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": 17,
   "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": 18,
   "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": 19,
   "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": 20,
   "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": 21,
   "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": 22,
   "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": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "configs = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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': 10,\n",
    "        'batch_size': 64\n",
    "    }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "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': 10,\n",
    "        'batch_size': 64\n",
    "    }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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': 10,\n",
    "        'batch_size': 64\n",
    "    }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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": 28,
   "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': 10,\n",
    "        'batch_size': 128\n",
    "    }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "vgg16_base_model = applications.VGG16(input_shape=(32,32,3), include_top= False)\n",
    "# freeze the VGG16 model\n",
    "vgg16_base_model.trainable = False\n",
    "\n",
    "configs['frozen VGG16 base model'] = {\n",
    "        'model': models.Sequential([\n",
    "            vgg16_base_model,\n",
    "            layers.Flatten(),\n",
    "            layers.Dense(64, activation='relu'),\n",
    "            layers.Dense(32, activation='relu'),\n",
    "            layers.Dense(10, activation='softmax'),\n",
    "        ]),\n",
    "        'optimizer': 'adam',\n",
    "        'loss': 'sparse_categorical_crossentropy',\n",
    "        'metrics': ['accuracy'],\n",
    "        'epochs': 5,\n",
    "        'batch_size': 64\n",
    "    }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "vgg16_base_model = applications.VGG16(input_shape=(32,32,3), include_top= False)\n",
    "\n",
    "configs['fully trainable VGG16 base model'] = {\n",
    "        'model': models.Sequential([\n",
    "            vgg16_base_model,\n",
    "            layers.Flatten(),\n",
    "            layers.Dense(64, activation='relu'),\n",
    "            layers.Dense(32, 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': 64\n",
    "    }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "vgg16_base_model = applications.VGG16(input_shape=(32,32,3), include_top= False)\n",
    "# freeze the VGG16 model\n",
    "vgg16_base_model.trainable = False\n",
    "for layer in vgg16_base_model.layers[-6:]:\n",
    "    layer.trainable=True\n",
    "\n",
    "configs['partly trainable VGG16 base model'] = {\n",
    "        'model': models.Sequential([\n",
    "            vgg16_base_model,\n",
    "            layers.Flatten(),\n",
    "            layers.Dense(64, activation='relu'),\n",
    "            layers.Dense(32, 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': 64\n",
    "    }"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Compile the Models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Already trained model \"regular Neural Network (3 hidden layers)\"\n",
      "Now training model \"regular Neural Network (5 hidden layers)\"\n",
      "Epoch 1/10\n",
      "573/573 [==============================] - 4s 6ms/step - loss: 2.1808 - accuracy: 0.2090 - val_loss: 2.0740 - val_accuracy: 0.2603\n",
      "Epoch 2/10\n",
      "573/573 [==============================] - 3s 5ms/step - loss: 1.9479 - accuracy: 0.2899 - val_loss: 2.0117 - val_accuracy: 0.2704\n",
      "Epoch 3/10\n",
      "573/573 [==============================] - 3s 5ms/step - loss: 1.8272 - accuracy: 0.3483 - val_loss: 1.8153 - val_accuracy: 0.3685\n",
      "Epoch 4/10\n",
      "573/573 [==============================] - 3s 5ms/step - loss: 1.7119 - accuracy: 0.3952 - val_loss: 1.7315 - val_accuracy: 0.3903\n",
      "Epoch 5/10\n",
      "573/573 [==============================] - 3s 5ms/step - loss: 1.6912 - accuracy: 0.4016 - val_loss: 1.7336 - val_accuracy: 0.3889\n",
      "Epoch 6/10\n",
      "573/573 [==============================] - 3s 5ms/step - loss: 1.6804 - accuracy: 0.4035 - val_loss: 1.7354 - val_accuracy: 0.3859\n",
      "Epoch 7/10\n",
      "573/573 [==============================] - 3s 5ms/step - loss: 1.6715 - accuracy: 0.4048 - val_loss: 1.7318 - val_accuracy: 0.3877\n",
      "Epoch 8/10\n",
      "573/573 [==============================] - 3s 5ms/step - loss: 1.6631 - accuracy: 0.4073 - val_loss: 1.7218 - val_accuracy: 0.3911\n",
      "Epoch 9/10\n",
      "573/573 [==============================] - 3s 5ms/step - loss: 1.6524 - accuracy: 0.4098 - val_loss: 1.7372 - val_accuracy: 0.3871\n",
      "Epoch 10/10\n",
      "573/573 [==============================] - 3s 5ms/step - loss: 1.5946 - accuracy: 0.4377 - val_loss: 1.6471 - val_accuracy: 0.4303\n",
      "Now training model \"regular Neural Network (10 hidden layers)\"\n",
