material/deep_learning/template_image_data_vanilla_cnn_classification.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Sv47DFcMmPRe"
      },
      "source": [
        "# 영상 데이터 분류 by vanilla CNN template"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "W7Fp4BNNcO8K"
      },
      "source": [
        "# 데이터 준비\n",
        "\n",
        "\n",
        "<br>\n",
        "\n",
        "데이터는 다음과 같이 구성됨\n",
        "\n",
        "```\n",
        "dogs/\n",
        "    train/\n",
        "        class1/\n",
        "            file1.jpg\n",
        "            file2.jpg\n",
        "             ...\n",
        "        class2/ \n",
        "            file21.jpg\n",
        "            file22.jpg\n",
        "             ...    \n",
        "        class3/\n",
        "            file31.jpg\n",
        "            file32.jpg\n",
        "             ...\n",
        "        ...\n",
        "    test/\n",
        "        class1/\n",
        "            file8.jpg\n",
        "            file9.jpg\n",
        "             ...\n",
        "        class2/ \n",
        "            file28.jpg\n",
        "            file29.jpg\n",
        "             ...    \n",
        "        class3/\n",
        "            file38.jpg\n",
        "            file39.jpg\n",
        "             ...\n",
        "        ...\n",
        "    \n",
        "```"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ywPDjMh0cSXA",
        "outputId": "29cc3b73-54b1-4c8c-dc3c-4bd4ccc6ca89"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "--2021-08-25 13:13:12--  https://github.com/dhrim/MDC_2021/raw/master/material/deep_learning/dogs_prepared.tar.gz\n",
            "Resolving github.com (github.com)... 140.82.121.4\n",
            "Connecting to github.com (github.com)|140.82.121.4|:443... connected.\n",
            "HTTP request sent, awaiting response... 302 Found\n",
            "Location: https://raw.githubusercontent.com/dhrim/MDC_2021/master/material/deep_learning/dogs_prepared.tar.gz [following]\n",
            "--2021-08-25 13:13:12--  https://raw.githubusercontent.com/dhrim/MDC_2021/master/material/deep_learning/dogs_prepared.tar.gz\n",
            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n",
            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 62898132 (60M) [application/octet-stream]\n",
            "Saving to: ‘dogs_prepared.tar.gz’\n",
            "\n",
            "dogs_prepared.tar.g 100%[===================>]  59.98M   271MB/s    in 0.2s    \n",
            "\n",
            "2021-08-25 13:13:14 (271 MB/s) - ‘dogs_prepared.tar.gz’ saved [62898132/62898132]\n",
            "\n",
            "total 61440\n",
            "drwxr-xr-x 1 root root     4096 Aug 25 13:13 .\n",
            "drwxr-xr-x 1 root root     4096 Aug 25 13:12 ..\n",
            "drwxr-xr-x 4 root root     4096 Aug 13 13:34 .config\n",
            "-rw-r--r-- 1 root root 62898132 Aug 25 13:13 dogs_prepared.tar.gz\n",
            "drwxr-xr-x 1 root root     4096 Aug 13 13:35 sample_data\n"
          ]
        }
      ],
      "source": [
        "!rm -rf dogs_prepared.tar.gz\n",
        "!wget https://github.com/dhrim/MDC_2021/raw/master/material/deep_learning/dogs_prepared.tar.gz\n",
        "!ls -al  "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kNsgn_iUjSrr",
        "outputId": "1d546773-d143-4087-af32-8bff0dbe7e94"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "dogs_prepared/\n",
            "dogs_prepared/._test\n",
            "dogs_prepared/test/\n",
            "dogs_prepared/train/\n",
            "dogs_prepared/train/n02085620-Chihuahua/\n",
