gen_cifar10_adv.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "from torch.utils.data import TensorDataset\n",
    "from torch.utils.data import DataLoader\n",
    "from torchvision.utils import save_image\n",
    "import argparse\n",
    "import os\n",
    "from robustness import model_utils, datasets\n",
    "import time\n",
    "\n",
    "%matplotlib inline\n",
    "os.environ['NOTEBOOK_MODE'] = '1'\n",
    "\n",
    "os.environ['CUDA_VISIBLE_DEVICES'] = '0,1,2,3'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cuda\n"
     ]
    }
   ],
   "source": [
    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
    "print(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "DATA = 'CIFAR'  # Choices: ['CIFAR', 'ImageNet', 'RestrictedImageNet']\n",
    "WORKERS=2\n",
    "BatchSize=128\n",
    "\n",
    "data_root='/data/zyh/data/'\n",
    "out_dir='./output/adv/'\n",
    "resume_path='./output/adv/train_time:1/checkpoint.pt.best'# 对抗训练模型路径\n",
    "# resume_path='../robust_representations-master/checkpoint.pt.best'\n",
    "gen_time=5\n",
    "save_path='./output/adv/gen_time:{}/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "==> Preparing dataset cifar..\n",
      "Files already downloaded and verified\n",
      "Files already downloaded and verified\n"
     ]
    }
   ],
   "source": [
    "# Load dataset\n",
    "dataset_function = getattr(datasets, DATA)\n",
    "dataset = dataset_function(data_root)\n",
    "train_loader, test_loader = dataset.make_loaders(workers=WORKERS,\n",
    "                                                 batch_size=BatchSize,\n",
    "                                                 data_aug=False)\n",
    "data_iterator = enumerate(test_loader)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=> loading checkpoint './output/adv/train_time:1/checkpoint.pt.best'\n",
      "=> loaded checkpoint './output/adv/train_time:1/checkpoint.pt.best' (epoch 101)\n"
     ]
    }
   ],
   "source": [
    "model_kwargs = {\n",
    "    'arch': 'resnet50',\n",
    "    'dataset': dataset,\n",
    "    'resume_path':resume_path ,\n",
    "    'parallel': True\n",
    "}\n",
    "model, _ = model_utils.make_and_restore_model(**model_kwargs)\n",
    "# model.eval()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "save_path = save_path.format(gen_time)\n",
    "if not os.path.exists(save_path):\n",
    "    os.mkdir(save_path)\n",
    "    for i in range(10):\n",
    "        os.mkdir(os.path.join(save_path, str(i)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Custom loss for inversion\n",
    "def inversion_loss(model, inp, targ):\n",
    "    _, rep = model(inp, with_latent=True, fake_relu=True)\n",
    "    loss = torch.div(torch.norm(rep - targ, dim=1), torch.norm(targ, dim=1))\n",
    "    return loss, None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def save_robust_dataset(data, labels, cnt):\n",
    "    dataset = TensorDataset(data, labels)\n",
    "    dloader = DataLoader(dataset, batch_size=1)\n",
    "\n",
    "    for i, (image, label) in enumerate(dloader):\n",
    "        save_image(image,\n",
    "                   os.path.join(save_path,\n",
    "                                '{}/{}.png'.format(label.item(), cnt * 1000 + i)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# PGD parameters\n",
    "kwargs = {\n",
    "    'custom_loss': inversion_loss,\n",
    "    'constraint': '2',\n",
    "    'eps': 500,\n",
    "    'step_size': 0.1,\n",
    "    'iterations': 1000,\n",
    "    'do_tqdm': True,\n",
    "    'targeted': True,\n",
    "    'use_best': False\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "start_time = time.time()\n",
    "for i, (image, label) in enumerate(train_loader):\n",
    "    inter_time = time.time()\n",
    "\n",
    "    rand_data = torch.randn_like(image) / args.noise_scala + 0.5  # Seed for inversion (x_0)\n",
    "\n",
    "    with torch.no_grad():\n",
    "        (_, rep), _ = model(image.cuda(), with_latent=True)  # Corresponding representation\n",
    "\n",
    "    _, xadv = model(rand_data.cuda(), rep.clone(), make_adv=True, **kwargs)  # Image inversion using PGD\n",
    "\n",
    "    save_robust_dataset(xadv, label, i)\n",
    "\n",
    "    # Measure the time\n",
    "    if (i + 1) % 10 == 1:\n",
    "        elapsed = time.time() - start_time\n",
    "        elapsed_tracking = time.time() - inter_time\n",
    "        print(\n",
    "            f'Robustified {(i + 1) * args.batchsize} images in {elapsed:0.3f} seconds; '\n",
    "            f'Took {elapsed_tracking:0.3f} seconds for this particular iteration')\n",
    "        # break"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# show img"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "from robustness.tools.vis_tools import show_image_row, show_image_column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "NOISE_SCALE = 20\n",
    "DATA = 'CIFAR'  # Choices: ['CIFAR', 'ImageNet', 'RestrictedImageNet']\n",
    "WORKERS=2\n",
    "BatchSize=8\n",
    "\n",
    "data_root='/data/zyh/data/'\n",
    "out_dir='./output/adv/'\n",
    "resume_path='./output/adv/train_time:1/checkpoint.pt.best'\n",
    "# resume_path='../robust_representations-master/checkpoint.pt.best'\n",
    "gen_time=5\n",
    "save_path='./output/adv/gen_time:{}/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=> loading checkpoint './output/adv/train_time:1/checkpoint.pt.best'\n",
      "=> loaded checkpoint './output/adv/train_time:1/checkpoint.pt.best' (epoch 101)\n"
     ]
    }
   ],
   "source": [
    "model_kwargs = {\n",
    "    'arch': 'resnet50',\n",
    "    'dataset': dataset,\n",
    "    'resume_path':resume_path ,\n",
    "    'parallel': True\n",
    "}\n",
    "model, _ = model_utils.make_and_restore_model(**model_kwargs)\n",
    "# model.eval()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "==> Preparing dataset cifar..\n",
      "Files already downloaded and verified\n",
      "Files already downloaded and verified\n"
     ]
    }
   ],
   "source": [
    "# Load dataset\n",
    "dataset_function = getattr(datasets, DATA)\n",
    "dataset = dataset_function(data_root)\n",
    "train_loader, test_loader = dataset.make_loaders(workers=WORKERS,\n",
    "                                                 batch_size=BatchSize,\n",
    "                                                 data_aug=False)\n",
    "data_iterator = enumerate(test_loader)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Select images to invert (random samples from the test set)\n",
    "\n",
    "_, (im, targ) = next(data_iterator) # Images to invert\n",
    "\n",
    "with torch.no_grad():\n",
    "    (_, rep), _ = model(im.cuda(), with_latent=True) # Corresponding representation "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# PGD parameters\n",
    "kwargs = {\n",
    "    'custom_loss': inversion_loss,\n",
    "    'constraint': '2',\n",
    "    'eps': 500,\n",
    "    'step_size': 0.1,\n",
    "    'iterations': 1000,\n",
    "    'do_tqdm': False,\n",
    "    'targeted': True,\n",
    "    'use_best': False\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
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      "\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "Current loss: 0.6624640226364136:   2%|▍                          | 16/1000 [00:01<01:12, 13.62it/s]\u001b[A\u001b[A\n",
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      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Current loss: 0.45811736583709717:   3%|▊                         | 29/1000 [00:02<01:14, 13.01it/s]\u001b[A\u001b[A\n",
