Main.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "outputs": [],
   "source": [
    "import sys"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-07-06T11:25:03.657234Z",
     "start_time": "2023-07-06T11:25:03.653506Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-07-06T11:25:03.996632Z",
     "start_time": "2023-07-06T11:25:03.657774Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5151cfac",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-06T11:25:06.202084Z",
     "start_time": "2023-07-06T11:25:03.997705Z"
    }
   },
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import keras\n",
    "from keras import layers\n",
    "import keras_tuner as kt\n",
    "import numpy as np\n",
    "from PIL import Image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "outputs": [],
   "source": [
    "sys.modules['Image'] = Image"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-07-06T11:25:06.202252Z",
     "start_time": "2023-07-06T11:25:06.196807Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "9553b337",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-06T11:25:06.202294Z",
     "start_time": "2023-07-06T11:25:06.196891Z"
    }
   },
   "outputs": [],
   "source": [
    "tf.get_logger().setLevel('ERROR')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "063c2882",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-06T11:25:21.613310Z",
     "start_time": "2023-07-06T11:25:06.196911Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Deleted 0 images\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "\n",
    "# folders = (\"H\", \"S\", \"U\", \"FG\", \"OP\", \"P\", \"C\")\n",
    "folders = (\"H\", \"S\", \"U\", \"F\", \"P\", \"C\", \"O\", \"0\")\n",
    "\n",
    "num_skipped = 0\n",
    "for folder_name in folders:\n",
    "    folder_path = os.path.join(\"Signs\", folder_name)\n",
    "    for fname in os.listdir(folder_path):\n",
    "        fpath = os.path.join(folder_path, fname)\n",
    "        try:\n",
    "            fobj = open(fpath, \"rb\")\n",
    "            is_jfif = tf.compat.as_bytes(\"PNG\") in fobj.peek(10)\n",
    "        finally:\n",
    "            fobj.close()\n",
    "\n",
    "        if not is_jfif:\n",
    "            num_skipped += 1\n",
    "            # Delete corrupted image\n",
    "            os.remove(fpath)\n",
    "\n",
    "print(\"Deleted %d images\" % num_skipped)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d8450da8",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-06T11:25:31.959506Z",
     "start_time": "2023-07-06T11:25:21.614080Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 133793 files belonging to 8 classes.\n",
      "Using 107035 files for training.\n",
      "Found 133793 files belonging to 8 classes.\n",
      "Using 26758 files for validation.\n"
     ]
    }
   ],
   "source": [
    "image_size = (40, 64)\n",
    "batch_size = 32\n",
    "\n",
    "train_ds = keras.utils.image_dataset_from_directory(\n",
    "    \"Signs\",\n",
    "    validation_split=0.2,\n",
    "    subset=\"training\",\n",
    "    seed=1337,\n",
    "    image_size=image_size,\n",
    "    batch_size=batch_size,\n",
    ")\n",
    "\n",
    "val_ds = keras.utils.image_dataset_from_directory(\n",
    "    \"Signs\",\n",
    "    validation_split=0.2,\n",
    "    subset=\"validation\",\n",
    "    seed=1337,\n",
    "    image_size=image_size,\n",
    "    batch_size=batch_size,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d01ef311",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-06T11:25:34.917851Z",
     "start_time": "2023-07-06T11:25:31.960079Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-07-06 13:25:31.965207: W tensorflow/tsl/platform/profile_utils/cpu_utils.cc:128] Failed to get CPU frequency: 0 Hz\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "107035\n",
      "['0', 'C', 'F', 'H', 'O', 'P', 'S', 'U']\n"
     ]
    }
   ],
   "source": [
    "class_names = train_ds.class_names\n",
    "print(len(np.concatenate([i for x, i in train_ds], axis=0)))\n",
