tensorflow/Basic_Performance.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000,
     "referenced_widgets": [
      "97651ef84a5d4f11a7e75670ce8300e7",
      "b479a3899aba49fd99294d9df2eec701",
      "7d2be1b24f2841fa93c57aae78f31a15",
      "4c181783977f4ebf9e1a2a49c3ec0120",
      "60170335e6514507bd87c6a4dac3d623",
      "058bb2545d34464b9ca0718261778026",
      "c418d6652e43445da8e239f0ca859846",
      "623e060e7e514a30aadda61cb7c1674b",
      "7d2279098f384e2eb2dbeaf53cde52c1",
      "a0273718b661442b8d1334943460ede6",
      "8b5f7d4f32c345de90ed6d1875bd6bd1",
      "f9129b445ddf4aee859db77ea883c8fe",
      "5adc2846a13b4a3d981cbd7fc0769cab",
      "ca45fa9c101d4051860f176bf1bab1da",
      "bf7a7a2133994732bfa850f7eb51fdf0",
      "7cbfd13104c44eba8aa1e8d1fb2fc7f1",
      "7b6f698700794454bcdd0cb4e5881b3e",
      "f580aa5ff4ec41c6ae48419661333f7f",
      "066346a946674cb7bd869ae6feb86b5e",
      "bd8c6c0cd07a4c4782db78b6f3c529cb",
      "ab368a038d174a9f979fcbb9f470b8c5",
      "53f3956ad0f945499c3daad7a104e392",
      "a8eb7ef889934f5ba61591bc338aafcd",
      "49c58f268866499c8ea5b5e86e7b629e",
      "7d62fc41e1a94a5eb9df853f7a2f16c2",
      "93f911153ab54aa0b625794462a91ad5",
      "e3cfed5314bb4867a4241cff2e0adac8",
      "46da9173be364dafaaaae3123ed61653",
      "bf9c745dd3784c2ab2562c5a6e1e002e",
      "9584b9a224444b7685e3eb883fa71015",
      "a08692e01d504d3fbf711c61f04f6ee6",
      "e4354c4c8da74a1ab1e44b987b221947"
     ]
    },
    "id": "Ka4FcloCqnH_",
    "outputId": "a698a483-d3d6-464b-9e25-7fe62f9741d6"
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import random\n",
    "\n",
    "from pathlib import Path\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras import layers\n",
    "from tensorflow.keras.datasets import cifar10\n",
    "\n",
    "\n",
    "PROJECT = 'apple_m1_pro'\n",
    "GROUP = 'keras'\n",
    "JOB_TYPE = 'M1Pro'\n",
    "\n",
    "# Set the random seeds\n",
    "os.environ['TF_CUDNN_DETERMINISTIC'] = '1' \n",
    "random.seed(hash(\"setting random seeds\") % 2**32 - 1)\n",
    "np.random.seed(hash(\"improves reproducibility\") % 2**32 - 1)\n",
    "tf.random.set_seed(hash(\"by removing stochasticity\") % 2**32 - 1)\n",
    "\n",
    "\n",
    "IMG_SIZE = 128\n",
    "BS = 64\n",
    "EPOCHS = 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 68
    },
    "id": "FBqlJEVarXP6",
    "outputId": "75b1d12d-c181-4327-c438-2b64ff1829ab"
   },
   "outputs": [],
   "source": [
    "import wandb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /var/folders/sf/tgv7vcv96x557p38bvvp1ms40000gn/T/ipykernel_1357/337460670.py:1: is_gpu_available (from tensorflow.python.framework.test_util) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use `tf.config.list_physical_devices('GPU')` instead.\n",
      "Metal device set to: Apple M1 Pro\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2021-11-22 09:51:58.136042: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:305] Could not identify NUMA node of platform GPU ID 0, defaulting to 0. Your kernel may not have been built with NUMA support.\n",
      "2021-11-22 09:51:58.136414: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:271] Created TensorFlow device (/device:GPU:0 with 0 MB memory) -> physical PluggableDevice (device: 0, name: METAL, pci bus id: <undefined>)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf.test.is_gpu_available()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2021-11-22 09:52:25.257768: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:305] Could not identify NUMA node of platform GPU ID 0, defaulting to 0. Your kernel may not have been built with NUMA support.\n",
      "2021-11-22 09:52:25.257802: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:271] Created TensorFlow device (/device:GPU:0 with 0 MB memory) -> physical PluggableDevice (device: 0, name: METAL, pci bus id: <undefined>)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'/device:GPU:0'"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf.test.gpu_device_name()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Name: /physical_device:GPU:0   Type: GPU\n"
     ]
    }
   ],
   "source": [
    "gpus = tf.config.list_physical_devices('GPU')\n",
    "for gpu in gpus:\n",
    "    print(\"Name:\", gpu.name, \"  Type:\", gpu.device_type)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def hardware():\n",
    "  \"\"\"Detect platform and accelerator\"\"\"\n",
    "  if platform.system() == \"Darwin\":\n",
    "    if platform.processor() == \"arm\":\n",
    "      return \"Apple M1\"\n",
    "    else:\n",
    "      return \"Apple Intel\"\n",
    "  elif len(tf.config.list_physical_devices('GPU')) > 0:\n",
    "    return \"Nvidia\"\n",
    "  return \"Linux Intel\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'platform' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m/var/folders/sf/tgv7vcv96x557p38bvvp1ms40000gn/T/ipykernel_1357/2333675197.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mhardware\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/var/folders/sf/tgv7vcv96x557p38bvvp1ms40000gn/T/ipykernel_1357/3459158288.py\u001b[0m in \u001b[0;36mhardware\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mhardware\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m   \u001b[0;34m\"\"\"Detect platform and accelerator\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m   \u001b[0;32mif\u001b[0m \u001b[0mplatform\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msystem\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"Darwin\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mplatform\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprocessor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"arm\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m       \u001b[0;32mreturn\u001b[0m \u001b[0;34m\"Apple M1\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'platform' is not defined"
     ]
    }
   ],
   "source": [
    "hardware()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "Finishing last run (ID:18ul76ty) before initializing another..."