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      "\n",
      "Current loss: 0.37024354934692383:   4%|█                         | 39/1000 [00:03<01:04, 14.99it/s]\u001b[A\u001b[A\u001b[A\n",
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      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Current loss: 0.3331643342971802:   4%|█▏                         | 45/1000 [00:03<01:12, 13.24it/s]\u001b[A\u001b[A\n",
      "\n",
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      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Current loss: 0.2557065188884735:   6%|█▋                         | 64/1000 [00:04<01:16, 12.25it/s]\u001b[A\u001b[A\n",
      "\n",
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      "\n",
      "\n",
      "Current loss: 0.2614494562149048:   7%|█▉                         | 72/1000 [00:05<01:14, 12.43it/s]\u001b[A\u001b[A\u001b[A\n",
      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
      "\n",
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      "\n",
      "\n",
      "Current loss: 0.26712626218795776:   7%|█▊                        | 69/1000 [00:05<01:15, 12.27it/s]\u001b[A\u001b[A\u001b[A\n",
      "\n",
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      "\n",
      "\n",
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      "\n",
      "Current loss: 0.24162153899669647:   7%|█▊                        | 72/1000 [00:05<01:15, 12.27it/s]\u001b[A\u001b[A\n",
      "\n",
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      "\n",
      "\n",
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      "\n",
      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
      "\n",
      "Current loss: 0.253579705953598:   8%|██▏                         | 76/1000 [00:05<01:15, 12.20it/s]\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "Current loss: 0.24885857105255127:   8%|██                        | 80/1000 [00:06<01:15, 12.20it/s]\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Current loss: 0.24647608399391174:   8%|██▏                       | 84/1000 [00:06<01:13, 12.54it/s]\u001b[A\u001b[A\u001b[A\n",
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      "\n",
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      "\n",
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      "\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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      "\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Current loss: 0.20289146900177002:  13%|███▏                     | 129/1000 [00:10<01:07, 12.90it/s]\u001b[A\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Current loss: 0.19482505321502686:  15%|███▋                     | 147/1000 [00:11<00:54, 15.55it/s]\u001b[A\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
      "\n",
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      "\n",
      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Current loss: 0.1834956407546997:  16%|████▎                     | 164/1000 [00:12<01:03, 13.14it/s]\u001b[A\u001b[A\n",
      "\n",
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      "\n",
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      "\n",
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      "\n",
      "\n",
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      "\n",
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      "\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Current loss: 0.19145677983760834:  18%|████▌                    | 181/1000 [00:13<00:59, 13.86it/s]\u001b[A\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Current loss: 0.18743711709976196:  20%|████▉                    | 199/1000 [00:15<01:04, 12.43it/s]\u001b[A\n",
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      "\n",
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      "\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Current loss: 0.18320518732070923:  22%|█████▍                   | 217/1000 [00:16<01:00, 12.97it/s]\u001b[A\n",
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      "\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Current loss: 0.17257604002952576:  23%|█████▊                   | 232/1000 [00:17<01:02, 12.27it/s]\u001b[A\u001b[A\u001b[A\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
      "\n",
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      "\n",
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      "\n",
      "\n",
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      "\n",
      "\n",
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      "\n",
      "\n",
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      "\n",
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      "\n",
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      "\n",
      "\n",
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      "\n",
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      "\n",
      "\n",
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      "\n",
      "\n",
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      "\n",
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      "\n",
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      "\n",
      "\n",
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      "\n",
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      "\n",
      "\n",
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      "\n",
      "\n",
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      "\n",
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      "\n",
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      "\n",
      "\n",
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      "\n",
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      "\n",
      "\n",
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      "\n",
      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
      "\n",
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      "\n",
      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Current loss: 0.16709083318710327:  25%|██████▏                  | 248/1000 [00:19<00:54, 13.81it/s]\u001b[A\u001b[A\n",
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      "\n",
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      "\n",
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      "\n",
      "\n",
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      "\n",
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      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Current loss: 0.16482999920845032:  26%|██████▌                  | 264/1000 [00:20<00:59, 12.45it/s]\u001b[A\u001b[A\n",
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      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Current loss: 0.16308216750621796:  28%|███████                  | 282/1000 [00:21<00:54, 13.12it/s]\u001b[A\u001b[A\n",
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      "\n",
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      "\n",
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      "\n",
      "\n",
      "Current loss: 0.1627226173877716:  29%|███████▍                  | 288/1000 [00:22<00:52, 13.51it/s]\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "Current loss: 0.16147959232330322:  29%|███████▏                 | 289/1000 [00:22<00:48, 14.68it/s]\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "Current loss: 0.16128233075141907:  29%|███████▎                 | 291/1000 [00:22<00:47, 15.05it/s]\u001b[A\u001b[A\n",