      "Epoch 1/10\n",
      "573/573 [==============================] - 5s 6ms/step - loss: 2.1620 - accuracy: 0.2086 - val_loss: 2.0289 - val_accuracy: 0.2637\n",
      "Epoch 2/10\n",
      "573/573 [==============================] - 3s 6ms/step - loss: 1.8554 - accuracy: 0.3278 - val_loss: 1.8178 - val_accuracy: 0.3646\n",
      "Epoch 3/10\n",
      "573/573 [==============================] - 3s 6ms/step - loss: 1.6382 - accuracy: 0.4278 - val_loss: 1.6134 - val_accuracy: 0.4429\n",
      "Epoch 4/10\n",
      "573/573 [==============================] - 4s 6ms/step - loss: 1.4941 - accuracy: 0.4835 - val_loss: 1.5625 - val_accuracy: 0.4769\n",
      "Epoch 5/10\n",
      "573/573 [==============================] - 4s 7ms/step - loss: 1.4252 - accuracy: 0.5136 - val_loss: 1.5296 - val_accuracy: 0.4912\n",
      "Epoch 6/10\n",
      "573/573 [==============================] - 4s 7ms/step - loss: 1.3940 - accuracy: 0.5287 - val_loss: 1.4993 - val_accuracy: 0.5050\n",
      "Epoch 7/10\n",
      "573/573 [==============================] - 4s 7ms/step - loss: 1.3718 - accuracy: 0.5373 - val_loss: 1.4939 - val_accuracy: 0.5019\n",
      "Epoch 8/10\n",
      "573/573 [==============================] - 4s 6ms/step - loss: 1.3595 - accuracy: 0.5405 - val_loss: 1.4698 - val_accuracy: 0.5136\n",
      "Epoch 9/10\n",
      "573/573 [==============================] - 3s 6ms/step - loss: 1.3512 - accuracy: 0.5465 - val_loss: 1.4504 - val_accuracy: 0.5230\n",
      "Epoch 10/10\n",
      "573/573 [==============================] - 3s 6ms/step - loss: 1.3413 - accuracy: 0.5512 - val_loss: 1.4415 - val_accuracy: 0.5292\n",
      "Already trained model \"convolutional neural network (1 convolution)\"\n",
      "Already trained model \"convolutional neural network (3 convolutions)\"\n",
      "Already trained model \"frozen VGG16 base model\"\n",
      "Already trained model \"fully trainable VGG16 base model\"\n",
      "Already trained model \"partly trainable 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": 40,
   "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": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'ok'"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tkinter.messagebox.showinfo(\"DONE\", \"DONE\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Save data for future use"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Keras weights file (<HDF5 file \"variables.h5\" (mode r+)>) saving:\n",
      "...layers\n",
      "......dense\n",
      ".........vars\n",
      "............0\n",
      "............1\n",
      "......dense_1\n",
      ".........vars\n",
      "............0\n",
      "............1\n",
      "......dense_2\n",
      ".........vars\n",
      "............0\n",
      "............1\n",
      "......dense_3\n",
      ".........vars\n",
      "............0\n",
      "............1\n",
      "......flatten\n",
      ".........vars\n",
      "...metrics\n",
      "......mean\n",
      ".........vars\n",
      "............0\n",
      "............1\n",
      "......mean_metric_wrapper\n",
      ".........vars\n",
      "............0\n",
      "............1\n",
      "...optimizer\n",
      "......vars\n",
      ".........0\n",
      ".........1\n",
      ".........10\n",
      ".........11\n",
      ".........12\n",
      ".........13\n",
      ".........14\n",
      ".........15\n",
      ".........16\n",
      ".........2\n",
      ".........3\n",
      ".........4\n",
      ".........5\n",
      ".........6\n",
      ".........7\n",
      ".........8\n",
      ".........9\n",
      "...vars\n",
      "Keras model archive saving:\n",
      "File Name                                             Modified             Size\n",
      "variables.h5                                   2023-02-01 01:27:30      2497920\n",
      "config.json                                    2023-02-01 01:27:30         2425\n",
      "metadata.json                                  2023-02-01 01:27:30           64\n",
      "Keras weights file (<HDF5 file \"variables.h5\" (mode r+)>) saving:\n",
      "...layers\n",
      "......dense\n",
      ".........vars\n",
      "............0\n",
      "............1\n",
      "......dense_1\n",
      ".........vars\n",
      "............0\n",
      "............1\n",
      "......dense_2\n",
      ".........vars\n",
      "............0\n",
      "............1\n",
      "......dense_3\n",
      ".........vars\n",
      "............0\n",
      "............1\n",
      "......dense_4\n",
      ".........vars\n",
      "............0\n",
      "............1\n",
      "......dense_5\n",
      ".........vars\n",
      "............0\n",
      "............1\n",
      "......flatten\n",
      ".........vars\n",
      "...metrics\n",
      "......mean\n",
      ".........vars\n",
      "............0\n",
      "............1\n",
      "......mean_metric_wrapper\n",
      ".........vars\n",
      "............0\n",
      "............1\n",
      "...optimizer\n",
      "......vars\n",
      ".........0\n",
      ".........1\n",
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      ".........7\n",
