Final_Project/Final_Project_Loedige.ipynb

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    "# Intelligent World Informatics LectureV: Assignment Week 12\n",
    "- Author: Paul Lödige (ploedige@g.ecc.u-tokyo.ac.jp)\n",
    "- Student ID: 37-229753"
   ]
  },
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   "source": [
    "## Assignment\n",
    "- Define your own project\n",
    "  - Describe the task and the goal you would like to achieve\n",
    "  - Experiment on any public dataset provided by Tensorflow:\n",
    "    - Full list: https://www.tensorflow.org/datasets/catalog/overview\n",
    "    - Simple datasets: https://www.tensorflow.org/api_docs/python/tf/keras/datasets\n",
    "  - Do one of the followings:\n",
    "    1. Apply two different deep learning techniques you learned from this class\n",
    "       - e.g.: Comparing Dense DNN vs RNN method on the same dataset (what pros and cons?)\n",
    "       - e.g.: Combining CNN and RNN on a dataset (why the combination is preferable?)\n",
    "    2. Apply transfer learning with two different base models\n",
    "       - Explain the reasons why the base models are reasonable choices.\n",
    "       - Survey on the internet with keywords such as “best model for imagenet/cifar10/mnist”, “tensorflow pre-trained models”, etc, to find the name of the model\n",
    "       - Download the base model through tensorflow\n",
    "         - Just like in the code, simply change the name of the base model. List of available base models in Tensorflow:\n",
    "           - https://www.tensorflow.org/api_docs/python/tf/keras/applications\n",
    "         - Add layers, dropouts, use different learning rate, epoch, batch_size, etc.\n",
    "\n",
    "### Additional Details\n",
    "- Write 1 ~ 2 page(s) (excluding the code) describing your project:\n",
    "  - Imagine the project as a kind of “mini research paper”\n",
    "  - What is the problem? What is your motivation?\n",
    "  - How are you solving them? What deep learning algorithms?\n",
    "  - Outline the experiment and test on the dataset.\n",
    "  - What are the results?\n",
    "    - Add at least 1 figure and insert your analysis\n",
    "  - Attach the code at the end.\n",
    "  - Include your name, affiliation, student number, as well as your university e-mail address in case if we need to contact you for clarification\n",
    "  - Put all the above in a single PDF and upload to ITC-LMS\n",
    "\n",
    "### How the project is graded\n",
    "- The purpose of the project is to make you familiar with machine learning experiments.\n",
    "  - Not intended to be a stressful project.\n",
    "    - So do not worry too much on achieving high accuracy, good parameter settings, etc\n",
    "    - Make sure to keep it simple\n",
    "- What we would like to see:\n",
    "  - Clarity of the project description\n",
    "  - Relevance between the problem and the chosen approaches\n",
    "  - Appropriate input/output design and execution of the experiments\n",
    "  - Proper evaluation methods, figures and tables\n",
    "  - Easy to understand writings, informative comments on the code\n",
    "- Overall grade for this course : 70% homeworks, 30% final project\n",
    "  - Bonus points for helping each other on slack\n",
    "    - Feel free to discuss your final project on slack if you have troubles"
   ]
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   "source": [
    "## Code\n",
    "### Import libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras import layers, models, applications, optimizers\n",
    "import tensorflow_datasets as tfds\n",
    "import tkinter.messagebox # for notifications"
   ]
  },
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   "source": [
    "#### Load Dataset\n",
    "https://www.tensorflow.org/datasets/keras_example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# split into training and test data\n",
    "ds_train, ds_test = tfds.load(\n",
