example_classifier.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# MNIST classifier\n",
    "\n",
    "This notebook illustrates the use of a `BaseModel` sub-class (`MNISTClassifier`) to classify `MNIST` data (28x28 images representing digits). All the projects are based on the `BaseModel` abstraction, allowing to define the following features : the data processing pipeline, the training / processing hyper-parameters (configuration), and the `keras.Model` architecture. \n",
    "\n",
    "1. The first step is to initialize the model by providing all the model-specific configurations (in this case, the `input_size` and the `labels`)\n",
    "    - The constructor initializes the directories to save the model configuration + the architecture weights\n",
    "    - The `build` function is then called to instantiate the `keras.Model` instance\n",
    "    - The configuration + architecture weights are saved in the model directory (`pretrained_models/{name}`)\n",
    "2. Once the model has been instanciated, it can be restored via the `get_pretrained` function, or by re-creating it with the same name.\n",
    "    - If a directory with the model name already exists, it will be restored based on the configuration files to restore configuration + deserialize the `keras.Model` architecture + load the expected checkpoint\n",
    "3. The second step is to `compile` the model : it means to define the optimizer, loss and metrics used during training\n",
    "    - In general, `BaseModel` sub-classes redefines this method, or defines the `_default_loss` attribute so that it can be compiled without providing any additional information. Nonetheless, it is possible to pass custom `{loss / metrics / optimizer}_config` as arguments\n",
    "4. The final step before training the model is to load the dataset ! For this purpose, the `get_dataset` method allows to automatically load the expected datase based on its name (and possible additional configurations)\n",
    "5. (Optional) A good practice is to check the processing pipeline to ensure that everything is working as expected. For this purpose, check the last cells of this notebook, that allow proper visualization of the processing impact on data\n",
    "6. Let's train the model ! :smile: The `fit` method of the `BaseModel` class handles the creation of a `tf.data` pipeline that properly processes the given dataset before passing it to the `keras.Model.fit` method !\n",
    "    - In addition, the `fit` method adds custom callbacks to track the training metrics / information to enable better understanding / reproductibility\n",
    "7. Once the training is finished, the model is automatically saved, and you can use it for prediction / evaluation !\n",
    "\n",
    "*This repository is **not** an official classification project, meaning that it is not fully integrated with the dedicated interfaces, and thus many features are not available. It is an example on how to build, traing, restore and predict with the `BaseModel` class*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Imports + model initialization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Keras version : 3.6.0 - backend (tensorflow) : 2.18.0\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "\n",
    "os.environ['KERAS_BACKEND'] = 'tensorflow'\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "from keras import tree\n",
    "\n",
    "from models import MNISTClassifier, get_pretrained\n",
    "from utils import set_display_options, plot, plot_multiple, plot_confusion_matrix\n",
    "from utils.keras_utils import show_version, ops\n",
    "from utils.datasets import get_dataset, prepare_dataset, test_dataset_time\n",
    "\n",
    "set_display_options()\n",
    "\n",
    "model_name = 'mnist_classifier'\n",
    "show_version()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initializing model with kwargs : {'architecture': 'simple_cnn', 'input_shape': (28, 28, 1), 'output_shape': 10, 'n_conv': 2, 'filters': 16, 'kernel_size': 3, 'strides': 1, 'pooling': 'max', 'dense_as_final': True, 'n_dense': 1, 'dense_size': 64, 'final_activation': 'softmax', 'final_name': 'classification_layer', 'name': 'cnn_classifier'}\n",
      "Set `use_mask = False` by default as the masking operation is not supported. To remove this message or change the behavior, explicitely pass `use_mask = ...`\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "I0000 00:00:1730359519.078622    3204 gpu_device.cc:2022] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 22444 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 3090 Ti, pci bus id: 0000:01:00.0, compute capability: 8.6\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Save `model` to pretrained_models/mnist_classifier/saving/ckpt-0000.weights.h5\n",
      "MNISTClassifier `mnist_classifier` initialized successfully !\n",
      "\n",
      "========== mnist_classifier ==========\n",
      "Model instance `model`\n",
      "- Inputs \t: (None, 28, 28, 1)\n",
      "- Outputs \t: (None, 10)\n",
      "- Number of layers \t: 18\n",
      "- Number of parameters \t: 28048\n",
      "- Model not compiled yet\n",
      "\n",
      "Already trained on 0 epochs (0 steps)\n",
      "\n",
      "- Labels : [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "input_shape = (28, 28, 1)\n",
    "model = MNISTClassifier(input_size = input_shape, labels = list(range(10)), nom = model_name)\n",
    "\n",
    "print(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"cnn_classifier\"</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mModel: \"cnn_classifier\"\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
