example_classifier_2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Fashion MNIST classifier\n",
    "\n",
    "This notebook is similar to `example_classifier` except that :\n",
    "- The code is executed with the `JAX` backend\n",
    "- The model architecture differs\n",
    "- A custom `learning-rate scheduler` is passed to the optimizer\n",
    "- The `Fashion MNIST` dataset is used instead of `MNIST`\n",
    "- It trains the model multiple times : it does not *restart* training but *continues* it and tracks configuration for all independant runs !"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Imports + model initialization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Keras version : 3.3.3 - backend (jax) : 0.4.28\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "\n",
    "os.environ['KERAS_BACKEND'] = 'jax'\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 = 'fashion_mnist_classifier'\n",
    "show_version()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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', 'bnorm': 'never', 'drop_rate': 0.5, 'dense_units': 28, 'dense_activation': None, 'dense_drop_rate': 0.0}\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",
      "Unable to initialize backend 'rocm': NOT_FOUND: Could not find registered platform with name: \"rocm\". Available platform names are: CUDA\n",
      "Unable to initialize backend 'tpu': INTERNAL: Failed to open libtpu.so: libtpu.so: cannot open shared object file: No such file or directory\n",
      "Save `model` to pretrained_models/fashion_mnist_classifier/saving/ckpt-0000.keras\n",
      "MNISTClassifier `fashion_mnist_classifier` initialized successfully !\n",
      "\n",
      "========== fashion_mnist_classifier ==========\n",
      "Model instance `model`\n",
      "- Inputs \t: (None, 28, 28, 1)\n",
      "- Outputs \t: (None, 10)\n",
      "- Number of layers \t: 12\n",
      "- Number of parameters \t: 24740\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": [
    "config = {\n",
    "    'labels'     : list(range(10)),\n",
    "    'input_size' : (28, 28, 1),\n",
    "    \n",
    "    'bnorm'  : 'never',\n",
    "    'drop_rate'   : 0.5,\n",
    "    'dense_units' : 28,\n",
    "    'dense_activation' : None,\n",
    "    'dense_drop_rate'  : 0.\n",
    "}\n",
    "model = MNISTClassifier(name = model_name, ** config)\n",
    "\n",
    "print(model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model instanciation + dataset loading\n",
    "\n",
    "The last cell shows how to visualize learning rate from custom learning rate schedulers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model compiled successfully !\n",
      "- Optimizer \t: {'name': 'SGD', 'learning_rate': {'module': 'custom_train_objects.optimizers.lr_schedulers', 'class_name': 'DivideByStep', 'config': {'minval': 9.999999747378752e-06, 'maxval': 0.009999999776482582, 'factor': 1.0}, 'registered_name': 'DivideByStep'}, '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, 'momentum': 0.0, 'nesterov': False}\n",
      "- Loss \t: crossentropy\n",
      "- Metric \t: accuracy\n",
      "\n",
      "\n",
      "========== fashion_mnist_classifier ==========\n",
      "Model instance `model`\n",
      "- Inputs \t: (None, 28, 28, 1)\n",
      "- Outputs \t: (None, 10)\n",
      "- Number of layers \t: 12\n",
      "- Number of parameters \t: 24740\n",
      "\n",
      "- Optimizer \t: {'name': 'SGD', 'learning_rate': {'module': 'custom_train_objects.optimizers.lr_schedulers', 'class_name': 'DivideByStep', 'config': {'minval': 9.999999747378752e-06, 'maxval': 0.009999999776482582, 'factor': 1.0}, 'registered_name': 'DivideByStep'}, '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, 'momentum': 0.0, 'nesterov': 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"
     ]
    },
    {
     "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\">Model: \"cnn_classifier\"</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mModel: \"cnn_classifier\"\u001b[0m\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\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\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",
       "│ 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",
       "│ 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",
       "│ 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",
       "│ 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\">28</span>)             │        <span style=\"color: #00af00; text-decoration-color: #00af00\">21,980</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\">280</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ custom_activation_2             │ (<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",
       "│ 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",
       "│ 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",
       "│ 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",
       "│ 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;34m28\u001b[0m)             │        \u001b[38;5;34m21,980\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;34m280\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ custom_activation_2             │ (\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": {
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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\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">24,740</span> (96.64 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m24,740\u001b[0m (96.64 KB)\n"
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     "metadata": {},
     "output_type": "display_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\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">24,740</span> (96.64 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m24,740\u001b[0m (96.64 KB)\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\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model = MNISTClassifier(name = model_name) # equivalent to get_pretrained(model_name)\n",
    "\n",
    "# equivalent to\n",
    "# lr = DivideByStep(maxval = 1e-2, minval = 1e-3, factor = 1.)\n",
    "\n",
    "lr = {'name' : 'DivideByStep', 'minval' : 1e-3, 'maxval' : 1e-2, 'factor' : 1.}\n",
    "\n",
    "model.compile(optimizer_config = {'lr' : lr}, optimizer = 'sgd')\n",
    "\n",
    "print(model)\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train : ((60000, 28, 28), (60000,)) - valid : ((10000, 28, 28), (10000,))\n"
     ]
    }
   ],
   "source": [
    "dataset_name = 'fashion_mnist' if 'fashion' in model_name else 'mnist'\n",
    "ds = get_dataset(dataset_name)\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",
    "Based on the below cells, it becomes possible to reproduce the model training by checking the different runs / parameters passed to the `fit` method at each round. In this case, 3 rounds have been performed of respectively 10, 15 and 25 epochs, with an early stopping for the 2 last rounds. The convergence criterion has not been reached, which is also visible in the metrics plots, where no convergence can be observed. This may indicated that the `SGD` optimizer is probably not the best one for this scenario, or that more training should be required ;)\n",
    "\n",
    "The training took 7sec for 10 epochs (1st round) 5sec for 15 epochs (2nd round) and 9sec for 25 epochs (3rd round). This indicates that the 3 rounds have been executed sequentially without restarting the kernel, because the 1st round is always longer due to XLA-compilation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.fit(\n",
    "    train,\n",
    "    validation_data = valid,\n",
    "    epochs     = ...,\n",
    "    batch_size = ...,\n",
    "    \n",
    "    additional_infos = {'dataset' : dataset_name},\n",
    "\n",
    "    add_early_stopping = ...\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "===== History =====\n",
      "    accuracy      loss  val_accuracy  val_loss\n",
      "0   0.131967  2.293011        0.1840  2.292314\n",
      "1   0.162583  2.281176        0.1703  2.285858\n",
      "2   0.175750  2.271803        0.1747  2.279865\n",
      "3   0.185067  2.263358        0.1833  2.274120\n",
      "4   0.189700  2.256872        0.2100  2.268800\n",
      "..       ...       ...           ...       ...\n",
      "45  0.310667  2.169927        0.3898  2.175428\n",
      "46  0.314517  2.168476        0.3930  2.174013\n",
      "47  0.314433  2.167945        0.3969  2.172622\n",
      "48  0.317183  2.166365        0.4005  2.171233\n",
      "49  0.317483  2.166347        0.4047  2.169837\n",
      "\n",
      "[50 rows x 4 columns]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "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": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training config :\n",
      "          start           end      time  interrupted  start_epoch  final_epoch\n",
      "0  1.717169e+09  1.717169e+09  7.416934        False            0           10\n",
      "1  1.717169e+09  1.717169e+09  5.570650        False           10           25\n",
      "2  1.717169e+09  1.717169e+09  8.919292        False           25           50\n",
      "\n",
      "Training parameters :\n",
      "   batch_size  augment_prct  epochs  initial_epoch add_early_stopping\n",
      "0         256          0.25      10              0                NaN\n",
      "1         256          0.25      25             10               True\n",
      "2         256          0.25      50             25               True\n",
      "\n",
      "Training informations :\n",
      "          start           end      time  interrupted  start_epoch  final_epoch                     dataset  \\\n",
      "0  1.717169e+09  1.717169e+09  7.416934        False            0           10  {'train': {}, 'valid': {}}   \n",
      "1  1.717169e+09  1.717169e+09  5.570650        False           10           25  {'train': {}, 'valid': {}}   \n",
      "2  1.717169e+09  1.717169e+09  8.919292        False           25           50  {'train': {}, 'valid': {}}   \n",
      "\n",
      "               additional_infos  \n",
      "0  {'dataset': 'fashion_mnist'}  \n",
      "1  {'dataset': 'fashion_mnist'}  \n",
      "2  {'dataset': 'fashion_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": 25,
   "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": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "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": 15,
   "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<06:24,  2.60it/s]2024-05-31 17:18:19.881724: W tensorflow/core/framework/local_rendezvous.cc:404] Local rendezvous is aborting with status: OUT_OF_RANGE: End of sequence\n",
      " 94%|█████████████████████████████████████████████████████████████████████████▏    | 938/1000 [00:00<00:00, 2065.74it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "938 batches generated in 453 ms (2067.828 batch / sec)\n",
      "- Initial batch time : 385 ms\n",
      "- Average batch time : 483 μs\n",
      "- Batch statistics   : [\n",
      "    \"shape : (32, 28, 28, 1) - min : 0.000 - max : 1.000 - mean : 0.272\",\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': 0.4536161422729492,\n",
       " 'initial batch time': 0.3855564594268799,\n",
       " 'average batch time': 0.00048359929879845335}"
      ]
     },
     "execution_count": 15,
     "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": "markdown",
   "metadata": {},
   "source": [
    "### Test learning-rate scheduler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from custom_train_objects.optimizers import get_lr_scheduler\n",
    "\n",
    "lr_config= {'scheduler' : 'DivideByStep', 'minval' : 1e-3, 'maxval' : 1e-2, 'factor' : 1.}\n",
    "\n",
    "lr_scheduler = get_lr_scheduler(** lr_config)\n",
    "lr_scheduler.plot(1000)"
   ]
  },
  {
   "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": "3.9.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}