    "    'svhn_cropped', \n",
    "    split=['train', 'test'],\n",
    "    shuffle_files=True, \n",
    "    as_supervised=True\n",
    "    )"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Confirm Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for image, label in ds_train.take(1):\n",
    "    plt.imshow(image)\n",
    "    plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Define normalization function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "def normalize_img(image, label):\n",
    "    return tf.cast(image, tf.float32) / 255.0, label"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Build a training pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds_train = ds_train.map(normalize_img, num_parallel_calls=tf.data.AUTOTUNE)\n",
    "ds_train = ds_train.cache()\n",
    "ds_train = ds_train.batch(128)\n",
    "ds_train = ds_train.prefetch(tf.data.AUTOTUNE)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Build an evaluation pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds_test = ds_test.map(normalize_img, num_parallel_calls=tf.data.AUTOTUNE)\n",
    "ds_test = ds_test.cache()\n",
    "ds_test = ds_test.batch(128)\n",
    "ds_test = ds_test.prefetch(tf.data.AUTOTUNE)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Configurations\n",
    "The following is a list of the neural network configurations that will be compared:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "configs = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "configs['regular Neural Network (3 hidden layers)'] = {\n",
    "        'model': models.Sequential([\n",
    "            layers.Flatten(input_shape=(32, 32, 3)),\n",
    "            layers.Dense(64, activation='relu'),\n",
    "            layers.Dense(64, activation='relu'),\n",
    "            layers.Dense(64, activation='relu'),\n",
    "            layers.Dense(10, activation='softmax')\n",
    "        ]),\n",
    "        'optimizer': 'adam',\n",
    "        'loss': 'sparse_categorical_crossentropy',\n",
    "        'metrics': ['accuracy'],\n",
    "        'epochs': 30,\n",
    "        'batch_size': 64\n",
    "    }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "configs['regular Neural Network (5 hidden layers)'] = {\n",
    "        'model': models.Sequential([\n",
    "            layers.Flatten(input_shape=(32, 32, 3)),\n",
    "            layers.Dense(64, activation='relu'),\n",
    "            layers.Dense(64, activation='relu'),\n",
    "            layers.Dense(64, activation='relu'),\n",
    "            layers.Dense(64, activation='relu'),\n",
    "            layers.Dense(64, activation='relu'),\n",
    "            layers.Dense(10, activation='softmax')\n",
    "        ]),\n",
    "        'optimizer': 'adam',\n",
    "        'loss': 'sparse_categorical_crossentropy',\n",
    "        'metrics': ['accuracy'],\n",
    "        'epochs': 30,\n",
    "        'batch_size': 64\n",
    "    }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "configs['regular Neural Network (10 hidden layers)'] = {\n",
    "        'model': models.Sequential([\n",
    "            layers.Flatten(input_shape=(32, 32, 3)),\n",
    "            layers.Dense(64, activation='relu'),\n",
    "            layers.Dense(64, activation='relu'),\n",
    "            layers.Dense(64, activation='relu'),\n",
    "            layers.Dense(64, activation='relu'),\n",
    "            layers.Dense(64, activation='relu'),\n",
    "            layers.Dense(64, activation='relu'),\n",
    "            layers.Dense(64, activation='relu'),\n",
    "            layers.Dense(64, activation='relu'),\n",
    "            layers.Dense(64, activation='relu'),\n",
    "            layers.Dense(64, activation='relu'),\n",
    "            layers.Dense(10, activation='softmax')\n",
    "        ]),\n",
    "        'optimizer': 'adam',\n",
    "        'loss': 'sparse_categorical_crossentropy',\n",
    "        'metrics': ['accuracy'],\n",
