Optuna_MLP_MNIST.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "919f77e8-2a34-41bb-9d38-221e20957d69",
   "metadata": {},
   "source": [
    "## OPTUNA MLP optimization "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "760b8098-f92a-4c2c-aa88-786dbfb0b15e",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TensorFlow 2.8.0\n",
      "Number of available GPUs : 1\n",
      "Not using GPU !!!\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/sznajder/WorkM1/miniforge3/envs/tf28/lib/python3.8/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    }
   ],
   "source": [
    "\"\"\"\n",
    "Optuna example that optimizes a Keras model\n",
    "\"\"\"\n",
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "from sklearn.metrics import roc_curve, auc\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn.datasets import load_wine\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from tensorflow.keras.backend import clear_session\n",
    "from tensorflow.keras.layers import Input, Dense, Flatten\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.optimizers import SGD, Adam\n",
    "from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint, ReduceLROnPlateau\n",
    "from tensorflow.keras import utils, regularizers\n",
    "from tensorflow.keras.datasets import mnist\n",
    "from tensorflow.keras import utils\n",
    " \n",
    "\n",
    "import optuna\n",
    "from optuna.integration.mlflow import MLflowCallback\n",
    "from optuna.integration import TFKerasPruningCallback\n",
    "from optuna.trial import TrialState\n",
    "\n",
    "\n",
    "print(f\"TensorFlow {tf.__version__}\")\n",
    "gpus = tf.config.list_physical_devices(\"GPU\")\n",
    "if gpus:\n",
    "    print(f\"Number of available GPUs : {len(gpus)}\")\n",
    "    tf.config.set_visible_devices(gpus[0],\"GPU\")\n",
    "    tf.config.experimental.set_memory_growth(gpus[0],True)\n",
    "else:\n",
    "    print(\"No GPU available, using CPU !!!\")    \n",
    "\n",
    "# To disable GPU use\n",
    "tf.config.set_visible_devices([], 'GPU')\n",
    "print(\"Not using GPU !!!\")    \n",
    "\n",
    "seed = 175\n",
    "nb_classes = 10\n",
    "NTRIALS = 100\n",
    "MTR='val_categorical_accuracy'\n",
    "NEPOCHS = 100\n",
    "BSIZES = [32,64,128]  # batch size for Optuna search\n",
    "#BSIZES = [16,32,64]  # batch size for Optuna search\n",
    "MINLR = 0.0001\n",
    "MAXLR = 0.0005\n",
    "MAXNL = 4 # maximum number of layer for Optuna search\n",
    "MINNEU = 8 # minimum number of neurons per layer for Optuna search\n",
    "MAXNEU = 96   # maximum number of neurons per layer for Optuna search\n",
    "PATIEN = 20 # maximum pacience for early stop and checkpoint\n",
    "REGL1 = 0.0001\n",
    "outdir = \"./optuna\"\n",
    "\n",
    "\n",
    "# OPTUNA trial syntax\n",
    "# filters=trial.suggest_categorical(\"filters\", [32, 64]),\n",
    "# activation=trial.suggest_categorical(\"activation\", [\"relu\", \"linear\"]),\n",
    "# learning_rate = trial.suggest_float(\"learning_rate\", 1e-5, 1e-1, log=True)\n",
    "# num_hidden = trial.suggest_int(\"n_units_l{}\".format(i), 4, 128, log=True)       "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c9a7c1b-f5fd-4045-8daf-58dad08a5ea0",
   "metadata": {},
   "source": [
    "## Load MNIST Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "0c843b59-74b6-495a-9e55-7962b94fd00c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "y_train= [5 0 4 ... 5 6 8]\n",
      "Y_Train= [[0. 0. 0. ... 0. 0. 0.]\n",
      " [1. 0. 0. ... 0. 0. 0.]\n",
      " [0. 0. 0. ... 0. 0. 0.]\n",
      " ...\n",
      " [0. 0. 0. ... 0. 0. 0.]\n",
      " [0. 0. 0. ... 0. 0. 0.]\n",
      " [0. 0. 0. ... 0. 1. 0.]]\n"
     ]
    }
   ],
   "source": [
    "# Load MNIST dataset (28x28 pixel images)\n",
    "(X_train, y_train), (X_test, y_test) = mnist.load_data()\n",
    "X_train = X_train.reshape(60000, 784)\n",
    "X_test = X_test.reshape(10000, 784)\n",
    "X_train = X_train.astype('float32')\n",
    "X_test = X_test.astype('float32')\n",
    "X_train /= 255\n",
    "X_test /= 255\n",
    "print(\"y_train=\",y_train)\n",
    "Y_train= utils.to_categorical(y_train, nb_classes)\n",
    "print(\"Y_Train=\",Y_train)\n",
    "Y_test = utils.to_categorical(y_test, nb_classes)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5176b6b8-1357-4737-ba18-462a60230f54",
   "metadata": {},
   "source": [
    "## Define Functions : Optuna objetive and model constrution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "51717293-c7ad-4b11-9c06-18e0a8bee2e2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Optuna objective function\n",
    "def objective(trial):\n",
    "    # Clear clutter from previous Keras session graphs.\n",
    "    clear_session()\n",
    "    \n",
    "    # early stopping callback\n",
    "    es = EarlyStopping(monitor=MTR, patience=PATIEN)\n",
    "\n",
    "    # Learning rate scheduler \n",