            "dogs_prepared/train/n02085782-Japanese_spaniel/\n",
            "dogs_prepared/train/n02085936-Maltese_dog/\n",
            "dogs_prepared/train/n02086079-Pekinese/\n",
            "dogs_prepared/train/n02086240-Shih-Tzu/\n",
            "dogs_prepared/train/n02086646-Blenheim_spaniel/\n",
            "dogs_prepared/train/n02086910-papillon/\n",
            "dogs_prepared/train/n02087046-toy_terrier/\n",
            "dogs_prepared/train/n02087394-Rhodesian_ridgeback/\n",
            "dogs_prepared/train/n02088094-Afghan_hound/\n",
            "dogs_prepared/train/n02088094-Afghan_hound/n02088094_1003.jpg\n",
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            "dogs_prepared/test/n02085782-Japanese_spaniel/n02085782_4698.jpg\n",
            "dogs_prepared/test/n02085782-Japanese_spaniel/n02085782_4772.jpg\n",
            "dogs_prepared/test/n02085782-Japanese_spaniel/n02085782_4780.jpg\n",
            "dogs_prepared/test/n02085782-Japanese_spaniel/n02085782_4798.jpg\n",
            "dogs_prepared/test/n02085782-Japanese_spaniel/n02085782_50.jpg\n",
            "dogs_prepared/test/n02085782-Japanese_spaniel/n02085782_516.jpg\n",
            "dogs_prepared/test/n02085782-Japanese_spaniel/n02085782_518.jpg\n",
            "dogs_prepared/test/n02085782-Japanese_spaniel/n02085782_564.jpg\n",
            "dogs_prepared/test/n02085782-Japanese_spaniel/n02085782_572.jpg\n",
            "dogs_prepared/test/n02085782-Japanese_spaniel/n02085782_593.jpg\n",
            "dogs_prepared/test/n02085782-Japanese_spaniel/n02085782_622.jpg\n",
            "dogs_prepared/test/n02085782-Japanese_spaniel/n02085782_626.jpg\n",
            "dogs_prepared/test/n02085782-Japanese_spaniel/n02085782_664.jpg\n",
            "dogs_prepared/test/n02085782-Japanese_spaniel/n02085782_665.jpg\n",
            "dogs_prepared/test/n02085782-Japanese_spaniel/n02085782_668.jpg\n",
            "dogs_prepared/test/n02085782-Japanese_spaniel/n02085782_676.jpg\n",
            "dogs_prepared/test/n02085782-Japanese_spaniel/n02085782_697.jpg\n",
            "dogs_prepared/test/n02085782-Japanese_spaniel/n02085782_698.jpg\n",
            "dogs_prepared/test/n02085782-Japanese_spaniel/n02085782_725.jpg\n",
            "dogs_prepared/test/n02085782-Japanese_spaniel/n02085782_749.jpg\n",
            "dogs_prepared/test/n02085782-Japanese_spaniel/n02085782_754.jpg\n",
            "dogs_prepared/test/n02085782-Japanese_spaniel/n02085782_757.jpg\n",
            "dogs_prepared/test/n02085782-Japanese_spaniel/n02085782_80.jpg\n",
            "dogs_prepared/test/n02085782-Japanese_spaniel/n02085782_806.jpg\n",
            "dogs_prepared/test/n02085782-Japanese_spaniel/n02085782_810.jpg\n",
            "dogs_prepared/test/n02085782-Japanese_spaniel/n02085782_82.jpg\n",
            "dogs_prepared/test/n02085782-Japanese_spaniel/n02085782_845.jpg\n",
            "dogs_prepared/test/n02085782-Japanese_spaniel/n02085782_866.jpg\n",
            "dogs_prepared/test/n02085782-Japanese_spaniel/n02085782_874.jpg\n",
            "dogs_prepared/test/n02085782-Japanese_spaniel/n02085782_919.jpg\n",
            "dogs_prepared/test/n02085782-Japanese_spaniel/n02085782_935.jpg\n",
            "dogs_prepared/test/n02085782-Japanese_spaniel/n02085782_940.jpg\n",
            "dogs_prepared/test/n02085782-Japanese_spaniel/n02085782_962.jpg\n",
            "dogs_prepared/test/n02085620-Chihuahua/n02085620_588.jpg\n",
            "dogs_prepared/test/n02085620-Chihuahua/n02085620_5927.jpg\n",
            "dogs_prepared/test/n02085620-Chihuahua/n02085620_6295.jpg\n",
            "dogs_prepared/test/n02085620-Chihuahua/n02085620_6399.jpg\n",
            "dogs_prepared/test/n02085620-Chihuahua/n02085620_6931.jpg\n",
