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": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "puN5QRLz2toU",
        "outputId": "3b0e5ef7-ccff-4868-b15d-1bf9b8c1e1c9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "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": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz\n",
            "11493376/11490434 [==============================] - 0s 0us/step\n",
            "11501568/11490434 [==============================] - 0s 0us/step\n"
          ]
        }
      ]
    },
    {
      "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": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cuW2ftNT2trB",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4e416e2b-9c81-4627-e727-df4419a24039"
      },
      "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": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv2d (Conv2D)             (None, 26, 26, 32)        320       \n",
            "                                                                 \n",
            " max_pooling2d (MaxPooling2D  (None, 13, 13, 32)       0         \n",
            " )                                                               \n",
            "                                                                 \n",
            " conv2d_1 (Conv2D)           (None, 11, 11, 64)        18496     \n",
            "                                                                 \n",
            " max_pooling2d_1 (MaxPooling  (None, 5, 5, 64)         0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " flatten (Flatten)           (None, 1600)              0         \n",
            "                                                                 \n",
            " dense (Dense)               (None, 10)                16010     \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 10)                110       \n",
            "                                                                 \n",
            " dense_2 (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 [==============================] - 15s 4ms/step - loss: 0.5675 - accuracy: 0.8174\n",
            "Epoch 2/5\n",
            "469/469 [==============================] - 2s 4ms/step - loss: 0.1775 - accuracy: 0.9488\n",
            "Epoch 3/5\n",
            "469/469 [==============================] - 2s 4ms/step - loss: 0.1254 - accuracy: 0.9622\n",
            "Epoch 4/5\n",
            "469/469 [==============================] - 2s 4ms/step - loss: 0.1002 - accuracy: 0.9702\n",
            "Epoch 5/5\n",
            "469/469 [==============================] - 2s 4ms/step - loss: 0.0840 - accuracy: 0.9747\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.0824 - accuracy: 0.9761\n",
            "loss= 0.08235092461109161\n",
            "acc= 0.9761000275611877\n",
            "[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": 5,
      "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": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SH90FFocI7GG",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "917c3e40-8fc5-4f39-8886-487f7ca3b081"
      },
      "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": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " input_2 (InputLayer)        [(None, 28, 28, 1)]       0         \n",
            "                                                                 \n",
            " conv2d_2 (Conv2D)           (None, 26, 26, 32)        320       \n",
            "                                                                 \n",
            " max_pooling2d_2 (MaxPooling  (None, 13, 13, 32)       0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " conv2d_3 (Conv2D)           (None, 11, 11, 32)        9248      \n",
            "                                                                 \n",
            " max_pooling2d_3 (MaxPooling  (None, 5, 5, 32)         0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " flatten_1 (Flatten)         (None, 800)               0         \n",
            "                                                                 \n",
            " dense_3 (Dense)             (None, 10)                8010      \n",
            "                                                                 \n",
            " dense_4 (Dense)             (None, 10)                110       \n",
            "                                                                 \n",
            " dense_5 (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: 0.5475 - accuracy: 0.8353\n",
            "Epoch 2/5\n",
            "469/469 [==============================] - 2s 3ms/step - loss: 0.1760 - accuracy: 0.9498\n",
            "Epoch 3/5\n",
            "469/469 [==============================] - 2s 3ms/step - loss: 0.1245 - accuracy: 0.9632\n",
            "Epoch 4/5\n",
            "469/469 [==============================] - 2s 3ms/step - loss: 0.1004 - accuracy: 0.9701\n",
            "Epoch 5/5\n",
            "469/469 [==============================] - 2s 3ms/step - loss: 0.0837 - accuracy: 0.9743\n",
            "313/313 [==============================] - 1s 3ms/step - loss: 0.0744 - accuracy: 0.9763\n",
            "loss= 0.074370376765728\n",
            "acc= 0.9763000011444092\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": "927a9866-9a7a-493e-fe9a-b15105950963"
      },
      "source": [
        "!git clone https://github.com/emanhamed/Houses-dataset"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cloning into 'Houses-dataset'...\n",
            "remote: Enumerating objects: 2166, done.\u001b[K\n",
            "remote: Counting objects: 100% (1/1), done.\u001b[K\n",
            "remote: Total 2166 (delta 0), reused 0 (delta 0), pack-reused 2165\u001b[K\n",
            "Receiving objects: 100% (2166/2166), 176.26 MiB | 26.88 MiB/s, done.\n",
            "Resolving deltas: 100% (20/20), done.\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": "8ba5024f-5054-485b-c7bf-ed216e704d36"
      },
      "source": [
        "!head 'Houses-dataset/Houses Dataset/HousesInfo.txt'"
      ],
      "execution_count": 9,
      "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": 10,
      "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": 11,
      "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": 12,
      "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": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ouQ27yABHR2w",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "8e6eab88-21dc-48bc-c72e-fde5a06eeb61"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 14,
      "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"
            ],
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              "        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-15e6bafd-1536-47cb-91c5-da1d88058567')\"\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-15e6bafd-1536-47cb-91c5-da1d88058567 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-15e6bafd-1536-47cb-91c5-da1d88058567');\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": 14
        }
      ]
    },
    {
      "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": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wZhmpoc8HNfQ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e9cbdf38-252e-486e-c0c3-08dfd898cfd4"
      },
      "source": [
        "print(images.shape)"
      ],
      "execution_count": 16,
      "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": "3e080f4e-d62e-4f67-f86b-19fbc61ef171",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        }
      },
      "execution_count": 17,
      "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": [
        "(trainAttrX, testAttrX, trainImagesX, testImagesX) = train_test_split(df, images, test_size=0.25, random_state=42)\n",
        "\n",
        "maxPrice = trainAttrX[\"price\"].max()\n",
        "trainY = trainAttrX[\"price\"] / maxPrice\n",
        "testY = testAttrX[\"price\"] / maxPrice\n",
        "(trainAttrX, testAttrX) = process_house_attributes(df, trainAttrX, testAttrX)"
      ],
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZoR7qloQHNYn",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3d3e72ff-1708-4102-d3cb-699633b8da29"
      },
      "source": [
        "print(\"tainAttrX.shape=\", trainAttrX.shape)\n",
        "print(\"trainImagesX.shape=\", trainImagesX.shape)\n",
        "print(\"trainY.shape=\", trainY.shape)\n",
        "print(\"testAttrX.shape=\", testAttrX.shape)\n",
        "print(\"testImageX.shape=\", testImagesX.shape)\n",
        "print(\"testY.shape=\", testY.shape)"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tainAttrX.shape= (271, 10)\n",
            "trainImagesX.shape= (271, 256, 256, 3)\n",
            "trainY.shape= (271,)\n",
            "testAttrX.shape= (91, 10)\n",
            "testImageX.shape= (91, 256, 256, 3)\n",
            "testY.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": 20,
      "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(trainAttrX[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": 53,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "roratdC_JdVs",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6abbf2bc-2939-463e-ba8d-2ee61fa4fba0"
      },
      "source": [
        "attr_model.compile(optimizer=\"Adam\", loss=\"mse\")\n",
        "attr_model.summary()\n",
        "\n",
        "attr_model.fit(trainAttrX, trainY, epochs=50, verbose=1, batch_size=128, validation_split=0.1)"
      ],
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model_5\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " input_10 (InputLayer)       [(None, 10)]              0         \n",
            "                                                                 \n",
            " dense_28 (Dense)            (None, 10)                110       \n",
            "                                                                 \n",