    "    ls = ReduceLROnPlateau(monitor=MTR, factor=0.8, patience=PATIEN, min_lr=0.00001)\n",
    "\n",
    "\n",
    "    # model checkpoint callback to save best model architecture + parameters into bestmodel.h5\n",
    "    chkp = ModelCheckpoint('bestmodel.h5', monitor=MTR, verbose=0, save_best_only=True, mode='auto', save_freq='epoch')\n",
    "\n",
    "    # Optuna pruner callback to drop non promising trials \n",
    "    # BE CAREFULL as this might bias towards faster learning models with lower performance at the end of traing !!!\n",
    "    pru = optuna.integration.TFKerasPruningCallback(trial, monitor=MTR)\n",
    "\n",
    "\n",
    "    # Create the model\n",
    "    model = create_model(trial)\n",
    "\n",
    "    # Fit the model\n",
    "    history = model.fit(X_train, Y_train, \n",
    "              batch_size= trial.suggest_categorical(\"bsize\", BSIZES),  \n",
    "              epochs=NEPOCHS, \n",
    "              verbose=0,\n",
    "              validation_split=0.2,\n",
    "              callbacks=[es,ls,chkp],\n",
    "              shuffle=True,\n",
    "              )\n",
    "\n",
    "    \n",
    "    # Load the best saved model\n",
    "    model = tf.keras.models.load_model( 'bestmodel.h5',)\n",
    "    \n",
    "\n",
    "    # Plot Loss and Accuracy versus Epoch\n",
    "    \n",
    "    plt.figure(figsize=(15,10))\n",
    "    ax = plt.subplot(2, 2, 1)\n",
    "    ax.plot(history.history['loss'], label='loss')\n",
    "    ax.plot(history.history['val_loss'], label='val loss')\n",
    "    ax.legend(loc=\"upper right\")\n",
    "    ax.set_xlabel('epoch')\n",
    "    ax.set_ylabel('loss')\n",
    "\n",
    "    # Plot training accuracy vs epoch\n",
    "    ax = plt.subplot(2, 2, 2)\n",
    "    #ax.plot(history.history['accuracy'], label='accuracy')\n",
    "    #ax.plot(history.history['val_accuracy'], label='val accuracy')\n",
    "    ax.plot(history.history['categorical_accuracy'], label='categorical_accuracy')\n",
    "    ax.plot(history.history['val_categorical_accuracy'], label='val categorical accuracy')\n",
    "    ax.legend(loc=\"upper left\")\n",
    "    ax.set_xlabel('epoch')\n",
    "    ax.set_ylabel('acc')\n",
    "\n",
    "    # Display plots\n",
    "    fig = plt.gcf()\n",
    "    plt.show()\n",
    "#    fig.savefig(fname+'.pdf')\n",
    "        \n",
    "    Y_pred = model.predict(X_test)\n",
    "    accuracy = tf.keras.metrics.CategoricalAccuracy()\n",
    "    accuracy.update_state(Y_test, Y_pred)\n",
    "    return accuracy.result().numpy()   \n",
    "    \n",
    "# Model constrution\n",
    "def create_model(trial):\n",
    "    model = Sequential()\n",
    "\n",
    "    # Define the MLP.\n",
    "    nlayers = trial.suggest_int(\"nlayers\", 1, MAXNL)\n",
    "    model = Sequential()\n",
    "    model.add(Input(shape=(784)))   # Flattened input shape\n",
    "    for i in range(nlayers):\n",
    "        nhidden = trial.suggest_int(\"nhidden_l{}\".format(i), MINNEU, MAXNEU, log=True)\n",
    "        model.add( Dense(nhidden, name=f'dense_{i}' ,activation=\"relu\" , kernel_regularizer=regularizers.L1(REGL1), bias_regularizer=regularizers.L1(REGL1) ) )\n",
    "    model.add( Dense(nb_classes, name=f'dense_out'  , activation=\"softmax\", kernel_regularizer=regularizers.L1(REGL1), bias_regularizer=regularizers.L1(REGL1) ) )\n",
    "\n",
    "    \n",
    "    # Define the optimizer\n",
    "    optim = Adam( learning_rate= trial.suggest_float(\"learning_rate\", MINLR, MAXLR, log=True) )\n",
    "\n",
    "# compile the model\n",
    "    model.compile(optimizer=optim, loss='categorical_crossentropy', metrics=['accuracy','categorical_accuracy'])\n",
    "    model.summary(expand_nested=True)\n",
    "    return model\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "2c3b6c03-830b-4f9b-ba15-8a60075c4175",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[I 2024-02-27 13:45:43,452] Using an existing study with name 'mlp_study' instead of creating a new one.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " dense_0 (Dense)             (None, 44)                34540     \n",
      "                                                                 \n",
      " dense_out (Dense)           (None, 10)                450       \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 34,990\n",
      "Trainable params: 34,990\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-02-27 13:45:43.589548: W tensorflow/core/platform/profile_utils/cpu_utils.cc:128] Failed to get CPU frequency: 0 Hz\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1500x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[I 2024-02-27 13:46:06,515] Trial 2 finished with value: 0.9693999886512756 and parameters: {'nlayers': 1, 'nhidden_l0': 44, 'learning_rate': 0.00015440196746668633, 'bsize': 128}. Best is trial 2 with value: 0.9693999886512756.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " dense_0 (Dense)             (None, 25)                19625     \n",