    "print(class_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "4a61c650",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-06T11:25:35.273069Z",
     "start_time": "2023-07-06T11:25:34.918801Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1000x1000 with 9 Axes>",
      "image/png": "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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 10))\n",
    "for images, labels in train_ds.take(1):\n",
    "    for i in range(9):\n",
    "        ax = plt.subplot(3, 3, i + 1)\n",
    "        plt.imshow(images[i].numpy().astype(\"uint8\"))\n",
    "        plt.title(int(labels[i]))\n",
    "        plt.axis(\"off\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "outputs": [],
   "source": [
    "data_augmentation = keras.Sequential(\n",
    "    [\n",
    "        layers.RandomFlip(\"horizontal\",\n",
    "                          input_shape=image_size + (3,),\n",
    "                          seed=1337),\n",
    "            layers.RandomTranslation(.5, .5,\n",
    "                                 fill_mode=\"nearest\",\n",
    "                                 seed=1337)\n",
    "    ]\n",
    ")"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-07-06T11:25:35.323545Z",
     "start_time": "2023-07-06T11:25:35.277685Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1000x1000 with 16 Axes>",
      "image/png": "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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 10))\n",
    "for images, _ in train_ds.take(1):\n",
    "    for i in range(16):\n",
    "        augmented_images = data_augmentation(images)\n",
    "        ax = plt.subplot(4, 4, i + 1)\n",
    "        plt.imshow(augmented_images[0].numpy().astype(\"uint8\"))\n",
    "        plt.axis(\"off\")"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-07-06T11:25:35.612067Z",
     "start_time": "2023-07-06T11:25:35.314271Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "outputs": [],
   "source": [
    "input_shape=image_size + (3,)\n",
    "num_classes=len(folders)\n",
    "epochs = 30"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-07-06T11:25:35.613543Z",
     "start_time": "2023-07-06T11:25:35.612280Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "outputs": [],
   "source": [
    "def model_builder(hp):\n",
    "    hp_last_units = hp.Int('units', min_value=288, max_value=768, step=32)\n",
    "    hp_last_activation = hp.Choice(\"activation\", [\"relu\", \"tanh\", \"softmax\"])\n",
    "    hp_dropout_rate = hp.Choice(\"hp_dropout_rate\", values=[.05, .1, .15, .2, .25, .3, .35, .4, .45, .5, .55, .6])\n",
    "\n",
    "    model_layers = [\n",
    "        data_augmentation,\n",
    "        layers.Rescaling(1./255, input_shape=input_shape),\n",
    "        layers.Conv2D(16, 3, padding='same', activation='relu'),\n",
    "        layers.BatchNormalization(),\n",
    "        layers.MaxPooling2D(),\n",
    "        layers.Conv2D(32, 3, padding='same', activation='relu'),\n",
    "        layers.MaxPooling2D(),\n",
    "        layers.Conv2D(64, 3, padding='same', activation='relu'),\n",
    "        layers.MaxPooling2D(),\n",
    "        layers.Dropout(hp_dropout_rate),\n",
    "        layers.Flatten(),\n",
    "        layers.Dense(hp_last_units, activation=hp_last_activation),\n",
    "        layers.Dense(num_classes, name=\"outputs\")\n",
    "    ]\n",
    "    model_layers = list(filter(lambda item: item is not None, model_layers))\n",
    "    model = keras.Sequential(model_layers)\n",
    "\n",
    "    hp_learning_rate = hp.Choice('learning_rate', values=[1e-2, 1e-3, 1e-4, 1e-5])\n",
    "    model.compile(\n",
    "        optimizer=keras.optimizers.Adam(learning_rate=hp_learning_rate),\n",
    "        loss=keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n",
    "        metrics=['accuracy']\n",
    "    )\n",
    "\n",
    "    return model"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-07-06T11:25:35.617695Z",
     "start_time": "2023-07-06T11:25:35.616480Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "outputs": [],
   "source": [