            " dense_29 (Dense)            (None, 10)                110       \n",
            "                                                                 \n",
            " dense_30 (Dense)            (None, 1)                 11        \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 231\n",
            "Trainable params: 231\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "Epoch 1/50\n",
            "2/2 [==============================] - 1s 227ms/step - loss: 0.0303 - val_loss: 0.0244\n",
            "Epoch 2/50\n",
            "2/2 [==============================] - 0s 37ms/step - loss: 0.0274 - val_loss: 0.0222\n",
            "Epoch 3/50\n",
            "2/2 [==============================] - 0s 33ms/step - loss: 0.0246 - val_loss: 0.0202\n",
            "Epoch 4/50\n",
            "2/2 [==============================] - 0s 65ms/step - loss: 0.0224 - val_loss: 0.0182\n",
            "Epoch 5/50\n",
            "2/2 [==============================] - 0s 36ms/step - loss: 0.0199 - val_loss: 0.0165\n",
            "Epoch 6/50\n",
            "2/2 [==============================] - 0s 57ms/step - loss: 0.0182 - val_loss: 0.0150\n",
            "Epoch 7/50\n",
            "2/2 [==============================] - 0s 42ms/step - loss: 0.0163 - val_loss: 0.0137\n",
            "Epoch 8/50\n",
            "2/2 [==============================] - 0s 39ms/step - loss: 0.0149 - val_loss: 0.0125\n",
            "Epoch 9/50\n",
            "2/2 [==============================] - 0s 33ms/step - loss: 0.0135 - val_loss: 0.0115\n",
            "Epoch 10/50\n",
            "2/2 [==============================] - 0s 46ms/step - loss: 0.0123 - val_loss: 0.0106\n",
            "Epoch 11/50\n",
            "2/2 [==============================] - 0s 74ms/step - loss: 0.0114 - val_loss: 0.0098\n",
            "Epoch 12/50\n",
            "2/2 [==============================] - 0s 59ms/step - loss: 0.0106 - val_loss: 0.0092\n",
            "Epoch 13/50\n",
            "2/2 [==============================] - 0s 38ms/step - loss: 0.0099 - val_loss: 0.0086\n",
            "Epoch 14/50\n",
            "2/2 [==============================] - 0s 39ms/step - loss: 0.0093 - val_loss: 0.0082\n",
            "Epoch 15/50\n",
            "2/2 [==============================] - 0s 68ms/step - loss: 0.0089 - val_loss: 0.0078\n",
            "Epoch 16/50\n",
            "2/2 [==============================] - 0s 46ms/step - loss: 0.0086 - val_loss: 0.0075\n",
            "Epoch 17/50\n",
            "2/2 [==============================] - 0s 93ms/step - loss: 0.0083 - val_loss: 0.0073\n",
            "Epoch 18/50\n",
            "2/2 [==============================] - 0s 76ms/step - loss: 0.0081 - val_loss: 0.0071\n",
            "Epoch 19/50\n",
            "2/2 [==============================] - 0s 60ms/step - loss: 0.0079 - val_loss: 0.0070\n",
            "Epoch 20/50\n",
            "2/2 [==============================] - 0s 67ms/step - loss: 0.0078 - val_loss: 0.0069\n",
            "Epoch 21/50\n",
            "2/2 [==============================] - 0s 31ms/step - loss: 0.0076 - val_loss: 0.0069\n",
            "Epoch 22/50\n",
            "2/2 [==============================] - 0s 35ms/step - loss: 0.0075 - val_loss: 0.0068\n",
            "Epoch 23/50\n",
            "2/2 [==============================] - 0s 41ms/step - loss: 0.0074 - val_loss: 0.0068\n",
            "Epoch 24/50\n",
            "2/2 [==============================] - 0s 51ms/step - loss: 0.0073 - val_loss: 0.0068\n",
            "Epoch 25/50\n",
            "2/2 [==============================] - 0s 74ms/step - loss: 0.0072 - val_loss: 0.0067\n",
            "Epoch 26/50\n",
            "2/2 [==============================] - 0s 35ms/step - loss: 0.0071 - val_loss: 0.0067\n",
            "Epoch 27/50\n",
            "2/2 [==============================] - 0s 38ms/step - loss: 0.0070 - val_loss: 0.0067\n",
            "Epoch 28/50\n",
            "2/2 [==============================] - 0s 61ms/step - loss: 0.0070 - val_loss: 0.0066\n",
            "Epoch 29/50\n",
            "2/2 [==============================] - 0s 48ms/step - loss: 0.0069 - val_loss: 0.0066\n",
            "Epoch 30/50\n",
            "2/2 [==============================] - 0s 39ms/step - loss: 0.0068 - val_loss: 0.0065\n",
            "Epoch 31/50\n",
            "2/2 [==============================] - 0s 38ms/step - loss: 0.0067 - val_loss: 0.0065\n",
            "Epoch 32/50\n",
            "2/2 [==============================] - 0s 56ms/step - loss: 0.0066 - val_loss: 0.0065\n",
            "Epoch 33/50\n",
            "2/2 [==============================] - 0s 37ms/step - loss: 0.0066 - val_loss: 0.0065\n",
            "Epoch 34/50\n",
            "2/2 [==============================] - 0s 36ms/step - loss: 0.0065 - val_loss: 0.0064\n",
            "Epoch 35/50\n",
            "2/2 [==============================] - 0s 34ms/step - loss: 0.0065 - val_loss: 0.0064\n",
            "Epoch 36/50\n",
            "2/2 [==============================] - 0s 81ms/step - loss: 0.0065 - val_loss: 0.0064\n",
            "Epoch 37/50\n",
            "2/2 [==============================] - 0s 57ms/step - loss: 0.0064 - val_loss: 0.0063\n",
            "Epoch 38/50\n",
            "2/2 [==============================] - 0s 45ms/step - loss: 0.0064 - val_loss: 0.0063\n",
            "Epoch 39/50\n",
            "2/2 [==============================] - 0s 91ms/step - loss: 0.0063 - val_loss: 0.0062\n",
            "Epoch 40/50\n",
            "2/2 [==============================] - 0s 39ms/step - loss: 0.0063 - val_loss: 0.0062\n",
            "Epoch 41/50\n",
            "2/2 [==============================] - 0s 34ms/step - loss: 0.0063 - val_loss: 0.0061\n",
            "Epoch 42/50\n",
            "2/2 [==============================] - 0s 37ms/step - loss: 0.0062 - val_loss: 0.0061\n",
            "Epoch 43/50\n",
            "2/2 [==============================] - 0s 63ms/step - loss: 0.0062 - val_loss: 0.0060\n",
            "Epoch 44/50\n",
            "2/2 [==============================] - 0s 42ms/step - loss: 0.0062 - val_loss: 0.0059\n",
            "Epoch 45/50\n",
            "2/2 [==============================] - 0s 87ms/step - loss: 0.0061 - val_loss: 0.0059\n",
            "Epoch 46/50\n",
            "2/2 [==============================] - 0s 88ms/step - loss: 0.0061 - val_loss: 0.0058\n",
            "Epoch 47/50\n",
            "2/2 [==============================] - 0s 63ms/step - loss: 0.0061 - val_loss: 0.0058\n",
            "Epoch 48/50\n",
            "2/2 [==============================] - 0s 37ms/step - loss: 0.0060 - val_loss: 0.0057\n",
            "Epoch 49/50\n",
            "2/2 [==============================] - 0s 34ms/step - loss: 0.0060 - val_loss: 0.0056\n",
            "Epoch 50/50\n",
            "2/2 [==============================] - 0s 35ms/step - loss: 0.0060 - val_loss: 0.0056\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7effbb0e9110>"
            ]
          },
          "metadata": {},
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Hk5WagwnJdTO",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "57afcb7f-5d0d-49c0-a681-bb162f559446"
      },
      "source": [
        "preds = attr_model.predict(testAttrX)\n",
        "\n",
        "diff = preds.flatten() - testY\n",
        "percentDiff = (diff / testY) * 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": 55,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "WARNING:tensorflow:5 out of the last 323 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7eff3202eef0> 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",
            "[INFO] mean: 51.62%, std: 86.15\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3L6Nr-1ZJdP_"
      },
      "source": [
        ""
      ],
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EY4cMiohJFKc"
      },
      "source": [
        "### 영상 모델"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JEbOBK0ULRlT"
      },
      "source": [
        "input = Input(trainImagesX[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": 56,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jprcg41pLRhL",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "89fa9574-d967-4c0b-e101-8992ac1e03d9"
      },
      "source": [
        "image_model.compile(optimizer=\"Adam\", loss=\"mse\")\n",
        "image_model.summary()\n",
        "\n",
        "image_model.fit(trainImagesX, trainY, epochs=50, verbose=1, batch_size=128, validation_split=0.1)"
      ],
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model_6\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " input_11 (InputLayer)       [(None, 256, 256, 3)]     0         \n",
            "                                                                 \n",
            " conv2d_14 (Conv2D)          (None, 256, 256, 32)      896       \n",
            "                                                                 \n",
            " max_pooling2d_14 (MaxPoolin  (None, 128, 128, 32)     0         \n",
            " g2D)                                                            \n",
            "                                                                 \n",
            " conv2d_15 (Conv2D)          (None, 128, 128, 32)      9248      \n",
            "                                                                 \n",
            " max_pooling2d_15 (MaxPoolin  (None, 64, 64, 32)       0         \n",
            " g2D)                                                            \n",
            "                                                                 \n",
            " flatten_7 (Flatten)         (None, 131072)            0         \n",
            "                                                                 \n",
            " dense_31 (Dense)            (None, 64)                8388672   \n",
            "                                                                 \n",
            " dense_32 (Dense)            (None, 10)                650       \n",
