{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "DCGAN-intro for Fashion MNIST",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "ckOHCuiArv5c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "outputId": "3fadcde2-83ed-41c4-de4b-654192c3e112"
      },
      "source": [
        "import tensorflow as tf\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import os\n",
        "import PIL\n",
        "import time\n",
        "from skimage.io import imshow\n",
        "\n",
        "from IPython.display import display\n",
        "tf.__version__"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'2.4.1'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1V2PJHrDrxf2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ae6e8fac-41ae-45f6-c4f8-b8fcc64b309b"
      },
      "source": [
        "(train_images, train_labels), (test_images, test_labels) = tf.keras.datasets.fashion_mnist.load_data()\n"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-labels-idx1-ubyte.gz\n",
            "32768/29515 [=================================] - 0s 0us/step\n",
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-images-idx3-ubyte.gz\n",
            "26427392/26421880 [==============================] - 0s 0us/step\n",
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-labels-idx1-ubyte.gz\n",
            "8192/5148 [===============================================] - 0s 0us/step\n",
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-images-idx3-ubyte.gz\n",
            "4423680/4422102 [==============================] - 0s 0us/step\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L0iNo21Arykj",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "edd7cb21-5751-44a9-9ba3-2f42d30b2057"
      },
      "source": [
        "train_images.dtype, train_images.shape"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(dtype('uint8'), (60000, 28, 28))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uAcCsSinus1d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 315
        },
        "outputId": "9d2dd99a-7a1f-4d0b-c725-b2c57d86aaaa"
      },
      "source": [
        "imshow(train_images[0])"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f1b752c1f90>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        },
        {
          "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": "uSDHJmuwtxT4"
      },
      "source": [
        "def img_to_float(img):\n",
        "  return (np.float32(img)[..., None]-127.5)/127.5\n",
        "def img_to_uint8(img):\n",
        "  return np.uint8(img*127.5+128).clip(0, 255)[...,0]"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9yGVvMP9vUn0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 315
        },
        "outputId": "d5247799-b649-4cd0-8d56-41c231d18050"
      },
      "source": [
        "train_img_f32 = img_to_float(train_images)\n",
        "imshow(img_to_uint8(train_img_f32[0]))"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f1b6cdb4b50>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        },
        {
          "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": "oSrY5b6ev0kO"
      },
      "source": [
        "BUFFER_SIZE = train_img_f32.shape[0]\n",
        "BATCH_SIZE = 32\n",
        "train_dataset = tf.data.Dataset.from_tensor_slices(train_img_f32).shuffle(BUFFER_SIZE).batch(BATCH_SIZE)"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZaJFsMs2v1R1"
      },
      "source": [
        "from tensorflow.keras.layers import Dense, BatchNormalization, LeakyReLU, Reshape, Conv2DTranspose\n",
        "latent_dim = 100\n",
        "generator = tf.keras.Sequential([\n",
        "    Dense(7*7*256, use_bias=False, input_shape=(latent_dim,)),\n",
        "    BatchNormalization(),\n",
        "    LeakyReLU(),\n",
        "    Reshape((7, 7, 256)),\n",
        "    Conv2DTranspose(128, (5, 5), strides=(1, 1), padding='same', use_bias=False),\n",
