material/deep_learning/data_augmentation.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "data_augmentation.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "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"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fMfxye5ETDgk"
      },
      "source": [
        "# 로딩한 데이터에서 증강"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oPclK8DGA_gw",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4736cde6-b923-42b8-a667-5770491303da"
      },
      "source": [
        "(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_x = train_x.reshape((60000, 28, 28, 1)) # COMMENT OUT\n",
        "# test_x = test_x.reshape((10000, 28, 28, 1)) # COMMENT OUT\n",
        "\n",
        "train_y = raw_train_y\n",
        "test_y = raw_test_y"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading data from https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz\n",
            "170500096/170498071 [==============================] - 12s 0us/step\n",
            "(50000, 32, 32, 3)\n",
            "(50000, 1)\n",
            "(10000, 32, 32, 3)\n",
            "(10000, 1)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lNy4AUQDylpU",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "8606970f-24bf-48da-fb05-8fc87e969c4c"
      },
      "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": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "9 truck\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "4 deer\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "7 horse\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "9 truck\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yo81ucmbFmVK"
      },
      "source": [
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
        "\n",
        "datagen = ImageDataGenerator(\n",
        "    rotation_range=10, # 0 ~ 180\n",
        "    width_shift_range=0.1,\n",
        "    height_shift_range=0.1,\n",
        "    fill_mode='nearest',\n",
        "    horizontal_flip=True,\n",
        "    vertical_flip=False \n",
        ")"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QS2Lib4UKH6s"
      },
      "source": [
        "BATCH_SIZE = 128\n",
        "iterator = datagen.flow(train_x, train_y, batch_size=BATCH_SIZE)"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9JNG0U3xNdyf"
      },
      "source": [
        "datage.flow()의 봔환값은 python의 iterator이다.\n",
        "\n",
        "next(iterator)를 호출하여 다음 아이템을 받을 수 있다.\n",
        "\n",
        "```\n",
        "item = next(iterator)\n",
        "augmented_x, augmented_y = item\n",
        "print(augmented_x.shape)\n",
        "print(augmented_y.shape)\n",
        "\n",
        "(128, 32, 32, 3)\n",
        "(128, 1)\n",
        "```"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FMeF15l5inLE",
        "outputId": "019e5eb6-1092-483a-af65-304fa721f83e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 798
        }
      },
      "source": [
        "item = next(iterator)\n",
        "augmented_x, augmented_y = item\n",