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      "\n",
      "\n",
      "Current loss: 0.16136392951011658:  29%|███████▎                 | 291/1000 [00:22<00:47, 15.05it/s]\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "Current loss: 0.1619318276643753:  29%|███████▍                  | 288/1000 [00:22<00:52, 13.58it/s]\u001b[A\u001b[A\n",
      "\n",
      "Current loss: 0.1619318276643753:  29%|███████▌                  | 290/1000 [00:22<00:49, 14.34it/s]\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "Current loss: 0.16236647963523865:  29%|███████▏                 | 290/1000 [00:22<00:52, 13.52it/s]\u001b[A\u001b[A\u001b[A\n",
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      "\n",
      "\n",
      "Current loss: 0.16236647963523865:  29%|███████▎                 | 292/1000 [00:22<00:49, 14.40it/s]\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "Current loss: 0.16154789924621582:  29%|███████▏                 | 290/1000 [00:22<00:49, 14.34it/s]\u001b[A\u001b[A\n",
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      "\n",
      "\n",
      "Current loss: 0.16118812561035156:  29%|███████▎                 | 293/1000 [00:22<00:45, 15.38it/s]\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "Current loss: 0.16206619143486023:  29%|███████▎                 | 292/1000 [00:22<00:49, 14.40it/s]\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "Current loss: 0.16206619143486023:  29%|███████▎                 | 294/1000 [00:22<00:46, 15.24it/s]\u001b[A\u001b[A\u001b[A\n",
      "\n",
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      "\n",
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      "\n",
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      "\n",
      "\n",
      "Current loss: 0.16095495223999023:  30%|███████▍                 | 295/1000 [00:22<00:43, 16.12it/s]\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "Current loss: 0.1617620587348938:  29%|███████▋                  | 294/1000 [00:22<00:46, 15.24it/s]\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "Current loss: 0.1617620587348938:  30%|███████▋                  | 296/1000 [00:22<00:44, 15.94it/s]\u001b[A\u001b[A\u001b[A\n",
      "\n",
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      "\n",
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      "\n",
      "\n",
      "Current loss: 0.1614447832107544:  30%|███████▋                  | 296/1000 [00:22<00:44, 15.94it/s]\u001b[A\u001b[A\u001b[A\n",
      "\n",
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      "\n",
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      "\n",
      "\n",
      "Current loss: 0.16145482659339905:  30%|███████▍                 | 296/1000 [00:22<00:44, 15.94it/s]\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "Current loss: 0.16145482659339905:  30%|███████▍                 | 298/1000 [00:22<00:43, 16.15it/s]\u001b[A\u001b[A\u001b[A\n",
      "\n",
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      "\n",
      "\n",
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      "\n",
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      "\n",
      "\n",
      "Current loss: 0.1611461043357849:  30%|███████▋                  | 298/1000 [00:22<00:43, 16.15it/s]\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "Current loss: 0.1611461043357849:  30%|███████▊                  | 300/1000 [00:22<00:43, 15.97it/s]\u001b[A\u001b[A\u001b[A\n",
      "\n",
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      "\n",
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      "\n",
      "\n",
      "Current loss: 0.16102725267410278:  30%|███████▌                 | 300/1000 [00:22<00:43, 15.97it/s]\u001b[A\u001b[A\u001b[A\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Current loss: 0.17086677253246307:  30%|███████▍                 | 299/1000 [00:22<00:44, 15.73it/s]\u001b[A\n",
      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
      "\n",
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      "\n",
      "\n",
      "Current loss: 0.15944363176822662:  31%|███████▊                 | 314/1000 [00:23<00:46, 14.66it/s]\u001b[A\u001b[A\u001b[A\n",
      "\n",
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      "\n",
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      "\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Current loss: 0.1592254936695099:  32%|████████▎                 | 318/1000 [00:23<00:46, 14.61it/s]\u001b[A\u001b[A\u001b[A\n",
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      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Current loss: 0.15741129219532013:  33%|████████▎                | 330/1000 [00:25<00:52, 12.73it/s]\u001b[A\u001b[A\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Current loss: 0.15516138076782227:  35%|████████▊                | 351/1000 [00:26<00:49, 13.02it/s]\u001b[A\n",
      "\n",
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      "\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Current loss: 0.16428345441818237:  36%|█████████▏               | 365/1000 [00:27<00:43, 14.61it/s]\u001b[A\n",
      "\n",
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      "\n",
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      "\n",
      "Current loss: 0.15433116257190704:  37%|█████████▏               | 366/1000 [00:27<00:42, 14.84it/s]\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "Current loss: 0.15369360148906708:  37%|█████████▏               | 368/1000 [00:27<00:42, 14.88it/s]\u001b[A\u001b[A\u001b[A\n",
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      "\n",
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      "\n",
      "\n",
      "Current loss: 0.15378451347351074:  37%|█████████▎               | 371/1000 [00:27<00:43, 14.42it/s]\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "Current loss: 0.15423306822776794:  37%|█████████▎               | 370/1000 [00:27<00:44, 14.24it/s]\u001b[A\u001b[A\u001b[A\n",
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      "\n",
      "\n",
      "Current loss: 0.15347468852996826:  37%|█████████▎               | 370/1000 [00:27<00:44, 14.24it/s]\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "Current loss: 0.15416188538074493:  37%|█████████▏               | 366/1000 [00:27<00:42, 14.84it/s]\u001b[A\u001b[A\n",
      "\n",
      "Current loss: 0.15416188538074493:  37%|█████████▏               | 368/1000 [00:27<00:44, 14.29it/s]\u001b[A\u001b[A\n",
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      "\n",
      "\n",
      "Current loss: 0.15401998162269592:  37%|█████████▎               | 370/1000 [00:27<00:44, 14.24it/s]\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "Current loss: 0.15401998162269592:  37%|█████████▎               | 372/1000 [00:27<00:43, 14.49it/s]\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "Current loss: 0.15371382236480713:  37%|█████████▏               | 368/1000 [00:27<00:44, 14.29it/s]\u001b[A\u001b[A\n",
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      "\n",
      "\n",
      "Current loss: 0.1533031165599823:  37%|█████████▋                | 372/1000 [00:28<00:43, 14.49it/s]\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "Current loss: 0.1539943516254425:  37%|█████████▌                | 368/1000 [00:28<00:44, 14.29it/s]\u001b[A\u001b[A\n",