      "                                                                 \n",
      " dense_out (Dense)           (None, 10)                260       \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 19,885\n",
      "Trainable params: 19,885\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[I 2024-02-27 13:47:06,964] Trial 3 finished with value: 0.9628999829292297 and parameters: {'nlayers': 1, 'nhidden_l0': 25, 'learning_rate': 0.00014405233163411815, 'bsize': 32}. Best is trial 2 with value: 0.9693999886512756.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " dense_0 (Dense)             (None, 74)                58090     \n",
      "                                                                 \n",
      " dense_out (Dense)           (None, 10)                750       \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 58,840\n",
      "Trainable params: 58,840\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[I 2024-02-27 13:47:36,084] Trial 4 finished with value: 0.974399983882904 and parameters: {'nlayers': 1, 'nhidden_l0': 74, 'learning_rate': 0.00011338482562865309, 'bsize': 128}. Best is trial 4 with value: 0.974399983882904.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " dense_0 (Dense)             (None, 86)                67510     \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 67)                5829      \n",
      "                                                                 \n",
      " dense_2 (Dense)             (None, 60)                4080      \n",
      "                                                                 \n",
      " dense_out (Dense)           (None, 10)                610       \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 78,029\n",
      "Trainable params: 78,029\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[I 2024-02-27 13:48:02,253] Trial 5 finished with value: 0.9761999845504761 and parameters: {'nlayers': 3, 'nhidden_l0': 86, 'nhidden_l1': 67, 'nhidden_l2': 60, 'learning_rate': 0.00032087823018588676, 'bsize': 64}. Best is trial 5 with value: 0.9761999845504761.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " dense_0 (Dense)             (None, 42)                32970     \n",
      "                                                                 \n",
      " dense_out (Dense)           (None, 10)                430       \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 33,400\n",
      "Trainable params: 33,400\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[I 2024-02-27 13:48:26,389] Trial 6 finished with value: 0.9700000286102295 and parameters: {'nlayers': 1, 'nhidden_l0': 42, 'learning_rate': 0.00014611196052649432, 'bsize': 128}. Best is trial 5 with value: 0.9761999845504761.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " dense_0 (Dense)             (None, 28)                21980     \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 48)                1392      \n",
      "                                                                 \n",
      " dense_2 (Dense)             (None, 11)                539       \n",
      "                                                                 \n",
      " dense_out (Dense)           (None, 10)                120       \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 24,031\n",
      "Trainable params: 24,031\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[W 2024-02-27 13:48:54,229] Trial 7 failed with parameters: {'nlayers': 3, 'nhidden_l0': 28, 'nhidden_l1': 48, 'nhidden_l2': 11, 'learning_rate': 0.00024127726825528417, 'bsize': 32} because of the following error: KeyboardInterrupt().\n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/sznajder/WorkM1/miniforge3/envs/tf28/lib/python3.8/site-packages/optuna/study/_optimize.py\", line 200, in _run_trial\n",
      "    value_or_values = func(trial)\n",
      "  File \"/var/folders/4k/3rlblhv55d10vc6y4lnyb0v40000gn/T/ipykernel_33308/828105472.py\", line 25, in objective\n",
      "    history = model.fit(X_train, Y_train,\n",
      "  File \"/Users/sznajder/WorkM1/miniforge3/envs/tf28/lib/python3.8/site-packages/keras/utils/traceback_utils.py\", line 64, in error_handler\n",
      "    return fn(*args, **kwargs)\n",
      "  File \"/Users/sznajder/WorkM1/miniforge3/envs/tf28/lib/python3.8/site-packages/keras/engine/training.py\", line 1384, in fit\n",
      "    tmp_logs = self.train_function(iterator)\n",
      "  File \"/Users/sznajder/WorkM1/miniforge3/envs/tf28/lib/python3.8/site-packages/tensorflow/python/util/traceback_utils.py\", line 150, in error_handler\n",
      "    return fn(*args, **kwargs)\n",
      "  File \"/Users/sznajder/WorkM1/miniforge3/envs/tf28/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py\", line 915, in __call__\n",
      "    result = self._call(*args, **kwds)\n",
      "  File \"/Users/sznajder/WorkM1/miniforge3/envs/tf28/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py\", line 947, in _call\n",