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<br/>Waiting for W&B process to finish, PID 94249... <strong style=\"color:green\">(success).</strong>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox(children=(Label(value=' 0.00MB of 0.00MB uploaded (0.00MB deduped)\\r'), FloatProgress(value=1.0, max=1.0)…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<style>\n",
       "    table.wandb td:nth-child(1) { padding: 0 10px; text-align: right }\n",
       "    .wandb-row { display: flex; flex-direction: row; flex-wrap: wrap; width: 100% }\n",
       "    .wandb-col { display: flex; flex-direction: column; flex-basis: 100%; flex: 1; padding: 10px; }\n",
       "    </style>\n",
       "<div class=\"wandb-row\"><div class=\"wandb-col\">\n",
       "</div><div class=\"wandb-col\">\n",
       "</div></div>\n",
       "Synced 6 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)\n",
       "<br/>Synced <strong style=\"color:#cdcd00\">feasible-oath-19</strong>: <a href=\"https://wandb.ai/tcapelle/apple_m1_pro/runs/18ul76ty\" target=\"_blank\">https://wandb.ai/tcapelle/apple_m1_pro/runs/18ul76ty</a><br/>\n",
       "Find logs at: <code>./wandb/run-20211119_192019-18ul76ty/logs</code><br/>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "Successfully finished last run (ID:18ul76ty). Initializing new run:<br/>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "\n",
       "                    Syncing run <strong><a href=\"https://wandb.ai/tcapelle/apple_m1_pro/runs/faz28dh6\" target=\"_blank\">brisk-violet-20</a></strong> to <a href=\"https://wandb.ai/tcapelle/apple_m1_pro\" target=\"_blank\">Weights & Biases</a> (<a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">docs</a>).<br/>\n",
       "\n",
       "                "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "run = wandb.init(project=PROJECT, group=GROUP, job_type=JOB_TYPE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'run' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m/var/folders/sf/tgv7vcv96x557p38bvvp1ms40000gn/T/ipykernel_1357/2789942315.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0martifact\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrun\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0muse_artifact\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'tcapelle/apple_m1_pro/PetImages:latest'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'dataset'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0martifact_dir\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0martifact\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdownload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'run' is not defined"
     ]
    }
   ],
   "source": [
    "artifact = run.use_artifact('tcapelle/apple_m1_pro/PetImages:latest', type='dataset')\n",
    "artifact_dir = artifact.download()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Deleted 0 images\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "from PIL import Image\n",
    "\n",
    "num_skipped = 0\n",
    "for folder_name in (\"Cat\", \"Dog\"):\n",
    "    folder_path = os.path.join(artifact_dir, 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(\"JFIF\") 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": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_data(data_dir, batch_size):\n",
    "    print(f'Reading from : {Path(data_dir).absolute()}')\n",
    "    train_ds = tf.keras.preprocessing.image_dataset_from_directory(\n",
    "        Path(data_dir),\n",
    "        validation_split=0.2,\n",
    "        subset=\"training\",\n",
    "        seed=1337,\n",
    "        image_size=(IMG_SIZE,IMG_SIZE),\n",
    "        batch_size=BS,\n",
    "    )\n",
    "    val_ds = tf.keras.preprocessing.image_dataset_from_directory(\n",
    "        Path(data_dir),\n",
    "        validation_split=0.2,\n",
    "        subset=\"validation\",\n",
    "        seed=1337,\n",
    "        image_size=(IMG_SIZE,IMG_SIZE),\n",
    "        batch_size=BS,\n",
    "    )\n",
    "    return train_ds, val_ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'artifact_dir' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m/var/folders/sf/tgv7vcv96x557p38bvvp1ms40000gn/T/ipykernel_1357/1649941138.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtrain_ds\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mval_ds\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martifact_dir\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m32\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'artifact_dir' is not defined"
     ]
    }
   ],
   "source": [
    "train_ds, val_ds = get_data(artifact_dir, 32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "#not supported on GPU in M1\n",
    "data_augmentation = keras.Sequential(\n",
    "    [\n",
    "        layers.RandomFlip(\"horizontal\"),\n",
    "        layers.RandomRotation(0.1),\n",
    "    ]\n",
    ")\n",
    "data_augmentation = lambda x: x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_ds = train_ds.prefetch(buffer_size=32)\n",
    "val_ds = val_ds.prefetch(buffer_size=32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_model(input_shape, num_classes):\n",
    "    inputs = keras.Input(shape=input_shape)\n",
    "    # Image augmentation block\n",
    "    x = data_augmentation(inputs)\n",
    "\n",
    "    # Entry block\n",
    "    x = layers.Rescaling(1.0 / 255)(x)\n",
    "    x = layers.Conv2D(32, 3, strides=2, padding=\"same\")(x)\n",
    "    x = layers.BatchNormalization()(x)\n",
    "    x = layers.Activation(\"relu\")(x)\n",
    "\n",
    "    x = layers.Conv2D(64, 3, padding=\"same\")(x)\n",
    "    x = layers.BatchNormalization()(x)\n",
    "    x = layers.Activation(\"relu\")(x)\n",
    "\n",
    "    previous_block_activation = x  # Set aside residual\n",
    "\n",
    "    for size in [128, 256, 512, 728]:\n",
    "        x = layers.Activation(\"relu\")(x)\n",
    "        x = layers.SeparableConv2D(size, 3, padding=\"same\")(x)\n",
    "        x = layers.BatchNormalization()(x)\n",
    "\n",
    "        x = layers.Activation(\"relu\")(x)\n",
    "        x = layers.SeparableConv2D(size, 3, padding=\"same\")(x)\n",
    "        x = layers.BatchNormalization()(x)\n",
    "\n",
    "        x = layers.MaxPooling2D(3, strides=2, padding=\"same\")(x)\n",
    "\n",
    "        # Project residual\n",
    "        residual = layers.Conv2D(size, 1, strides=2, padding=\"same\")(\n",
    "            previous_block_activation\n",
    "        )\n",
    "        x = layers.add([x, residual])  # Add back residual\n",
    "        previous_block_activation = x  # Set aside next residual\n",
    "\n",
    "    x = layers.SeparableConv2D(1024, 3, padding=\"same\")(x)\n",
    "    x = layers.BatchNormalization()(x)\n",
    "    x = layers.Activation(\"relu\")(x)\n",
    "\n",
    "    x = layers.GlobalAveragePooling2D()(x)\n",
    "    if num_classes == 2:\n",
    "        activation = \"sigmoid\"\n",
    "        units = 1\n",
    "    else:\n",
    "        activation = \"softmax\"\n",
    "        units = num_classes\n",
    "\n",
    "    x = layers.Dropout(0.5)(x)\n",
    "    outputs = layers.Dense(units, activation=activation)(x)\n",
    "    return keras.Model(inputs, outputs)\n",
    "\n",
    "\n",
