230705_multi_fashion_mnist.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyPeehdIymGkD6tX6m/pY+/9",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/khwwang/coding-training/blob/master/230705_multi_fashion_mnist.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 이미지 다중분류"
      ],
      "metadata": {
        "id": "tCNRcLDUchhm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import tensorflow as tf"
      ],
      "metadata": {
        "id": "0eC9fwnjcjxy"
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from keras.datasets import fashion_mnist\n",
        "\n",
        "(X_train, y_train), (X_test, y_test) = fashion_mnist.load_data()"
      ],
      "metadata": {
        "id": "e7qbf9mYc6Hy"
      },
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train.shape, y_train.shape, X_test.shape, y_test.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PEkewJlrdZ4I",
        "outputId": "48a87904-d9df-432f-a575-3aceedd752c3"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((60000, 28, 28), (60000,), (10000, 28, 28), (10000,))"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat', 'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']"
      ],
      "metadata": {
        "id": "YMH5BA2PeNxI"
      },
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "samples = np.random.randint(6000, size=9)\n",
        "samples"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GjrR0LmPes99",
        "outputId": "fb72a6a3-096b-412c-dfbf-e0f3ccdd02ff"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([3890, 2469, 3487, 1593, 1249, 1117, 2141, 2140, 4148])"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(8,8))\n",
        "for i, idx in enumerate(samples):\n",
        "    plt.subplot(3, 3, i+1)\n",
        "    plt.imshow(X_train[idx], cmap='gray')\n",
        "    plt.axis('off')\n",
        "    plt.title(class_names[y_train[idx]])\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 675
        },
        "id": "k1bs18zAe42i",
        "outputId": "aa2aaa2a-f92f-4348-c3d6-22f7c1ae03cb"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x800 with 9 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "X_train, X_val, y_train, y_val = train_test_split(X_train, y_train, test_size=0.2, random_state=42)"
      ],
      "metadata": {
        "id": "W8eko9HGgLOm"
      },
      "execution_count": 45,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### MinMaxScaling"
      ],
      "metadata": {
        "id": "UnEyTokQiJki"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X_train = X_train/225.\n",
        "X_val = X_val/255.\n",
        "X_test = X_test/255."
      ],
      "metadata": {
        "id": "6d9OtXhVhtSb"
      },
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from keras.utils import to_categorical\n",
        "\n",
        "y_train_o =  to_categorical(y_train)\n",
        "y_val_o = to_categorical(y_val)"
      ],
      "metadata": {
        "id": "OmlQL1RjiV6M"
      },
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_val_o.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cQXCixZ9kM_V",
        "outputId": "e54ae203-89e4-4cf9-a210-17c782262491"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(12000, 10)"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train = X_train.reshape(-1, 28*28)# 이미지 데이터를 일열로"
      ],
      "metadata": {
        "id": "VcgrjNPBkgFH"
      },
      "execution_count": 49,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_val = X_val.reshape(-1, 28*28)\n",
        "X_test = X_test.reshape(-1, 28*28)"
      ],
      "metadata": {
        "id": "11Q_EN-ak7Sk"
      },
      "execution_count": 50,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow import keras\n",
        "from keras import layers\n",
        "\n",
        "model = keras.Sequential([\n",
        "    layers.Dense(units=256, activation='relu', input_shape=(28*28,)),\n",
        "    layers.Dense(units=64, activation='relu'),\n",
        "    layers.Dense(units=64, activation='relu'),\n",
        "    layers.Dense(units=10, activation='softmax'),\n",
        "])# 마지막 layer에는 y_val의 열개수로\n",
        "\n",
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "t7bCUnCUiyVX",
        "outputId": "97f20ce3-c2ad-497c-cb42-e7f29e3199e5"
      },
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_3\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " dense_10 (Dense)            (None, 256)               200960    \n",
            "                                                                 \n",
            " dense_11 (Dense)            (None, 64)                16448     \n",
            "                                                                 \n",