    "tuner = kt.Hyperband(\n",
    "    model_builder,\n",
    "    objective='val_accuracy',\n",
    "    max_epochs=8,\n",
    "    factor=3,\n",
    "    directory='kt',\n",
    "    project_name='erebus'\n",
    ")"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-07-06T11:25:35.761207Z",
     "start_time": "2023-07-06T11:25:35.620573Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "outputs": [],
   "source": [
    "stop_early = keras.callbacks.EarlyStopping(monitor='val_loss', patience=7)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-07-06T11:25:35.764301Z",
     "start_time": "2023-07-06T11:25:35.762041Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Search: Running Trial #8\n",
      "\n",
      "Value             |Best Value So Far |Hyperparameter\n",
      "352               |640               |units\n",
      "tanh              |relu              |activation\n",
      "0.3               |0.2               |hp_dropout_rate\n",
      "0.0001            |0.0001            |learning_rate\n",
      "8                 |8                 |tuner/epochs\n",
      "0                 |3                 |tuner/initial_epoch\n",
      "0                 |1                 |tuner/bracket\n",
      "0                 |1                 |tuner/round\n",
      "\n",
      "Epoch 1/8\n",
      "3345/3345 [==============================] - 57s 17ms/step - loss: 0.8506 - accuracy: 0.7282 - val_loss: 0.3608 - val_accuracy: 0.8922\n",
      "Epoch 2/8\n",
      "3345/3345 [==============================] - 58s 17ms/step - loss: 0.4958 - accuracy: 0.8451 - val_loss: 0.1859 - val_accuracy: 0.9422\n",
      "Epoch 3/8\n",
      "3345/3345 [==============================] - 59s 18ms/step - loss: 0.4129 - accuracy: 0.8728 - val_loss: 0.1402 - val_accuracy: 0.9639\n",
      "Epoch 4/8\n",
      "3345/3345 [==============================] - 58s 17ms/step - loss: 0.3605 - accuracy: 0.8912 - val_loss: 0.0921 - val_accuracy: 0.9791\n",
      "Epoch 5/8\n",
      "2454/3345 [=====================>........] - ETA: 14s - loss: 0.3408 - accuracy: 0.8973"
     ]
    },
    {
     "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[18], line 1\u001B[0m\n\u001B[0;32m----> 1\u001B[0m \u001B[43mtuner\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43msearch\u001B[49m\u001B[43m(\u001B[49m\u001B[43mtrain_ds\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mvalidation_data\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mval_ds\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mepochs\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mepochs\u001B[49m\u001B[43m,\u001B[49m\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\u001B[43m \u001B[49m\u001B[43mcallbacks\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43m[\u001B[49m\u001B[43mstop_early\u001B[49m\u001B[43m]\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m      3\u001B[0m \u001B[38;5;66;03m# Get the optimal hyperparameters\u001B[39;00m\n\u001B[1;32m      4\u001B[0m best_hps \u001B[38;5;241m=\u001B[39m tuner\u001B[38;5;241m.\u001B[39mget_best_hyperparameters(num_trials\u001B[38;5;241m=\u001B[39m\u001B[38;5;241m1\u001B[39m)[\u001B[38;5;241m0\u001B[39m]\n",
      "File \u001B[0;32m~/.local/share/virtualenvs/Erebus-Image-Recog-N9ivHk-d/lib/python3.10/site-packages/keras_tuner/engine/base_tuner.py:230\u001B[0m, in \u001B[0;36mBaseTuner.search\u001B[0;34m(self, *fit_args, **fit_kwargs)\u001B[0m\n\u001B[1;32m    227\u001B[0m         \u001B[38;5;28;01mcontinue\u001B[39;00m\n\u001B[1;32m    229\u001B[0m     \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mon_trial_begin(trial)\n\u001B[0;32m--> 230\u001B[0m     \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_try_run_and_update_trial\u001B[49m\u001B[43m(\u001B[49m\u001B[43mtrial\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43mfit_args\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[43mfit_kwargs\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m    231\u001B[0m     \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mon_trial_end(trial)\n\u001B[1;32m    232\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mon_search_end()\n",