      "    return self._stateless_fn(*args, **kwds)  # pylint: disable=not-callable\n",
      "  File \"/Users/sznajder/WorkM1/miniforge3/envs/tf28/lib/python3.8/site-packages/tensorflow/python/eager/function.py\", line 2955, in __call__\n",
      "    filtered_flat_args) = self._maybe_define_function(args, kwargs)\n",
      "  File \"/Users/sznajder/WorkM1/miniforge3/envs/tf28/lib/python3.8/site-packages/tensorflow/python/eager/function.py\", line 3262, in _maybe_define_function\n",
      "    graph_function = self._function_cache.lookup(cache_key,\n",
      "  File \"/Users/sznajder/WorkM1/miniforge3/envs/tf28/lib/python3.8/site-packages/tensorflow/python/eager/function_cache.py\", line 159, in lookup\n",
      "    if key in self._primary:\n",
      "  File \"/Users/sznajder/WorkM1/miniforge3/envs/tf28/lib/python3.8/site-packages/tensorflow/python/eager/function_cache.py\", line 102, in __eq__\n",
      "    if not isinstance(other, FunctionCacheKey):\n",
      "KeyboardInterrupt\n",
      "[W 2024-02-27 13:48:54,266] Trial 7 failed with value None.\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[4], line 12\u001b[0m\n\u001b[1;32m      2\u001b[0m study \u001b[38;5;241m=\u001b[39m optuna\u001b[38;5;241m.\u001b[39mcreate_study(\n\u001b[1;32m      3\u001b[0m       study_name\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmlp_study\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m      4\u001b[0m       sampler\u001b[38;5;241m=\u001b[39moptuna\u001b[38;5;241m.\u001b[39msamplers\u001b[38;5;241m.\u001b[39mTPESampler(),\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m      7\u001b[0m       load_if_exists\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m,\n\u001b[1;32m      8\u001b[0m   )\n\u001b[1;32m     11\u001b[0m \u001b[38;5;66;03m# Optimize\u001b[39;00m\n\u001b[0;32m---> 12\u001b[0m \u001b[43mstudy\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptimize\u001b[49m\u001b[43m(\u001b[49m\u001b[43mobjective\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mn_trials\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mNTRIALS\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mgc_after_trial\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/WorkM1/miniforge3/envs/tf28/lib/python3.8/site-packages/optuna/study/study.py:451\u001b[0m, in \u001b[0;36mStudy.optimize\u001b[0;34m(self, func, n_trials, timeout, n_jobs, catch, callbacks, gc_after_trial, show_progress_bar)\u001b[0m\n\u001b[1;32m    348\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21moptimize\u001b[39m(\n\u001b[1;32m    349\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m    350\u001b[0m     func: ObjectiveFuncType,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    357\u001b[0m     show_progress_bar: \u001b[38;5;28mbool\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m,\n\u001b[1;32m    358\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m    359\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Optimize an objective function.\u001b[39;00m\n\u001b[1;32m    360\u001b[0m \n\u001b[1;32m    361\u001b[0m \u001b[38;5;124;03m    Optimization is done by choosing a suitable set of hyperparameter values from a given\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    449\u001b[0m \u001b[38;5;124;03m            If nested invocation of this method occurs.\u001b[39;00m\n\u001b[1;32m    450\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[0;32m--> 451\u001b[0m     \u001b[43m_optimize\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    452\u001b[0m \u001b[43m        \u001b[49m\u001b[43mstudy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m    453\u001b[0m \u001b[43m        \u001b[49m\u001b[43mfunc\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfunc\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    454\u001b[0m \u001b[43m        \u001b[49m\u001b[43mn_trials\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mn_trials\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    455\u001b[0m \u001b[43m        \u001b[49m\u001b[43mtimeout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtimeout\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    456\u001b[0m \u001b[43m        \u001b[49m\u001b[43mn_jobs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mn_jobs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    457\u001b[0m \u001b[43m        \u001b[49m\u001b[43mcatch\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mtuple\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mcatch\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43misinstance\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mcatch\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mIterable\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43m(\u001b[49m\u001b[43mcatch\u001b[49m\u001b[43m,\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    458\u001b[0m \u001b[43m        \u001b[49m\u001b[43mcallbacks\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcallbacks\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    