            " dense_12 (Dense)            (None, 64)                4160      \n",
            "                                                                 \n",
            " dense_13 (Dense)            (None, 10)                650       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 222,218\n",
            "Trainable params: 222,218\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.compile(\n",
        "    optimizer='adam',\n",
        "    loss='categorical_crossentropy',\n",
        "    metrics=['accuracy']\n",
        ")"
      ],
      "metadata": {
        "id": "TETVjJSJk4fd"
      },
      "execution_count": 55,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# from keras.optimizers import Adam\n",
        "# opti = Adam(learning_rate=0.002)\n",
        "\n",
        "# model.compile(\n",
        "#     optimizer=opti,\n",
        "#     loss='categorical_crossentropy',\n",
        "#     metrics=['accuarcy']\n",
        "# )"
      ],
      "metadata": {
        "id": "YQvsGe-0mDXh"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "EPOCHS = 30\n",
        "BATCH = 256\n",
        "\n",
        "history = model.fit(\n",
        "    X_train, y_train_o,\n",
        "    epochs = EPOCHS,\n",
        "    batch_size = BATCH,\n",
        "    validation_data =(X_val, y_val_o),\n",
        "    verbose = 1\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "D5UOwTaymSJe",
        "outputId": "b55a2d71-c0fc-4309-cedb-251cca700565"
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/30\n",
            "188/188 [==============================] - 3s 14ms/step - loss: 0.6102 - accuracy: 0.7865 - val_loss: 0.4416 - val_accuracy: 0.8446\n",
            "Epoch 2/30\n",
            "188/188 [==============================] - 2s 13ms/step - loss: 0.4111 - accuracy: 0.8534 - val_loss: 0.4312 - val_accuracy: 0.8492\n",
            "Epoch 3/30\n",
            "188/188 [==============================] - 2s 12ms/step - loss: 0.3607 - accuracy: 0.8700 - val_loss: 0.3636 - val_accuracy: 0.8711\n",
            "Epoch 4/30\n",
            "188/188 [==============================] - 4s 19ms/step - loss: 0.3294 - accuracy: 0.8802 - val_loss: 0.3616 - val_accuracy: 0.8700\n",
            "Epoch 5/30\n",
            "188/188 [==============================] - 3s 13ms/step - loss: 0.3121 - accuracy: 0.8853 - val_loss: 0.3474 - val_accuracy: 0.8743\n",
            "Epoch 6/30\n",
            "188/188 [==============================] - 2s 13ms/step - loss: 0.2953 - accuracy: 0.8911 - val_loss: 0.3182 - val_accuracy: 0.8865\n",
            "Epoch 7/30\n",
            "188/188 [==============================] - 2s 13ms/step - loss: 0.2796 - accuracy: 0.8971 - val_loss: 0.3228 - val_accuracy: 0.8833\n",
            "Epoch 8/30\n",
            "188/188 [==============================] - 2s 13ms/step - loss: 0.2661 - accuracy: 0.9018 - val_loss: 0.3194 - val_accuracy: 0.8834\n",
            "Epoch 9/30\n",
            "188/188 [==============================] - 3s 18ms/step - loss: 0.2568 - accuracy: 0.9036 - val_loss: 0.3204 - val_accuracy: 0.8848\n",
            "Epoch 10/30\n",
            "188/188 [==============================] - 2s 12ms/step - loss: 0.2474 - accuracy: 0.9086 - val_loss: 0.3222 - val_accuracy: 0.8791\n",
            "Epoch 11/30\n",
            "188/188 [==============================] - 2s 13ms/step - loss: 0.2357 - accuracy: 0.9111 - val_loss: 0.3160 - val_accuracy: 0.8868\n",
            "Epoch 12/30\n",
            "188/188 [==============================] - 2s 12ms/step - loss: 0.2296 - accuracy: 0.9136 - val_loss: 0.3307 - val_accuracy: 0.8809\n",
            "Epoch 13/30\n",
            "188/188 [==============================] - 2s 13ms/step - loss: 0.2259 - accuracy: 0.9154 - val_loss: 0.3057 - val_accuracy: 0.8907\n",
            "Epoch 14/30\n",
            "188/188 [==============================] - 4s 19ms/step - loss: 0.2135 - accuracy: 0.9215 - val_loss: 0.3179 - val_accuracy: 0.8837\n",
            "Epoch 15/30\n",
            "188/188 [==============================] - 2s 12ms/step - loss: 0.2070 - accuracy: 0.9220 - val_loss: 0.3104 - val_accuracy: 0.8930\n",
            "Epoch 16/30\n",
            "188/188 [==============================] - 2s 13ms/step - loss: 0.2009 - accuracy: 0.9255 - val_loss: 0.3293 - val_accuracy: 0.8862\n",
            "Epoch 17/30\n",
            "188/188 [==============================] - 2s 12ms/step - loss: 0.1993 - accuracy: 0.9252 - val_loss: 0.3147 - val_accuracy: 0.8914\n",
            "Epoch 18/30\n",
            "188/188 [==============================] - 3s 15ms/step - loss: 0.1886 - accuracy: 0.9294 - val_loss: 0.3099 - val_accuracy: 0.8928\n",