      "\n",
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      "\n",
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      "\n",
      "\n",
      "Current loss: 0.15377914905548096:  37%|█████████▎               | 372/1000 [00:28<00:43, 14.49it/s]\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "Current loss: 0.15377914905548096:  37%|█████████▎               | 374/1000 [00:28<00:42, 14.81it/s]\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "Current loss: 0.15385887026786804:  37%|█████████▎               | 370/1000 [00:28<00:42, 14.76it/s]\u001b[A\u001b[A\n",
      "\n",
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      "\n",
      "\n",
      "Current loss: 0.153230220079422:  38%|██████████▏                | 375/1000 [00:28<00:41, 15.15it/s]\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "Current loss: 0.15344752371311188:  37%|█████████▎               | 372/1000 [00:28<00:40, 15.39it/s]\u001b[A\u001b[A\n",
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      "\n",
      "\n",
      "Current loss: 0.15352261066436768:  37%|█████████▎               | 374/1000 [00:28<00:42, 14.81it/s]\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "Current loss: 0.15352261066436768:  38%|█████████▍               | 376/1000 [00:28<00:41, 15.07it/s]\u001b[A\u001b[A\u001b[A\n",
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      "\n",
      "\n",
      "Current loss: 0.15297004580497742:  38%|█████████▍               | 376/1000 [00:28<00:41, 15.07it/s]\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "Current loss: 0.15370561182498932:  37%|█████████▎               | 372/1000 [00:28<00:40, 15.39it/s]\u001b[A\u001b[A\n",
      "\n",
      "Current loss: 0.15370561182498932:  37%|█████████▎               | 374/1000 [00:28<00:41, 15.16it/s]\u001b[A\u001b[A\n",
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      "\n",
      "\n",
      "Current loss: 0.15326112508773804:  38%|█████████▍               | 376/1000 [00:28<00:41, 15.07it/s]\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "Current loss: 0.15326112508773804:  38%|█████████▍               | 378/1000 [00:28<00:39, 15.57it/s]\u001b[A\u001b[A\u001b[A\n",
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      "\n",
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      "\n",
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      "\n",
      "Current loss: 0.1535107046365738:  38%|█████████▊                | 376/1000 [00:28<00:40, 15.39it/s]\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "Current loss: 0.152780219912529:  38%|██████████▏                | 378/1000 [00:28<00:39, 15.57it/s]\u001b[A\u001b[A\u001b[A\n",
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      "\n",
      "\n",
      "Current loss: 0.15298593044281006:  38%|█████████▍               | 378/1000 [00:28<00:39, 15.57it/s]\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "Current loss: 0.15291838347911835:  38%|█████████▌               | 381/1000 [00:28<00:39, 15.68it/s]\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "Current loss: 0.1530805081129074:  38%|█████████▊                | 376/1000 [00:28<00:40, 15.39it/s]\u001b[A\u001b[A\n",
      "\n",
      "Current loss: 0.15330860018730164:  38%|█████████▍               | 376/1000 [00:28<00:40, 15.39it/s]\u001b[A\u001b[A\n",
      "\n",
      "Current loss: 0.15330860018730164:  38%|█████████▍               | 378/1000 [00:28<00:39, 15.73it/s]\u001b[A\u001b[A\n",
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      "\n",
      "\n",
      "Current loss: 0.15281331539154053:  38%|█████████▌               | 381/1000 [00:28<00:39, 15.68it/s]\u001b[A\u001b[A\u001b[A\n",
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      "\n",
      "\n",
      "Current loss: 0.15283799171447754:  38%|█████████▌               | 383/1000 [00:28<00:40, 15.32it/s]\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "Current loss: 0.1527518332004547:  38%|█████████▉                | 382/1000 [00:28<00:40, 15.38it/s]\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "Current loss: 0.15270096063613892:  38%|█████████▌               | 383/1000 [00:28<00:40, 15.32it/s]\u001b[A\u001b[A\n",
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      "\n",
      "Current loss: 0.1531372219324112:  38%|█████████▊                | 378/1000 [00:28<00:39, 15.73it/s]\u001b[A\u001b[A\n",
      "\n",
      "Current loss: 0.1531372219324112:  38%|█████████▉                | 380/1000 [00:28<00:41, 15.08it/s]\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "Current loss: 0.15242472290992737:  38%|█████████▌               | 382/1000 [00:28<00:40, 15.38it/s]\u001b[A\u001b[A\u001b[A\n",
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      "\n",
      "\n",
      "Current loss: 0.15276341140270233:  38%|█████████▌               | 383/1000 [00:28<00:40, 15.32it/s]\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "Current loss: 0.15276341140270233:  38%|█████████▋               | 385/1000 [00:28<00:41, 14.99it/s]\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "Current loss: 0.1525961458683014:  38%|██████████                | 385/1000 [00:28<00:41, 14.99it/s]\u001b[A\u001b[A\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Current loss: 0.16280627250671387:  38%|█████████▌               | 383/1000 [00:28<00:40, 15.09it/s]\u001b[A\n",
      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Current loss: 0.15150229632854462:  40%|██████████               | 401/1000 [00:29<00:38, 15.53it/s]\u001b[A\u001b[A\u001b[A\n",
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      "\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Current loss: 0.14973127841949463:  42%|██████████▍              | 416/1000 [00:30<00:39, 14.86it/s]\u001b[A\u001b[A\u001b[A\n",
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      "\n",
      "\n",
      "Current loss: 0.14953196048736572:  42%|██████████▍              | 418/1000 [00:31<00:42, 13.85it/s]\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "Current loss: 0.14953196048736572:  42%|██████████▌              | 420/1000 [00:31<00:43, 13.26it/s]\u001b[A\u001b[A\u001b[A\n",
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      "\n",
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      "\n",
      "Current loss: 0.1507364809513092:  42%|██████████▊               | 418/1000 [00:31<00:43, 13.40it/s]\u001b[A\u001b[A\n",
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      "\n",
      "\n",
      "Current loss: 0.14974626898765564:  42%|██████████▌              | 420/1000 [00:31<00:43, 13.26it/s]\u001b[A\u001b[A\u001b[A\n",
      "Current loss: 0.1602133959531784:  42%|██████████▉               | 421/1000 [00:31<00:39, 14.72it/s]\u001b[A\n",
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      "\n",
      "Current loss: 0.14992119371891022:  42%|██████████▌              | 423/1000 [00:31<00:42, 13.51it/s]\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "Current loss: 0.14992119371891022:  42%|██████████▋              | 425/1000 [00:31<00:42, 13.65it/s]\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "Current loss: 0.14935436844825745:  42%|██████████▌              | 422/1000 [00:31<00:43, 13.21it/s]\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "Current loss: 0.15058884024620056:  42%|██████████▍              | 418/1000 [00:31<00:43, 13.40it/s]\u001b[A\u001b[A\n",
      "\n",