        "print(augmented_x.shape)\n",
        "print(augmented_y.shape)\n",
        "\n",
        "for i in range(3):\n",
        "  plt.imshow(augmented_x[i])\n",
        "  plt.show()"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(128, 32, 32, 3)\n",
            "(128, 1)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8nF8ZfNEKNY_",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "26e1fcc6-7971-4230-b68a-9ec55e9f855d"
      },
      "source": [
        "data_count = len(train_x)\n",
        "print(\"data_count :\", data_count)\n",
        "\n",
        "WANTED_MULTIPLE_COUNT = 2\n",
        "print(\"WANTED_MULTIPLE_COUNT :\", WANTED_MULTIPLE_COUNT)\n",
        "\n",
        "STEP_COUNT = int(data_count/BATCH_SIZE)+1\n",
        "print(\"STEP_COUNT :\", STEP_COUNT)\n",
        "\n",
        "FETCH_COUNT = STEP_COUNT*WANTED_MULTIPLE_COUNT\n",
        "print(\"FETCH_COUNT :\", FETCH_COUNT)\n",
        "\n",
        "\n",
        "augmented_x_list = []\n",
        "augmented_y_list = []\n",
        "for i in range(FETCH_COUNT):\n",
        "  item = next(iterator)\n",
        "  augmented_x, augmented_y = item\n",
        "  augmented_x_list.extend(augmented_x)\n",
        "  augmented_y_list.extend(augmented_y)\n",
        "  # print(len(augmented_x_list))\n",
        "# python list를 numpy로 변환\n",
        "augmented_train_x = np.array(augmented_x_list)\n",
        "augmented_train_y = np.array(augmented_y_list)\n",
        "\n",
        "print(augmented_train_x.shape)\n",
        "print(augmented_train_y.shape)"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "data_count : 50000\n",
            "WANTED_MULTIPLE_COUNT : 2\n",
            "STEP_COUNT : 391\n",
            "FETCH_COUNT : 782\n",
            "(100000, 32, 32, 3)\n",
            "(100000, 1)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VaEcFdYzEepA",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 721
        },
        "outputId": "7bb8ffff-a07b-4db5-d91c-76dc07891b6c"
      },
      "source": [
        "for i in [0,2,4,6,8,10,12,14]:\n",
        "  plt.figure(figsize=(1,1))\n",
        "  plt.imshow(augmented_train_x[i])\n",
        "  plt.show()"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 72x72 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 72x72 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 72x72 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 72x72 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 72x72 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 72x72 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 72x72 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 72x72 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OQoneoRgTJ6y"
      },
      "source": [
        "# 파일 시스템에서 증강"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4lC85qLTTMqe",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fb7f93eb-5f59-4bca-dd7d-b4b3a2bf44f0"
      },
      "source": [
        "!rm -rf dogs_prepared.tar.gz\n",