    "model = make_model(input_shape=(IMG_SIZE, IMG_SIZE) + (3,), num_classes=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2021-11-19 19:21:14.311909: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:185] None of the MLIR Optimization Passes are enabled (registered 2)\n",
      "2021-11-19 19:21:14.315356: W tensorflow/core/platform/profile_utils/cpu_utils.cc:128] Failed to get CPU frequency: 0 Hz\n",
      "2021-11-19 19:21:14.315483: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:112] Plugin optimizer for device_type GPU is enabled.\n",
      "Corrupt JPEG data: 2226 extraneous bytes before marker 0xd9\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  6/293 [..............................] - ETA: 2:28 - loss: 1.0128 - accuracy: 0.5208WARNING:tensorflow:Callback method `on_train_batch_end` is slow compared to the batch time (batch time: 0.1761s vs `on_train_batch_end` time: 0.3246s). Check your callbacks.\n",
      "126/293 [===========>..................] - ETA: 1:24 - loss: 0.6724 - accuracy: 0.6336"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Corrupt JPEG data: 228 extraneous bytes before marker 0xd9\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "141/293 [=============>................] - ETA: 1:17 - loss: 0.6654 - accuracy: 0.6395"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning: unknown JFIF revision number 0.00\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "180/293 [=================>............] - ETA: 57s - loss: 0.6376 - accuracy: 0.6632"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Corrupt JPEG data: 128 extraneous bytes before marker 0xd9\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "185/293 [=================>............] - ETA: 54s - loss: 0.6344 - accuracy: 0.6653"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Corrupt JPEG data: 65 extraneous bytes before marker 0xd9\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "191/293 [==================>...........] - ETA: 51s - loss: 0.6316 - accuracy: 0.6673"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Corrupt JPEG data: 396 extraneous bytes before marker 0xd9\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "194/293 [==================>...........] - ETA: 50s - loss: 0.6298 - accuracy: 0.6688"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Corrupt JPEG data: 239 extraneous bytes before marker 0xd9\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "293/293 [==============================] - ETA: 0s - loss: 0.5843 - accuracy: 0.7013"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2021-11-19 19:23:43.002272: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:112] Plugin optimizer for device_type GPU is enabled.\n",
      "Corrupt JPEG data: 252 extraneous bytes before marker 0xd9\n",
      "Corrupt JPEG data: 1153 extraneous bytes before marker 0xd9\n",
      "Corrupt JPEG data: 162 extraneous bytes before marker 0xd9\n",
      "Corrupt JPEG data: 214 extraneous bytes before marker 0xd9\n",
      "Corrupt JPEG data: 99 extraneous bytes before marker 0xd9\n",
      "Corrupt JPEG data: 1403 extraneous bytes before marker 0xd9\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "293/293 [==============================] - 159s 538ms/step - loss: 0.5843 - accuracy: 0.7013 - val_loss: 0.8676 - val_accuracy: 0.4957\n",
      "Epoch 2/10\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Corrupt JPEG data: 2226 extraneous bytes before marker 0xd9\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "126/293 [===========>..................] - ETA: 1:25 - loss: 0.4167 - accuracy: 0.8140"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Corrupt JPEG data: 228 extraneous bytes before marker 0xd9\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "141/293 [=============>................] - ETA: 1:18 - loss: 0.4122 - accuracy: 0.8162"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning: unknown JFIF revision number 0.00\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "180/293 [=================>............] - ETA: 57s - loss: 0.3962 - accuracy: 0.8245"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Corrupt JPEG data: 128 extraneous bytes before marker 0xd9\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "185/293 [=================>............] - ETA: 55s - loss: 0.3934 - accuracy: 0.8258"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Corrupt JPEG data: 65 extraneous bytes before marker 0xd9\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "191/293 [==================>...........] - ETA: 52s - loss: 0.3908 - accuracy: 0.8273"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Corrupt JPEG data: 396 extraneous bytes before marker 0xd9\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "194/293 [==================>...........] - ETA: 50s - loss: 0.3901 - accuracy: 0.8275"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Corrupt JPEG data: 239 extraneous bytes before marker 0xd9\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "293/293 [==============================] - ETA: 0s - loss: 0.3622 - accuracy: 0.8413"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Corrupt JPEG data: 252 extraneous bytes before marker 0xd9\n",
      "Corrupt JPEG data: 1153 extraneous bytes before marker 0xd9\n",
      "Corrupt JPEG data: 162 extraneous bytes before marker 0xd9\n",
      "Corrupt JPEG data: 214 extraneous bytes before marker 0xd9\n",
      "Corrupt JPEG data: 99 extraneous bytes before marker 0xd9\n",
      "Corrupt JPEG data: 1403 extraneous bytes before marker 0xd9\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "293/293 [==============================] - 159s 543ms/step - loss: 0.3622 - accuracy: 0.8413 - val_loss: 0.8233 - val_accuracy: 0.6025\n",
      "Epoch 3/10\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Corrupt JPEG data: 2226 extraneous bytes before marker 0xd9\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "126/293 [===========>..................] - ETA: 1:25 - loss: 0.2653 - accuracy: 0.8859"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Corrupt JPEG data: 228 extraneous bytes before marker 0xd9\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "141/293 [=============>................] - ETA: 1:17 - loss: 0.2618 - accuracy: 0.8871"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning: unknown JFIF revision number 0.00\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "180/293 [=================>............] - ETA: 57s - loss: 0.2581 - accuracy: 0.8895"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Corrupt JPEG data: 128 extraneous bytes before marker 0xd9\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "185/293 [=================>............] - ETA: 55s - loss: 0.2571 - accuracy: 0.8901"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Corrupt JPEG data: 65 extraneous bytes before marker 0xd9\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "191/293 [==================>...........] - ETA: 52s - loss: 0.2562 - accuracy: 0.8905"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Corrupt JPEG data: 396 extraneous bytes before marker 0xd9\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "194/293 [==================>...........] - ETA: 50s - loss: 0.2549 - accuracy: 0.8912"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Corrupt JPEG data: 239 extraneous bytes before marker 0xd9\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "293/293 [==============================] - ETA: 0s - loss: 0.2412 - accuracy: 0.8982"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Corrupt JPEG data: 252 extraneous bytes before marker 0xd9\n",