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     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ zero_padding2d (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">ZeroPadding2D</span>)  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">30</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">30</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)      │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>)     │           <span style=\"color: #00af00; text-decoration-color: #00af00\">160</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ norm_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>)     │            <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ custom_activation               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>)     │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">CustomActivation</span>)              │                        │               │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ max_pooling2d (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>)     │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dropout (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>)     │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ zero_padding2d_1                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>)     │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">ZeroPadding2D</span>)                 │                        │               │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>)     │         <span style=\"color: #00af00; text-decoration-color: #00af00\">2,320</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ norm_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>)     │            <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ custom_activation_1             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>)     │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">CustomActivation</span>)              │                        │               │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ max_pooling2d_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">7</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">7</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>)       │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dropout_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">7</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">7</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>)       │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ flatten (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Flatten</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">784</span>)            │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)             │        <span style=\"color: #00af00; text-decoration-color: #00af00\">25,120</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ custom_activation_2             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)             │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">CustomActivation</span>)              │                        │               │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dropout_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)             │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ classification_layer (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">10</span>)             │           <span style=\"color: #00af00; text-decoration-color: #00af00\">320</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ custom_activation_3             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">10</span>)             │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">CustomActivation</span>)              │                        │               │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ zero_padding2d (\u001b[38;5;33mZeroPadding2D\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m30\u001b[0m, \u001b[38;5;34m30\u001b[0m, \u001b[38;5;34m1\u001b[0m)      │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv_1 (\u001b[38;5;33mConv2D\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m16\u001b[0m)     │           \u001b[38;5;34m160\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ norm_1 (\u001b[38;5;33mBatchNormalization\u001b[0m)     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m16\u001b[0m)     │            \u001b[38;5;34m64\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ custom_activation               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m16\u001b[0m)     │             \u001b[38;5;34m0\u001b[0m │\n",
       "│ (\u001b[38;5;33mCustomActivation\u001b[0m)              │                        │               │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ max_pooling2d (\u001b[38;5;33mMaxPooling2D\u001b[0m)    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m16\u001b[0m)     │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dropout (\u001b[38;5;33mDropout\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m16\u001b[0m)     │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ zero_padding2d_1                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m)     │             \u001b[38;5;34m0\u001b[0m │\n",
       "│ (\u001b[38;5;33mZeroPadding2D\u001b[0m)                 │                        │               │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv_2 (\u001b[38;5;33mConv2D\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m16\u001b[0m)     │         \u001b[38;5;34m2,320\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ norm_2 (\u001b[38;5;33mBatchNormalization\u001b[0m)     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m16\u001b[0m)     │            \u001b[38;5;34m64\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ custom_activation_1             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m16\u001b[0m)     │             \u001b[38;5;34m0\u001b[0m │\n",