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      ".........9\n",
      "...vars\n",
      "Keras model archive saving:\n",
      "File Name                                             Modified             Size\n",
      "variables.h5                                   2023-02-01 01:27:30      2605296\n",
      "config.json                                    2023-02-01 01:27:30         3278\n",
      "metadata.json                                  2023-02-01 01:27:30           64\n",
      "Keras weights file (<HDF5 file \"variables.h5\" (mode r+)>) saving:\n",
      "...layers\n",
      "......dense\n",
      ".........vars\n",
      "............0\n",
      "............1\n",
      "......dense_1\n",
      ".........vars\n",
      "............0\n",
      "............1\n",
      "......dense_10\n",
      ".........vars\n",
      "............0\n",
      "............1\n",
      "......dense_2\n",
      ".........vars\n",
      "............0\n",
      "............1\n",
      "......dense_3\n",
      ".........vars\n",
      "............0\n",
      "............1\n",
      "......dense_4\n",
      ".........vars\n",
      "............0\n",
      "............1\n",
      "......dense_5\n",
      ".........vars\n",
      "............0\n",
      "............1\n",
      "......dense_6\n",
      ".........vars\n",
      "............0\n",
      "............1\n",
      "......dense_7\n",
      ".........vars\n",
      "............0\n",
      "............1\n",
      "......dense_8\n",
      ".........vars\n",
      "............0\n",
      "............1\n",
      "......dense_9\n",
      ".........vars\n",
      "............0\n",
      "............1\n",
      "......flatten\n",
      ".........vars\n",
      "...metrics\n",
      "......mean\n",
      ".........vars\n",
      "............0\n",
      "............1\n",
      "......mean_metric_wrapper\n",
      ".........vars\n",
      "............0\n",
      "............1\n",
      "...optimizer\n",
      "......vars\n",
      ".........0\n",
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      "...vars\n",
      "Keras model archive saving:\n",
      "File Name                                             Modified             Size\n",
      "variables.h5                                   2023-02-01 01:27:30      2876328\n",
      "config.json                                    2023-02-01 01:27:30         5403\n",
      "metadata.json                                  2023-02-01 01:27:30           64\n",
      "Keras weights file (<HDF5 file \"variables.h5\" (mode r+)>) saving:\n",
      "...layers\n",
      "......conv2d\n",
      ".........vars\n",
      "............0\n",
      "............1\n",
      "......dense\n",
      ".........vars\n",
      "............0\n",
      "............1\n",
      "......dense_1\n",
      ".........vars\n",
      "............0\n",
      "............1\n",
      "......dense_2\n",
      ".........vars\n",
      "............0\n",
      "............1\n",
      "......flatten\n",
      ".........vars\n",
      "......max_pooling2d\n",
      ".........vars\n",
      "...metrics\n",
      "......mean\n",
      ".........vars\n",
      "............0\n",
      "............1\n",
      "......mean_metric_wrapper\n",
      ".........vars\n",
      "............0\n",
      "............1\n",
      "...optimizer\n",
      "......vars\n",
      ".........0\n",
      ".........1\n",
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      ".........5\n",
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      ".........9\n",
      "...vars\n",
      "Keras model archive saving:\n",
      "File Name                                             Modified             Size\n",
      "variables.h5                                   2023-02-01 01:27:30      5630656\n",
      "config.json                                    2023-02-01 01:27:30         2761\n",
      "metadata.json                                  2023-02-01 01:27:30           64\n",
      "Keras weights file (<HDF5 file \"variables.h5\" (mode r+)>) saving:\n",
      "...layers\n",
      "......conv2d\n",
      ".........vars\n",
      "............0\n",
      "............1\n",
      "......conv2d_1\n",
      ".........vars\n",
      "............0\n",
      "............1\n",
      "......conv2d_2\n",
      ".........vars\n",
      "............0\n",
      "............1\n",
      "......dense\n",
      ".........vars\n",
      "............0\n",
      "............1\n",
      "......dense_1\n",
      ".........vars\n",
      "............0\n",
      "............1\n",
      "......dense_2\n",
      ".........vars\n",
      "............0\n",
      "............1\n",
      "......flatten\n",
      ".........vars\n",
      "......max_pooling2d\n",
      ".........vars\n",
      "......max_pooling2d_1\n",
      ".........vars\n",
      "......max_pooling2d_2\n",
      ".........vars\n",
      "...metrics\n",
      "......mean\n",
      ".........vars\n",
      "............0\n",
      "............1\n",
      "......mean_metric_wrapper\n",
      ".........vars\n",