            "                                                                 \n",
            " dense_33 (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 1/50\n",
            "2/2 [==============================] - 1s 223ms/step - loss: 0.9715 - val_loss: 3.0598\n",
            "Epoch 2/50\n",
            "2/2 [==============================] - 0s 113ms/step - loss: 1.6814 - val_loss: 0.0148\n",
            "Epoch 3/50\n",
            "2/2 [==============================] - 0s 112ms/step - loss: 0.0227 - val_loss: 609.9528\n",
            "Epoch 4/50\n",
            "2/2 [==============================] - 0s 111ms/step - loss: 322.5199 - val_loss: 0.1836\n",
            "Epoch 5/50\n",
            "2/2 [==============================] - 0s 107ms/step - loss: 0.1072 - val_loss: 0.0149\n",
            "Epoch 6/50\n",
            "2/2 [==============================] - 0s 110ms/step - loss: 0.0163 - val_loss: 0.0150\n",
            "Epoch 7/50\n",
            "2/2 [==============================] - 0s 115ms/step - loss: 0.0164 - val_loss: 0.0150\n",
            "Epoch 8/50\n",
            "2/2 [==============================] - 0s 116ms/step - loss: 0.0165 - val_loss: 0.0151\n",
            "Epoch 9/50\n",
            "2/2 [==============================] - 0s 112ms/step - loss: 0.0165 - val_loss: 0.0152\n",
            "Epoch 10/50\n",
            "2/2 [==============================] - 0s 110ms/step - loss: 0.0166 - val_loss: 0.0152\n",
            "Epoch 11/50\n",
            "2/2 [==============================] - 0s 109ms/step - loss: 0.0166 - val_loss: 0.0152\n",
            "Epoch 12/50\n",
            "2/2 [==============================] - 0s 117ms/step - loss: 0.0166 - val_loss: 0.0153\n",
            "Epoch 13/50\n",
            "2/2 [==============================] - 0s 110ms/step - loss: 0.0167 - val_loss: 0.0153\n",
            "Epoch 14/50\n",
            "2/2 [==============================] - 0s 109ms/step - loss: 0.0167 - val_loss: 0.0153\n",
            "Epoch 15/50\n",
            "2/2 [==============================] - 0s 112ms/step - loss: 0.0167 - val_loss: 0.0153\n",
            "Epoch 16/50\n",
            "2/2 [==============================] - 0s 112ms/step - loss: 0.0167 - val_loss: 0.0153\n",
            "Epoch 17/50\n",
            "2/2 [==============================] - 0s 110ms/step - loss: 0.0167 - val_loss: 0.0153\n",
            "Epoch 18/50\n",
            "2/2 [==============================] - 0s 109ms/step - loss: 0.0167 - val_loss: 0.0153\n",
            "Epoch 19/50\n",
            "2/2 [==============================] - 0s 110ms/step - loss: 0.0167 - val_loss: 0.0153\n",
            "Epoch 20/50\n",
            "2/2 [==============================] - 0s 109ms/step - loss: 0.0167 - val_loss: 0.0153\n",
            "Epoch 21/50\n",
            "2/2 [==============================] - 0s 108ms/step - loss: 0.0167 - val_loss: 0.0153\n",
            "Epoch 22/50\n",
            "2/2 [==============================] - 0s 110ms/step - loss: 0.0167 - val_loss: 0.0153\n",
            "Epoch 23/50\n",
            "2/2 [==============================] - 0s 112ms/step - loss: 0.0167 - val_loss: 0.0153\n",
            "Epoch 24/50\n",
            "2/2 [==============================] - 0s 107ms/step - loss: 0.0167 - val_loss: 0.0153\n",
            "Epoch 25/50\n",
            "2/2 [==============================] - 0s 111ms/step - loss: 0.0167 - val_loss: 0.0153\n",
            "Epoch 26/50\n",
            "2/2 [==============================] - 0s 113ms/step - loss: 0.0167 - val_loss: 0.0153\n",
            "Epoch 27/50\n",
            "2/2 [==============================] - 0s 110ms/step - loss: 0.0167 - val_loss: 0.0153\n",
            "Epoch 28/50\n",
            "2/2 [==============================] - 0s 116ms/step - loss: 0.0167 - val_loss: 0.0153\n",
            "Epoch 29/50\n",
            "2/2 [==============================] - 0s 116ms/step - loss: 0.0167 - val_loss: 0.0153\n",
            "Epoch 30/50\n",
            "2/2 [==============================] - 0s 111ms/step - loss: 0.0167 - val_loss: 0.0152\n",
            "Epoch 31/50\n",
            "2/2 [==============================] - 0s 110ms/step - loss: 0.0167 - val_loss: 0.0152\n",
            "Epoch 32/50\n",
            "2/2 [==============================] - 0s 111ms/step - loss: 0.0166 - val_loss: 0.0152\n",
            "Epoch 33/50\n",
            "2/2 [==============================] - 0s 115ms/step - loss: 0.0166 - val_loss: 0.0152\n",
            "Epoch 34/50\n",
            "2/2 [==============================] - 0s 123ms/step - loss: 0.0166 - val_loss: 0.0152\n",
            "Epoch 35/50\n",
            "2/2 [==============================] - 0s 111ms/step - loss: 0.0166 - val_loss: 0.0152\n",
            "Epoch 36/50\n",
            "2/2 [==============================] - 0s 112ms/step - loss: 0.0166 - val_loss: 0.0152\n",
            "Epoch 37/50\n",
            "2/2 [==============================] - 0s 110ms/step - loss: 0.0166 - val_loss: 0.0152\n",
            "Epoch 38/50\n",
            "2/2 [==============================] - 0s 111ms/step - loss: 0.0166 - val_loss: 0.0152\n",
            "Epoch 39/50\n",
            "2/2 [==============================] - 0s 112ms/step - loss: 0.0166 - val_loss: 0.0151\n",
            "Epoch 40/50\n",
            "2/2 [==============================] - 0s 109ms/step - loss: 0.0166 - val_loss: 0.0151\n",
            "Epoch 41/50\n",
            "2/2 [==============================] - 0s 110ms/step - loss: 0.0165 - val_loss: 0.0151\n",
            "Epoch 42/50\n",
            "2/2 [==============================] - 0s 110ms/step - loss: 0.0165 - val_loss: 0.0151\n",
            "Epoch 43/50\n",
            "2/2 [==============================] - 0s 109ms/step - loss: 0.0165 - val_loss: 0.0151\n",
            "Epoch 44/50\n",
            "2/2 [==============================] - 0s 112ms/step - loss: 0.0165 - val_loss: 0.0151\n",
            "Epoch 45/50\n",
            "2/2 [==============================] - 0s 110ms/step - loss: 0.0165 - val_loss: 0.0151\n",
            "Epoch 46/50\n",
            "2/2 [==============================] - 0s 108ms/step - loss: 0.0165 - val_loss: 0.0151\n",
            "Epoch 47/50\n",
            "2/2 [==============================] - 0s 108ms/step - loss: 0.0165 - val_loss: 0.0150\n",
            "Epoch 48/50\n",
            "2/2 [==============================] - 0s 111ms/step - loss: 0.0165 - val_loss: 0.0150\n",
            "Epoch 49/50\n",
            "2/2 [==============================] - 0s 115ms/step - loss: 0.0164 - val_loss: 0.0150\n",
            "Epoch 50/50\n",
            "2/2 [==============================] - 0s 102ms/step - loss: 0.0164 - val_loss: 0.0150\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7effb8db9e90>"
            ]
          },
          "metadata": {},
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OROOOvr9LRet",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "97729b61-4ca0-48ce-a5a9-f00aabd7f6a5"
      },
      "source": [
        "preds = image_model.predict(testImagesX)\n",
        "\n",
        "diff = preds.flatten() - testY\n",
        "percentDiff = (diff / testY) * 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": 58,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "WARNING:tensorflow:5 out of the last 13 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7efeecf279e0> 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",
            "[INFO] mean: 100.23%, std: 0.26\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "k7DX0yusIn78"
      },
      "source": [
        "## 다중 입력 모델로 학습 실행"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P7iDDfb-LRb6"
      },
      "source": [
        "attr_input = Input(trainAttrX[0].shape)\n",
        "x = Dense(10, activation='relu')(attr_input)\n",
        "attr_out = Dense(10, activation='relu')(x)\n",
        "\n",
        "image_input = Input(trainImagesX[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",
        "combined_input = concatenate([attr_out, image_output])\n",
        "\n",
        "x = Dense(10, activation=\"relu\")(combined_input)\n",
        "output = Dense(1)(x)\n",
        "\n",
        "\n",
        "combined_model = Model([attr_input, image_input], output)"
      ],
      "execution_count": 66,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2fVpru2CLRYu",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "73a5a33b-c22f-48f2-87c3-b615769344a7"
      },
      "source": [
        "combined_model.compile(optimizer=\"Adam\", loss=\"mse\")\n",
        "combined_model.summary()\n",
        "\n",
        "combined_model.fit([trainAttrX, trainImagesX], trainY, epochs=50, verbose=1, batch_size=128, validation_split=0.1)"
      ],
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model_9\"\n",
            "__________________________________________________________________________________________________\n",
            " Layer (type)                   Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            " input_17 (InputLayer)          [(None, 256, 256, 3  0           []                               \n",
            "                                )]                                                                \n",
            "                                                                                                  \n",
            " conv2d_20 (Conv2D)             (None, 256, 256, 32  896         ['input_17[0][0]']               \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " max_pooling2d_20 (MaxPooling2D  (None, 128, 128, 32  0          ['conv2d_20[0][0]']              \n",
            " )                              )                                                                 \n",
            "                                                                                                  \n",