            "Epoch 19/30\n",
            "188/188 [==============================] - 4s 20ms/step - loss: 0.1820 - accuracy: 0.9324 - val_loss: 0.3098 - val_accuracy: 0.8943\n",
            "Epoch 20/30\n",
            "188/188 [==============================] - 2s 12ms/step - loss: 0.1810 - accuracy: 0.9317 - val_loss: 0.3240 - val_accuracy: 0.8907\n",
            "Epoch 21/30\n",
            "188/188 [==============================] - 2s 12ms/step - loss: 0.1706 - accuracy: 0.9349 - val_loss: 0.3307 - val_accuracy: 0.8898\n",
            "Epoch 22/30\n",
            "188/188 [==============================] - 2s 12ms/step - loss: 0.1717 - accuracy: 0.9345 - val_loss: 0.3317 - val_accuracy: 0.8867\n",
            "Epoch 23/30\n",
            "188/188 [==============================] - 2s 13ms/step - loss: 0.1656 - accuracy: 0.9372 - val_loss: 0.3178 - val_accuracy: 0.8956\n",
            "Epoch 24/30\n",
            "188/188 [==============================] - 4s 20ms/step - loss: 0.1551 - accuracy: 0.9411 - val_loss: 0.3356 - val_accuracy: 0.8912\n",
            "Epoch 25/30\n",
            "188/188 [==============================] - 2s 12ms/step - loss: 0.1490 - accuracy: 0.9434 - val_loss: 0.3177 - val_accuracy: 0.8958\n",
            "Epoch 26/30\n",
            "188/188 [==============================] - 2s 13ms/step - loss: 0.1509 - accuracy: 0.9423 - val_loss: 0.3314 - val_accuracy: 0.8907\n",
            "Epoch 27/30\n",
            "188/188 [==============================] - 3s 18ms/step - loss: 0.1428 - accuracy: 0.9465 - val_loss: 0.3440 - val_accuracy: 0.8918\n",
            "Epoch 28/30\n",
            "188/188 [==============================] - 3s 15ms/step - loss: 0.1394 - accuracy: 0.9472 - val_loss: 0.3530 - val_accuracy: 0.8928\n",
            "Epoch 29/30\n",
            "188/188 [==============================] - 3s 16ms/step - loss: 0.1318 - accuracy: 0.9499 - val_loss: 0.3497 - val_accuracy: 0.8954\n",
            "Epoch 30/30\n",
            "188/188 [==============================] - 2s 12ms/step - loss: 0.1311 - accuracy: 0.9504 - val_loss: 0.3599 - val_accuracy: 0.8934\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def plot_history(history):\n",
        "    hist = pd.DataFrame(history.history)\n",
        "    hist['epoch']=history.epoch\n",
        "\n",
        "    plt.figure(figsize=(16, 8))\n",
        "    plt.subplot(1, 2, 1)\n",
        "    plt.xlabel('Epoch')\n",
        "    plt.ylabel('Loss')\n",
        "    plt.plot(hist['epoch'], hist['loss'], label='Train Loss')\n",
        "    plt.plot(hist['epoch'], hist['val_loss'], label='Val Loss')\n",
        "    plt.legend()\n",
        "\n",
        "    plt.subplot(1, 2, 2)\n",
        "    plt.xlabel('Epoch')\n",
        "    plt.ylabel('Accuracy')\n",
        "    plt.plot(hist['epoch'], hist['accuracy'], label='Train Accuracy')\n",
        "    plt.plot(hist['epoch'], hist['val_accuracy'], label='Val Accuracy')\n",
        "\n",
        "    plt.legend()\n",
        "    plt.show()"
      ],
      "metadata": {
        "id": "3FqMziXKnd4h"
      },
      "execution_count": 57,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plot_history(history)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 652
        },
        "id": "JE8bTUkvneru",
        "outputId": "c125b1dc-effb-4a3c-a64a-3ed9566cf551"
      },
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x800 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = model.predict(X_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DrZuazgfrpkM",
        "outputId": "af25cfaa-2413-4f26-cc83-760e0e5852a3"
      },
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "313/313 [==============================] - 1s 3ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = np.argmax(y_pred, axis=1)"
      ],
      "metadata": {
        "id": "h_OXy93Prpom"
      },
      "execution_count": 60,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_test_o  =to_categorical(y_test)"
      ],
      "metadata": {
        "id": "DNLTmOCrsoZU"
      },
      "execution_count": 61,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.evaluate(X_test, y_test_o)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "T3n2DDQWr84U",
        "outputId": "e64dfca2-5797-48bc-b619-49e76bbdec85"
      },
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "313/313 [==============================] - 1s 3ms/step - loss: 0.3797 - accuracy: 0.8856\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.37965643405914307, 0.8855999708175659]"
            ]
          },
          "metadata": {},