      "............0\n",
      "............1\n",
      "...optimizer\n",
      "......vars\n",
      ".........0\n",
      ".........1\n",
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      "...vars\n",
      "Keras model archive saving:\n",
      "File Name                                             Modified             Size\n",
      "variables.h5                                   2023-02-01 01:27:30      1615704\n",
      "config.json                                    2023-02-01 01:27:30         4286\n",
      "metadata.json                                  2023-02-01 01:27:30           64\n",
      "Keras weights file (<HDF5 file \"variables.h5\" (mode r+)>) saving:\n",
      "...layers\n",
      "......dense\n",
      ".........vars\n",
      "............0\n",
      "............1\n",
      "......dense_1\n",
      ".........vars\n",
      "............0\n",
      "............1\n",
      "......dense_2\n",
      ".........vars\n",
      "............0\n",
      "............1\n",
      "......flatten\n",
      ".........vars\n",
      "......functional\n",
      ".........layers\n",
      "............conv2d\n",
      "...............vars\n",
      "..................0\n",
      "..................1\n",
      "............conv2d_1\n",
      "...............vars\n",
      "..................0\n",
      "..................1\n",
      "............conv2d_10\n",
      "...............vars\n",
      "..................0\n",
      "..................1\n",
      "............conv2d_11\n",
      "...............vars\n",
      "..................0\n",
      "..................1\n",
      "............conv2d_12\n",
      "...............vars\n",
      "..................0\n",
      "..................1\n",
      "............conv2d_2\n",
      "...............vars\n",
      "..................0\n",
      "..................1\n",
      "............conv2d_3\n",
      "...............vars\n",
      "..................0\n",
      "..................1\n",
      "............conv2d_4\n",
      "...............vars\n",
      "..................0\n",
      "..................1\n",
      "............conv2d_5\n",
      "...............vars\n",
      "..................0\n",
      "..................1\n",
      "............conv2d_6\n",
      "...............vars\n",
      "..................0\n",
      "..................1\n",
      "............conv2d_7\n",
      "...............vars\n",
      "..................0\n",
      "..................1\n",
      "............conv2d_8\n",
      "...............vars\n",
      "..................0\n",
      "..................1\n",
      "............conv2d_9\n",
      "...............vars\n",
      "..................0\n",
      "..................1\n",
      "............input_layer\n",
      "...............vars\n",
      "............max_pooling2d\n",
      "...............vars\n",
      "............max_pooling2d_1\n",
      "...............vars\n",
      "............max_pooling2d_2\n",
      "...............vars\n",
      "............max_pooling2d_3\n",
      "...............vars\n",
      "............max_pooling2d_4\n",
      "...............vars\n",
      ".........vars\n",
      "...metrics\n",
      "......mean\n",
      ".........vars\n",
      "............0\n",
      "............1\n",
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      ".........vars\n",
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      "...optimizer\n",
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      ".........0\n",
      ".........1\n",
      ".........10\n",
      ".........11\n",
      ".........12\n",
      ".........2\n",
      ".........3\n",
      ".........4\n",
      ".........5\n",
      ".........6\n",
      ".........7\n",
      ".........8\n",
      ".........9\n",
      "...vars\n",
      "Keras model archive saving:\n",
      "File Name                                             Modified             Size\n",
      "variables.h5                                   2023-02-01 01:27:31     59356288\n",
      "config.json                                    2023-02-01 01:27:30        11981\n",
      "metadata.json                                  2023-02-01 01:27:30           64\n",
      "Keras weights file (<HDF5 file \"variables.h5\" (mode r+)>) saving:\n",
      "...layers\n",
      "......dense\n",
      ".........vars\n",
      "............0\n",
      "............1\n",
      "......dense_1\n",
      ".........vars\n",
      "............0\n",
      "............1\n",
      "......dense_2\n",
      ".........vars\n",
      "............0\n",
      "............1\n",
      "......flatten\n",
      ".........vars\n",
      "......functional\n",
      ".........layers\n",
      "............conv2d\n",
      "...............vars\n",
      "..................0\n",
      "..................1\n",
      "............conv2d_1\n",
      "...............vars\n",
      "..................0\n",
      "..................1\n",
      "............conv2d_10\n",
      "...............vars\n",
      "..................0\n",
      "..................1\n",
      "............conv2d_11\n",