    "        'epochs': 30,\n",
    "        'batch_size': 64\n",
    "    }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "configs['convolutional neural network (1 convolution)'] = {\n",
    "        'model': models.Sequential([\n",
    "            layers.Conv2D(32, (3,3), activation='relu', input_shape=(32,32,3)),\n",
    "            layers.MaxPooling2D((2,2)),\n",
    "            layers.Flatten(),\n",
    "            layers.Dense(64, activation='relu'),\n",
    "            layers.Dense(64, activation='relu'),\n",
    "            layers.Dense(10, activation='softmax')\n",
    "        ]),\n",
    "        'optimizer': 'adam',\n",
    "        'loss': 'sparse_categorical_crossentropy',\n",
    "        'metrics': ['accuracy'],\n",
    "        'epochs': 10,\n",
    "        'batch_size': 64\n",
    "    }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "configs['convolutional neural network (3 convolutions)'] = {\n",
    "        'model': models.Sequential([\n",
    "            layers.Conv2D(32, (3,3), activation='relu', input_shape=(32,32,3)),\n",
    "            layers.MaxPooling2D((2,2)),\n",
    "            layers.Conv2D(64, (3,3), activation='relu'),\n",
    "            layers.MaxPooling2D((2,2)),\n",
    "            layers.Conv2D(128, (3,3), activation='relu'),\n",
    "            layers.MaxPooling2D((2,2)),\n",
    "            layers.Flatten(),\n",
    "            layers.Dense(64, activation='relu'),\n",
    "            layers.Dense(64, activation='relu'),\n",
    "            layers.Dense(10, activation='softmax')\n",
    "        ]),\n",
    "        'optimizer': 'adam',\n",
    "        'loss': 'sparse_categorical_crossentropy',\n",
    "        'metrics': ['accuracy'],\n",
    "        'epochs': 30,\n",
    "        'batch_size': 128\n",
    "    }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "densenet121_base_model = applications.DenseNet121(input_shape=(32,32,3), include_top=False, weights='imagenet')\n",
    "# activate only the last 20 layers for training\n",
    "densenet121_base_model.trainable = False\n",
    "for layer in densenet121_base_model.layers[-20:]:\n",
    "    layer.trainable = True\n",
    "\n",
    "configs['with DenseNet121 base model'] = {\n",
    "        'model': models.Sequential([\n",
    "            densenet121_base_model,\n",
    "            layers.Flatten(),\n",
    "            layers.Dense(1024, activation='relu'),\n",
    "            layers.Dropout(0.1),\n",
    "            layers.Dense(512, activation='relu'),\n",
    "            layers.Dropout(0.2),\n",
    "            layers.Dense(512, activation='relu'),\n",
    "            layers.Dense(10, activation='softmax'),\n",
    "        ]),\n",
    "        'optimizer': optimizers.Adam(learning_rate=0.0001),\n",
    "        'loss': 'sparse_categorical_crossentropy',\n",
    "        'metrics': ['accuracy'],\n",
    "        'epochs': 20,\n",
    "        'batch_size': 128\n",
    "    }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "vgg16_base_model = applications.VGG16(input_shape=(32,32,3), include_top= False)\n",
    "# activate only the last 6 layers for training\n",
    "vgg16_base_model.trainable = False\n",
    "for layer in vgg16_base_model.layers[-6:]:\n",
    "    layer.trainable = True\n",
    "\n",
    "configs['with VGG16 base model'] = {\n",
    "        'model': models.Sequential([\n",
    "            vgg16_base_model,\n",
    "            layers.Flatten(),\n",
    "            layers.Dense(1024, activation='relu'),\n",
    "            layers.Dropout(0.1),\n",
    "            layers.Dense(512, activation='relu'),\n",
    "            layers.Dropout(0.2),\n",
    "            layers.Dense(512, activation='relu'),\n",
    "            layers.Dense(10, activation='softmax'),\n",
    "        ]),\n",
    "        'optimizer': optimizers.Adam(learning_rate=0.001),\n",
    "        'loss': 'sparse_categorical_crossentropy',\n",
    "        'metrics': ['accuracy'],\n",