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# CNN"
      ],
      "metadata": {
        "id": "IyPlhuiNtli6"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "이미지와 같은 데이터의 공간적인 특성은 무시된다\n",
        "- 이미지의 특성을 잘 나타내는 필터를 학습시키는 것이 목표\n",
        "\n",
        "> 합성곱 신경망\n",
        "- 이미지의 특징을 추출하여 NN에 적용한다\n",
        "\n",
        "> convolution\n",
        "- 필터라고 생각\n",
        "- 필터의 개수에 다라 z축이 설정된다\n",
        "(ex) input 6x6x3 -> 출력도 마지막z값은 3"
      ],
      "metadata": {
        "id": "7wK0QUXft9JK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = keras.Sequential([\n",
        "    layers.Conv2D(filters=32, kernel_size=(3,3), activation='relu', input_shape=(28,28,1)),\n",
        "    layers.MaxPool2D(pool_size=2),\n",
        "\n",
        "    layers.Conv2D(filters=64, kernel_size=(3,3), activation='relu'),\n",
        "    layers.MaxPool2D(pool_size=2),\n",
        "    layers.Flatten(),\n",
        "    layers.Dense(units=32, activation='relu'),\n",
        "    layers.Dense(units=10, activation='softmax')\n",
        "])"
      ],
      "metadata": {
        "id": "utieIZ22yOg8"
      },
      "execution_count": 63,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.compile(\n",
        "    optimizer='adam',\n",
        "    loss='categorical_crossentropy',\n",
        "    metrics=['accuracy']\n",
        ")"
      ],
      "metadata": {
        "id": "YtO7ruD78zKD"
      },
      "execution_count": 64,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "EPOCHS = 30\n",
        "BATCH = 256\n",
        "\n",
        "history = model.fit(\n",
        "    X_train, y_train_o,\n",
        "    epochs = EPOCHS,\n",
        "    batch_size = BATCH,\n",
        "    validation_data =(X_val, y_val_o),\n",
        "    verbose = 1\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 684
        },
        "id": "oLt_bsXr85YD",
        "outputId": "43a490a5-2062-4b01-898d-fe09530ea42a"
      },
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/30\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-65-5443ff59c540>\u001b[0m in \u001b[0;36m<cell line: 4>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mBATCH\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m256\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m history = model.fit(\n\u001b[0m\u001b[1;32m      5\u001b[0m     \u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train_o\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m     \u001b[0mepochs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mEPOCHS\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/keras/utils/traceback_utils.py\u001b[0m in \u001b[0;36merror_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     68\u001b[0m             \u001b[0;31m# To get the full stack trace, call:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     69\u001b[0m             \u001b[0;31m# `tf.debugging.disable_traceback_filtering()`\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 70\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwith_traceback\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfiltered_tb\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     71\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     72\u001b[0m             \u001b[0;32mdel\u001b[0m \u001b[0mfiltered_tb\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/keras/engine/training.py\u001b[0m in \u001b[0;36mtf__train_function\u001b[0;34m(iterator)\u001b[0m\n\u001b[1;32m     13\u001b[0m                 \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     14\u001b[0m                     \u001b[0mdo_return\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 15\u001b[0;31m                     \u001b[0mretval_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mag__\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconverted_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mag__\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mld\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstep_function\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mag__\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mld\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mag__\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mld\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miterator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfscope\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     16\u001b[0m                 \u001b[0;32mexcept\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     17\u001b[0m                     \u001b[0mdo_return\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: in user code:\n\n    File \"/usr/local/lib/python3.10/dist-packages/keras/engine/training.py\", line 1284, in train_function  *\n        return step_function(self, iterator)\n    File \"/usr/local/lib/python3.10/dist-packages/keras/engine/training.py\", line 1268, in step_function  **\n        outputs = model.distribute_strategy.run(run_step, args=(data,))\n    File \"/usr/local/lib/python3.10/dist-packages/keras/engine/training.py\", line 1249, in run_step  **\n        outputs = model.train_step(data)\n    File \"/usr/local/lib/python3.10/dist-packages/keras/engine/training.py\", line 1050, in train_step\n        y_pred = self(x, training=True)\n    File \"/usr/local/lib/python3.10/dist-packages/keras/utils/traceback_utils.py\", line 70, in error_handler\n        raise e.with_traceback(filtered_tb) from None\n    File \"/usr/local/lib/python3.10/dist-packages/keras/engine/input_spec.py\", line 298, in assert_input_compatibility\n        raise ValueError(\n\n    ValueError: Input 0 of layer \"sequential_4\" is incompatible with the layer: expected shape=(None, 28, 28, 1), found shape=(None, 784)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = model.predict(X_test)"
      ],
      "metadata": {
        "id": "BApTAMfA-Z8Q"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = np.argmax(y_pred, axis=1)"
      ],
      "metadata": {
        "id": "ggOdzz_2-aXL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_test_o  =to_categorical(y_test)"
      ],
      "metadata": {
        "id": "vVJt4Eln-aab"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.evaluate(X_test, y_test_o)"
      ],
      "metadata": {
        "id": "3bqVw1Ic-eYf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# cnn-cifar"
      ],
      "metadata": {
        "id": "paYZG-dE-xH3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import tensorflow as tf"
      ],
      "metadata": {
        "id": "Ox6L7dso-5cY"
      },
      "execution_count": 66,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.datasets import cifar10\n",
        "\n",
        "(X_train, y_train), (X_test,y_test) = cifar10.load_data()"
      ],
      "metadata": {
        "id": "x2IBKQiu_nT4"
      },
      "execution_count": 159,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RGEZjgDdDGjR",
        "outputId": "315365c6-ddeb-4e2f-fd1c-f0f89f572b57"
      },
      "execution_count": 160,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(50000, 32, 32, 3)"
            ]
          },
          "metadata": {},
          "execution_count": 160
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_train= y_train.reshape(-1)\n",
        "y_test= y_test.reshape(-1)"
      ],
      "metadata": {
        "id": "eKywyqnbBWJk"
      },
      "execution_count": 161,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class_names = ['airplane','automobile','bird','cat','deer','dog','frog','horse','ship','truck']"
      ],
      "metadata": {
        "id": "8zLmT92R_92A"
      },
      "execution_count": 162,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "samples = np.random.randint(50000, size=9)\n",
        "samples"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PXMjxIOeA5qS",
        "outputId": "bf0cb544-8553-4861-b87a-24ca2a567b51"
      },
      "execution_count": 163,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([15129, 22401, 14181, 14381, 34347, 39966, 11733,  8613, 18117])"
            ]
          },
          "metadata": {},
          "execution_count": 163
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(8,8))\n",
        "for i, idx in enumerate(samples):\n",
        "    plt.subplot(3, 3, i+1)\n",
        "    plt.imshow(X_train[idx])\n",
        "    plt.axis('off')\n",
        "    plt.title(class_names[y_train[idx]])\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 675
        },
        "id": "GstwMp7PBC_Y",
        "outputId": "9a91fd19-4cdb-49a8-8eeb-f07a22fbabc9"
      },
      "execution_count": 164,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x800 with 9 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_val, y_train, y_val = train_test_split(X_train, y_train, test_size=0.2, random_state=42)"
      ],
      "metadata": {
        "id": "RmR3ETnOB--3"
      },
      "execution_count": 165,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 이미지데이터 스케일링\n",
        "#타켓값 one_hot encoding\n",
        "# 모델만들기, 컴파일 학습\n",
        "# 그래프\n",
        "# 예측\n",
        "# 정확도, confusion matrix"
      ],
      "metadata": {
        "id": "hD3y_ecPCK3U"
      },
      "execution_count": 136,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train = X_train/225.\n",
        "X_val = X_val/255.\n",
        "X_test = X_test/255."