      "File \u001B[0;32m~/.local/share/virtualenvs/Erebus-Image-Recog-N9ivHk-d/lib/python3.10/site-packages/keras_tuner/engine/base_tuner.py:270\u001B[0m, in \u001B[0;36mBaseTuner._try_run_and_update_trial\u001B[0;34m(self, trial, *fit_args, **fit_kwargs)\u001B[0m\n\u001B[1;32m    268\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21m_try_run_and_update_trial\u001B[39m(\u001B[38;5;28mself\u001B[39m, trial, \u001B[38;5;241m*\u001B[39mfit_args, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mfit_kwargs):\n\u001B[1;32m    269\u001B[0m     \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[0;32m--> 270\u001B[0m         \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_run_and_update_trial\u001B[49m\u001B[43m(\u001B[49m\u001B[43mtrial\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43mfit_args\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[43mfit_kwargs\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m    271\u001B[0m         trial\u001B[38;5;241m.\u001B[39mstatus \u001B[38;5;241m=\u001B[39m trial_module\u001B[38;5;241m.\u001B[39mTrialStatus\u001B[38;5;241m.\u001B[39mCOMPLETED\n\u001B[1;32m    272\u001B[0m         \u001B[38;5;28;01mreturn\u001B[39;00m\n",
      "File \u001B[0;32m~/.local/share/virtualenvs/Erebus-Image-Recog-N9ivHk-d/lib/python3.10/site-packages/keras_tuner/engine/base_tuner.py:235\u001B[0m, in \u001B[0;36mBaseTuner._run_and_update_trial\u001B[0;34m(self, trial, *fit_args, **fit_kwargs)\u001B[0m\n\u001B[1;32m    234\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21m_run_and_update_trial\u001B[39m(\u001B[38;5;28mself\u001B[39m, trial, \u001B[38;5;241m*\u001B[39mfit_args, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mfit_kwargs):\n\u001B[0;32m--> 235\u001B[0m     results \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mrun_trial\u001B[49m\u001B[43m(\u001B[49m\u001B[43mtrial\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43mfit_args\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[43mfit_kwargs\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m    236\u001B[0m     \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39moracle\u001B[38;5;241m.\u001B[39mget_trial(trial\u001B[38;5;241m.\u001B[39mtrial_id)\u001B[38;5;241m.\u001B[39mmetrics\u001B[38;5;241m.\u001B[39mexists(\n\u001B[1;32m    237\u001B[0m         \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39moracle\u001B[38;5;241m.\u001B[39mobjective\u001B[38;5;241m.\u001B[39mname\n\u001B[1;32m    238\u001B[0m     ):\n\u001B[1;32m    239\u001B[0m         \u001B[38;5;66;03m# The oracle is updated by calling `self.oracle.update_trial()` in\u001B[39;00m\n\u001B[1;32m    240\u001B[0m         \u001B[38;5;66;03m# `Tuner.run_trial()`. For backward compatibility, we support this\u001B[39;00m\n\u001B[1;32m    241\u001B[0m         \u001B[38;5;66;03m# use case. No further action needed in this case.\u001B[39;00m\n\u001B[1;32m    242\u001B[0m         warnings\u001B[38;5;241m.\u001B[39mwarn(\n\u001B[1;32m    243\u001B[0m             \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mThe use case of calling \u001B[39m\u001B[38;5;124m\"\u001B[39m\n\u001B[1;32m    244\u001B[0m             \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124m`self.oracle.update_trial(trial_id, metrics)` \u001B[39m\u001B[38;5;124m\"\u001B[39m\n\u001B[0;32m   (...)\u001B[0m\n\u001B[1;32m    250\u001B[0m             stacklevel\u001B[38;5;241m=\u001B[39m\u001B[38;5;241m2\u001B[39m,\n\u001B[1;32m    251\u001B[0m         )\n",