            " conv2d_21 (Conv2D)             (None, 128, 128, 32  9248        ['max_pooling2d_20[0][0]']       \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " max_pooling2d_21 (MaxPooling2D  (None, 64, 64, 32)  0           ['conv2d_21[0][0]']              \n",
            " )                                                                                                \n",
            "                                                                                                  \n",
            " input_16 (InputLayer)          [(None, 10)]         0           []                               \n",
            "                                                                                                  \n",
            " flatten_10 (Flatten)           (None, 131072)       0           ['max_pooling2d_21[0][0]']       \n",
            "                                                                                                  \n",
            " dense_46 (Dense)               (None, 10)           110         ['input_16[0][0]']               \n",
            "                                                                                                  \n",
            " dense_48 (Dense)               (None, 64)           8388672     ['flatten_10[0][0]']             \n",
            "                                                                                                  \n",
            " dense_47 (Dense)               (None, 10)           110         ['dense_46[0][0]']               \n",
            "                                                                                                  \n",
            " dense_49 (Dense)               (None, 10)           650         ['dense_48[0][0]']               \n",
            "                                                                                                  \n",
            " concatenate_3 (Concatenate)    (None, 20)           0           ['dense_47[0][0]',               \n",
            "                                                                  'dense_49[0][0]']               \n",
            "                                                                                                  \n",
            " dense_50 (Dense)               (None, 10)           210         ['concatenate_3[0][0]']          \n",
            "                                                                                                  \n",
            " dense_51 (Dense)               (None, 1)            11          ['dense_50[0][0]']               \n",
            "                                                                                                  \n",
            "==================================================================================================\n",
            "Total params: 8,399,907\n",
            "Trainable params: 8,399,907\n",
            "Non-trainable params: 0\n",
            "__________________________________________________________________________________________________\n",
            "Epoch 1/50\n",
            "2/2 [==============================] - 1s 255ms/step - loss: 170.6057 - val_loss: 1.6435\n",
            "Epoch 2/50\n",
            "2/2 [==============================] - 0s 112ms/step - loss: 1.7200 - val_loss: 0.2604\n",
            "Epoch 3/50\n",
            "2/2 [==============================] - 0s 121ms/step - loss: 0.3627 - val_loss: 0.1741\n",
            "Epoch 4/50\n",
            "2/2 [==============================] - 0s 112ms/step - loss: 0.1082 - val_loss: 0.2393\n",
            "Epoch 5/50\n",
            "2/2 [==============================] - 0s 113ms/step - loss: 0.1972 - val_loss: 0.0968\n",
            "Epoch 6/50\n",
            "2/2 [==============================] - 0s 112ms/step - loss: 0.2977 - val_loss: 0.0189\n",
            "Epoch 7/50\n",
            "2/2 [==============================] - 0s 113ms/step - loss: 0.0355 - val_loss: 0.0471\n",
            "Epoch 8/50\n",
            "2/2 [==============================] - 0s 112ms/step - loss: 0.0354 - val_loss: 0.0071\n",
            "Epoch 9/50\n",
            "2/2 [==============================] - 0s 112ms/step - loss: 0.0090 - val_loss: 0.0065\n",
            "Epoch 10/50\n",
            "2/2 [==============================] - 0s 110ms/step - loss: 0.0089 - val_loss: 0.0065\n",
            "Epoch 11/50\n",
            "2/2 [==============================] - 0s 111ms/step - loss: 0.0091 - val_loss: 0.0067\n",
            "Epoch 12/50\n",
            "2/2 [==============================] - 0s 113ms/step - loss: 0.0096 - val_loss: 0.0070\n",
            "Epoch 13/50\n",
            "2/2 [==============================] - 0s 111ms/step - loss: 0.0099 - val_loss: 0.0071\n",
            "Epoch 14/50\n",
            "2/2 [==============================] - 0s 106ms/step - loss: 0.0100 - val_loss: 0.0070\n",
            "Epoch 15/50\n",
            "2/2 [==============================] - 0s 113ms/step - loss: 0.0098 - val_loss: 0.0068\n",
            "Epoch 16/50\n",
            "2/2 [==============================] - 0s 111ms/step - loss: 0.0094 - val_loss: 0.0065\n",
            "Epoch 17/50\n",
            "2/2 [==============================] - 0s 114ms/step - loss: 0.0090 - val_loss: 0.0064\n",
            "Epoch 18/50\n",
            "2/2 [==============================] - 0s 115ms/step - loss: 0.0088 - val_loss: 0.0064\n",
            "Epoch 19/50\n",
            "2/2 [==============================] - 0s 118ms/step - loss: 0.0087 - val_loss: 0.0065\n",
            "Epoch 20/50\n",
            "2/2 [==============================] - 0s 119ms/step - loss: 0.0088 - val_loss: 0.0067\n",
            "Epoch 21/50\n",
            "2/2 [==============================] - 0s 113ms/step - loss: 0.0089 - val_loss: 0.0068\n",
            "Epoch 22/50\n",
            "2/2 [==============================] - 0s 113ms/step - loss: 0.0089 - val_loss: 0.0068\n",
            "Epoch 23/50\n",
            "2/2 [==============================] - 0s 114ms/step - loss: 0.0089 - val_loss: 0.0067\n",
            "Epoch 24/50\n",
            "2/2 [==============================] - 0s 109ms/step - loss: 0.0088 - val_loss: 0.0066\n",
            "Epoch 25/50\n",
            "2/2 [==============================] - 0s 139ms/step - loss: 0.0087 - val_loss: 0.0064\n",
            "Epoch 26/50\n",
            "2/2 [==============================] - 0s 199ms/step - loss: 0.0086 - val_loss: 0.0063\n",
            "Epoch 27/50\n",
            "2/2 [==============================] - 0s 173ms/step - loss: 0.0086 - val_loss: 0.0063\n",
            "Epoch 28/50\n",
            "2/2 [==============================] - 0s 112ms/step - loss: 0.0086 - val_loss: 0.0062\n",
            "Epoch 29/50\n",
            "2/2 [==============================] - 0s 110ms/step - loss: 0.0086 - val_loss: 0.0062\n",
            "Epoch 30/50\n",
            "2/2 [==============================] - 0s 206ms/step - loss: 0.0086 - val_loss: 0.0062\n",
            "Epoch 31/50\n",
            "2/2 [==============================] - 0s 241ms/step - loss: 0.0086 - val_loss: 0.0062\n",
            "Epoch 32/50\n",
            "2/2 [==============================] - 0s 112ms/step - loss: 0.0085 - val_loss: 0.0062\n",
            "Epoch 33/50\n",
            "2/2 [==============================] - 0s 112ms/step - loss: 0.0085 - val_loss: 0.0062\n",
            "Epoch 34/50\n",
            "2/2 [==============================] - 0s 212ms/step - loss: 0.0085 - val_loss: 0.0063\n",
            "Epoch 35/50\n",
            "2/2 [==============================] - 0s 222ms/step - loss: 0.0085 - val_loss: 0.0063\n",
            "Epoch 36/50\n",
            "2/2 [==============================] - 0s 117ms/step - loss: 0.0084 - val_loss: 0.0063\n",
            "Epoch 37/50\n",
            "2/2 [==============================] - 0s 111ms/step - loss: 0.0084 - val_loss: 0.0062\n",
            "Epoch 38/50\n",
            "2/2 [==============================] - 0s 109ms/step - loss: 0.0084 - val_loss: 0.0062\n",
            "Epoch 39/50\n",
            "2/2 [==============================] - 0s 112ms/step - loss: 0.0084 - val_loss: 0.0062\n",
            "Epoch 40/50\n",
            "2/2 [==============================] - 0s 113ms/step - loss: 0.0084 - val_loss: 0.0062\n",
            "Epoch 41/50\n",
            "2/2 [==============================] - 0s 113ms/step - loss: 0.0084 - val_loss: 0.0061\n",
            "Epoch 42/50\n",
            "2/2 [==============================] - 0s 117ms/step - loss: 0.0083 - val_loss: 0.0061\n",
            "Epoch 43/50\n",
            "2/2 [==============================] - 0s 113ms/step - loss: 0.0083 - val_loss: 0.0061\n",
            "Epoch 44/50\n",
            "2/2 [==============================] - 0s 111ms/step - loss: 0.0083 - val_loss: 0.0061\n",
            "Epoch 45/50\n",
            "2/2 [==============================] - 0s 111ms/step - loss: 0.0083 - val_loss: 0.0061\n",
            "Epoch 46/50\n",
            "2/2 [==============================] - 0s 112ms/step - loss: 0.0083 - val_loss: 0.0061\n",
            "Epoch 47/50\n",
            "2/2 [==============================] - 0s 113ms/step - loss: 0.0082 - val_loss: 0.0061\n",
            "Epoch 48/50\n",
            "2/2 [==============================] - 0s 115ms/step - loss: 0.0082 - val_loss: 0.0061\n",
            "Epoch 49/50\n",
            "2/2 [==============================] - 0s 112ms/step - loss: 0.0082 - val_loss: 0.0061\n",
            "Epoch 50/50\n",
            "2/2 [==============================] - 0s 94ms/step - loss: 0.0082 - val_loss: 0.0061\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7eff1c02aa90>"
            ]
          },
          "metadata": {},
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cdJXO5maFwh0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "cd22b41e-6228-4881-ed6a-27cdd038b019"
      },
      "source": [
        "preds = combined_model.predict([testAttrX, testImagesX])\n",