            "dogs_prepared/test/n02085620-Chihuahua/n02085620_7.jpg\n",
            "dogs_prepared/test/n02085620-Chihuahua/n02085620_712.jpg\n",
            "dogs_prepared/test/n02085620-Chihuahua/n02085620_7292.jpg\n",
            "dogs_prepared/test/n02085620-Chihuahua/n02085620_730.jpg\n",
            "dogs_prepared/test/n02085620-Chihuahua/n02085620_735.jpg\n",
            "dogs_prepared/test/n02085620-Chihuahua/n02085620_7436.jpg\n",
            "dogs_prepared/test/n02085620-Chihuahua/n02085620_7440.jpg\n",
            "dogs_prepared/test/n02085620-Chihuahua/n02085620_7613.jpg\n",
            "dogs_prepared/test/n02085620-Chihuahua/n02085620_7700.jpg\n",
            "dogs_prepared/test/n02085620-Chihuahua/n02085620_7738.jpg\n",
            "dogs_prepared/test/n02085620-Chihuahua/n02085620_806.jpg\n",
            "dogs_prepared/test/n02085620-Chihuahua/n02085620_8420.jpg\n",
            "dogs_prepared/test/n02085620-Chihuahua/n02085620_8491.jpg\n",
            "dogs_prepared/test/n02085620-Chihuahua/n02085620_8558.jpg\n",
            "dogs_prepared/test/n02085620-Chihuahua/n02085620_8578.jpg\n",
            "dogs_prepared/test/n02085620-Chihuahua/n02085620_8585.jpg\n",
            "dogs_prepared/test/n02085620-Chihuahua/n02085620_8611.jpg\n",
            "dogs_prepared/test/n02085620-Chihuahua/n02085620_8636.jpg\n",
            "dogs_prepared/test/n02085620-Chihuahua/n02085620_8637.jpg\n",
            "dogs_prepared/test/n02085620-Chihuahua/n02085620_9351.jpg\n",
            "dogs_prepared/test/n02085620-Chihuahua/n02085620_9357.jpg\n",
            "dogs_prepared/test/n02085620-Chihuahua/n02085620_9399.jpg\n",
            "dogs_prepared/test/n02085620-Chihuahua/n02085620_9414.jpg\n",
            "dogs_prepared/test/n02085620-Chihuahua/n02085620_949.jpg\n",
            "dogs_prepared/test/n02085620-Chihuahua/n02085620_952.jpg\n",
            "dogs_prepared/test/n02085620-Chihuahua/n02085620_9654.jpg\n",
            "total 20\n",
            "drwxr-xr-x  4  501 staff 4096 Jul 23  2019 .\n",
            "drwxr-xr-x  1 root root  4096 Aug 25 13:13 ..\n",
            "drwxr-xr-x 12  501 staff 4096 Aug 25 13:13 test\n",
            "-rwxr-xr-x  1  501 staff  120 Jul 23  2019 ._test\n",
            "drwxr-xr-x 12  501 staff 4096 Jul 23  2019 train\n"
          ]
        }
      ],
      "source": [
        "!rm -rf dogs_prepared\n",
        "!tar xvfz dogs_prepared.tar.gz\n",
        "!ls -al dogs_prepared"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ub3sOp6Onox6"
      },
      "source": [
        "# Template"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "B5LVL0anlYhG",
        "outputId": "8a569f0b-5f28-45dd-d81a-13247b88a901"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Model: \"sequential_9\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d_62 (Conv2D)           (None, 224, 224, 64)      1792      \n",
            "_________________________________________________________________\n",
            "conv2d_63 (Conv2D)           (None, 224, 224, 64)      36928     \n",
            "_________________________________________________________________\n",
            "max_pooling2d_27 (MaxPooling (None, 112, 112, 64)      0         \n",
            "_________________________________________________________________\n",
            "conv2d_64 (Conv2D)           (None, 112, 112, 128)     73856     \n",
            "_________________________________________________________________\n",
            "conv2d_65 (Conv2D)           (None, 112, 112, 128)     147584    \n",
            "_________________________________________________________________\n",
