GAN_Tutorial.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "GAN Tutorial.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true,
      "authorship_tag": "ABX9TyMpGHBVYHcujYzj1EIfRrI7"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "g7-tNwkOLuNZ",
        "colab_type": "code",
        "outputId": "c94feadb-b415-4809-a71c-d8dd359aef5b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 306
        }
      },
      "source": [
        "#check GPU status\n",
        "gpu_info = !nvidia-smi\n",
        "gpu_info = '\\n'.join(gpu_info)\n",
        "if gpu_info.find('failed') >= 0:\n",
        "  print('Select the Runtime → \"Change runtime type\" menu to enable a GPU accelerator, ')\n",
        "  print('and then re-execute this cell.')\n",
        "else:\n",
        "  print(gpu_info)"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Wed May  6 19:44:33 2020       \n",
            "+-----------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 440.82       Driver Version: 418.67       CUDA Version: 10.1     |\n",
            "|-------------------------------+----------------------+----------------------+\n",
            "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
            "|===============================+======================+======================|\n",
            "|   0  Tesla P100-PCIE...  Off  | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   33C    P0    26W / 250W |      0MiB / 16280MiB |      0%      Default |\n",
            "+-------------------------------+----------------------+----------------------+\n",
            "                                                                               \n",
            "+-----------------------------------------------------------------------------+\n",
            "| Processes:                                                       GPU Memory |\n",
            "|  GPU       PID   Type   Process name                             Usage      |\n",
            "|=============================================================================|\n",
            "|  No running processes found                                                 |\n",
            "+-----------------------------------------------------------------------------+\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gHr59AEzKr1_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import os\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras import models, layers\n",
        "%matplotlib inline"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OBBsmBA0Gqv2",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#simple monomial function\n",
        "def calculate(x):\n",
        "  return x*x*x"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MNsQh8DwLqmS",
        "colab_type": "code",
        "outputId": "91d8a102-9307-43ad-8956-53e7b48c195c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "inputs = [round(x,1) for x in np.arange(-0.5,0.6,0.1)]\n",
        "print(inputs)"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[-0.5, -0.4, -0.3, -0.2, -0.1, -0.0, 0.1, 0.2, 0.3, 0.4, 0.5]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4IFCdXhVG14-",
        "colab_type": "code",
        "outputId": "ad35f8ba-3a5a-4b35-9db8-3b01ab9c9cc4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        }
      },
      "source": [
        "outputs = [calculate(x) for x in inputs]\n",
        "print(outputs)"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[-0.125, -0.06400000000000002, -0.027, -0.008000000000000002, -0.0010000000000000002, -0.0, 0.0010000000000000002, 0.008000000000000002, 0.027, 0.06400000000000002, 0.125]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LFMbnxEdG-gE",
        "colab_type": "code",
        "outputId": "55addc27-e4d9-46c3-c76d-e82c66dda78f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        }
      },
      "source": [
        "plt.plot(inputs, outputs)"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f0d27e7b3c8>]"
            ]
          },
          "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": "_ZCRnZf-HQUv",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#example generation of samples\n",
        "def generate_sample(n=100):\n",
        "  x = np.random.rand(n) - 0.5\n",
        "  y = calculate(x)\n",
        "  x = x.reshape((n,1))\n",
        "  y = y.reshape((n,1))\n",
        "  return np.hstack((x,y))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WHJ4_wK7I5yL",
        "colab_type": "code",
        "outputId": "3451590a-a14d-48c5-deb0-cf853bc30b44",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        }
      },
      "source": [
        "data = generate_sample()\n",
        "plt.scatter(data[:,0], data[:,1])"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7f0d279df390>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        },
        {
          "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": "0KDIULZdJsFz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#define the discriminator\n",
        "def define_discriminator(n_inputs=2):\n",
        "  model = tf.keras.Sequential()\n",
        "  model.add(layers.Dense(25, activation='relu', kernel_initializer='he_uniform', \n",
        "                         input_dim=n_inputs))\n",
        "  model.add(layers.Dense(1, activation='sigmoid'))\n",
        "  model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
        "  return model"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z0Q1NGIXKSIe",
        "colab_type": "code",
        "outputId": "7e6be916-c803-4fad-971a-ff8a4b8eb129",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        }
      },
      "source": [
        "model = define_discriminator()\n",