      ],
      "metadata": {
        "id": "jlo7z-j4CoA2"
      },
      "execution_count": 166,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from keras.utils import to_categorical\n",
        "\n",
        "y_train_o =  to_categorical(y_train)\n",
        "y_val_o = to_categorical(y_val)"
      ],
      "metadata": {
        "id": "77qmZzuECtAw"
      },
      "execution_count": 167,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_train_o.shape, y_val_o.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zKK1vnX5CzMD",
        "outputId": "48cfa51c-9182-48d4-83a4-e769830a1022"
      },
      "execution_count": 168,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((40000, 10), (10000, 10))"
            ]
          },
          "metadata": {},
          "execution_count": 168
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_val.shape, X_train.shape,X_test.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "s9dUD9stDM0V",
        "outputId": "95368df0-ffb7-4ee6-ed6d-f36fa9cab5c1"
      },
      "execution_count": 169,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((10000, 32, 32, 3), (40000, 32, 32, 3), (10000, 32, 32, 3))"
            ]
          },
          "metadata": {},
          "execution_count": 169
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_val = X_val.reshape(-1, 32,32,3)\n",
        "X_test = X_test.reshape(-1, 32,32,3)\n",
        "X_train = X_train.reshape(-1, 32,32,3)\n",
        "X_val.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rkZG_XFcC1u2",
        "outputId": "046bd848-d9a8-417d-a2fc-6196eae34317"
      },
      "execution_count": 170,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(10000, 32, 32, 3)"
            ]
          },
          "metadata": {},
          "execution_count": 170
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model = keras.Sequential([\n",
        "    layers.Conv2D(filters=32, kernel_size=(3,3), activation='relu', input_shape=(32,32,3)),\n",
        "    layers.MaxPool2D(pool_size=2),\n",
        "\n",
        "    layers.Conv2D(filters=64, kernel_size=(3,3), activation='relu'),\n",
        "    layers.MaxPool2D(pool_size=2),\n",
        "    layers.Flatten(),\n",
        "    layers.Dense(units=32, activation='relu'),\n",
        "    layers.Dense(units=10, activation='softmax')\n",
        "])"
      ],
      "metadata": {
        "id": "usbuVWwIDV8R"
      },
      "execution_count": 171,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.compile(\n",
        "    optimizer='adam',\n",
        "    loss='categorical_crossentropy',\n",
        "    metrics=['accuracy']\n",
        ")"
      ],
      "metadata": {
        "id": "s0gEpfnhDbzD"
      },
      "execution_count": 172,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "EPOCHS = 30\n",
        "BATCH = 256\n",
        "\n",
        "history = model.fit(\n",
        "    X_train, y_train_o,\n",
        "    epochs = EPOCHS,\n",
        "    batch_size = BATCH,\n",
        "    validation_data =(X_val, y_val_o),\n",
        "    verbose = 1\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fEUK0SHSDgKq",
        "outputId": "e12e42dc-0244-4c95-815d-08f3322d916c"
      },
      "execution_count": 173,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/30\n",
            "157/157 [==============================] - 57s 357ms/step - loss: 1.9369 - accuracy: 0.2850 - val_loss: 1.6697 - val_accuracy: 0.3925\n",
            "Epoch 2/30\n",
            "157/157 [==============================] - 56s 356ms/step - loss: 1.5386 - accuracy: 0.4432 - val_loss: 1.4767 - val_accuracy: 0.4737\n",
            "Epoch 3/30\n",
            "157/157 [==============================] - 60s 380ms/step - loss: 1.4107 - accuracy: 0.4975 - val_loss: 1.4124 - val_accuracy: 0.4989\n",
            "Epoch 4/30\n",
            "157/157 [==============================] - 55s 350ms/step - loss: 1.3325 - accuracy: 0.5265 - val_loss: 1.3347 - val_accuracy: 0.5283\n",
            "Epoch 5/30\n",
            "157/157 [==============================] - 54s 343ms/step - loss: 1.2689 - accuracy: 0.5531 - val_loss: 1.2625 - val_accuracy: 0.5552\n",
            "Epoch 6/30\n",
            "157/157 [==============================] - 54s 344ms/step - loss: 1.2216 - accuracy: 0.5711 - val_loss: 1.2678 - val_accuracy: 0.5623\n",
            "Epoch 7/30\n",
            "157/157 [==============================] - 54s 342ms/step - loss: 1.1778 - accuracy: 0.5888 - val_loss: 1.2222 - val_accuracy: 0.5733\n",