        "    BatchNormalization(),\n",
        "    LeakyReLU(),\n",
        "    Conv2DTranspose(64, (5, 5), strides=(2, 2), padding='same', use_bias=False),\n",
        "    BatchNormalization(),\n",
        "    LeakyReLU(),\n",
        "    Conv2DTranspose(1, (5, 5), strides=(2, 2), padding='same', use_bias=False, activation='tanh')\n",
        "]\n",
        ")"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kqdl08rxvWiS"
      },
      "source": [
        "from tensorflow.keras.layers import Conv2D, Dropout, Flatten\n",
        "discriminator =  tf.keras.Sequential([\n",
        "   Conv2D(64, (5, 5), strides=(2, 2), padding='same', input_shape=(28,28, 1)),\n",
        "   LeakyReLU(),\n",
        "   Dropout(0.3),\n",
        "   Conv2D(128, (5, 5), strides=(2, 2), padding='same'),\n",
        "   LeakyReLU(),\n",
        "   Dropout(0.3),\n",
        "   Flatten(),\n",
        "   Dense(1)]\n",
        ")"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nd8zcnhjzKWq"
      },
      "source": [
        "loss_fn = tf.keras.losses.BinaryCrossentropy(from_logits=True)\n",
        "def generator_loss(generated_output):\n",
        "    return loss_fn(tf.ones_like(generated_output), generated_output)"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fgWHoC6FzbUA"
      },
      "source": [
        "def discriminator_loss(real_output, generated_output):\n",
        "    # [1,1,...,1] with real output since it is true and we want our generated examples to look like it\n",
        "    real_loss = loss_fn(tf.ones_like(real_output), real_output)\n",
        "\n",
        "    # [0,0,...,0] with generated images since they are fake\n",
        "    generated_loss = loss_fn(tf.zeros_like(generated_output), generated_output)\n",
        "\n",
        "    total_loss = real_loss + generated_loss\n",
        "\n",
        "    return total_loss"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KCYwQqxgz8o_"
      },
      "source": [
        "generator_optimizer = tf.keras.optimizers.Adam(1e-4)\n",
        "discriminator_optimizer = tf.keras.optimizers.Adam(1e-4)"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Rb2-KU6K1J0I"
      },
      "source": [
        "EPOCHS = 50\n",
        "num_examples_to_generate = 16\n",
        "\n",
        "# We'll re-use this random vector used to seed the generator so\n",
        "# it will be easier to see the improvement over time.\n",
        "random_vector_for_generation = tf.random.normal([num_examples_to_generate,\n",
        "                                                 latent_dim])"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Xnu88uGR1etc"
      },
      "source": [
        "@tf.function\n",
        "def train_step(images):\n",
        "   # generating noise from a normal distribution\n",
        "      noise = tf.random.normal([BATCH_SIZE, latent_dim])\n",
        "      \n",
        "      with tf.GradientTape() as gen_tape, tf.GradientTape() as disc_tape:\n",
        "        generated_images = generator(noise, training=True)\n",
        "      \n",
        "        real_output = discriminator(images, training=True)\n",
        "        generated_output = discriminator(generated_images, training=True)\n",
        "         \n",
        "        gen_loss = generator_loss(generated_output)\n",
        "        disc_loss = discriminator_loss(real_output, generated_output)\n",
        "        \n",
        "      gradients_of_generator = gen_tape.gradient(gen_loss, generator.trainable_variables)\n",
        "      gradients_of_discriminator = disc_tape.gradient(disc_loss, discriminator.trainable_variables)\n",
        "      \n",
        "      generator_optimizer.apply_gradients(zip(gradients_of_generator, generator.trainable_variables))\n",
        "      discriminator_optimizer.apply_gradients(zip(gradients_of_discriminator, discriminator.trainable_variables))\n",