      "Corrupt JPEG data: 1153 extraneous bytes before marker 0xd9\n",
      "Corrupt JPEG data: 162 extraneous bytes before marker 0xd9\n",
      "Corrupt JPEG data: 214 extraneous bytes before marker 0xd9\n",
      "Corrupt JPEG data: 99 extraneous bytes before marker 0xd9\n",
      "Corrupt JPEG data: 1403 extraneous bytes before marker 0xd9\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "293/293 [==============================] - 160s 543ms/step - loss: 0.2412 - accuracy: 0.8982 - val_loss: 0.3012 - val_accuracy: 0.8661\n",
      "Epoch 4/10\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Corrupt JPEG data: 2226 extraneous bytes before marker 0xd9\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "126/293 [===========>..................] - ETA: 1:25 - loss: 0.1764 - accuracy: 0.9249"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Corrupt JPEG data: 228 extraneous bytes before marker 0xd9\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "141/293 [=============>................] - ETA: 1:17 - loss: 0.1755 - accuracy: 0.9251"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning: unknown JFIF revision number 0.00\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "180/293 [=================>............] - ETA: 57s - loss: 0.1758 - accuracy: 0.9262"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Corrupt JPEG data: 128 extraneous bytes before marker 0xd9\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "185/293 [=================>............] - ETA: 55s - loss: 0.1760 - accuracy: 0.9263"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Corrupt JPEG data: 65 extraneous bytes before marker 0xd9\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "191/293 [==================>...........] - ETA: 52s - loss: 0.1762 - accuracy: 0.9268"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Corrupt JPEG data: 396 extraneous bytes before marker 0xd9\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "194/293 [==================>...........] - ETA: 50s - loss: 0.1752 - accuracy: 0.9273"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Corrupt JPEG data: 239 extraneous bytes before marker 0xd9\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "293/293 [==============================] - ETA: 0s - loss: 0.1702 - accuracy: 0.9304"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Corrupt JPEG data: 252 extraneous bytes before marker 0xd9\n",
      "Corrupt JPEG data: 1153 extraneous bytes before marker 0xd9\n",
      "Corrupt JPEG data: 162 extraneous bytes before marker 0xd9\n",
      "Corrupt JPEG data: 214 extraneous bytes before marker 0xd9\n",
      "Corrupt JPEG data: 99 extraneous bytes before marker 0xd9\n",
      "Corrupt JPEG data: 1403 extraneous bytes before marker 0xd9\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "293/293 [==============================] - 159s 543ms/step - loss: 0.1702 - accuracy: 0.9304 - val_loss: 0.4854 - val_accuracy: 0.8138\n",
      "Epoch 5/10\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Corrupt JPEG data: 2226 extraneous bytes before marker 0xd9\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "126/293 [===========>..................] - ETA: 1:25 - loss: 0.1326 - accuracy: 0.9472"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Corrupt JPEG data: 228 extraneous bytes before marker 0xd9\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "141/293 [=============>................] - ETA: 1:17 - loss: 0.1302 - accuracy: 0.9488"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning: unknown JFIF revision number 0.00\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "180/293 [=================>............] - ETA: 57s - loss: 0.1336 - accuracy: 0.9469"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Corrupt JPEG data: 128 extraneous bytes before marker 0xd9\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "185/293 [=================>............] - ETA: 54s - loss: 0.1339 - accuracy: 0.9465"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Corrupt JPEG data: 65 extraneous bytes before marker 0xd9\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "191/293 [==================>...........] - ETA: 51s - loss: 0.1338 - accuracy: 0.9466"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Corrupt JPEG data: 396 extraneous bytes before marker 0xd9\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "194/293 [==================>...........] - ETA: 50s - loss: 0.1331 - accuracy: 0.9468"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Corrupt JPEG data: 239 extraneous bytes before marker 0xd9\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "293/293 [==============================] - ETA: 0s - loss: 0.1272 - accuracy: 0.9491"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Corrupt JPEG data: 252 extraneous bytes before marker 0xd9\n",
      "Corrupt JPEG data: 1153 extraneous bytes before marker 0xd9\n",
      "Corrupt JPEG data: 162 extraneous bytes before marker 0xd9\n",
      "Corrupt JPEG data: 214 extraneous bytes before marker 0xd9\n",
      "Corrupt JPEG data: 99 extraneous bytes before marker 0xd9\n",
      "Corrupt JPEG data: 1403 extraneous bytes before marker 0xd9\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "293/293 [==============================] - 159s 541ms/step - loss: 0.1272 - accuracy: 0.9491 - val_loss: 0.3413 - val_accuracy: 0.8971\n",
      "Epoch 6/10\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Corrupt JPEG data: 2226 extraneous bytes before marker 0xd9\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "125/293 [===========>..................] - ETA: 1:25 - loss: 0.1024 - accuracy: 0.9574"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Corrupt JPEG data: 228 extraneous bytes before marker 0xd9\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "140/293 [=============>................] - ETA: 1:18 - loss: 0.1028 - accuracy: 0.9583"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning: unknown JFIF revision number 0.00\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "179/293 [=================>............] - ETA: 58s - loss: 0.1059 - accuracy: 0.9576"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Corrupt JPEG data: 128 extraneous bytes before marker 0xd9\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "184/293 [=================>............] - ETA: 55s - loss: 0.1059 - accuracy: 0.9576"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Corrupt JPEG data: 65 extraneous bytes before marker 0xd9\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "190/293 [==================>...........] - ETA: 52s - loss: 0.1073 - accuracy: 0.9567"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Corrupt JPEG data: 396 extraneous bytes before marker 0xd9\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "193/293 [==================>...........] - ETA: 50s - loss: 0.1071 - accuracy: 0.9569"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Corrupt JPEG data: 239 extraneous bytes before marker 0xd9\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "293/293 [==============================] - ETA: 0s - loss: 0.1076 - accuracy: 0.9570"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Corrupt JPEG data: 252 extraneous bytes before marker 0xd9\n",