            "Epoch 8/30\n",
            "157/157 [==============================] - 54s 342ms/step - loss: 1.1358 - accuracy: 0.6041 - val_loss: 1.1705 - val_accuracy: 0.5925\n",
            "Epoch 9/30\n",
            "157/157 [==============================] - 54s 342ms/step - loss: 1.1021 - accuracy: 0.6184 - val_loss: 1.1615 - val_accuracy: 0.5904\n",
            "Epoch 10/30\n",
            "157/157 [==============================] - 54s 347ms/step - loss: 1.0719 - accuracy: 0.6266 - val_loss: 1.1478 - val_accuracy: 0.5998\n",
            "Epoch 11/30\n",
            "157/157 [==============================] - 56s 356ms/step - loss: 1.0379 - accuracy: 0.6422 - val_loss: 1.0812 - val_accuracy: 0.6228\n",
            "Epoch 12/30\n",
            "157/157 [==============================] - 56s 355ms/step - loss: 1.0197 - accuracy: 0.6473 - val_loss: 1.0842 - val_accuracy: 0.6252\n",
            "Epoch 13/30\n",
            "157/157 [==============================] - 62s 394ms/step - loss: 0.9954 - accuracy: 0.6560 - val_loss: 1.0763 - val_accuracy: 0.6222\n",
            "Epoch 14/30\n",
            "157/157 [==============================] - 60s 385ms/step - loss: 0.9758 - accuracy: 0.6619 - val_loss: 1.0514 - val_accuracy: 0.6373\n",
            "Epoch 15/30\n",
            "157/157 [==============================] - 58s 372ms/step - loss: 0.9603 - accuracy: 0.6681 - val_loss: 1.0730 - val_accuracy: 0.6177\n",
            "Epoch 16/30\n",
            "157/157 [==============================] - 54s 344ms/step - loss: 0.9541 - accuracy: 0.6701 - val_loss: 1.0151 - val_accuracy: 0.6450\n",
            "Epoch 17/30\n",
            "157/157 [==============================] - 54s 343ms/step - loss: 0.9252 - accuracy: 0.6827 - val_loss: 1.0271 - val_accuracy: 0.6448\n",
            "Epoch 18/30\n",
            "157/157 [==============================] - 56s 357ms/step - loss: 0.9133 - accuracy: 0.6859 - val_loss: 1.0519 - val_accuracy: 0.6326\n",
            "Epoch 19/30\n",
            "157/157 [==============================] - 75s 476ms/step - loss: 0.8974 - accuracy: 0.6903 - val_loss: 1.0128 - val_accuracy: 0.6469\n",
            "Epoch 20/30\n",
            "157/157 [==============================] - 71s 451ms/step - loss: 0.8842 - accuracy: 0.6949 - val_loss: 0.9929 - val_accuracy: 0.6559\n",
            "Epoch 21/30\n",
            "157/157 [==============================] - 54s 346ms/step - loss: 0.8721 - accuracy: 0.6985 - val_loss: 1.0355 - val_accuracy: 0.6341\n",
            "Epoch 22/30\n",
            "157/157 [==============================] - 57s 360ms/step - loss: 0.8627 - accuracy: 0.7016 - val_loss: 1.0103 - val_accuracy: 0.6486\n",
            "Epoch 23/30\n",
            "157/157 [==============================] - 61s 390ms/step - loss: 0.8436 - accuracy: 0.7087 - val_loss: 0.9915 - val_accuracy: 0.6562\n",
            "Epoch 24/30\n",
            "157/157 [==============================] - 83s 532ms/step - loss: 0.8354 - accuracy: 0.7118 - val_loss: 1.0051 - val_accuracy: 0.6496\n",
            "Epoch 25/30\n",
            "157/157 [==============================] - 62s 393ms/step - loss: 0.8317 - accuracy: 0.7120 - val_loss: 0.9606 - val_accuracy: 0.6655\n",
            "Epoch 26/30\n",
            "157/157 [==============================] - 55s 353ms/step - loss: 0.8117 - accuracy: 0.7200 - val_loss: 0.9623 - val_accuracy: 0.6668\n",
            "Epoch 27/30\n",
            "157/157 [==============================] - 65s 416ms/step - loss: 0.8082 - accuracy: 0.7221 - val_loss: 0.9907 - val_accuracy: 0.6529\n",
            "Epoch 28/30\n",
            "157/157 [==============================] - 65s 416ms/step - loss: 0.7982 - accuracy: 0.7251 - val_loss: 0.9568 - val_accuracy: 0.6681\n",
            "Epoch 29/30\n",
            "157/157 [==============================] - 64s 408ms/step - loss: 0.7915 - accuracy: 0.7260 - val_loss: 0.9818 - val_accuracy: 0.6534\n",
            "Epoch 30/30\n",
            "157/157 [==============================] - 67s 430ms/step - loss: 0.7808 - accuracy: 0.7318 - val_loss: 0.9760 - val_accuracy: 0.6587\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def plot_history(history):\n",
        "    hist = pd.DataFrame(history.history)\n",
        "    hist['epoch']=history.epoch\n",
        "\n",
        "    plt.figure(figsize=(16, 8))\n",
        "    plt.subplot(1, 2, 1)\n",
        "    plt.xlabel('Epoch')\n",
        "    plt.ylabel('Loss')\n",
        "    plt.plot(hist['epoch'], hist['loss'], label='Train Loss')\n",
