ycgY9a+LPBnh02X7Rul39zc2rC4165ItUlHmxvmYgMvUY2jP+8vrWFRwTjzrW+nqd2G9o41FB6W19Ln2VbRYTJ6mnPHmpIxhAPanMtdC0OBlQjaaaeMVYkQ0wKGyD1p3KRF5oxz1pfMVuM81BeGO2ZQxPzZNZ0usKmQkbH8KTkkNJs05phEMnjFZscsuo3UkcR2QJ8ryD17qPesv7bc39zJGf3Kxkb3b+D6+/tWzbTwWlk3kkMka8beRn69zVRT3ZlOSvyom2LiQqMRQrsXHT3qhbajcaLqdvf2pK3FuwlX3x1B9iMirsimGwjiJ+eUhW/Hk/1qldpm9iTH3k5H1NYVoKcXFnVh6jpyUk9j6e0bVIdb0m0v7c5huIxIvtnt+B4q8vWvLfgPrputCutKlbMlnKSgJ/gJ5/Ij9a9SXrXwFel7GrKn2P1bC1liKMaq6r8eo6iiisDqCiiigAooooAKpaxqcej6VeX0p/d20Tyt9FBNXa8V/a5+JafC/4P3N+9s939suobIRRyBGIbLNyfZCPxrWlDnnGPdmNafs6cprojzmylk1HV0up8tNPC8zk9yzZNXdMQ4vLfoyv5i/Qj/GvEvB37VPgfUjCdQubnQ7pIxGVvYGKYBP8aZHTHp0r03TviT4T1m4iu9L8S6XdHG140u0DFT/skg8Hmv0ODilZH5JWjPmbkjpmj+2WZjB2SocoT/Cw6VyfjPV47bS4JxIiXUUnmJA77WJU4ZRnqeeK7CSMnFzb/vEYc7OQR6iuA+L3hKPxR4Q1WFQPOeFnX/fC5B/HGD+Brpja5wSO3t7xbiximU5V1DAn6UR889M15F+zr4+HjD4WacZJhNPaO9k7g55Q4APuAQK9ZtJVkYLnmudvWx2qLSNW3c+WMjmrUaVUt8dDxV1WAHvTZOw7bind+OaCcdqB3rMkjcZr4Z8WIdC/avlkd1hYeIYJg2cfK7If5NX3Soy1Zb+GdJOoyah/ZlkdSkILXhtkMpwMDLkZqrmkXy3NBD8voRwRTiMikAAANLzQZEbLxioHXB44qy9QyEVRVzP1NfOhB/jQ5FY1zP5WGzjFa16+Aa5DWLw7yin61DetkbKKtcw9N8RJd+KtTknt5XsY5SsciMMJIMbjt75GOa75JVvpoIoyPKUCVzjHHYV5j4Q0L7Hfy2gu7jUJdTZtXuPPRUS3BkaKOFMckbYg2TycmvTrW3MEZigBlnc/M/v6/QV13Timjz3GUZuL/rr+pZDG8vzj7kQ2j/ePX9KhVPP1V2/hRgg/D/65rM8QePPDPgKzP9q63Y2cgBOyWZd7H/dGW/SvI/Ef7XXhPw/aSf2Rb3et3ig7GdPs8BY9yzfMRn0WuebXU7aUJS2R9EfDPWl0Dxf58riK1lnaCVmOFCscAk+m7FfRq9a/MDwZ8OPjN+1FqtreGCTQvC6SrKtzcB7azGGzuVT807e+CPcV+nsKlUUM25gAC3r718bmcoTqpxettT9FyenUpUXGotL3RJRRRXjnvBRRRQAUUUUAFZuv+G9J8Vac9hrWmWmrWLnLW17As0ZPrtYEZ561pU3ijYTV9GfP/iz9hP4OeKGaSPw3Jodw3WXRruS3/wDHMlP/AB2vIPFP/BMTSZ/Mfw943vbX+5DqtlHcD/vpCh/Q19wHr0oHtXXDF16fwzf5nJUweHqfFBfkfmxqf7BHxm8JkHQdd0/UlT7hsdUmtHx2+VwAP++qzLb4RftO6ZfRaTLZ62be6dbd7iSe3u4olY7S5fcxAAJOfav06HWm7QT0Fd0M1xEVrZnm1Mlw03fVf15o+RNe+DmnfB7Wb6w0G2+y6VczfbIo1HALqN2P+BK1OsZmkvLZBkOCd30r3j40aXFc+HY7plHmwyYDd8EHivDNIhP255j/AArtFe3garr0oylvt9x8pmdBYbESjHZ6/eb0Tn7YijpyTV4tiVE9Tms3TiZr6Z+yfKKuI/maiF67VzXqnhtal8nJFPximDk08k4qBMReuaQ8mnKcU3gGmFwxkY9KQD0pSQM0Cgkjk4FVLh8DirkvC81QufmjPPIquhaVzK1KTyypPQ1yGrW7xmVlycgla669HmQ89RVbSdPGp6xZWpG4SzxxkexYA1hKXJ7x1wh7T3UcF8ZfhN8ZvDnjDS7r4babJf6Xf6FY29w8SwP5dxErB8+aRtzuBz0OfasbS/2RPj98QY9virxdb+H7NvvwPetK2P8ArnAAh/Fq/QYIoUDAwOlLxjivlP7UxCgoKx98snwvPztf1p/kfH3hH/gmx4L04rL4j8R6xr8/Vkt9lnET3zjc5/76r2/wX+zD8LvAEsc2jeCdLS6jxturqL7VMCO4eUsQfpivUh0pa4J4mtU+OTPSp4WhS+CCQwKFGAMY7Uo606iuc6gooooAKKKKACiiigAppp1JzQAlA5o5oFAAOpo70DrR3oA4v4toW8HykAkCaPP0JI/rXhFuv2eBj3719IeNbP7f4X1GIrk+XvA91IP9K+bb+TasmO7N/OvqMql+7a7M+Hz2H76Mu6X4XNHQ122pc9XJap9Lbzbi5m6jdtH4VFC4tNP54CJ/Sp9CjKabGW6v8x/HmvoeiPk3u2aS9fWnZ5601OakK5qWQw+6KZ19qU9MUChAKetJ9aXGaQUCGS8JzWZK3JBrTk+6ax5WxIehp9DSJSmHDA+tbXwzsftvjXTRjISXzD/wEE/0FYt18rH0xXb/AATtPN8RPN/zzt3b8yB/WvOxkuWlN+T/AMj2cBDnr04+a/z/AEPcaPWlpO1fEH6WFLSCloAKKKKACiiigAooooAKKKKACm/jSmk/CgBTSLQRQPyoAO5o/wA5oHU0Y9+KAKmr4/su8z08l/8A0E18u3kZN8kJ6q5Jr6quI1lhkVsbWUg/TFfLT/vL25lIx8+xfoP8ivocp+38j5DP1rTfr+ganOVsCg6v8tdBaL5VtGg7KBXMXf72WCP+84rqkOFA9K+nTuz4tqyJU4605TnvTf4adGOKTMwboaQYxQ3pR6cUhCnpSYo/SgnPHamgGSH5CaxJj+9YVtS/dNYM7ZuGHvih7G0FcgvDw57AV6R8D49t/eEjpbDn6v8A/WrzW4XzdqA/ebn6AV6p8GlCX+pjusMY/wDHjXl4/TDzf9bnuZX/AL3TXr+TPVzRQaO1fGH6MApaQUtABRRRQAUUUUAFFFFABRRRQAh6UZ6UGkoAD9aB+dKQM0g9qAAdaOKB1o6mgCO4G6CUDqVI/SvlrUlezuXH/LJjkH3r6pPpXzh4hshHc3cBAPlTOufoxFfQZS7ucfT9T5LP00qcvVfkYFn/AKRqMB6hBk11C4PrXOaJDtunPYV0kfavpobXPi6mjsOZtq0+M1DK2B6c1LHzVPYx3HsaOmMUhHNFSAppp6dKcTTSKaERyH5T3rn7g/6Q3oK35s7TWEyeZdv7UM3gLZ2/mN5jdFBA+tejfBeXzNX1dR08qP8A9CNcKR5VqcdcE123wLjxdaoxOW8uPd+LH/CvMzHTDyX9bntZRri4P+tmewUdqWk7Gviz9HAUtIOlLQAUUUUAFFFFABRRRQAUUUUAFNJp1Jn2oASgc0ZoBoAB1NGMfSgdaO9AARyK+c9Qm+2Xl9JnO6Vzn33Gvop2wpb0Ga+aLJmeO6c5ILyYP6/419DlEdZy9P1PkOIJWVOPq/usVtFQiOSQ/wATZrciIYDuazbKPyreNOmFGatxfu245U19So2R8RJ3dya4OPwp8RyAaidgSeKfD0oFuTjNKPypBwM04DIqGIQ008A0p/KmZzmhARzn9230rNigw2W6nmr87bVPpVJmOeOvtVNGsdgu32wsBycYrv8A4ERbU1hzycxjP/fVeeTqdvNekfA4hYdXT+LdEfww1ePmf+7y+X5nv5N/vUfn+R6pSUtJ2r44/RBaKQUtABRRRQAUUUUAFFFFABRRRQAUnI70tNx+dACmkWjGaAKAF70nf3oA5owfwoAhvn8q0uH/ALsbH9DXzlbMlrokMknWVtyj1yx/pX0LrzFNE1F/7ttIf/HDXzfKWvLS1jUj9yuwj8B/jX0OVJuMrd1+CZ8hnrSlC/8AK/xaRpRCKRQVxjtipgAO1YQhubD97G25O61pWmpLOuTwa+pufD6ItNHnPtQilT7UBwec0oY56ZphcmVqdxUSn070/dj64qGhCk1ExpSajY1SAimG8hew61DhVqVsc1GWGP6Uy12IbgZUmu7+CEw/tHVo88+VG3/jxrg5ySDxj2rr/glKR4m1JD0a24/BxXk5kv8AZ5HuZO7YuH9dD2qijrRXxR+kgKWkFLQAUUUUAFFFFABRRRQAUUUUAFFFJ3oAWiiigBKMUUUAZniZxH4b1RieBay/+gmvmaItHJEQ2BLGHHuQMH+lfS3iw48LasSP+XWX/wBBNfN1vatc6TbbeJozuQ/iQRX0+UK9OfqvyPiOIG1Vp+j/ADNazkWWMKwwe9V7nTzBL5kH4qKSJJF2yAYYfeU960yQ65r6Q+PZQhuCpG4ED3q6PUGopYdwOBzSplQOcD0oETqOueKXd75pnvz+NKWA70AISKjYgZxilZqjY5HTmmA12FMzkjinlNxz2p6xqOtIq5XnOIyRXSfB6byfGLRHrJbv/Q1z8yblIHer/wAN7jyPiNYjPDBo/r8hrzsfG9CS8j1ssly4qD8z6D5o7Ug57mlr4U/UAFLRRQAUUUUAFFFFABRRRQAUUUUAFFFFABSUtFACYooooAyPFwz4V1YZ/wCXaT/0E185W8yqLa3VtjbWOfqTj+VfRni//kVdV/69n/lXzZFb/aINgO2WFjtb8a+myn4Jev6HxOf/AMWP+F/maLRXKcqytjtjrUlvfEqNwwakspzPCu8YkHBHvTpbKN2J6E9cV9NofFu6F+0qaPNB/ixURsgvQk0+ODHX86Y7kqsD1JoZvekO2McGowS3sKLDHE8U0tx6UpH403ge9ADTIR0/SkzI5wOKeQSeBipYoznmgCMQFRl3J4yeab4IlEHjnRpGOA1yBz7gj+tPuyfL2jqaoaVKLXxLpkgOBHcIefrXHilzUpLyZ34KXLXi/NfmfT/aigUV+fH6yLRRRQAUUUUAFFFFABRRRQAUUUUAFFFJ3oAWiiigBKKKOpoAw/G77PCWqHp+4YV862qsL6bkYIJ4+tfQPxFuRb+DdSz1ePYPqa+foIsXiyIeH5I9K+oyhfu5Pz/Q+Hz9/vYr+7+poRiVckKvPcVOssiY3gAeoNMUEdCMU7PBB5r6U+NuTBtwz+lIXx2zVfAXlTj2pC/rQFx7Pk9OKXJ6VGDknCnNOGTntQFxwyMmkzjtSHn3pQOelAXHD6VMvyg4pirSkgDigRWuWOfr39Kx53MV1E4J+Vwc/jWxcH5TisS+PNYVleLR1UHaSZ9UWMvn2VvJn78at+YFT1l+FpxdeG9LlBzutYzn/gIrUr87krNo/XoO8UxaKKKksKKKKACiiigAooooAKKKKACiiigAooooASjt7UUUAcB8ZroweFo4wxUSzgEgegNeNWo3BCCCy9+lfRviPwxZeKrFba9V9itvVo22lTXFT/BOBM/ZdTkT0E0Qb9QRX0OX4yhQhyVHZ+h8hm2XYnFVfaUldW7/AOZ5wqswBDEH60hjmPTBru5PhBqcefLvbWUf7QZT/I1Wk+F2ux/dW2lx/dmwf1Fe6sfhntUX9fI+XlleMjvSf9fM4orNzmMH8aTfIePL/Kusk+HniGM/8eO8eqTof61C/gzX4+Dptwfddp/rWyxVB7TX3o53gsVHenL7mc0rOP4SKkXd6Gt0+EddB/5Bd0f+Ainp4P15+mlXI+uB/Wn9Yo/zr71/mT9UxD/5dy+5/wCRhBGx92nAPx8tbw8D+ID00uX8XQf+zVKvw+8Qyf8AMO2+7TIP61LxVBbzX3otYHFPalL7mc7k5pST34FdQnw019hzBAh/2ph/Spo/hTrMnLy2kX1kZv5LUPG4Zb1Eaxy3GS2pS+7/AIJw85BB+YVi37JzyWPoBXrkXwauZOJtTiQH/nnEW/mRV63+CWlbgbu8urnH8KlYwfyBP61yVcywyVlK/oj0KOTYxvWFvVr/AIJofB7UX1DwPaBzkwO8PPYA5A/I123aqGi6HY+H7FbPT4FtrdSTtXJyT1JJ5Jq/Xx9WSnOUls2fodCEqdKMJbpAKWiisjYKKKKACiiigAooooAKKKKACiiigApCcUUUAH+FHbNFFAAOlLRRQAUncUUUALSbR6UUUAIVHpS7R6UUUAGMUtFFABRRRQAUh6UUUAGKWiigAooooAKKKKACiiigAooooA//2Q==\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "data[0]= (96, 96, 3)\n",
            "categoryLabels[0]= shirt\n",
            "colorLabels[0]= blue\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPsAAAD7CAYAAACscuKmAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nOy92autW5bg9Rtzfs1qdnf6e+I2ceOGmVmRkZFkZUalllpQWBQICvUmKoiCkE+Cgg8m/gX15B+QoOCDIIKCPhRIUdhCWVWRkmhGNlZmNHnb0+2z917d18w5hw9zfs1ae+19mnvOjRP3nnFYZ6/1tbMZ/RhzTFFV3sJbeAtffzC/6Aa8hbfwFr4aeEvsb+EtfEPgLbG/hbfwDYG3xP4W3sI3BN4S+1t4C98QeEvsb+EtfEPgSxG7iPzrIvLnIvIXIvL7r6pRb+EtvIVXD/KycXYRscD/B/xd4BPgnwH/jqr+yatr3lt4C2/hVUH2Je79XeAvVPUnACLy3wJ/D7iS2G/fvq0ffvjhl3jlNxcUBQUEBNk5dzXI3iu6+3XPsedpy/47rnva/nPxaCdvRJ6/DW9hP/zsZz/j8ePHewfyyxD7u8DHo9+fAP/i7kUi8nvA7wF88MEH/OhHP/oSrxzgeTQSVX0pBHpZpNNIksNzOiROZ7trrnnz6DO+B0LwBPWIGKxko7PgQiCoplsFQbCpD1ZCelRAVEFM/ADgL79Xhxaz8xUgAF5j37KdljqBINE2zLqDqZFelIBiRDBIPKEBVUU1ttGYDJGrLMsxi7l6fvaeeUU85FVnm74O5vbDH/7wynNfhtifC1T1D4A/SA35SnNzrxvMl2UEz4arpOjwV/detwcntftPERQj8XcgEkcI4EPg9OkZq/WaLMvJ85wiz5hPZ1hrEGvTfSYqBjomFtu/51KrpTu1zaQCDFysa59090giRel5hqZnOw1JMTEj5teL9KtGYGhbuuat7H95+DLE/inw/uj3e+nYLwW8LpVRxmQjHVrLiJquVoKVjljG7CARRUdgKEpANUpY5wPL1Zqzs3PKoqQsSyZlQZkVECwWA0Z61V87wmQwB7SjO0mtEI3HhBGpd/+6a+KN24yra70QEqUrkbF64jOtkDSLjomNxmprSMYcaGi07o7xpVHcuWfPSH9T4csQ+z8DfkVEvkMk8n8b+HdfSat+aWEkrbYO71qpu7/i323Z1ZFVJ1EFj3C6WPHzz59QtZ5F5Wlc4MHpGYvVmrba0K7XGA3Y4DFAYQUrUBQZWWYoipzJtCTPMg5mM3KbMZ/NyPOc2bRkUpZkWUZZFhgRxEhSumN7fIhMhqBIgBACVbXBecfFes2mrtisK5bLNU3TcHa+wIWAMxY1ws2TGxwfH3Hr+JAP3rnLpMg5nk3JrMGaZAP046hASEMSjYYX8yy8JfMxvDSxq6oTkf8I+J+J+uB/pao/fmUt+1rAgHCXWcCYMexzX4VemivgMTiEB+cb/vGf/oyLdcMXFzWbNvBkU7FqWs4+/5wnn3xCs1mxevwAfEtBwAgcHpRMJjkHBzNObh4zm0z41u3bTCcl79y9y8Fsxu2bN7hxfMR0MuHo8BBjhNyYpAXFtrQufjRA8ErbOk7PTqnqms8fPuDp+RmPn5zy2WcPWCxX/Ozjz6hbhy8KsBkffPQh33r/XX7tw/f5279dcnIwY5JPMWIw0nW5I/IAuETihueNFOtbqb4XvpTNrqr/APgHr6gtrxWuUttfpdOlU8I797JuSenx106Sb9vDvSxTpQmKV2XTBtqgVD5QeeWvniz45NEZq6rlaRXPObFIOaU4OubgboOva6YHcwieQqLqXGRCbiHLDI1Y8JaHy5q88qzcY8qy4PDJBfPplKLImU6n0ZkmJvn9Yl9CkCjZEVCDD4HlZk3jHIvVkk3VsPKWdnaEmgnzb+UUIRDyEqwlv3kHPz3ktAr88U8/4Wg2YblcMZ+U3LtzwsF8SmYgt4KoYMQOLjkda0D7GKRssU/ZPf2ScBWOfFm/z/i5X0Uk4rU76L55MPZqD4cuB8uS9Op/Q8DgUeqgPKkigX921rCoHefriotNzc9//jn/9E9/TuMckk2QLGN27z7l4THF4TE33nsfI0JuDUaESZZjRGiWC9xmzXpxxuLJQ9ZtyycP1njvaOvPCd7hnSM4l1qUSCrE7yIWwWAzwVqDsZa8LMBapIiEPJ/PmZQTyumM2Tt3yLOc98o5YizkJRiLEQ8S+MnpE/7wH/1jprnlo/u3ODmc87f+pb/Od779LodFxlGZk4lhCogoJjkp+4HtPH+QfCPp8xWG737ZQoW/tMT+qgb6VU6Y7oSt9kWzR29OBKW4ZAc7DdQeaq8smkDlA0unLB2sHWycUnmlDYHWBURbREF9QBSMsRgrGGPI8hxrDNZmGART19DUeOepFiuatmG9WeO9p6lrvIvE7r0DBDEGEELy3ouxgMFag7VCPimR+RxjM8hLxGZoMUXLCZQTmEwhy7HTROxZAWLQ0KC+pfHKYlPhnOWiajB5zqJxLBpPJG1PLtCaqJlMLJikpUifa9DJ786Wv8xk47xsz8AvG5G+KvilJfY3FfoEkb0no6/OpzBYSLLqvPUs28BZssPbAOtgcSpUamnV0lpgYrGzA2aHR5jNhuXFCq1qdLHCSIEUBikNAWUTUtC7riEo7ukT/PkZj/75n/Hxj/4vggZCkYExmKLAWEtW5JRZRjmdMD86wNgMmyRyEINiCApelRv37vHd3/wtbDmhJsOLwaR+++BpQ4tiaEwOCOo9qEdcg4QW7xyqhiA5oTygLeZ8soL6cU1mGjIRMlFKUaaZ4Ts3ZxwUGbPCUFoZCJ8unDjANYG8bzS8JfZXCtuyfDc/JTmx8cms9whelbWHZauc1YHH65Y2CA1JrbeWIEIQCzZgs5xiUuK9R3VBcIHQNGjTIDZDNEdVCemj3qPe45qaUG2oLs5ZPXxAEMXMp5jMks/mSJ4hZkJmIZfANDPYzGAnGWIsHkPA4ILSemU6m3Hr3j2y6Yx1MDgVgnNoCNRNhavXoILX6FRTr2gISAgY5wghDPFzm6NZwcYJF3VigQIZSokyLyx32oCxivUxcpchfbTODm4SOmkvMszDLnzVtvKbAm+J/VXDjurYoZJH8EDtA+dVS+MDT9aOqg0sPawDbNrAWjKCiUSiApq80UYCIsrJyTG/9r3vc3F+zmr9xywXSy5WC6qm5vDWCcezmzGpJSQzwRhULW3rqTYbWlcTjEMI5AomGPK2xQRDFnJsk+PXhovzTzHGkhUlIkLrlaCwaZVNA1mzIfz2D8HmtN7SBgitIziPV4/FRMdjNPpTGM+w3mxYLs6plkvUBaxYjo6OOTo+xpYTnNg+ChGUmGXXCj95WlPahlkGpYGjScbNaUFhDYdFhhWSXQ/SZQ0+I9vuZeE6x9xV515fEtfzw9eY2Pd5ay+fee77detXuuJyCoeIJCmznW4Sg0iR2M/qlk3j+fi0YtkEKjE0GIIYgljGSW5CAI3OKTEwn8+YTD7gdP6UP/vzv0SXa9bVmk21ojgoe9W2t7WxqAmEoLRtg/ctajyinkwVEwTrGkwQTLCY1hA0sPEtIkKW5ZHYW4/zgVUNixpObtxEmwa8x7dEv0Pt8K1DbMCYOA4aPCjkWIwxLJqGzXJJu6nQEDAizGYHzA8OMHmO75xtSVobFbxXvlg2iChTUQpR7h4UWCPMcss0t9Bl78l2roOMf2059NKhLs14PIt7Do1Bdu4f/47zf5mwf9GEDl9bYt8l5+vi3fvvHwhZRrltO8/U9BFBJdrfPmWNeYSgynLTULWO2kPlofLKWe2jE04tlRhaiQkzKkLYykYDlc4VFbFfU5LLZDbhw48+5OatGzz47FPOz85YLRY8/OIL8smU+Y1bUf0ODg1KeXSMMVCUBcc3b2JEmZY2SsQQ8+abtqF1LaFtcXVFCCFqAiEgTYN4R+Yzpj4jv32fRjLwgvee4ME5h3MtVgWbR0IIIToPO2LUtsVtKoJzGGuxeU45m1LOZhhrhxlKUxYkMjpNOfNCwKGctqCLltI6zjYNhTXcmk+Y5JbCRHvfAEbDMGVXSfoePXQrAeoqhOm1tisI+E0g7H3wNSX2Lwdd7hoooXc7DWhiu4tCiGqqiYtLlJgCEoBGhFaVTxYVp4sNq1ZZNorDUEuGR2jI8Eb6d13KwBul2/Y4KIIYmB3O+fXf+B5VVfFPNiuenj7m/PwpF4sLjk5uMD88weaG1jm8KrObt8nu3qP81V9nlmdkmTAps9i7dQ2t59HZU54szglVhbtY0DQ1p2ePaNsaqdfgWspsQm6nFO9+QCUF3oNrPd4H2qahbVuy3EbvvELwIYno5Lyra9rlCgRsbsknBbOjA+ZHh9GXwVgFV0Lqd0gP8BhElU2lPK5qMjwTbZnmhl/91m1OjOVAhJkIVgOF+p3xzC6Jeh2J8WeRaSe1n0XwV933i4S3xL4HZBzC7Y9qUqcH6NDSByVooPbKynm8Qq3gVFnUnrWLUr0JgheJDEEiU4iItk/rGMJJOhwazomQ5TmFKkcnx9y8fZuqqtlUFd45NsslWV5AlmGNYci6F5wxkf5cC95TPXxAu1yzrFa09RrfNLj1hoAyPTygYE7JrRg9MDkqBYcnt1EMPig+BHwIkWl1aQZJ6zEKGpSqWqHO01QVQZVJWXJwdMjRyQnWZpcE6BBQG3wfOvrlSUxQDUYF8cL5piUgNFaoMqE0MLfRX5CZ+Lwu4NnPafd03Z6FqzTAjmBf1Gb/RRM6vCX2K6FbqjEkaIZe6nTpHT55yVcusG4d55uGT54uaYNSp9h57YVWoQ3RLleVFMbaRrvncyZ16KgghqwsMVnGR7/yK9y5d5e/+tlf8ZO//ClN1fDFX31MURTcvX+fcjYjBI93ykaVTQhIaLDVOW614C//9/+N848/4eCoYH5Q4AO0Pqr+d3/rb1Ae36S49QF2doJpPdJ6NEX2XOupnIvRAVHIowTW4BFVcqJ6//knn7K8uCDUNcF7jm7c4Hs/+D7T+YxsMsH3XGJMdMOYdCwvIHTLeD0WLwEXhCoof/loSS7KPDNMreF4XvLOyQGlFU4KIRPICEn3Gpb6boXuZNDq5LnmZGeGnkHUv0gJ/zUl9qsH8zJXvwyd9JCxLTeCoNGKb0LAhZjSumoDy9azbDxtUJoQ7XaHjSErBrt875uFLXtRZOdU37jUKImIZYDJbIpqYH5wwGw2wzuPaxtQxTUNPsvQPEfTbAcB8QG8x3mPax3ONXgnBC+9daIaKMoJk9mM8uCI7OAG0rRI3eK8o3LRrg9pPX2f7NJJ9hDwbYtrWpqqoqkqLJDlGUVZMjs4YDKdIMaMvCqXZ2r7Z6ehxEEKaoiGVaDynlYDGsBZxeaBZRtog6EwSmFgYjRK+RdQ3V8l/CIl/NeU2F8E9imQ0LvEU1qmigAmhs9UaYPy2dmSi03N0hsWXqi9cu4zgtJL74BFJSajhITRY9df1OI71XDsMN7nSIrOQOkYhgBimBwckE0mfMfm3L51h9PHT/jzP/5T2qrm4aefYbOM+c0bTA8PkCLDUGIMZPNjssmcX/ubfwu/XLCpzqmqBd4FfO0oD46YTU6YZIdkajEuxHMuqu39Xx/w6skzi7WChIB4T7PecPb557R1TbtaY0Pg+OYNDo+PuHP/Hocnx2R5hhizzSx2Z0PS4KTzmvwomhgLWcwtaCSDEGiBVVAu1sqjZkVhlJuFMs2Eb9+YczLLyRBs7wSN77Jd6O7azKjxdFwvpd8EO30M3yBi3ydPk312lZDvHMNJkqtED3urUAflomo4XdUsgmUZLE5hox0JZ9FL30V/RVLcvFMRd17aF2fYDuMMbdEtcT8m+KwoYrbbiWFeTsErmTW0dWC9WkX7fjqhmEQil9yiYpG8wOYF8/vvYtuG06cPcGePMI3HZA359JAsm5DZAkMkYkJK2AmKBo1aQKo4A4qIiX3TgG8b1oslbZ2878BkUnJ4fMT0YE5eFlhrGfvLO/462OjSHx9R/sCMRaJzVKLGhemKeyh1G1g1LYUooY2JQncOA/MuLNnx9fTqjoVszf/WFGw75a4Ksz0P/CIYwTeI2HfhekJXIj7VHtqgrKqGp6sNbYCKeOzJOrDylkrjx2tU3WOkKSJUrOzSPXOUwz0GuXzscpz2clu7Z1kMYgSbF6hYbt+7y2/+zm+zWi756U9/ymKxZLVaUjUVxWTCZD6jmEzI8ttolpFPjzFTmE4OMHfeS2l+is0L7K3bhLwk5BYVhxeHx+HU0QZPCD5mxYVoOvgQaDZrqsWCtq6o6w2iysmtG0wmJfc/+IA79+8zm8+wedGbIn0n6YX4aJYYaURmIPR0sA9Xmhi6U40eF8VjXJT0562wDvDnTyo+WbQcF5bj0jLJLTemeSyqsZU5sT0jV618u45gX/bc64JvILE/K9Ie2XmUFFGCbzw8Xjf8/NGCVpUKQ0CosDgyWgwtJtZnAyKamP1vkysIfvuS7tul45cWdShxGaoqkhkkyzkpCg4OD1lcLHj45AnLzYb1eoVzjulkgjYN4eCAyY3bkGWEyQRszuTkDpM8wxiDNRYEXKodF3wbP3g8AUdU3UPwEALiQ4yftw3L8zPOnjyJCTWhJc9zjk5iltw7777LO++9N3RNZJuw9xDV2As/unG4r3NjmG7MYyRAU85DUHBeEA/LpsIK3Jvn3J1n3JgWHJcZ1u6IeS4J9l96+IYR+/WE3qmNQZVFHVNazyvPovFcVC1ryWMWnEZibzExeWbLaxzo1XTZUUO7a2Q4pshgIw4X7sGyqNhuq/Z0vrohbCAx1GTLgvJgxvsffcjRzRMePXjI+dkZglKt13iEYnFO0TZMjIk+iSBIMD2R6PhFLlamUa8E9bFIZAgQPL6u8W1LtVjQrNc01QZCIM9yDo+OKScT7rzzDkfHx8wODjDGXspdGbqpO9OU1gb2Orf0fTb9Fb29lRyE2nFB1GaRG4Q4L0ENqLJsFakCVWgBmGSGu7OcMhPi+r7rU2L75r6AhP5F2/DfMGK/CpKjhiiZ26A8XG5YVA0Plw2n6xZnCho7I4jQSFz62RNE+k8IaT0bKT9bejHdW+nS2Zmj90pH8B2h7yDEjr3e35nwukv7CSZ62sksVnIOZiW/+cPfwTnHj//oj/j5X/6E5fk5Tx8+JqtqmBSU0ynzIqfMTOx8cvoFjTXjQtdJH4kdHwjBo8FB8KhztKsVbVVx/ugxq/NzjBFMZpnMS9778EMODg/54DsfcnR8jLFxYU1nk6cO9nZ63znVHdYsW+cGVT+NrNJrBSZdp0YIRY4GxbQtBMVrICg8qZQnjaeQls/OVhyVGb/13gknNmeWsu/Gsr0j0i9T7OQX7az7xhL7MGcD0QaNiTCtVyqnbFyMl7cqeI0VWmIyTOdsGz+wk+4mSR7TE86gfg4q6KX2yDUhoB3HXLxh6+7hmi1+IeSFwdq42OTmrVsYhM1yjWSWUDc4hHq1IRNLNp2SiUWM9kysyzZVH1L6a1xF552jrWt809DUVfzu2hSum3F4dMDB0VGU5vM5RTnBZjldjHysu+uoiz0x7aj3Mu5zP4y6fWw0p91Pg4CE6MQzAXomLajGBKeNg8wq51UbzxQWk8c6uK9yf7QXzbp71fCNJXaIxK3ETLgAOK+sa8fGeR6vA+e1sAoFLitoMTQBVAJBzA7tJmmtsZqLIL2DTlKoLJC8xTugV5P49ZCwWYkMKKq6Q3MigUgsLZUZvvMvfJf33n+fTz/5hGI+o1quOf/sIZtwwcPak0+nnNy7x9HtO6nNqRxUUn+9BgIB71pC29Cs15w/ekLbNKwvLqJzrm2RzPDOt+7zve9/n+nBjJv37sRCGkUeq9roNsvrbPFeju8ZDiFp5rsMduv6TosaPlYjsQYUl4ES8Najqhg1GAwueFZeaTaBH39+xjSD771zg/vHMwqUCdsE+mUJ9a0a/7rgEmbs6orDZVFTVWrnqdtAE4hZcBjURCkQVJPNOIiTHgFFtp+bJLokRiBJ9RxA9jqAnhcV+hakzJ8uDNeF5LqEIEmSdDqdMplMOVxccHh0hAlwAX0Kq1elqWpc2yDGxhAdikl6vFffE7tvG1zT0FZVzIdvGrxrsdaQZSWzgzknN04oZ1Omsxkmy7bDa6Me9IxJhwVHXV/Gl0aC775ENjGynLvZiN81alhb+lRiBGpM9Ddoui4tQkJh2Tich7XzVD7OVzG2uq6chctMe9/1b9X4rwzGLqcdEZjOVK3ns6dr1m1g6TNaMyCpeMWEmBLaSe2Y4BKXpqqYxAMGSzLiXZLuKEaHNoz50K52+iJ96l8pHbKPtVwZ7PkkPU9u3uSv/cb3OT99iveO9XLFarVgs1pz/lhxzYJ8OmN6fCMyrBDv0+R1X549Zfn0FNe0VKsVPgScc4gRPvjo29x75x1u37nD8e1b2CzD2LxndrtW+OV+J4LfkeCS+Kts3Tcard7/0SndKaFJUsxdFYMBNZj0oC43QCSGLg2CE0sF/NXTlvP1Oe/MS947KskMFGYg5/Eov0E5M8+EbxCxwyANhunSFMBWoPWB803Dug3UWY4zQ8KHiN9yCnUSpiNmkFgkbYtkpRMeDKS426KrpfsznUGdnSuDSiyjN3Xfx8+fzmZMZjOyPGf+6QnBCsv1OW2zoVoZCC2lb7HTKWIsQeOiGfEOQmCzXHFx+pTgPb5t43gawUjGya2bvPft95kfHDKZz+gTXlJPe6HcdXxPXzrJLqNjqG4x0sgMdkauM5v6Ad9OWxI1W0k0cRFSXGufDK8UYYGzylPVLRNruHtQApD3ToOuDd3qt+4Ng6/hTYVvGLFvQ4eEF+uaR4s1iyZw7g21WBoEx6APqBiMzdN3EiVZkmUMYi4Ts+ysgx8b1juK316i79TVZzmA991M53TsylqOO61MJ1M+/OgjqtWKeZaxODtjvdlQLTc4D04t1lqKfIIgbNYxSaZarwkhjozJLGU54aPvfpfD4yPuv/ctDo+OKcpybDqPaXbcuT0HR8O09VtGm0eMju3c1TPi/sg+P0Cn7BuMEjP+Uny+G6tGBBXl0cZjHi2YF5Z7BwWFFSaZJdtxpvZduFLdfzPgG0vsOvp7sa742RdP2WA5N3OcdBJ9pHiKwdi02i0egGSzI4ZhQ8LBOpWxNLjMCUb/s43du6guz6J2eiLeXUETdGQHj66dTqd89NFHtE1D7gNns8d8/PHHPH18itQtm6oly3KOD+JmEedPHrJeLRAT+ypiMDZjfjDnB7/5A+7dfwexdij/Svf3WdrJfqLfP1zPYxf3gzH0umOGo1eYtHYhPjaeCCEunG3E4AUebmrOFytuzHKKXGIdAGuxRnptKjLj5/eyv1319ppgv8wYztY+LmjZ+JgV12DSslWiij6+R7hMNHueueW00V0EkPHjdq4bP+OyzL8qVXYf6AjJo8NuH8HFY0aE+dExqHC2XDFfrfDe41xF8DUtLQZBmwrRQJGXlMnZd3Jyk8PDIybzWSR0M/QnEtflkk2X+5DMlWtDj3u/bvcm+UO0f89Y0ZddtSK1p/sl/SREX4vGKsDG0picdTA8XjtmuVLYmIpbmJiirKOZGHazYdSOna689ca/PhjIT+h2LU0uJ5Z1y6L2nNbKQnNasUmFiyEr6WLkceYG7AjdE7eJUkdcvntj/HL9BO/WNNs52z/ilW4ZrIoGh4hy91vvEu69SysZTizLs0c8+fQvCK5lc9pGus2mZDbn8GDOjXv3uHvnHt//jR9QTiYxrGZS5l0v5WLbX16SXWGbPFfXhndeVvyl/z2o9AO3FRQ1ARWllSziRPCsTytmmVBYIUwz5nnGNK2b63evTcU14z5Wb55C/zUl9su22q7nVxXq1rOuW2oXcGnNedhBhq2bRAbuPQpt9Tb8iBa3p3rs2LkaCS5rAuNzr0P9i3Ipes2F6XzG4dER2ixZWEsIcTU+qmTGYLKM6XTK4dEx88NDJrMZRVEQdnUHYVCRn9HvqxnYi/V1XCpq/3N0D+Hvu7Qn/+SfiVK/DoLxsGochVFyMZQmbodtnsNaeRPga0rsMMj0EdEySPmg8Ph8zSenCy4oWcukz44bOP4A0kt66O1yI9GW79R+7VRu7T38LwLSI9tlIrgamXef8bxaRMwd6DdhFuHuvbscHx/y8OMSf/45brMhrAwaFJ0fo+UB773/bT76/q9TliUYwQU/5K0LsR7f8Lbn6fZzw/P3bYCheuxoYdJIFiixKCYCxkjS+CWtDzARJ4whZAavnp88XjLF86vv3KC8mZMDWed2GJkL15kbX2Zp7JeBrzexj2d2h+ADULWO1aalynJ8braqyOh2PecR+W/LB0nxuN2Ju6yEjhjBCHrpJ+MjlxFhFzm+bI72sEBn8AdMJiWTMmN9OqcsCoxzOJuhEpCyRKZT5gcHHB2fYLoVZh1TS8k7lxbqXOrr5VOvEun3lXnufAh6CRUkMeUOV8Y40o1RqmMgMelqWXua0LBpHS7Ektn9/Cl7057Hc/eqMvFeBr7GxB5BIDqqkvRSjXupNT6wdrD0UBvBYeJ1Gok4jMKmMQYLZoTYPe3Ltjk/pHbvl9BXQucXuKofO5L9eSX9vudEMKjJIkp7RdVhRBGJyL9ulcaB8xmC4f6d+xzfuceNW7fJsowuDbhv/nWS6rJv7Ev342Vgy1k4+ja27zvm20+vdg63eLaWAifCg0WFhJab8wnvnhxgRPCpcKFhu5u/6My5Dr72xB4JOE6UkDZQDErjNdZyD4LTmGE2KPDb8ruDoCMO0J2S4fc+xJWddLBdtN4WNvtDOPtCOy+jBu56xsXYxJ0con6wPyGmC3twwWJNxvzohFt37zE/PMJY249KL9lTJ/ZHLK7s8LVt7G95QWZweQ6uv77z3m/r992u8INJpoATixc4rxqM25Ab0JMZQ0WiF2rqVwpfY2LfN+qK84HTxYZl41h5H/dSM4ZuZZRJnlSVuMtItOkUY2KYalDpE6KHgDUGk+qoXdmUZ1DAvm0oru3dC0rEyyGw7TemrRjigh0xcf81G6vSgKFxnrqpcb6lW48qSa0ZltL3RsnV3X3NxHD9mFzPaXaPbpkdmsbIxHJjVWi5aJB50j8AACAASURBVJQTB7VKTLTRUSDuDST6rz+x9wkp0S5zzvHgbMHTTcOiFXyeE2xcGxWl3SjfHSEEnzYhFNRGu9Qmwgkh9PHZKAW4tOCjJyjZRcQ9oaUXjDY9i+CvjfOm8CIazRuE5LPQuAWVLVEb8NKiKjRtS1VtcG2bein9P8xorX7f7/FLn79Prxw6O27vMO3aFPsfoSKDBmMzVC2rpqZulZMWKgwFUGiK5ciuIr/nmW8ddK8GhkTUTi0bUk4DsHGedetoNY9EbYb52U1vjQwgpVOGyBCMsSMbfcCiXm50yNFNaCfqZK+XaHT3oDFcl4zS3/EcCHN9Yof2RE5atqvJOdjVhjPE7ZuCb/GuibF5FNEu+6x/6O5bLjG4K4NsrwjpL/lJOjNLr9I1UiSCfaSp0bTrGCKMioyAYvBi2bSBpxcbprnhZhmz6+if2j1qD1N566B7dbBvaoNAq8rTquHRumadW5zNY801O+DGoIZJ3I9MLM45mqbGGIPkkjYu7JBlQK4xocNIQU/4dp001isk0FXE8GUk+5Z/wtq0l1ryDZiY/+0Fcm0RDYR6Sb0u8e1Nupq5QFpnHtX5sCfc+GoccFczxn1Ds132Sxm4kg63jx89cqsI9NVpNfU0mnXSVbpCULzJ8UZ4vHT8+KcPuDkvOHz/FkWZv4kaPPA1JvZtiBPnFZxCE5QmaO9xjyAjpOgOdUg1fFSToy7E50aWf3n55j7oBHtfBnoX6cZScJwFNg7d9K68/c68q947vGSHQEY+xzFNGol11DM8ogH1juBaNOwYKrLzN4Wwtoh89+HXwV5md+nIdn8uvb/7Ol4fr72EH7sQo8TeHteYJhHNs77tW3y4S6uVmG7tHJvc0gbFp36OWGd39cgF+NVuTNHB15bYdwezCYFlE7ioPUvNWUkgUyEPcQeREOLijmDj3b0GmCbfWEM5mRKCp64bVJU8t33d89ATPqDbSx/3ZcYJoEPFxHTwGqJ4QXv+qoeMJXvfFvFAiAUkAmQamIgnM46pbBBx2HaDqyq886gagon14VXSvpZbJlDHVIYQJn1UYuRDYVvD6I7t8sDd9uroyL4hGYZ+vGClO7YzvqodLffPbZzHe0+R50wKi/pA8K7XzCBqiSqBRpWVKpkLPKpaNgonhTCxnWkUV0N26yNDYvTPtupfPTyzxJaIvC8i/4uI/ImI/FhE/uN0/KaI/EMR+efp743X39yXAyVK48YFGq80KrTYNOhxrbSmzR3GO6ZuE0Ss5WaMjVsmedd733eU+e13JylxSZWVwcHV2cjRlBt+v2oYq7eXT8YQk2jMJ8gEMlEKceQ4JDi8d0myd1Kw+3+0wbXIKO+g+5I+Mhwa+jiKaY8IeDCpdnSrfny2Hz30Y+d67T6jrMiU4dgTfpqfqLWBDzE8G3TUHyXa650qQCR4j9Kq0qiybgNr53EhpBTiy+U6tnSKHS3kxXMuXwyeR7I74D9V1f9bRA6BPxSRfwj8B8A/UtW/LyK/D/w+8J+9vqa+HHQ1xOvW8Xix4bwOqBoyExdvBDGp+AKginMtiJDnBWIk7kI6wiYRIcsyQggYuT41dJ+t2kvWfZJ6Jybft3//458LOsIavzthfK9yx80VDNj4rmw65/DWPepiwvn5OS5UWCfYtI4gNl1iZmxHtJLGqW+/bFlGvUZ/pbPsalBlLwO+5B8Yf9ERYccXp3u077ui+KC0zvc1AhWwNiPLM6wIrWsRBZvHktQhmWxxC2lLUIsTw7r1fPLolHlhObh/i1lR9tqOACrbjkAZVyz9ikT8M4ldVT8HPk/fFyLyp8C7wN8D/na67L8G/lfeMGIfI0PjPGfrikUTdwyJmyAIIYXajMTJdj563LOC3ljsLL0OjDF9mG6frQiXCX17Ndzovl3c30cLX5LQt7SEnfdGIoo7qXR1qbNywvzkFoilyebUDVgHpvG0Plx+9khC71OxNV0/YPu2ufIsC0V3LrqKXXRMqKd07bIHdHhIt24hEXYIntY5VBWXbI78oCAvCoJ3ONfGLZ+z6Hjz3sWtr6yBYAkmEvvGOR4+XTLLDN+5fQKpYOdo24pLU/BVwwvZ7CLyIfDXgX8C3EuMAOAL4N4V9/we8HsAH3zwwcu28wVhQLkuv9kFuKhalq0StEQwUbIbwUpXEVYH9bBXvceEEgk8y6Nkd64lBM+kLMmsZYzeYwfVePHD0K6B61+afumesifVpkf4/dZq51faCwL78ts0dVpC2pZaiEtWxdJKRi0ZWQDjPUE1Lv9lROi9YSxbzsBOlqbh5IUcdZd6Rk/hg9W//SxhGLNOeelryneKdZrXkOg+7i8fC+YXeY6kBKnuRUE1laPeNsliN6LlHcQgYnFiacWwdoFl45gaw9SYqO4nzaKrZySdk0C6foyZ5ethCM9N7CJyAPz3wH+iqhc7qZsqV5RTUdU/AP4A4Ic//OHrNUr2QPSFWmqnPFptWDtwtsSIxRuLWtOv2oqqaUTgoCHukCKJP3dxeGPIbUEIgc1mHbchNoayLHuhBYPKPCb49CWFcbr4/YhAdrT4F/fZdgxr9z7diz19CkCqvNJnBRkhWIu3GZUUbHBYD7Zx+BCwQq/Cd1L9MtkN/3dFJMfNeVFE6Alcx0e2jN4RoXQMqV/KQlA/ss2VoHGFY1cw0xjLZFJi87x/clCNm1d2dfQlMofgO6YdcQvJ4pyauHfdRe0p1zW3yoJpYVAUpwERYmnLcbN77Wog+NcFz0XsIpITCf2/UdX/IR1+ICL3VfVzEbkPPHxdjXxhGCFEnNSYE99qDL2NVfKtSnBJNY3qXYheeGMxdr+iGTdN2I6pP7NdnfqcpEwv5bdMhueHq8JSW4kz+67VbRLtTIuYHy8YM6QOj62OcePG6wi2TJOdS8fpxb2+1Qn5nQ5fctr34nzQFIbjY6NhewxUuxBbGElj7feS9xodcUFDdLzaaNZJamfXNJGukEkv2tGUKdcVN+lWSKrEJbGb1rOqWw5tBvlOA3Xn71cIzyR2iVjzXwJ/qqr/xejU/wT8+8DfT3//x9fSwpeEzrvZKDRBWAdhrRmVEuuLmMjZY/pEhCjZYzXVumkIPlBOpxS2GJ7b4V6IhO77bYuv58xjYumYQpeR15U36nLNXw5k/629prFzWEilrTuBHle8iYHcCmWeUWUZ1sSIRWYUaxQrsST2uIprnzfwzBYyGohdC3//dd2ONEFDb6JEurvMxHbZZSCgIW4K4dPfxvm464/ztN6T5wXT+UHyw5ieeQtgxWCzPG5nlcywEOJuOEWWkYnFY3BBMBjUFDhRHlzULFYNkzuGu9MyMfNuhvcJjq/Gin8eyf6vAP8e8P+KyB+lY/85kcj/OxH5D4GfA//W62niy0HH970qjY+ruHyS8l0W1OChZZBgCXHH0qB74rZGAJ3Ii9fvIvMAW4c6qTFW80eGWq/RvyDRb/ngrsGlnjBGmr2MeyYDQRmJmXI27X1mRfpVcb2K/ox2bgnuQY2JzrNOWm4pBVvm4TNVnagBd2bCcHHUDgJo2JHow/dubo21MbLSK0ODZDe7VYN7jaELl3baTdqAAqV2AdIy6rEwGT8j6Q3Xjt2rhufxxv+fXN2qv/Nqm/OqQbioWh6sWh6valpv8BqRWEyAroZYb9klGwwly3PURmRrXYsYi81kyK5SxWaWvCh6bSAi7bY6exWudkQ+4LsSk/RfHAH2MoZ9hy6p2amtsp3hpSGAqzG+YqYVQkVZlNhJTpllxGSRZ7fzRU2S7bYO5oiq9ouP4kCngesN9fhXNaW5dup2CAQfFzLVbcyLcEmbs1lGWU6x1kZmgJKlDSedD3jvyKzF2EgiPi16yqzFipAZizVRbVeVtGOsIaAsmoq1dyx9oJaoOWVjs2lkYe0LlLwu+Hpm0HVcXiOXvahaVo3vJbsOmRZ0umEvqZP9ZYwFE00BHzxWtiu8KqSceoukYovxtXssyJEXetsU1b6d8acOEu95uzomil032V5VY+RfSAQj3e/RL0JMpMlxBPEUBrIsIvhWP65t3NafXX/ac/cNojY2ODsvWwMxvNbZ5wENAQ2K9x4fQoylq+IlOmStsWR5ntTzQbuJzCXgnY/S3nYMMDIZk9IFTZdsM8h5MDaajkHBBZqgOJQMTTXqrxmfjg+8Ror/ehL7CNbO8WRTsagjsWtSPUWiBAuqaJaBjRl1IamZNrMIStO2tM6jKkkbGMBYi01qYd00WGti+KbTF0YcfIvUdzF9x2nzvMsfx5fo6CGDVXjVMzqbNl6z7fISwJCJMC0y3r19hKtyzhE2TR1rq8tomeALgWyp7H0J6T3huN1STl2ueQiDHR4leMD7WO89+LHHPW7N7H28x/n4205yTJbFHWXTjj8xvAY2qeghdI5Xeobokzd/rO33Q9xv1BvH0mFQDMvWc7qpmGfCpIzh3V9ALk0PX3ti37SO003FuoEQBO08zAbUBdRDMKZH9K5ijbUGY6BuW5xzgGCzvE8kkeSxttaiIdA0DVmWkdls2AVqZ/eXMUrvm2gdSfaXW+/cKeJXvWH3yqF9W6nCIlgjTPOMk1vHhKakfrpiua4J3sd97Z4TVa/qd9+3zuv2LIJP94ReavtI0CHg2jYSeOiIPbIxH+JW2yFA6wKIUGQ5WVEg3Uq/5HTT9GyDpEiM9tpPdPBFEyEznaaWOma0J3hNE++xeAIr53m6qZEy41ZRbjsyv4yN85LwRhO7Xvo1VALRPReO0c8THXJOuxAL0TYV7Xc1idv+xQk1JhWr6FXFqN5ZYynyHMTgXEuXLhtfGLPvWu9wbYMCEw0ol4lBkxdP+h9cosdxttvgPBt3MxkbW/6BsfrdN2s0MJ2GMajA4+f25kunxgJZljOZHdCEwKNVQ71ac7FxNC6OZX/LqJHX6RC9mZDMirHa2hH6M/F+4BLR8iHa0aqKGJs2jkwMoqsnaASDxQelIWoybeuihM9zbB5r8NkUcotFSoiJNdYmlZ0UeYk2u4oZeLgMjrq4K1BqmMSlsVUb9w4sJQqRDis0taXzlWwtUNmXF/yK4I0mdtiWVYZtTBv8ojvLFlVoVGgUai+0XvBBEaMxP8ZkMa7qWgI+qe1xx1bxLj3DogGKPKfIc+qmYbXZYIwgMkWSl1aMoWkdy+WG+VSZTScpByd5cbtUreQ86uzyvhskTWOLWHfXjw0j0Um44Zj0cnkbQXTrml0YM5Vx+WwDlJMJx7ff4VQtf/bFgqenp0hRQpZR+5G/Q9JzZPTGHS4sbNvYWzwiteHKcl6MpLskhmFiK0OIqbsiQmZjqahu+W1nFkSizWm9Z1MJwQeqqkb9hsl8ylSmGCPkNjplXUqbNSYjn+Spcmxsg2vTOYmBc+mcmgbE2pib0On4YkGU88rz8VOHUeGjkxjeSz5fGokCKdsZk9cJbzyxD7Df0B1vvzNGNpfWrMdkJxnRVKeGD7u99Kq5DsgZpc1oRZrsU1zjW4MOjqA+ZNevdBnY1b4y0koXsx53Ldmye942/rs1EqODV2v/nVrc9Tsyxm7ji67FcR83i1ibGKZig2LDtra99zVjrWLX6TQm+nHoMf1+ZlGP1HTZYY79WF0yB4TOjm6do/WBTIQsS1J7xORU6UNyxjIk0ux0rX+zdsuaU/y8u1573QWvsXZCG8CTIjkkvXT07EFQjXX7V0/+v0TEDpcV+8sDosTY+qJyLJpA1SomK7ESMMEj1mKzHJtliEQpLkBmDF4DzsUJDD5OiLW2t8+zPN9xikUkcc5TNy15nuG9A2zc4LVXj3eWL44kYZSsnXqufaLLVRTbswAZrum0x5eBfehlRDCZQaxBbU7IcpSUWtp5u5G+XNcWkfbtHj/5GW3Y8VFcTjEG6XPLY+w7yJDZ1jv4Rqp8CNH+1hCompYnT5/ifOD9e/c5PjhM69GHvAHVQNO2hBAosoIsS5V30+KYzMZVb5nJMCIE7/Dep7CgSZI9agjWCMFGjakOhlqFmpgXP0l6lEmzb/pI/FWs/NXBLw+x7xrp+wKUw5zTOE/V+Ki+J/VKJPRSXTobi0hAY67dIYwqGDOQQr9AgstSOnqJQ6pEm7KwBGL9uyjdxwthtlTfrtGie5EeOlN1LA3SNTKcf/YAjhfjbA9af3s/EEkVMiaVrBrlGFz7mnGbd/pxDf2Pr70s5bU3BfqlQ1dpW72XXtP6hoAPnqZpcT466brlqr6L2TNiEro9HmOn6fBO6TWBTqPo27Jj1nik9x25zjchyQoaJcnvz6x7tfDGE/tlRUrpXBpdjDNCHEgvhhblycWKB+cbzrXASx6THyQ5WIg4acRgbbSanPPxycnZ07jO22tjGWljKcsS39l9qmR5gTE2OrQmU4zNqOsG7y12MkFtp1YqnZd4K/Y9ks7dz754zQ4/6y4YI/ygvna9f9ZIbo+W7BZCTGeMEfIsi47IpLPGaFsKWeqQZ/46imz0LR5rCd2miQkMhtzYnhjH/hDRkGLlSt16qqrGpfTY0/ML6taTFzlZnpEZIU/iPc/jdk7GRC+9cw7XtIik6IzEmoQigrYt3mtcS9BvAxbv0+AJ3hGyArUFayd8cbbmILcUB3l8Vu8QHetV33Bih24IxmqObB3dWkWExG16Ng2nizV1LmhRxvimjNMbo21qTdzZ1Seu36mmaOjzpEPQuLTVZoDDuZiV1YXirLXkKUGjdS5OeBl6satjCdITsWx1qSfgzq7dW92CHTV59Oe5iG5E8Dq20mFMSV2fjDWj4R7W73eLeDop/qx3D2G2rSZvnb/kzxiF5qJLobNVOnU+Lk1WQsqb18QQ4t/IqKHxSuuieu59YLWucB6mswlTSoI10WRJTtposqU8eB9XxFljyFOOfCfd42u0D7f1tj+k9/towElGE+B81RBKyzuzDLLkhd/lz2NB8Brgl4LYnw8i4nb14DZNy6qq8Wbax9GTiAYx0VOb2ZQcq1RNHYm2LHobvdMA+qo0KbsusxYvBu8DIbRcnF/w8NEjisxyMCuZFAXTssAaEz3EHRLuSt9LBdJeL2ffepMIEoZm9K/XeM5YQ5ZllJMJ09mMg6NDJpMJh0dH3eKCa4n8qhTepChce9+1pkJ3SnVYE56SYXr1PZlUzgfa1uOdJzMWMhN3rM1sCr/Gd7XORZ+MzSIDUcUTUmA2xPz/tKR3a6x6zSx9ZLysduj06elTTp98zK35hHd/+D1mxUGS6COv41cAbzSxXzbxrndiKPTLWddNy8W6JisC1thE7AJGEBuJ3eYZYoS2aajrKqpxZSpHZTpFKyGPkFJFY9USEwJN6/HecXr6lE8/+ZRJmXPjaM58NuPWyTFFlqEp82psGw/ydPAZdN/7WPq4jzv4ELWSy31/Ltm+E8cfv6RXOIzBYMmzjOl0wvxgzr17dzk6Pub45DhqPy9K6C8Alwhedz/RcTaM5ZBNp73vJOCdo6kbvA9kxmJS4RE78saHEGhCi7WWWTmJ+e4atQWvPpl+SR0zHYlqWv8v28TeSXgZttEKAR4/PuUv/umP+NbNI/7lX/+Q2yeHkalqmrXXaAqN4Y0mdriMlLpzplfKk1rqNZYXUgyY5Eknqn1b3lsN9EUOtggp5ZKZuPNL53EdHD/JRsMQq7JqTJMtClBluVhEp5D3204eAJKpgAzU2c331r/9o9C1U7YbvGURvMzIXmaqEfIi594773BwcMjJzRvM5nOms1nfhn1E/VyEnl7YC+l443C+s3W2km1S7v/IfBjXk+vmR5NjLqRFMN45gleMpkKZKTzaM1+JmZHWmL4JGgI+ZeHZzGITHvVtoxPq0q//F0mZeN6xuliwWi1Az1EMFw8+Y