13_MNIST_CNN_Todo.ipynb

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    "colab": {
      "name": "13_MNIST_Todo.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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    "accelerator": "GPU"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5uySOSqQcSSw",
        "colab_type": "text"
      },
      "source": [
        "# MNIST with CNN"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tPpSE3fgZazy",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "%matplotlib inline\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import keras\n",
        "from keras.datasets import mnist\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense, Dropout, Flatten\n",
        "from keras.layers import Conv2D, MaxPooling2D"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cThfuu0cs3K1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# input image dimensions\n",
        "img_rows, img_cols = 28, 28\n",
        "input_shape = (img_rows, img_cols, 1)\n",
        "num_classes = 10"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tKf-qrBEZj_Z",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# the data, shuffled and split between train and test sets\n",
        "(x_train, y_train), (x_test, y_test) = mnist.load_data()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-vUwF8uKsJ3a",
        "colab_type": "code",
        "outputId": "9d094dc5-671a-4b91-915b-ced68bca88cf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "print('x_train shape:', x_train.shape)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "x_train shape: (60000, 28, 28)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nL65o8Ejvepy",
        "colab_type": "code",
        "outputId": "941f7b92-fae3-430d-9854-9f85b1fd9f35",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1476
        }
      },
      "source": [
        "# The color is encoded as a number from 0 to 255\n",
        "x_train[0]"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
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          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xtCd3vKFZ2Ej",
        "colab_type": "code",
        "outputId": "e3b20531-280e-4250-a88c-3663dc385bb1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# Keras wants the picture not in a 3D tensor but in a 4D tensor\n",
        "# first dimension is the different pictures\n",
        "# second and third dimensions are the 28x28 x and y axis of the picture\n",
        "# fourth dimension: Color channel (we only have one color (gray))\n",
        "# reshape train and test data to 4D tensor\n",
        "x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1)\n",
        "x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1)\n",
        "print('x_train shape:', x_train.shape)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "x_train shape: (60000, 28, 28, 1)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sOprR4sDsdrN",
        "colab_type": "code",
        "outputId": "97f6cd5f-0417-43ce-ce20-d59b0649db51",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        }
      },
      "source": [
        "# gray scale is encodes an integer between 0 to 255\n",
        "# the neural network needs at as a float\n",
        "x_train = x_train.astype('float32')\n",