        "!wget https://github.com/dhrim/cau_2022_summer/raw/master/material/deep_learning/dogs_prepared.tar.gz\n",
        "!ls -al "
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2021-08-10 00:55:52--  https://github.com/dhrim/cau_2022_summer/raw/master/material/deep_learning/dogs_prepared.tar.gz\n",
            "Resolving github.com (github.com)... 192.30.255.112\n",
            "Connecting to github.com (github.com)|192.30.255.112|:443... connected.\n",
            "HTTP request sent, awaiting response... 302 Found\n",
            "Location: https://raw.githubusercontent.com/dhrim/cau_2022_summer/master/material/deep_learning/dogs_prepared.tar.gz [following]\n",
            "--2021-08-10 00:55:53--  https://raw.githubusercontent.com/dhrim/cau_2022_summer/master/material/deep_learning/dogs_prepared.tar.gz\n",
            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.109.133, 185.199.108.133, 185.199.111.133, ...\n",
            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.109.133|: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  79.6MB/s    in 0.8s    \n",
            "\n",
            "2021-08-10 00:55:58 (79.6 MB/s) - ‘dogs_prepared.tar.gz’ saved [62898132/62898132]\n",
            "\n",
            "total 61440\n",
            "drwxr-xr-x 1 root root     4096 Aug 10 00:55 .\n",
            "drwxr-xr-x 1 root root     4096 Aug 10 00:50 ..\n",
            "drwxr-xr-x 4 root root     4096 Jul 16 13:19 .config\n",
            "-rw-r--r-- 1 root root 62898132 Aug 10 00:55 dogs_prepared.tar.gz\n",
            "drwxr-xr-x 1 root root     4096 Jul 16 13:20 sample_data\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G9b0JYbXT5QO",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b3432744-9b3c-4d6d-8eb8-963fbf9b1ad2"
      },
      "source": [
        "!rm -rf dogs_prepared\n",
        "!tar xvfz dogs_prepared.tar.gz\n",
        "!ls -al dogs_prepared"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "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",
            "dogs_prepared/train/n02088094-Afghan_hound/n02088094_1007.jpg\n",
            "dogs_prepared/train/n02088094-Afghan_hound/n02088094_1023.jpg\n",
            "dogs_prepared/train/n02088094-Afghan_hound/n02088094_10263.jpg\n",
            "dogs_prepared/train/n02088094-Afghan_hound/n02088094_10715.jpg\n",
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            "dogs_prepared/test/n02085620-Chihuahua/\n",
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            "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 10 00:55 ..\n",
            "drwxr-xr-x 12  501 staff 4096 Aug 10 00:55 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"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6JodORR8UDRN",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9bd641ab-9df0-4785-e798-89e3aacab14b"
      },
      "source": [
        "\n",
        "import numpy as np\n",
        "\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",
        "def my_preprocess_input(image):\n",
        "  return image/255\n",
        "\n",