        "model.summary()"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "dense (Dense)                (None, 25)                75        \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 1)                 26        \n",
            "=================================================================\n",
            "Total params: 101\n",
            "Trainable params: 101\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BFamugJWP7SL",
        "colab_type": "code",
        "outputId": "b9796cc8-8aac-4f6b-f03d-3e513ad36c31",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 312
        }
      },
      "source": [
        "from tensorflow.keras.utils import plot_model\n",
        "plot_model(model, to_file='discriminator_plot.png', show_shapes=True, show_layer_names=True)"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T5aEzuWZQYBA",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def generate_real_samples(n=100):\n",
        "  x1 = np.random.rand(n) - 0.5\n",
        "  x2 = calculate(x1)\n",
        "  x1 = x1.reshape((n,1))\n",
        "  x2 = x2.reshape((n,1))\n",
        "  X = np.hstack((x1,x2))\n",
        "  y = np.ones((n,1))\n",
        "  return X, y"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "REtq5TlWZK4F",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def train_discriminator(model, n_epochs=1000, n_batch=128):\n",
        "  half_batch = int(n_batch / 2)\n",
        "  for i in range(n_epochs):\n",
        "    X_real, y_real = generate_real_samples(half_batch)\n",
        "    model.train_on_batch(X_real, y_real)\n",
        "    X_fake, y_fake = generate_fake_samples(half_batch)\n",
        "    model.train_on_batch(X_fake, y_fake)\n",
        "    _, acc_real = model.evaluate(X_real, y_real, verbose=0)\n",
        "    _, acc_fake = model.evaluate(X_fake, y_fake, verbose=0)\n",
        "    print(i, acc_real, acc_fake)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C_mQFB6Ua0pp",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def define_generator(latent_dims=5, n_outputs=2):\n",
        "  model = tf.keras.Sequential()\n",
        "  model.add(layers.Dense(15, activation='relu', kernel_initializer='he_uniform',\n",
        "                         input_dim=latent_dims))\n",
        "  model.add(layers.Dense(n_outputs, activation='linear'))\n",
        "  return model"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Oz3JeEDHkS4O",
        "colab_type": "code",
        "outputId": "ef63b851-fffd-4788-b39e-154db6d20a35",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 516
        }
      },
      "source": [
        "model = define_generator(5)\n",
        "model.summary()\n",
        "plot_model(model, to_file='generator_plot.png', show_shapes=True, show_layer_names=True)"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_2\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "dense_4 (Dense)              (None, 15)                90        \n",
            "_________________________________________________________________\n",
            "dense_5 (Dense)              (None, 2)                 32        \n",
            "=================================================================\n",
            "Total params: 122\n",
            "Trainable params: 122\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bmmI_06MkZZe",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def generate_latent_points(latent_dims, n):\n",
        "  x = np.random.randn(latent_dims*n)\n",
        "  x = x.reshape((n, latent_dims))\n",
        "  return x"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NgZOvFj3lmIZ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def generate_fake_samples(generator, latent_dim, n):\n",
        "  x_input = generate_latent_points(latent_dim, n)\n",
        "  X = generator.predict(x_input)\n",
        "  y = np.zeros((n, 1))\n",
        "  return X, y"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6OhlC4sJm3G7",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def define_gan(generator, discriminator):\n",
        "  discriminator.trainable = False\n",
        "  model = tf.keras.Sequential()\n",
        "  model.add(generator)\n",
        "  model.add(discriminator)\n",
        "  model.compile(loss='binary_crossentropy' ,optimizer='adam')\n",
        "  return model"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4NPoWqRToYKB",
        "colab_type": "code",
        "outputId": "67316704-2211-4728-cdaa-6f44d4654783",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 516
        }
      },
      "source": [
        "latent_dim = 5\n",
        "discriminator = define_discriminator()\n",
        "generator = define_generator(latent_dim)\n",
        "gan_model = define_gan(generator, discriminator)\n",
        "gan_model.summary()\n",
        "plot_model(gan_model, to_file='gan_plot.png', show_shapes=True, show_layer_names=True)"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_5\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "sequential_4 (Sequential)    (None, 2)                 122       \n",
            "_________________________________________________________________\n",
            "sequential_3 (Sequential)    (None, 1)                 101       \n",
            "=================================================================\n",
            "Total params: 223\n",
            "Trainable params: 122\n",
            "Non-trainable params: 101\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FR2S8pxqIuZc",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def summarize_performance(epoch, generator, discriminator, latent_dim, n=100):\n",
        "  x_real, y_real = generate_real_samples(n)\n",
        "  _, acc_real = discriminator.evaluate(x_real, y_real, verbose=0)\n",
        "\n",
        "  x_fake, y_fake = generate_fake_samples(generator, latent_dim, n)\n",
        "  _, acc_fake = discriminator.evaluate(x_fake, y_fake, verbose=0)\n",