        "      return gen_loss, disc_loss"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1DPQkOVx2yon",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 703
        },
        "outputId": "ec9e3c03-57a5-4643-eb3e-81d3b15eacc2"
      },
      "source": [
        "for epoch in range(15):\n",
        "  start_time = time.time()\n",
        "  for images in train_dataset:\n",
        "      train_step(images)\n",
        "  fake = generator(random_vector_for_generation, training=False)\n",
        "  fake_concat = np.transpose(img_to_uint8(fake), [1,0,2]).reshape((28,-1))\n",
        "  print(epoch, time.time()-start_time)\n",
        "  display(PIL.Image.fromarray(fake_concat))\n",
        "  \n",
        "  "
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0 89.9477813243866\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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MgBBgpOp0RlS2eUvX3bd3ShsQHLXN8c2j10u7EnKkXwu2aqr6aO/u33oA0BMfmzxzMjDx1EUydwWFZcc3tXxMkQmhQCIkohOgjDAqjDuDarw9HZU3t0UUhqBAUAPQgG3NcJJNEJIKynagXSEAgnkQBAwYVGpt/+gH98X7+tNpkEEOf8kRzMtEEKMqskC5FlMotmfjnX9+Z4hQQCGiy+aVoakbFQkn5/zrJ04x4+yJFAEAy819vDx3qeu5X08bLxXC3EJV94bvSQBl4Cs8EwDUQIQ7LWhr0Wj/QPrg7+4LUAIoBLlQfL2v+cpaXLtxI7r4xlRtegVcXTeE8Lx7nNWj6V/86mJidEZSwe871LGZUgAALqEqEzR4eNut6d237tO1Pl3TP5sd+uTfIQAQAX6LPan36b7UurA021ws660qAajbC5/LvfCLzHf/+8vNxxcjQGD/bxsBAAAdUHAlV7u4d7lkDreWeRvz1EhaDKgDB29FSWfxkI0eIxy0e7JUS4Gk76pvP/DqWUEU3yeBNLczcSV+J41uOhCReYKKHmx/IUikQFMdfiUzq413Gd1rqAuRfKTSlinfQNCFBrrjqVqTGrs3i20ZVeY6d9tTyJkqaRKzBQv6VvbWMEYGZe6pOhtKLiJxhMhTRiRdhZEDXS3s8ymJSpTLFr42eANk01N8JnQS62prxiIEWKyG955eFcQn/oxNrqXP7L7QUlm6uueSOp05sbWeAQAwlEyjyy+H9zfK/b0pK2QAKu31Lfc8fUGAJsdCoumFU43Y5u1bGW9HzdiO23f7L3CCICwn+ErUG552i1jy/NiNPtubD+XrjqwYkSbpK9vulurOtl5PBFBu/8LsWKTYBNC5YoRdbNkRGuwY1Ek2KUktAQadsO/pSAORqlxoYoQ6SvxSQlZGo57cGOocR6JGR5qh3tVcIzH54U+0clSQ3bf3a0eY2rQBUdR5rLxiDlYhTYr6zowhhzURb3jzESnZdW73+kkRYeWeghENl5gMEa7/x2L9SnqBCcjc/XR/yx6fxtVqV1BV1WYxGGj6W76wk7DanbW/z9JtNcL7WcBMKSRRGPzqynMslo8ma+nQNR7puHHwWqRj5GRWlRRAmVx1JT8QWuqplSQ9W77/bFhtz8WZ5vPAZuBAEQExIAK7+1YT/V3t6UWXKojIRLnxyJ8q0fVh87w0Ii73ks7w0HSSyLof2pLPI+McsOmBNbY5f79lrAzRQ+Mt+21OPvoSAGids5sO/Esj3bkmazGDOECA2MbvrM4RykOZtYPSE8EtB4582hmKREqKBgEePtgIknANETypcaCjvxoz0hgJD1Wl2IqL9IodrEnx5icWrrTsCU0NBbqCAgw5NqD0hczvmrKrCCe948XWtj3kLjdF5aakMOrLkgebfzqASEi0MRI4XEtJttyV3kn0llBkOwFCgGTf+PyBN6r+COiVOxTKZcKyxcdwOe9sPy8QXeZsbs2s3ALhYgAjisFQ6GbB/PLXpOZsZqYY8Aloq9JoXbVLg55fS+eUdGIVOLCFX9dn16/uyDTW0+VVDFim3W5yM/U/VccLPlOfK49JW1uTbz2w2Lr071dHT/3x8X6UiqRuKfWqKqRUpZkKOOfxrQep2Yyu5WzJAa9q5KpU1CYSP/bqn4ba/JBaLe+xlmXJIz4BUddGWCbZrmQCFe1qq2I3s1LjcvlHpl835kxVWwlHG+rs0ERh5R5SXuueXi4DRwLgAjjjpdS+uRt+a/bxGH31V4e0bwAA2DODp/PXtCejPXqifK8t8kNes9z5sqNXOLiVzrVGSCnM94xSOGSWFneXq/XdK74WrAECeD78+MmHbzeqlxXzxuu9jeqFVKGexxpgs+Plku08vZUtwujdXKwfMCv5Qy+nEuse+EKtTMqyW8isExb3LIy4nOs+cuMpBQHFurgGT+yPrOTtC+Uzva4ZP2gudSzXmDu587lX1/1vCyWw+uTTWnDmNmfhV3/4xf4CXgAEcASePXFftMYWg9XI+Vvt5sLtpfKP6t/0qLq9+3e/1Ju4dzB26x67qrkuypLs96jZZAciQKdsrrRW1v3eZDK+qQgB6TpoHol9pYGS+tm0uRLMXXw1UT63Upyx5PZW5bF9hxIZ5LKjhlDeHtl5190W1fwiUEa8SJMKglKbvnmIkGTAbeu1XCGqlqizsibHCUUQgNy2D2qDt3aZJCGWOVMcz8+/fb5SogE2vIeIkYToaitdXxu9UhYSLYSZAgA3k1Ysjv9wdubir0b9xdORXacLcQIAJJn4gwON3Aet+fzgtYIQxwUjBfj8nZ0BGVj/4Mc/2xb6cI/Zu9019UDNUUnZzkiDwchG/oVL/vHvnA2kC/MumVmIiFq+iShxCGU/tcctdBUulkML15uVgq3oYf9T27eqGpCIcdstyd6PJKIdvZIaClmgiiJKHrKHygBAGDYC/otz/SlTDykL5rXRSogPpAwuEi2PfYC7u3m9dnD9SM2qo9R9QPr6X6S0IAAC+P6kemXWRhUJBUHiuk8dvxJwpaSyPdgbfHHX7nPT+/6dvhaKckZDBGMxzCkh39OSNTO8qyUe3MP9lnZmJh+UMeYtHx/OyZGSEynliVwvtIsr8tBKWNymJFO3/irPoIm1iGwEux2ndRidge0MVSabu/cuIQtB2nBjVS0O1rYRo5Ee1PyUlDJ7Bm54jCMAEL25NwzRvrpo/72Qoimk2Nr11nxcyASwrRTyWpYOVNZ+cdh0OGhqo0cGJDfzcuDRRnjJUypeomNmpWcmRDlANNVfzxx+pzEsZvAh+8Lm223i9Qjo/6M/aJJscCisffBcZ9BZ7h4OrSf6bVcalEmm3yhIN2vT3to9UKmZST/ck+OxwHqHUeNyPNnmq/svRD3txo5hvNwTrOpelirtfVKDZo3gtoFUY7jXp8EU8GAERSTGhCHEO7tOEJAo5aR+KCZa2qo9XV6ufUcI++46LmnB2JCd6Z1wDLtQHrp69L5BS7K6PTkanfYJAQRCfDXUSUKpkpy4MxxgW6KV7szpsk6zt3V1DbX/3uEDPV/roVrHqkkpEUQRpvkxjEQiex4O7mnZ2drGQnqbrkey1AQJROunmi27s7u/GFKH9wU71J6WfZ4TCxllEje6kl8nnmc1hdkixcmHIKBvsQxDlhQSjlSJjKkdAwfuC2+968Ntn0llQyM0kegLRqRsmPKWjT4O92f9UCagRgwnplFZZkCeqkrl6Nbk8Ac7D9zSro4s59d2vLTEZKleJ1jB9wsWAp3foorSZdy288EH7u3fe6cAgOyHaHBXkEU2ixEpnj7MaxLFAgkHulIfJ5m7tnQNJ35nZEf3w+lMeEQhatRziMGi5Es3i23gZNMjak93JLg1m7zF9ESg4gd46kM9PYfDW/fs0e5uBiI7FxqMSTVMKDFyEFr6hju2wMejMb2FyUxllMnoAwNIf6RJgKit2JX6nfSm7CZt5/b7Rj4VDWnpaHTovwc6HjEin+hkH8hawbfr0kNvjINQRtd5sciTiAggUGl0dWzV4qmEHY4oQZVo6kWn3ZFqRz6vnUz5U6G95yq1FpZbyhK+3OasP2e9wm0anbcrqfOVW5Q6ZO283190ah2CC7gWrl65ffSIqZ0ULfTSiS15ePdjPp/dy1lhy+PERjBXvZxDWs+SsdQcGzMkxzdEqTWSh9LkLvdcLDpBO6edZUVvXtui15c20/y8VNgoxOHE9L42UC21oTsSeMKvX1X9GjYnB9bn/eybQbaOjeZPRe6B1MpYB5524T37AyT0GCnq6zt/Ert77Jtts0cQANxffHH9hxf5t9yBNjp/+9zy+DMV91K/I2sfeJaWjzwm3m4k3hjAOZtHIJ8LFysr7QrPtf/8plagvVSzVk5nzPXL97dMW7W1OkADrSP/YfWdtdQzkZKTnB8MTi4MRGvHonL9VXoG694QmXaXmjzAwDGYA4bvOJoQHJcjAsBbwPXi9w7bxWIwlFtovlv2Pmi4/PbnVfvxv2x+ZyX4rQK3Cgu1X7OjD6nu//vfpzfNY36DN2ze6O9uWOPxOr26J1Qvhflq1FgCClkts/3yeD9catt06o3Zl58uuG5utlyqFV0BUujgtvryAX2MtwsTUrNrzDniEdtyusfsULX9dreSNEqldGW9UZqbM601V3ECi5s4CMEkz9+UsLra43rMoB6i70r1ikWEAzVpWzm3PTQt87XVYr6xXJ8pTVkc7JtIiL75zzlF0qQWQxYSMblVnmmAh2ItQKfejdarQtSLubn5V+bUxWMzDntffgCiQeaCDWv8oH2ps+vSSoAAgB1e6j24OHtITL296di7F2aOjq1e/7FjuNHQ7wor0NB6j/+0yztSk1fz9eqFs7NXzi9hNbDa4r6nFJWnC+1DvtJ1yI7sHuDxDAGGpjYba519syU3v1K78Ppb754+MUbX3616rAFAgm2DXdcLff5xoVtNQUoN0Rx3iWk3YhcAACJoqVueud7Tx9mm6qXC8pF55qXFY5Vmx0Lkw6VCL/G9hj+/ulh5dkJq/vPVAhfvUcLF6IwiBd2EqttaIMDVZsVEj9Ymjk2dmp1ylsPPBjPTX9m5ZaImixszoaLnSEENndpWL9jXOS/a9JK+1b1oPMjcUKj5zF5cG3/9GUIsX6lNSqvXtPYza+odjEcizVMqUKIYKgTiWattR7+ZHVBdSWH1tiGPM3fp9MpFDUK16PEO1jiZVd25ASVYijng043awbra1Qt6QCF6WAMppJzd0Wt5zC9PXjtZXbDngrPt3C+nYkI24ur6ZgbwfqGYRbUHU1rolvhwuHi6ZpURAIrXXnn7e0JcXm9OXsi98JPlwHdeE49yCESlY4H66HOzPyrcqJ+N/NIv5F6Olt58Pt4NblKHOX3jUGTaHp9Fk31dO3bs6rj/Dn9XFDzavPrjo/+3WsqNmidY8cZrrdqbVV/MjOKo7xKd5JzGlkUj3DtNDVKSAt6iPgRuUGs+fw+lkBVMxCYPYlP0YOj8zHUlGnRjUfLrTPXo4289VhHLlhBVFBzJr1s95eT0hAAESoAwylOdA1asNQFte1rlcEfU3PwpvylTN1W5rl/wluSjPavm5f5/ej1fWf3zJx9/cdSnggWSxv7cQW8H7xExjIFs9wHhKkL00ZKUZfAxVFt3uQlFKyZnlxeM6TlHBhm/AsgFl6xOu7e0A9vcPjcEMhKuseMeEyav3UitZdOZiZZTMyfaT1yf6V+4OKs2pnyGN5tRS+fKMqWEIiWUMOyOj/lE2Lw2v1aqT5XH9UXXldaKU7L5T794+UoT3sdQ5OZ/K9r8Ydgf3NlSdkQDAEB0zV/a0xT2YBEv0bwyXblefvWXDkUp85xiKYUxecpearucnpHH6JFXji0+8/MF3WPNv5AAgABQofJD0ibsb2a9SKBxIyoKnAhTmRqtlNycs+q/VC2So6vnfvn6vzzxd99dEIhSJtNoHR3Jt+Uilm7JHq8HXE/ICLHPrBHQgreKgcxjywN6vLzmnJzny6OvGGaz9NUg7StOjDiYBAEOAKCYPFM9M1t4/aYBUsR4uYMMKgk568SZJmuY0K55IS4tz26+NGlX3nKsdP5i44jiLolaualNOehCZU25PL46ubhQgVIt7RWmMqe3OmkN7Pyr7vpy9PVZU7qi0OaNVa+q+QuXjNX5mHO59jceEeAUxFS53cyrK4UINERnc7WFr+SBg9nQxy7XrJ96C1yfccuehZHxxaJ6zgIfyM2JjHpeIAdBffREHq6+4aMgji3mfQFnDZ9aAjzmr7xbwTX61G8gKFD6zYrnvND9zkz/zxBBAACsLoSfXfHwEhd4skCE582b2yfzIbIg/sgr5Ftm5k3+fbnuzjKRqFTlcoS/sWf17ea3bYCN9quzfH1hbi6iXjdIreDOLQByaNTZD5qCnAfhF1ywGG9OLGAdlxAQlq9HKucvpNjJ880EM+K41lroMDUZmuWTFkfRPAPmzLeujp+fCQz8nYuI5IUvFx32QmNlSfvrEsJJ8Z5CVouPA4ILNwmhmIfp/lq1WvPUMurgUCsvSAWo6V0zG5VrYV6eW7UbZsF6p2E288tVAMSGn1NTpbOb+LEXks0r7wbPn16rHyvz+vopZ8LDcjHlNDpba7YmUQrcAne2ZFnnFoocAYCKms/O5x0+vlZeuTxTWr44s1LhgL5oFtG72C3yE/Pl8tjK4tLMtalXX54SiEjIBhzWnwCJIAWoV3nx+hGPAyAXggv0CTo+KkA4rWiSW28I8hsCFFb7CnfW3qrnnq28J1jHLyYlIdKSEDYCcqCRelMmrvVLavp8pWDzuiGs6UJ9/nLZr1v54rHZ+mJ13YP3aDmba7RUluS+Si0gjhUkBAAuHA9RSFRwhwjgYFu68GocAKDgnlfDy09l3Xcfj08f+TF569hk7lhZVErvNK8iiHwaF21t/s9i0vPfCAsE4BPV8ZmX19a4aHqAqL0X/oKf/Bz7N9YIghA3XjSNUIlJ0roPzky5drkJgrpOc7yB4uqS37Qawgmn4+6Bw2HSxQAIt3Orr6BTPO/NTK/VGQ0WZoO7zbJR/PFfRT3PzU0gKS2hKHPkiT7G994xM0HedGxKCWgK5dH1wJzcdKaN4rVz4sryhU0eIkHBRRG85phf8y2/FBT4zgej8ug2AkCCOjAAALWPM4pIcL1mTVvugIZAUCAiIDgUdwQIRIc7Krt7FF8HALgpeCIb1x8OkbXsCq6E3uPc8/yrTYQ1G6AhOBBV7nrgv1Q86cbFNyTue2UPMF8EuzHv5XWJ8GySnQ5f4k1KCBACQOVsZX82RBPoILftWjsBACHQEQC+B4IIQFkPhW9LMKEDADj1K6f+XjhXfkXWr41b+bpWmqW77HKo8uOvxgn4ySnCnVeh+KMnnUVjw3GfvK2+8AuDc1HlCO57MQsd7gh30Q0lohQkCr4RbfPiwbAc1qhL0B6PNjxBKAJaCGD5nlhiXrvXxT4ZObPuUhcAObprHS7Mbi1Wngu/Onpy9NcrL59/0V382vlQCQAbVBBNdsWqsMKl+lLv6Lf+5YmfjQnkIAiGRRvdPD0wp7xhH51/4/Qrv/zWj35ZQEBEtDj3Cg3OV33Ox8ul5rOXVubfdgDQd0EBAPDPyJw44JHc9bkb36/MVhHwJiQ6th244ohuv83/XPySebOH8178av1VYZ6z1Qr3G7+huw5szKMIghQkeKT22lW//J9LrosALgC4QvCcWGttOK5UXZwYP3KBA6IARABhLRyekscOFjrmI+TaRevixgzHzRFBBAdQFQr9xsGiv9GSR8at3T5hnzW96/svNZfoFa1cnqDFfzqXLgDRYARC3V0oJuvTlR8iAGDt2Of+eLnLxA0u3jdA+YHt6SrZmGwQAjgHqC2/kV6Xj+mr6hWsl37y2oVRAUgo3gQbwlJVn7SpH+2Ifj3flE0ECDksggXJ8G2SsAJKfAKgvZz8tPtNEwkiUgBJ4iiCNqcJhv2JM6bsFgFA5agbhajRu7wvMXp9MDRaDgeWGNAiAlBESoTCHIEEAQgBSXMFyCYCxERtg4vB46631Mf/dnVVPdmxpq4h3PSPQAgxMuWKFNsZ39f424ZLBLz/D0LSruJKgVDCAd/X5I225MYiJNVI7ew60nH/5e/7BP4t/qEBFvASM0JoLkblvE+AbLhQkPXPUuOlR3T13N1T37YahG/gGcBG9Cv7AMFQb6b1CZMTAQARW45YVRpzHdFWComBayywZyr12+5f1AQhKEek1bCLNlCk/ObtQR0rEjo3CREbC6I/ugV2NYoEAUASggACCce/IilPfUFZe+Ue9tPGqp1DAEreEx8gtqNmbVtKey0eucm/52jCVQ3fj6bN/O0x2d8STJ9vMABBAJChpyR8NFIghdKgOa5TRQIAFNCTsNbk80eqMfNKk5g5kF1LpxtcIwGPyAhANt7eQl9s5F0hohMAwLklWht72R65dlZLFs1uBf4tV2CCOK2o16LnvOs3611wE+7ArxTXHCF8wcX7sc3GLCZQBkBkKvdvatvCrPrrVQL4fg2HAvqdNYwlkQERZg0AN2gDAKwfX6wuPTtjH/2ukrSQ/abPZUhAQ4l3znevyTdfTAjbUoQfbbO9ZG+9entP2t+Z7L5UURAEQb9ERNUDRGAc33NxZZNz/71zZQKEAkDwZa3e4mwkVu/FBu7Sz2R/6SkXJp5U2/KFKAUAonFvwxKQyuARRQt4ySXPUlwBJOx4EheCIaACihNuBBoh1/BDS8AZ8wQBBAaCyOCDRpAHG8TRTAQSsTxJcKAESdChrmYzX+HU000ORPU5AQQmECkiUEBgiCj5CKRPmdCaAoCSxH2nOq9sP9U2aVhGI9jwkWi+TxCBMM5U30dZYiJQQi75CBCyfSYEEIIk2ECkCEgRNwxTEx5BQLph7dRgkpfMqVb7pEAqCX9jnBoJlQSorhAyR1RcRGY4G7VZAlKiijzgqWa4Ap7kI4CMPgEESgShwIkkUx6oCs44AoQdn3EUsgBUQPYSjWg1XY+V45PoM9lBxnEDDQCAblgzZYioeCgIAqEggAkBJBROd7zJqpKPABHHYUIAoaD25UgzUzCqyUIobzQ5kP8PRlz2JvAj1+sAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<PIL.Image.Image image mode=L size=448x28 at 0x7F1B602DF610>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "1 59.20023536682129\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<PIL.Image.Image image mode=L size=448x28 at 0x7F1ABF2EC850>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "2 59.25111627578735\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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73YQQIMZDnRHdm+TKBk5khnuowLmbvkQejiHjcsFfYCq3qOX1ZRVWm9+SV2VAxgEwYgjlubSNZempARw+TXyzB/2b2L9v2GZEADShZreSDcleWir2Vi6eFBbhMl7qikwq2D7qzBMlG1KGkJBBU9LBrkuFctsqR/R8T03JhMPlRwCgPKCJYHHd5fEZNxmF2e5zecY8x23c9+YQCRK+ploO77ixqjBpp52aY+xZ3R6/S1pGCQOgk7E393ffcOTpCzXK4QfcgrywYQdiWtOqBhLFec59q7jT46bJ0zd94Yau0lYkHBqjceOpn9ZfN30oSWb/I2M491eRiuiPLatQG+xMngy6Lz+h+i89byjofjbeMPzY/obMDzpEBGSc5+xVGq7h1AIYcs8eu8QjMVbj4xEkHHEpTDy3N7PlyvMDtyb+bc+W2Rf75+IMAHhEpCpj6C2y1YhD4nBVh9O57bVTtb5ThWaFI6SqzzzqVMhqHplQF6L5dztOtd4/WntU5SlIYXHck/Kv5Pji9OoXS0Tza1f2HrpoYxyxRJEBQKw4Q9+6hxYPO+3LrySDiJLMgOk0OKwss6jbytrLJpwTeZbbtWjew471v08AMMVUTsrtUa8TzhY7B4SZT3lQq8vkF60Wa/7TE6mQIvkxhgwYaGdFha949hZ5+32PbF//7tjr6+WkzACAMF4WnHLpmmcqw8aK6zy+b3mk3Te/9otouxF4s2t6rW7xbLIURX0hL60c7vHf54rVfGcwxVEAoFduFNZUXInU9STxhpUdm387D+aE133JTIC9+Q26bqOsr3vmps76B65W/1OPAmCS2J4hki7zy9XlIhVW9YK0uzXkvmcsBSwjJwaDfb7K2JE9lSRadNOAn9vja7/pu6dPX2CINKax4Ppk5v6ERRea/rYCxNt6f7k1IDFms5A7MOVaaw0oFcet5JZNHzdb38hePRMw8naTyFdnXMpVxQRr+TWKZWoIGku3eorAxPn5JrUVpDka18f5DAnlz7W/C5Ca4QEoI/70OqN5wjKZORsK+eXbLKf/648786IE6mrWODhE/NHqB77wZdZkEdPWSDJel2cAghyR+NLkzuRY33TclGkW3EolNzve3CgGnQAA1JIqnEwOcWfbTyinDkwInzpg2r1l9umL0WHj7LLZueQUyV2aMIVpk2st3zIeHZkYiiXvGNQ0PwAA5ZiJt9P0haqydYmy2aGjKt4c7ep9e6cdOe6OL/3iB2vg6w+arnjNhfKavCa736uP5W/xpzwAADD+fuLG8TcaA8OdFX/6zStPLNy2mKSqPa30rm030b/y6ck07nI+Xa8OsIJaL48Flk2ba9nXdgbe5IXEtCnPldncPPWKyc/GOAZUGvaNVPR3HpUmQlMkaImtnzz6BNMPjQEABU6wlc3aee3MpcNtH5hgFekb+JwZMohCwtEIkap9kSfLDcn3b7zc99PutxNUAADgrY0rLjtm+F2J8v6Lnk5zmTlVzJeWbaj2GkpO2Mv98m7JdrFg83CwMqnFrLqVaxjfNWsPX3dSFkEXNTtP7z0HhcQ1wTWXtiqtx35mGTfwrh9tlMfePn+EawVMuKbKVVupiytySzKnM0k0YvF1HvOmZfJMbY/BaXUbpQrjSrFOW+29AgAM+TauGCJrj9OJteP+fG5h0cm58subzp/2xS5KnOXGC5KSnN/YUsLpBuIrqdbq/rD3z4a1XmOKIgMQXAbZ1Ticyq9LrePqDve4bdU3OPI6XzSM89ytTzgYcFRroJriHyU+qyFP9GVWJpx6de0wAMCJ86rtEW6mvnKocL3WMDe/K8rbtzuwLC9xoqxGKPCacMQjlDqKzRUz3r4b/mDeeVPvctGC28REuGicRB3pPLE+uTti13e+GwZO8/NbJnaYgxPrp/hY46xkrNI9yTJXeUwTgKLJsU65vl9qGS46sUas/+7tzZcnAQD5IveV6KS5u2yW72weA/3mtHigAXmjAgBEWS6vbbm+tMo6Q9JnC6smO7Tg1HGbcOEfOzqSMgaLVVvB6IGQQemCguBbxd5LT801ht5vyZdZhgplNRWZTF84EItx1eRuR9uH3xozhqvNGHrhyV++9+zZkoL2cGfHpc4jr4WGRzu1sUuhUIaCz+gzNbQaziQ9rp6Gitk3Veh9Xhc6/jqojAID4K62d3Vk1k0fjngjFwv4/IVtMgNAAN5gGqO20yM2uWriQCikLiSoyWLwma8r7vrTUbny5P7NA6/MBeMAAFp0Jt3SNr15bbnjMjt1qSBwm9D+9k8GazWrzrpisakXknD5sW7adZjCwJPxTO/zs4lTe3lLV5ZrWg/Zbh59t6T4ZPulssZ5WTL8sHJyWBp8LaB3/ed4ZOo3FfzxB//rzLlfFtgyaTKsvoiGc/eMVke7HMXxHztMvY9M+qeeqVmmgCgUOHffmP+Oe6+1s3gle7qi7Mw3IpWpC59vZaAxYdtav808Fu0duRTZxH1PPzb72+xRgvW20Vc1+7FvzGwZ/vW2PR0vDd+yfKS60IEAwNu35p263xZ1zgWN7hKfqZwapZLCd2VvrU2kXBmnNpsjX/nQEioyJ21FtXmRJm+ckY2OypI5AkoyT72j3zUieXrW8muq+nd0vufuq2icU6a7DysFK79yavk663dMXlPDhvZa2RlN8BnG6aaVpskv1qY3i8zBLXcaihqcarXPylXvNyUZAwA1bQy4So1r5FB3iXumPLAUQhmg0e4tSZSHtrjq6RVl6W2aYpZs1DJQORqsvlhb6ywJfxjMHh9VLTFaWlG/2sjvzAw9WN1k/LBkzU/JQZ9o0md9ELtBIkUPW9J1+UA3rQGaV5ERN9Si4pkEAGAMMF2+MdHS7o50lry4cJ+3Of+9dTbjmo3LFF8tNS9v8iQ3Oc5hpJW3ekJ1Ze3bJXnlTgNTSnlo+H/2TPX3BWv6Nl60pUWJ7nWNbQkmU83lImn0m9SbGvSwuTpWZI5RovJ7UmsjSszsiN9ZVlIcuusXw7lln8lJfTRjaBCv+mub/6vVERMH0RoSs+fATP/1qZmRiO7KlHHapJIZPhiLnTpCyNjbFp0MrExPB0SJS1kNBiJJCcIXcIEaZzKYAmQCgwTExtGjO1SzclW92GiEffsVmmYD0fTo0Xvfei56YPTsqX84d/To610Hrk5OM6oBKzLcZlUUU4VhqKFAF9GRYSZveMDUEDiVXXdQNmCJzaHPTJSOnD6uLb2DAQSWGdSMdqlNHv3t5VPqkhZpWVWhORUWaot6myfDh0amBlNWAACmKdyMMszaJjEaYaqa7qUXAkrqli0Cp6ZfLLz7P8YvMDX8TTo5NhBNRpJU1CYdtSzgy7IlRGx21VnPz9rGp37XMj8ZfXXf2Utnn7cmp9tiscR5iA2enuXHf7+9Wg+AfuWHqiMVsd7Ev1duVnXeltRFPzdbWaLNSqzU8zWDapKqC8QCGy/wVsqLpS6lwqZMqcCjDRRubFjDgqC/g7a/8d8/XNBO15lgwBt2CEdXs2NxTnOevz7THSkmAMALXD7HDQ5E68+PV0wPjzOl7VhV4pj94GH7LGe07hCK52z8dkMKbDTDcwpVBCxivGAEwnO2pvVELyUkbfVkLCqdONcVNtYMrvlAAwqg6zA69hdDeZgO/UTqlo9FdZABgJWuL1U9aatSOVtuNMwmnArGBE1hqVjqagIAgCliJBQya+X9RfZ9YmGhJM/7DhkaGUmNt10IecZGQ5M0PX/IIMShNH0z71VAT6C80ybeyBmiLe9y7hADIITnqawGdFufkfT0PTgV6gyNRkyRE1JcV/6atnf8lHN58PKIbUT+9pBi2umYFpwmTsgDAAC+qYsWFJBN43f6d//+r2hXbk+sngoEutN4xFF+YlvN1MhXtJGekelXlM4amkHlfPrVlenCVGGoQLVoaQPqqBLZwpCYzXm37pUKiU3ZlLDzAtU4I+icwNUgX7DxZdVZ4G1tnOqWJlVRTqHdGLZ+NLm4etC5A/eN5SeblOaPy/s2tq1x7ztj1zpkAD70E8289z26iQV6jk3Wj00kxoYPhk2z7yZijGjntBU+b8E73V8omgrYYLhzteH98yuWtX288q0hRhTRM+jvDlVyE2CbYCcrRt6coeNPR/n4fNIgTlPVEQddKxhJzaPd9FFTuqyusidojp7kTHJXRy2d622GNw94Jqaz1XcZ4dKej7u4ooHL3ivlC7DFUc4RYgbDXDEPQ9ExObrkWsSgFdja6wf7y3YcnTJXXBz4ct3QH412kegATNfJ5sOxONfd445d7ay55GtPKix2IpFWESgAQxJJUdbjSaf+MF0vPcfVkJm3n5EPAgCAGNBpT8XJl3a3tL26Xw2F5xNinEGEcb32oHwpUWBuH62q/PVoCJ+Xvt4zo52m0lNPeH/X9qXKQbNDHPpwS+GRozdVdb7612ODNPGhL68l79Dph+yzqofM/Ok+w+tvPumL9bo7wgymLfxzmqS7XVeiQuiiMhhbuEpCCqharKO/+Grd6faNrW98sLdu8p1i2zkNAPh0r8y6zCZrgz+c3+CZTUvB4kvmeFJjAFSdEA7cXphZUVClcmJhPFRlg6usyT2sRHQN0BCOe68m7R5jxlppihj6rJOqKAZkns07kEFCTdhEm6FCnVrIllB/cOKm0ul2w4p30X5dR/tYKO9PVlfBwYlZXc1eeRKGgtlUC7PEGS19O73gJaKHATPqwN3H0y4uxif5xSs2f2F/2ucX7NFZVuea9pdwhsFkDMYpMqAaSxzg9FFXLK/Hlr8sVtFjnAPAsMxy1XWm8QoyRUFxiMtog/n3GSdbJDENgAzF+jG4sltpcZrhOvfU1XnIZgwgQVDVzJgylKPuHZ3rkI1SuMxt0iiDVG9kVCirvoTWwtCcyaAdExutiWWUqejJP/k1s+rb7VPMRGSuz1uhpcYnslq9jlEdLv8uQ9PlXJXH5H5Fgvd9J/XF6Zg+HRPcUmK6hE5NWUsEY/5w7ApFovMszSCdBnw/fs6DwWmra3bcEQ/O3wPH1rlVtDFlOD/dfVhxn/ufOfs/dd6y/r2PGONlhyFC7Smtv3i4/23R0RFUWSaDqC7ZWci8YI9Ro3pkwX+gng2IR8RA3cevtbvhhSSSQR3NPQrHJ2iubsDQfI+0KThz8NFjRzvu714IBwrAGFLoyrOOj9T61CUlwtCIxzwt7xjeOrUtGD3+aDRzpuWCNlc5pAIA03FMTB01RA2iHEqd906rDEBPL5SDkeiMgR5RkHEnENhDsZLHzz2JgMCZKkSb+dts80rxt18+fOHHz5FrFmRGSVBYxrVfrfAcTTjn4lFtz8e/1wEYFpqlOq3ruf3Byx+EHAd7sOmJDzc8cOQHSMPnt4OmF08pPdV8OjmdJ5x86S7vyTvMY48zlkzzE4mMb7poYEXg8kGHopzlFqCMAXBrTHtaDS8/GADlm6ek45+r+W1k29sZulhlA17nNKQIAPNlcyAlBdv3i/pHJeMfWIa7EvNvfwiIMuPrjcbmjN5D5KAQTiWBfvLEjd4Akyo175ylom8gvfjOkFAQrAY5dk3NeVFQQFFlJ2oDQ/b3bgy849p5Xw63iJh7I0cK9HDRZEEyQhcYEEuGmZx365P7M07sv7pT6BdmPjokOqYoAOF1RnJPYT6z6mzZ/GcKQDh1SYVf0IArXf+2ZLzneWVKyqSNZ1ZBz9CW29uXCssxYi4tSJUFCkZ7vL0ZBoCCThki3/pr8+ty9CW/2qksVExQVMDwr27nGp2ELJHvlFR/d/5ijtzS6P1+GkSFAQDHvEW1E/4jq2mbcyS5xDLksW9NjVd2F0rDxcDpqQZtX5PxtxOULpbUCCWMAiAwfn57jjyrXFOrPq+mVAbXlOQYECO1V/TwMSmtIy7ZDi4QEalKuAISTeaHU3TpUOAMJM0+7b+Fszo8defzP/3mFecb26cf+nJuKALk3l3xvIzUrnExtmQcEntd5spDZb3v7phq+t1jw21HgQMZABAZGFNLeH+SHN4BxoA3xj8hPphZhhlS7HDzz//5vamBn72QWrlmiczIEJCXnJqSNqV0qiw4Cphg/XX+52dVHcknHnpxDrqi8i7jt6dn9aUviDwZzxBDYEAoIFpdNaOp2Q2zy9+R5YW5AGDLsy/+4l/I8H88vvzlP+xrWvegmR+dYUve+DA9m0QMF5OJaTg0SRKfNjUDYAoLBCiDzKca5/soFHSYpqhP6tcOBT312cmw8POVVVU7826c2Efl13BJY7ZAqTOgqJjlRZcwAKbHp4rLoucNG23Csv36TJhTFleQFPwf2QcAEI0yANA+4T9gkGQMMohK/Nb8MqfzGTb02lIIZcCAqbqqAkstfabBAGniH0aikAX9a4hG8GTXmV71E+LUhKZY7kUnMJaiJgiJ4RXXv6sv8gSA7jPRwukybq9lkJbgEFfQls3uxXrgYkwsmZeqmc/wX7ZJo5+CzaUkmggg8CLHKH6q8bMHzvcTH1Fu/s/Wrq/+vXB8r/FTnQhnRLDs+kRKoahZv5Tqu/cR69nq/fFDG0qBkU/P+xkkiZ/ZDRlFApTbUBWu/5L51c230V/9s/RpPXRkn7ACMkomEp85FaOaFuv9FFxd8kWXPOQTbZU78w0br38/tuAHBAD0nL/9OdOzn/+7zJv3755+obZAWYSmnCL5HEFCiNWwCKscAv4fVhANXK7p0x2QIwAGBwLHGUSJXMOCIADw0mdy5TDX1Wyt+FuPUPlVAynMlY5B4HKxxueLEi/krXdKC+hBAAANRt5tlCrutIu+++2iz4soEAAQDTwu9gIAXBKzEg8gOnMPda4lIfePM1uqnvVw5T82LL6n4AUuq7dgFAhHcOlwIhBA7rNjh3D4GeYyEQEA+FwBFwu21W4t9u/+x62F9qX6gdEi2EVivVEkRhMBfl6W/wUR0et9HNQb3AAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<PIL.Image.Image image mode=L size=448x28 at 0x7F1B60325550>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "3 59.24855279922485\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<PIL.Image.Image image mode=L size=448x28 at 0x7F1B601E07D0>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "4 59.167911767959595\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<PIL.Image.Image image mode=L size=448x28 at 0x7F1B602BDC90>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "5 59.27145791053772\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<PIL.Image.Image image mode=L size=448x28 at 0x7F1ABF2E7290>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "6 59.30877327919006\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<PIL.Image.Image image mode=L size=448x28 at 0x7F1B603232D0>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "7 59.37397885322571\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<PIL.Image.Image image mode=L size=448x28 at 0x7F1B602E3890>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "8 59.345611572265625\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<PIL.Image.Image image mode=L size=448x28 at 0x7F1B603A1F90>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "9 59.34937334060669\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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sq7sqmd8dak3y4luLFh0S60XY15pqyAvJ01oNxR22+Lgh+xVSVpyFVoNYFEa1gE5NyJoCcFT58G80ksOhWHQqSBmvdVNsxcWdyulmR2LFB/2UjrkcAIP12tXfWHwe/ixYTba/fRX061KG5I+3Suylr9y2MDdYcIyYzrM+CtOXylkcMdJk2JmcOr0gE0mzMgpXyrEffmWiLPDYvnAk3xS294Boz6fC1XIh7KyRo55wznBrvX9uX0QyXEaPedR8zgZCxuCNEWtPHfIclBytrNzj5qrzJkqJCZJH4oAcW3SJLXoLa7cvZRTgeK766M5pPgTIACPkeJFyFOyhGMtN3Oth96aKy0JEOguGYFNy5hy1r6d8XbWPpPoHfIwHIe+eTAIX9BzfUzAxMiWjRMawnqszi1V0TDoRHC0sUHM3HDxdV34ufbpnIJnHnJmb6nTveXu/RLHZT795z1MHfvm7F36VLh8jr+feGM5XKZo0cq4xUxZnlmwrmH2uECmG0oCNXk6i0pDvi1ZMjL5o0byRIhQnmLmzsZIjBgIAqNiTiabIglFqmGlWCTJhAChf4K9ZfzmVgdqGqOl36kNfM8d7ju2T7WOKzjpO//Sn4hjVpfFcRD0Uy3FRw+LN08jeyVQUJSvyYQQQJxG8Y7JIO/DnQ5YhbQxl9WjqpFWls6iqLRswPHGrLdY/EC+/mO1lI4ZWGNFznIJ5u8H7vGmTj7K3vaMdHg3YEMWgFE0cVhMnBBlPtb5q0mbYkxHSvATmtZQ04xREXoMj6Tyr5gp0Ihelrhsvpb3+f69v0/mbFHHQzth7bKHmtE/qjDSFKz9V4IG/8Eu6LQJx5s2yKW5jbEkP8NhiACUqG1oErtC+1zbCbcIN0irXm9kFrsXHON+Bqt+eLHZ6tp12p3353r1HjIll0t5+8QazyXW7ecVYRdzebVEbVB8XTLnNW/unbiN2yTWUyWpRRL9Ydax0jQEAGV0E9Qyfb9JKj4gEAICze0f18lhryG2sfwMjQwGAyv8ZafdRYisKxJbn1jD28isv8MdvONbCOMraL7fhz5li6HpzS4JP2SI+hopbUaRQbt4UK859slipAYFy4fbB1ZNlmSw01oWdX7BTYyYsJm2yZq9iI2kL3ZQ3i8bYauu5qpVsZYZTbVSZ7kIMUhUzvSzP3ORN3OPsFwIGl9HUJOUFQWFxVXcDrtEnYhGMXG7GFhdkDLZjn5+cnMUXl02SqDMOEyqLOajmCQBD5i90D