      "File \u001B[0;32m~/.local/share/virtualenvs/Erebus-Image-Recog-N9ivHk-d/lib/python3.10/site-packages/keras_tuner/tuners/hyperband.py:425\u001B[0m, in \u001B[0;36mHyperband.run_trial\u001B[0;34m(self, trial, *fit_args, **fit_kwargs)\u001B[0m\n\u001B[1;32m    423\u001B[0m     fit_kwargs[\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mepochs\u001B[39m\u001B[38;5;124m\"\u001B[39m] \u001B[38;5;241m=\u001B[39m hp\u001B[38;5;241m.\u001B[39mvalues[\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mtuner/epochs\u001B[39m\u001B[38;5;124m\"\u001B[39m]\n\u001B[1;32m    424\u001B[0m     fit_kwargs[\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124minitial_epoch\u001B[39m\u001B[38;5;124m\"\u001B[39m] \u001B[38;5;241m=\u001B[39m hp\u001B[38;5;241m.\u001B[39mvalues[\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mtuner/initial_epoch\u001B[39m\u001B[38;5;124m\"\u001B[39m]\n\u001B[0;32m--> 425\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43msuper\u001B[39;49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mrun_trial\u001B[49m\u001B[43m(\u001B[49m\u001B[43mtrial\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43mfit_args\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[43mfit_kwargs\u001B[49m\u001B[43m)\u001B[49m\n",
      "File \u001B[0;32m~/.local/share/virtualenvs/Erebus-Image-Recog-N9ivHk-d/lib/python3.10/site-packages/keras_tuner/engine/tuner.py:287\u001B[0m, in \u001B[0;36mTuner.run_trial\u001B[0;34m(self, trial, *args, **kwargs)\u001B[0m\n\u001B[1;32m    285\u001B[0m     callbacks\u001B[38;5;241m.\u001B[39mappend(model_checkpoint)\n\u001B[1;32m    286\u001B[0m     copied_kwargs[\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mcallbacks\u001B[39m\u001B[38;5;124m\"\u001B[39m] \u001B[38;5;241m=\u001B[39m callbacks\n\u001B[0;32m--> 287\u001B[0m     obj_value \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_build_and_fit_model\u001B[49m\u001B[43m(\u001B[49m\u001B[43mtrial\u001B[49m\u001B[43m,\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[43mcopied_kwargs\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m    289\u001B[0m     histories\u001B[38;5;241m.\u001B[39mappend(obj_value)\n\u001B[1;32m    290\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m histories\n",
      "File \u001B[0;32m~/.local/share/virtualenvs/Erebus-Image-Recog-N9ivHk-d/lib/python3.10/site-packages/keras_tuner/engine/tuner.py:214\u001B[0m, in \u001B[0;36mTuner._build_and_fit_model\u001B[0;34m(self, trial, *args, **kwargs)\u001B[0m\n\u001B[1;32m    212\u001B[0m hp \u001B[38;5;241m=\u001B[39m trial\u001B[38;5;241m.\u001B[39mhyperparameters\n\u001B[1;32m    213\u001B[0m model \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_try_build(hp)\n\u001B[0;32m--> 214\u001B[0m results \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mhypermodel\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mfit\u001B[49m\u001B[43m(\u001B[49m\u001B[43mhp\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mmodel\u001B[49m\u001B[43m,\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    215\u001B[0m tuner_utils\u001B[38;5;241m.\u001B[39mvalidate_trial_results(\n\u001B[1;32m    216\u001B[0m     results, \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39moracle\u001B[38;5;241m.\u001B[39mobjective, \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mHyperModel.fit()\u001B[39m\u001B[38;5;124m\"\u001B[39m\n\u001B[1;32m    217\u001B[0m )\n\u001B[1;32m    218\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m results\n",
      "File \u001B[0;32m~/.local/share/virtualenvs/Erebus-Image-Recog-N9ivHk-d/lib/python3.10/site-packages/keras_tuner/engine/hypermodel.py:144\u001B[0m, in \u001B[0;36mHyperModel.fit\u001B[0;34m(self, hp, model, *args, **kwargs)\u001B[0m\n\u001B[1;32m    120\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21mfit\u001B[39m(\u001B[38;5;28mself\u001B[39m, hp, model, \u001B[38;5;241m*\u001B[39margs, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs):\n\u001B[1;32m    121\u001B[0m \u001B[38;5;250m    \u001B[39m\u001B[38;5;124;03m\"\"\"Train the model.\u001B[39;00m\n\u001B[1;32m    122\u001B[0m \n\u001B[1;32m    123\u001B[0m \u001B[38;5;124;03m    Args:\u001B[39;00m\n\u001B[0;32m   (...)\u001B[0m\n\u001B[1;32m    142\u001B[0m \u001B[38;5;124;03m        If return a float, it should be the `objective` value.\u001B[39;00m\n\u001B[1;32m    143\u001B[0m \u001B[38;5;124;03m    \"\"\"\u001B[39;00m\n\u001B[0;32m--> 144\u001B[0m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mmodel\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mfit\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",