       "│ (\u001b[38;5;33mCustomActivation\u001b[0m)              │                        │               │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ max_pooling2d_1 (\u001b[38;5;33mMaxPooling2D\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m16\u001b[0m)       │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dropout_1 (\u001b[38;5;33mDropout\u001b[0m)             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m16\u001b[0m)       │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ flatten (\u001b[38;5;33mFlatten\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m784\u001b[0m)            │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense (\u001b[38;5;33mDense\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m)             │        \u001b[38;5;34m25,120\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ custom_activation_2             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m)             │             \u001b[38;5;34m0\u001b[0m │\n",
       "│ (\u001b[38;5;33mCustomActivation\u001b[0m)              │                        │               │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dropout_2 (\u001b[38;5;33mDropout\u001b[0m)             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m)             │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ classification_layer (\u001b[38;5;33mDense\u001b[0m)    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m)             │           \u001b[38;5;34m320\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ custom_activation_3             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m)             │             \u001b[38;5;34m0\u001b[0m │\n",
       "│ (\u001b[38;5;33mCustomActivation\u001b[0m)              │                        │               │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">28,048</span> (109.56 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m28,048\u001b[0m (109.56 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">27,984</span> (109.31 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m27,984\u001b[0m (109.31 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">64</span> (256.00 B)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m64\u001b[0m (256.00 B)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model instanciation + dataset loading\n",
    "\n",
    "Once initialized, the model can be restored just by passing its name as argument. Furthermore, all models are `Singleton`, meaning that if your model has already been restored, it will return the existing instance (and will not restore it) (*unless explicitely required via the `reload = True` argument*) !"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model compiled successfully !\n",
      "- Optimizer \t: {'name': 'adam', 'learning_rate': 0.0010000000474974513, 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'loss_scale_factor': None, 'gradient_accumulation_steps': None, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}\n",
      "- Loss \t: crossentropy\n",
      "- Metric \t: accuracy\n",
      "\n",
      "\n",
      "========== mnist_classifier ==========\n",
      "Model instance `model`\n",
      "- Inputs \t: (None, 28, 28, 1)\n",
      "- Outputs \t: (None, 10)\n",
      "- Number of layers \t: 18\n",
      "- Number of parameters \t: 28048\n",
      "\n",
      "- Optimizer \t: {'name': 'adam', 'learning_rate': 0.0010000000474974513, 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'loss_scale_factor': None, 'gradient_accumulation_steps': None, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}\n",
      "- Loss \t: crossentropy\n",
      "\n",
      "Already trained on 0 epochs (0 steps)\n",
      "\n",
      "- Labels : [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "model = get_pretrained(model_name) # Equivalent to `MNISTClassifier(name = model_name)`\n",
    "\n",
    "model.compile()\n",
    "\n",
    "print(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz\n",
      "\u001b[1m11490434/11490434\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 0us/step\n",
      "train : ((60000, 28, 28), (60000,)) - valid : ((10000, 28, 28), (10000,))\n"
     ]
    }
   ],
   "source": [
    "ds = get_dataset('mnist')\n",
    "\n",
    "if isinstance(ds, dict):\n",
    "    train, valid = ds['train'], ds['test']\n",
    "    print(train)\n",
    "    print(valid)\n",
    "else:\n",
    "    train, valid = ds\n",
    "    print('train : {} - valid : {}'.format(\n",
    "        tree.map_structure(ops.shape, train), tree.map_structure(ops.shape, valid)\n",
    "    ))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training + history analysis\n",
    "\n",
    "Note that the training has finished after 9 epochs, while 15 were expected. This is due to the `EarlyStopping` callback, allowing to stop the training when a convergence criterion is reached. In this case, no improvement of the `val_loss` during 3 (`patience`) epochs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training config :\n",
      "HParams :\n",
      "- augment_prct\t: 0.25\n",
      "- x\t: <_PrefetchDataset element_spec=(TensorSpec(shape=(None, 28, 28, 1), dtype=tf.float32, name=None), TensorSpec(shape=(None,), dtype=tf.int32, name=None))>\n",
      "- epochs\t: 15\n",