      "Corrupt JPEG data: 1153 extraneous bytes before marker 0xd9\n",
      "Corrupt JPEG data: 162 extraneous bytes before marker 0xd9\n",
      "Corrupt JPEG data: 214 extraneous bytes before marker 0xd9\n",
      "Corrupt JPEG data: 99 extraneous bytes before marker 0xd9\n",
      "Corrupt JPEG data: 1403 extraneous bytes before marker 0xd9\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "293/293 [==============================] - 159s 542ms/step - loss: 0.1076 - accuracy: 0.9570 - val_loss: 0.3643 - val_accuracy: 0.8889\n",
      "Epoch 7/10\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Corrupt JPEG data: 2226 extraneous bytes before marker 0xd9\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "125/293 [===========>..................] - ETA: 1:25 - loss: 0.0834 - accuracy: 0.9668"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Corrupt JPEG data: 228 extraneous bytes before marker 0xd9\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "140/293 [=============>................] - ETA: 1:17 - loss: 0.0810 - accuracy: 0.9673"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning: unknown JFIF revision number 0.00\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "179/293 [=================>............] - ETA: 57s - loss: 0.0785 - accuracy: 0.9688"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Corrupt JPEG data: 128 extraneous bytes before marker 0xd9\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "184/293 [=================>............] - ETA: 55s - loss: 0.0786 - accuracy: 0.9688"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Corrupt JPEG data: 65 extraneous bytes before marker 0xd9\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "190/293 [==================>...........] - ETA: 52s - loss: 0.0782 - accuracy: 0.9689"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Corrupt JPEG data: 396 extraneous bytes before marker 0xd9\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "193/293 [==================>...........] - ETA: 50s - loss: 0.0782 - accuracy: 0.9691"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Corrupt JPEG data: 239 extraneous bytes before marker 0xd9\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "293/293 [==============================] - ETA: 0s - loss: 0.0812 - accuracy: 0.9687"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Corrupt JPEG data: 252 extraneous bytes before marker 0xd9\n",
      "Corrupt JPEG data: 1153 extraneous bytes before marker 0xd9\n",
      "Corrupt JPEG data: 162 extraneous bytes before marker 0xd9\n",
      "Corrupt JPEG data: 214 extraneous bytes before marker 0xd9\n",
      "Corrupt JPEG data: 99 extraneous bytes before marker 0xd9\n",
      "Corrupt JPEG data: 1403 extraneous bytes before marker 0xd9\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "293/293 [==============================] - 159s 542ms/step - loss: 0.0812 - accuracy: 0.9687 - val_loss: 1.1136 - val_accuracy: 0.7774\n",
      "Epoch 8/10\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Corrupt JPEG data: 2226 extraneous bytes before marker 0xd9\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "126/293 [===========>..................] - ETA: 1:25 - loss: 0.0680 - accuracy: 0.9741"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Corrupt JPEG data: 228 extraneous bytes before marker 0xd9\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "141/293 [=============>................] - ETA: 1:17 - loss: 0.0653 - accuracy: 0.9752"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning: unknown JFIF revision number 0.00\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "180/293 [=================>............] - ETA: 57s - loss: 0.0656 - accuracy: 0.9754"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Corrupt JPEG data: 128 extraneous bytes before marker 0xd9\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "185/293 [=================>............] - ETA: 55s - loss: 0.0649 - accuracy: 0.9758"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Corrupt JPEG data: 65 extraneous bytes before marker 0xd9\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "191/293 [==================>...........] - ETA: 52s - loss: 0.0647 - accuracy: 0.9759"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Corrupt JPEG data: 396 extraneous bytes before marker 0xd9\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "194/293 [==================>...........] - ETA: 50s - loss: 0.0650 - accuracy: 0.9758"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Corrupt JPEG data: 239 extraneous bytes before marker 0xd9\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "293/293 [==============================] - ETA: 0s - loss: 0.0642 - accuracy: 0.9754"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Corrupt JPEG data: 252 extraneous bytes before marker 0xd9\n",
      "Corrupt JPEG data: 1153 extraneous bytes before marker 0xd9\n",
      "Corrupt JPEG data: 162 extraneous bytes before marker 0xd9\n",
      "Corrupt JPEG data: 214 extraneous bytes before marker 0xd9\n",
      "Corrupt JPEG data: 99 extraneous bytes before marker 0xd9\n",
      "Corrupt JPEG data: 1403 extraneous bytes before marker 0xd9\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "293/293 [==============================] - 159s 543ms/step - loss: 0.0642 - accuracy: 0.9754 - val_loss: 0.2818 - val_accuracy: 0.9020\n",
      "Epoch 9/10\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Corrupt JPEG data: 2226 extraneous bytes before marker 0xd9\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "125/293 [===========>..................] - ETA: 1:25 - loss: 0.0641 - accuracy: 0.9740"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Corrupt JPEG data: 228 extraneous bytes before marker 0xd9\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "140/293 [=============>................] - ETA: 1:18 - loss: 0.0629 - accuracy: 0.9744"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning: unknown JFIF revision number 0.00\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "179/293 [=================>............] - ETA: 58s - loss: 0.0618 - accuracy: 0.9754"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Corrupt JPEG data: 128 extraneous bytes before marker 0xd9\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "184/293 [=================>............] - ETA: 55s - loss: 0.0617 - accuracy: 0.9755"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Corrupt JPEG data: 65 extraneous bytes before marker 0xd9\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "190/293 [==================>...........] - ETA: 52s - loss: 0.0624 - accuracy: 0.9752"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Corrupt JPEG data: 396 extraneous bytes before marker 0xd9\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "193/293 [==================>...........] - ETA: 51s - loss: 0.0629 - accuracy: 0.9750"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Corrupt JPEG data: 239 extraneous bytes before marker 0xd9\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "293/293 [==============================] - ETA: 0s - loss: 0.0613 - accuracy: 0.9760"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Corrupt JPEG data: 252 extraneous bytes before marker 0xd9\n",