459\u001b[0m \u001b[43m        \u001b[49m\u001b[43mgc_after_trial\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgc_after_trial\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    460\u001b[0m \u001b[43m        \u001b[49m\u001b[43mshow_progress_bar\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mshow_progress_bar\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    461\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/WorkM1/miniforge3/envs/tf28/lib/python3.8/site-packages/optuna/study/_optimize.py:66\u001b[0m, in \u001b[0;36m_optimize\u001b[0;34m(study, func, n_trials, timeout, n_jobs, catch, callbacks, gc_after_trial, show_progress_bar)\u001b[0m\n\u001b[1;32m     64\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m     65\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m n_jobs \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[0;32m---> 66\u001b[0m         \u001b[43m_optimize_sequential\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m     67\u001b[0m \u001b[43m            \u001b[49m\u001b[43mstudy\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     68\u001b[0m \u001b[43m            \u001b[49m\u001b[43mfunc\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     69\u001b[0m \u001b[43m            \u001b[49m\u001b[43mn_trials\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     70\u001b[0m \u001b[43m            \u001b[49m\u001b[43mtimeout\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     71\u001b[0m \u001b[43m            \u001b[49m\u001b[43mcatch\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     72\u001b[0m \u001b[43m            \u001b[49m\u001b[43mcallbacks\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     73\u001b[0m \u001b[43m            \u001b[49m\u001b[43mgc_after_trial\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     74\u001b[0m \u001b[43m            \u001b[49m\u001b[43mreseed_sampler_rng\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m     75\u001b[0m \u001b[43m            \u001b[49m\u001b[43mtime_start\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m     76\u001b[0m \u001b[43m            \u001b[49m\u001b[43mprogress_bar\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mprogress_bar\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     77\u001b[0m \u001b[43m        \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     78\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m     79\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m n_jobs \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m:\n",
      "File \u001b[0;32m~/WorkM1/miniforge3/envs/tf28/lib/python3.8/site-packages/optuna/study/_optimize.py:163\u001b[0m, in \u001b[0;36m_optimize_sequential\u001b[0;34m(study, func, n_trials, timeout, catch, callbacks, gc_after_trial, reseed_sampler_rng, time_start, progress_bar)\u001b[0m\n\u001b[1;32m    160\u001b[0m         \u001b[38;5;28;01mbreak\u001b[39;00m\n\u001b[1;32m    162\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 163\u001b[0m     frozen_trial \u001b[38;5;241m=\u001b[39m \u001b[43m_run_trial\u001b[49m\u001b[43m(\u001b[49m\u001b[43mstudy\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfunc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcatch\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    164\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m    165\u001b[0m     \u001b[38;5;66;03m# The following line mitigates memory problems that can be occurred in some\u001b[39;00m\n\u001b[1;32m    166\u001b[0m     \u001b[38;5;66;03m# environments (e.g., services that use computing containers such as GitHub Actions).\u001b[39;00m\n\u001b[1;32m    167\u001b[0m     \u001b[38;5;66;03m# Please refer to the following PR for further details:\u001b[39;00m\n\u001b[1;32m    168\u001b[0m     \u001b[38;5;66;03m# https://github.com/optuna/optuna/pull/325.\u001b[39;00m\n\u001b[1;32m    169\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m gc_after_trial:\n",
      "File \u001b[0;32m~/WorkM1/miniforge3/envs/tf28/lib/python3.8/site-packages/optuna/study/_optimize.py:251\u001b[0m, in \u001b[0;36m_run_trial\u001b[0;34m(study, func, catch)\u001b[0m\n\u001b[1;32m    244\u001b[0m         \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28;01mFalse\u001b[39;00m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mShould not reach.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    246\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m (\n\u001b[1;32m    247\u001b[0m     frozen_trial\u001b[38;5;241m.\u001b[39mstate \u001b[38;5;241m==\u001b[39m TrialState\u001b[38;5;241m.\u001b[39mFAIL\n\u001b[1;32m    248\u001b[0m     \u001b[38;5;129;01mand\u001b[39;00m func_err \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m    249\u001b[0m     \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(func_err, catch)\n\u001b[1;32m    250\u001b[0m ):\n\u001b[0;32m--> 251\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m func_err\n\u001b[1;32m    252\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m frozen_trial\n",