            "max_pooling2d_28 (MaxPooling (None, 56, 56, 128)       0         \n",
            "_________________________________________________________________\n",
            "conv2d_66 (Conv2D)           (None, 56, 56, 256)       295168    \n",
            "_________________________________________________________________\n",
            "conv2d_67 (Conv2D)           (None, 56, 56, 256)       590080    \n",
            "_________________________________________________________________\n",
            "conv2d_68 (Conv2D)           (None, 56, 56, 256)       590080    \n",
            "_________________________________________________________________\n",
            "max_pooling2d_29 (MaxPooling (None, 28, 28, 256)       0         \n",
            "_________________________________________________________________\n",
            "conv2d_69 (Conv2D)           (None, 28, 28, 512)       1180160   \n",
            "_________________________________________________________________\n",
            "conv2d_70 (Conv2D)           (None, 28, 28, 512)       2359808   \n",
            "_________________________________________________________________\n",
            "conv2d_71 (Conv2D)           (None, 28, 28, 512)       2359808   \n",
            "_________________________________________________________________\n",
            "max_pooling2d_30 (MaxPooling (None, 14, 14, 512)       0         \n",
            "_________________________________________________________________\n",
            "conv2d_72 (Conv2D)           (None, 14, 14, 512)       2359808   \n",
            "_________________________________________________________________\n",
            "conv2d_73 (Conv2D)           (None, 14, 14, 512)       2359808   \n",
            "_________________________________________________________________\n",
            "conv2d_74 (Conv2D)           (None, 14, 14, 512)       2359808   \n",
            "_________________________________________________________________\n",
            "max_pooling2d_31 (MaxPooling (None, 7, 7, 512)         0         \n",
            "_________________________________________________________________\n",
            "flatten_6 (Flatten)          (None, 25088)             0         \n",
            "_________________________________________________________________\n",
            "dense_12 (Dense)             (None, 64)                1605696   \n",
            "_________________________________________________________________\n",
            "batch_normalization_6 (Batch (None, 64)                256       \n",
            "_________________________________________________________________\n",
            "dense_13 (Dense)             (None, 10)                650       \n",
            "=================================================================\n",
            "Total params: 16,321,290\n",
            "Trainable params: 16,321,162\n",
            "Non-trainable params: 128\n",
            "_________________________________________________________________\n",
            "Found 1531 images belonging to 10 classes.\n",
            "Found 388 images belonging to 10 classes.\n",
            "Epoch 1/10\n",
            "23/23 [==============================] - 30s 1s/step - loss: 2.4332 - acc: 0.1332 - val_loss: 18.5523 - val_acc: 0.0979\n",
            "Epoch 2/10\n",
            "23/23 [==============================] - 24s 975ms/step - loss: 2.2784 - acc: 0.1581 - val_loss: 6.6520 - val_acc: 0.1005\n",
            "Epoch 3/10\n",
            "23/23 [==============================] - 23s 974ms/step - loss: 2.2373 - acc: 0.1822 - val_loss: 7.4742 - val_acc: 0.1340\n",
            "Epoch 4/10\n",