7NaUs9zfPB4VazCdjRpe45fB7zxxL4NV4xE732FjQts2kCQLIbcjCUnesaDtVGyE6VtV27IjO1RjWEym0W1XdXTtp3X1QPEgpNJ/UOUyXTCyckJi/MzPv34Uw4PD/i1734XDuiXkiqdOi+9GtwTasLtbesv9XhHEu8npqvI9TlHdY9aEESYHRzyu7/7u1GzSVLN5nlfg+9lQHf+7n7verJl9Gy9KupvaVe6yLQTAQefJLKLC1/apqWtqhghcSkq4x3BW4LPopM1s8wmBUYic5fkmGudoyhyisl0S+/qmIoBchMTaTIDqOKbmraq+OznP+Pxwwecn55z/vSCLGyYuHPWE6FuG9oQiMKiixK91FC+MLzxxH4lCutl0g8+sNk0LOuWxXLJYrGgxFKKRY2JzhhrIGR9YkbnkLEJgWO4JqnsSa8dwkJpjzOTFO+k7ud5xmQyYb00NE1NU+ex1HTwUUsYxYEH8UEMvejz2b/7qn7tG5t9Xvn9eez77pa+z2h00mWzKQBtSjjB7JfoV8OIk+x55e6hS56NJOHHbGI7lDms1xvXINDOlk+fbq2eSfF5kzS3XtvbMR1SwbAhpKbJbEhps8G1eNeiQam8j+ZD29DUFavFgs1ySbVcUC/PKUrhxuEBJ4cHqVbhvhEae+VfD7zRxP5cskrpHWfrTcWf/NlPeHi24P/40Z/xzz95xPz4hIPjE45unPDuhx9QTqZkN25gy4IsK7DWYsnJrcWHQF1VgDCZzmJJ4cxgTFS/GucSc8gxBsoiJ88zVG9QlhNcvaHeVFiE1cUFs6Igzy2ZNX3YSlIIjj7ef5kYVRWj+9W6mJ23a98niXiVY+tK/NHR/92DIsHbrkx2UqU7XjM2JfblA2w/+9U4n6Ka3yWfBMBHXwjR6959Qspi7EqKiQhFFp2keeSuZGWBmU6ZTiZMJpNIZEFRojYAGh2TRRGHIgyec9VAaFqCd5w9ecLy4pyzp0958NnneNfi64rgHe3igtDUSF1xTM33v/0h/8bf+ZvcvnnM/VsnFKmkYR9d6MZ4qzT5q4c3mtgjdFjW/zcc3sEx7zxnZ+c8fvyULz5/wGeffsHBcsXhckXbNty8fRNUcfMZVqDfUztxfh8CbdMCUBRln0RjJG4G0En2DpGNjcUDy7IAhLzI8c7h2pa2bWjbBmOKaNexbSpI37XtKjEpSXfocqdAdogh8f1D17W78ZlD+Hx016V/Dr+6jJarbr+8lv4Kd+GWtbHd4F2GsU9D6W30PZ8uIaYj9K4F1nQ9ilVzMxM98nGFYpcl52IYrStQkZEKkkQGIp1TUAOuafDOUa1WrC4uOHv8mC8++QSXiF00MBUlBwoCZWa4e3LIX/vutzk+OmA6KWMhjq0hGv94fRL+DSf2fUoe9AMxphKih73IDJPccFAIx6WgbcXy6Sm+qXBNhc1z8pQAY7M8ViIxBkyWQiwx3nrr9m0mkwl5UZDneUyzzeMCGjWWbmGzIFiBwgq5Bmy9RvDUVc2mamKkj4zMxjplYECidA4kbm66cwOSe+3SKjvJPzijdtFBLhHiFcS2e7hDui032HBpnzGnsZ1G+iU3O4+9TrLvh5HL4ornJa2jo9zuqpGa3hG4Cz6uQU9+G019UxQNLd55qqoBsZQGJkVOlmLpRmNtIpDE0D0Xp09omobVasViscC3Lc16HaveNBvUOer1ErfZsFmt0OUZ08xy8+YRs0nBr337W9w+PuTGrOR4VvDOnZvcOTmiLHIyGfd8Z9BfM7zRxL6NCHr1SQHSbpu5FQorTHNhlsG6rdlsKur1kovzMxTpK8yKzRBrEZtj8pIsz5jMZhRFQb1aM5/Pmc3nTKZTyumE2cEBkima+biwKSGUBcQaMgKmqUCUtmmpm5aiEDJL3Hmmr45DQkZQCRjNdnraqXYCGuI+6uNxSPf3XU+40zGFfWsGrh/nHY1JusIWSi9wtxKSdEsD2ffE53vvVYS++107zkOf0DNiBrG2XBgRu/QDo+oJoaWuK0QsBijyWFqrWyVn+3GLz1pdXHBxfs7p06c8fPiIpkoCw7VoU4F3ZOrJNCDBI95RzGbcPZxx4/iQv/H9X+Xb797jnVtH3L1xhPQ6pPbd2R2J3gH4TbXZYeQFTXCVWapAWRS89613ODo6wqvl1777YSw26ZXWBzaNo25avnjylKpqWGxqNm3cvyw0HrzFqYM65+yhsC5LysmEvIj1xLNyEkss53ly5kXCMyamSrrlgt/5wffIioLjgzmZTeukNZZICsnzarqqOC4uxDFZjiHvVdne7aTRVNjbZU3EtiXVkxTeM4iXnzHWjgYk7NdkdydleKZu3T1S9bcefr0ausO29lwlA0EnO3kLegHfMYDhexdJiVpJHPuM6F1fn5/jfECLv+L09Bzn4xJZq4HMxT3nVR2qgeXZU+r1mvVqiV9cYEPgRq5kk4JbRzeZFjk3D6ccTkuMBqx6JkXBnZs3mU8nfHj/NreO58zKYihic9WYdEx610x9DfDGE/uLwHQ64Xu/8l2CKr/1g+8RwoD5F8sNn31xytnFgj/8f37M49MzfvrpQx6drqldoHJRtW7WOY0Im7PHMXnE2FS+SAhi0xZREcG8c4QQmE4mTCdTvvfhu/yb/9q/imQ5P7loWLuQkMjjg8RqrcZiyEEV37axbnleQLFt8wYf19mr2i0Jur04Zke2Xme374OeguOXvRF+2V5/EBF32yMvjG3250fYPQrt6E2DBjHOlhvKUWlva3QFI/vNFJNz0xARvGkazh48YL2pefj4As1KlpsNq80GcQ5bV3GL6jwufLJNjfEu1eFrmU5L7t26yeHBhN/+wa9y99YNfuU77/L+/TuxzHYqE23Sp8gM1uyO5lXM7yvS4fklIPZd/B1syj2ySoQsIac1ZkAkAfWBG0czcgMf3L/D8XxCWRTcvXVC65Xah7hDiMZthlsNqQy14rxSO8+m8XF3z7aNqbIuhtdcozQEDI6bRzOwGbPK40nZual9qWZhv43xoJLu9HQcqtvTTx1J9f7p16SY9mN2LV7Jnu+69dhu4dH+TNjnQNqrtLLdtveJMyOiHrdwLNFJTE+7hKQhs66whsmkgLrksMwR51g1Fa6q0abBtA05MC8z8sxwcFjGuLsoIDT+CwAAIABJREFUhSiWQCaBSVlw8+SY+WzKe3ducPPkmP+/vbeLsW277jp/Y86169Q5537bV8a+ThPTjoAICYKuUKKgViuh1emASB6iVhAPFoqUFwSBbgkS8YBa6pdIiJAHhGQlQhFCkG531IncEt20Ca+mnQ5qhTjpmAQSGwff2HEc7vU5tdeag4cxP9fH3ruqdtWpqr3Gvfvsvdaaa865Vs3/HJ9zzNefPubpmaUg80n6if1MLr3p+1ym2wD9nQb7oui5QFJ9krkrHXfnG5584HXCm6/xkQ+9n2EwV9o27koasBTC7z27YLsdeOdrX+O95xd86ctf48u/95/4ytd+ny986cu89/w5X/rKM55ve57plp4B7bc879/lkX+L//JDryDdhq8+v+D8vQu+HgIX8UEGxRLWBFvBZZluiYkuIqdL47uSSsZUVpvtd9VkDjyqK08YO17v7PwxB/Ra5b+sdLGDRMvMmOL4c/opTE9XNAf5DENJ+6Uh8MrjDR9+/AbvPT5Dv/w+vvZ7v8+v/vsv8Dtf/RqPOs/jruONl1/iG//AB3j5pSd89KNv8cpLj/nga6/y6pMnnJ95njzyJVLOOZ6c24Rw1nk6hhg0lSwc6SV4lN2BR1fLMXg9utNgnyOdVxRHIqE2BivBOL07M3A8Pj8zc0keOEb9MPDu1y+42PY8Ou9479kFXfTVnnXCdvuc955t0KE3sG+3bIfBvDeqvPbkEU/PO3AdjzeOr3fCthe2g+ZpR7Gy+c8sJUTWpNLUo/I0Yz23/pHHSyUtlKqlKqQjcbulw8JcG4WiiRE6hLIheobaWHiN8Qi5qebb7CDacHawSEiNUXAisOkcT883dMMj3v/qU84k8M6Xz+kvnrE527A52/DGK0/5A2+8wisvP+Wt97/GKy8/4UNvvM7rT59yftbx5Dxu1RyNec7VxrSx0e1wA9ttAx3uDdgvpwe2QtH81kRCpY+qBVVsnPDy4w3hUcfTR44+DPwXb77K823P8+3Ae8+2DDHzSQiB7RCXvwqIKO9/7RXeONtwEZQPPN1w7uGL724JzwcG5xmkQyHuGmr7gFs0l+nl9Q40Usv/NSVuN6OsL69pn1EVZg6vQk1Q4I76xg6FicVvsQH7J2ksRVDHbDKxbotxd7gg+MG2Yn7lfMNbbzyhe+Mxf/QDrxKGga+++x7PLrZxt1bhUdfxyuNzNl3Hyy/b95PNhjNvC2dS9GMtlhc5LBpIiwUuXhmduCN0x8F+uRd24Jxq/0ajs6SAkaiMpnzxj8/OAdCX6jm8bMuspD3eTC/3RaFEL3qennmCBr7yfKDbqrl6nIvuIdsiuEtAF1eYeax7pw43g6rdyStG7L+6Z1aU3wPCQ3Gay4/Fj30cfoGjJ4nNDJfMc3YHqOnwjzrHS+dnPD3zvO/JYzrn6GletRnYKCzBwpvjcZxoxnYKbYBf2Ev7Jo+ozxyJ7jjYj0OHmEdUUvabBALNY8vOtxKCgu2TEEVGJ2TL8ZkX3v/yE56cD/zuxXu8u33OMxW2aYDGe8Q5xFUTSCUuWvx9/C+Pf83W54MTRkzeRG1xK8mQ6gE9KwfcEKNKoe85D13qS5r4Kv96e58FUdU8NEWdBwF18KjzvHR+xuPOtmxK1vkW7Ja1eApfq7htN00qzJy9+3SnwX5ZDnK1RtICiKKJ2RLUMiSmqgFlT21puQKibDy87+VzXh7gP/z+wFfeG+gD6FDWXadVT2mrYChgT8tyG2Ew+eCj6DqX2WXM6Q7VCxPQ81sQWuFhoZr673OQYW5cJlaQga6kyFyIak0dTJMmuvhwtkWXlsUu0ehBZO48iivazp00KZvbR5uLCYzdzYuPimTShjLfL7rTYK856+TSzEAebyO08CeMX3Xyg2np0EJt5k+8688teAQvShe3hHq+HZC4xl3qzSpiIog8rodgK8tiiqRmgpm4qKpuyw5wRwTNc+p6khuXmRHxRwdjTba9fdyfWSPEYommi2nyjbNRWbVY/u4ptUT6TulH0hUZ10vRrzOca8En9W5kH5lZgEjzh4Dpu7sDdLfBfiUaz8ZzDCXkK2Woh6q0MEhHqFxbQnF0CUnMrIdPGU4i8cUKPN50PDmH51zgtoOBzhnYnXeI7wDbBTYMll0lp6/2rYhff1qI7n8jekDRpoiMLtwy2aRY5XvLM5sQLXKm6qRTJD1b8RpwqnTa7s0z/wrGf1lKadVqaXF991ihq+tSDnGJvgi6x2DPWuzMn2AK+HmqeVkBbL3xRF0yfc/P7BF+UbxPckHy0eZj0lgtgzaOq6b3JTVxdV5HBUlhJOObl59yH83JRlod7vOpX8p/rJMfixwYKEFBSZJvFYl8jxPT35clndFBLfVM2LnmiT21v/NdSvN1p+h+gX2BO9Xe8pafp3Pjm2zmDYyHWSGvAbSvzki13ngkNVSDIO2MmkRIhw08LxZpJRBF93Eet6q/cTKQKiOMxkCRbK0eh8qWbs5SE/J+CZoLbktGtWnZYk9YSjjZ3mCfkEMKtelkkWBoTGipqiyiR5982mOx844z59h0tQFvzIFTatJyTpKJrwH8NNnEfaX7BfbRYJkbvIcJtodVPq0nGWjKyrJFJqbVgKw4uxPM8i/1mnHNKmnuS+L6dZURDGUAxx/aZlmRsUiw84UsTwE5andcxSVnjWnf5hvTrG80L6PpTw3cFJtQk2CSlHcx3ZidbCWyBVFnUY2ZE5XuId0vsI9IZn5d7f6Z85J2BK/OHiCeCnEzgyAQBEtF3AMDboDgBOfiGnoUZYiJE8A2kUh6avzEpoch0Pdb25TCd3GH0bn+FCE4C+FR522epgln2/1cS61MbCEHvJ9J1F3WZITKx0hZwaaW4TUUsOdUX8HCZXuXJiazfWw65XGnPPLGq9N69VFnqRe37n8L95/uNth3vP2pELscClpT3paJEYecNLBwdUe4mNQcSdMIDGhMcIliCzbiDiLGamK47WD3lk0Li0PIguZCdMs5iwWP2VDnFJipMrPvvUxgu6c8UzWieh+l3/PtZg4t6b40daTealswzgFWXeVqoxLEo8QkongPZ53QueTClGwrGT/j7BqBybM2X/eW7jbYb4BuNia5miDyz5iaOgFZQMTnHORgHD35+p1zeV9wuzuJ+eaD9y4G5DCvNzd0ZZ2mLBOde8JddR4s4S/oYArRZ95E+VeNVjdm8ULLzUDnPednjrPO53e10iV8BCLiReQXReST8fgjIvJpEfmciPy0iJzdXDfvD6Us9Wl8CorEDQDTppLOecT5OBeUTQ0U21OuTtVcgK4m7idOWPnml+gQSWf2vgyg2Qe8Ni2bOaZySLuUV4rgBBQkp4Uz9o423vP40Rlnmy5z9ZUu5xD8IeCz1fGPAj+mqh8Ffhf4gWN27L7SGHwO6Jzgq9TFpXDFr1LYbUzVrFWZxL2a9dt1JRR7Xl39QcN8py4zLXoMmjG9tT+1GCO14tjVy2ruK8ZPSxHdeePqXQynXcnoILCLyIeBPwv8RDwW4DuAT8QiPwV870108CZo77bD124AUmjnmXM89p5H3rPxDu+Trh4XXAQix4750NKGinHAp2uaMl9kHfWaw/iGXsHBvarnPC1/E20+mi5OJzPR7Dp1Gt2b3tF1nsdnHS+fbzjf+FWGr+hQzv73gL9BCTN7H/BV1eyI/jzw1tyNIvKDIvIZEfnMO++8c63OHotuTm9v2WTi1J2zNEXOJXfbWP9sY76Lbz2W0abWkT5PLjsTcxO/9ijZNWPdNxEuXB5NRfFR5jaU0IXSozI51j3WRWt2VKgCmKp3JCZFdel9z9VeRSKeEu0Fu4j8OeBLqvoLV2lAVT+uqm+r6ttvvvnmVaq4XySYyh43jH185nnl8YYnjzZszjo2XRd9TVH/DGp6e+RTSQ3NXCyYi8m2Ue7y/uBUVewas8Uo2A775Nufv/dIINhlT6jtbOP+NfeV5B4OaWa1PBeEgES3pRPhzHuebDrOVjG+oUOs8d8O/HkR+W7gHHgF+HHgNRHpInf/MPCFm+vmlO7yrFwz784Lm87RecU7R6+KhJI0gwZwyQ1UD9HCgdo91lqt/ZBBPd0Oub1z6Z1eWRJKkkUVBbhcML2LYlmvt7yqQ2NtV5zq9mbiMMB3ztlmDBz+bnb38f7TXs6uqj+iqh9W1W8Evh/4F6r6F4GfB74vFvsY8LM31st7REoMw1XbdPDMOx5vOs687QRfL6eU0FrqESlZUuOYH4bA0FtmG0tZbZNBse6XSLyD6A6zusYomSQPUo76OenE/i2uTSAC/cw5ugUx/lTpOstz/ibwP4jI5zAd/ieP06Xbo2PP4lkTrXy/nXM88r4ZeBmgMQosbRGtMo3XD9Eth5I3Jax5+0GDeWyhl+mFu8HRxnqFNlx7XDSRAb14JVzU2f1SYNSJ0qWCalT1XwL/Mv7+deBPHb9Lt0c3nfRPBDMWkVxuldEpzQpKo6c3tmi1TLL90ONdh3ddyU1XXM6XA7yOAX8cWlocc8lackWNoa8BNoBme4VUH5sIbTuuzuKWGJkMr9vBe013c+HtfScpATTeOc686Y8lKEYrsKeNFKMZKl1W4oYUPX3fo6p47/LW0ilN1i6b9rUe4Cp3zRne537va/VSzWsDdh/F903OC3gzb+g+0smEyx7LALP7/nqXtZIdxQmI2JLKZEYvWxwFFI9ITDxZRYUl1JdgmpSsiuyBq8LHs+tJKcDLGsU+mrWGp1ov886K0a8JiBkX2VuHVr+hmQHyK7BrIU2U8VwW3mWxhpOkkwH7bZBUYLekSODiziLCAPRxUG5AYau2n3jXbfB+A842HLRKAoKF0upg2Wglud2qiaT47W37qkpan/w6nK42Iepkkliuur5aP0OxWrQcOdUYRPMs59RMd1vpcMA5aptrNvetRrpEK9gruin3S5kEWhCEaG22tl3LjqUS+akYbwql1bKyPkn/mcNLgXwd577LRrHgyZr0eYkOf2XS/EoWdfu32set4ewS30clMVTdU0oWmZKHrm2r6cEN22ruKq1gv2Fq7cFVltTGnGyGqVQuqKJ9b7u/1ktyFwaplqwOc1ebcpcd6LdhxLT8+7vk/Qj+OYNbEW+wyS1k8f40Ib1MK9hvhKbDzDLKUrxJ2pYvy+RtMigutxFn30EFzMeRTK4L9Mn9B7D/1Ptm/so/5u4v0+lkld+qrDe0WuNvhApnSePMiaPrOrxzlmElhXc6lxNSJoNc0MAwDIQwZPfdHPDK0G6vHUsLuRXfe+2VqJn3yP1Wx/aOsZ8CktKa/7sRM3D3aAX7DVEDP41g9wb2tJrNAkBcy401