        "\n",
        "diff = preds.flatten() - testY\n",
        "percentDiff = (diff / testY) * 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": 68,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[INFO] mean: 109.97%, std: 178.11\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZAh3boxuFwez"
      },
      "source": [
        ""
      ],
      "execution_count": 29,
      "outputs": []
    },
    {
      "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": "86b9808c-6491-4732-f86f-5f2e810d1cd4"
      },
      "source": [
        "!wget https://github.com/dhrim/cau_2022_summer/raw/master/material/deep_learning/fashion_dataset.zip"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2022-07-25 04:24:51--  https://github.com/dhrim/cau_2022_summer/raw/master/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/cau_2022_summer/master/material/deep_learning/fashion_dataset.zip [following]\n",
            "--2022-07-25 04:24:51--  https://raw.githubusercontent.com/dhrim/cau_2022_summer/master/material/deep_learning/fashion_dataset.zip\n",
            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.111.133, 185.199.108.133, 185.199.109.133, ...\n",
            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.111.133|: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",
            "2022-07-25 04:24:52 (316 MB/s) - ‘fashion_dataset.zip’ saved [26991216/26991216]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mFt96FMqvjah",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1e9a3102-1838-4ce3-f6bf-13c7d1597ab7"
      },
      "source": [
        "!ls -alh"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "total 26M\n",
            "drwxr-xr-x 1 root root 4.0K Jul 25 04:24 .\n",
            "drwxr-xr-x 1 root root 4.0K Jul 25 04:10 ..\n",
            "drwxr-xr-x 4 root root 4.0K Jul 13 13:42 .config\n",
            "-rw-r--r-- 1 root root  26M Jul 25 04:24 fashion_dataset.zip\n",
            "drwxr-xr-x 4 root root 4.0K Jul 25 04:23 Houses-dataset\n",
            "drwxr-xr-x 1 root root 4.0K Jul 13 13:43 sample_data\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zYCavDIjh5RK",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ae693e83-ea85-4fb7-d292-eb5ab7e1ce20"
      },
      "source": [
        "!unzip fashion_dataset.zip"
      ],
      "execution_count": 32,
      "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",
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          ]
        }
      ]
    },
    {
      "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": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pD_ONY_4jtzK"
      },
      "source": [
        "IMAGE_DIMS = (96, 96, 3)"
      ],
      "execution_count": 34,
      "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": "005330e1-525b-4525-d365-b1c18cc58f4b"
      },
      "source": [
        "imagePaths = sorted(list(paths.list_images('fashion_dataset')))\n",
        "random.shuffle(imagePaths)\n",
        "print(len(imagePaths))\n",
        "print(imagePaths[:3])"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "755\n",
            "['fashion_dataset/black_jeans/00000098.jpg', 'fashion_dataset/black_shoes/00000041.jpg', 'fashion_dataset/blue_shirt/00000119.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": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IME5dUwqilgV",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b4e558fb-99eb-4a1b-c252-c7fdc9598260"
      },
      "source": [
        "print(categoryLabels[:10])\n",
        "print(colorLabels[:10])"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['jeans', 'shoes', 'shirt', 'jeans', 'shirt', 'shirt', 'dress', 'shoes', 'dress', 'shirt']\n",
            "['black', 'black', 'blue', 'black', 'blue', 'blue', 'red', 'black', 'red', 'blue']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FHIVMXWyjxov",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 601
        },
        "outputId": "aa03156d-7ae3-4240-8710-8314cf8dbf27"
      },
      "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": 38,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "755 755 755\n",
            "iamgePaths[0]= fashion_dataset/black_jeans/00000098.jpg\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Image object>"
            ],
            "image/jpeg": 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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "data[0]= (96, 96, 3)\n",
            "categoryLabels[0]= jeans\n",
            "colorLabels[0]= black\n"
          ]
        },
        {
          "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": "markdown",
      "metadata": {
        "id": "4CcZd2FUnTAb"
      },
      "source": [
        "### numpy로 변환"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CFPjrQRCnSSb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "465c5b7f-adf2-41b4-ab9f-988fc9ecf4d2"
      },
      "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": 39,
      "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": 40,
      "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": "0b36a848-f51e-470d-d1cf-810dd4f4b820"
      },
      "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": 41,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['jeans' 'shoes' 'shirt' 'jeans' 'shirt']\n",
            "[[0 1 0 0]\n",
            " [0 0 0 1]\n",
            " [0 0 1 0]\n",
            " [0 1 0 0]\n",
            " [0 0 1 0]]\n",
            "['black' 'black' 'blue' 'black' 'blue']\n",
            "[[1 0 0]\n",
            " [1 0 0]\n",
            " [0 1 0]\n",
            " [1 0 0]\n",
            " [0 1 0]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AXcJIhU1noje"
      },
      "source": [
        "### 데이터 분리"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H6H1tL3unAOv",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c62ca652-a771-40b8-e2e9-bf8fbe437a15"
      },
      "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": 42,
      "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": "decd379f-731b-4775-95dc-71f83ed3c102"
      },
      "source": [
        "CATEGORY_COUNT = categoryLabels.shape[-1]\n",
        "COLOR_COUNT = colorLabels.shape[-1]\n",
        "\n",
        "print(CATEGORY_COUNT)\n",
        "print(COLOR_COUNT)"
      ],
      "execution_count": 43,
      "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": 44,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Vh-cWdnw9YIM",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d7803d3e-4633-4230-bd5b-18f992b66fd4"
      },
      "source": [
        "print(trainX.shape)\n",
        "print(trainCategoryY.shape)\n",
        "print(CATEGORY_COUNT)"
      ],
      "execution_count": 45,
      "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": "1fd69f8c-4732-4c74-cb8b-0f316d46fda6"
      },
      "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": 46,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_1\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv2d_8 (Conv2D)           (None, 94, 94, 32)        896       \n",
            "                                                                 \n",
            " max_pooling2d_8 (MaxPooling  (None, 47, 47, 32)       0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " conv2d_9 (Conv2D)           (None, 45, 45, 64)        18496     \n",
            "                                                                 \n",
            " max_pooling2d_9 (MaxPooling  (None, 22, 22, 64)       0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " flatten_4 (Flatten)         (None, 30976)             0         \n",
            "                                                                 \n",
            " dense_18 (Dense)            (None, 10)                309770    \n",
            "                                                                 \n",
            " dense_19 (Dense)            (None, 10)                110       \n",
            "                                                                 \n",
            " dense_20 (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 38ms/step - loss: 1.5497 - accuracy: 0.2914\n",
            "Epoch 2/50\n",
            "5/5 [==============================] - 0s 16ms/step - loss: 1.4283 - accuracy: 0.2632\n",
            "Epoch 3/50\n",
            "5/5 [==============================] - 0s 15ms/step - loss: 1.2420 - accuracy: 0.3858\n",
            "Epoch 4/50\n",
            "5/5 [==============================] - 0s 15ms/step - loss: 1.0783 - accuracy: 0.3825\n",
            "Epoch 5/50\n",
            "5/5 [==============================] - 0s 15ms/step - loss: 0.9881 - accuracy: 0.6192\n",
            "Epoch 6/50\n",
            "5/5 [==============================] - 0s 17ms/step - loss: 0.9110 - accuracy: 0.6954\n",
            "Epoch 7/50\n",
            "5/5 [==============================] - 0s 19ms/step - loss: 0.8380 - accuracy: 0.7053\n",
            "Epoch 8/50\n",
            "5/5 [==============================] - 0s 14ms/step - loss: 0.7692 - accuracy: 0.7732\n",
            "Epoch 9/50\n",
            "5/5 [==============================] - 0s 16ms/step - loss: 0.6855 - accuracy: 0.7831\n",
            "Epoch 10/50\n",
            "5/5 [==============================] - 0s 16ms/step - loss: 0.6041 - accuracy: 0.7864\n",
            "Epoch 11/50\n",
            "5/5 [==============================] - 0s 15ms/step - loss: 0.5585 - accuracy: 0.7699\n",
            "Epoch 12/50\n",
            "5/5 [==============================] - 0s 14ms/step - loss: 0.4969 - accuracy: 0.7964\n",
            "Epoch 13/50\n",
            "5/5 [==============================] - 0s 15ms/step - loss: 0.4111 - accuracy: 0.8328\n",
            "Epoch 14/50\n",
            "5/5 [==============================] - 0s 15ms/step - loss: 0.4191 - accuracy: 0.8162\n",
            "Epoch 15/50\n",
            "5/5 [==============================] - 0s 15ms/step - loss: 0.3635 - accuracy: 0.8427\n",