      "Current loss: 0.15058884024620056:  42%|██████████▌              | 420/1000 [00:31<00:43, 13.49it/s]\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "Current loss: 0.14961598813533783:  42%|██████████▌              | 422/1000 [00:31<00:43, 13.21it/s]\u001b[A\u001b[A\u001b[A\n",
      "Current loss: 0.15001028776168823:  42%|██████████▋              | 425/1000 [00:31<00:42, 13.65it/s]\u001b[A\n",
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      "\n",
      "\n",
      "Current loss: 0.14917761087417603:  42%|██████████▌              | 422/1000 [00:31<00:43, 13.21it/s]\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "Current loss: 0.14917761087417603:  42%|██████████▌              | 424/1000 [00:31<00:42, 13.49it/s]\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "Current loss: 0.14995735883712769:  43%|██████████▋              | 427/1000 [00:31<00:42, 13.53it/s]\u001b[A\u001b[A\n",
      "\n",
      "Current loss: 0.15044750273227692:  42%|██████████▌              | 420/1000 [00:31<00:43, 13.49it/s]\u001b[A\u001b[A\n",
      "\n",
      "Current loss: 0.15044750273227692:  42%|██████████▌              | 422/1000 [00:31<00:42, 13.49it/s]\u001b[A\u001b[A\n",
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      "\n",
      "\n",
      "Current loss: 0.14986693859100342:  43%|██████████▋              | 427/1000 [00:31<00:42, 13.53it/s]\u001b[A\u001b[A\u001b[A\n",
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      "\n",
      "Current loss: 0.14994663000106812:  43%|██████████▋              | 427/1000 [00:31<00:42, 13.53it/s]\u001b[A\u001b[A\n",
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      "\n",
      "\n",
      "Current loss: 0.14901483058929443:  42%|██████████▌              | 424/1000 [00:31<00:42, 13.49it/s]\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "Current loss: 0.14901483058929443:  43%|██████████▋              | 426/1000 [00:31<00:43, 13.16it/s]\u001b[A\u001b[A\u001b[A\n",
      "Current loss: 0.1496732234954834:  43%|███████████▏              | 429/1000 [00:31<00:41, 13.60it/s]\u001b[A\n",
      "Current loss: 0.15979497134685516:  43%|██████████▋              | 427/1000 [00:31<00:40, 14.19it/s]\u001b[A\n",
      "\n",
      "\n",
      "Current loss: 0.14938423037528992:  43%|██████████▋              | 426/1000 [00:31<00:43, 13.16it/s]\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "Current loss: 0.15030014514923096:  42%|██████████▌              | 422/1000 [00:31<00:42, 13.49it/s]\u001b[A\u001b[A\n",
      "\n",
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      "\n",
      "\n",
      "Current loss: 0.14887157082557678:  43%|██████████▋              | 426/1000 [00:31<00:43, 13.16it/s]\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "Current loss: 0.15012910962104797:  42%|██████████▌              | 424/1000 [00:31<00:43, 13.21it/s]\u001b[A\u001b[A\n",
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      "\n",
      "\n",
      "Current loss: 0.14887157082557678:  43%|██████████▋              | 428/1000 [00:31<00:42, 13.52it/s]\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "Current loss: 0.14934007823467255:  43%|██████████▋              | 428/1000 [00:31<00:42, 13.52it/s]\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "Current loss: 0.1501656472682953:  42%|███████████               | 424/1000 [00:31<00:43, 13.21it/s]\u001b[A\u001b[A\n",
      "\n",
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      "\n",
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      "\n",
      "\n",
      "Current loss: 0.14873474836349487:  43%|██████████▋              | 428/1000 [00:31<00:42, 13.52it/s]\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "Current loss: 0.149750754237175:  43%|███████████▋               | 433/1000 [00:31<00:42, 13.34it/s]\u001b[A\u001b[A\u001b[A\n",
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      "\n",
      "\n",
      "Current loss: 0.14921265840530396:  43%|██████████▊              | 430/1000 [00:31<00:42, 13.39it/s]\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "Current loss: 0.1492653787136078:  43%|███████████▎              | 433/1000 [00:31<00:42, 13.34it/s]\u001b[A\u001b[A\n",
      "\n",
      "Current loss: 0.15003445744514465:  43%|██████████▋              | 428/1000 [00:31<00:42, 13.58it/s]\u001b[A\u001b[A\n",
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      "\n",
      "\n",
      "Current loss: 0.1486029326915741:  43%|███████████▏              | 430/1000 [00:32<00:42, 13.39it/s]\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "Current loss: 0.14960327744483948:  43%|██████████▊              | 433/1000 [00:32<00:42, 13.34it/s]\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "Current loss: 0.14960327744483948:  44%|██████████▉              | 435/1000 [00:32<00:41, 13.69it/s]\u001b[A\u001b[A\n",
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      "\n",
      "\n",
      "Current loss: 0.1490807980298996:  43%|███████████▏              | 432/1000 [00:32<00:41, 13.76it/s]\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "Current loss: 0.1490660011768341:  44%|███████████▎              | 435/1000 [00:32<00:41, 13.69it/s]\u001b[A\u001b[A\n",
      "\n",
      "Current loss: 0.14943918585777283:  44%|██████████▉              | 437/1000 [00:32<00:38, 14.71it/s]\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "Current loss: 0.14843760430812836:  43%|██████████▊              | 432/1000 [00:32<00:41, 13.76it/s]\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "Current loss: 0.14843760430812836:  43%|██████████▊              | 434/1000 [00:32<00:39, 14.48it/s]\u001b[A\u001b[A\u001b[A\n",
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      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Current loss: 0.14887577295303345:  44%|██████████▉              | 437/1000 [00:32<00:38, 14.71it/s]\u001b[A\u001b[A\n",
      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
      "\n",
      "Current loss: 0.14761260151863098:  44%|███████████              | 444/1000 [00:32<00:38, 14.50it/s]\u001b[A\u001b[A\u001b[A\n",
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      "\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Current loss: 0.1477941870689392:  45%|███████████▋              | 450/1000 [00:33<00:40, 13.53it/s]\u001b[A\u001b[A\u001b[A\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Current loss: 0.15780873596668243:  47%|███████████▋             | 467/1000 [00:34<00:39, 13.33it/s]\u001b[A\n",
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      "\n",
      "\n",
      "Current loss: 0.14709222316741943:  48%|███████████▉             | 475/1000 [00:34<00:40, 12.92it/s]\u001b[A\u001b[A\u001b[A\n",
      "\n",
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      "Current loss: 0.1462281346321106:  47%|████████████▏             | 470/1000 [00:34<00:41, 12.79it/s]\u001b[A\u001b[A\u001b[A\n",
      "\n",
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      "Current loss: 0.1462281346321106:  47%|████████████▎             | 472/1000 [00:34<00:41, 12.78it/s]\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "Current loss: 0.14777132868766785:  47%|███████████▋             | 468/1000 [00:34<00:40, 13.24it/s]\u001b[A\u001b[A\n",
      "\n",
      "Current loss: 0.14777132868766785:  47%|███████████▊             | 470/1000 [00:34<00:41, 12.64it/s]\u001b[A\u001b[A\n",
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      "\n",
      "Current loss: 0.14756333827972412:  47%|███████████▊             | 470/1000 [00:35<00:41, 12.64it/s]\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "Current loss: 0.1469559669494629:  48%|████████████▍             | 477/1000 [00:35<00:40, 12.98it/s]\u001b[A\u001b[A\u001b[A\n",