      "...............vars\n",
      "..................0\n",
      "..................1\n",
      "............conv2d_12\n",
      "...............vars\n",
      "..................0\n",
      "..................1\n",
      "............conv2d_2\n",
      "...............vars\n",
      "..................0\n",
      "..................1\n",
      "............conv2d_3\n",
      "...............vars\n",
      "..................0\n",
      "..................1\n",
      "............conv2d_4\n",
      "...............vars\n",
      "..................0\n",
      "..................1\n",
      "............conv2d_5\n",
      "...............vars\n",
      "..................0\n",
      "..................1\n",
      "............conv2d_6\n",
      "...............vars\n",
      "..................0\n",
      "..................1\n",
      "............conv2d_7\n",
      "...............vars\n",
      "..................0\n",
      "..................1\n",
      "............conv2d_8\n",
      "...............vars\n",
      "..................0\n",
      "..................1\n",
      "............conv2d_9\n",
      "...............vars\n",
      "..................0\n",
      "..................1\n",
      "............input_layer\n",
      "...............vars\n",
      "............max_pooling2d\n",
      "...............vars\n",
      "............max_pooling2d_1\n",
      "...............vars\n",
      "............max_pooling2d_2\n",
      "...............vars\n",
      "............max_pooling2d_3\n",
      "...............vars\n",
      "............max_pooling2d_4\n",
      "...............vars\n",
      ".........vars\n",
      "...metrics\n",
      "......mean\n",
      ".........vars\n",
      "............0\n",
      "............1\n",
      "......mean_metric_wrapper\n",
      ".........vars\n",
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      "............1\n",
      "...optimizer\n",
      "......vars\n",
      ".........0\n",
      ".........1\n",
      ".........10\n",
      ".........11\n",
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      ".........63\n",
      ".........64\n",
      ".........7\n",
      ".........8\n",
      ".........9\n",
      "...vars\n",
      "Keras model archive saving:\n",
      "File Name                                             Modified             Size\n",
      "variables.h5                                   2023-02-01 01:27:31    177093096\n",
      "config.json                                    2023-02-01 01:27:31        12379\n",
      "metadata.json                                  2023-02-01 01:27:31           64\n",
      "Keras weights file (<HDF5 file \"variables.h5\" (mode r+)>) saving:\n",
      "...layers\n",
      "......dense\n",
      ".........vars\n",
      "............0\n",
      "............1\n",
      "......dense_1\n",
      ".........vars\n",
      "............0\n",
      "............1\n",
      "......dense_2\n",
      ".........vars\n",
      "............0\n",
      "............1\n",
      "......flatten\n",
      ".........vars\n",
      "......functional\n",
      ".........layers\n",
      "............conv2d\n",
      "...............vars\n",
      "..................0\n",
      "..................1\n",
      "............conv2d_1\n",
      "...............vars\n",
      "..................0\n",
      "..................1\n",
      "............conv2d_10\n",
      "...............vars\n",
      "..................0\n",
      "..................1\n",
      "............conv2d_11\n",
      "...............vars\n",
      "..................0\n",
      "..................1\n",
      "............conv2d_12\n",
      "...............vars\n",
      "..................0\n",
      "..................1\n",
      "............conv2d_2\n",
      "...............vars\n",
      "..................0\n",
      "..................1\n",
      "............conv2d_3\n",
      "...............vars\n",
      "..................0\n",
      "..................1\n",
      "............conv2d_4\n",
      "...............vars\n",
      "..................0\n",
      "..................1\n",
      "............conv2d_5\n",
      "...............vars\n",
      "..................0\n",
      "..................1\n",
      "............conv2d_6\n",
      "...............vars\n",
      "..................0\n",
      "..................1\n",
      "............conv2d_7\n",
      "...............vars\n",
      "..................0\n",
      "..................1\n",
      "............conv2d_8\n",
      "...............vars\n",
      "..................0\n",
      "..................1\n",
      "............conv2d_9\n",
      "...............vars\n",
      "..................0\n",
      "..................1\n",
      "............input_layer\n",
      "...............vars\n",
      "............max_pooling2d\n",
      "...............vars\n",
      "............max_pooling2d_1\n",
      "...............vars\n",
      "............max_pooling2d_2\n",
      "...............vars\n",
      "............max_pooling2d_3\n",
      "...............vars\n",
      "............max_pooling2d_4\n",