            "Epoch 16/50\n",
            "5/5 [==============================] - 0s 17ms/step - loss: 0.3318 - accuracy: 0.8593\n",
            "Epoch 17/50\n",
            "5/5 [==============================] - 0s 15ms/step - loss: 0.3084 - accuracy: 0.8841\n",
            "Epoch 18/50\n",
            "5/5 [==============================] - 0s 15ms/step - loss: 0.2559 - accuracy: 0.9156\n",
            "Epoch 19/50\n",
            "5/5 [==============================] - 0s 20ms/step - loss: 0.2084 - accuracy: 0.9288\n",
            "Epoch 20/50\n",
            "5/5 [==============================] - 0s 16ms/step - loss: 0.1796 - accuracy: 0.9470\n",
            "Epoch 21/50\n",
            "5/5 [==============================] - 0s 15ms/step - loss: 0.1762 - accuracy: 0.9503\n",
            "Epoch 22/50\n",
            "5/5 [==============================] - 0s 16ms/step - loss: 0.2853 - accuracy: 0.9056\n",
            "Epoch 23/50\n",
            "5/5 [==============================] - 0s 16ms/step - loss: 0.1789 - accuracy: 0.9354\n",
            "Epoch 24/50\n",
            "5/5 [==============================] - 0s 15ms/step - loss: 0.1763 - accuracy: 0.9470\n",
            "Epoch 25/50\n",
            "5/5 [==============================] - 0s 15ms/step - loss: 0.1347 - accuracy: 0.9652\n",
            "Epoch 26/50\n",
            "5/5 [==============================] - 0s 15ms/step - loss: 0.1338 - accuracy: 0.9454\n",
            "Epoch 27/50\n",
            "5/5 [==============================] - 0s 16ms/step - loss: 0.1005 - accuracy: 0.9685\n",
            "Epoch 28/50\n",
            "5/5 [==============================] - 0s 16ms/step - loss: 0.0786 - accuracy: 0.9801\n",
            "Epoch 29/50\n",
            "5/5 [==============================] - 0s 17ms/step - loss: 0.0751 - accuracy: 0.9818\n",
            "Epoch 30/50\n",
            "5/5 [==============================] - 0s 15ms/step - loss: 0.0669 - accuracy: 0.9818\n",
            "Epoch 31/50\n",
            "5/5 [==============================] - 0s 17ms/step - loss: 0.0652 - accuracy: 0.9768\n",
            "Epoch 32/50\n",
            "5/5 [==============================] - 0s 15ms/step - loss: 0.0517 - accuracy: 0.9868\n",
            "Epoch 33/50\n",
            "5/5 [==============================] - 0s 15ms/step - loss: 0.0498 - accuracy: 0.9868\n",
            "Epoch 34/50\n",
            "5/5 [==============================] - 0s 15ms/step - loss: 0.0432 - accuracy: 0.9884\n",
            "Epoch 35/50\n",
            "5/5 [==============================] - 0s 16ms/step - loss: 0.0386 - accuracy: 0.9901\n",
            "Epoch 36/50\n",
            "5/5 [==============================] - 0s 16ms/step - loss: 0.0387 - accuracy: 0.9884\n",
            "Epoch 37/50\n",
            "5/5 [==============================] - 0s 16ms/step - loss: 0.0353 - accuracy: 0.9917\n",
            "Epoch 38/50\n",
            "5/5 [==============================] - 0s 16ms/step - loss: 0.0352 - accuracy: 0.9917\n",
            "Epoch 39/50\n",
            "5/5 [==============================] - 0s 16ms/step - loss: 0.0349 - accuracy: 0.9901\n",
            "Epoch 40/50\n",
            "5/5 [==============================] - 0s 15ms/step - loss: 0.0376 - accuracy: 0.9901\n",
            "Epoch 41/50\n",
            "5/5 [==============================] - 0s 16ms/step - loss: 0.0308 - accuracy: 0.9934\n",
            "Epoch 42/50\n",
            "5/5 [==============================] - 0s 15ms/step - loss: 0.0270 - accuracy: 0.9917\n",
            "Epoch 43/50\n",
            "5/5 [==============================] - 0s 15ms/step - loss: 0.0250 - accuracy: 0.9967\n",
            "Epoch 44/50\n",
            "5/5 [==============================] - 0s 14ms/step - loss: 0.0219 - accuracy: 0.9950\n",
            "Epoch 45/50\n",
            "5/5 [==============================] - 0s 15ms/step - loss: 0.0200 - accuracy: 0.9950\n",
            "Epoch 46/50\n",
            "5/5 [==============================] - 0s 15ms/step - loss: 0.0185 - accuracy: 0.9950\n",
            "Epoch 47/50\n",
            "5/5 [==============================] - 0s 15ms/step - loss: 0.0169 - accuracy: 0.9950\n",
            "Epoch 48/50\n",
            "5/5 [==============================] - 0s 15ms/step - loss: 0.0158 - accuracy: 0.9950\n",
            "Epoch 49/50\n",
            "5/5 [==============================] - 0s 14ms/step - loss: 0.0147 - accuracy: 0.9967\n",
            "Epoch 50/50\n",
            "5/5 [==============================] - 0s 14ms/step - loss: 0.0139 - accuracy: 0.9967\n",
            "5/5 [==============================] - 0s 9ms/step - loss: 0.2239 - accuracy: 0.9470\n",
            "loss= 0.22394804656505585\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": "5e627817-c669-40ce-eb73-3a3dc6f9495a"
      },
      "source": [
        "print(trainX.shape)\n",
        "print(trainColorY.shape)\n",
        "print(COLOR_COUNT)"
      ],
      "execution_count": 47,
      "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": "2da44cfd-f3ad-46b6-ac8a-beaccddd899a"
      },
      "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": 48,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_2\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv2d_10 (Conv2D)          (None, 94, 94, 32)        896       \n",
            "                                                                 \n",
            " max_pooling2d_10 (MaxPoolin  (None, 47, 47, 32)       0         \n",
            " g2D)                                                            \n",
            "                                                                 \n",
            " conv2d_11 (Conv2D)          (None, 45, 45, 64)        18496     \n",
            "                                                                 \n",
            " max_pooling2d_11 (MaxPoolin  (None, 22, 22, 64)       0         \n",
            " g2D)                                                            \n",
            "                                                                 \n",
            " flatten_5 (Flatten)         (None, 30976)             0         \n",
            "                                                                 \n",
            " dense_21 (Dense)            (None, 10)                309770    \n",
            "                                                                 \n",
            " dense_22 (Dense)            (None, 10)                110       \n",
            "                                                                 \n",
            " dense_23 (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: 1.1016 - accuracy: 0.3742\n",
            "Epoch 2/50\n",
            "5/5 [==============================] - 0s 16ms/step - loss: 0.9525 - accuracy: 0.5629\n",
            "Epoch 3/50\n",
            "5/5 [==============================] - 0s 15ms/step - loss: 0.7833 - accuracy: 0.6606\n",
            "Epoch 4/50\n",
            "5/5 [==============================] - 0s 16ms/step - loss: 0.5953 - accuracy: 0.7152\n",
            "Epoch 5/50\n",
            "5/5 [==============================] - 0s 15ms/step - loss: 0.5112 - accuracy: 0.7252\n",
            "Epoch 6/50\n",
            "5/5 [==============================] - 0s 16ms/step - loss: 0.4499 - accuracy: 0.7732\n",
            "Epoch 7/50\n",
            "5/5 [==============================] - 0s 15ms/step - loss: 0.4051 - accuracy: 0.9437\n",
            "Epoch 8/50\n",
            "5/5 [==============================] - 0s 15ms/step - loss: 0.3189 - accuracy: 0.9222\n",
            "Epoch 9/50\n",
            "5/5 [==============================] - 0s 16ms/step - loss: 0.2063 - accuracy: 0.9321\n",
            "Epoch 10/50\n",
            "5/5 [==============================] - 0s 15ms/step - loss: 0.1283 - accuracy: 0.9603\n",
            "Epoch 11/50\n",
            "5/5 [==============================] - 0s 14ms/step - loss: 0.0822 - accuracy: 0.9801\n",
            "Epoch 12/50\n",
            "5/5 [==============================] - 0s 15ms/step - loss: 0.0655 - accuracy: 0.9818\n",
            "Epoch 13/50\n",
            "5/5 [==============================] - 0s 15ms/step - loss: 0.0464 - accuracy: 0.9851\n",
            "Epoch 14/50\n",
            "5/5 [==============================] - 0s 14ms/step - loss: 0.0382 - accuracy: 0.9934\n",
            "Epoch 15/50\n",
            "5/5 [==============================] - 0s 15ms/step - loss: 0.0314 - accuracy: 0.9934\n",
            "Epoch 16/50\n",
            "5/5 [==============================] - 0s 22ms/step - loss: 0.0245 - accuracy: 0.9950\n",
            "Epoch 17/50\n",
            "5/5 [==============================] - 0s 15ms/step - loss: 0.0212 - accuracy: 0.9983\n",
            "Epoch 18/50\n",
            "5/5 [==============================] - 0s 15ms/step - loss: 0.0151 - accuracy: 0.9983\n",
            "Epoch 19/50\n",
            "5/5 [==============================] - 0s 16ms/step - loss: 0.0127 - accuracy: 0.9983\n",
            "Epoch 20/50\n",
            "5/5 [==============================] - 0s 15ms/step - loss: 0.0104 - accuracy: 0.9983\n",
            "Epoch 21/50\n",
            "5/5 [==============================] - 0s 15ms/step - loss: 0.0079 - accuracy: 0.9983\n",
            "Epoch 22/50\n",
            "5/5 [==============================] - 0s 15ms/step - loss: 0.0064 - accuracy: 1.0000\n",
            "Epoch 23/50\n",
            "5/5 [==============================] - 0s 16ms/step - loss: 0.0064 - accuracy: 1.0000\n",
            "Epoch 24/50\n",
            "5/5 [==============================] - 0s 14ms/step - loss: 0.0048 - accuracy: 1.0000\n",
            "Epoch 25/50\n",
            "5/5 [==============================] - 0s 14ms/step - loss: 0.0040 - accuracy: 1.0000\n",
            "Epoch 26/50\n",
            "5/5 [==============================] - 0s 15ms/step - loss: 0.0034 - accuracy: 1.0000\n",
            "Epoch 27/50\n",