            "23/23 [==============================] - 23s 975ms/step - loss: 2.2206 - acc: 0.1920 - val_loss: 24.5249 - val_acc: 0.1649\n",
            "Epoch 5/10\n",
            "23/23 [==============================] - 23s 973ms/step - loss: 2.2043 - acc: 0.2077 - val_loss: 7.4883 - val_acc: 0.1675\n",
            "Epoch 6/10\n",
            "23/23 [==============================] - 23s 974ms/step - loss: 2.1985 - acc: 0.2358 - val_loss: 11.2146 - val_acc: 0.1031\n",
            "Epoch 7/10\n",
            "23/23 [==============================] - 23s 971ms/step - loss: 2.1627 - acc: 0.2162 - val_loss: 6.3220 - val_acc: 0.1598\n",
            "Epoch 8/10\n",
            "23/23 [==============================] - 23s 975ms/step - loss: 2.1590 - acc: 0.2306 - val_loss: 5.3508 - val_acc: 0.1701\n",
            "Epoch 9/10\n",
            "23/23 [==============================] - 24s 977ms/step - loss: 2.0906 - acc: 0.2476 - val_loss: 2.5089 - val_acc: 0.2165\n",
            "Epoch 10/10\n",
            "23/23 [==============================] - 24s 977ms/step - loss: 2.2191 - acc: 0.2097 - val_loss: 3.3025 - val_acc: 0.2165\n"
          ]
        },
        {
          "data": {
            "image/png": 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FrfQD/PBToWZCea7Uk0r5M9lim/97IIlG6NgDG14OXYlIUSv9AI9XwLEXwOuPQse+0NUU1ubXYd+26FzAzDiwQo+aUUQOpfQDHGDuYj9E+52nQ1dSWAdW4IlYgI+rh7EzdCFTZADlEeBHfBSqxsDK+0NXUljJZhg5yY9wjJpEo0ZkigygPAK8ohqOOdcPq+/sCF1N4bSmV6A3C13J4NU3wrYkbF8XuhKRolUeAQ6+GWX3+/De86ErKYzdW3wbeFT6f/eWafbRWbhIv8onwI86GypqymeptQMTWEU0wKcdDxUjFOAih1A+AV41yi/VtepB372u1CWbweIw/aTQlQxNRZWvXRcyRfpVPgEOfnKrHev9IJFSl2zyZ7FVI0NXMnSJhbD+JWjfG7oSkaJUXgF+zLkQq4RVJd4bpbMD1i6LXvfB3hKLINUO65eHrkSkKJVXgNeMhyPO8KMynQtdTf5sehXad0W3/TujPl2/mlFE+lReAQ6+GWXru6U9TDtz4S/qAT66FibM0oVMkX6UX4DPvgAsVtpTzCabYUwdjEuErmT4Eov891PKfzGJDFH5BfioyXD4KaXdnTDZ5M++oziAp7dEI+zaBB+sCV2JSNEpvwAH34zSthraXg9dSe7t2OibiOoj3nySoYmtRPpVpgF+ob8txWaUzFJkUe+BkjFlLlSNLv4LmZ3tsH196CqkzJRngI+dDvULSzPAk00Qr4K6D4WuJDdicb8gczEH+L6d8NNL4AdaSUgKqzwDHPxKPetfKr221eQSv65kRXXoSnKnvhE2vuqDstjs3QZ3XgrvveAXDrnrs7BzU+iqpEyUcYAv9rerHgpbRy517IN1L0a/+2BviUV+YeZiG0G7ewv89GI/aOr3fwJX3gN7tsDdnyuvWS8lmPIN8Imz/FDzUmpGWb8COveVzgXMjPr0gszF1B981/vw08X+L4PL7/SzXU47Hi76Aax5Bp68KXSFUgbKN8DBn4Unm2DHhtCV5EZriQzg6a1mPNTO7vr+Qtu5CX5yAWx+A674JRx7Xtdz8z8NC78Az/8QXv3PcDVKWVCAQ+n0CU82wfjDYcy00JXkXmaFntAzSW5fBz8+33fV/MP/8DNc9nbuP/hmn/uuh02rC1+jlI3yDvDaY2HS0aUR4M75gCu1s++MxCLYuxXefyNcDVuTPrx3bIAr74VZp/e9X0UV/P4dfgrjX/0h7N1e2DqlbJR3gJv5tss1z/oLUlG2rdVPlVsq/b97OzCxVaBmlC3v+PDevQU+ex8c/uFD7z+2zl/Y3PIO3PclTQUgeTFggJvZ7Wa2ycxe6bZtopk9bmZvpG8n5LfMPJpzEbhOWP1w6EqGJ9NPun5h2DryZdJRvpteiP7gm9/04b1/B1z1QNdF1YHMPAV+7+9g9UPw3PfzW6OUpWzOwH8CnNdr21eBJ51zRwNPph9HU90JMP6w6DejtC6BypEwdV7oSvIjFvNn4YU+A9+0Gn78cejcD1c/DNNPGNzrT/4SHHcpPPm38PZv8lKilK8BA9w59zTQu33hYuCO9P07gEtyXFfhmPmLmW8/Fe22ymQTzFgA8YrQleRPYiFsfg32fFCY99vwMvzkfD975TWPwNTjBn8MM1j8Q5h8rO8fvjWZ+zqlbA21DXyqcy4z8cMGYGp/O5rZdWbWYmYtbW1tQ3y7PJtzkT/DeuOx0JUMzf7dPmxK9QJmRqZ9v7Ul/++1dhn85EK/sPI1j/gL3kNVPRo+/XM/X8pdn9EScZIzw76I6ZxzQL9XaJxztzjnGpxzDbW1tcN9u/yob4TRU2FlRJdaW/cipDpK9wJmxvST/ELN+W4HTy7xIyxHjPXhPenI4R9z0pHwiX/z/1b/9T+HfzwRhh7gG82sDiB9G+3JH2IxmH0hvPmEP5uNmlK/gJlRPdo3Y+SzHfzd5+Fnl/h5469+BCbMzN2xZ58Pp/05LPspLL1j4P1FBjDUAH8AuCp9/yogoqeu3cxdDO274a0nQ1cyeK1LfH/2kRNDV5J/iUV+TpR8zDXy9m/gzsv8bJVXPwLj87Ci0cf+Co48Ex758+Kb20UiJ5tuhL8AXgCONbNWM7sW+CZwjpm9AZydfhxth5/iu6lFrTeKc+kVeEq8+SQjsQj274RNK3N73DeegH+/3J9xX/2w78edD7E4XHYbjJ4Gd13l51QRGaJseqFc4Zyrc85VOufqnXO3Oefed86d5Zw72jl3tnMu4qNggHglHHsBvPYodOwPXU32trwNu9/3PTTKQeb7zOW8KKsfgV9eAZOPgasegtFTcnfsvoycCJf/zM+pcs/nINWZ3/eTklXeIzF7m7sY9m2Dd34bupLsJUtsBZ6BjD/cX3DOVTv4q/f5niHTjveDdEZNys1xBzL9BLjwu77Z5