    "        'epochs': 5,\n",
    "        'batch_size': 128\n",
    "    }"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Compile the Models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "for config in configs.values():\n",
    "    if 'history' not in config.keys():\n",
    "        config['model'].compile(\n",
    "            optimizer=config['optimizer'],\n",
    "            loss=config['loss'],\n",
    "            metrics=config['metrics']\n",
    "        )"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Train and Evaluate the Models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Already trained model \"regular Neural Network (3 hidden layers)\"\n",
      "Already trained model \"regular Neural Network (5 hidden layers)\"\n",
      "Already trained model \"regular Neural Network (10 hidden layers)\"\n",
      "Already trained model \"convolutional neural network (1 convolution)\"\n",
      "Now training model \"convolutional neural network (3 convolutions)\"\n",
      "Epoch 1/30\n",
      "573/573 [==============================] - 44s 75ms/step - loss: 1.2724 - accuracy: 0.5829 - val_loss: 0.7224 - val_accuracy: 0.7973\n",
      "Epoch 2/30\n",
      "573/573 [==============================] - 42s 73ms/step - loss: 0.5971 - accuracy: 0.8280 - val_loss: 0.6116 - val_accuracy: 0.8272\n",
      "Epoch 3/30\n",
      "573/573 [==============================] - 43s 76ms/step - loss: 0.4751 - accuracy: 0.8613 - val_loss: 0.5721 - val_accuracy: 0.8365\n",
      "Epoch 4/30\n",
      "573/573 [==============================] - 41s 71ms/step - loss: 0.4117 - accuracy: 0.8789 - val_loss: 0.4737 - val_accuracy: 0.8675\n",
      "Epoch 5/30\n",
      "573/573 [==============================] - 37s 65ms/step - loss: 0.3686 - accuracy: 0.8910 - val_loss: 0.4212 - val_accuracy: 0.8802\n",
      "Epoch 6/30\n",
      "573/573 [==============================] - 36s 64ms/step - loss: 0.3372 - accuracy: 0.8993 - val_loss: 0.4049 - val_accuracy: 0.8844\n",
      "Epoch 7/30\n",
      "573/573 [==============================] - 38s 67ms/step - loss: 0.3121 - accuracy: 0.9080 - val_loss: 0.3934 - val_accuracy: 0.8863\n",
      "Epoch 8/30\n",
      "573/573 [==============================] - 41s 71ms/step - loss: 0.2916 - accuracy: 0.9141 - val_loss: 0.3929 - val_accuracy: 0.8881\n",
      "Epoch 9/30\n",
      "573/573 [==============================] - 45s 79ms/step - loss: 0.2743 - accuracy: 0.9197 - val_loss: 0.3793 - val_accuracy: 0.8929\n",
      "Epoch 10/30\n",
      "573/573 [==============================] - 44s 77ms/step - loss: 0.2584 - accuracy: 0.9242 - val_loss: 0.3878 - val_accuracy: 0.8931\n",
      "Epoch 11/30\n",
      "573/573 [==============================] - 45s 79ms/step - loss: 0.2467 - accuracy: 0.9283 - val_loss: 0.3807 - val_accuracy: 0.8955\n",
      "Epoch 12/30\n",
      "573/573 [==============================] - 46s 80ms/step - loss: 0.2347 - accuracy: 0.9317 - val_loss: 0.3857 - val_accuracy: 0.8949\n",
      "Epoch 13/30\n",
      "573/573 [==============================] - 44s 78ms/step - loss: 0.2250 - accuracy: 0.9345 - val_loss: 0.3756 - val_accuracy: 0.8986\n",
      "Epoch 14/30\n",
      "573/573 [==============================] - 45s 79ms/step - loss: 0.2174 - accuracy: 0.9363 - val_loss: 0.3865 - val_accuracy: 0.8947\n",
      "Epoch 15/30\n",
      "573/573 [==============================] - 46s 81ms/step - loss: 0.2088 - accuracy: 0.9386 - val_loss: 0.3882 - val_accuracy: 0.8952\n",
      "Epoch 16/30\n",
      "573/573 [==============================] - 42s 73ms/step - loss: 0.1964 - accuracy: 0.9426 - val_loss: 0.4134 - val_accuracy: 0.8892\n",
      "Epoch 17/30\n",
      "573/573 [==============================] - 41s 71ms/step - loss: 0.1912 - accuracy: 0.9436 - val_loss: 0.4202 - val_accuracy: 0.8889\n",
      "Epoch 18/30\n",
      "573/573 [==============================] - 40s 70ms/step - loss: 0.1827 - accuracy: 0.9462 - val_loss: 0.4349 - val_accuracy: 0.8913\n",