      "\n",
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      "\n",
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      "\n",
      "\n",
      "Current loss: 0.14618107676506042:  47%|███████████▊             | 472/1000 [00:35<00:41, 12.78it/s]\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "Current loss: 0.14618107676506042:  47%|███████████▊             | 474/1000 [00:35<00:41, 12.65it/s]\u001b[A\u001b[A\u001b[A\n",
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      "\n",
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      "\n",
      "\n",
      "Current loss: 0.14677056670188904:  48%|███████████▉             | 479/1000 [00:35<00:39, 13.35it/s]\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "Current loss: 0.14749085903167725:  47%|███████████▊             | 472/1000 [00:35<00:40, 12.89it/s]\u001b[A\u001b[A\n",
      "\n",
      "Current loss: 0.14749085903167725:  47%|███████████▊             | 474/1000 [00:35<00:39, 13.46it/s]\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "Current loss: 0.14615386724472046:  47%|███████████▊             | 474/1000 [00:35<00:41, 12.65it/s]\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "Current loss: 0.14615386724472046:  48%|███████████▉             | 476/1000 [00:35<00:39, 13.25it/s]\u001b[A\u001b[A\u001b[A\n",
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      "\n",
      "\n",
      "Current loss: 0.14590878784656525:  48%|███████████▉             | 476/1000 [00:35<00:39, 13.25it/s]\u001b[A\u001b[A\u001b[A\n",
      "\n",
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      "\n",
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      "\n",
      "\n",
      "Current loss: 0.14613521099090576:  48%|███████████▉             | 478/1000 [00:35<00:37, 13.75it/s]\u001b[A\u001b[A\u001b[A\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
      "\n",
      "Current loss: 0.14669395983219147:  48%|████████████▏            | 485/1000 [00:35<00:33, 15.47it/s]\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "Current loss: 0.14611242711544037:  48%|████████████             | 480/1000 [00:35<00:37, 13.77it/s]\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "Current loss: 0.14730535447597504:  48%|███████████▉             | 476/1000 [00:35<00:39, 13.41it/s]\u001b[A\u001b[A\n",
      "\n",
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      "\n",
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      "\n",
      "\n",
      "Current loss: 0.1466153860092163:  49%|████████████▋             | 487/1000 [00:35<00:32, 15.75it/s]\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "Current loss: 0.14606019854545593:  48%|████████████             | 480/1000 [00:35<00:37, 13.77it/s]\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "Current loss: 0.14724847674369812:  48%|███████████▉             | 478/1000 [00:35<00:38, 13.64it/s]\u001b[A\u001b[A\n",
      "\n",
      "Current loss: 0.14724847674369812:  48%|████████████             | 480/1000 [00:35<00:36, 14.32it/s]\u001b[A\u001b[A\n",
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      "\n",
      "\n",
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      "\n",
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      "\n",
      "\n",
      "Current loss: 0.14556601643562317:  48%|████████████             | 482/1000 [00:35<00:36, 14.30it/s]\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "Current loss: 0.1465621292591095:  49%|████████████▋             | 487/1000 [00:35<00:32, 15.75it/s]\u001b[A\u001b[A\n",
      "\n",
      "Current loss: 0.1465621292591095:  49%|████████████▋             | 489/1000 [00:35<00:32, 15.70it/s]\u001b[A\u001b[A\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Current loss: 0.1570540964603424:  48%|████████████▌             | 483/1000 [00:35<00:35, 14.45it/s]\u001b[A\n",
      "\n",
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      "\n",
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      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Current loss: 0.1462438404560089:  50%|████████████▉             | 498/1000 [00:36<00:31, 16.19it/s]\u001b[A\u001b[A\n",
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      "\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Current loss: 0.14405211806297302:  52%|████████████▉            | 516/1000 [00:37<00:30, 16.04it/s]\u001b[A\u001b[A\u001b[A\n",
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      "\n",
      "Current loss: 0.14515815675258636:  52%|█████████████            | 520/1000 [00:38<00:30, 15.62it/s]\u001b[A\u001b[A\n",
      "\n",
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      "Current loss: 0.14433163404464722:  53%|█████████████▏           | 527/1000 [00:38<00:30, 15.58it/s]\u001b[A\u001b[A\u001b[A\n",
      "\n",
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      "\n",
      "\n",
      "Current loss: 0.1438412219285965:  52%|█████████████▌            | 522/1000 [00:38<00:29, 15.97it/s]\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "Current loss: 0.14468182623386383:  53%|█████████████▏           | 529/1000 [00:38<00:30, 15.47it/s]\u001b[A\u001b[A\u001b[A\n",
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      "\n",
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      "\n",
      "Current loss: 0.1442156732082367:  53%|█████████████▊            | 529/1000 [00:38<00:30, 15.47it/s]\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "Current loss: 0.14358079433441162:  52%|█████████████            | 524/1000 [00:38<00:30, 15.56it/s]\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "Current loss: 0.1449642777442932:  52%|█████████████▌            | 522/1000 [00:38<00:31, 15.12it/s]\u001b[A\u001b[A\n",
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      "\n",
      "\n",
      "Current loss: 0.14387255907058716:  52%|█████████████            | 524/1000 [00:38<00:30, 15.56it/s]\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "Current loss: 0.14387255907058716:  53%|█████████████▏           | 526/1000 [00:38<00:31, 15.15it/s]\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "Current loss: 0.14487774670124054:  52%|█████████████            | 522/1000 [00:38<00:31, 15.12it/s]\u001b[A\u001b[A\n",
      "\n",
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      "\n",
      "\n",
      "Current loss: 0.14344725012779236:  53%|█████████████▏           | 526/1000 [00:38<00:31, 15.15it/s]\u001b[A\u001b[A\u001b[A\n",
      "\n",
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      "\n",
      "\n",
      "Current loss: 0.14384782314300537:  53%|█████████████▏           | 526/1000 [00:38<00:31, 15.15it/s]\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "Current loss: 0.14384782314300537:  53%|█████████████▏           | 528/1000 [00:38<00:30, 15.39it/s]\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "Current loss: 0.14476007223129272:  52%|█████████████            | 524/1000 [00:38<00:31, 15.05it/s]\u001b[A\u001b[A\n",
      "\n",
      "Current loss: 0.14476007223129272:  53%|█████████████▏           | 526/1000 [00:38<00:31, 15.27it/s]\u001b[A\u001b[A\n",
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      "\n",
      "\n",
      "Current loss: 0.14332208037376404:  53%|█████████████▏           | 528/1000 [00:38<00:30, 15.39it/s]\u001b[A\u001b[A\u001b[A\n",
      "\n",