      "File \u001B[0;32m~/.local/share/virtualenvs/Erebus-Image-Recog-N9ivHk-d/lib/python3.10/site-packages/keras/utils/traceback_utils.py:65\u001B[0m, in \u001B[0;36mfilter_traceback.<locals>.error_handler\u001B[0;34m(*args, **kwargs)\u001B[0m\n\u001B[1;32m     63\u001B[0m filtered_tb \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;01mNone\u001B[39;00m\n\u001B[1;32m     64\u001B[0m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[0;32m---> 65\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     66\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     67\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~/.local/share/virtualenvs/Erebus-Image-Recog-N9ivHk-d/lib/python3.10/site-packages/keras/engine/training.py:1685\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   1677\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   1678\u001B[0m     \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mtrain\u001B[39m\u001B[38;5;124m\"\u001B[39m,\n\u001B[1;32m   1679\u001B[0m     epoch_num\u001B[38;5;241m=\u001B[39mepoch,\n\u001B[0;32m   (...)\u001B[0m\n\u001B[1;32m   1682\u001B[0m     _r\u001B[38;5;241m=\u001B[39m\u001B[38;5;241m1\u001B[39m,\n\u001B[1;32m   1683\u001B[0m ):\n\u001B[1;32m   1684\u001B[0m     callbacks\u001B[38;5;241m.\u001B[39mon_train_batch_begin(step)\n\u001B[0;32m-> 1685\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   1686\u001B[0m     \u001B[38;5;28;01mif\u001B[39;00m data_handler\u001B[38;5;241m.\u001B[39mshould_sync:\n\u001B[1;32m   1687\u001B[0m         context\u001B[38;5;241m.\u001B[39masync_wait()\n",
      "File \u001B[0;32m~/.local/share/virtualenvs/Erebus-Image-Recog-N9ivHk-d/lib/python3.10/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~/.local/share/virtualenvs/Erebus-Image-Recog-N9ivHk-d/lib/python3.10/site-packages/tensorflow/python/eager/polymorphic_function/polymorphic_function.py:894\u001B[0m, in \u001B[0;36mFunction.__call__\u001B[0;34m(self, *args, **kwds)\u001B[0m\n\u001B[1;32m    891\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    893\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--> 894\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    896\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    897\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~/.local/share/virtualenvs/Erebus-Image-Recog-N9ivHk-d/lib/python3.10/site-packages/tensorflow/python/eager/polymorphic_function/polymorphic_function.py:926\u001B[0m, in \u001B[0;36mFunction._call\u001B[0;34m(self, *args, **kwds)\u001B[0m\n\u001B[1;32m    923\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    924\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    925\u001B[0m   \u001B[38;5;66;03m# defunned version which is guaranteed to never create variables.\u001B[39;00m\n\u001B[0;32m--> 926\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_no_variable_creation_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    927\u001B[0m \u001B[38;5;28;01melif\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_variable_creation_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    928\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    929\u001B[0m   \u001B[38;5;66;03m# in parallel.\u001B[39;00m\n\u001B[1;32m    930\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~/.local/share/virtualenvs/Erebus-Image-Recog-N9ivHk-d/lib/python3.10/site-packages/tensorflow/python/eager/polymorphic_function/tracing_compiler.py:143\u001B[0m, in \u001B[0;36mTracingCompiler.__call__\u001B[0;34m(self, *args, **kwargs)\u001B[0m\n\u001B[1;32m    140\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    141\u001B[0m   (concrete_function,\n\u001B[1;32m    142\u001B[0m    filtered_flat_args) \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_maybe_define_function(args, kwargs)\n\u001B[0;32m--> 143\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mconcrete_function\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_call_flat\u001B[49m\u001B[43m(\u001B[49m\n\u001B[1;32m    144\u001B[0m \u001B[43m    \u001B[49m\u001B[43mfiltered_flat_args\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mcaptured_inputs\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mconcrete_function\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mcaptured_inputs\u001B[49m\u001B[43m)\u001B[49m\n",