tffKMx7SslRgHd3xEehaky0pphNNkH1ON/PuByYpSe2ykFPlBV3wd3XwIwG+Mx9vgmtkE68EhZcA89+L3pNd1IUFODdVVTDMef6YfVRmZA/2eyHdsfK6J+yvtGvpDSclW9evBPuvc5f+7jyHt9lMISP/18/AOs/v1Sai2xLXpXR//oszbnItym/90LoSga2d7u/mFcuzScZme93qM0oLbfD/dfDkR+DP7jLd08MpaIaPvVTf/urK4tz2TgpWgrw3o4+x4++i0IzytoWwJXPBcyMuvl+4eahNKP87mZ46E/h6HPh07+AqpG5r2+wxtXDJ2/3U+Xef71mLpSsKcB7qxoFR53t18oMvXjAQJJLAPNtuOWkcoSfhKx1kCvAP/t9ePSrftDW5Xf64xSLI86As2+ClffBC/8cuhqJCAV4X+Yshh3rin+gRbLJj0wM1X4bUqLRz1eSbZfP334LnvgbmHeZn6e7oiqv5Q3JR/4E5l4Mj/8NvPNM6GokAhTgfTnmXIhVwqoiHmCaSvlJnUp9+Hx/Eo1+AecNKw69n3Pw5Dfgqb+H+VfApbf6Pv/FyAwu/hc/b8p/XA3b1oauSIqcArwvNeP9n7SrHize9sjNr/k+6+V2ATMjmxV6nIPHvgbP/BOc9Fm4+F/9SMhiVj0GLv85dOyFuz4LHftCVyRFTAHenzmLfVe1DS+HrqRvmQt4pT6FbH/G1sG4w/q/kJlKwX/9hW9PbrwOLvxBdLpa1h4Dl/yrv0j96F+GrkaKWER+owOYfYGfyL9YB1gkm2HkJJh4ROhKwkn0s0JPKgUP3QjNt8CHvwwf/1Z0wjtj7sVwyg3Qchss//fQ1UiRithvdQGNmuwHeRRrd8Jks28+MQtdSTiJRf5i87bWrm2pTt8Vb9kdcNpX/JqUUf0ZnfnXMOt03+1x/Uuhq5EipAA/lDkXQdvq4hsht3uL7zNcrhcwMzL93zPNKJ3tcO8X4KV/h4/9bzjrr6Mb3uCXx/vkj/1fWr+60v+7i3SjAD+UORf522I7C8/0fy7XC5gZU+f5hZyTzb474d3XwCv3wNlfhzP+InR1uTFqMnzqZ7BjA9zzec1cKD0owA9l7HR/lltsAZ5sglgFTD8xdCVhxSv9PCJrnvMzCq56EM77Jpx6Y+jKcqt+gW/Hf+tJ+M0/hq5GiogCfCBzLvLtjx+8G7oSr32PD6xpxxfHMPDQ6hfCxpfh9Ufhgu/CyV8KXVF+LLjaz1749Lfhtf8KXY0UCQX4QA40owTojZJKweY3YPkv4OGvwL+dAf9YD8nf+Qus4gddVdTA4n+GhdeGriZ/zOD87/gpBO79Irz/VuiKpAiYK+BAlYaGBtfS0lKw98uZm0/1c6Rc+9/5fZ9dm/3oyrUt/nbdMti7zT9XNdo3mdQ3+LlPjjoLKmvyW09UdHb4C37lYOt7/oN8zDT4/BP+91JKnpktdc4dNOlRmfzWD9PcxfDUP/gLSWOm5eaY7Xtg/YqusF67FLamm2ksBlOOg+M+4cO6vsEv91XsowhDKZfwBhh/GHzyNvjZpfDAn8BlP4p2TxsZljL6zR+GOYv9XBqrH4KFnx/861MpeP/NnmG98RVIpReNGFvvL1Qt/Ly/KDf9BJ1ZSf+OPBPO/Jpfiq2+oXTb/WVACvBs1B4Lk46GlQ9kF+A723xYr12aDuxlft4S8Eu2zTjRzzw3Y4H/D5irs3opH6f+mf+9euxrfn70wz8SuiIJQAGeDTPfjPLs9/1gipETu55r3+N7qRwI6xbfTglgcZgyF+apKURyLBaDT9wMt57pZy784tM6EShDCvBszbkInvkOLLkNxs3oCuuNr/bRFPIFH9Z189UUIvkzYpxfmOLWs+Cuq+CqB4tznnPJGwV4tupO8BeQnvo7/7h7U0h9g28O0RmQFNqUOXDxD+Huz/nmlPO/FboiKSAFeLbM/LwUbat9WKspRIrFvMugdSn87l/87+b8y0NXJAWiAB+M+nQ7tkixOefrsH45PHgDTJ3rR+pKydNITJFSEK/0a33WjPcDfW49y6+t+eYTsG9n6OokT3QGLlIqRk+Bax7xC0C884xfjei57/uJz2YsgJmnwszT/CyWmkenJGgovUip2r8L3vsdrHkW1jzj+427TohX+W6ts07zoV7fCJUjQlcrh9DfUHoFuEi52LfDB/o7T/tQX78cXAri1X55upmn+VCfsQAqqkNXK90owEWkp73b4N0X/Nn5O0+nF/B2fnbHRGP6DP10P4ma+pcHpcmsRKSnEePg2PP8F/hRxu+94NvP1zwDv06PeagcCYednD5DP92PiSinCcSKmP4VRMQbORFmX+C/AHa9D+8+lz5Dfwae/LrfXjUGDv9w10XRuvkaExGIAlxE+jZqkp8DaO5i/3hnmw/zNc/4NvQ3HvPbq8f5ybRmnuqbXaYe7+dqkbxTgItIdkbXwrxL/Rf4+fHXPNt1UfT19FJvI8b7MF9wNRx9TrByy8GwAtzM1gA7gE6go69GdhEpUWOmwfGf9F8A29amuyw+DW895efPP/l6OPsmXQTNk1ycgX/MObc5B8cRkSgbN8PPwzL/cmjfC4//Hz8/y3vPwydvh4lHhK6w5KihSkRyr3IEnP9t+NTPYMvbfnj/K/eGrqrkDDfAHfCYmS01s+v62sHMrjOzFjNraWtrG+bbiUikzF0MX3zGz9559zXw4I1+ERTJieEG+KnOuZOAjwPXm9npvXdwzt3inGtwzjXU1tYO8+1EJHImHA6fexROuQGW/thPtNX2WuiqSsKwAtw5tzZ9uwn4T6AxF0WJSImJV8I5fwt/eDfs3AC3fNRPuiXDMuQAN7NRZjYmcx/4PeCVXBUmIiXo6HPgj56D6SfBfV+Ce7+o6W6HYThn4FOBZ83sJaAZeNg592huyhKRkjW2Dq56AM74Kqz4FdxyRnoeFhmsIQe4c+5t59z89Ndxzrm/z2VhIlLCYnH42F/6IN+307eLL/kRFHByvVKgboQiEs6s0+GPnvVD8B/+Ctz1WdizNXRVkaEAF5GwRtfCH/wHnP11WP0w/NtpfpFmGZACXETCi8Xg1Bt9d0Pn4Pbfg+d/CKlU6MqKmgJcRIpHohH+6Bk45jx47Gvwi0/7aW2lTwpwESkuNRPg8jvh49+Gt5+C/3cqrHkudFVFSQEuIsXHDBZdB9c+7udVueNC+O23IdUZurKiogAXkeI1/QT44tMw7zJ46u/gZ5f4ecgFUICLSLGrHgOX3gqL/xmSS3yTyptPhq6qKCjARaT4mcFJn4HrnoKRk+HOS+GJm6CzPXRl/XMOtrXCygd8rVuTOX8LLakmItExZQ584dfw6P+CZ78H7z4Pl90G4xOhK/O9ZdYtg7XLum53bfLPxSrgsA/nvE4FuIhES9VIWPxDmHUGPHiDb1K55GaYfX7hati3A9Yt7xnYW99LP2l+/vOjzvKTds04CabO8xdjc0wBLiLRdPwnYfqJfqGIX14Bi74E53wdKqpz+z7te2HjKz3PrDe/jl/PBhh/mA/qhZ/3t3XzYcTY3NbQDwW4iETXpCN9V8PH/xqabk6vv/ljv30oOjugbVVXWK97ETa+CqkO//yoKf6Met5l/nb6iTBqcu6+n0EyV8DZvxoaGlxLS0vB3k9Eysiqh+D+/+GH31/0fX+GfiiplF+vs3szyPoV0JFe8m3EOB/QmWaQ6SfC2Bn+gmqBmdlS51xD7+06AxeR0jDnQqj7ENx9LdxzLdDzuKQAAAbESURBVLzzNJz3Td9m7hxsX9uzGWTdcti3zb+2osY3fTRc0xXYE2b5OVqKmAJcRErH+MPgmkfgqb/3vVSSTTBh5sE9QqYeB/MuTZ9ZnwS1syEevTiMRMXfe/x1HlyxjpFVcWoq44yojB+4X1MVp6aygpqqWPpxRfo2lt7u9xtZ5V9XUxVnZPq2uiKGBfhzSETyKF4JZ98EM0+Fh/4MPlhTkB4hIUQiwGdMqGFO3Vj27u9k9/5OduztoG3HPnbv72RPe6ff3t5JZ2pw7flm+LDv/qGQCfpuHxIjuoV+ZTxGPGbEzIjHIGaZ+0YsZsQM4ubvx9PbzSCefhzr9drMsTL34zEw63ptzIxYrOcxY+aP6b8MS38vRno76e3d73fbh8xr08/Fur2W/o5F1/uJRMJRZ8ONK0JXkVeRCPBPNST4VMOhO8A752jvdOxp72RPOtj9bQd79qfY097J7v0d7E1v350Ofr89/UGQub+/k62797Nua6/t7Z1a8QkO/cGQfhw78Jy/pZ8Pllj6Sev22t4fHnQ/Zq/3ptd+6U0HXtO1zQ7a1vN7sh6v7b5vX8fp8Xy3DQc+wK3rgztmXR/umQ/rWLfneuxn3faL9bFft5OAg/ZL3+/r+zR6bjj4+d4/jz5+RgMco7d4zKiIx6iImf+KGxWx9ON4LP04vS19P3OCVBk34gf29fv4bV37VMRsSCcUzjn2d6bY255iX3sn+zpS7O3jdm97in0dA9we6vUdnexr79r2o6saOO3o2kHXeyiRCPBsmBlVFUZVRYxxNZV5eQ/nHJ0pR6dzpFL4W+dIpbq2O4e/n0o/l36cct22pV/bmXIHHTPlMvcz+9Pztc7Rmd4PBw7/ng7St90fp+871+15/5hu+6dcz9fS/bW9j9vjWAfvQ7f36Pe4vV7v/3DKHP/Qx025Xt+ff2nXvxFd31vXv1vP5w56vo9t9HWcXr8LvbelXOb77vo3bu9MHfh3dK7nv3Pm9yOV+f1IP3dgv27PHbRf5njpY5eriljPUK9Mh33Xh4DR3ul6BO++jtSwTsSqKmJUV8QYURk/cDuiMkZ1hb8dW1PJiMoYIyriVKe3V1fGmD6+JnffeFrJBHghmKXPBkIXItJLj6AfIJ16P+1wh3ze79P7GG6A531NHSlHRypFR2f6fmcqfZve3v1+7336fZ1/3J5ydPazT3vKf3B2dDoq49YjbKsrYlT3CN/4IQM5czuiMk5VPHbgr5xioCwSKQGxmBE7qCFESl1xd3IUEZF+KcBFRCJKAS4iElEKcBGRiFKAi4hElAJcRCSiFOAiIhGlABcRiaiCLuhgZm3Au0N8+WRgcw7LiTr9PLroZ9GTfh49lcLP43Dn3EETqRQ0wIfDzFr6WpGiXOnn0UU/i5708+iplH8eakIREYkoBbiISERFKcBvCV1AkdHPo4t+Fj3p59FTyf48ItMGLiIiPUXpDFxERLpRgIuIRFQkAtzMzjOz18zsTTP7auh6QjGzhJk9ZWYrzexVM7shdE3FwMziZvaimT0UupbQzGy8md1tZqvNbJWZfTh0TaGY2Z+m/5+8Yma/MLPSWIq+m6IPcDOLA/8CfByYC1xhZnPDVhVMB/AV59xc4GTg+jL+WXR3A7AqdBFF4gfAo8652cB8yvTnYmYzgD8BGpxz84A48OmwVeVe0Qc40Ai86Zx72zm3H/glcHHgmoJwzq13zi1L39+B/885I2xVYZlZPXAB8KPQtYRmZuOA04HbAJxz+51zW8NWFVQFUGNmFcBIYF3genIuCgE+A0h2e9xKmYcWgJnNBE4EmsJWEtz3gb8AUqELKQKzgDbgx+kmpR+Z2ajQRYXgnFsL/BPwHrAe2OaceyxsVbkXhQCXXsxsNHAPcKNzbnvoekIxswuBTc65paFrKRIVwEnAzc65E4FdQFleMzKzCfi/1GcB04FRZnZl2KpyLwoBvhZIdHtcn95WlsysEh/eP3fO3Ru6nsBOARab2Rp809qZZnZn2JKCagVanXOZv8ruxgd6OTobeMc51+acawfuBT4SuKaci0KALwGONrNZZlaFvxDxQOCagjAzw7dvrnLOfTd0PaE55/7SOVfvnJuJ/734tXOu5M6ysuWc2wAkzezY9KazgJUBSwrpPeBkMxuZ/n9zFiV4QbcidAEDcc51mNmXgf/GX0m+3Tn3auCyQjkF+AzwspktT2/7K+fcIwFrkuLyx8DP0yc7bwPXBK4nCOdck5ndDSzD9956kRIcUq+h9CIiERWFJhQREemDAlxEJKIU4CIiEaUAFxGJKAW4iEhEKcBFRCJKAS4iElH/H9e57kRjyGkEAAAAAElFTkSuQmCC",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "6/6 [==============================] - 2s 249ms/step - loss: 3.3025 - acc: 0.2165\n",
            "loss= 3.3025217056274414\n",
            "acc= 0.2164948433637619\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "import os\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "\n",
        "from tensorflow import keras\n",
        "from tensorflow.keras import optimizers\n",
        "from tensorflow.keras import models\n",
        "from tensorflow.keras import layers\n",
        "from tensorflow.keras import optimizers\n",
        "from tensorflow.keras import Sequential\n",
        "from tensorflow.keras.layers import Dropout, Flatten, Dense, BatchNormalization, Conv2D, MaxPooling2D, Input\n",
        "from tensorflow.keras import Model\n",
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
        "from tensorflow.keras.preprocessing.image import load_img\n",
        "from tensorflow.keras.preprocessing.image import img_to_array\n",
        "\n",
        "from tensorflow.keras.applications import efficientnet\n",
        "from tensorflow.keras.applications import EfficientNetB2\n",
        "\n",
        "model = keras.Sequential()\n",
        "model.add(Input((224,224,3)))\n",