        "x_test = x_test.astype('float32')\n",
        "# We rescale the tensor so that the numbers are between 0 and 1\n",
        "x_train /= 255\n",
        "x_test /= 255\n",
        "print(x_train.shape[0], 'train samples')\n",
        "print(x_test.shape[0], 'test samples')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "60000 train samples\n",
            "10000 test samples\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0_g0Oo9GaPiH",
        "colab_type": "code",
        "outputId": "440a715a-09e0-4b20-ba84-2e0090acc009",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 304
        }
      },
      "source": [
        "# Let us us look at an example\n",
        "img_index = 20\n",
        "print(\"true class:\", y_train[img_index])\n",
        "plt.imshow(np.reshape(x_train[img_index],(28,28)),cmap='gray')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "true class: 4\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f54800c0f60>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PqlRN8ZfbDR7",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# convert class vectors to one-hot endoded binary class matrices\n",
        "# TODO"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uaKWDbEucJm6",
        "colab_type": "code",
        "outputId": "506c7345-01f0-4eaa-9d14-070397b09b86",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 304
        }
      },
      "source": [
        "# Show the image 20 with the true class\n",
        "img_index = 20\n",
        "print(\"true class:\", y_train[img_index])\n",
        "plt.imshow(np.reshape(x_train[img_index],(28,28)),cmap='gray')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "true class: [0. 0. 0. 0. 1. 0. 0. 0. 0. 0.]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f547d82fd30>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bC1g4auJbD7u",
        "colab_type": "code",
        "outputId": "3946bf95-d7ab-4c7d-9101-8ad493639ece",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 141
        }
      },
      "source": [
        "# Create a Sequential Model\n",
        "# Add a Convolutional Layer with the 32 Kernels, each with the size 3x3, user Relu as the activation function, and define the input shape\n",
        "# Add another Convolutional Layer with the 64 Kernels, each with the size 3x3, user Relu as the activation function\n",
        "# Add a Max Pooling Layer with pool size of 2x2\n",
        "# Add then a Dropout of 25%\n",
        "# Add then a Flatten Operator to get a one dimensional vector\n",
        "# Add a dense layer with 128 node and Relu activation\n",
        "# Add then a Dropout of 50%\n",
        "# Finally add a dense layer for the output. Think about the number of nodes and the activation function you need\n",
        "# TODO\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Colocations handled automatically by placer.\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:3445: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JFj5b-akbRpn",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Compile the model\n",
        "# Think about the appropiate loss function\n",
        "# Use as the Optimizer Adadelta\n",
        "# Add as another metric Accuracy\n",
        "# TODO\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g8sU7Wz9bGwn",