            "5/5 [==============================] - 0s 15ms/step - loss: 0.0030 - accuracy: 1.0000\n",
            "Epoch 28/50\n",
            "5/5 [==============================] - 0s 16ms/step - loss: 0.0026 - accuracy: 1.0000\n",
            "Epoch 29/50\n",
            "5/5 [==============================] - 0s 15ms/step - loss: 0.0025 - accuracy: 1.0000\n",
            "Epoch 30/50\n",
            "5/5 [==============================] - 0s 15ms/step - loss: 0.0023 - accuracy: 1.0000\n",
            "Epoch 31/50\n",
            "5/5 [==============================] - 0s 16ms/step - loss: 0.0022 - accuracy: 1.0000\n",
            "Epoch 32/50\n",
            "5/5 [==============================] - 0s 15ms/step - loss: 0.0019 - accuracy: 1.0000\n",
            "Epoch 33/50\n",
            "5/5 [==============================] - 0s 15ms/step - loss: 0.0019 - accuracy: 1.0000\n",
            "Epoch 34/50\n",
            "5/5 [==============================] - 0s 16ms/step - loss: 0.0017 - accuracy: 1.0000\n",
            "Epoch 35/50\n",
            "5/5 [==============================] - 0s 16ms/step - loss: 0.0016 - accuracy: 1.0000\n",
            "Epoch 36/50\n",
            "5/5 [==============================] - 0s 16ms/step - loss: 0.0015 - accuracy: 1.0000\n",
            "Epoch 37/50\n",
            "5/5 [==============================] - 0s 16ms/step - loss: 0.0014 - accuracy: 1.0000\n",
            "Epoch 38/50\n",
            "5/5 [==============================] - 0s 17ms/step - loss: 0.0013 - accuracy: 1.0000\n",
            "Epoch 39/50\n",
            "5/5 [==============================] - 0s 15ms/step - loss: 0.0012 - accuracy: 1.0000\n",
            "Epoch 40/50\n",
            "5/5 [==============================] - 0s 16ms/step - loss: 0.0011 - accuracy: 1.0000\n",
            "Epoch 41/50\n",
            "5/5 [==============================] - 0s 16ms/step - loss: 0.0011 - accuracy: 1.0000\n",
            "Epoch 42/50\n",
            "5/5 [==============================] - 0s 19ms/step - loss: 0.0010 - accuracy: 1.0000\n",
            "Epoch 43/50\n",
            "5/5 [==============================] - 0s 16ms/step - loss: 0.0010 - accuracy: 1.0000\n",
            "Epoch 44/50\n",
            "5/5 [==============================] - 0s 16ms/step - loss: 9.2715e-04 - accuracy: 1.0000\n",
            "Epoch 45/50\n",
            "5/5 [==============================] - 0s 15ms/step - loss: 8.9119e-04 - accuracy: 1.0000\n",
            "Epoch 46/50\n",
            "5/5 [==============================] - 0s 15ms/step - loss: 8.6719e-04 - accuracy: 1.0000\n",
            "Epoch 47/50\n",
            "5/5 [==============================] - 0s 15ms/step - loss: 7.9631e-04 - accuracy: 1.0000\n",
            "Epoch 48/50\n",
            "5/5 [==============================] - 0s 16ms/step - loss: 7.8038e-04 - accuracy: 1.0000\n",
            "Epoch 49/50\n",
            "5/5 [==============================] - 0s 15ms/step - loss: 7.2527e-04 - accuracy: 1.0000\n",
            "Epoch 50/50\n",
            "5/5 [==============================] - 0s 13ms/step - loss: 8.2646e-04 - accuracy: 1.0000\n",
            "5/5 [==============================] - 0s 6ms/step - loss: 0.1403 - accuracy: 0.9801\n",
            "loss= 0.14027619361877441\n",
            "acc= 0.9801324605941772\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BlumccIP-YUu"
      },
      "source": [
        ""
      ],
      "execution_count": 48,
      "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": 49,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "49vnw8ZSAEYN",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "82eb0bfa-4a4a-4021-8383-2605702a6d71"
      },
      "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": 50,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model_4\"\n",
            "__________________________________________________________________________________________________\n",
            " Layer (type)                   Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            " input_9 (InputLayer)           [(None, 96, 96, 3)]  0           []                               \n",
            "                                                                                                  \n",
            " conv2d_12 (Conv2D)             (None, 94, 94, 32)   896         ['input_9[0][0]']                \n",
            "                                                                                                  \n",
            " max_pooling2d_12 (MaxPooling2D  (None, 47, 47, 32)  0           ['conv2d_12[0][0]']              \n",
            " )                                                                                                \n",
            "                                                                                                  \n",
            " conv2d_13 (Conv2D)             (None, 45, 45, 64)   18496       ['max_pooling2d_12[0][0]']       \n",
            "                                                                                                  \n",
            " max_pooling2d_13 (MaxPooling2D  (None, 22, 22, 64)  0           ['conv2d_13[0][0]']              \n",
            " )                                                                                                \n",
            "                                                                                                  \n",
            " flatten_6 (Flatten)            (None, 30976)        0           ['max_pooling2d_13[0][0]']       \n",
            "                                                                                                  \n",
            " dense_24 (Dense)               (None, 10)           309770      ['flatten_6[0][0]']              \n",
            "                                                                                                  \n",
            " dense_26 (Dense)               (None, 10)           309770      ['flatten_6[0][0]']              \n",
            "                                                                                                  \n",
            " dense_25 (Dense)               (None, 10)           110         ['dense_24[0][0]']               \n",
            "                                                                                                  \n",
            " dense_27 (Dense)               (None, 10)           110         ['dense_26[0][0]']               \n",
            "                                                                                                  \n",
            " category_output (Dense)        (None, 4)            44          ['dense_25[0][0]']               \n",
            "                                                                                                  \n",
            " color_output (Dense)           (None, 3)            33          ['dense_27[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": "3dee0709-e55a-49e1-d358-5504a5c2e501"
      },
      "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": 51,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/50\n",
            "5/5 [==============================] - 1s 19ms/step - loss: 3.0351 - category_output_loss: 1.6214 - color_output_loss: 1.4137 - category_output_accuracy: 0.3526 - color_output_accuracy: 0.5298\n",
            "Epoch 2/50\n",
            "5/5 [==============================] - 0s 16ms/step - loss: 2.2195 - category_output_loss: 1.2535 - color_output_loss: 0.9660 - category_output_accuracy: 0.3874 - color_output_accuracy: 0.5132\n",
            "Epoch 3/50\n",
            "5/5 [==============================] - 0s 16ms/step - loss: 2.0206 - category_output_loss: 1.1814 - color_output_loss: 0.8392 - category_output_accuracy: 0.3775 - color_output_accuracy: 0.6490\n",
            "Epoch 4/50\n",
            "5/5 [==============================] - 0s 16ms/step - loss: 1.8207 - category_output_loss: 1.0844 - color_output_loss: 0.7362 - category_output_accuracy: 0.5033 - color_output_accuracy: 0.6573\n",
            "Epoch 5/50\n",
            "5/5 [==============================] - 0s 17ms/step - loss: 1.7035 - category_output_loss: 0.9985 - color_output_loss: 0.7050 - category_output_accuracy: 0.5017 - color_output_accuracy: 0.7070\n",
            "Epoch 6/50\n",
            "5/5 [==============================] - 0s 18ms/step - loss: 1.5402 - category_output_loss: 0.9299 - color_output_loss: 0.6103 - category_output_accuracy: 0.5232 - color_output_accuracy: 0.8460\n",
            "Epoch 7/50\n",
            "5/5 [==============================] - 0s 17ms/step - loss: 1.4141 - category_output_loss: 0.8759 - color_output_loss: 0.5382 - category_output_accuracy: 0.5265 - color_output_accuracy: 0.9156\n",
            "Epoch 8/50\n",
            "5/5 [==============================] - 0s 18ms/step - loss: 1.3043 - category_output_loss: 0.8415 - color_output_loss: 0.4627 - category_output_accuracy: 0.5331 - color_output_accuracy: 0.9454\n",
            "Epoch 9/50\n",
            "5/5 [==============================] - 0s 16ms/step - loss: 1.2547 - category_output_loss: 0.8248 - color_output_loss: 0.4299 - category_output_accuracy: 0.5265 - color_output_accuracy: 0.9586\n",
            "Epoch 10/50\n",
            "5/5 [==============================] - 0s 17ms/step - loss: 1.1945 - category_output_loss: 0.7929 - color_output_loss: 0.4016 - category_output_accuracy: 0.5546 - color_output_accuracy: 0.9636\n",
            "Epoch 11/50\n",
            "5/5 [==============================] - 0s 16ms/step - loss: 1.1430 - category_output_loss: 0.7593 - color_output_loss: 0.3837 - category_output_accuracy: 0.5579 - color_output_accuracy: 0.9752\n",
            "Epoch 12/50\n",
            "5/5 [==============================] - 0s 17ms/step - loss: 1.1009 - category_output_loss: 0.7368 - color_output_loss: 0.3641 - category_output_accuracy: 0.5596 - color_output_accuracy: 0.9834\n",