        "BATCH_SIZE = 128\n",
        "\n",
        "data_aug_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=my_preprocess_input\n",
        ")\n",
        "\n",
        "iterator = data_aug_generator.flow_from_directory(\n",
        "      \"dogs_prepared/train\",\n",
        "      target_size=(224,224),\n",
        "      batch_size=BATCH_SIZE,\n",
        "      class_mode='sparse'\n",
        ")\n"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 1531 images belonging to 10 classes.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SOZh_QGkUm5A",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6e683392-f415-4dd6-ab2b-f039802e2b6c"
      },
      "source": [
        "data_count = len(iterator.labels)\n",
        "print(\"data_count :\", data_count)\n",
        "\n",
        "WANTED_MULTIPLE_COUNT = 2\n",
        "print(\"WANTED_MULTIPLE_COUNT :\", WANTED_MULTIPLE_COUNT)\n",
        "\n",
        "STEP_COUNT = int(data_count/BATCH_SIZE)+1\n",
        "print(\"STEP_COUNT :\", STEP_COUNT)\n",
        "\n",
        "FETCH_COUNT = STEP_COUNT*WANTED_MULTIPLE_COUNT\n",
        "print(\"FETCH_COUNT :\", FETCH_COUNT)\n",
        "\n",
        "augmented_x_list = []\n",
        "augmented_y_list = []\n",
        "for i in range(FETCH_COUNT):\n",
        "  item = next(iterator)\n",
        "  augmented_x, augmented_y = item\n",
        "  augmented_x_list.extend(augmented_x)\n",
        "  augmented_y_list.extend(augmented_y)\n",
        "\n",
        "# python list를 numpy로 변환\n",
        "augmented_train_x = np.array(augmented_x_list)\n",
        "augmented_train_y = np.array(augmented_y_list)\n",
        "\n",
        "print(augmented_train_x.shape)\n",
        "print(augmented_train_y.shape)"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "data_count : 1531\n",
            "WANTED_MULTIPLE_COUNT : 2\n",
            "STEP_COUNT : 12\n",
            "FETCH_COUNT : 24\n",
            "(3062, 224, 224, 3)\n",
            "(3062,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fY3MxbdHU0oN",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 729
        },
        "outputId": "2ddedcb0-a9b2-422e-e059-4dd764f1c438"
      },
      "source": [
        "for i in [0,2,4,6,8,10,12,14]:\n",
        "  plt.figure(figsize=(1,1))\n",
        "  plt.imshow(augmented_train_x[i])\n",
        "  plt.show()"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 72x72 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 72x72 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 72x72 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 72x72 