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      "\n",
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      "\n",
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      "Current loss: 0.1437835842370987:  53%|█████████████▊            | 530/1000 [00:38<00:31, 15.13it/s]\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "Current loss: 0.14468058943748474:  53%|█████████████▏           | 526/1000 [00:38<00:31, 15.27it/s]\u001b[A\u001b[A\n",
      "\n",
      "Current loss: 0.14468058943748474:  53%|█████████████▏           | 528/1000 [00:38<00:31, 15.11it/s]\u001b[A\u001b[A\n",
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      "\n",
      "\n",
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      "\n",
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      "\n",
      "\n",
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      "\n",
      "Current loss: 0.14462187886238098:  53%|█████████████▏           | 528/1000 [00:38<00:31, 15.11it/s]\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "Current loss: 0.14374595880508423:  53%|█████████████▎           | 532/1000 [00:38<00:31, 14.78it/s]\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "Current loss: 0.14462187886238098:  53%|█████████████▎           | 530/1000 [00:38<00:31, 14.89it/s]\u001b[A\u001b[A\n",
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      "\n",
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      "\n",
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      "Current loss: 0.14313173294067383:  53%|█████████████▎           | 532/1000 [00:38<00:31, 14.78it/s]\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "Current loss: 0.1445671021938324:  53%|█████████████▊            | 530/1000 [00:38<00:31, 14.89it/s]\u001b[A\u001b[A\n",
      "\n",
      "Current loss: 0.14393958449363708:  54%|█████████████▍           | 539/1000 [00:38<00:29, 15.76it/s]\u001b[A\u001b[A\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Current loss: 0.15472137928009033:  53%|█████████████▎           | 533/1000 [00:38<00:31, 14.90it/s]\u001b[A\n",
      "\n",
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      "\n",
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      "\n",
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      "\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Current loss: 0.15411579608917236:  55%|█████████████▋           | 549/1000 [00:40<00:28, 16.09it/s]\u001b[A\n",
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      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Current loss: 0.14274844527244568:  57%|██████████████▎          | 573/1000 [00:41<00:28, 15.15it/s]\u001b[A\u001b[A\n",
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      "\n",
      "Current loss: 0.14290927350521088:  57%|██████████████▏          | 570/1000 [00:41<00:27, 15.56it/s]\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "Current loss: 0.14190015196800232:  58%|██████████████▍          | 579/1000 [00:41<00:26, 15.70it/s]\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "Current loss: 0.14158205687999725:  57%|██████████████▎          | 572/1000 [00:41<00:27, 15.59it/s]\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "Current loss: 0.14158205687999725:  57%|██████████████▎          | 574/1000 [00:41<00:26, 15.86it/s]\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "Current loss: 0.14291232824325562:  57%|██████████████▏          | 570/1000 [00:41<00:27, 15.56it/s]\u001b[A\u001b[A\n",
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      "\n",
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      "\n",
      "\n",
      "Current loss: 0.14172989130020142:  57%|██████████████▎          | 574/1000 [00:41<00:26, 15.86it/s]\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "Current loss: 0.14291250705718994:  57%|██████████████▎          | 572/1000 [00:41<00:27, 15.82it/s]\u001b[A\u001b[A\n",
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      "\n",
      "\n",
      "Current loss: 0.14153918623924255:  57%|██████████████▎          | 574/1000 [00:41<00:26, 15.86it/s]\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "Current loss: 0.14153918623924255:  58%|██████████████▍          | 576/1000 [00:41<00:26, 15.94it/s]\u001b[A\u001b[A\u001b[A\n",
      "\n",
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      "\n",
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      "\n",
      "\n",
      "Current loss: 0.141658216714859:  58%|███████████████▌           | 576/1000 [00:41<00:26, 15.94it/s]\u001b[A\u001b[A\u001b[A\n",
      "\n",
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      "\n",
      "\n",
      "Current loss: 0.14151626825332642:  58%|██████████████▍          | 576/1000 [00:41<00:26, 15.94it/s]\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "Current loss: 0.14151626825332642:  58%|██████████████▍          | 578/1000 [00:41<00:26, 15.97it/s]\u001b[A\u001b[A\u001b[A\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
      "\n",
      "Current loss: 0.14157205820083618:  58%|██████████████▍          | 578/1000 [00:41<00:26, 15.97it/s]\u001b[A\u001b[A\u001b[A\n",
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      "\n",
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      "\n",
      "\n",
      "Current loss: 0.14147932827472687:  58%|██████████████▍          | 578/1000 [00:41<00:26, 15.97it/s]\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "Current loss: 0.14147932827472687:  58%|██████████████▍          | 580/1000 [00:41<00:27, 15.30it/s]\u001b[A\u001b[A\u001b[A\n",
      "\n",
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      "\n",
      "\n",
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      "\n",
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      "\n",
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      "\n",
      "\n",
      "Current loss: 0.1414298117160797:  58%|███████████████           | 580/1000 [00:42<00:27, 15.30it/s]\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "Current loss: 0.14277753233909607:  58%|██████████████▍          | 580/1000 [00:42<00:27, 15.27it/s]\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "Current loss: 0.1414298117160797:  58%|███████████████▏          | 582/1000 [00:42<00:27, 14.98it/s]\u001b[A\u001b[A\u001b[A\n",
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      "\n",
      "\n",
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      "\n",
      "Current loss: 0.14280760288238525:  58%|██████████████▍          | 580/1000 [00:42<00:27, 15.27it/s]\u001b[A\u001b[A\n",
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      "\n",
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      "\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Current loss: 0.14137758314609528:  58%|██████████████▌          | 582/1000 [00:42<00:27, 14.98it/s]\u001b[A\u001b[A\u001b[A\n",
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      "\n",
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      "\n",
      "\n",
      "Current loss: 0.14082291722297668:  60%|██████████████▉          | 596/1000 [00:43<00:27, 14.74it/s]\u001b[A\u001b[A\u001b[A\n",
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      "\n",
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      "\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Current loss: 0.15195387601852417:  60%|██████████████▉          | 599/1000 [00:43<00:28, 14.21it/s]\u001b[A\n",