      "...............vars\n",
      ".........vars\n",
      "...metrics\n",
      "......mean\n",
      ".........vars\n",
      "............0\n",
      "............1\n",
      "......mean_metric_wrapper\n",
      ".........vars\n",
      "............0\n",
      "............1\n",
      "...optimizer\n",
      "......vars\n",
      ".........0\n",
      ".........1\n",
      ".........10\n",
      ".........11\n",
      ".........12\n",
      ".........2\n",
      ".........3\n",
      ".........4\n",
      ".........5\n",
      ".........6\n",
      ".........7\n",
      ".........8\n",
      ".........9\n",
      "...vars\n",
      "Keras model archive saving:\n",
      "File Name                                             Modified             Size\n",
      "variables.h5                                   2023-02-01 01:27:32     59356288\n",
      "config.json                                    2023-02-01 01:27:31        12391\n",
      "metadata.json                                  2023-02-01 01:27:31           64\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:Found untraced functions such as _update_step_xla while saving (showing 1 of 1). These functions will not be directly callable after loading.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: saved_models/regular Neural Network (3 hidden layers)/assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: saved_models/regular Neural Network (3 hidden layers)/assets\n",
      "WARNING:absl:Found untraced functions such as _update_step_xla while saving (showing 1 of 1). These functions will not be directly callable after loading.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: saved_models/regular Neural Network (5 hidden layers)/assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: saved_models/regular Neural Network (5 hidden layers)/assets\n",
      "WARNING:absl:Found untraced functions such as _update_step_xla while saving (showing 1 of 1). These functions will not be directly callable after loading.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: saved_models/regular Neural Network (10 hidden layers)/assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: saved_models/regular Neural Network (10 hidden layers)/assets\n",
      "WARNING:absl:Found untraced functions such as _jit_compiled_convolution_op, _update_step_xla while saving (showing 2 of 2). These functions will not be directly callable after loading.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: saved_models/convolutional neural network (1 convolution)/assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: saved_models/convolutional neural network (1 convolution)/assets\n",
      "WARNING:absl:Found untraced functions such as _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op, _update_step_xla while saving (showing 4 of 4). These functions will not be directly callable after loading.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: saved_models/convolutional neural network (3 convolutions)/assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: saved_models/convolutional neural network (3 convolutions)/assets\n",
      "WARNING:absl:Found untraced functions such as _update_step_xla, _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op while saving (showing 5 of 14). These functions will not be directly callable after loading.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: saved_models/frozen VGG16 base model/assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: saved_models/frozen VGG16 base model/assets\n",
      "WARNING:absl:Found untraced functions such as _update_step_xla, _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op while saving (showing 5 of 14). These functions will not be directly callable after loading.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: saved_models/fully trainable VGG16 base model/assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: saved_models/fully trainable VGG16 base model/assets\n",
      "WARNING:absl:Found untraced functions such as _update_step_xla, _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op while saving (showing 5 of 14). These functions will not be directly callable after loading.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: saved_models/partly trainable VGG16 base model/assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: saved_models/partly trainable VGG16 base model/assets\n"
     ]
    }
   ],
   "source": [
    "# store histories with pickle\n",
    "import pickle\n",
    "histories = {}\n",
    "for config_name, config in configs.items():\n",
    "    histories[config_name] = config['history']\n",
    "with open('savepoint.pkl', 'wb') as out:\n",
    "    pickle.dump(histories, out)\n",
    "\n",
    "# store models with tensorflow\n",
    "for config_name, config in configs.items():\n",
    "    config['model'].save(f'saved_models/{config_name}')\n",
    "\n",
    "# store models in HDF5 format\n",
    "for config_name, config in configs.items():\n",
    "    config['model'].save(f'hdf5_models/{config_name}.h5')\n"
   ]
  }
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