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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l16NQYWUwYD6fk2WW3Z0F/XKAxfLk448zX4xZPzDi9NkzrI8PMlo9xMpwvXMkkIKVtVXaFNnol5is2z6fTyZcuPAyp0+fpsgsB44dxRnH9tYONIbxYorLFCRSFDkhBrK88+YoBgP6PUPKcrarBd77r/EM+UbYF0SE4JnVU0Lm0HKAZDliHaONI0SF8WTB2koPlzmm4z1KhdGoZD4ZY/OclBrqeoKRjPPnJmxv7WBdt5G4srKCNQbrhLpZIDhMr0/T1vRWh5BbsEJdVYgkLpy9wGxvxtFDh9lYW2N9bQVOHkGxzGZTqnpO2Sspy5KyVxI0UPmWalGT2sSsasicI2nkejyb9gUR7bziuS98ER2MqBuHDRscvu0Y4iyrKwOaxZRRf41dewmvNcY65m1Dv1/irGFzc8ygv4bLHOcunKNNDbcdPsaho0c4nuekqJTFkBAg+IRYoeg7xCWsExZ1TVM1XDx/gaqpOHv+HL71nD93httvO8TaasFotMaJEwcRjlHXnslkjBPoFX1WRiOSGFh4dnf2eOHsy+D9N3IH/BrsCyJm29t8/MP/jY27X8+x2+4lbB9AgN7GBkmEejqjWl3l9mMnKIs+oW1YVDXOOlpNxAgn73wdm1ubHD16grX1Qxw6cpiNjQ1WV9domxbvU2et8wnfNjSLBSYGUlWzszdmd2uXhz/3OR57+gsUeQZUjFaHTOaGmArqqmJvZ8podJgiL1ldHbFYTKnmDXuzGQllKDmxabFVTS8EzM1GhFMY7ky4cOkRdrMXqJzjwOvv4rt/6AdYPX4bTVtx5vQ57jp5B2ur68xnE3zbUC1qer2SgwcPkuWO0fqIvCgwLiMvy85dPuUI4KyiyRNoOgc26wDD1vYOp0+/zPOnnmdna4t+NuCO40c5cfsRjh89RL8oyUxG0evjXMF0uqBatFhnyTKL+kg1XnDu3FnG586zff4CFybb7OxuEW82BzNR6AXLejBYr/TxzJ44xeODP+H+9343brXPRGE2r+gPSoaDVSqZ4dsGRWjairPnXmR94wArKz3mVYNvG/K8pFosiCkiBnxoCbGh9S3NYkFQaFvP6soaB0frjM9fxE0adk6dYegjK2oZnbyLrOihUbBFRqJmb7JHlmXE4CkwpCYwcAOkN6TOLLGu8YsaTTfZiMAa1gYr9KIjmhXmksgWMy792ee49MXH0CMH+NGf/Rmyoo81jmg8rjBgHEWvIKXIZDalOnuOEBL9wYDpdE7R61H0+/TKHr4JpKjMZzUxJfaaivmiYdhb5ZFPP8QX/+TTpL0JK2KZhcATz56mfsuc4WCDA3cMKcoeCx/pr4/obYzY2d5htL6OpEjMSja3piz25lRbY5rNmp4fIOkmW0ekpSvKcHQEO9hguDHgoElsXrxElSK3v/4eZtM9qrbCmwwrdL5ETct4MqfX61FNZlzY3ARjiT5y8dwFjh89ysrakLXRBv3eChcvbONcwSOPPsab3/V23njv63nqyceZNzOO338Pb7z3XjbKAcePHGNQlFw4d44zL5wl7s4oDq1z8Laj7E3HmNwxGq0w3t1jZzImL4YMV/oskkdrjzZCXcXrMdDtDyKMgEGYTSaECrzxHLv3Hr7jnd+FlgUMS1YOH2Eyn9Ps1gxKy2x3m73NTZp5hQBnz51luliQYoIqsP3CWTbzPqPhKnmvR50S07ahToHBaJ1CLGdPPcOZJx/jEIGDt99Gr26RWc258xeZ7Ozx+c99jvFkzOrGOuVohXd/73sZHD3Amb0tirUV7rjrTiYTz+75l7GTCqMNQTyVTdS5Jd10e00Kpk0k120hh/MzNv0ZBtkGd7/lTYyOH8E6h28i2rbMKs/ayipHDx3qXN9j4r4HHsAOBrz4/Ev84a//NqefeYEL04ZVVxCNwVvD8Ngh3vP97+d1b36AZ577MjunX+Klhx+mOX2OCT3WpEcp3Ra218R6PUe8Z3r2DGmz4JQIK7cdIj844vHPP8LsvgfY3t5j2C95/uknOfvUlzBSsGgi0Zruwa4R+4MIBOdKkuQMpSD3QrW1wxOf+RRPP/0wG8eOMeyv0UbPdD7GAgcOrLN2cMTwwBr33vcGNg4fRYdDbr+nz3t/QNm5/y2ceeIp9s6fZ210gI2jx7nrTQ9w5xvvZWexx2Rvm3OnX+L8iy8zqhJBhXnyTDJP5iyZsZTGspH1WMtLUuupXjiLjiekXkHygWcuTLEm59lLFwBP4Q1V0+JMQS4O0ZvMVAogYsmdkIWGzCoaFuS1IdtriNsTFtIjN46h9xgjTMJpmrUh5zPDY/JxzMaIAydP8q73vY8H7n2QvcPHuOtND5ALmKzk3MVtnjl3jq1Tpzh8eIP5YsrmhUukVmmayEwaKgOVBpwXsqDYoPTynLLIsYWFFDHTObPtHaQo2Vlskbse0npqakId8a3BlSVQItdhKn3VA96/FRwv+/qPT95H5hylT120aCagnRlSg6HXWyXFSNvUCIakSjSGmW+pSNTO0YilPHyIv/1jP8roxGEaGxkdPcT5S9tUTWTYH5Lahi8+/BBPfuZTZLPAam0Ynz1PiA2NNKyWBb2sQNrAfDJFjCErc/q5wxkgJeoY2QuRmXV4DyEos1iRAhgG6GCDWg0PX3iEaTPd3+G9VyMBVYqEmDrv7taSSUEvL2h9BSR8WBBINKbFiKVpWpJXVOl2YTEY60g7l/i9X/kV7nzgDZSjNb7vh3+A9byHakWWCfWkxm9P8JemWBUqD5W2VH6ByYRIRkoW8Z7MWoJEaq3ROCRGYRESVYgkMaQm0sTI2Lc0zrKwhkYbQrWJisGncM19sC+I6NwfIh6DtQ4vEULFwhuM6hUT5bypcL0CnxJzbRmUJRKV2DTEmPB1QuqaAcLm559Asoz/8cSXiIOc0V3HMc5yZLBG88Jp1qMjLBZU0xmuDazZEmcdg8aQkzDRQjYgOqWWSEhC5T07ixkLjWieocYhpWUaI/MYmCVYxEikcx6IN5uDmSCUvZIqBergkWQQI0QEjYncFnjvaa2iBMazMcEHZk3FICtYGwwZOYdRIXlFmoiTQBaFvZdexq32mO9sMasWbEYlzWr60RBbz8gUSFEg1pFEML6Lw1CrNESCFSKRUNe4pKy6gszAblsTcsFrYiaBcfDMIwQMxrluWr3Zdl/FCNZYrCaKPCN4T9t4jLHkeYYCtW9I1lCNF6BKYR0Dl7Oa9SiwhDYg1uGsQWMktAscOasIfloT28CGNaTWY0xOvyiJLlDYHNQwbxvUObbcnCCJWdMSUEQyfFTmse7i8QrLIgbmLrHXzmib7twWIaoBYzAIWZGxmN1sWpMxFIMeOoOjqwdAEuPFhGbRUuQ9ymGfMFF2J2P6RU5OomcdJlr8PLBHYGEqchX6xnF49QD91SHOZMzrGZI8ISgaFWMGqAqtF5wt8Qbm8znqDDaz1FbZnc7xbQRrWNRTgokYDOIck1ix4yumMVAFJagQbI4zPbIgRANSOMrRABnba+6CfUHEPHge291k1eS07YJhUZIP1lkf5hgVDh1cRxAG4hhmjtJBU1fM2oQf9mgWFZlPHD1wAGlbnCQg0LaRnsso1BBipIkJ30aCClvSMp5MqX2D2G5DsPEttYBzBU4KRBxlWbCIFY22zOqKHb9gognvSmJednF/FgwOly2dk11GNCXX42H2TYkQkRPArwJH6Bx1PqSqvywiG8CvAyeBF4EfUdVd6UIpfxn4ILAAflxVH/lGdVSqPLSYkGlk0I4ptWCgBRvFkFWbUexukcKcPBd2U0TmnsJY+oMBRbSUQ8eR1dvIC8tkNmayWBAWcwbFkEP9NYqsZDafkwDNHHWIVNWC3ckerXROAUa6wEfKHExGCJY2QRMDe23DrK2o1eOdJWU9rCnJpNtqNyYhy2DtghLNcpwrEXkV1xEicgw4pqqPiMgK8OfADwE/Duyo6i8ss5Stq+rPicgHgZ9ZEvEdwC+r6jdME1SUPb3t+DE0RQyWFELnYJwcWYRcFPDY3JDnGYU48ggrNqcnlpWsZDXrMVwtKQvLMMtpxwvCrCbFSG4MqLKIgSmWvRAYLxY0JHbbOZIZcmMY9fq0zjNeLKiCYY4wToG2EEyed0GXyy0QmyyZ5hixBFE0NiiJRROocRhXsHPuFL5ZvDrrCFU9D5xffp6KyNN0OZl+EPhry9M+DHwS+Lll+a9qx/BnRWQkIseW9/m6yMRwtL9G03giQqQmIyEKkURjhISFBFm0TDVhjXDRLzCayNspvWBxE8egV3CoN+CA7XHn8dtI7YQSSzPtIkPHdcV2WzOpGpITvINgA9vtgotxj6kG1FjyvEfAgskwzpGIBN9cCbL0qaVKC8AgapBUg0Z8skTb7zSn18obXEROAm8FHgKOXNW5F+imLvj6ibOOsyTzqntdyddUZDmr+YDgIh5FU6/zTAZCWLouiqApXXFTcS7DOoe1Fo0J7xNzDezEyNZiTh6mPLuY8MCxo9x5+Bj9jcSF55+naWd4PDOb0MwxCZ7xbAa2cxCLeQYiVE1LTOAxRGewVhC62O/LOTquuMyIIKmLgBUVRBQJ6bWxWYvIEPhN4GdVdfJVuYtU5Dq2GrtrPgR8CKBf9nQ8HmOsIVpDvzeg6BW03pPlStM2V2Ksne3CrhKGIIZWDYjB5UJIhlm9YBJaDHBuMeH0szPWXjrDelFiU6C1ylgCm6kmRsciBWJekJcFunS3t8bhEGxULIpkDnGCLkPMLnvwdZGvXayfUUthS+pWESy5sZhXU1gvK8zoSPjvqvpby+KLl6ecpRy5tCy/psRZV8OaLrVDiJGmqZnOpjjnGPQHGGNoQtM9tEAUQVNcdkjspooEzhRdWrgUQSNiDcZZqjIjoOy1czK6/SufFYi1uMxRaEEUIXP5lS06A+RiMSghBiJKrZ6wzJZzOQi/GxFdwKUjI8znRA/JBMx17LzCtWlNAvxX4GlV/aWrDv0u8A+BX1j+/Z2ryn9aRH6NTliPv5F86OqA/soQANvW1G2DbxoW1fxKep48z9FlhoIUQ9chyzwbVmzne6qJXpEvtUbtpq6iU4GNCE4MMURyoLCGEANljPgYSJpo2raLuwDqlLo0E95jEGKKRCBll6OCDMUyHV1UwYpBxWEAa3PyvPeqp4D4buDHgMdF5NFl2b9YEvAbIvKTwEvAjyyPfZROYzpFp77+xDerIKlStQ39fp/ClIgx9PKyS24S5wgJwVDmRRfbkJS4TIRipZMT2M6HNi0TV1lrCSHShgXO2G7kqKJL2SOpe9svZ6TxMXQr9sLRti25y4ghkFnXeYEYS0wRTUIGaAQniqAY6Z4BZ5AIucsYlD3Mdaiv16I1fZpXXpl839c5X4F/cs0toBNyu7u7XWod161GL2cbgK8ksWqbBmdsF9Om2mlV3tMsKvwyA+VfEKKAcZZoHXYp7Fl6VkhSog+4PCemZfoh1e7eKWGdXYZk5cwXC5x0yRbzLMcmiHQ+rj5FYgI1SkBpUySPkVyuL03QvrBHiMgU+PKNbse3iYPA1leV3amqh67l4n2xxQF8WVXfcaMb8e1ARB7+dp7hVpL2fYJbROwT7BciPnSjG/Aq4Nt6hn0hrG9h/4yI/+9xw4m4Gb70Q0ROiMgnROQpEXlSRP7psnxDRD4mIs8u/64vy0VE/v3ymb4oIm/7ppVcTtF5I37pUlY/B9wN5MBjwAM3sk2v0M5jwNuWn1fovqjkAeDfAD+/LP954F8vP38Q+F90C+HvBB76ZnXc6BFxU3zph6qev2xlVNUpcLVN5sPL0z5MZzCDq2wyqvpZYLTcGH1F3GgiXsl2sW/xbdpkXhE3moibCl9tk7n62HKP7VtWQW80Eddtu7hR+EY2meXxb8smc6OJ+La/9OP/Ba7BJgNfa5P5B0vt6Tu5BpvMftBIPkinhTwH/Msb3Z5XaOP30E07XwQeXf5+EDgA/DHwLPBHwMbyfAH+4/KZHgfe8c3quLWy3ie40VPTLSxxi4h9gltE7BPcImKf4BYR+wS3iNgnuEXEPsEtIvYJ/i+ErUeFO9wQNQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 72x72 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 72x72 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 72x72 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 72x72 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-a4k3wPFJWXU"
      },
      "source": [
        ""
      ],
      "execution_count": 15,
      "outputs": []
    }
  ]
}