material/deep_learning/cnn_cifar10.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "cnn_cifar10.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c503DSvxvOU5"
      },
      "source": [
        "# 이전 CNN 코드"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pH--ADDvhXQM"
      },
      "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, BatchNormalization, Input, Reshape\n",
        "\n",
        "import time"
      ],
      "execution_count": null,
      "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_y = raw_train_y\n",
        "test_y = raw_test_y"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cuW2ftNT2trB",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f157d035-4f8f-46e9-82fd-8170d86d9cfe"
      },
      "source": [
        "model = keras.Sequential()\n",
        "model.add(Input((28,28)))\n",
        "model.add(Reshape((28,28,1)))\n",
        "model.add(Conv2D(32, (3, 3), padding='same'))\n",
        "model.add(MaxPooling2D((2, 2)))\n",
        "model.add(Conv2D(64, (3, 3), padding='same'))\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",
        "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": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_31\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "reshape_4 (Reshape)          (None, 28, 28, 1)         0         \n",
            "_________________________________________________________________\n",
            "conv2d_62 (Conv2D)           (None, 28, 28, 32)        320       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_62 (MaxPooling (None, 14, 14, 32)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_63 (Conv2D)           (None, 14, 14, 64)        18496     \n",
            "_________________________________________________________________\n",
            "max_pooling2d_63 (MaxPooling (None, 7, 7, 64)          0         \n",
            "_________________________________________________________________\n",
            "flatten_31 (Flatten)         (None, 3136)              0         \n",
            "_________________________________________________________________\n",
            "dense_93 (Dense)             (None, 10)                31370     \n",
            "_________________________________________________________________\n",
            "dense_94 (Dense)             (None, 10)                110       \n",
            "_________________________________________________________________\n",
            "dense_95 (Dense)             (None, 10)                110       \n",
            "=================================================================\n",
            "Total params: 50,406\n",
            "Trainable params: 50,406\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "Epoch 1/5\n",
            "469/469 [==============================] - 2s 4ms/step - loss: 0.4971 - accuracy: 0.8418\n",
            "Epoch 2/5\n",
            "469/469 [==============================] - 2s 3ms/step - loss: 0.1279 - accuracy: 0.9625\n",
            "Epoch 3/5\n",
            "469/469 [==============================] - 2s 4ms/step - loss: 0.0887 - accuracy: 0.9733\n",
            "Epoch 4/5\n",
            "469/469 [==============================] - 2s 4ms/step - loss: 0.0733 - accuracy: 0.9777\n",
            "Epoch 5/5\n",
            "469/469 [==============================] - 2s 3ms/step - loss: 0.0615 - accuracy: 0.9807\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 0.0637 - accuracy: 0.9818\n",
            "loss= 0.06371552497148514\n",
            "acc= 0.9818000197410583\n",
            "[7 2 1 ... 4 5 6]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "54-uB1LWA-5R"
      },
      "source": [
        "# CIFAR10 적용\n",
        "\n",
        "refered https://www.cs.toronto.edu/~kriz/cifar.html\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zHPDKioauhfZ"
      },
      "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, BatchNormalization, Input, Reshape\n",
        "\n",
        "import time"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oPclK8DGA_gw",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1904e52f-dd8b-4966-f2ee-6c800a850d44"
      },
      "source": [
        "# (raw_train_x, raw_train_y), (raw_test_x, raw_test_y) = tf.keras.datasets.mnist.load_data()\n",
        "(raw_train_x, raw_train_y), (raw_test_x, raw_test_y) = tf.keras.datasets.cifar10.load_data()\n",
        "\n",
        "print(raw_train_x.shape)\n",
        "print(raw_train_y.shape)\n",
        "print(raw_test_x.shape)\n",
        "print(raw_test_y.shape)\n",
        "\n",
        "\n",
        "train_x = raw_train_x/255.\n",
        "test_x = raw_test_x/255.\n",
        "\n",
        "train_y = raw_train_y\n",
        "test_y = raw_test_y"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(50000, 32, 32, 3)\n",
            "(50000, 1)\n",
            "(10000, 32, 32, 3)\n",
            "(10000, 1)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SrbJDeptkD3j",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7d54518d-573e-463a-b731-3d8209cbfde2"
      },
      "source": [
        "print(raw_train_x[0])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[[ 59  62  63]\n",
            "  [ 43  46  45]\n",