      "- callbacks\t: [<History epochs=0 steps=0>, <keras.src.callbacks.terminate_on_nan.TerminateOnNaN object at 0x7f1687423160>, <keras.src.callbacks.early_stopping.EarlyStopping object at 0x7f16874223b0>, <custom_train_objects.callbacks.checkpoint_callback.CheckpointCallback object at 0x7f1663708670>]\n",
      "- initial_epoch\t: 0\n",
      "- validation_data\t: <_PrefetchDataset element_spec=(TensorSpec(shape=(None, 28, 28, 1), dtype=tf.float32, name=None), TensorSpec(shape=(None,), dtype=tf.int32, name=None))>\n",
      "\n",
      "Epoch 1/15\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n",
      "I0000 00:00:1730359524.130796    3286 service.cc:148] XLA service 0x7f151c0141c0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n",
      "I0000 00:00:1730359524.139341    3286 service.cc:156]   StreamExecutor device (0): NVIDIA GeForce RTX 3090 Ti, Compute Capability 8.6\n",
      "2024-10-31 08:25:24.164066: I tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc:268] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable.\n",
      "I0000 00:00:1730359524.259563    3286 cuda_dnn.cc:529] Loaded cuDNN version 90300\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m 45/938\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m3s\u001b[0m 4ms/step - accuracy: 0.2157 - loss: 2.2333"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "I0000 00:00:1730359525.529623    3286 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m938/938\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 4ms/step - accuracy: 0.6189 - loss: 1.8517 - val_accuracy: 0.9641 - val_loss: 1.4984\n",
      "Epoch 2/15\n",
      "\u001b[1m938/938\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - accuracy: 0.9266 - loss: 1.5393 - val_accuracy: 0.9723 - val_loss: 1.4898\n",
      "Epoch 3/15\n",
      "\u001b[1m938/938\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 3ms/step - accuracy: 0.9420 - loss: 1.5218 - val_accuracy: 0.9775 - val_loss: 1.4839\n",
      "Epoch 4/15\n",
      "\u001b[1m938/938\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - accuracy: 0.9476 - loss: 1.5154 - val_accuracy: 0.9798 - val_loss: 1.4820\n",
      "Epoch 5/15\n",
      "\u001b[1m938/938\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 3ms/step - accuracy: 0.9537 - loss: 1.5090 - val_accuracy: 0.9825 - val_loss: 1.4796\n",
      "Epoch 6/15\n",
      "\u001b[1m938/938\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 4ms/step - accuracy: 0.9554 - loss: 1.5070 - val_accuracy: 0.9811 - val_loss: 1.4802\n",
      "Epoch 7/15\n",
      "\u001b[1m938/938\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 4ms/step - accuracy: 0.9574 - loss: 1.5054 - val_accuracy: 0.9834 - val_loss: 1.4780\n",
      "Epoch 8/15\n",
      "\u001b[1m938/938\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 3ms/step - accuracy: 0.9601 - loss: 1.5024 - val_accuracy: 0.9829 - val_loss: 1.4786\n",
      "Epoch 9/15\n",
      "\u001b[1m938/938\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 3ms/step - accuracy: 0.9621 - loss: 1.5005 - val_accuracy: 0.9834 - val_loss: 1.4779\n",
      "Epoch 10/15\n",
      "\u001b[1m938/938\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - accuracy: 0.9630 - loss: 1.4990 - val_accuracy: 0.9838 - val_loss: 1.4777\n",
      "Loading `model` weights from pretrained_models/mnist_classifier/saving/best.weights.h5\n",
      "Training finished after 30.577 sec !\n",
      "Save `model` to pretrained_models/mnist_classifier/saving/ckpt-0001.weights.h5\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<History epochs=10 steps=9380>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(\n",
    "    train,\n",
    "    validation_data = valid,\n",
    "    epochs     = 15,\n",
    "    batch_size = 64,\n",
    "    \n",
    "    additional_infos = {'dataset' : 'mnist'},\n",
    "    \n",
    "    add_early_stopping = True,\n",
    "    patience = 3\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "===== History =====\n",
      "   accuracy      loss  val_accuracy  val_loss\n",
      "0  0.787300  1.683872        0.9641  1.498367\n",
      "1  0.930717  1.534305        0.9723  1.489805\n",
      "2  0.944283  1.519484        0.9775  1.483922\n",
      "3  0.949050  1.513885        0.9798  1.482030\n",
      "4  0.954383  1.508457        0.9825  1.479635\n",
      "5  0.956350  1.506167        0.9811  1.480220\n",
      "6  0.959217  1.503443        0.9834  1.477970\n",
      "7  0.961150  1.501222        0.9829  1.478593\n",
      "8  0.962117  1.500198        0.9834  1.477919\n",
      "9  0.963517  1.498178        0.9838  1.477721\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(model.history)\n",
    "model.plot_history()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training config :\n",
      "          start           end       time  interrupted  start_epoch  final_epoch\n",
      "0  1.730360e+09  1.730360e+09  30.552843        False            0           10\n",
      "\n",
      "Training parameters :\n",
      "   batch_size  add_early_stopping  patience  augment_prct  epochs  initial_epoch\n",
      "0          64                True         3          0.25      15              0\n",
      "\n",
      "Training informations :\n",
      "          start           end       time  interrupted  start_epoch  final_epoch                     dataset  \\\n",
      "0  1.730360e+09  1.730360e+09  30.552843        False            0           10  {'train': {}, 'valid': {}}   \n",
      "\n",
      "       additional_infos  \n",
      "0  {'dataset': 'mnist'}  \n"
     ]
    }
   ],
   "source": [
    "print(\"Training config :\")\n",
    "print(pd.DataFrame(model.training_logs))\n",
    "print(\"\\nTraining parameters :\")\n",
    "print(pd.DataFrame(model.training_config))\n",