QAiEMBCGCHafstIsmN1mONe+te6H0u0BvnzmGXn7QK2BL6XyqsC+4n1CJyPG395sr9lolChr49VqK1XFO1uCmaLict41LeJ7Wv+e/fC5zpGUuqjPW5/2i+S6ODnsfHezzvE2hHfu/sZr0PgO2z7ZV7RpVEZK2zPPbhLvohF0HDo8b6ib7c8JGO1OBuy3SYavGNEd3UoalDAM6GDfkFaxxcw2ChDMOBfiCjgUJy4u7nANx8sLQXaM0dqaf5s0hxsdcWVt4T77HMUeZ3uwm0HO5ckwBAsz9lEVcjrgJJwEcK9CJwn2Jav0sbj/mFGZwTjGtUvtbMotV5/cGbs3LXYZlb5rNI+veWlhrzNPtd3KS1qDXUnKWTWeRZ3Ey+/3vmw3QScJ9mPM/POiICR3UD3QFLXFGV2HDwPIkN1vhftq9iRp1YaIRHE/xQCQ/eqH0fGs84eTdbJdsz62zM/dpuVaraMo6BAMvsnV1mA9nXcw2pF1hXqhkwQ7TMF6PDdT5CgjQCbu3ujeIwNUTlZTGaNK3HztT6+Gc4o5me1T3foyTV2Bh5OUaJa2T1ekZLfI9afzpICZ9sqUy9PcUX6POnmCdLJgvy7tnhzKQBOiy0Ojzl5vyTwZmNG4F0LW252z7Yx2u97uDl0Z6EpJFdVK8LHeNAWUve27zRniHM47e5951koSwqyz/mRpBfsN0Jj/pOEXgsbxVwn5meloYbFxgQzYRJFy12nNrYoYUI6XROUbHedTkb0+v3jLzLGO/Ospt9zkdlVEnFnhJX4mATXpfayifKIV7NyQ2yWP2KJrD6oENfCm2HYrOh3UeYjmlXALnF0jKIS8fXG6OLF4J5oxhJfd6i9Ll3x3s8Z3mw5FYrqpaMeoVffajpErEIlr1ilGvSwZ1OL9CnY4YbDf+M4wKQ9vlNeDQh/stBdXmA62Ldzchg4K4MQ2lGgd16g0qM6/x2u8dDLQG6vZCHyHbaF1WRq73aY28mSgTJ8iuIS6RL1IJib1VMG2g7KGkCpGgUk7p00nCfZ6s4Wj150NR0DamBFtuXPiPpJs9UqQZOxqXUq7klBNr5QzebWbgGrr8pt96mxnm9kZddd7qswNsxXG+3Od1cTUFNXUOtlLsehw1GjsdL5k94kTQel7fe8KeDjBcNl6IB/iV7+y713Kx8ax5C2fwhAIw5BXtYUUHqshc9ZkgRcnOaR2PHzrT9vn+E1hhPkxjs24d80c9Zmdy2unsQTQSjs2dxUpRsThN5Za24xzcZss1UoaWEFe08lx9mk65f3ldx3XVHYfTYWr+9K9Ysp1GrwKtiVx5MRSVTAOkZ3r9STjaqxnTpItNqspf98XQjq3D/qNYWnhz5P97LFvIs7E9ny9rIdfzXJTOjmww03r66kRsjptErtj4z19AHwPQRlCIMRMVVkK0AELwjFunrizJlWgqn6JVIlx48AMx4yl9tRywPMt92BSYHn75hjjvmO1mr0Dsyd453BOcJ1tgElcVKRDgCHgdFhXvS3QSYL9OnSpgZT0ZFOd88aDaZeWQIhZ6ZKsn6eGHICTzh7QM5g6oHYVvWEqjezfUqoI7OOirfaRdsGVidSRt3me1dlXghXsR6WlIe1EYy46xQtszjZIUPrntBwt6aTO2TZPzkzOaaDDnF47bbl2WR0y5JfBeDvC8H5JS7LkI97hvUcRNAwQBojBSl40rj+48S7fS1rBfk1aXFSTdEtNO5pYCkovQtd5W8++7SvfkuYoMRcU1zlEPFMzXO0kL2bwPbb25s6qlwdEvCVJY76GXHe2C4wt6BUX1rqXpa/t+5vpUH70uPrPmV1ZQ1whqCHr7tN+Hob8q6Tsum+0gv2atDhAqjHrAC/CmXf0Ak5j4grvbb16bwkrlGBZKM2a14jy6Vzy2Nn3OBSmiP1j92IKuKmnjctptpcBwiFyRSuGt+J39UTRqOlELLYgLXWlLCQKYUBC4EyS52Lcl8MB/5DpwYH9Rc7QcyJ2WtSyEeFR5+kHkCHgnNJtOgiK0qNbKx+wte7JL5+zsBRHOCmj7GX3KBVoo+wuRZe98fCyS9snJ5ccUtJoj+0YqsowDIgOSCd47x88h74qPTiwv3CaMDXToJ0TzjrPBaEK2XYx2CWCW5NYWw30cmJnk3WsyiKHqvF6EHYvLw4fVc+XwtklKkJN3VVyjuZ17+nHLpfiQ6YHB/brzOq1VHBdCUHbQE8ebRyvPu0Izwfk2RB93g7nQV0AUYIoIWemKVztgNCf6qvt81jjby7cAFkcw9XuHd8n1TvIIcMCZXqztNEuSjl5Q6wmgmjl8okeHNgvS7VuW4P7KkBPtvLKkZSvOQfeCz6mks3cuxLZkWIIK+L7uI2dT7N8euHGY4m8egBHzW0u17IQypyMDQnQ4/rKmz+Es58qnTTYDxXhjmEHcAJnHWxK0hlb/YYZntLQzKGjlS+5jrsZ9Wx0XImnydA3Wfo6X9NxadfsQtNtw/DI05C1F3sPiEzsE/kpRBDnzOsRtjHXfriVp7xvdNJgP5QOBfriirHIuH3m6BF/IvliEVlJo9hE+BFOJyaBLNnGsrUCrzXsps+QbNrQitA3Z9+S/GiFC48bK3aLqnDTpxR/lLMCJHtHsxBg5exjWsF+w5QwNAzwfAvbrWVFDUFRH9dvV4Ab1MxQ6hJXS9eXjWUZzJqSY2gDjoBZ4bPLrlbxJySLV45B48kquc+K+B6vJjVGCtBFaNQF0QHVwBBsk8eN8zxyjs470jb2KxVawX4LFDBX+sUF9FtiIIgSGllAoskpH1biLJmrz/Jnrbl3DMwZ6fuNtb6uZ/qDmwb8pD9jP3sCeJR0VCwKMV1OE1rZUMMeym88Z53SObeK8TN0b8F+jFTQk/jqyQqyK+jqWe0sa9kFi5XpB6UPJQNLAmgKgjEu5uLGEGWn8RRbX4xUVXMLXDrnbIvsMFnJ68SYTYebe9saD3kH8xF0o3p3KhXprGR7ZR1r0NgjUidTqLFEW0e17mBHI6N+n86UcG/Bfmya+6NfbyDUI04ZgvJsO3AxDHGr5uJWCkCIQHDOI9IRs8/BTPbWmcOGGgnhSsbF43P1yUScDQyFspttFuxtPbaFlm2qYUAnrog7LQBfhu4t2K8TDHEd6/pl9nAv3N3W0A/xU4uwDZ+VYolPHNx02iiqCxPruo5+zPVq8XkXX8ENifF7qkwAL9PkvDCeMtC2RspiyFta1HvqdFCmGhF5TUQ+ISK/IiKfFZFvE5E3ROSfi8ivxe/Xb7qzh9I+IN7mzC+RQ/dB+XrfczEM2VgsKjEVlUPFgbMVXc67bJxTLVyMENduRwOfJZtujO/lo63h6/YjxXa3lybBLLpHqabrOotvz9ccqJs8D/H+nOBDbPmwryxzN5FP7z7ToWmpfhz4Z6r6R4A/DnwW+GHgU6r6TcCn4vGdoOtw7ZuZCExUH6LlWJMYXyFVE1dzdYqmqQ2h/oZkEThsHbvkO/ZNhuVd7HsfNQiLrj9ff5mIRsUmDLyySI7aqr9r1p6mjOSK3AX0ts/tBLLr2n2nvWAXkVeB/wr4SQBVvVDVrwLfA/xULPZTwPfeVCfvMyUwDgGeDYGLEBgs7gNB8FFn77GMsc5vcH5DiQMvRjrLVVelYapE9zHEnHPVdtCVBUFnsHX4w1y3wKSMWSoWxHWIenlogD7esDKL/6sIv5MO4ewfAd4B/qGI/KKI/ISIPAU+oKpfjGV+G/jA3M0i8oMi8hkR+cw777xznF7fEB1vJi9+84TCoGaJN86ueZBXgZ5ojgE3oKuS9fPEOSd2rmod/AEPONfNw+lAHKV+znJHrcsU1p7fQ9PHsox1qpa0HRtNaaWth8OYr02HgL0D/iTwD1T1W4B3GYnsOp5q22sfV9W3VfXtN99887r9vQc0HYzJ9bZVpVf7nYNIIFudbUOI+KnutVRMMcOs2L1BdSKqLk1Ut8nrLj1XxufI4v2ojqXqSrBhcrXJNUSW06BDwP554POq+ul4/AkM/P9RRD4IEL+/dDNdvG80Lx0EoFfoE1BViyc9r+xymatnqnTn5G9OBrs58X1Mwl3DQOtJKGq+WdgPnywkI16oM+lIlgZWtt7SXrCr6m8DvyUifzie+k7gl4GfAz4Wz30M+Nkb6eEN03ENMCNjTwUzxdxuAYtuE6SkVUJjBpYkzicgtEaqxlgWY19TiM51re5N7PkIdJczXEaQHdSP2Of8FGPxf66fo8CZCuiq0aahy3aAU6ZD/ex/BfjHInIG/Drwl7CJ4n8RkR8A/j3w399MF2+Wbsb6PjVCDapsdSCowzlviK4MbuIcOCHuQk7KQ2Ocr8S650i4yMGaHVyuOG9NX8GRJsA6JmBS5cjgpvPQzGrO6N68e46oeTCwjDW9mlvyrskzd4EOAruq/mvg7ZlL33nc7jwg0ihQprGejFZEcUqKw0wAF3X1OudcGrKJ0kKQxsKeQNSAqb5zXMsytQtNqtp2cukqAKeahKZl2j5qfVxL3LE6Tb/rVTDWmebJ6vYVKltGLaq03ThVurcRdHeW6mB1xbLPYGGdOliwyKaL2zFH7tS7jkE6E+9dFM/TTic5djQdVruikIZ6URXKFZrfJYrOgKAVHmoGfLigM22jHFWe/BmjW6axjzvVlVf7WYfqW0X7/G4UD1hm2YDS68BWAoOWyWSNnC10MmA/JMx1biHMmPZG5zHmWprDwDVYDLcTbZxutnlElZ5hhKPG8pwup8CbJP6KMhdvPu17NVM0164G9JbLxjOjLsy/sxH/H88dY/tE6mRzj5A3z1RbRRgo+n41t41uO80Z4GTAfjtUDeA80GwPcRMxB5wK3lkKaYcHHF5ctJSm0M8o1jPPP5vvSvQuFumrGeoK4HdNGjPicVPJzumm3B8NcYIFAE37HJ9Dxr2Rpo5iADURvg7AWamlewv2u5ghdNKL0UBN/XRiueicGNidOpzWUIgJJxfaGZ+vQZ7aOfT9tBPFGPDjpxqLHFXdMzbCabvFmFir624sUbVNTCWAGuRVu7aDa1gdbgt0b8F+F8l01QokiS0hFvbZDzgcXiyn2rbfEgL00jFgPnZxPqv9V1U4zWKvzTFMwVdL9ePz803vNvbtA9l8bniaeWViZ6wOso09Pp9hPnJ6ta2wB0LMvxfPn6bEPksr2I9KguCyzp6MzAqEoAz9Fu82dNFX/O7FBdth4MJtCK7DubiOfZ51H9aDSvEfg2vWX34kwDd2ih1UJAHNC35aTM9XUuSBeIeIuTWqpX9hCAw6RIs87MijcZJ0MmA/RNQ/hjrQiKBZ3Kx9Sxr/1xwvb9b6ADoA5n7z3qOqreFuT7uNygB7JolWTFcdcVla3I2DbmqI1jHuBxk1l95zbS+YLdJODXXeuuR2X653QbI4IWPdyYB9TMdIDz2pE7IY72o7UtaLbVOD5HC/GALP+8AgA0GUQQK4gHOOzWZTxcNP+9mAL/4T8sHyuJ/FUMHqpem6E+Tk/rGBcPw8WTAo1xPQ1QmasuymsqeD5b10b8F+2UE2BveNz+hScaecWqrkmwNbEJOkUOtjQIOVDcHSUeYAGqn7LM04ruUGqnM7uzfzq9RZ1zgyns0Y4pYanOPoMvGrt23X/Up72k1u0OkpiX0r1a8oH9O9Bftl6ehcfD5wu+iWKSgmLT8QIYjp4ec6AAAd20lEQVQjIPT9gCL06thC3IZYovRqIbTPnveICJvOOLzvYgYbkbhYpu7MLhn2Mg81NdodfU4ch+nJvOjf3FL9WwwhlQMy9juEwKCBMHqGlYweFNgvkx/uJtoe2bpHR5KRozGlVNC4WaFUWz2p9X+IMfNOBFWX2Z44Z2mVxxzswGdeekcy0QtoANMsO53Tyw9qPbeW78i2hdoll0T3LEW0hryyUXUr6dSr3RqpIev2pz0DPCiw3ybtGjitYKwZzBL3Fu+HgIpDzjZ4HN4gjxdsKyhV+sEE/KC2/7gOEDQgzuG7zrDvEs/zHJqhRaMl7qoZXXZPpJdjqUV9aeXypoYI3tDyd9oJI6XaKttbz6k2p073FuxjX/JdoCJMzxvUXBTVhxBsx5fNBu86vAZcGGyZq4vP5QZUle12G/V3kBDw3ufJw5GMd5c0NipoZJu7Q3e0we/e933In2Ok8zeGtlHr7W1FIS82vOJjT9649DxByZGL1+HnD0kauLdgh6v9IXaJ+sdQA1rOU4numKV4UOW5qi3mGIYqJ1ylg4rgnfVDvRIkZMNeCIoMaglt4pp2EQsTHb+Pxfczc7oNwkndTu+julGna9oaACdxfOEVZgOgprcyVXjS/ToW2OvVOho9G6LZkGcBSZo3yjT7w1QnuYwL7ia8Ni+K7jXY7xzVIqj4dIrMj9TRh8BFGEAU5y5sUwjnjaurOYxd9LMDOPGEyOH7oSeoAgMuOBxm2AvarnffR9dJyqi0BraRBF7OzUTwWZmyRm+sebftJMXdvlw+XxYGC7avvYhhWlxUZ8RZ6i/Razzpw6MHCfbDtivaoXPvuLaf6xdOolXAuaWSstVYyU4vIcRyztxSlXstGeycs2spW2xqIhmjQphy9OYZmlC4ywu19rgJnjOWu/m75iqZJRn9mAmnz3VGhQXVOk1n+U5G0CQFWdfLM981te+26UGC/YVRUnOlHYaKLdIYhmCpqRKbCr3tRIoi2tnqt2TMi6GkznuSv917b9lY+t4s9sNgunucCJLI2QTiNBPBdNCXhI2Fan/1ZSTYrIiMPRNNe1WZfS639AjVfQ28kzqUKzbvRlBhCBpVIVNz2tZPk1awX5L2cv0ZD1Y6SNwmcfwUdqoRXUu6pIjgVFEtgM7txe90vkkGcYDLSaP+XAfvtG62yZOknuXydVt1v6eJKZbF6ktx3WirmNwhJdlkyWF32gCvaQX7DZNWn4C5y3zno3HNriSDkmjI3Drp7AmEPmaeTVsjmS9+yAO7Fufr7yz6X4Ima2WuK/2O/OgCs5PS+DhpIDVfbrPgxMnMO8R2bzYJKih9CHRmxWQFvNEK9hulKmtqHnO2p5mN+EC9xXFQ0+NrkTyRiCXBANsPLoTAEAZrpeLwh3gblq9f4skWZ4D5rLDxUioxqWepvmJyWN60qlZbbFKNm3HkZH9x8tvb94dNK9hviGZ19hCQTvBdB6I8e7YlDAPd5pwu+s8tHXI1gLPvuAx2cYITR7fjzzcGURKr835oM8g+Bgam4fBjMOsEdLtThS23Zc/gst89BIUQ6Afj7INeXqp5yLSC/QapiO+aUyY5PF3nUaDvB7bbC1z3KEbXpYEfLEQWLBGDi5a/OPIT50/fOR1zvj9tFVXE+7zv2y1KtHW/4pnmWv09R+N96vJ5ot1D0jToQOJ22CEwRMAPPmS1YaUV7DdA7fBKeqZUR2kSMCt69Mc3xjSxfPIxxLaxpycOXX2P29eZxJNzov5BcfKHPvWBYoGOvkcNT46L522suU9rTf73oBoXxLhsFJ2+kXl6KAE0c7SC/UZIR/C2X4IFzVhkl+Bdh3pAoe97vPd436Gq9H0P0PrXI4117yTyG+CcRdSFtmxtsT+g+0dhh0s2gjTZxUL21d44C/z44qZu/PxtPvhtUJ4NgfO4TFh1/pEeMrDnaAX7UWk0qqpBKaNjAOc8pqq7ia7bACWK8nNDswZz+T3T2Gx3dQqq3Nn54tNzUwliOfR0obk5jl4bJ/Pz1NtqlL6OFwGqmv4e5txzJ0wr2I9OyW89PZ0CtlVt6er5+TkgbPuefhiyni0idJ39aTQE+qA47/Gdb6qcC0wZi+hL526C5vTw5Mc/RFVY7Kcp6dVBlJOkOidCXDDAEJRtPzCELl9LSTNOjZvXtIL9NmjMXuKx+dIlLmcNswAtRq7dYvi+RTCz91164Bf/9+TKzMnkYpvfPV4WhIpdfZLqKypIKXBGkk/euH8IFp5c7qmWGp0o4FewH5XmjEdxPDpwneC80Lm4/DIYCLz3Zo0XA8CUa5moYNLsAXp3tL4fKs2P9Y5ln/eCMr+njbmQ3J1UNZPDdieVCuCBwbavHgKo4HD0Q+D5tmcbNqaz75lCToVWsB+bau4cvzWed15wTvBx7A/RUpzdYoxdVclWVbnlxvGsdcEaGFLbn3ehcc7fvlz+MgEpzQ6zB1KWAqrb6oUvzVOJAw1xgZHmqTYEZTsMOdvPSkYr2I9N2UNk7KlOKGnAVXwUNodg69ODcwQns/UkS7zQut0W293XvQm7t36WOWQX0A9r47JU11vAXEfiVXEEUUrI7jQV0rqCFI0YNE6kodhPVs6+gv0GqOWolrACW1stihMTPlFF+2Bx3G5gcMbBnZj4rYNxpa7rqqWt1RrwQ11otGAax6RLdhPcHByWQneTQXJ0YRIoZAXrXW0tvZeII4YnZpUo+9ljnHxxfa60gv1GqA3hSLHdScS3ZZmOlGpJYnZZKVamDOYQo8LyklcBl+6tfPlLdFtx4G2mm/3wml2dR3JYjNQZKWFJVItbxMUJo14qI2mRUIiJPlLNV7BHPjBawX4TVLuB4wjO2zILDMMAMQYeBOcdbtOhFTcirnobhoEQF8eksFfvbRC7OPCLH7pu9xCr/TzHzVevOFEccl+bZNJ+h/h7PHFIDEBK1vZ4IQJbiMuBcE6Q4Ag6RNdbyB7PE8c5sIL9qCRZn6wNY+0ws4CPmKEmRdPlexXJm0FW9VYrugrXE0samUbzvr7tYWuqY6Dvr3Mf7bbq72+oTuIhUlx1O5NNSXG9hfJyV2IF+w1SWt4agRmNTEEDF8MQwzotq4o/62yn9pnQ2LTYJYSQs8wOwQx8qsXVNzee96XeqgEE8661Y2kBi9y+6sB462a7HJN3xL6GMOO1l1hPTCe7HQLP+i3bft2+uaYV7Eelym3Uuq7jb436pHFkS7Yg+Eq3bNZmx5VtuZo6Qi1Z5g/tmrS+5rKYpr02GxxzWcTPhNDu714VV0CZA+o0W9n0oWUibaCf7BwB2zAzrFCv6aAFvyLy10Xk34jIL4nIPxGRcxH5iIh8WkQ+JyI/LSJnN93Ze0FzbEctgCaobXWw6TZl40bnGEJg2/emm9eRXlScLXL94pMnSwrZel0NfzNSl9+lQhjHnl/qcfbeMG8HmPvsozYphZL4tOW402zPsJNRRYrPHBCCHKzlnATtBbuIvAX8VeBtVf1jmOfo+4EfBX5MVT8K/C7wAzfZ0ftF0XedQkE0fWzYdZ2n812OmrO9223gSv6PDMokxnrvZ4GSAa61r7ySAhqq9PK5rsue61eiqG5cAfB1X5r+VKpNdSoaOSFI2RlmJaNDU3l0wGMR6YAnwBeB7wA+Ea//FPC9x+/e/aQ5HdpUSuPKIbqFOu/ZdF3URQ3sGgJpTWZdj3F3A7zzlpqq62xxjC2N9XjvbPPHmK9OxNxT2etXSQm2Z9wMAGN7dRLMS3FHaX8mdXqM63r9/jSOfyoN2D0F9CXExp5RnMtZfW2p68rRx7RXZ1fVL4jI3wF+E/g68H8BvwB8VVX7WOzzwFs31ssHQCIO5z1EH7CgdN0ZnesYthdc9Fu8OLQbzJfsXFLUizgf492DJr02m6dNVajcVhM/diwrLoF6JoY/R6m1NoLqIdp2R+3EQovW730Revup8nNUlvaUWz8QmsnglkIM7g0dIsa/DnwP8BHgQ8BT4LsObUBEflBEPiMin3nnnXeu3NH7TiJxr7cUIUbFvVxJG2U6eJiMVGk45jj3TSozLyZfRmzWmn02qJXR91VoN/qSd6Dte2usy32sulVLHsVe0Z5b6TBr/J8BfkNV3wEQkZ8Bvh14TUS6yN0/DHxh7mZV/TjwcYC33377NN77DB5EBO8dkFZohSiEOjbdBuk8qGWsceLoNpZKUVxMRIlOuejc25QyCaR26+/mdp1+T5mvG92/+0+4K6vt7NqzarZKEsf4vvQ7LfVNOo6IIGq6uXF24/2DKoNG3b3ccfLu9kN09t8EvlVEnoi9/e8Efhn4eeD7YpmPAT97M128P6RQ6Y3VSSjGtjgwawt51qMlWu7r+yYNYCte46eM/eRzr4NQRhyeKYdXHVXe9L6dKGouW6sMYylikaT+ZEMC2XiXVIB0fYFKeO1EEcmIrkX5xv5QrKX572Cf2o8x9y7uPx2is39aRD4B/L9AD/wixqn/D+Cfisj/HM/95E129L5QgYlmcTLEJZqdeJCYLilyIoGSKdUV4xTioqEpmdiXdeHDVmvPQWNuF9VRU8JcifkWZMeqvKa++XPNFDMql2zuWVXPk6IQgoA6RHpEbDutAdgqPAu2CYcT4tLidiJrSUe/50yt95cOCqpR1b8N/O3R6V8H/tTRe3SfaSzpxu+gClo51ZLYjFrIa5G5y7ekZBXk1WlzoG5E3ZlfE2rV3RHgR0E6Cxb0K1M9g4zrppIamL7K5nexzUXuLXEWiJGFYpPDgNIrdKpZ4jrAanG5Z7pHtEbQ3SRVIr3p3CXRgoq5h4xDRREyxsqnoBGhHf1jUTmF0ubmFjjqIkiTGLvj+mVo72SQ0bYbdiWQZqbuaFjINowsSdQfu3cIysW2x6tDzzxU09op0gr2G6PEN6PIrpoXZxjHrjTFFP0WtBJTa4u7lCqZ18eB/SI0U0BON3LYo3cfUOd1aN6QqPk7N1UiiGakBHt3Q1C2254NHjZ+lxngJGgF+01QlkHr4A7J4rkTh2agFiCLK8BO14rk2wrxST/OPHIk5mv1b/trei0dFdF5v8DbivvZancw37ws7gzXCx6BKD5lbVzJk2w/BAbfZgya3jzXs4cnAaxgv2GqjXQqPsbLhAj2OskkFtXm/Gw9mYtrWeARKGJs7XcfmQwm/WmvmT5bg8Xam7kpNcB8QR2Xba7sgve+67U6I9noXiatpAbRPEc/mBi/EbObBIS4B+7udhaP7zetYL8mTUXYkbV3xO1yzEr6RzXr42Mxc16cljjeW3RqrGcuxeI4iWNO8awN5JfF95nTxVjW3lc/bTm/DzSHqSK55jjpGeCrNoTGnWd6eyiq064HmnD0hwV0WMF+ZMoCeznMTEmz7t4PAyogfW9cqnOId+wbYHWIaFlk0+ZrS77q8br4OV196dpVdfZj6e5zgJ+oHgnwgrkuIepBGkONPUHhou/Z+pHKc6K0gv2KdJWBnQI8DPjFh+6bMofvsT7Xl6UdWJbqOyZA99Fc3w/3IMyUEygb32tbMor7jVF0lz9PZg8fFK1gPzKlOCwXJfQhWPhmiPqmOBc3b4Q+uuOcxp1aK1fYdCVYmQDGYaQllNR6kModQrNgq9WDJK3cgil7ws21BXDm4Mk40ETFxVLiLJ88Qq+ms2+74qI8Ze7+oMB+XXH0+lQsXWkAajSome1IAEsXHbJKP9YnbTiO/ed1aGpWFyRZqdtnHeO8BU3rxpujOZv6Prde6ddh73xpMpvtz+K8FS1yVFjPuBeCQh8CfQiXDRl4kPSgwH5nSIgLNGxhy8XFwHY7xKyyMdecpOywMSY+u9oOA8uuwVtPApP7ohe6sSc0E8lSnYf062qT67jq1p4Qv1maSioRXtsyIQS2F4G+81niOmVawX5kkvqHQr8deP58y8VFYOgDIuB9TAtNBXaKb30f7eZS+/3EtXX+EGloUdQfn76CjFyq1hmJpFRbUkcqLjfV6urpptQNi6Dbst36aYquE6QV7Jek3bpwPZxsFPfBsslayqmYQpqURDKG0OKqIM89FvmZH80dia1r6UcjLWhque7lbhr3qjZ2NV72AyWDSX+quuq3KyRJpP5dePQYvLWyolg++edBuNCY8EPbUtRpu8c2j+rpHkrk3Qr2I5NUk0FAed4PvPt8y8UQELEsNAGHqqtCZ80en4dZ42E6YKTVE1C0FUjW5alcdWlKSVzOJp8ahrVkMAbSDB+tn3zyq0g5Mi23UJGMDsoGGEP2NIC92wxIKf1VioH+uQq/r46z4Bgom1BYqbSlVt3oOM5OODxz292nBwX22zfIzVNKdKgIvcI2WProZIxzOYar8HNNevQM1SDTMQLnD6o7d/Uz/SvUGkSBjeQykp+p3Cmp36PWlOz52iuxKKQ9MHf20zDfxr4V4aatIPJj4+6K7bXXzKFKLTPUf4WHTA8K7C+aFGEQH5dXCr0o/2lQvraFdwd4Fsz1tpEOwVVhro6gIOKQzElsdKZdTVKWlhQjrlRjPG4aaSQVg67zq1bJIbLXIBnmighbT0JLg3+CzVqkX5Tm0/KUKdUwbdWU/IAMVPnzoeLQAVGNn/jgaTlxsJDZ7aBsh0A/KBtR/IygMd+ru8E8jkUr2C9JuwJe4tCL3zAAvcJFUPpg1yTp7OKyaN1w9VHoqda/Mj4rY1PEw5IOnjlnuksqfizkDRfsmUolTT2a5OkCxgz4GfU7ygocYv8eqwY195XRrzHvldS35rj9tnUJ6VP0m3HP2ulNqBYYPxhawX5EEsBrtBvHrZdl6KG/QNTROR9H1YCNxrHbjWyqIyW1SECt/Gm1lRqImyHMc6JkQygLRUq9WTCPhqqJrj1DtQab0milviSQFdG44tRSF9D2vkaNn3kGFJ8zzMU6Jc58EiBmp4GAiqLBknZqTM3dh4GLYeBicDyWqsFJa65t44EBfgX7FWka6AJowCcnkUbRPAzo0CPS4cVHtNhyFYnZZkWy1m7r2Gs5uBE5JYO15cZCqGXp5qLdU7TvyNyaQJs5nXfUdiWeN7hNj16heqxRtFw0qiMjiaD0XatnlXzOxQmy4e6SvBlxIpCQjzVn6Q0MIdAPA8PgCd61z9Y8et1hacs9AFrBfg2aW6xhw862Hkq/C8dyoPGDoCFydld0aRuoUsTsSaNFj08r5RxJJaBSc5MwnTo3I1LXcnv1ZXUzuZaSZZYrkgtLDdTRO5HqoHD/Une9CUSjJzQVFcnFmLqiATTvVa9RPQIVj4raLjrx9ebQWrSE3abKaiVIp+/iodAK9iOSAoNUOruQXT4pbbTiTO4Wc7+R1qlHFGjk+gnwCbKpjFmVI9idgdzj8iDNYrHEvHX1wNYyhhNzn8PWnIGt5KewjDp1Ftu6kNaqhtQTgx06laihlAlOtIBd849WOx/UFxtAVMY1puMGTHyXABq3snbgNLARpcP6HEKwdz75qy1ZPB4WrWA/IkXenYeOB56edbz6+BEdHahljB1CMrnZYLWxnbhmAjJQpaYq1rDIK/Mx1FBtsK3xWtbRa9eVzgL94AelTVXZSr9VjcU6iIo0nn2JZctTyKgzRfWojZUS35PTgIgiYmZR5xXTlEzW2QTlcXjCK+cd5xvPxrvZbaHnH/HhAX8F+xFJgK7yBXcI3/D6S7z20mN+bxv4yvOB7QDvPVN6VZ73PYMqF0HYBrPSB+LmjZr0+cjRQwK4y+J7Ed2T/JC4exmoaRFOyXRDA/gC1iz/TwEnRdJIiDZpxaSQSdiJ1JOOVRbiT3Wu9C+K12LyeFyDLxD17dq2kBYWSdTdvQ54ejxw7gKdwEtnjo0Tnm42PN44HnVPePpow5kTXt04NgLdyNjQWiseNodfwX5UMv6WhooDHnnbcHAQYavKRa+4weK2N0JMdQxbis4PIL4ChJQgnWzYqsCUM61W3DZJwQGNzDW6/XRucI/3eW/F71ImTQRWPkjrA2h08xrP6bREcRspqgYgMc2Wk7oNykYYkIPbXDzvAK9Ch/LYi4G9E8688NKZGNg3HU/PN3iBM1OgEMwfX1JLNz2cPPVDohXsRyRFGeKA6RgQ4IkPPHLKYw9vPOpsS+HeZWAH4ALYVoa8BGpF6LEh2AeLBAtB6QdLyLAdLOXSs17ZBggaGAbzBqS9zpKBcIj3NJFkLhq02vCyotBDliDqHHgQmvJOElgtJMhsjgZoHyUT5yQvAvJxb7vOm6PRO2/gdbY1deccnXM4bIA6lA1RgtCuyC9q5jmfynjbCKJzQudMUkgbQ3gNUxvD6K+XaJTI+8HQCvYdtCtTzGJQjZDXVIsondhLPkOiFVhwIVrPnYH6AjKoUz0DhqstxHXZEfADXGwDgyrPt4E+KBuFCxH6QRkkmHEwc3Zrc6uDhexSEmlYYxJdVNVwV7UKImjBgCNRxBaNrsN4QwK7lwL45B/onEkUCcgbF/De0bnAprNtqLsMcpt/zrxwFr2UG2zCeATMpuKM3D8pQdnWSXrGbPWLakj5O6R3PQfqhwZ0WMF+VDJ7e9wIMZ+LgrlUqRErI5YgdCSduVDSph9FLj9EI34QGOLmEMPGau+HyPXVx91nijqQOHxI2XKypt0o1hkQefBXhm+SfUAKsNpnjBMDSQrI2kc1WZRvCy8QvIucPZWJXfLOuLVgdTpNWzdN3zj5vL39ZZTW4np7ds7Q+RBpBfsRyQbneLR5ijBdKaPVADMxdAr4umbTd61gcuPV7ZoyPq0hSQg17R7SRZOd78lURx9LxnWZuXbrp5fJxWRUOBR4UtkUd4joAsVXL6PSD5GPT2kF+w1QbZwa/6pkTAq8tRl+MirbiJrjWPD0PfJ3pxvdqPllCLXGqTEc6jLjs+P2x0Cua54APc1/RfdpJ8O6wxNMtst8rPzMEyagj/4MtTaz+7kfBq1gPzItcbVdVw4dXLLwe1dN81tOLNMh9uid+NtDO8uPZ7mrNLBU8VwGngPOPCRawX5FWkrpLG2h/fUc0lbdxshNduntkRcoh6Q3de8vc0hTlwb4JWhe9hiVGSe3vFJL95/kmJvy7W1M5B3gXeB3bq3R49D7uX99hvvZ77XP16M/qKpvzl24VbADiMhnVPXtW230mnQf+wz3s99rn2+OHk6CrZVWWmknrWBfaaUToRcB9o+/gDavS/exz3A/+732+Ybo1nX2lVZa6cXQKsavtNKJ0Ar2lVY6Ebo1sIvId4nIr4rI50Tkh2+r3cuSiHyDiPy8iPyyiPwbEfmheP4NEfnnIvJr8fv1F93XMYmIF5FfFJFPxuOPiMin4zv/aRE5e9F9rElEXhORT4jIr4jIZ0Xk2+7Je/7rcWz8koj8ExE5v+vvGm4J7CLigb8P/HfANwN/QUS++TbavgL1wP+oqt8MfCvwl2Nffxj4lKp+E/CpeHzX6IeAz1bHPwr8mKp+FPhd4AdeSK+W6ceBf6aqfwT441jf7/R7FpG3gL8KvK2qfwyLSP5+7v67Jm9mf5Mf4NuA/7M6/hHgR26j7SP0/WeB/wb4VeCD8dwHgV990X0b9fPDGDi+A/gkFhX6O0A39zd40R/gVeA3iEbi6vxdf89vAb8FvIGFm38S+G/v8rtOn9sS49MLSvT5eO5Ok4h8I/AtwKeBD6jqF+Ol3wY+8IK6tUR/D/gblKXs7wO+qqp9PL5r7/wjwDvAP4yqx0+IyFPu+HtW1S8Afwf4TeCLwO8Bv8DdftfAaqBbJBF5CfjfgL+mql+rr6lN33fGZykifw74kqr+wovuyyWoA/4k8A9U9VuwNRONyH7X3jNAtCF8DzZZfQh4CnzXC+3UgXRbYP8C8A3V8YfjuTtJIrLBgP6PVfVn4un/KCIfjNc/CHzpRfVvhr4d+PMi8u+Af4qJ8j8OvCYiaWXjXXvnnwc+r6qfjsefwMB/l98zwJ8BfkNV31HVLfAz2Pu/y+8auD2w/z/AN0WL5Rlm0Pi5W2r7UiS2HvIngc+q6t+tLv0c8LH4+2OYLn8nSFV/RFU/rKrfiL3bf6GqfxH4eeD7YrG71uffBn5LRP5wPPWdwC9zh99zpN8EvlVEnsSxkvp9Z991pls0bHw38P8D/xb4Wy/aWLGjn38aEx3/P+Bfx893Yzrwp4BfA/5v4I0X3deF/v/XwCfj7z8E/Cvgc8D/Cjx60f0b9fVPAJ+J7/p/B16/D+8Z+J+AXwF+CfhHwKO7/q5VdQ2XXWmlU6HVQLfSSidCK9hXWulEaAX7SiudCK1gX2mlE6EV7CutdCK0gn2llU6EVrCvtNKJ0H8GlmeUA4EUpCoAAAAASUVORK5CYII=\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4CcZd2FUnTAb"
      },
      "source": [
        "### numpy로 변환"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CFPjrQRCnSSb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f336907e-b438-463e-e0ef-6c6740570666"
      },
      "source": [
        "data = np.array(data, dtype=\"float\")\n",
        "print(\"[INFO] data matrix: {} images ({:.2f}MB)\".format(len(imagePaths), data.nbytes / (1024 * 1000.0)))\n",
        "\n",
        "# convert the label lists to NumPy arrays prior to binarization\n",
        "categoryLabels = np.array(categoryLabels)\n",
        "colorLabels = np.array(colorLabels)"
      ],
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[INFO] data matrix: 755 images (163.08MB)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wJEmMrAXrmDP"
      },
      "source": [
        "## normalization"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r8Xeo-Rorjwd"
      },
      "source": [
        "data = data/255."