      "File \u001B[0;32m~/.local/share/virtualenvs/Erebus-Image-Recog-N9ivHk-d/lib/python3.10/site-packages/tensorflow/python/eager/polymorphic_function/monomorphic_function.py:1757\u001B[0m, in \u001B[0;36mConcreteFunction._call_flat\u001B[0;34m(self, args, captured_inputs, cancellation_manager)\u001B[0m\n\u001B[1;32m   1753\u001B[0m possible_gradient_type \u001B[38;5;241m=\u001B[39m gradients_util\u001B[38;5;241m.\u001B[39mPossibleTapeGradientTypes(args)\n\u001B[1;32m   1754\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m (possible_gradient_type \u001B[38;5;241m==\u001B[39m gradients_util\u001B[38;5;241m.\u001B[39mPOSSIBLE_GRADIENT_TYPES_NONE\n\u001B[1;32m   1755\u001B[0m     \u001B[38;5;129;01mand\u001B[39;00m executing_eagerly):\n\u001B[1;32m   1756\u001B[0m   \u001B[38;5;66;03m# No tape is watching; skip to running the function.\u001B[39;00m\n\u001B[0;32m-> 1757\u001B[0m   \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_build_call_outputs(\u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_inference_function\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mcall\u001B[49m\u001B[43m(\u001B[49m\n\u001B[1;32m   1758\u001B[0m \u001B[43m      \u001B[49m\u001B[43mctx\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43margs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mcancellation_manager\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mcancellation_manager\u001B[49m\u001B[43m)\u001B[49m)\n\u001B[1;32m   1759\u001B[0m forward_backward \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_select_forward_and_backward_functions(\n\u001B[1;32m   1760\u001B[0m     args,\n\u001B[1;32m   1761\u001B[0m     possible_gradient_type,\n\u001B[1;32m   1762\u001B[0m     executing_eagerly)\n\u001B[1;32m   1763\u001B[0m forward_function, args_with_tangents \u001B[38;5;241m=\u001B[39m forward_backward\u001B[38;5;241m.\u001B[39mforward()\n",
      "File \u001B[0;32m~/.local/share/virtualenvs/Erebus-Image-Recog-N9ivHk-d/lib/python3.10/site-packages/tensorflow/python/eager/polymorphic_function/monomorphic_function.py:381\u001B[0m, in \u001B[0;36m_EagerDefinedFunction.call\u001B[0;34m(self, ctx, args, cancellation_manager)\u001B[0m\n\u001B[1;32m    379\u001B[0m \u001B[38;5;28;01mwith\u001B[39;00m _InterpolateFunctionError(\u001B[38;5;28mself\u001B[39m):\n\u001B[1;32m    380\u001B[0m   \u001B[38;5;28;01mif\u001B[39;00m cancellation_manager \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m:\n\u001B[0;32m--> 381\u001B[0m     outputs \u001B[38;5;241m=\u001B[39m \u001B[43mexecute\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mexecute\u001B[49m\u001B[43m(\u001B[49m\n\u001B[1;32m    382\u001B[0m \u001B[43m        \u001B[49m\u001B[38;5;28;43mstr\u001B[39;49m\u001B[43m(\u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43msignature\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mname\u001B[49m\u001B[43m)\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m    383\u001B[0m \u001B[43m        \u001B[49m\u001B[43mnum_outputs\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_num_outputs\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m    384\u001B[0m \u001B[43m        \u001B[49m\u001B[43minputs\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43margs\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m    385\u001B[0m \u001B[43m        \u001B[49m\u001B[43mattrs\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mattrs\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m    386\u001B[0m \u001B[43m        \u001B[49m\u001B[43mctx\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mctx\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m    387\u001B[0m   \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[1;32m    388\u001B[0m     outputs \u001B[38;5;241m=\u001B[39m execute\u001B[38;5;241m.\u001B[39mexecute_with_cancellation(\n\u001B[1;32m    389\u001B[0m         \u001B[38;5;28mstr\u001B[39m(\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39msignature\u001B[38;5;241m.\u001B[39mname),\n\u001B[1;32m    390\u001B[0m         num_outputs\u001B[38;5;241m=\u001B[39m\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_num_outputs,\n\u001B[0;32m   (...)\u001B[0m\n\u001B[1;32m    393\u001B[0m         ctx\u001B[38;5;241m=\u001B[39mctx,\n\u001B[1;32m    394\u001B[0m         cancellation_manager\u001B[38;5;241m=\u001B[39mcancellation_manager)\n",