/rjbKYqlmMWd+hg/nQomPSfSbXIPcrPCqYTtVFTllaR4ZFTspXRM3pWDVst9ssvyBbKrF0JNHzSnHSlyPmWjrE9KwpWrdneNAXgADZsIe4yc7xo7MNbqi5a2iOSiFkls3xkVKoIRyPeeDLYWHGp2GyxzkTdYw6dHfZMyZog9Xy1XKV7kWnmawtAoilRFmnuCYQpbQ0hlE6rABdeuWJuX13HWI2IgNcM0ifGXtELx+AgdsLizuQA82n7n4VWwSBSbTSmWLOZeLWqBkXPlNBtsXqVphzyynTRCcRfMTJ65blyLOdxIRDXRmuUMcUBUpdfj8iF2vDIWllL5XKsZFdlBJg2eFWPmVmT3XKO9qyPKRqootGSVREmumyzk5i10jQu2cDJ85UuRgOPYTVyM9Sr14UUkqmMkOPthjEJYGHYd7MgJbLlOKBIzhUjI2PRs32GGk6LMqdnQP2FadmNTAOT1xkm79Td6SFbmA71LKRUFWIoOmoMZrN4ER/MWc6m3u67nMcaRZU+g+is7sKUf2qxHH6I9Y4citeu80juWJUrbji5MGdlEFHE4qs+NjNBP3HGvUbm7ZGZKBluh+Avo7E0Db/xG/e4s99bEOINeajBxJwtGyaQ+CNBx19iFgWuazME3Y0ynQUdAIgegX3wAvqYRZpn5TdCQvdqkAWRU1ii5/Npe6IoeJU1gwzHofVMstih0oXMhTPgarhRTcZ1giiKygpZYJiwJZr5RPEF+qjuJZjE5ER5sNLgZJTXZDZqNlg4Vc9GyPnanDNqkfBorcJKokxdWHLGMbmdG1LXAxWMARFVuH7C9xSdTw8y2TWThNPutBq0T9yW0LkHsUn3mZLrq/RghA33aTg7HoTEIQcAEIK+PZb7LJbF5aaQMSZdZgOiZuL7VQAAQvKgdtDZWtkXsnLykKIAgA7FiApAGwlAvIw9OgVg4cr47/OIYGxzZj2NluHbTJX9zSNouFjpcnmcSfKIRbGDsx4jyIGWFa/36szobADF06vyVSFx1N9TbcY6TVREAAgQXZcPwx8wQ5DCKlPxEAECegEg1/8aQdiJ5crGVeRGrTxcEckWTzT4vRUHvi7ZZSFecjVpQLYLYKB7CKjrQ+KYP0YPlqmOWG04nkx/el2v/XhNl5OK8dawNUUIlmooE7AZUkjkqT0thxu6hJ76WMvp9nT69MKsZVjIUm9M3V0NpYTuhAqImKTpQSiAis/UZh2KXaGzjIGrJwAEw8hAH8Dv5033IST1CwpxZUyvihCh6mQ7BJSxSbuRHKJCTldm0Ns7RwoD7wTaxjk2zSGZgGHEYhA6C/DhhpghVSW6iqxGc4tOU6aC2ZSefRcasSirOW9h+ru6AQAQ9flcrDNkY1uGeCAFo1B6WU10HQBmP26Mj4XoEYBk5+vs/31AZ/srz35QZG0QVmjcisr46r7SZCbDQKBrRymOwhaT3i2TuiVcYVBMjOhXli1tzHhylNc82BdUXhmT28Vdq2RO8+DcSdNXblvQrq/hI1E/akhR0mKxjLJfrknddumdScFyjuo/RSvCAEJnZoyy1WinCQHdrHtUDmrHC6duYj4r04VooHaPf+qXAOD05B04AaJBps/RaduvzJXXlI014r56fo4bzVLSPwSAnqn/qFK2FhxYTJllMzNL7ME1FiVnloOz5dBbZCFYGXFEq8IYSB7me/6Zn4g8/Pj0ErRMaU36mLqQOwJs6ZMXAVBVyO53cYjQaevgu3ohqmnEuPz01iw9GEupk0kynfD/KKqc8tSejy/PARI5oW5zuvZSq9rr3J6yd/3pzXv2nlaL1sdKeJcDIEc5Z76VwCrNljVlLQTLrEHJE9v01b7Iz9Bz6oBCAHTJEuzNzfPMjWEQib+8NvGcJlAWidZNht87O/9GXc316Ar58ex2jrQRaZS5qCvvRxgIzEqo+U+QjSaiTauP1A7p6q7fz2ITNYYAaDmpEwIABIOe6zCU2HTPr+sD0HsMkRQAJAB6P1atkkfnsXYg/ykhz8qlk9ToW35ZgZjuH57pLRbRBTZrpWyyRaZ0Rh73B1eb1PRZpHT/+96P58HvOAIr8HOsls8iWsKSOUoBmzb5/bO4pAOzkGen3dVjhABA6IKEuZun3MMeYgaM6rhZja+Z6F4IMQk2xb9rz//vuSrohqYXdEU3CCEEDABQErPfbgFKsxELeqIwFOzt/t7+o7pBMqX4IeK1rGSAq7P4Zq7ADB6kdJQoyFSKwtTEy/tfA0CS78/Pl3XWDqgGUPc1SrShcsrJWjm2zmqazd2oCaQzPO2YO+5I1ECkDnjdRxxIM0DHuVkR0LVTvlndji2OKtIoaM65rIVoMKbni4wZ15R1M500MOxJNxiyEpgJYCOIaUZjgScmjdbxXw9A+Izx7jZgH+h3Xps81WqoQ1wWJC5rmyOR6J+P7iedY6um9EOgIxoAOqrKxJ4qw5bx5JQsEL3rWpJf5qEcpUkhzmcdWWzjMyV6ooktiqIInmFp3G69+sTLmURQD7xpAaqr4b0ZHIS1MsLnTGaTbKb1wkQq/H9uD5wU+R8nKafO1/QMHPyXa5NbuGVZtHoD6QuFLx6b7X3R/otD+Bdt8dKXQkJUANjZxBfs6wjqHUxIOgAZu4YU5PmItAV08aH3n6EpPVSiLaIRpeoIgMUYCqRkCiNEcTWXpblkXRXhU2UHo1j5zYoLM0dSu2Fj3tvnsfQCkxizDI53+f8BY1ryI/PCGHEAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<PIL.Image.Image image mode=L size=448x28 at 0x7F1B602E0650>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "10 59.33029747009277\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<PIL.Image.Image image mode=L size=448x28 at 0x7F1B602C0D10>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "11 59.294721841812134\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<PIL.Image.Image image mode=L size=448x28 at 0x7F1B60506AD0>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "12 59.325294971466064\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<PIL.Image.Image image mode=L size=448x28 at 0x7F1B60349E10>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "13 59.30851936340332\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<PIL.Image.Image image mode=L size=448x28 at 0x7F1ABF30A6D0>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "14 59.251888036727905\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<PIL.Image.Image image mode=L size=448x28 at 0x7F1ABF30AA90>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "d7ABl9sd5l48"
      },
      "source": [
        ""
      ]
    }
  ]
}