      ],
      "execution_count": 69,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3G8ZqLqfnYRy"
      },
      "source": [
        "### 레이블 데이터를 one-hot 인코딩"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m2KnUkTEieV5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "41f1dc2a-0c73-461c-abe6-45cdc6380f49"
      },
      "source": [
        "print(categoryLabels[:5])\n",
        "categoryLB = LabelBinarizer()\n",
        "categoryLabels = categoryLB.fit_transform(categoryLabels)\n",
        "print(categoryLabels[:5])\n",
        "\n",
        "\n",
        "print(colorLabels[:5])\n",
        "colorLB = LabelBinarizer()\n",
        "colorLabels = colorLB.fit_transform(colorLabels)\n",
        "print(colorLabels[:5])"
      ],
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['shirt' 'dress' 'jeans' 'shirt' 'shoes']\n",
            "[[0 0 1 0]\n",
            " [1 0 0 0]\n",
            " [0 1 0 0]\n",
            " [0 0 1 0]\n",
            " [0 0 0 1]]\n",
            "['blue' 'red' 'blue' 'blue' 'black']\n",
            "[[0 1 0]\n",
            " [0 0 1]\n",
            " [0 1 0]\n",
            " [0 1 0]\n",
            " [1 0 0]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AXcJIhU1noje"
      },
      "source": [
        "### 데이터 분리"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H6H1tL3unAOv",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6740d2f6-c9eb-48ff-e9e2-65fc2a0ff36f"
      },
      "source": [
        "(trainX, testX, trainCategoryY, testCategoryY, trainColorY, testColorY) = train_test_split(data, categoryLabels, colorLabels, test_size=0.2, random_state=42)\n",
        "\n",
        "print(trainX.shape)\n",
        "print(trainCategoryY.shape)\n",
        "print(trainColorY.shape)\n",
        "\n",
        "print(testX.shape)\n",
        "print(testCategoryY.shape)\n",
        "print(testColorY.shape)"
      ],
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(604, 96, 96, 3)\n",
            "(604, 4)\n",
            "(604, 3)\n",
            "(151, 96, 96, 3)\n",
            "(151, 4)\n",
            "(151, 3)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_vHgevIupt6_",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d428568f-dc3a-45f0-8def-0b348df416b2"
      },
      "source": [
        "CATEGORY_COUNT = categoryLabels.shape[-1]\n",
        "COLOR_COUNT = colorLabels.shape[-1]\n",
        "\n",
        "print(CATEGORY_COUNT)\n",
        "print(COLOR_COUNT)"
      ],
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "4\n",
            "3\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dAcKr4uko_51"
      },
      "source": [
        "## 개별 모델로 학습 실행"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X3gbBIm7oBMX"
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "from tensorflow.keras import optimizers\n",
        "from tensorflow.keras.layers import Dense, Flatten, Conv2D, MaxPooling2D, Input\n",
        "\n",
        "\n",
        "def build_category_model():\n",
        "\n",
        "  model = keras.Sequential()\n",
        "  model.add(Input(IMAGE_DIMS))\n",
        "  model.add(Conv2D(32, (3, 3)))\n",
        "  model.add(MaxPooling2D((2, 2)))\n",
        "  model.add(Conv2D(64, (3, 3)))\n",
        "  model.add(MaxPooling2D((2, 2)))\n",
        "  model.add(Flatten())\n",
        "  model.add(Dense(10, activation='relu'))\n",
        "  model.add(Dense(10, activation='relu'))\n",
        "  model.add(Dense(CATEGORY_COUNT, activation='softmax'))\n",
        "\n",
        "  return model\n",
        "\n",
        "def build_color_model():\n",
        "\n",
        "  model = keras.Sequential()\n",
        "  model.add(Input(IMAGE_DIMS))\n",
        "  model.add(Conv2D(32, (3, 3)))\n",
        "  model.add(MaxPooling2D((2, 2)))\n",
        "  model.add(Conv2D(64, (3, 3)))\n",
        "  model.add(MaxPooling2D((2, 2)))\n",
        "  model.add(Flatten())\n",
        "  model.add(Dense(10, activation='relu'))\n",
        "  model.add(Dense(10, activation='relu'))\n",
        "  model.add(Dense(COLOR_COUNT, activation='softmax'))\n",
        "\n",
        "  return model  "
      ],
      "execution_count": 73,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Vh-cWdnw9YIM",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7df175b9-a734-4d18-a313-34e0256bcdfa"
      },
      "source": [
        "print(trainX.shape)\n",
        "print(trainCategoryY.shape)\n",
        "print(CATEGORY_COUNT)"
      ],
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(604, 96, 96, 3)\n",
            "(604, 4)\n",
            "4\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DnwbSGt4I5Er"
      },
      "source": [
        "### 카테고리 모델"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O0CE4sh2pXrm",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "094c271a-2423-4b10-839a-e6e9a000da7e"
      },
      "source": [
        "category_model = build_category_model()\n",
        "\n",
        "category_model.compile(optimizer=\"adam\", loss=\"categorical_crossentropy\", metrics=[\"accuracy\"])\n",
        "category_model.summary()\n",
        "\n",
        "\n",
        "category_model.fit(trainX, trainCategoryY, epochs=50, verbose=1, batch_size=128)\n",
        "\n",
        "\n",
        "loss, acc = category_model.evaluate(testX, testCategoryY)\n",
        "print(\"loss=\",loss)\n",
        "print(\"acc=\",acc)\n"
      ],
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_1\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv2d_14 (Conv2D)          (None, 94, 94, 32)        896       \n",
            "                                                                 \n",
            " max_pooling2d_14 (MaxPoolin  (None, 47, 47, 32)       0         \n",
            " g2D)                                                            \n",
            "                                                                 \n",
            " conv2d_15 (Conv2D)          (None, 45, 45, 64)        18496     \n",
            "                                                                 \n",
            " max_pooling2d_15 (MaxPoolin  (None, 22, 22, 64)       0         \n",
            " g2D)                                                            \n",
            "                                                                 \n",
            " flatten_7 (Flatten)         (None, 30976)             0         \n",
            "                                                                 \n",
            " dense_33 (Dense)            (None, 10)                309770    \n",
            "                                                                 \n",
            " dense_34 (Dense)            (None, 10)                110       \n",
            "                                                                 \n",
            " dense_35 (Dense)            (None, 4)                 44        \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 329,316\n",
            "Trainable params: 329,316\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "Epoch 1/50\n",
            "5/5 [==============================] - 1s 39ms/step - loss: 1.6357 - accuracy: 0.3013\n",
            "Epoch 2/50\n",
            "5/5 [==============================] - 0s 16ms/step - loss: 1.2238 - accuracy: 0.4536\n",
            "Epoch 3/50\n",
            "5/5 [==============================] - 0s 15ms/step - loss: 0.9556 - accuracy: 0.5894\n",
            "Epoch 4/50\n",
            "5/5 [==============================] - 0s 15ms/step - loss: 0.7424 - accuracy: 0.6921\n",
            "Epoch 5/50\n",
            "5/5 [==============================] - 0s 16ms/step - loss: 0.6300 - accuracy: 0.7368\n",
            "Epoch 6/50\n",
            "5/5 [==============================] - 0s 15ms/step - loss: 0.5283 - accuracy: 0.8278\n",
            "Epoch 7/50\n",
            "5/5 [==============================] - 0s 17ms/step - loss: 0.4553 - accuracy: 0.8593\n",
            "Epoch 8/50\n",
            "5/5 [==============================] - 0s 15ms/step - loss: 0.3753 - accuracy: 0.8825\n",
            "Epoch 9/50\n",
            "5/5 [==============================] - 0s 16ms/step - loss: 0.2968 - accuracy: 0.9172\n",
            "Epoch 10/50\n",
            "5/5 [==============================] - 0s 16ms/step - loss: 0.2673 - accuracy: 0.9222\n",
            "Epoch 11/50\n",
            "5/5 [==============================] - 0s 15ms/step - loss: 0.2137 - accuracy: 0.9387\n",
            "Epoch 12/50\n",
            "5/5 [==============================] - 0s 14ms/step - loss: 0.2264 - accuracy: 0.9288\n",
            "Epoch 13/50\n",
            "5/5 [==============================] - 0s 14ms/step - loss: 0.1892 - accuracy: 0.9470\n",
            "Epoch 14/50\n",
            "5/5 [==============================] - 0s 15ms/step - loss: 0.1558 - accuracy: 0.9437\n",
            "Epoch 15/50\n",
            "5/5 [==============================] - 0s 16ms/step - loss: 0.1377 - accuracy: 0.9636\n",
            "Epoch 16/50\n",
            "5/5 [==============================] - 0s 16ms/step - loss: 0.1138 - accuracy: 0.9636\n",
            "Epoch 17/50\n",
            "5/5 [==============================] - 0s 15ms/step - loss: 0.1075 - accuracy: 0.9652\n",
            "Epoch 18/50\n",
            "5/5 [==============================] - 0s 17ms/step - loss: 0.1015 - accuracy: 0.9636\n",
            "Epoch 19/50\n",
            "5/5 [==============================] - 0s 17ms/step - loss: 0.0823 - accuracy: 0.9801\n",
            "Epoch 20/50\n",
            "5/5 [==============================] - 0s 16ms/step - loss: 0.0678 - accuracy: 0.9818\n",
            "Epoch 21/50\n",
            "5/5 [==============================] - 0s 17ms/step - loss: 0.0798 - accuracy: 0.9735\n",
            "Epoch 22/50\n",
            "5/5 [==============================] - 0s 18ms/step - loss: 0.0715 - accuracy: 0.9752\n",
            "Epoch 23/50\n",
            "5/5 [==============================] - 0s 15ms/step - loss: 0.0643 - accuracy: 0.9801\n",
            "Epoch 24/50\n",
            "5/5 [==============================] - 0s 15ms/step - loss: 0.0659 - accuracy: 0.9768\n",
            "Epoch 25/50\n",
            "5/5 [==============================] - 0s 15ms/step - loss: 0.0409 - accuracy: 0.9950\n",
            "Epoch 26/50\n",
            "5/5 [==============================] - 0s 16ms/step - loss: 0.0507 - accuracy: 0.9868\n",
            "Epoch 27/50\n",
            "5/5 [==============================] - 0s 15ms/step - loss: 0.0445 - accuracy: 0.9868\n",
            "Epoch 28/50\n",
            "5/5 [==============================] - 0s 15ms/step - loss: 0.0463 - accuracy: 0.9851\n",
            "Epoch 29/50\n",
            "5/5 [==============================] - 0s 15ms/step - loss: 0.0323 - accuracy: 0.9967\n",
            "Epoch 30/50\n",
            "5/5 [==============================] - 0s 17ms/step - loss: 0.0270 - accuracy: 0.9967\n",
            "Epoch 31/50\n",
            "5/5 [==============================] - 0s 13ms/step - loss: 0.0235 - accuracy: 0.9967\n",
            "Epoch 32/50\n",
            "5/5 [==============================] - 0s 16ms/step - loss: 0.0244 - accuracy: 0.9983\n",
            "Epoch 33/50\n",
            "5/5 [==============================] - 0s 15ms/step - loss: 0.0184 - accuracy: 0.9983\n",
            "Epoch 34/50\n",
            "5/5 [==============================] - 0s 15ms/step - loss: 0.0171 - accuracy: 0.9983\n",
            "Epoch 35/50\n",
            "5/5 [==============================] - 0s 17ms/step - loss: 0.0158 - accuracy: 0.9983\n",
            "Epoch 36/50\n",
            "5/5 [==============================] - 0s 15ms/step - loss: 0.0153 - accuracy: 0.9983\n",
            "Epoch 37/50\n",
            "5/5 [==============================] - 0s 16ms/step - loss: 0.0210 - accuracy: 0.9967\n",
            "Epoch 38/50\n",
            "5/5 [==============================] - 0s 16ms/step - loss: 0.0181 - accuracy: 0.9967\n",
            "Epoch 39/50\n",
            "5/5 [==============================] - 0s 16ms/step - loss: 0.0116 - accuracy: 1.0000\n",
            "Epoch 40/50\n",
            "5/5 [==============================] - 0s 15ms/step - loss: 0.0153 - accuracy: 0.9967\n",
            "Epoch 41/50\n",
            "5/5 [==============================] - 0s 16ms/step - loss: 0.0136 - accuracy: 1.0000\n",
            "Epoch 42/50\n",
            "5/5 [==============================] - 0s 17ms/step - loss: 0.0149 - accuracy: 0.9967\n",
            "Epoch 43/50\n",
            "5/5 [==============================] - 0s 16ms/step - loss: 0.0387 - accuracy: 0.9868\n",
            "Epoch 44/50\n",
            "5/5 [==============================] - 0s 15ms/step - loss: 0.0470 - accuracy: 0.9818\n",
            "Epoch 45/50\n",
            "5/5 [==============================] - 0s 16ms/step - loss: 0.0573 - accuracy: 0.9818\n",
            "Epoch 46/50\n",
            "5/5 [==============================] - 0s 16ms/step - loss: 0.0210 - accuracy: 0.9983\n",
            "Epoch 47/50\n",
            "5/5 [==============================] - 0s 16ms/step - loss: 0.0177 - accuracy: 0.9983\n",
            "Epoch 48/50\n",
            "5/5 [==============================] - 0s 17ms/step - loss: 0.0248 - accuracy: 0.9950\n",
            "Epoch 49/50\n",
            "5/5 [==============================] - 0s 16ms/step - loss: 0.0195 - accuracy: 0.9983\n",
            "Epoch 50/50\n",
            "5/5 [==============================] - 0s 16ms/step - loss: 0.0231 - accuracy: 0.9917\n",
            "5/5 [==============================] - 0s 10ms/step - loss: 0.2917 - accuracy: 0.9470\n",
            "loss= 0.2916771471500397\n",
            "acc= 0.9470198750495911\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "g_h8W_oHI_CZ"
      },
      "source": [
        "### 컬러 모델"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z-p1AcRT-IMJ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f7a26df2-7922-47ac-d0ae-5f6aa755f09c"
      },
      "source": [
        "print(trainX.shape)\n",
        "print(trainColorY.shape)\n",
        "print(COLOR_COUNT)"
      ],
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(604, 96, 96, 3)\n",
            "(604, 3)\n",
            "3\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Qgm2tq2I-Sym",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7ebe46f8-e410-4ede-8f54-368023e03e55"
      },
      "source": [
        "color_model = build_color_model()\n",
        "\n",
        "color_model.compile(optimizer=\"adam\", loss=\"categorical_crossentropy\", metrics=[\"accuracy\"])\n",
        "color_model.summary()\n",
        "\n",
        "\n",
        "color_model.fit(trainX, trainColorY, epochs=50, verbose=1, batch_size=128)\n",
        "\n",
        "\n",
        "loss, acc = color_model.evaluate(testX, testColorY)\n",
        "print(\"loss=\",loss)\n",
        "print(\"acc=\",acc)\n"
      ],
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_2\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv2d_16 (Conv2D)          (None, 94, 94, 32)        896       \n",
            "                                                                 \n",
            " max_pooling2d_16 (MaxPoolin  (None, 47, 47, 32)       0         \n",
            " g2D)                                                            \n",
            "                                                                 \n",
            " conv2d_17 (Conv2D)          (None, 45, 45, 64)        18496     \n",
            "                                                                 \n",
            " max_pooling2d_17 (MaxPoolin  (None, 22, 22, 64)       0         \n",
            " g2D)                                                            \n",
            "                                                                 \n",
            " flatten_8 (Flatten)         (None, 30976)             0         \n",
            "                                                                 \n",
            " dense_36 (Dense)            (None, 10)                309770    \n",
            "                                                                 \n",
            " dense_37 (Dense)            (None, 10)                110       \n",
            "                                                                 \n",
            " dense_38 (Dense)            (None, 3)                 33        \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 329,305\n",
            "Trainable params: 329,305\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "Epoch 1/50\n",
            "5/5 [==============================] - 1s 16ms/step - loss: 1.4585 - accuracy: 0.3874\n",
            "Epoch 2/50\n",
            "5/5 [==============================] - 0s 15ms/step - loss: 0.7526 - accuracy: 0.6076\n",
            "Epoch 3/50\n",
            "5/5 [==============================] - 0s 15ms/step - loss: 0.5734 - accuracy: 0.6556\n",
            "Epoch 4/50\n",
            "5/5 [==============================] - 0s 18ms/step - loss: 0.3560 - accuracy: 0.8328\n",
            "Epoch 5/50\n",
            "5/5 [==============================] - 0s 16ms/step - loss: 0.2771 - accuracy: 0.9619\n",
            "Epoch 6/50\n",
            "5/5 [==============================] - 0s 15ms/step - loss: 0.1897 - accuracy: 0.9603\n",
            "Epoch 7/50\n",
            "5/5 [==============================] - 0s 16ms/step - loss: 0.1403 - accuracy: 0.9553\n",
            "Epoch 8/50\n",
            "5/5 [==============================] - 0s 15ms/step - loss: 0.1174 - accuracy: 0.9619\n",
            "Epoch 9/50\n",
            "5/5 [==============================] - 0s 16ms/step - loss: 0.0907 - accuracy: 0.9785\n",
            "Epoch 10/50\n",
            "5/5 [==============================] - 0s 17ms/step - loss: 0.0803 - accuracy: 0.9752\n",
            "Epoch 11/50\n",
            "5/5 [==============================] - 0s 15ms/step - loss: 0.0677 - accuracy: 0.9818\n",
            "Epoch 12/50\n",
            "5/5 [==============================] - 0s 15ms/step - loss: 0.0582 - accuracy: 0.9834\n",
            "Epoch 13/50\n",
            "5/5 [==============================] - 0s 15ms/step - loss: 0.0492 - accuracy: 0.9851\n",
            "Epoch 14/50\n",
            "5/5 [==============================] - 0s 15ms/step - loss: 0.0385 - accuracy: 0.9901\n",
            "Epoch 15/50\n",
            "5/5 [==============================] - 0s 15ms/step - loss: 0.0366 - accuracy: 0.9917\n",
            "Epoch 16/50\n",
            "5/5 [==============================] - 0s 16ms/step - loss: 0.0331 - accuracy: 0.9884\n",
            "Epoch 17/50\n",
            "5/5 [==============================] - 0s 15ms/step - loss: 0.0289 - accuracy: 0.9901\n",
            "Epoch 18/50\n",
            "5/5 [==============================] - 0s 16ms/step - loss: 0.0253 - accuracy: 0.9934\n",
            "Epoch 19/50\n",
            "5/5 [==============================] - 0s 18ms/step - loss: 0.0185 - accuracy: 0.9950\n",
            "Epoch 20/50\n",
            "5/5 [==============================] - 0s 15ms/step - loss: 0.0134 - accuracy: 1.0000\n",
            "Epoch 21/50\n",
            "5/5 [==============================] - 0s 16ms/step - loss: 0.0147 - accuracy: 0.9950\n",
            "Epoch 22/50\n",
            "5/5 [==============================] - 0s 14ms/step - loss: 0.0126 - accuracy: 1.0000\n",
            "Epoch 23/50\n",
            "5/5 [==============================] - 0s 16ms/step - loss: 0.0078 - accuracy: 1.0000\n",
            "Epoch 24/50\n",
            "5/5 [==============================] - 0s 16ms/step - loss: 0.0073 - accuracy: 1.0000\n",
            "Epoch 25/50\n",
            "5/5 [==============================] - 0s 16ms/step - loss: 0.0071 - accuracy: 1.0000\n",
            "Epoch 26/50\n",
            "5/5 [==============================] - 0s 17ms/step - loss: 0.0056 - accuracy: 1.0000\n",
            "Epoch 27/50\n",
            "5/5 [==============================] - 0s 16ms/step - loss: 0.0046 - accuracy: 1.0000\n",
            "Epoch 28/50\n",
            "5/5 [==============================] - 0s 16ms/step - loss: 0.0036 - accuracy: 1.0000\n",
            "Epoch 29/50\n",
            "5/5 [==============================] - 0s 15ms/step - loss: 0.0034 - accuracy: 1.0000\n",
            "Epoch 30/50\n",
            "5/5 [==============================] - 0s 17ms/step - loss: 0.0028 - accuracy: 1.0000\n",
            "Epoch 31/50\n",
            "5/5 [==============================] - 0s 15ms/step - loss: 0.0029 - accuracy: 1.0000\n",
            "Epoch 32/50\n",
            "5/5 [==============================] - 0s 14ms/step - loss: 0.0025 - accuracy: 1.0000\n",
            "Epoch 33/50\n",
            "5/5 [==============================] - 0s 17ms/step - loss: 0.0024 - accuracy: 1.0000\n",
            "Epoch 34/50\n",
            "5/5 [==============================] - 0s 15ms/step - loss: 0.0022 - accuracy: 1.0000\n",
            "Epoch 35/50\n",
            "5/5 [==============================] - 0s 16ms/step - loss: 0.0021 - accuracy: 1.0000\n",
            "Epoch 36/50\n",
            "5/5 [==============================] - 0s 16ms/step - loss: 0.0019 - accuracy: 1.0000\n",
            "Epoch 37/50\n",
            "5/5 [==============================] - 0s 15ms/step - loss: 0.0019 - accuracy: 1.0000\n",
            "Epoch 38/50\n",
            "5/5 [==============================] - 0s 15ms/step - loss: 0.0018 - accuracy: 1.0000\n",
            "Epoch 39/50\n",
            "5/5 [==============================] - 0s 15ms/step - loss: 0.0017 - accuracy: 1.0000\n",
            "Epoch 40/50\n",