      "File \u001b[0;32m~/WorkM1/miniforge3/envs/tf28/lib/python3.8/site-packages/optuna/study/_optimize.py:200\u001b[0m, in \u001b[0;36m_run_trial\u001b[0;34m(study, func, catch)\u001b[0m\n\u001b[1;32m    198\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m get_heartbeat_thread(trial\u001b[38;5;241m.\u001b[39m_trial_id, study\u001b[38;5;241m.\u001b[39m_storage):\n\u001b[1;32m    199\u001b[0m     \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 200\u001b[0m         value_or_values \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtrial\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    201\u001b[0m     \u001b[38;5;28;01mexcept\u001b[39;00m exceptions\u001b[38;5;241m.\u001b[39mTrialPruned \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m    202\u001b[0m         \u001b[38;5;66;03m# TODO(mamu): Handle multi-objective cases.\u001b[39;00m\n\u001b[1;32m    203\u001b[0m         state \u001b[38;5;241m=\u001b[39m TrialState\u001b[38;5;241m.\u001b[39mPRUNED\n",
      "Cell \u001b[0;32mIn[3], line 25\u001b[0m, in \u001b[0;36mobjective\u001b[0;34m(trial)\u001b[0m\n\u001b[1;32m     22\u001b[0m model \u001b[38;5;241m=\u001b[39m create_model(trial)\n\u001b[1;32m     24\u001b[0m \u001b[38;5;66;03m# Fit the model\u001b[39;00m\n\u001b[0;32m---> 25\u001b[0m history \u001b[38;5;241m=\u001b[39m \u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX_train\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mY_train\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\n\u001b[1;32m     26\u001b[0m \u001b[43m          \u001b[49m\u001b[43mbatch_size\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mtrial\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msuggest_categorical\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mbsize\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mBSIZES\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m  \u001b[49m\n\u001b[1;32m     27\u001b[0m \u001b[43m          \u001b[49m\u001b[43mepochs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mNEPOCHS\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\n\u001b[1;32m     28\u001b[0m \u001b[43m          \u001b[49m\u001b[43mverbose\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m     29\u001b[0m \u001b[43m          \u001b[49m\u001b[43mvalidation_split\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m0.2\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m     30\u001b[0m \u001b[43m          \u001b[49m\u001b[43mcallbacks\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m[\u001b[49m\u001b[43mes\u001b[49m\u001b[43m,\u001b[49m\u001b[43mls\u001b[49m\u001b[43m,\u001b[49m\u001b[43mchkp\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     31\u001b[0m \u001b[43m          \u001b[49m\u001b[43mshuffle\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m     32\u001b[0m \u001b[43m          \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     35\u001b[0m \u001b[38;5;66;03m# Load the best saved model\u001b[39;00m\n\u001b[1;32m     36\u001b[0m model \u001b[38;5;241m=\u001b[39m tf\u001b[38;5;241m.\u001b[39mkeras\u001b[38;5;241m.\u001b[39mmodels\u001b[38;5;241m.\u001b[39mload_model( \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mbestmodel.h5\u001b[39m\u001b[38;5;124m'\u001b[39m,)\n",
      "File \u001b[0;32m~/WorkM1/miniforge3/envs/tf28/lib/python3.8/site-packages/keras/utils/traceback_utils.py:64\u001b[0m, in \u001b[0;36mfilter_traceback.<locals>.error_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     62\u001b[0m filtered_tb \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m     63\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m---> 64\u001b[0m   \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     65\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:  \u001b[38;5;66;03m# pylint: disable=broad-except\u001b[39;00m\n\u001b[1;32m     66\u001b[0m   filtered_tb \u001b[38;5;241m=\u001b[39m _process_traceback_frames(e\u001b[38;5;241m.\u001b[39m__traceback__)\n",
      "File \u001b[0;32m~/WorkM1/miniforge3/envs/tf28/lib/python3.8/site-packages/keras/engine/training.py:1384\u001b[0m, in \u001b[0;36mModel.fit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)\u001b[0m\n\u001b[1;32m   1377\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m tf\u001b[38;5;241m.\u001b[39mprofiler\u001b[38;5;241m.\u001b[39mexperimental\u001b[38;5;241m.\u001b[39mTrace(\n\u001b[1;32m   1378\u001b[0m     \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtrain\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[1;32m   1379\u001b[0m     epoch_num\u001b[38;5;241m=\u001b[39mepoch,\n\u001b[1;32m   1380\u001b[0m     step_num\u001b[38;5;241m=\u001b[39mstep,\n\u001b[1;32m   1381\u001b[0m     batch_size\u001b[38;5;241m=\u001b[39mbatch_size,\n\u001b[1;32m   1382\u001b[0m     _r\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m):\n\u001b[1;32m   1383\u001b[0m   callbacks\u001b[38;5;241m.\u001b[39mon_train_batch_begin(step)\n\u001b[0;32m-> 1384\u001b[0m   tmp_logs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtrain_function\u001b[49m\u001b[43m(\u001b[49m\u001b[43miterator\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1385\u001b[0m   \u001b[38;5;28;01mif\u001b[39;00m data_handler\u001b[38;5;241m.\u001b[39mshould_sync:\n\u001b[1;32m   1386\u001b[0m     context\u001b[38;5;241m.\u001b[39masync_wait()\n",