        "  \n",
        "  print(epoch, acc_real, acc_fake)\n",
        "\n",
        "  plt.scatter(x_real[:, 0], x_real[:, 1], color='red')\n",
        "  plt.scatter(x_fake[:, 0], x_fake[:, 1], color='blue')\n",
        "\n",
        "  filename = '/content/generated_plot_e%03d.png' % (epoch+1) \n",
        "  plt.savefig(filename)\n",
        "  plt.close()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0-eZjqmhoyfE",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def train(g_model, d_model, gan_model, latent_dim, n_epochs=10000, n_batch=128, n_eval=2000):\n",
        "\n",
        "  half_batch = int(n_batch/2)\n",
        "\n",
        "  for i in range(n_epochs):\n",
        "    x_real, y_real = generate_real_samples(half_batch)\n",
        "    x_fake, y_fake = generate_fake_samples(g_model, latent_dim, half_batch)\n",
        "    \n",
        "    d_model.train_on_batch(x_real, y_real)\n",
        "    d_model.train_on_batch(x_fake, y_fake)\n",
        "\n",
        "    x_gan = generate_latent_points(latent_dim, n_batch)\n",
        "    y_gan = np.ones((n_batch,1))\n",
        "\n",
        "    gan_model.train_on_batch(x_gan, y_gan)\n",
        "\n",
        "    if (i+1) % n_eval == 0:\n",
        "      summarize_performance(i, g_model, d_model, latent_dim)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1kq0HmZB6Jme",
        "colab_type": "code",
        "outputId": "ed2f8c32-af7c-4c60-cbef-db039db18bd5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 867
        }
      },
      "source": [
        "latent_dim = 5\n",
        "discriminator = define_discriminator()\n",
        "generator = define_generator(latent_dim)\n",
        "gan_model = define_gan(generator, discriminator)\n",
        "train(generator, discriminator, gan_model, latent_dim, n_eval=200)"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "199 0.6299999952316284 0.800000011920929\n",
            "399 0.5299999713897705 0.6600000262260437\n",
            "599 0.7200000286102295 0.20000000298023224\n",
            "799 0.9900000095367432 0.27000001072883606\n",
            "999 1.0 0.1899999976158142\n",
            "1199 0.75 0.28999999165534973\n",
            "1399 0.6700000166893005 0.47999998927116394\n",
            "1599 0.6100000143051147 0.47999998927116394\n",
            "1799 0.44999998807907104 0.6700000166893005\n",
            "1999 0.5 0.5899999737739563\n",
            "2199 0.550000011920929 0.41999998688697815\n",
            "2399 0.4099999964237213 0.5299999713897705\n",
            "2599 0.28999999165534973 0.6299999952316284\n",
            "2799 0.4399999976158142 0.5699999928474426\n",
            "2999 0.5799999833106995 0.5199999809265137\n",
            "3199 0.5299999713897705 0.5899999737739563\n",
            "3399 0.4399999976158142 0.6800000071525574\n",
            "3599 0.4000000059604645 0.9900000095367432\n",
            "3799 0.47999998927116394 0.05000000074505806\n",
            "3999 0.3400000035762787 0.2199999988079071\n",
            "4199 0.3799999952316284 0.9800000190734863\n",
            "4399 0.5199999809265137 0.3799999952316284\n",
            "4599 0.6100000143051147 0.4300000071525574\n",
            "4799 0.07999999821186066 0.9900000095367432\n",
            "4999 0.9100000262260437 0.09000000357627869\n",
            "5199 0.4000000059604645 0.5699999928474426\n",
            "5399 0.6399999856948853 0.41999998688697815\n",
            "5599 0.7400000095367432 0.07000000029802322\n",
            "5799 0.38999998569488525 1.0\n",
            "5999 0.6000000238418579 0.5199999809265137\n",
            "6199 0.3400000035762787 0.6000000238418579\n",
            "6399 0.0 1.0\n",
            "6599 0.1899999976158142 0.9200000166893005\n",
            "6799 0.6200000047683716 0.4699999988079071\n",
            "6999 0.5199999809265137 0.3199999928474426\n",
            "7199 0.1899999976158142 0.5600000023841858\n",
            "7399 0.6200000047683716 0.1899999976158142\n",
            "7599 0.550000011920929 0.8100000023841858\n",
            "7799 0.47999998927116394 0.3100000023841858\n",
            "7999 0.07000000029802322 0.9599999785423279\n",
            "8199 0.550000011920929 0.7400000095367432\n",
            "8399 0.23999999463558197 0.5899999737739563\n",
            "8599 0.5799999833106995 0.41999998688697815\n",
            "8799 0.46000000834465027 0.6100000143051147\n",
            "8999 0.2800000011920929 0.5899999737739563\n",
            "9199 0.30000001192092896 0.4099999964237213\n",
            "9399 0.3400000035762787 0.3700000047683716\n",
            "9599 0.5299999713897705 0.5199999809265137\n",
            "9799 0.23000000417232513 0.30000001192092896\n",
            "9999 0.6299999952316284 0.5099999904632568\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HBeMa8BvkTik",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 436
        },
        "outputId": "853317aa-34ae-4b58-8c1d-114ce5294f3d"
      },
      "source": [
        "img = plt.imread('/content/generated_plot_e600.png')\n",
        "plt.figure(figsize=(10,10))\n",
        "plt.title(\"After epoch 600\")\n",
        "plt.imshow(img)\n",
        "plt.axis('off')"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(-0.5, 431.5, 287.5, -0.5)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bLjrF-5JlSbd",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 436
        },
        "outputId": "fbf5d10a-0f9d-4155-a566-9cd0ae1168cf"
      },
      "source": [
        "img = plt.imread('/content/generated_plot_e10000.png')\n",
        "plt.figure(figsize=(10,10))\n",
        "plt.title(\"After epoch 10000\")\n",
        "plt.imshow(img)\n",
        "plt.axis('off')"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(-0.5, 431.5, 287.5, -0.5)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}