            "5/5 [==============================] - 0s 15ms/step - loss: 0.0016 - accuracy: 1.0000\n",
            "Epoch 41/50\n",
            "5/5 [==============================] - 0s 16ms/step - loss: 0.0015 - accuracy: 1.0000\n",
            "Epoch 42/50\n",
            "5/5 [==============================] - 0s 17ms/step - loss: 0.0015 - accuracy: 1.0000\n",
            "Epoch 43/50\n",
            "5/5 [==============================] - 0s 16ms/step - loss: 0.0014 - accuracy: 1.0000\n",
            "Epoch 44/50\n",
            "5/5 [==============================] - 0s 15ms/step - loss: 0.0014 - accuracy: 1.0000\n",
            "Epoch 45/50\n",
            "5/5 [==============================] - 0s 19ms/step - loss: 0.0013 - accuracy: 1.0000\n",
            "Epoch 46/50\n",
            "5/5 [==============================] - 0s 16ms/step - loss: 0.0012 - accuracy: 1.0000\n",
            "Epoch 47/50\n",
            "5/5 [==============================] - 0s 18ms/step - loss: 0.0012 - accuracy: 1.0000\n",
            "Epoch 48/50\n",
            "5/5 [==============================] - 0s 16ms/step - loss: 0.0012 - accuracy: 1.0000\n",
            "Epoch 49/50\n",
            "5/5 [==============================] - 0s 16ms/step - loss: 0.0011 - accuracy: 1.0000\n",
            "Epoch 50/50\n",
            "5/5 [==============================] - 0s 15ms/step - loss: 0.0011 - accuracy: 1.0000\n",
            "5/5 [==============================] - 0s 6ms/step - loss: 0.0178 - accuracy: 0.9934\n",
            "loss= 0.017849484458565712\n",
            "acc= 0.9933775067329407\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BlumccIP-YUu"
      },
      "source": [
        ""
      ],
      "execution_count": 77,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Z1fdpl5c-iW_"
      },
      "source": [
        "## 다중 출력 모델로 학습 실행"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KOyQbsVo-ke8"
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "from tensorflow.keras import optimizers, Model\n",
        "from tensorflow.keras.layers import Dense, Flatten, Conv2D, MaxPooling2D, Input\n",
        "\n",
        "\n",
        "def common_input_branch(input):\n",
        "\n",
        "  x = Conv2D(32, (3, 3))(input)\n",
        "  x = MaxPooling2D((2, 2))(x)\n",
        "  x = Conv2D(64, (3, 3))(x)\n",
        "  x = MaxPooling2D((2, 2))(x)\n",
        "  x = Flatten()(x)\n",
        "\n",
        "  return x\n",
        "\n",
        "\n",
        "def category_branch(x):\n",
        "\n",
        "  x = Dense(10, activation='relu')(x)\n",
        "  x = Dense(10, activation='relu')(x)\n",
        "  x = Dense(CATEGORY_COUNT, activation='softmax', name='category_output')(x)\n",
        "\n",
        "  return x\n",
        "\n",
        "def color_branch(x):\n",
        "\n",
        "  x = Dense(10, activation='relu')(x)\n",
        "  x = Dense(10, activation='relu')(x)\n",
        "  x = Dense(COLOR_COUNT, activation='softmax', name='color_output')(x)\n",
        "\n",
        "  return x"
      ],
      "execution_count": 78,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "49vnw8ZSAEYN",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6b2a8840-7e44-4062-c8d5-bf31e18ced4e"
      },
      "source": [
        "input = Input(IMAGE_DIMS)\n",
        "\n",
        "common_input = common_input_branch(input)\n",
        "category_output = category_branch(common_input)\n",
        "color_output = color_branch(common_input)\n",
        "\n",
        "multi_output_model = Model(input, [category_output, color_output])\n",
        "\n",
        "multi_output_model.summary()"
      ],
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model_8\"\n",
            "__________________________________________________________________________________________________\n",
            " Layer (type)                   Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            " input_14 (InputLayer)          [(None, 96, 96, 3)]  0           []                               \n",
            "                                                                                                  \n",
            " conv2d_18 (Conv2D)             (None, 94, 94, 32)   896         ['input_14[0][0]']               \n",
            "                                                                                                  \n",
            " max_pooling2d_18 (MaxPooling2D  (None, 47, 47, 32)  0           ['conv2d_18[0][0]']              \n",
            " )                                                                                                \n",
            "                                                                                                  \n",
            " conv2d_19 (Conv2D)             (None, 45, 45, 64)   18496       ['max_pooling2d_18[0][0]']       \n",
            "                                                                                                  \n",
            " max_pooling2d_19 (MaxPooling2D  (None, 22, 22, 64)  0           ['conv2d_19[0][0]']              \n",
            " )                                                                                                \n",
            "                                                                                                  \n",
            " flatten_9 (Flatten)            (None, 30976)        0           ['max_pooling2d_19[0][0]']       \n",
            "                                                                                                  \n",
            " dense_39 (Dense)               (None, 10)           309770      ['flatten_9[0][0]']              \n",
            "                                                                                                  \n",
            " dense_41 (Dense)               (None, 10)           309770      ['flatten_9[0][0]']              \n",
            "                                                                                                  \n",
            " dense_40 (Dense)               (None, 10)           110         ['dense_39[0][0]']               \n",
            "                                                                                                  \n",
            " dense_42 (Dense)               (None, 10)           110         ['dense_41[0][0]']               \n",
            "                                                                                                  \n",
            " category_output (Dense)        (None, 4)            44          ['dense_40[0][0]']               \n",
            "                                                                                                  \n",
            " color_output (Dense)           (None, 3)            33          ['dense_42[0][0]']               \n",
            "                                                                                                  \n",
            "==================================================================================================\n",
            "Total params: 639,229\n",
            "Trainable params: 639,229\n",
            "Non-trainable params: 0\n",
            "__________________________________________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SftqPQsKAHCU",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2f67259f-1f5c-460c-d015-cbb49cd25ef6"
      },
      "source": [
        "losses = { \n",
        "    \"category_output\":\"categorical_crossentropy\", \n",
        "    \"color_output\":\"categorical_crossentropy\"\n",
        "}\n",
        "multi_output_model.compile(optimizer=\"adam\", loss=losses, metrics=[\"accuracy\"])\n",
        "\n",
        "multi_output_model.fit(trainX, {\"category_output\":trainCategoryY, \"color_output\":trainColorY}, epochs=50, verbose=1, batch_size=128)"
      ],
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/50\n",
            "5/5 [==============================] - 1s 16ms/step - loss: 2.5594 - category_output_loss: 1.4301 - color_output_loss: 1.1293 - category_output_accuracy: 0.3278 - color_output_accuracy: 0.2864\n",
            "Epoch 2/50\n",
            "5/5 [==============================] - 0s 18ms/step - loss: 2.7229 - category_output_loss: 1.4105 - color_output_loss: 1.3124 - category_output_accuracy: 0.4255 - color_output_accuracy: 0.3874\n",
            "Epoch 3/50\n",
            "5/5 [==============================] - 0s 19ms/step - loss: 2.4370 - category_output_loss: 1.3339 - color_output_loss: 1.1030 - category_output_accuracy: 0.3825 - color_output_accuracy: 0.2682\n",
            "Epoch 4/50\n",
            "5/5 [==============================] - 0s 17ms/step - loss: 2.3705 - category_output_loss: 1.2772 - color_output_loss: 1.0933 - category_output_accuracy: 0.4123 - color_output_accuracy: 0.2616\n",
            "Epoch 5/50\n",
            "5/5 [==============================] - 0s 17ms/step - loss: 2.2981 - category_output_loss: 1.2203 - color_output_loss: 1.0779 - category_output_accuracy: 0.3841 - color_output_accuracy: 0.2616\n",
            "Epoch 6/50\n",
            "5/5 [==============================] - 0s 16ms/step - loss: 2.2382 - category_output_loss: 1.1783 - color_output_loss: 1.0598 - category_output_accuracy: 0.4553 - color_output_accuracy: 0.2632\n",
            "Epoch 7/50\n",
            "5/5 [==============================] - 0s 18ms/step - loss: 2.1808 - category_output_loss: 1.1371 - color_output_loss: 1.0436 - category_output_accuracy: 0.5149 - color_output_accuracy: 0.3046\n",
            "Epoch 8/50\n",
            "5/5 [==============================] - 0s 18ms/step - loss: 2.1425 - category_output_loss: 1.1170 - color_output_loss: 1.0255 - category_output_accuracy: 0.5199 - color_output_accuracy: 0.4818\n",
            "Epoch 9/50\n",
            "5/5 [==============================] - 0s 18ms/step - loss: 2.1105 - category_output_loss: 1.0972 - color_output_loss: 1.0133 - category_output_accuracy: 0.5579 - color_output_accuracy: 0.5116\n",
            "Epoch 10/50\n",
            "5/5 [==============================] - 0s 18ms/step - loss: 2.0693 - category_output_loss: 1.0756 - color_output_loss: 0.9937 - category_output_accuracy: 0.5464 - color_output_accuracy: 0.5215\n",
            "Epoch 11/50\n",
            "5/5 [==============================] - 0s 19ms/step - loss: 1.9926 - category_output_loss: 1.0333 - color_output_loss: 0.9593 - category_output_accuracy: 0.5563 - color_output_accuracy: 0.5894\n",
            "Epoch 12/50\n",
            "5/5 [==============================] - 0s 17ms/step - loss: 1.7504 - category_output_loss: 0.9603 - color_output_loss: 0.7901 - category_output_accuracy: 0.5315 - color_output_accuracy: 0.6391\n",
            "Epoch 13/50\n",
            "5/5 [==============================] - 0s 18ms/step - loss: 1.6606 - category_output_loss: 0.9320 - color_output_loss: 0.7286 - category_output_accuracy: 0.5977 - color_output_accuracy: 0.7583\n",
            "Epoch 14/50\n",
            "5/5 [==============================] - 0s 18ms/step - loss: 1.4908 - category_output_loss: 0.8412 - color_output_loss: 0.6496 - category_output_accuracy: 0.5877 - color_output_accuracy: 0.7368\n",
            "Epoch 15/50\n",
            "5/5 [==============================] - 0s 19ms/step - loss: 1.3424 - category_output_loss: 0.7746 - color_output_loss: 0.5678 - category_output_accuracy: 0.5745 - color_output_accuracy: 0.8659\n",
            "Epoch 16/50\n",
            "5/5 [==============================] - 0s 19ms/step - loss: 1.1968 - category_output_loss: 0.7352 - color_output_loss: 0.4616 - category_output_accuracy: 0.5993 - color_output_accuracy: 0.9023\n",
            "Epoch 17/50\n",
            "5/5 [==============================] - 0s 18ms/step - loss: 1.0305 - category_output_loss: 0.6619 - color_output_loss: 0.3686 - category_output_accuracy: 0.6209 - color_output_accuracy: 0.9189\n",
            "Epoch 18/50\n",
            "5/5 [==============================] - 0s 18ms/step - loss: 0.8818 - category_output_loss: 0.6065 - color_output_loss: 0.2753 - category_output_accuracy: 0.6490 - color_output_accuracy: 0.9288\n",
            "Epoch 19/50\n",
            "5/5 [==============================] - 0s 17ms/step - loss: 0.7868 - category_output_loss: 0.5528 - color_output_loss: 0.2340 - category_output_accuracy: 0.6887 - color_output_accuracy: 0.9305\n",
            "Epoch 20/50\n",
            "5/5 [==============================] - 0s 18ms/step - loss: 0.7490 - category_output_loss: 0.5198 - color_output_loss: 0.2293 - category_output_accuracy: 0.7053 - color_output_accuracy: 0.9205\n",
            "Epoch 21/50\n",
            "5/5 [==============================] - 0s 17ms/step - loss: 0.6860 - category_output_loss: 0.4992 - color_output_loss: 0.1868 - category_output_accuracy: 0.7136 - color_output_accuracy: 0.9404\n",
            "Epoch 22/50\n",
            "5/5 [==============================] - 0s 17ms/step - loss: 0.6020 - category_output_loss: 0.4554 - color_output_loss: 0.1466 - category_output_accuracy: 0.7500 - color_output_accuracy: 0.9470\n",
            "Epoch 23/50\n",
            "5/5 [==============================] - 0s 17ms/step - loss: 0.5357 - category_output_loss: 0.4020 - color_output_loss: 0.1337 - category_output_accuracy: 0.8162 - color_output_accuracy: 0.9536\n",
            "Epoch 24/50\n",
            "5/5 [==============================] - 0s 18ms/step - loss: 0.4553 - category_output_loss: 0.3199 - color_output_loss: 0.1355 - category_output_accuracy: 0.8576 - color_output_accuracy: 0.9470\n",
            "Epoch 25/50\n",
            "5/5 [==============================] - 0s 17ms/step - loss: 0.4031 - category_output_loss: 0.2751 - color_output_loss: 0.1279 - category_output_accuracy: 0.8957 - color_output_accuracy: 0.9570\n",
            "Epoch 26/50\n",
            "5/5 [==============================] - 0s 18ms/step - loss: 0.3486 - category_output_loss: 0.2431 - color_output_loss: 0.1055 - category_output_accuracy: 0.9073 - color_output_accuracy: 0.9685\n",
            "Epoch 27/50\n",
            "5/5 [==============================] - 0s 17ms/step - loss: 0.2958 - category_output_loss: 0.1983 - color_output_loss: 0.0975 - category_output_accuracy: 0.9321 - color_output_accuracy: 0.9669\n",
            "Epoch 28/50\n",
            "5/5 [==============================] - 0s 18ms/step - loss: 0.2809 - category_output_loss: 0.1921 - color_output_loss: 0.0888 - category_output_accuracy: 0.9354 - color_output_accuracy: 0.9818\n",
            "Epoch 29/50\n",
            "5/5 [==============================] - 0s 16ms/step - loss: 0.2928 - category_output_loss: 0.1955 - color_output_loss: 0.0973 - category_output_accuracy: 0.9338 - color_output_accuracy: 0.9702\n",
            "Epoch 30/50\n",
            "5/5 [==============================] - 0s 20ms/step - loss: 0.2971 - category_output_loss: 0.1748 - color_output_loss: 0.1222 - category_output_accuracy: 0.9487 - color_output_accuracy: 0.9503\n",
            "Epoch 31/50\n",
            "5/5 [==============================] - 0s 17ms/step - loss: 0.2390 - category_output_loss: 0.1442 - color_output_loss: 0.0948 - category_output_accuracy: 0.9503 - color_output_accuracy: 0.9669\n",
            "Epoch 32/50\n",
            "5/5 [==============================] - 0s 17ms/step - loss: 0.2077 - category_output_loss: 0.1308 - color_output_loss: 0.0769 - category_output_accuracy: 0.9553 - color_output_accuracy: 0.9834\n",
            "Epoch 33/50\n",
            "5/5 [==============================] - 0s 17ms/step - loss: 0.2260 - category_output_loss: 0.1550 - color_output_loss: 0.0710 - category_output_accuracy: 0.9470 - color_output_accuracy: 0.9818\n",
            "Epoch 34/50\n",
            "5/5 [==============================] - 0s 16ms/step - loss: 0.2155 - category_output_loss: 0.1505 - color_output_loss: 0.0649 - category_output_accuracy: 0.9520 - color_output_accuracy: 0.9868\n",
            "Epoch 35/50\n",
            "5/5 [==============================] - 0s 18ms/step - loss: 0.2334 - category_output_loss: 0.1540 - color_output_loss: 0.0794 - category_output_accuracy: 0.9387 - color_output_accuracy: 0.9752\n",
            "Epoch 36/50\n",
            "5/5 [==============================] - 0s 17ms/step - loss: 0.2120 - category_output_loss: 0.1390 - color_output_loss: 0.0730 - category_output_accuracy: 0.9520 - color_output_accuracy: 0.9834\n",
            "Epoch 37/50\n",
            "5/5 [==============================] - 0s 18ms/step - loss: 0.2045 - category_output_loss: 0.1214 - color_output_loss: 0.0831 - category_output_accuracy: 0.9603 - color_output_accuracy: 0.9685\n",
            "Epoch 38/50\n",
            "5/5 [==============================] - 0s 18ms/step - loss: 0.2092 - category_output_loss: 0.1137 - color_output_loss: 0.0954 - category_output_accuracy: 0.9669 - color_output_accuracy: 0.9619\n",
            "Epoch 39/50\n",
            "5/5 [==============================] - 0s 18ms/step - loss: 0.1900 - category_output_loss: 0.1154 - color_output_loss: 0.0746 - category_output_accuracy: 0.9669 - color_output_accuracy: 0.9818\n",
            "Epoch 40/50\n",
            "5/5 [==============================] - 0s 18ms/step - loss: 0.1702 - category_output_loss: 0.1047 - color_output_loss: 0.0654 - category_output_accuracy: 0.9685 - color_output_accuracy: 0.9884\n",
            "Epoch 41/50\n",
            "5/5 [==============================] - 0s 17ms/step - loss: 0.1457 - category_output_loss: 0.0990 - color_output_loss: 0.0467 - category_output_accuracy: 0.9652 - color_output_accuracy: 0.9950\n",
            "Epoch 42/50\n",
            "5/5 [==============================] - 0s 17ms/step - loss: 0.1322 - category_output_loss: 0.0888 - color_output_loss: 0.0435 - category_output_accuracy: 0.9719 - color_output_accuracy: 0.9917\n",
            "Epoch 43/50\n",
            "5/5 [==============================] - 0s 17ms/step - loss: 0.1245 - category_output_loss: 0.0866 - color_output_loss: 0.0379 - category_output_accuracy: 0.9702 - color_output_accuracy: 0.9934\n",
            "Epoch 44/50\n",
            "5/5 [==============================] - 0s 18ms/step - loss: 0.1100 - category_output_loss: 0.0743 - color_output_loss: 0.0357 - category_output_accuracy: 0.9768 - color_output_accuracy: 0.9934\n",
            "Epoch 45/50\n",
            "5/5 [==============================] - 0s 16ms/step - loss: 0.1027 - category_output_loss: 0.0751 - color_output_loss: 0.0276 - category_output_accuracy: 0.9752 - color_output_accuracy: 0.9950\n",
            "Epoch 46/50\n",
            "5/5 [==============================] - 0s 18ms/step - loss: 0.0987 - category_output_loss: 0.0699 - color_output_loss: 0.0288 - category_output_accuracy: 0.9785 - color_output_accuracy: 0.9950\n",
            "Epoch 47/50\n",
            "5/5 [==============================] - 0s 18ms/step - loss: 0.0900 - category_output_loss: 0.0605 - color_output_loss: 0.0295 - category_output_accuracy: 0.9834 - color_output_accuracy: 0.9934\n",
            "Epoch 48/50\n",
            "5/5 [==============================] - 0s 18ms/step - loss: 0.0821 - category_output_loss: 0.0580 - color_output_loss: 0.0241 - category_output_accuracy: 0.9834 - color_output_accuracy: 0.9950\n",
            "Epoch 49/50\n",
            "5/5 [==============================] - 0s 18ms/step - loss: 0.0810 - category_output_loss: 0.0593 - color_output_loss: 0.0216 - category_output_accuracy: 0.9818 - color_output_accuracy: 0.9967\n",
            "Epoch 50/50\n",
            "5/5 [==============================] - 0s 18ms/step - loss: 0.0733 - category_output_loss: 0.0533 - color_output_loss: 0.0200 - category_output_accuracy: 0.9834 - color_output_accuracy: 0.9983\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7f9447ee0590>"
            ]
          },
          "metadata": {},
          "execution_count": 80
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NHH3al7zB4uK",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fd191ced-2f28-40ba-c1b2-b23f00814995"
      },
      "source": [
        "loss, category_loss, color_loss, category_acc, color_acc = multi_output_model.evaluate(testX, {\"category_output\":testCategoryY, \"color_output\":testColorY})\n",
        "print(\"loss=\",loss)\n",
        "print(\"categoyr_loss=\", category_loss)\n",
        "print(\"color_loss=\", color_loss)\n",
        "print(\"category_acc=\", category_acc)\n",
        "print(\"color_acc=\", color_acc)"
      ],
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "5/5 [==============================] - 0s 6ms/step - loss: 0.6876 - category_output_loss: 0.4119 - color_output_loss: 0.2758 - category_output_accuracy: 0.9007 - color_output_accuracy: 0.9205\n",
            "loss= 0.6876477599143982\n",
            "categoyr_loss= 0.4118959903717041\n",
            "color_loss= 0.2757517099380493\n",
            "category_acc= 0.9006622433662415\n",
            "color_acc= 0.9205297827720642\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nGPuLHteCIso",
        "outputId": "c6fecfe5-d49e-4284-881d-8b4459caef54",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 736
        }
      },
      "source": [
        "import tensorflow as tf\n",
        "tf.keras.utils.plot_model(model=multi_output_model, rankdir=\"TD\", dpi=72, show_shapes=True)"
      ],
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<IPython.core.display.Image object>"
            ],
            "image/png": "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\n"
          },
          "metadata": {},
          "execution_count": 82
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "sam0aSXwUzBA"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}