      "File \u001B[0;32m~/.local/share/virtualenvs/Erebus-Image-Recog-N9ivHk-d/lib/python3.10/site-packages/tensorflow/python/eager/execute.py:52\u001B[0m, in \u001B[0;36mquick_execute\u001B[0;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001B[0m\n\u001B[1;32m     50\u001B[0m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[1;32m     51\u001B[0m   ctx\u001B[38;5;241m.\u001B[39mensure_initialized()\n\u001B[0;32m---> 52\u001B[0m   tensors \u001B[38;5;241m=\u001B[39m \u001B[43mpywrap_tfe\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mTFE_Py_Execute\u001B[49m\u001B[43m(\u001B[49m\u001B[43mctx\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_handle\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mdevice_name\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mop_name\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m     53\u001B[0m \u001B[43m                                      \u001B[49m\u001B[43minputs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mattrs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mnum_outputs\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m     54\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m core\u001B[38;5;241m.\u001B[39m_NotOkStatusException \u001B[38;5;28;01mas\u001B[39;00m e:\n\u001B[1;32m     55\u001B[0m   \u001B[38;5;28;01mif\u001B[39;00m name \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[0;31mKeyboardInterrupt\u001B[0m: "
     ]
    }
   ],
   "source": [
    "tuner.search(train_ds, validation_data=val_ds, epochs=epochs, validation_split=0.2, callbacks=[stop_early])\n",
    "\n",
    "# Get the optimal hyperparameters\n",
    "best_hps = tuner.get_best_hyperparameters(num_trials=1)[0]"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-07-06T13:08:49.353703Z",
     "start_time": "2023-07-06T13:04:19.278223Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "print(f\"\"\"\n",
    "units: {best_hps.get('units')}\n",
    "activation: {best_hps.get('activation')}\n",
    "hp_dropout_rate: {best_hps.get('hp_dropout_rate')}\n",
    "learning_rate: {best_hps.get('learning_rate')}\n",
    "\"\"\")"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "730d3e93",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = tuner.hypermodel.build(best_hps)\n",
    "history = model.fit(\n",
    "    train_ds,\n",
    "    validation_data=val_ds,\n",
    "    epochs=epochs,\n",
    "    validation_split=0.2\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "acc = history.history['accuracy']\n",
    "val_acc = history.history['val_accuracy']\n",
    "\n",
    "loss = history.history['loss']\n",
    "val_loss = history.history['val_loss']\n",
    "\n",
    "epochs_range = range(epochs)\n",
    "\n",
    "plt.figure(figsize=(8, 8))\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.plot(epochs_range, acc, label='Training Accuracy')\n",
    "plt.plot(epochs_range, val_acc, label='Validation Accuracy')\n",
    "plt.legend(loc='lower right')\n",
    "plt.title('Training and Validation Accuracy')\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.plot(epochs_range, loss, label='Training Loss')\n",
    "plt.plot(epochs_range, val_loss, label='Validation Loss')\n",
    "plt.legend(loc='upper right')\n",
    "plt.title('Training and Validation Loss')\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "50afb0ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.save('augmentation-flip_translate.keras')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false
   }
  }
 ],
 "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": "3.10.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}