            "Epoch 13/50\n",
            "5/5 [==============================] - 0s 18ms/step - loss: 1.0646 - category_output_loss: 0.6988 - color_output_loss: 0.3657 - category_output_accuracy: 0.5712 - color_output_accuracy: 0.9702\n",
            "Epoch 14/50\n",
            "5/5 [==============================] - 0s 17ms/step - loss: 1.0072 - category_output_loss: 0.6506 - color_output_loss: 0.3566 - category_output_accuracy: 0.6374 - color_output_accuracy: 0.9768\n",
            "Epoch 15/50\n",
            "5/5 [==============================] - 0s 16ms/step - loss: 0.9432 - category_output_loss: 0.6119 - color_output_loss: 0.3312 - category_output_accuracy: 0.7003 - color_output_accuracy: 0.9884\n",
            "Epoch 16/50\n",
            "5/5 [==============================] - 0s 16ms/step - loss: 0.8798 - category_output_loss: 0.5630 - color_output_loss: 0.3168 - category_output_accuracy: 0.7417 - color_output_accuracy: 0.9917\n",
            "Epoch 17/50\n",
            "5/5 [==============================] - 0s 17ms/step - loss: 0.8392 - category_output_loss: 0.5330 - color_output_loss: 0.3062 - category_output_accuracy: 0.7831 - color_output_accuracy: 0.9950\n",
            "Epoch 18/50\n",
            "5/5 [==============================] - 0s 17ms/step - loss: 0.7904 - category_output_loss: 0.4876 - color_output_loss: 0.3028 - category_output_accuracy: 0.7881 - color_output_accuracy: 0.9950\n",
            "Epoch 19/50\n",
            "5/5 [==============================] - 0s 16ms/step - loss: 0.7391 - category_output_loss: 0.4417 - color_output_loss: 0.2974 - category_output_accuracy: 0.7914 - color_output_accuracy: 0.9934\n",
            "Epoch 20/50\n",
            "5/5 [==============================] - 0s 17ms/step - loss: 0.7014 - category_output_loss: 0.4101 - color_output_loss: 0.2913 - category_output_accuracy: 0.8129 - color_output_accuracy: 0.9934\n",
            "Epoch 21/50\n",
            "5/5 [==============================] - 0s 19ms/step - loss: 0.6659 - category_output_loss: 0.3819 - color_output_loss: 0.2840 - category_output_accuracy: 0.8642 - color_output_accuracy: 0.9967\n",
            "Epoch 22/50\n",
            "5/5 [==============================] - 0s 17ms/step - loss: 0.6326 - category_output_loss: 0.3534 - color_output_loss: 0.2792 - category_output_accuracy: 0.9023 - color_output_accuracy: 0.9967\n",
            "Epoch 23/50\n",
            "5/5 [==============================] - 0s 17ms/step - loss: 0.6141 - category_output_loss: 0.3384 - color_output_loss: 0.2757 - category_output_accuracy: 0.9189 - color_output_accuracy: 0.9967\n",
            "Epoch 24/50\n",
            "5/5 [==============================] - 0s 16ms/step - loss: 0.5817 - category_output_loss: 0.3088 - color_output_loss: 0.2729 - category_output_accuracy: 0.9387 - color_output_accuracy: 0.9950\n",
            "Epoch 25/50\n",
            "5/5 [==============================] - 0s 16ms/step - loss: 0.5704 - category_output_loss: 0.3032 - color_output_loss: 0.2672 - category_output_accuracy: 0.9338 - color_output_accuracy: 0.9983\n",
            "Epoch 26/50\n",
            "5/5 [==============================] - 0s 17ms/step - loss: 0.5434 - category_output_loss: 0.2815 - color_output_loss: 0.2619 - category_output_accuracy: 0.9454 - color_output_accuracy: 0.9967\n",
            "Epoch 27/50\n",
            "5/5 [==============================] - 0s 18ms/step - loss: 0.5208 - category_output_loss: 0.2621 - color_output_loss: 0.2587 - category_output_accuracy: 0.9652 - color_output_accuracy: 0.9983\n",
            "Epoch 28/50\n",
            "5/5 [==============================] - 0s 16ms/step - loss: 0.5131 - category_output_loss: 0.2585 - color_output_loss: 0.2546 - category_output_accuracy: 0.9685 - color_output_accuracy: 0.9983\n",
            "Epoch 29/50\n",
            "5/5 [==============================] - 0s 18ms/step - loss: 0.4983 - category_output_loss: 0.2477 - color_output_loss: 0.2506 - category_output_accuracy: 0.9669 - color_output_accuracy: 0.9983\n",
            "Epoch 30/50\n",
            "5/5 [==============================] - 0s 18ms/step - loss: 0.4890 - category_output_loss: 0.2428 - color_output_loss: 0.2463 - category_output_accuracy: 0.9719 - color_output_accuracy: 0.9983\n",
            "Epoch 31/50\n",
            "5/5 [==============================] - 0s 16ms/step - loss: 0.4746 - category_output_loss: 0.2318 - color_output_loss: 0.2428 - category_output_accuracy: 0.9719 - color_output_accuracy: 0.9983\n",
            "Epoch 32/50\n",
            "5/5 [==============================] - 0s 15ms/step - loss: 0.4659 - category_output_loss: 0.2265 - color_output_loss: 0.2394 - category_output_accuracy: 0.9785 - color_output_accuracy: 0.9983\n",
            "Epoch 33/50\n",
            "5/5 [==============================] - 0s 18ms/step - loss: 0.4547 - category_output_loss: 0.2195 - color_output_loss: 0.2352 - category_output_accuracy: 0.9884 - color_output_accuracy: 0.9983\n",
            "Epoch 34/50\n",
            "5/5 [==============================] - 0s 18ms/step - loss: 0.4447 - category_output_loss: 0.2129 - color_output_loss: 0.2318 - category_output_accuracy: 0.9834 - color_output_accuracy: 0.9983\n",
            "Epoch 35/50\n",
            "5/5 [==============================] - 0s 15ms/step - loss: 0.4156 - category_output_loss: 0.1874 - color_output_loss: 0.2282 - category_output_accuracy: 0.9834 - color_output_accuracy: 0.9983\n",
            "Epoch 36/50\n",
            "5/5 [==============================] - 0s 17ms/step - loss: 0.3264 - category_output_loss: 0.1001 - color_output_loss: 0.2263 - category_output_accuracy: 0.9669 - color_output_accuracy: 0.9983\n",
            "Epoch 37/50\n",
            "5/5 [==============================] - 0s 17ms/step - loss: 0.2815 - category_output_loss: 0.0591 - color_output_loss: 0.2223 - category_output_accuracy: 0.9801 - color_output_accuracy: 0.9983\n",
            "Epoch 38/50\n",
            "5/5 [==============================] - 0s 16ms/step - loss: 0.2822 - category_output_loss: 0.0629 - color_output_loss: 0.2193 - category_output_accuracy: 0.9818 - color_output_accuracy: 0.9934\n",
            "Epoch 39/50\n",
            "5/5 [==============================] - 0s 18ms/step - loss: 0.2755 - category_output_loss: 0.0586 - color_output_loss: 0.2170 - category_output_accuracy: 0.9851 - color_output_accuracy: 0.9983\n",
            "Epoch 40/50\n",
            "5/5 [==============================] - 0s 17ms/step - loss: 0.2481 - category_output_loss: 0.0349 - color_output_loss: 0.2132 - category_output_accuracy: 0.9934 - color_output_accuracy: 0.9967\n",
            "Epoch 41/50\n",
            "5/5 [==============================] - 0s 16ms/step - loss: 0.2417 - category_output_loss: 0.0319 - color_output_loss: 0.2098 - category_output_accuracy: 0.9934 - color_output_accuracy: 0.9967\n",
            "Epoch 42/50\n",
            "5/5 [==============================] - 0s 18ms/step - loss: 0.2316 - category_output_loss: 0.0275 - color_output_loss: 0.2041 - category_output_accuracy: 0.9934 - color_output_accuracy: 0.9983\n",
            "Epoch 43/50\n",
            "5/5 [==============================] - 0s 18ms/step - loss: 0.2234 - category_output_loss: 0.0248 - color_output_loss: 0.1986 - category_output_accuracy: 0.9934 - color_output_accuracy: 0.9983\n",
            "Epoch 44/50\n",
            "5/5 [==============================] - 0s 16ms/step - loss: 0.2163 - category_output_loss: 0.0179 - color_output_loss: 0.1985 - category_output_accuracy: 0.9967 - color_output_accuracy: 0.9983\n",
            "Epoch 45/50\n",
            "5/5 [==============================] - 0s 16ms/step - loss: 0.2105 - category_output_loss: 0.0182 - color_output_loss: 0.1923 - category_output_accuracy: 0.9983 - color_output_accuracy: 1.0000\n",
            "Epoch 46/50\n",
            "5/5 [==============================] - 0s 17ms/step - loss: 0.2075 - category_output_loss: 0.0177 - color_output_loss: 0.1898 - category_output_accuracy: 0.9983 - color_output_accuracy: 1.0000\n",
            "Epoch 47/50\n",
            "5/5 [==============================] - 0s 17ms/step - loss: 0.1989 - category_output_loss: 0.0152 - color_output_loss: 0.1837 - category_output_accuracy: 0.9983 - color_output_accuracy: 1.0000\n",
            "Epoch 48/50\n",
            "5/5 [==============================] - 0s 16ms/step - loss: 0.1930 - category_output_loss: 0.0136 - color_output_loss: 0.1794 - category_output_accuracy: 0.9983 - color_output_accuracy: 1.0000\n",
            "Epoch 49/50\n",
            "5/5 [==============================] - 0s 18ms/step - loss: 0.1872 - category_output_loss: 0.0115 - color_output_loss: 0.1757 - category_output_accuracy: 0.9983 - color_output_accuracy: 1.0000\n",
            "Epoch 50/50\n",
            "5/5 [==============================] - 0s 15ms/step - loss: 0.1823 - category_output_loss: 0.0104 - color_output_loss: 0.1719 - category_output_accuracy: 0.9983 - color_output_accuracy: 1.0000\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7effbb3f2f90>"
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NHH3al7zB4uK",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d05ed620-0fe9-457e-d418-e2cbacfb1750"
      },
      "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": 52,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "5/5 [==============================] - 0s 6ms/step - loss: 0.7000 - category_output_loss: 0.3723 - color_output_loss: 0.3277 - category_output_accuracy: 0.9272 - color_output_accuracy: 0.9801\n",
            "loss= 0.7000102996826172\n",
            "categoyr_loss= 0.37234002351760864\n",
            "color_loss= 0.32767030596733093\n",
            "category_acc= 0.9271523356437683\n",
            "color_acc= 0.9801324605941772\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nGPuLHteCIso"
      },
      "source": [
        ""
      ],
      "execution_count": 52,
      "outputs": []
    }
  ]
}