        "colab_type": "code",
        "outputId": "71a24cd9-4b7a-4e1c-8860-1925f9eda3e7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 523
        }
      },
      "source": [
        "# Fit the model\n",
        "# save the learning history in a variable history\n",
        "# use batch size 128, and 12 epochs\n",
        "# TODO\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use tf.cast instead.\n",
            "Train on 60000 samples, validate on 10000 samples\n",
            "Epoch 1/12\n",
            "60000/60000 [==============================] - 8s 134us/step - loss: 0.2622 - acc: 0.9187 - val_loss: 0.0554 - val_acc: 0.9818\n",
            "Epoch 2/12\n",
            "60000/60000 [==============================] - 5s 78us/step - loss: 0.0887 - acc: 0.9740 - val_loss: 0.0388 - val_acc: 0.9861\n",
            "Epoch 3/12\n",
            "60000/60000 [==============================] - 5s 78us/step - loss: 0.0687 - acc: 0.9802 - val_loss: 0.0370 - val_acc: 0.9875\n",
            "Epoch 4/12\n",
            "60000/60000 [==============================] - 5s 77us/step - loss: 0.0561 - acc: 0.9831 - val_loss: 0.0341 - val_acc: 0.9885\n",
            "Epoch 5/12\n",
            "60000/60000 [==============================] - 5s 79us/step - loss: 0.0492 - acc: 0.9851 - val_loss: 0.0319 - val_acc: 0.9893\n",
            "Epoch 6/12\n",
            "60000/60000 [==============================] - 5s 79us/step - loss: 0.0414 - acc: 0.9872 - val_loss: 0.0287 - val_acc: 0.9906\n",
            "Epoch 7/12\n",
            "60000/60000 [==============================] - 5s 79us/step - loss: 0.0379 - acc: 0.9883 - val_loss: 0.0297 - val_acc: 0.9901\n",
            "Epoch 8/12\n",
            "60000/60000 [==============================] - 5s 77us/step - loss: 0.0332 - acc: 0.9898 - val_loss: 0.0288 - val_acc: 0.9909\n",
            "Epoch 9/12\n",
            "60000/60000 [==============================] - 5s 77us/step - loss: 0.0309 - acc: 0.9903 - val_loss: 0.0277 - val_acc: 0.9908\n",
            "Epoch 10/12\n",
            "60000/60000 [==============================] - 5s 77us/step - loss: 0.0291 - acc: 0.9911 - val_loss: 0.0284 - val_acc: 0.9910\n",
            "Epoch 11/12\n",
            "60000/60000 [==============================] - 5s 77us/step - loss: 0.0288 - acc: 0.9910 - val_loss: 0.0292 - val_acc: 0.9900\n",
            "Epoch 12/12\n",
            "60000/60000 [==============================] - 5s 77us/step - loss: 0.0252 - acc: 0.9917 - val_loss: 0.0258 - val_acc: 0.9919\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "30TFyx8BbUmA",
        "colab_type": "code",
        "outputId": "fa2b18a2-67d8-4e8d-be52-c9d0c3aff095",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        }
      },
      "source": [
        "# Test the model based on the test data\n",
        "# Print the Test loss and the test accuracy\n",
        "# TODO\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Test loss: 0.025847555106781873\n",
            "Test accuracy: 0.9919\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9E84NcCxbYY6",
        "colab_type": "code",
        "outputId": "80eba6fb-689b-4c04-ceaf-d312adf78095",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        }
      },
      "source": [
        "# Create a plot to show Training and Validation Accuracy\n",