      "Epoch 19/30\n",
      "573/573 [==============================] - 38s 66ms/step - loss: 0.1785 - accuracy: 0.9479 - val_loss: 0.4358 - val_accuracy: 0.8937\n",
      "Epoch 20/30\n",
      "573/573 [==============================] - 44s 78ms/step - loss: 0.1713 - accuracy: 0.9501 - val_loss: 0.4541 - val_accuracy: 0.8942\n",
      "Epoch 21/30\n",
      "573/573 [==============================] - 41s 72ms/step - loss: 0.1635 - accuracy: 0.9518 - val_loss: 0.4651 - val_accuracy: 0.8928\n",
      "Epoch 22/30\n",
      "573/573 [==============================] - 43s 75ms/step - loss: 0.1576 - accuracy: 0.9531 - val_loss: 0.4903 - val_accuracy: 0.8893\n",
      "Epoch 23/30\n",
      "573/573 [==============================] - 40s 69ms/step - loss: 0.1511 - accuracy: 0.9551 - val_loss: 0.4916 - val_accuracy: 0.8917\n",
      "Epoch 24/30\n",
      "573/573 [==============================] - 36s 64ms/step - loss: 0.1449 - accuracy: 0.9565 - val_loss: 0.4928 - val_accuracy: 0.8932\n",
      "Epoch 25/30\n",
      "573/573 [==============================] - 41s 71ms/step - loss: 0.1387 - accuracy: 0.9586 - val_loss: 0.4998 - val_accuracy: 0.8950\n",
      "Epoch 26/30\n",
      "573/573 [==============================] - 39s 68ms/step - loss: 0.1363 - accuracy: 0.9586 - val_loss: 0.5161 - val_accuracy: 0.8913\n",
      "Epoch 27/30\n",
      "573/573 [==============================] - 39s 69ms/step - loss: 0.1356 - accuracy: 0.9583 - val_loss: 0.5425 - val_accuracy: 0.8897\n",
      "Epoch 28/30\n",
      "573/573 [==============================] - 37s 65ms/step - loss: 0.1308 - accuracy: 0.9591 - val_loss: 0.5365 - val_accuracy: 0.8904\n",
      "Epoch 29/30\n",
      "573/573 [==============================] - 41s 71ms/step - loss: 0.1249 - accuracy: 0.9612 - val_loss: 0.5667 - val_accuracy: 0.8877\n",
      "Epoch 30/30\n",
      "573/573 [==============================] - 45s 79ms/step - loss: 0.1199 - accuracy: 0.9627 - val_loss: 0.5609 - val_accuracy: 0.8886\n",
      "Already trained model \"with DenseNet121 base model\"\n",
      "Already trained model \"with VGG16 base model\"\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    for config_name, config in configs.items():\n",
    "        if 'history' in config.keys():\n",
    "            print(f'Already trained model \"{config_name}\"')\n",
    "        else:\n",
    "            print(f'Now training model \"{config_name}\"')\n",
    "            config['history'] = config['model'].fit(ds_train, epochs=config['epochs'], validation_data=ds_test, batch_size=config['batch_size'])#, verbose=0)\n",
    "except Exception as e:\n",
    "    print(e)\n",
    "    tkinter.messagebox.showerror(\"ERROR\", f\"ERROR: {e}\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Plot the accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,5))\n",
    "for config_name, config in configs.items():\n",
    "    plt.plot(config['history'].history['accuracy'], label=config_name)\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Accuracy')\n",
    "# plt.ylim([0.75, 1])\n",
    "plt.legend(loc='lower right')\n",
    "plt.title(\"Accuracy\")\n",
    "plt.show()\n",
    "\n",
    "plt.figure(figsize=(10,5))\n",
    "for config_name, config in configs.items():\n",
    "    plt.plot(config['history'].history['val_accuracy'], label=config_name)\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Accuracy')\n",
    "# plt.ylim([0.8, 1])\n",
    "plt.legend(loc='lower right')\n",
    "plt.title(\"Validation Accuracy\")\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Notify when done"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'ok'"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tkinter.messagebox.showinfo(\"DONE\", \"DONE\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.9"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "767d51c1340bd893661ea55ea3124f6de3c7a262a8b4abca0554b478b1e2ff90"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}