        "    plt.plot(hist['epoch'], hist['val_loss'], label='Val Loss')\n",
        "    plt.legend()\n",
        "\n",
        "    plt.subplot(1, 2, 2)\n",
        "    plt.xlabel('Epoch')\n",
        "    plt.ylabel('Accuracy')\n",
        "    plt.plot(hist['epoch'], hist['accuracy'], label='Train Accuracy')\n",
        "    plt.plot(hist['epoch'], hist['val_accuracy'], label='Val Accuracy')\n",
        "\n",
        "    plt.legend()\n",
        "    plt.show()"
      ],
      "metadata": {
        "id": "MPBscazSD1ZI"
      },
      "execution_count": 174,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plot_history(history)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 652
        },
        "id": "ZHd0QgW_D2Cq",
        "outputId": "3f16f9c5-4428-46ab-8bb1-b02d58f02414"
      },
      "execution_count": 175,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x800 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = model.predict(X_test)\n",
        "y_pred = np.argmax(y_pred, axis=1)\n",
        "y_test_o  =to_categorical(y_test)\n",
        "model.evaluate(X_test, y_test_o)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "D2o2FtEAEAWz",
        "outputId": "8240db18-8ed9-401e-e5f0-8237c88eaffe"
      },
      "execution_count": 176,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "313/313 [==============================] - 4s 13ms/step\n",
            "313/313 [==============================] - 4s 13ms/step - loss: 0.9861 - accuracy: 0.6603\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.9860618114471436, 0.6603000164031982]"
            ]
          },
          "metadata": {},
          "execution_count": 176
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Early stopping"
      ],
      "metadata": {
        "id": "gejInBqgKxCW"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### callback\n",
        "어떠한 조건을 달성 하면 학습을 조기 종료한다"
      ],
      "metadata": {
        "id": "bN4FMESnLJiT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = keras.Sequential([\n",
        "    layers.Conv2D(filters=32, kernel_size=(3,3), activation='relu', input_shape=(32,32,3)),\n",
        "    layers.MaxPool2D(pool_size=2),\n",
        "\n",
        "    layers.Conv2D(filters=64, kernel_size=(3,3), activation='relu'),\n",
        "    layers.MaxPool2D(pool_size=2),\n",
        "    layers.Flatten(),\n",
        "    layers.Dense(units=32, activation='relu'),\n",
        "    layers.Dense(units=10, activation='softmax')\n",
        "])"
      ],
      "metadata": {
        "id": "TGzNf4UoKzyZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.compile(\n",
        "    optimizer='adam',\n",
        "    loss='categorical_crossentropy',\n",
        "    metrics=['accuracy']\n",
        ")"
      ],
      "metadata": {
        "id": "Hm_AfCo3K8V9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from keras.callbacks import EarlyStopping, ModelCheckpoint\n",
        "\n",
        "es = EarlyStopping(monitor='val_loss', patience=5)\n",
        "ckpt = './ckpt/cifar10.ckpt'\n",
        "mc = ModelCheckpoint(ckpt, monitor ='val_loss', save_weights_only=True, save_best_only=True, verbose=1)"
      ],
      "metadata": {
        "id": "7Pu7bhkHMCg_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "EPOCHS = 30\n",
        "BATCH = 256\n",
        "\n",
        "history = model.fit(\n",
        "    X_train, y_train_o,\n",
        "    epochs = EPOCHS,\n",
        "    batch_size = BATCH,\n",
        "    validation_data =(X_val, y_val_o),\n",
        "    callbacks=[es, mc],\n",
        "    verbose = 1\n",
        ")"
      ],
      "metadata": {
        "id": "7pqZ7NXYK_OC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.load_weights(ckpt)"
      ],
      "metadata": {
        "id": "t8u-4ycBOI3R"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.evaluate(X_test, y_test_o)"
      ],
      "metadata": {
        "id": "39u-wnrVOZ7_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# reduce\n",
        "from keras.callbacks import EarlyStopping, ModelCheckpoint, ReduceLROnPlateau\n",
        "\n",
        "es = EarlyStopping(monitor='val_loss', patience=5)\n",
        "ckpt = './ckpt/cifar10.ckpt'\n",
        "mc = ModelCheckpoint(ckpt, monitor ='val_loss', save_weights_only=True, save_best_only=True, verbose=1)\n",
        "\n",
        "reduce_lr = ReduceLROnPlateau(monitor)"
      ],
      "metadata": {
        "id": "2VpQSaCDOhgn"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}