        "# TODO\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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f7x1hc3UDOw410u7/cHT1WaMzOHdKHjPcaqbigkxy0hKjHLUxZrCN3MShCvvfdhq6q56G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\nn6SwZQO/YQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TxHqY0P5baoX",
        "colab_type": "code",
        "outputId": "b624f2e6-a5a8-4487-c3a5-bbe1ecdaab35",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 232
        }
      },
      "source": [
        "# Shows Test Pictures and predicted class\n",
        "grid_x = 8\n",
        "grid_y = 3\n",
        "plt.figure(figsize=(1*grid_x, 2*grid_y))\n",
        "for y in range(grid_y):\n",
        "    for x in range(grid_x):\n",
        "        index = x + (grid_x * y)\n",
        "        plt_index = x + (grid_x * y)\n",
        "        plt.subplot(2*grid_y, grid_x, 1+plt_index)\n",
        "        img_index = index+500\n",
        "        pred_data = np.reshape(x_test[img_index], (1, 28, 28, 1))\n",
        "        pred = model.predict(pred_data, verbose=0)\n",
        "        plt.imshow(np.reshape(x_test[img_index],(28,28)),cmap='gray')\n",
        "        plt.xticks([],[])\n",
        "        plt.yticks([],[])\n",
        "        plt.title(str(np.argmax(pred)))\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ioqLo2bOn+bl3794A9OrVy8Go3KlUqVIALFu2jPvuu489e0IzNUjhwoWpUaMG\nAHPmzKFv377mtWPHjplz6LnnnuPYsWMAvP7664EPNIA+/fRTwCqbLFmyAHD77bd77UW45ZZbmD17\nNgAPP/wwe/fuDUicTrDLwL7O2OzzRCRVtmxZAIYMGcI999xDzpw5AZg7dy6///47YE12t4djW7du\nnW6xONJgyZkzJxs2bDAXXIAtW7YAMHHiRPr37w/AtGnTzPDIrl27Ah5neqpYsSJgVZLw8OT/N/z7\n779mXPXixYuMHz8egB07drB79+5AhBlwUVFRSS6q9kWlZ8+eqb6gxMTEZOgZ/3Xr1gWsxnzt2rX5\n5ptvUjy+cuXKtG3bFoClS5cyYcKEdI8xEPbu3Uv37t0BmDJlSpLXBg4caOYpvP/++7Rv3x6Abdu2\n8fnnnwc20AA6e/YsgBkCA+smKXfu3ABUqFCBCRMmmGF5gPLlywNWwyYUGiye15mYmBi5KUpG2bJl\nmTFjBmDN93n55ZdZvXo1YH0n58mTB4BWrVqZhnF6kiEhIYQQQrheQFcJ2UMdM2fOpGrVqub5ZcuW\n0a5dOwA2bdpknr/tttto1KgRAIMHD/bpM4JtFnbLli05efIkAPnz5+fbb7/lwoULgNWiTUhI8E+Q\nLl/VYIuKijK5EfzBl9wKwbSqwdNLL70EwKhRo3jhhRe89rA0aNAAgO+++458+fIBMGHCBJ555plr\nfkaw1aeUrF27lkqVKgGwb98+ihcvfj1vFxT1KSV169Zl4sSJAOa8AKucHn30UcDq5U0tt9cnu2fl\nyuuMUqkL27M3uHbt2ixduhRIefg62OpTmzZtzFDz3Llzr3rd7mFZs2YNy5cvB6BFixZp+Sj3rRK6\n6aabAExjZdGiRQA0a9bMfGl72rJlixkqykgyZcrEV199BVjjxfbs/MaNGzsZlivExMQkuXAk1317\npdq1a4fkDP+HHnromsdER0czadIk83vHjh0BGD58eLrF5VZ9+vRh6tSpgDU8Yg9JZ9Th1WtZtGiR\nmcvj2ditUqUKY8eOBS4PO2YkyV0rfB1u7tWr11VzX65834w0tDRy5MgUX8+fPz8AJUuWZNy4ceke\njwwJCSGEEML9tNY+PwB9PY9Vq1bpVatWaa21Pnr0qM6VK5fOlSvXdb3nlY/U/D3+eqQ2xhYtWujE\nxESdmJio//rrL50zZ06dM2dOv5ZDMo9VwVA2fvj/n4Rbzxl/lM3ChQv1woULdWJiom7RokWS1woW\nLKgLFiyod+7cac617777LkPWp9Q87LJISEjQL7zwgn7hhRdCuj5VrVpVV61aVcfFxZmySUxM1OvW\nrdPr1q1Lax10dX1KzrX+TVQibiXsAAAgAElEQVRUlI6Kikr2314pKirKVWWTnvWpV69eulevXvrI\nkSPX+14+1aeADQlFR0eboaD4+HgefvjhZIeBQkGxYsXMz7feeiuHDh1K8vr58+cBa4XDRx99BFir\nhIR3yXXDpmfGSjfwnN9kD68ChIWFmSWGpUqVYuvWrUDGX8qbGocPHzbzN0LZmjVrAOvcsIeBwLou\nATz44IMsXLjQkdgCwdehIG/z6ux/7zksHRUVlaFXJ9qqVatGmzZtAPjjjz8C8pkyJCSEEEII10v3\nHpZs2bIB0K5dOzOZ8vTp0/zyyy/p/dGuNX78eJ588knAWgkVHx8PWHfM4eHhZuZ1//79OXHiBBCa\nkyR95ZlsDi6vCMrodzkHDhwwP0dGRrJv3z7Aqmt22uzz58+b3XoPHz4c+CBdxJ5wC1aPpZ2rJFTM\nmDHDrPoZPnw4GzduNK9duRooe/bsAEnytGQEaZkQ6613pU6dOuYa48+VjW5nb/fwySefcObMGQD+\n97//BeSz073B8tprrwFJV8DY+1eEqr1791KlShXAStZkLxs7deoUuXPnpmHDhoC19PSDDz4AYMGC\nBWzfvt2ZgF3O82IRExOT4YeCbPZwYYsWLYiOjjar8Dw3AHz//feZNWuWI/G5hd1V//DDD5ubpn79\n+jkYUWDZK8MaNGhgkns9/vjjbNq0iREjRgBWRtxQlNJNjX1duXJVkT0MFBMT4zUJXUaVK1cuM5Ra\no0YNnnvuOQD+/vvvwASQ3pN3unbtqrt27aq11nrDhg16w4YNOlu2bOk2CSgjTGpSSmmllO7QoYM+\nf/68Pn/+vI6NjdV58+bVefPmTddJTW4vmyv+X18lWM4Zf5SNfZ48+uij+uLFi0kmTU6dOlVPnTpV\nh4eHh3R9qlGjhj516pQ+deqUTkhI0FOmTNFTpkzxx3sHRX0qXLiwPn78uD5+/HiS88PXxxNPPBEU\n54yvZdOrVy/tKbXXlysn1CbHbWVzvee6vTimTp06+uDBg+bcmDdvno6IiNAREREBq08yh0UIIYQQ\nrpfuQ0IlSpQwP9ub+dWvXz/JMUuXLjVzNQR2q5ihQ4fy3nvvAXDjjTfSqVMnIGMlJkqOt2Rx3hI2\nedJapzpjZbCyz5MZM2Ywffp0mjVrZl6zk8WF8uqyiIgIunbtajYa3bFjB3369HE4qsDq1KkTuXLl\nAqyNIO0s2nny5Emy94tSypxPoSKlFUJXXmM9h4FsV85byYhDQSNGjODpp58GrPoElzMC16tXjzlz\n5gBWRtzNmzenf0Dp2bV0ww036N27d+vdu3cn23VmO3HihI6Li9NxcXG6b9++umjRorpo0aJp6loK\nxi63lB7t2rXT7dq104mJifrAgQP6wIEDOk+ePOnW5eaGsrkeKeVAcNM548/zJkeOHDo2NtZ01Y4b\nN05ny5bNL0OvwVgudhf2tGnTdEJCgl6/fr1ev369zp49uz/rZlDUp1OnTpnz4ssvv9Tly5fX5cuX\n1126dNF//fWXPnv2rD579mzIDAl56tWrl0/HLVmyJMlrS5Ys0UuWLNFXcmvZpOX8njx5sp48ebK+\nePGiTkhI0AkJCXrXrl26XLlyOl++fDpfvny6fv36etOmTXrTpk162bJlAalPMiQkhBBCCNdL1yGh\nihUrJhkSSkxMBODcuXMA/PbbbwBs377dLMPs3r272dTt/vvvD/mVMaNGjQKgdevWZmWRvVQ8o0ou\nGVNy7C7Y3r17Z8juWF917tyZfPnycfDgQcAaBrCXyoea8uXLM2bMGMBKbDV//nyaNGkCXL7uhKqI\niAizN9vGjRsZPnw4K1euBKxyS07z5s356aefAPjvv/8CE2g68lzZ4yt7U0PA7rG4iq8J6IJFXFwc\nYH03f/fdd4C1FP7IkSPmmPnz5zN58mQA3n33Xe6//34Afv755/QLLL27lrZu3aq3bt2qtdY6Ojpa\nR0dHJ3vcvffeq++991596tQp08U2bNiwoOiO9DW2TJky6UyZMummTZv6/PeEhYXpsLAwvWrVKtNN\nW7hw4XTrcnNDd2Ryjytn92vtWxesW88Zf5RN9uzZdfbs2c3M/T59+ug+ffr4rVzcXp88H5GRkToy\nMlKvXbvWdGEvXLhQ33LLLX4tj2CrT55DQomJiXrixIl64sSJevTo0XrBggU+rRSKiYnRMTExXq/d\nbjhnfC0bz+tISkNCyV1vkmMPD2W0+uTro0aNGrpGjRo6ISFBN2zYUDds2DBd65MMCQkhhBDC9QK2\nlxCkPIu6aNGiwOUsegClS5dO75ACasiQIQCcPXuWH374wad/0759ewCqVq1quuNCqWvbnq1/5Qqh\nUEkOl5LPP/8cgIIFC3L48GEGDRrkcETOiIiIMAkWK1asyP79+wHo1q2bT0PKefPm5cEHH7zq+V27\ndrFq1Sr/BhtgZ86cSXJNfeKJJ1L9HnZXf7Vq1Rg5ciRgJSe0hw2CVe3atZP87rk60ZcViaGUpNKb\nihUrBvTz0r3BMnv2bMDaXMvOdmsvubSXRzVv3pzPPvssyXMAP/74Y3qHF1D2RlHexouvVKxYMbp2\n7Wp+ty8Wx44d839wLpTchSO55YWhKFeuXDRo0MD83rVr1wwxxyAtevbsyaOPPmp+nzlzJgDNmjVj\n3LhxSY61ry+XusoByJIlC8WLFwes65W9DPrHH38M+gZLgwYNzMaY10qzb2crHTlyJEWKFAHgrbfe\nMq/nyJHDpGDv0KEDjRo1ApJuvBlMoqKikpwHvrKvPaHeWAGrEQtw9OhR5s6dm+6fJ0NCQgghhHC/\n9J68Y0+m1VqbXCvjx4/X48eP13PmzNFz5sy5aiLTmDFj9JgxY9I0ecfNk5rsNPvDhg0zk2mTO87O\nobF69Woz8S0+Pl5Xr15dV69ePV0nNblhwldUVJSOioq66rxIaZLc9TycKJe0lo39GDVqlDk3Ro8e\nnW7bXbi5XAoVKqQLFSpk0u778rDLbPHixXrIkCF6yJAhumvXrrpw4cK6cOHCOkuWLKb+XWNbg6Cp\nT23atNFt2rTR8fHx+sKFC/rChQs6MTFRX7x40WyX8uabb+oSJUroEiVKaEAXLFhQFyxYUK9Zs8aU\nmdba/Hz27Fl911136bvuussV50xqysZbHhVf2denjFafrvW48runTZs25jvND5P9fapP6tIf5BOl\nlO8HX5IpUyYAnn32Wb7++mv7fbwe//3339OyZUvA2mk2tbTWAU9z6mu52H9PeHg4ixcvBmDDhg1m\n2Azg7rvvNt3b1atXN938Tz31lFlemEartdbVr+cN0iK150xUVFSyO5+m53ixE+cMpK0+2b788kta\ntWoFWEMf06dP91tcntxcn8LCrA7ir776ymzC9s8//yQZBho5cmSyQ6jnz583WV/TKCjq05XseRq3\n3347cXFxZnjem/z587Nw4ULAmq9w+vRpAPr27et1zlSw1KdevXqZeSxXbl7ouZTZM72C5w7NaeHm\n+uTNF198AcDYsWNZsWIFTZs2BWDYsGEmNUnz5s2vN5WCT/VJhoSEEEII4Xrp3sPiyU7g1K1bN6pX\nv9yY+vHHH5kyZQpg7c/w77//pvkz3NyCrVq1KmDdHdv7e5QqVcrcKdrsBHvLli3j/fffB/wysS0o\n7giXLFmS7Fbt6TnBLVjuCD2NHDmSO++8E4AHHniA48eP+y0uT26uTw4LivrkhGCsT4ESjPVpw4YN\nAEyfPp0TJ06Yidjz5s3jnXfeAWDfvn3XGaVv9SmgDZZACLYT4r777qNHjx4AREZGsnjxYrPkedas\nWf4J0OLqC6zdSPEcDoqJiQnIqqBgvMD+/vvvDB48GIAJEyb4LaYrBVt9CiBX1ycnBWN9CpRgrE/2\nENCIESPInz8/H374IQA9evS43mFVTzIkJIQQQoiMIaCJ48TVli9fniSfRqiTZEy+iYmJoWzZsk6H\nIYTI4Owef1+TnaYnGRLyg2AoF6QL2yvpwvZO6pNXUp+8kPrkndQnr2RISAghhBAZgzRYhBBCCOF6\nqZ3DEgvsSY9A/KSkQ5/r9nIBKRtvnCoXkLLxxu3lAlI23kh98k7OGe98KptUzWERQgghhHCCDAkJ\nIYQQwvWkwSKEEEII15MGixBCCCFcTxosQgghhHA9abAIIYQQwvWkwSKEEEII15MGixBCCCFcTxos\nQgghhHA9abAIIYQQwvWkwSKEEEII15MGixBCCCFcTxosQgghhHA9abAIIYQQwvWkwSKEEEII1wt4\ng0UplVUpNVoptUcpdUoptU4pFR3oONxMKdVcKfWXUuq0UmqHUqqW0zE5TSkVo5SKV0r9d+mxxemY\n3EYpdeulMvrW6VjcQilVSik1Ryl1TCl1UCn1mVIq3Om4nKaUul0ptVgpdUIptV0p1dTpmNzA4/pi\nPxKUUkOdjssNlFKvKaVWKaXOKaW+diIGJ3pYwoF/gNpAbqA7MEkpVcqBWFxHKVUPGAi8CEQA9wM7\nHQ3KPV7TWue89LjN6WBc6HPgD6eDcJlhwGGgCFAZ67rT3tGIHHapwTYDmAXkA14BvlVKRToamAt4\nXF9yAoWBs8Bkh8Nyi/3A+8AYpwIIeINFa31aa91La71ba52otZ4F7AKqBToWl+oN9NFa/3qpfPZp\nrfc5HZRwN6VUc+A4sMjpWFymNDBJax2vtT4IzAXKORyT08oCRYHBWusErfViYAXwvLNhuc5jWI3d\nZU4H4gZa62la6+lAnFMxOD6HRSlVCIgENjkdi9OUUpmA6kCBS920/17qws7udGwuMUApFauUWqGU\ninI6GLdQSuUC+gD/czoWFxoCNFdK3aCUKgZEYzVaRFIKKO90EC7TEvhGa62dDkRYHG2wKKUyA+OB\nsVrrv52MxSUKAZmBx4FaWF3YVbCGzULd20AZoBjwBTBTKXWzsyG5Rl9gtNb6X6cDcaGfsXpUTgL/\nAquA6Y5G5LwtWD0HbyqlMiul6mMNld3gbFjuoZQqiVUmY52ORVzmWINFKRUGjAPOA685FYfLnL30\n36Fa6wNa61jgE6CRgzG5gtb6N631Ka31Oa31WKwu7JAvF6VUZeBBYLDTsbjNpWvMXGAakAPID+TF\nmiMWsrTWF4AmwEPAQeANYBJWg05YngeWa613OR2IuMyR2fJKKQWMxupRaHSpAoU8rfUxpdS/gGcX\npHRHJk9jdWOHuiigFLDXqlbkBDIppe7QWld1MC43yAeUAD7TWp8DzimlvsKaOPiWo5E5TGv9J1YP\nAgBKqV+Q3gRPLYAPnA5CJOVUD8tw4Hagsdb67LUODjFfAR2UUgWVUnmBzliz+UOWUiqPUqqBUiqb\nUipcKfUs1uopmYtgDY/djDV8WBkYAcwGGjgZlBtc6qHcBbS7dN7kwZqX8KezkTlPKVXxUn26QSnV\nBWsV1dcOh+UKSql7sIaeZXWQh0t1KBuQCeumKFugUwQ4kYelJNAG6+J60GO9+7OBjsWl+mItTd0K\n/AWsBfo5GpHzMmPdFR8BYoEOQBOt9VZHo3IBrfUZrfVB+wH8B8RrrY84HZtLNAMaYp0724ELWDcB\noe554ADWXJa6QL1LvVDCatRO01qfcjoQl+mONW3hHeC5Sz8HdH6lkgnQQgghhHA7x5c1CyGEEEJc\nizRYhBBCCOF60mARQgghhOtJg0UIIYQQrpeqJUlKKdfP0NVaBzw3RzCUCxCrtS4Q6A8NhrJx4pwB\nKRtvgqFckPrkldQn76Q+eeVTfZIeltCxx+kAhMhApD4J4T8+1SfHGiwFChTg4sWLXLx4Ea01S5cu\nZenSpVSsWNGpkIQQQgjhUo41WBo1akRYWBhhYWForalVqxa1atVi0aJFdOnShS5duhAWFlodQOXL\nl2fp0qUUL16c4sWLOx2OEEII4Rqh1SIQQgghRFByZPNDgIMHD7JkyRIAfvzxR95++20AChcuzKBB\ngwBYtmwZv/32m1MhBky2bNkA+OmnnyhWrBitW7cGoGfPnk6GJUSGUL58eQD69+/PHXfcAUCZMmVo\n3749AHPnzmX37t1OhSeE8JXW2ucH1g656fKoWLGirlixoo6NjdWJiYk6MTFRDxw4MNXvk5q/x1+P\n6/3bc+TIoXPkyKETExN1QkKCbtq0qW7atKm/y3hVMJaNvx7ly5d31TnjprLJaPXJ81GtWjUdFxen\n4+LidEJCQpLHiRMn9IkTJ/TEiRN19uzZdfbs2aU+Bek5I2XjznJ58sknzff5sWPHdLVq1XS1atXS\nXJ9kSEgIIYQQrufYkNCV/vzT2vF94MCBDBw4EIDnnnvODBVlZI8//rj5edKkSfzwww8ORuMut9xy\nCwAPPfQQO3fuZOHChQCcPXvWHKOU4oYbbgCgQoUKNG3aFIDo6GhzTOnSpdm1a5f5/e+//wbgySef\nTN8/wCF2eUybNo39+/cD8MYbb3Ds2DEnwwqoHDly8Nlnn5my6Nu3L9OmTTOv//fff8DlIVmAJ554\ngs2bNwOwadOmAEYbeJUrV2bevHkARERE8NBDDwGYoXoh0ioyMhKwhmEv9fKwfPlytmzZcl3v65oG\ni+2LL74wDZZQ8f777zsdgqvkzZsXsE52+yJarFgxAP755x/AutgmJCSY41599VUAUzkAzp8/z4oV\nKwA4cuRIks+wnw82r732GgAlSpSgR48eAJw7d+6q46pUqQJAgwYNzHNTp05l9uzZAYjSHSpUqMBd\nd91F165dAczcuJS89NJLjB8/HsjYDZbu3bvTrl07ChS4nKtr6tSpANx5553s2LHDqdAcV7hwYQ4e\nPJjkd5vn8yKpvn37AnD69Gnatm0LwLZt25g0aRIAH3/8sblJSCsZEhJCCCGE67muhyXU9OjRgyJF\nigDwwQcfmLu7UJU9e3YzPFikSBEmTpwIwJ49eyhdujSPPPIIYA11XLhwAYD77rvP3B0OGDCAEydO\nAHDx4kX27MlYCUkLFSoEwJtvvskvv/wCwPTp06867rHHHjM/nz9/HoC4uLgAROge9913H0opn3pW\n7J6r+vXr89FHH6V3aI4ICwsz9adXr14cOHCAmjVrAla9iYqKAqzhoYzAHuqrV6+e6XH0VKpUKfM3\ne8qZM