            "  [ 50  48  43]\n",
            "  ...\n",
            "  [158 132 108]\n",
            "  [152 125 102]\n",
            "  [148 124 103]]\n",
            "\n",
            " [[ 16  20  20]\n",
            "  [  0   0   0]\n",
            "  [ 18   8   0]\n",
            "  ...\n",
            "  [123  88  55]\n",
            "  [119  83  50]\n",
            "  [122  87  57]]\n",
            "\n",
            " [[ 25  24  21]\n",
            "  [ 16   7   0]\n",
            "  [ 49  27   8]\n",
            "  ...\n",
            "  [118  84  50]\n",
            "  [120  84  50]\n",
            "  [109  73  42]]\n",
            "\n",
            " ...\n",
            "\n",
            " [[208 170  96]\n",
            "  [201 153  34]\n",
            "  [198 161  26]\n",
            "  ...\n",
            "  [160 133  70]\n",
            "  [ 56  31   7]\n",
            "  [ 53  34  20]]\n",
            "\n",
            " [[180 139  96]\n",
            "  [173 123  42]\n",
            "  [186 144  30]\n",
            "  ...\n",
            "  [184 148  94]\n",
            "  [ 97  62  34]\n",
            "  [ 83  53  34]]\n",
            "\n",
            " [[177 144 116]\n",
            "  [168 129  94]\n",
            "  [179 142  87]\n",
            "  ...\n",
            "  [216 184 140]\n",
            "  [151 118  84]\n",
            "  [123  92  72]]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lNy4AUQDylpU",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "39ee87ed-6fb7-4ef8-e480-44a61e1c2916"
      },
      "source": [
        "labels = [\"airplane\", \"automobile\", \"bird\", \"cat\", \"deer\", \"dog\", \"frog\", \"horse\", \"ship\", \"truck\"]\n",
        "\n",
        "def show_sample(i):\n",
        "  print(raw_train_y[i][0], labels[raw_train_y[i][0]])\n",
        "  plt.imshow(raw_train_x[i])\n",
        "  plt.show()\n",
        "\n",
        "for i in [2, 10, 12, 14]:\n",
        "  show_sample(i)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "9 truck\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "4 deer\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "7 horse\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "9 truck\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "om_gEsuDA_jp",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b420b024-436a-4e71-8ef5-0337335a178c"
      },
      "source": [
        "model = keras.Sequential()\n",
        "# model.add(Input((28,28)))\n",
        "# model.add(Reshape((28,28,1)))\n",
        "model.add(Input((32,32,3)))\n",
        "model.add(Conv2D(32, (3, 3), padding='same'))\n",
        "model.add(MaxPooling2D((2, 2)))\n",
        "model.add(Conv2D(64, (3, 3), padding='same'))\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",
        "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": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_25\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d_50 (Conv2D)           (None, 32, 32, 32)        896       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_50 (MaxPooling (None, 16, 16, 32)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_51 (Conv2D)           (None, 16, 16, 64)        18496     \n",
            "_________________________________________________________________\n",
            "max_pooling2d_51 (MaxPooling (None, 8, 8, 64)          0         \n",
            "_________________________________________________________________\n",
            "flatten_25 (Flatten)         (None, 4096)              0         \n",
            "_________________________________________________________________\n",
            "dense_75 (Dense)             (None, 10)                40970     \n",
            "_________________________________________________________________\n",
            "dense_76 (Dense)             (None, 10)                110       \n",
            "_________________________________________________________________\n",
            "dense_77 (Dense)             (None, 10)                110       \n",
            "=================================================================\n",
            "Total params: 60,582\n",
            "Trainable params: 60,582\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "Epoch 1/5\n",
            "79/79 [==============================] - 1s 4ms/step - loss: 2.2262 - accuracy: 0.1663\n",
            "Epoch 2/5\n",
            "79/79 [==============================] - 0s 4ms/step - loss: 2.0485 - accuracy: 0.2346\n",
            "Epoch 3/5\n",
            "79/79 [==============================] - 0s 4ms/step - loss: 1.9633 - accuracy: 0.2780\n",
            "Epoch 4/5\n",
            "79/79 [==============================] - 0s 4ms/step - loss: 1.9069 - accuracy: 0.3038\n",
            "Epoch 5/5\n",
            "79/79 [==============================] - 0s 4ms/step - loss: 1.8266 - accuracy: 0.3369\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 1.7722 - accuracy: 0.3526\n",
            "loss= 1.7722152471542358\n",
            "acc= 0.35260000824928284\n",
            "[3 8 8 ... 3 1 7]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6Oj5QSsQ_EZu"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}