      "File \u001b[0;32m~/WorkM1/miniforge3/envs/tf28/lib/python3.8/site-packages/tensorflow/python/util/traceback_utils.py:150\u001b[0m, in \u001b[0;36mfilter_traceback.<locals>.error_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    148\u001b[0m filtered_tb \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m    149\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 150\u001b[0m   \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    151\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m    152\u001b[0m   filtered_tb \u001b[38;5;241m=\u001b[39m _process_traceback_frames(e\u001b[38;5;241m.\u001b[39m__traceback__)\n",
      "File \u001b[0;32m~/WorkM1/miniforge3/envs/tf28/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py:915\u001b[0m, in \u001b[0;36mFunction.__call__\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m    912\u001b[0m compiler \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mxla\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_jit_compile \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnonXla\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    914\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m OptionalXlaContext(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_jit_compile):\n\u001b[0;32m--> 915\u001b[0m   result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    917\u001b[0m new_tracing_count \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mexperimental_get_tracing_count()\n\u001b[1;32m    918\u001b[0m without_tracing \u001b[38;5;241m=\u001b[39m (tracing_count \u001b[38;5;241m==\u001b[39m new_tracing_count)\n",
      "File \u001b[0;32m~/WorkM1/miniforge3/envs/tf28/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py:947\u001b[0m, in \u001b[0;36mFunction._call\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m    944\u001b[0m   \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_lock\u001b[38;5;241m.\u001b[39mrelease()\n\u001b[1;32m    945\u001b[0m   \u001b[38;5;66;03m# In this case we have created variables on the first call, so we run the\u001b[39;00m\n\u001b[1;32m    946\u001b[0m   \u001b[38;5;66;03m# defunned version which is guaranteed to never create variables.\u001b[39;00m\n\u001b[0;32m--> 947\u001b[0m   \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_stateless_fn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m  \u001b[38;5;66;03m# pylint: disable=not-callable\u001b[39;00m\n\u001b[1;32m    948\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_stateful_fn \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m    949\u001b[0m   \u001b[38;5;66;03m# Release the lock early so that multiple threads can perform the call\u001b[39;00m\n\u001b[1;32m    950\u001b[0m   \u001b[38;5;66;03m# in parallel.\u001b[39;00m\n\u001b[1;32m    951\u001b[0m   \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_lock\u001b[38;5;241m.\u001b[39mrelease()\n",
      "File \u001b[0;32m~/WorkM1/miniforge3/envs/tf28/lib/python3.8/site-packages/tensorflow/python/eager/function.py:2955\u001b[0m, in \u001b[0;36mFunction.__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   2952\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Calls a graph function specialized to the inputs.\"\"\"\u001b[39;00m\n\u001b[1;32m   2953\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_lock:\n\u001b[1;32m   2954\u001b[0m   (graph_function,\n\u001b[0;32m-> 2955\u001b[0m    filtered_flat_args) \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_maybe_define_function\u001b[49m\u001b[43m(\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   2956\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m graph_function\u001b[38;5;241m.\u001b[39m_call_flat(\n\u001b[1;32m   2957\u001b[0m     filtered_flat_args, captured_inputs\u001b[38;5;241m=\u001b[39mgraph_function\u001b[38;5;241m.\u001b[39mcaptured_inputs)\n",