2eSnoCcOXOan//77z86d+4MwIwZM/wccfDKmjUrlStXBqwca6dPnwbgo48+MsOO/uDqBktG\nXtKcPXt2AB555BGTIG/btm0ZuhvaF/Xr16do0aIArF27lmeffTbJ6/fddx8An332mRnWeeutt1i/\nfn1gA3VIfHy8+fnmm29O9pjq1aubL2CAxYsXA/Drr7+mb3Au89hjj7Fq1aprHjdjxgwaNmwIwFNP\nPWXmSWU0lStXTjKHp2vXruYa27x5czPcceONNzoSn7/Z14fkGivXkj9/fq/PDx8+HIBffvnlqqHm\nUNWrVy8aNWpkfm/cuDEAS5cu9evnyJCQEEIIIVzP1T0sGzZscDqEdGPfHVerVi1N/95e+XD33Xdn\nqFn99gRJgBdeeOGq15cvXw7APffcY547c+ZMusflFlmyZDE/L1iwINljHnnkETJnzmx+79evX7rH\n5UbZsmXj1KlTXl//+OOPAWjYsKEZis3IPXVNmjQxP69fv56FCxdSokQJADPMATBu3DjKlCkDJO3R\nCzZ27CtXrrzmsdu2bTPDx//++y9wuWeyQIECZiVny5YtzRD+yJEjadasmd/jDib2Kst33nnHLHiw\n9wVMD65rsHguYx4zZoyDkbiPvcR3xowZZi7DiRMn6NixI0CGWAFiN8QAatasycaNG5M9LpQaKZ5e\neeUV8/OV83Nq1KgBWPNbPO3duzf9Awsyr7zyivmSHjlyJG+99RZAig2cYHfTTTeZn3v16sXBgwd5\n/vnnAejUqZN5LX/+/BliH7d7773XL++zY8cOM5xavHhxnnjiCQAefvhhv7x/sBo6dCjNmzcHIDEx\nkTlz5gDw9NNPp9tnBv9ZKYQQQogMz+89LNmzZzdd+evXr+f3338HrBUbKbG72Vq1amW65DLy3U69\nevXMz4mJiQDExsZedZy9D0rdunXNHaHdjQuQL18+09LPCD0sn376KSNHjgRg+PDh5k5v9OjR1zyH\nQlm1atX48MMPgaSJ0FavXs3hw4edCssRlSpVSvJfexL3/v37TTLBESNG8M033wDQrl07B6J0lr3C\nLLnJpd98803I9mB6Ex5ufVUWL17c4Ujc49VXXzXDQBs3buSTTz4BuO5cKynxW4PFHq6YN28epUuX\nNs/v27cPsDY4HDp0KHA5y6inVq1aAdYMdTsJ2NGjR/0VnquEhYUl6U60s7bOnDkzyXGPPfYYX3/9\nNWBl7bTNnj2bAQMGANb8BHu+QlhYmGn8BKuvvvrKfNG8+uqrZkZ+xYoV6devn8naGooaNmxohgI9\n5cmThy+//NIsK4TLjeDevXsH9TyEtLDPkU2bNnHHHXdQt25dwEpIaDfqVq1aFTINFfv6cPvttyd5\nvkmTJvTu3duJkIKOPXfMHnYFK8P2XXfdddWx9k16RpM1a1befPNNKlSoYJ6zG75NmjQJSNoE5ZkZ\n9JoHK+X1YHt53J133mnyYGTLlo2sWbOaY+wekw8++IBp06aZHVLLlStnJjidP3+eggULpu6v8KC1\nVmn+x2mUUrkkJ3fu3ElSpNtr1u38B3Zq+e+//95cbGJjY82ysbVr15oGzG+//WbyRowePTqlj12t\nta6emjj9IbVlA5helaeffpouXboAVoMlMTGRIUOGAPD555/7bYddJ84ZSH3ZVK5c2VwgsmfPzoMP\nPghY8xHs5d42uw7myZPnumIMhvrkzUsvvUTbtm1Nz9ytt95qJmC2b9/eZE1Oo6CpT7bhw4fTpk0b\nwNoCJTo6mooVKwLWddf+Ul67dq35YrZzHaVGsNQnT3nz5uWll15K9rXq1aubcipbtuw13yul+T/B\nWJ969uwJwG233cZTTz1lng8LC+OZZ54BYMKECdfzEeBjfZI5LEIIIYRwP39tU20bNmyYea5s2bJ6\n5MiReuTIkXr79u1mm2n7sXr1avOwn4uOjs7w23e/8cYbScph8uTJevLkyRrQ99xzj46Pj9fx8fE6\nMTFRr127Vq9du1ZXrlw5yXvY/+bYsWO6fv36un79+tf6XJ+273a6bK58lClTRpcpU0a//PLLeteu\nXTohIUEnJCToI0eO6EqVKulKlSpd9xboTpRLWsumc+fOunPnzlprbc4frbU+c+aMeWit9fHjx/Xx\n48eDsmyuN2b70aVLF52QkGDKac2aNX5532CtT7fddps+efKkPnnypCmTZcuW6WXLlum6deua52Ji\nYnR4eLgODw8PmnPmestmxIgRV30/+fI4dOiQPnTokJ4yZYpu3bq1bt26tevK5nrP9X/++Uf/888/\n5m9esmSJXrJkiT/rks/1ye+TbocNG2Z+/vvvv00XZO7cuU130rvvvkuRIkWSzUD4+uuvc/LkScDa\nwTm5ibe5c+fm0UcfBTAT54LZ9u3bAWv+zkcffWS6ZmNjY83wkOfwR9euXc36/3bt2jF//vzABhxA\nO3fuNP9dsGAB7733HmDlaLEzkrZu3Tpk0mR//vnngLX82z4Hdu/ezbfffms2Q6xSpYrs+I01kV8p\nZZbGP/fccw5H5KwtW7aYoZ5OnTqRPXt2c732TMe/ffv2kJvg/s0331C/fn3AStnvaePGjWZo1XNp\n+KuvvsrYsWOBjJtmYejQoWbe3KJFi/jkk09YtmyZY/HIkJAQQgghXM9vk27t96lQoYLXZF+2iIgI\nfvvtt2tOYNq+fTtbtmy56vny5cvz5ZdfAldn8dRBMKnpjTfeMKsVANNbVLJkST799FOzqqNmzZom\n82bhwoUZOHAgYN0p2i37V155xde7IVdPErR7leyN+ryxJ3HXr1/f9CIopczW5n379iUhISFVMTpx\nzoD/JpeCdVe4evVqwFrqbmdxtSctp1Uw1Kcr2cnQvvzyS8LDw03Sr6ioqDRNIvXC1fUptQoWLGj2\nEhozZgytW7dO83sFa32ye1GS62GxRwqGDh3K8ePHAahatWqqJ/4HW33SWpv9kurUqZOee935VJ/8\nPiT06quvJrtcMHPmzCZ/SExMDMWKFWPcuHEA9O/fnzvuuAOAxx9/3CwVK126tFkuffToUbORWcuW\nLX1Ktxws7BPCLjd75+Fdu3aZ5zp27GgaeF9//bXJeJpRum7t5dv2rHNvzp07B1hLwO0hxkGDBpnh\nkEOHDpml0KHkxhtvJF++fE6H4bisWbPSrVs3wFrtcuTIER566CHA2jgzI21jIfzLboisW7cuxePs\nvEb+WqXoNjfffDP9+/cHrAaLvWGmGzbmlSEhIYQQQrie33pYduzYAcDLL7/MgQMHzHN2ZsBHHnkk\nSdKdzz//3OxfkZiYaIZ+PCcL1qhRw2Tt3LlzZ4bZE+XKIbNZs2YBmDvkBg0aANYdop2ELz4+3kzA\nnTt3bobpWbHVqlULsLpjfb1zsXuijh8/zvTp0wFMT12os5OlhZrevXtz6623AvDRRx+xceNG08NS\ntWpV6WER4hoGDBjAY489BljTF0aNGuVwRJf5rcFij/ENGTKEXr16JXuM/UXUr18/vv7662tmZbXH\nnjOahQsXmpU99evXv6or3zNd9qBBgwArU7CdNCwjsmffz5kzJ9WNjkWLFpnGy7PPPkuHDh38Hl+w\n8cw2HUqeeOIJlLKmCezfv59y5cqZ34V3/tooMKPK6BsdRkZGmoZ9ZGQkP//8MwCDBw92MqyryJCQ\nEEIIIVzPbz0sixYtAqxN/Vq2bAlYk3fs9Nfz5s0zwz522vBQlZCQYLYob9WqlWm92934dqr+KlWq\nmL2YMjp7eHDw4MFmZdScOXNMrpXkhons/aeaNWtGyZIlgcsTmEPN8ePHTb3KnTs38+bNcziiwMqb\nNy9g9SzZKxb79+9PtmzZSM1KyFBVpUoV0xNVt25dsxrPnuQe6jz349q2bZuDkaSPqVOnmp7tOXPm\n8PTTTzscUfL8vkro4Pq35BAAAAJKSURBVMGDZvmt8M5OiPd///d/ZlPINm3a0L17d1MhQqWxApdX\nCZUtW5a2bdsC1hL5t99+26d/b8/cf+edd9IlPrfbsWOHSbJXpUqV690nJ0Ow57+tXbsWgEmTJjkZ\njuvZDbs1a9b4c/l3huC5Ea89RyojsOeVFihQwMytfOGFF9J1x+XrIUNCQgghhHA9v/ewiNSzJx8P\nHz48JHOIAGY7hk6dOvHXX3+Z5+0die0hH08rVqwA4K+//jIz2UO5C9teqZfclhcZnb37ebt27Xjg\ngQcAq4dl7dq1XhcBiOT169fvmgsiQk2FChXMzxmpbOyFHKdPnzYTbOPi4pwMKUXSYBGucuHCBTNE\nBiT5WaRs8eLFgJV8sWbNmg5H44yRI0cycuRIp8MIahUqVGDNmjVOh+Fau3btcjoEv3vllVeYMGGC\n02FckwwJCSGEEML1pIdFiAzCTqG9b98+XnvtNYejEcFkw4YNJuGnveJTXOY5CdXesysjCAsLrj4L\nv21+6BbBtrlUAGWozdr8KVg3awsEqU9eSX3yIiPWp6JFiwJwyy23mBQLacm8LvXJK5/qU3A1r4QQ\nQggRkmRISAghhEjB/v37k/xXOCO1DZZYYE96BOInV699DQy3lwtI2XjjVLmAlI03bi8XkLLxRuqT\nd3LOeOdT2aRqDosQQgghhBNkDosQQgghXE8aLEIIIYRwPWmwCCGEEML1pMEihBBCCNeTBosQQggh\nXE8aLEIIIYRwPWmwCCGEEML1pMEihBBCCNeTBosQQgghXO//AYQjJ0c/71eDAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 576x432 with 24 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Hm_0kj13bu44",
        "colab_type": "code",
        "outputId": "92b32ef4-cd99-4cb7-a0fb-c0ffafffcb29",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 416
        }
      },
      "source": [
        "# Show a Model Summary\n",
        "# TODO"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d_1 (Conv2D)            (None, 26, 26, 32)        320       \n",
            "_________________________________________________________________\n",
            "conv2d_2 (Conv2D)            (None, 24, 24, 64)        18496     \n",
            "_________________________________________________________________\n",
            "max_pooling2d_1 (MaxPooling2 (None, 12, 12, 64)        0         \n",
            "_________________________________________________________________\n",
            "dropout_1 (Dropout)          (None, 12, 12, 64)        0         \n",
            "_________________________________________________________________\n",
            "flatten_1 (Flatten)          (None, 9216)              0         \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 128)               1179776   \n",
            "_________________________________________________________________\n",
            "dropout_2 (Dropout)          (None, 128)               0         \n",
            "_________________________________________________________________\n",
            "dense_2 (Dense)              (None, 10)                1290      \n",
            "=================================================================\n",
            "Total params: 1,199,882\n",
            "Trainable params: 1,199,882\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UrG3UV1mcO-6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}