      "Corrupt JPEG data: 1153 extraneous bytes before marker 0xd9\n",
      "Corrupt JPEG data: 162 extraneous bytes before marker 0xd9\n",
      "Corrupt JPEG data: 214 extraneous bytes before marker 0xd9\n",
      "Corrupt JPEG data: 99 extraneous bytes before marker 0xd9\n",
      "Corrupt JPEG data: 1403 extraneous bytes before marker 0xd9\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "293/293 [==============================] - 159s 542ms/step - loss: 0.0613 - accuracy: 0.9760 - val_loss: 0.3614 - val_accuracy: 0.9065\n",
      "Epoch 10/10\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Corrupt JPEG data: 2226 extraneous bytes before marker 0xd9\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "126/293 [===========>..................] - ETA: 1:24 - loss: 0.0574 - accuracy: 0.9788"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Corrupt JPEG data: 228 extraneous bytes before marker 0xd9\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "141/293 [=============>................] - ETA: 1:17 - loss: 0.0568 - accuracy: 0.9787"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning: unknown JFIF revision number 0.00\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "180/293 [=================>............] - ETA: 57s - loss: 0.0548 - accuracy: 0.9789"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Corrupt JPEG data: 128 extraneous bytes before marker 0xd9\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "185/293 [=================>............] - ETA: 54s - loss: 0.0544 - accuracy: 0.9790"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Corrupt JPEG data: 65 extraneous bytes before marker 0xd9\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "191/293 [==================>...........] - ETA: 51s - loss: 0.0553 - accuracy: 0.9785"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Corrupt JPEG data: 396 extraneous bytes before marker 0xd9\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "194/293 [==================>...........] - ETA: 50s - loss: 0.0551 - accuracy: 0.9786"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Corrupt JPEG data: 239 extraneous bytes before marker 0xd9\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "293/293 [==============================] - ETA: 0s - loss: 0.0542 - accuracy: 0.9793"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Corrupt JPEG data: 252 extraneous bytes before marker 0xd9\n",
      "Corrupt JPEG data: 1153 extraneous bytes before marker 0xd9\n",
      "Corrupt JPEG data: 162 extraneous bytes before marker 0xd9\n",
      "Corrupt JPEG data: 214 extraneous bytes before marker 0xd9\n",
      "Corrupt JPEG data: 99 extraneous bytes before marker 0xd9\n",
      "Corrupt JPEG data: 1403 extraneous bytes before marker 0xd9\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "293/293 [==============================] - 159s 541ms/step - loss: 0.0542 - accuracy: 0.9793 - val_loss: 0.4073 - val_accuracy: 0.9052\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x2830a4f40>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "callbacks = [\n",
    "    wandb.keras.WandbCallback(save_model=False),\n",
    "]\n",
    "model.compile(\n",
    "    optimizer=keras.optimizers.Adam(1e-3),\n",
    "    loss=\"binary_crossentropy\",\n",
    "    metrics=[\"accuracy\"],\n",
    ")\n",
    "model.fit(\n",
    "    train_ds, epochs=EPOCHS, callbacks=callbacks, validation_data=val_ds,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<br/>Waiting for W&B process to finish, PID 94302... <strong style=\"color:green\">(success).</strong>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox(children=(Label(value=' 0.01MB of 0.01MB uploaded (0.00MB deduped)\\r'), FloatProgress(value=1.0, max=1.0)…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<style>\n",
       "    table.wandb td:nth-child(1) { padding: 0 10px; text-align: right }\n",
       "    .wandb-row { display: flex; flex-direction: row; flex-wrap: wrap; width: 100% }\n",
       "    .wandb-col { display: flex; flex-direction: column; flex-basis: 100%; flex: 1; padding: 10px; }\n",
       "    </style>\n",
       "<div class=\"wandb-row\"><div class=\"wandb-col\">\n",
       "<h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>accuracy</td><td>▁▅▆▇▇▇████</td></tr><tr><td>epoch</td><td>▁▂▃▃▄▅▆▆▇█</td></tr><tr><td>loss</td><td>█▅▃▃▂▂▁▁▁▁</td></tr><tr><td>val_accuracy</td><td>▁▃▇▆██▆███</td></tr><tr><td>val_loss</td><td>▆▆▁▃▂▂█▁▂▂</td></tr></table><br/></div><div class=\"wandb-col\">\n",
       "<h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>accuracy</td><td>0.97928</td></tr><tr><td>best_epoch</td><td>7</td></tr><tr><td>best_val_loss</td><td>0.28175</td></tr><tr><td>epoch</td><td>9</td></tr><tr><td>loss</td><td>0.05418</td></tr><tr><td>val_accuracy</td><td>0.90517</td></tr><tr><td>val_loss</td><td>0.40733</td></tr></table>\n",
       "</div></div>\n",
       "Synced 6 W&B file(s), 1 media file(s), 0 artifact file(s) and 0 other file(s)\n",
       "<br/>Synced <strong style=\"color:#cdcd00\">brisk-violet-20</strong>: <a href=\"https://wandb.ai/tcapelle/apple_m1_pro/runs/faz28dh6\" target=\"_blank\">https://wandb.ai/tcapelle/apple_m1_pro/runs/faz28dh6</a><br/>\n",
       "Find logs at: <code>./wandb/run-20211119_192058-faz28dh6/logs</code><br/>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "wandb.finish()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Old code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'platform' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m/var/folders/sf/tgv7vcv96x557p38bvvp1ms40000gn/T/ipykernel_94235/2064899850.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      8\u001b[0m default_config = {\n\u001b[1;32m      9\u001b[0m     \u001b[0;34m\"batch_size\"\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;36m64\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"epochs\"\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;36m10\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"dropout\"\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;36m0.4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"base_model\"\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mBASE_MODEL\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 10\u001b[0;31m     \u001b[0;34m\"init_lr\"\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;36m0.0005\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"decay\"\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;36m0.96\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"num_classes\"\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mN_CLASSES\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"hardware\"\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mhardware\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     11\u001b[0m     \u001b[0;34m\"train_size\"\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"test_size\"\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     12\u001b[0m     \u001b[0;34m\"dataset\"\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mDATASET\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"img_dim\"\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mIMG_DIM\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"trainable\"\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/var/folders/sf/tgv7vcv96x557p38bvvp1ms40000gn/T/ipykernel_94235/3459158288.py\u001b[0m in \u001b[0;36mhardware\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mhardware\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m   \u001b[0;34m\"\"\"Detect platform and accelerator\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m   \u001b[0;32mif\u001b[0m \u001b[0mplatform\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msystem\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"Darwin\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mplatform\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprocessor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"arm\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m       \u001b[0;32mreturn\u001b[0m \u001b[0;34m\"Apple M1\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'platform' is not defined"
     ]
    }
   ],
   "source": [
    "# get data\n",
    "(x_train, y_train), (x_test, y_test) = cifar10.load_data()\n",
    "\n",
    "# Subsetting train data and normalizing to [0., 1.]\n",
    "x_train, x_test = x_train / 255., x_test / 255.\n",
    "\n",
    "\n",
    "default_config = {\n",
    "    \"batch_size\": 64, \"epochs\": 10, \"dropout\": 0.4, \"base_model\": BASE_MODEL, \n",
    "    \"init_lr\": 0.0005, \"decay\": 0.96, \"num_classes\": N_CLASSES, \"hardware\": hardware(), \n",
    "    \"train_size\": len(x_train), \"test_size\": len(x_test),\n",
    "    \"dataset\": DATASET, \"img_dim\": IMG_DIM, \"trainable\": True,\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train():\n",
    "  \"\"\"Run transfer learning on the configured model and dataset\"\"\"\n",
    "  global IMG_DIM, N_CLASSES, DS_CACHE\n",
    "  with wandb.init(project=\"m1-benchmark\", config=default_config) as run:\n",
    "    # Set global defaults when running in sweep mode\n",
    "    IMG_DIM = run.config.img_dim\n",
    "    N_CLASSES = run.config.num_classes\n",
    "    DS_CACHE = os.path.join(tempfile.mkdtemp(), str(hash(frozenset(run.config.items()))))\n",
    "\n",
    "    # Setup base model to transfer from, optionally fine-tune\n",
    "    base_model = getattr(tf.keras.applications, run.config.base_model)(\n",
    "        input_shape=(run.config.img_dim, run.config.img_dim, 3),\n",
    "        include_top=False, weights='imagenet')\n",
    "    base_model.trainable = run.config.trainable\n",
    "\n",
    "    # Decay learning rate\n",
    "    lr_schedule = tf.keras.optimizers.schedules.ExponentialDecay(\n",
    "      run.config.init_lr, decay_steps=run.config.train_size, decay_rate=run.config.decay)\n",
    "\n",
    "    # Compile model for this dataset\n",
    "    model = tf.keras.Sequential([\n",
    "      base_model,\n",
    "      tf.keras.layers.GlobalAveragePooling2D(),\n",
    "      tf.keras.layers.Dropout(run.config.dropout),\n",
    "      tf.keras.layers.Dense(run.config.num_classes, activation='softmax')\n",
    "    ])\n",
    "    model.compile(optimizer=tf.keras.optimizers.Adam(lr_schedule),\n",
    "                  loss='categorical_crossentropy',\n",
    "                  metrics=['accuracy', 'top_k_categorical_accuracy'])\n",
    "    \n",
    "    # Update config and print summary\n",
    "    run.config.update({\n",
    "      \"total_params\": model.count_params(),\n",
    "      \"trainable_params\": trainable_params(model),\n",
    "    })\n",
    "    print(\"Model {}:\".format(run.config.base_model))\n",
    "    print(\"  trainable parameters:\", run.config.trainable_params)\n",
    "    print(\"      total parameters:\", run.config.total_params)\n",
    "    print(\"Dataset {}:\".format(run.config.dataset))\n",
    "    print(\"  training: \", run.config.train_size)\n",
    "    print(\"      test: \", run.config.test_size)\n",
    "    print(\"     shape: {}\\n\".format((run.config.img_dim, run.config.img_dim, 3)))\n",
    "    \n",
    "    # Train the model\n",
    "    history = model.fit(x_train, y_train,  \n",
    "                        epochs=run.config.epochs, \n",
    "                        validation_data=(x_test, y_test),\n",
    "                        callbacks=[wandb.keras.WandbCallback(save_model=False)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "train()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "collapsed_sections": [],
   "name": "Basic Performance.ipynb",
   "provenance": []
  },
  "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": "3.9.7"
  },
  "widgets": {
   "application/vnd.jupyter.widget-state+json": {
    "058bb2545d34464b9ca0718261778026": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "066346a946674cb7bd869ae6feb86b5e": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "LabelModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "LabelModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "LabelView",
      "description": "",
      "description_tooltip": null,
      "layout": "IPY_MODEL_53f3956ad0f945499c3daad7a104e392",
      "placeholder": "​",
      "style": "IPY_MODEL_ab368a038d174a9f979fcbb9f470b8c5",
      "value": " 1.86MB of 1.86MB uploaded (0.00MB deduped)\r"
     }
    },
    "46da9173be364dafaaaae3123ed61653": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "FloatProgressModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "FloatProgressModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "ProgressView",
      "bar_style": "",
      "description": "",
      "description_tooltip": null,
      "layout": "IPY_MODEL_e4354c4c8da74a1ab1e44b987b221947",
      "max": 1,
      "min": 0,
      "orientation": "horizontal",
      "style": "IPY_MODEL_a08692e01d504d3fbf711c61f04f6ee6",
      "value": 1
     }
    },
    "49c58f268866499c8ea5b5e86e7b629e": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "4c181783977f4ebf9e1a2a49c3ec0120": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "FloatProgressModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "FloatProgressModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "ProgressView",
      "bar_style": "",
      "description": "",
      "description_tooltip": null,
      "layout": "IPY_MODEL_623e060e7e514a30aadda61cb7c1674b",
      "max": 1,
      "min": 0,
      "orientation": "horizontal",
      "style": "IPY_MODEL_c418d6652e43445da8e239f0ca859846",
      "value": 1
     }
    },
    "53f3956ad0f945499c3daad7a104e392": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "5adc2846a13b4a3d981cbd7fc0769cab": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "DescriptionStyleModel",
     "state": {
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "DescriptionStyleModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "StyleView",
      "description_width": ""
     }
    },
    "60170335e6514507bd87c6a4dac3d623": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "DescriptionStyleModel",
     "state": {
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "DescriptionStyleModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "StyleView",
      "description_width": ""
     }