      "File \u001b[0;32m~/WorkM1/miniforge3/envs/tf28/lib/python3.8/site-packages/tensorflow/python/eager/function.py:3262\u001b[0m, in \u001b[0;36mFunction._maybe_define_function\u001b[0;34m(self, args, kwargs)\u001b[0m\n\u001b[1;32m   3257\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m   3258\u001b[0m   \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\n\u001b[1;32m   3259\u001b[0m       \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mArguments supplied to `defun`-generated functions must be \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m   3260\u001b[0m       \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhashable.  Original error: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00me\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m-> 3262\u001b[0m graph_function \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_function_cache\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlookup\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcache_key\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3263\u001b[0m \u001b[43m                                             \u001b[49m\u001b[43mUSE_FUNCTION_SUBTYPING\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   3264\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m graph_function \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m   3265\u001b[0m   \u001b[38;5;28;01mreturn\u001b[39;00m graph_function, filtered_flat_args\n",
      "File \u001b[0;32m~/WorkM1/miniforge3/envs/tf28/lib/python3.8/site-packages/tensorflow/python/eager/function_cache.py:159\u001b[0m, in \u001b[0;36mFunctionCache.lookup\u001b[0;34m(self, key, use_function_subtyping)\u001b[0m\n\u001b[1;32m    156\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m use_function_subtyping:\n\u001b[1;32m    157\u001b[0m   \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_primary\u001b[38;5;241m.\u001b[39mget(key, \u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[0;32m--> 159\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[43mkey\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_primary\u001b[49m:\n\u001b[1;32m    160\u001b[0m   \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_primary[key]\n\u001b[1;32m    162\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m key \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_dispatch_cache:\n",
      "File \u001b[0;32m~/WorkM1/miniforge3/envs/tf28/lib/python3.8/site-packages/tensorflow/python/eager/function_cache.py:102\u001b[0m, in \u001b[0;36mFunctionCacheKey.__eq__\u001b[0;34m(self, other)\u001b[0m\n\u001b[1;32m     99\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(other, trace\u001b[38;5;241m.\u001b[39mTraceType):\n\u001b[1;32m    100\u001b[0m   \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mNotImplemented\u001b[39m\n\u001b[0;32m--> 102\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;43misinstance\u001b[39;49m(other, FunctionCacheKey):\n\u001b[1;32m    103\u001b[0m   \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[1;32m    105\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcall_context \u001b[38;5;241m==\u001b[39m other\u001b[38;5;241m.\u001b[39mcall_context \u001b[38;5;129;01mand\u001b[39;00m\n\u001b[1;32m    106\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfunction_signature \u001b[38;5;241m==\u001b[39m other\u001b[38;5;241m.\u001b[39mfunction_signature)\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "  # Set Optuna\n",
    "study = optuna.create_study(\n",
    "        study_name=\"mlp_study\",\n",
    "        sampler=optuna.samplers.TPESampler(),\n",
    "        direction=\"maximize\",\n",
    "        storage=\"sqlite:///optuna_mlp_mnist.db\",\n",
    "        load_if_exists=True,\n",
    "    )\n",
    "\n",
    "\n",
    "# Optimize\n",
    "study.optimize(objective, n_trials=NTRIALS, gc_after_trial=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d194304b-4726-480a-a249-2cc0a5425fe1",
   "metadata": {},
   "source": [
    "## Print Optuna Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e6aeafa5-474e-4ec5-b17d-bf73096a6327",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# Results\n",
    "\n",
    "pruned_trials = study.get_trials(deepcopy=False, states=[TrialState.PRUNED])\n",
    "complete_trials = study.get_trials(deepcopy=False, states=[TrialState.COMPLETE])\n",
    "print(\"Study statistics: \")\n",
    "print(\"  Number of finished trials: \", len(study.trials))\n",
    "print(\"  Number of pruned trials: \", len(pruned_trials))\n",
    "print(\"  Number of complete trials: \", len(complete_trials))\n",
    "\n",
    "print(\"Best trial:\")\n",
    "trial = study.best_trial\n",
    "print(\"  Value: \", trial.value)\n",
    "print(\"  Params: \")\n",
    "for key, value in trial.params.items():\n",
    "    print(\"    {}: {}\".format(key, value))\n",
    "\n",
    "\n",
    "\n",
    "#trials = study.get_trials(deepcopy=False, states=[TrialState.PRUNED])\n",
    "#trials = study.get_trials(deepcopy=False)\n",
    "#print(\"Study statistics: \")\n",
    "#print(\"  Number of finished trials: \", len(study.trials))\n",
    "#print(\"  Number of trials: \", len(trials))\n",
    "\n",
    "    \n",
    "# reload study and continue optimizing\n",
    "#study = optuna.load_study(study_name=\"example-study\", storage=\"sqlite:///example.db\")\n",
    "#study.optimize(objective, n_trials=30)    \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4afacf06-1071-4914-a78e-f6a6106775e5",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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