    "print(\"\\nTraining informations :\")\n",
    "print(pd.DataFrame(model.training_infos))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x2000 with 16 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "predictions = {}\n",
    "for i, data in enumerate(zip(* valid)):\n",
    "    if i >= 16: break\n",
    "    image, label = model.prepare_data(* data)\n",
    "    \n",
    "    pred, score, probs = model.predict([image])[0]\n",
    "    predictions['Pred #{}\\nClass : {} ({} ({:.2f}%))'.format(i, label, pred, score * 100)] = image\n",
    "\n",
    "plot_multiple(** predictions, ncols = 4, plot_type = 'imshow')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Confusion matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 750x750 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "images, labels = [], []\n",
    "for i, data in enumerate(zip(* valid)):\n",
    "    image, label = data #model.prepare_data(* data)\n",
    "    images.append(image)\n",
    "    labels.append(label)\n",
    "\n",
    "pred = model.predict(images)\n",
    "pred = [p[0] for p in pred] # p[1] is the confidence score\n",
    "\n",
    "plot_confusion_matrix(true = labels, pred = pred, norm = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Test processing pipeline\n",
    "\n",
    "When it is the first time you train a model, it is recommanded to assess the correctness of the processing pipeline. The below cells illustrate how to check the data statistics (values within a batch), as well as a visual analysis of the processing result. \n",
    "\n",
    "- The `get_dataset_config` returns the processing functions defined by the model that have to be forwarded to the `prepare_dataset` method\n",
    "- The `prepare_dataset` instanciates a `tf.data.Dataset` instance based on the given data (also supports `pd.DataFrame` as well as other dataset formats)\n",
    "\n",
    "This analysis demonstrates the correct alignment between images and labels, as well as correct value ranges for the image pixels (i.e., between 0 and 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original dataset : <_TensorSliceDataset element_spec=(TensorSpec(shape=(28, 28), dtype=tf.uint8, name=None), TensorSpec(shape=(), dtype=tf.uint8, name=None))>\n",
      "- Dataset after preparation : <_ParallelMapDataset element_spec=(TensorSpec(shape=(28, 28, 1), dtype=tf.float32, name=None), TensorSpec(shape=(), dtype=tf.int32, name=None))>\n",
      "- Dataset is cached\n",
      "- Dataset is shuffled\n",
      "- Dataset after batch : <_BatchDataset element_spec=(TensorSpec(shape=(None, 28, 28, 1), dtype=tf.float32, name=None), TensorSpec(shape=(None,), dtype=tf.int32, name=None))>\n",
      "- Dataset is prefetched\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|▏                                                                                                                                          | 1/1000 [00:00<06:20,  2.62it/s]2024-10-31 08:26:04.030196: I tensorflow/core/framework/local_rendezvous.cc:405] Local rendezvous is aborting with status: OUT_OF_RANGE: End of sequence\n",
      " 94%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋        | 938/1000 [00:00<00:00, 2156.74it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "938 batches generated in 434 ms (2157.114 batch / sec)\n",
      "- Initial batch time : 382 ms\n",
      "- Average batch time : 463 μs\n",
      "- Batch statistics   : [\n",
      "    \"shape : (32, 28, 28, 1) - min : 0.000 - max : 1.000 - mean : 0.128\",\n",
      "    \"shape : (32,) - min : 0 - max : 9\"\n",
      "]\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'steps': 938,\n",
       " 'batch_size': 64,\n",
       " 'total time': 0.43484020233154297,\n",
       " 'initial batch time': 0.38204121589660645,\n",
       " 'average batch time': 0.0004635823052575085}"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from loggers import set_level\n",
    "\n",
    "set_level('debug', 'utils.datasets') # the 2nd argument tells to only set the `datasets.*` module to debug\n",
    "\n",
    "config = model.get_dataset_config('train', batch_size = 64)\n",
    "\n",
    "ds_train = prepare_dataset(train, ** config)\n",
    "test_dataset_time(ds_train, steps = 1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original dataset : <_TensorSliceDataset element_spec=(TensorSpec(shape=(28, 28), dtype=tf.uint8, name=None), TensorSpec(shape=(), dtype=tf.uint8, name=None))>\n",
      "- Dataset after preparation : <_ParallelMapDataset element_spec=(TensorSpec(shape=(28, 28, 1), dtype=tf.float32, name=None), TensorSpec(shape=(), dtype=tf.int32, name=None))>\n",
      "- Dataset is cached\n",
      "- Dataset is shuffled\n",
      "- Dataset after batch : <_BatchDataset element_spec=(TensorSpec(shape=(None, 28, 28, 1), dtype=tf.float32, name=None), TensorSpec(shape=(None,), dtype=tf.int32, name=None))>\n",
      "- Dataset is prefetched\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2500x2500 with 25 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Initializes the `tf.data.Dataset` instance with all defined processing functions\n",
    "config = model.get_dataset_config('train', batch_size = 25)\n",
    "\n",
    "ds_train = prepare_dataset(train, ** config)\n",
    "\n",
    "# Get the 1st batch of data\n",
    "inp, out = next(iter(ds_train))\n",
    "\n",
    "data = {\n",
    "    'Data #{} : {}'.format(i + 1, label) : img\n",
    "    for i, (img, label) in enumerate(zip(inp, out))\n",
    "}\n",
    "\n",
    "plot_multiple(** data, ncols = 5, plot_type = 'imshow')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.10.15"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}