    },
    "623e060e7e514a30aadda61cb7c1674b": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "7b6f698700794454bcdd0cb4e5881b3e": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "VBoxModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "VBoxModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "VBoxView",
      "box_style": "",
      "children": [
       "IPY_MODEL_066346a946674cb7bd869ae6feb86b5e",
       "IPY_MODEL_bd8c6c0cd07a4c4782db78b6f3c529cb"
      ],
      "layout": "IPY_MODEL_f580aa5ff4ec41c6ae48419661333f7f"
     }
    },
    "7cbfd13104c44eba8aa1e8d1fb2fc7f1": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "7d2279098f384e2eb2dbeaf53cde52c1": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "VBoxModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "VBoxModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "VBoxView",
      "box_style": "",
      "children": [
       "IPY_MODEL_8b5f7d4f32c345de90ed6d1875bd6bd1",
       "IPY_MODEL_f9129b445ddf4aee859db77ea883c8fe"
      ],
      "layout": "IPY_MODEL_a0273718b661442b8d1334943460ede6"
     }
    },
    "7d2be1b24f2841fa93c57aae78f31a15": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "LabelModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "LabelModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "LabelView",
      "description": "",
      "description_tooltip": null,
      "layout": "IPY_MODEL_058bb2545d34464b9ca0718261778026",
      "placeholder": "​",
      "style": "IPY_MODEL_60170335e6514507bd87c6a4dac3d623",
      "value": " 1.79MB of 1.79MB uploaded (0.00MB deduped)\r"
     }
    },
    "7d62fc41e1a94a5eb9df853f7a2f16c2": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "VBoxModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "VBoxModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "VBoxView",
      "box_style": "",
      "children": [
       "IPY_MODEL_e3cfed5314bb4867a4241cff2e0adac8",
       "IPY_MODEL_46da9173be364dafaaaae3123ed61653"
      ],
      "layout": "IPY_MODEL_93f911153ab54aa0b625794462a91ad5"
     }
    },
    "8b5f7d4f32c345de90ed6d1875bd6bd1": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "LabelModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "LabelModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "LabelView",
      "description": "",
      "description_tooltip": null,
      "layout": "IPY_MODEL_ca45fa9c101d4051860f176bf1bab1da",
      "placeholder": "​",
      "style": "IPY_MODEL_5adc2846a13b4a3d981cbd7fc0769cab",
      "value": " 0.92MB of 0.92MB uploaded (0.00MB deduped)\r"
     }
    },
    "93f911153ab54aa0b625794462a91ad5": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "9584b9a224444b7685e3eb883fa71015": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "97651ef84a5d4f11a7e75670ce8300e7": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "VBoxModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "VBoxModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "VBoxView",
      "box_style": "",
      "children": [
       "IPY_MODEL_7d2be1b24f2841fa93c57aae78f31a15",
       "IPY_MODEL_4c181783977f4ebf9e1a2a49c3ec0120"
      ],
      "layout": "IPY_MODEL_b479a3899aba49fd99294d9df2eec701"
     }
    },
    "a0273718b661442b8d1334943460ede6": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "a08692e01d504d3fbf711c61f04f6ee6": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "ProgressStyleModel",
     "state": {
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "ProgressStyleModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "StyleView",
      "bar_color": null,
      "description_width": ""
     }
    },
    "a8eb7ef889934f5ba61591bc338aafcd": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "ProgressStyleModel",
     "state": {
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "ProgressStyleModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "StyleView",
      "bar_color": null,
      "description_width": ""
     }
    },
    "ab368a038d174a9f979fcbb9f470b8c5": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "DescriptionStyleModel",
     "state": {
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "DescriptionStyleModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "StyleView",
      "description_width": ""
     }
    },
    "b479a3899aba49fd99294d9df2eec701": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "bd8c6c0cd07a4c4782db78b6f3c529cb": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "FloatProgressModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "FloatProgressModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "ProgressView",
      "bar_style": "",
      "description": "",
      "description_tooltip": null,
      "layout": "IPY_MODEL_49c58f268866499c8ea5b5e86e7b629e",
      "max": 1,
      "min": 0,
      "orientation": "horizontal",
      "style": "IPY_MODEL_a8eb7ef889934f5ba61591bc338aafcd",
      "value": 1
     }
    },
    "bf7a7a2133994732bfa850f7eb51fdf0": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "ProgressStyleModel",
     "state": {
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "ProgressStyleModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "StyleView",
      "bar_color": null,
      "description_width": ""
     }
    },
    "bf9c745dd3784c2ab2562c5a6e1e002e": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "DescriptionStyleModel",
     "state": {
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "DescriptionStyleModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "StyleView",
      "description_width": ""
     }
    },
    "c418d6652e43445da8e239f0ca859846": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "ProgressStyleModel",
     "state": {
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "ProgressStyleModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "StyleView",
      "bar_color": null,
      "description_width": ""
     }
    },
    "ca45fa9c101d4051860f176bf1bab1da": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "e3cfed5314bb4867a4241cff2e0adac8": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "LabelModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "LabelModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "LabelView",
      "description": "",
      "description_tooltip": null,
      "layout": "IPY_MODEL_9584b9a224444b7685e3eb883fa71015",
      "placeholder": "​",
      "style": "IPY_MODEL_bf9c745dd3784c2ab2562c5a6e1e002e",
      "value": " 1.61MB of 1.61MB uploaded (0.00MB deduped)\r"
     }
    },
    "e4354c4c8da74a1ab1e44b987b221947": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "f580aa5ff4ec41c6ae48419661333f7f": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "f9129b445ddf4aee859db77ea883c8fe": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "FloatProgressModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "FloatProgressModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "ProgressView",
      "bar_style": "",
      "description": "",
      "description_tooltip": null,
      "layout": "IPY_MODEL_7cbfd13104c44eba8aa1e8d1fb2fc7f1",
      "max": 1,
      "min": 0,
      "orientation": "horizontal",
      "style": "IPY_MODEL_bf7a7a2133994732bfa850f7eb51fdf0",
      "value": 1
     }
    }
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}