        "model.add(Conv2D(64, (3,3), padding='same'))\n",
        "model.add(Conv2D(64, (3,3), padding='same'))\n",
        "model.add(MaxPooling2D())\n",
        "model.add(Conv2D(128, (3,3), padding='same'))\n",
        "model.add(Conv2D(128, (3,3), padding='same'))\n",
        "model.add(MaxPooling2D())\n",
        "model.add(Conv2D(256, (3,3), padding='same'))\n",
        "model.add(Conv2D(256, (3,3), padding='same'))\n",
        "model.add(Conv2D(256, (3,3), padding='same'))\n",
        "model.add(MaxPooling2D())\n",
        "model.add(Conv2D(512, (3,3), padding='same'))\n",
        "model.add(Conv2D(512, (3,3), padding='same'))\n",
        "model.add(Conv2D(512, (3,3), padding='same'))\n",
        "model.add(MaxPooling2D())\n",
        "model.add(Conv2D(512, (3,3), padding='same'))\n",
        "model.add(Conv2D(512, (3,3), padding='same'))\n",
        "model.add(Conv2D(512, (3,3), padding='same'))\n",
        "model.add(MaxPooling2D())\n",
        "model.add(Flatten())\n",
        "model.add(Dense(64, activation='relu'))\n",
        "model.add(BatchNormalization())\n",
        "model.add(Dense(10, activation='softmax'))\n",
        "\n",
        "model.compile(loss='sparse_categorical_crossentropy', optimizer=\"RMSprop\", metrics=['acc'])\n",
        "model.summary()\n",
        "\n",
        "def normalize(image):\n",
        "  return image/255.0\n",
        "\n",
        "preprocessor = normalize\n",
        "BATCH_SIZE = 64\n",
        "\n",
        "train_data_generator = ImageDataGenerator(\n",
        "      rotation_range=10,\n",
        "      width_shift_range=0.1,\n",
        "      height_shift_range=0.1,\n",
        "      horizontal_flip=True,\n",
        "      vertical_flip=False,\n",
        "      preprocessing_function=preprocessor\n",
        ").flow_from_directory(\n",
        "      \"dogs_prepared/train\",\n",
        "      target_size=(224,224),\n",
        "      batch_size=BATCH_SIZE,\n",
        "      class_mode='sparse'\n",
        ")\n",
        "\n",
        "test_data_generator = ImageDataGenerator(\n",
        "      preprocessing_function=preprocessor\n",
        ").flow_from_directory(\n",
        "      \"dogs_prepared/test\",\n",
        "      target_size=(224,224),\n",
        "      batch_size=BATCH_SIZE,\n",
        "      class_mode='sparse'\n",
        ")\n",
        "\n",
        "\n",
        "history = model.fit(\n",
        "      train_data_generator,\n",
        "      validation_data=test_data_generator,\n",
        "      steps_per_epoch=train_data_generator.samples/BATCH_SIZE,  \n",
        "      validation_steps=test_data_generator.samples/BATCH_SIZE,      \n",
        "      epochs=10\n",
        ")\n",
        "\n",
        "\n",
        "plt.plot(history.history['loss'])\n",
        "plt.plot(history.history['val_loss'])\n",
        "plt.show()\n",
        "\n",
        "\n",
        "loss, acc = model.evaluate(\n",
        "      test_data_generator,\n",
        "      steps=test_data_generator.samples/BATCH_SIZE\n",
        ")\n",
        "print(\"loss=\", loss)\n",
        "print(\"acc=\", acc)\n",
        "\n",
        "\n",
        "\n",
        "test_x, test_y = next(iter(test_data_generator))\n",
        "y_ = model.predict(test_x)\n",
        "predicted = np.argmax(y_, axis=-1)\n",
        "\n",
        "plt.plot(test_y[:100], \"o\")\n",
        "plt.plot(predicted[:100], '.')\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Ra4JmMC7f6Hn"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "name": "template_image_data_vannilla_cnn_classification.ipynb",
      "provenance": []
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