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      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Current loss: 0.14047466218471527:  62%|███████████████▌         | 623/1000 [00:44<00:24, 15.33it/s]\u001b[A\u001b[A\n",
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      "\n",
      "Current loss: 0.14000225067138672:  63%|███████████████▋         | 629/1000 [00:44<00:24, 15.16it/s]\u001b[A\u001b[A\n",
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      "\n",
      "\n",
      "Current loss: 0.14014247059822083:  62%|███████████████▌         | 622/1000 [00:44<00:26, 14.25it/s]\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "Current loss: 0.13982366025447845:  63%|███████████████▊         | 631/1000 [00:44<00:25, 14.70it/s]\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "Current loss: 0.13999229669570923:  62%|███████████████▌         | 622/1000 [00:44<00:26, 14.25it/s]\u001b[A\u001b[A\u001b[A\n",
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      "\n",
      "\n",
      "Current loss: 0.13999229669570923:  62%|███████████████▌         | 624/1000 [00:44<00:26, 14.19it/s]\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "Current loss: 0.1414187103509903:  62%|████████████████          | 620/1000 [00:44<00:26, 14.30it/s]\u001b[A\u001b[A\n",
      "\n",
      "Current loss: 0.1414187103509903:  62%|████████████████▏         | 622/1000 [00:44<00:27, 13.87it/s]\u001b[A\u001b[A\n",
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      "\n",
      "\n",
      "Current loss: 0.13987642526626587:  63%|███████████████▊         | 631/1000 [00:45<00:25, 14.70it/s]\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "Current loss: 0.14137545228004456:  62%|███████████████▌         | 622/1000 [00:45<00:27, 13.87it/s]\u001b[A\u001b[A\n",
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      "\n",
      "\n",
      "Current loss: 0.13993403315544128:  62%|███████████████▌         | 624/1000 [00:45<00:26, 14.19it/s]\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "Current loss: 0.13978078961372375:  63%|███████████████▊         | 631/1000 [00:45<00:25, 14.70it/s]\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "Current loss: 0.13978078961372375:  63%|███████████████▊         | 633/1000 [00:45<00:25, 14.51it/s]\u001b[A\u001b[A\n",
      "\n",
      "Current loss: 0.14129909873008728:  62%|███████████████▌         | 624/1000 [00:45<00:26, 14.26it/s]\u001b[A\u001b[A\n",
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      "\n",
      "\n",
      "Current loss: 0.1397785246372223:  63%|████████████████▍         | 633/1000 [00:45<00:25, 14.51it/s]\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "Current loss: 0.14129355549812317:  62%|███████████████▌         | 624/1000 [00:45<00:26, 14.26it/s]\u001b[A\u001b[A\n",
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      "\n",
      "\n",
      "Current loss: 0.1398915946483612:  63%|████████████████▎         | 626/1000 [00:45<00:25, 14.43it/s]\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "Current loss: 0.1397550255060196:  64%|████████████████▌         | 635/1000 [00:45<00:24, 14.79it/s]\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "Current loss: 0.14119186997413635:  62%|███████████████▌         | 624/1000 [00:45<00:26, 14.26it/s]\u001b[A\u001b[A\n",
      "\n",
      "Current loss: 0.14119186997413635:  63%|███████████████▋         | 626/1000 [00:45<00:25, 14.56it/s]\u001b[A\u001b[A\n",
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      "\n",
      "\n",
      "Current loss: 0.13973112404346466:  64%|███████████████▉         | 635/1000 [00:45<00:24, 14.79it/s]\u001b[A\u001b[A\u001b[A\n",
      "\n",
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      "\n",
      "\n",
      "Current loss: 0.13985887169837952:  63%|███████████████▋         | 628/1000 [00:45<00:25, 14.83it/s]\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "Current loss: 0.13985887169837952:  63%|███████████████▊         | 630/1000 [00:45<00:24, 15.08it/s]\u001b[A\u001b[A\u001b[A\n",
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      "\n",
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      "\n",
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      "\n",
      "\n",
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      "\n",
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      "\n",
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      "\n",
      "Current loss: 0.14102864265441895:  63%|███████████████▊         | 630/1000 [00:45<00:24, 15.13it/s]\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "Current loss: 0.13979269564151764:  64%|███████████████▉         | 639/1000 [00:45<00:24, 14.84it/s]\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
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      "\n",
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      "\n",
      "\n",
      "Current loss: 0.13965880870819092:  64%|███████████████▉         | 639/1000 [00:45<00:24, 14.84it/s]\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "Current loss: 0.14095038175582886:  63%|███████████████▊         | 630/1000 [00:45<00:24, 15.13it/s]\u001b[A\u001b[A\n",
      "\n",
      "Current loss: 0.14095038175582886:  63%|███████████████▊         | 632/1000 [00:45<00:23, 15.50it/s]\u001b[A\u001b[A\n",
      "\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Current loss: 0.13976328074932098:  64%|███████████████▉         | 639/1000 [00:45<00:24, 14.84it/s]\u001b[A\u001b[A\u001b[A\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Current loss: 0.13863632082939148:  66%|████████████████▍        | 657/1000 [00:46<00:22, 15.52it/s]\u001b[A\u001b[A\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Current loss: 0.1380235105752945:  68%|█████████████████▌        | 675/1000 [00:47<00:19, 16.44it/s]\u001b[A\n",
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      "\n",
      "Current loss: 0.14011850953102112:  67%|████████████████▋        | 668/1000 [00:48<00:20, 16.03it/s]\u001b[A\u001b[A\n",
      "\n",
      "Current loss: 0.13786840438842773:  68%|████████████████▉        | 677/1000 [00:48<00:20, 16.01it/s]\u001b[A\u001b[A\n",
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      "\n",
      "\n",
      "Current loss: 0.1386040449142456:  67%|█████████████████▍        | 670/1000 [00:48<00:20, 16.34it/s]\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "Current loss: 0.1386040449142456:  67%|█████████████████▍        | 672/1000 [00:48<00:21, 15.48it/s]\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "Current loss: 0.13818085193634033:  68%|████████████████▉        | 679/1000 [00:48<00:20, 15.78it/s]\u001b[A\u001b[A\n",
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      "\n",
      "\n",
      "Current loss: 0.13882265985012054:  67%|████████████████▊        | 672/1000 [00:48<00:21, 15.48it/s]\u001b[A\u001b[A\u001b[A\n",
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      "\n",
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      "\n",
      "Current loss: 0.13784219324588776:  68%|█████████████████        | 681/1000 [00:48<00:20, 15.37it/s]\u001b[A\u001b[A\n",
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      "\n",
      "\n",
      "Current loss: 0.13854330778121948:  67%|████████████████▊        | 672/1000 [00:48<00:21, 15.48it/s]\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "Current loss: 0.13808465003967285:  68%|█████████████████        | 681/1000 [00:48<00:20, 15.37it/s]\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "Current loss: 0.1400092989206314:  67%|█████████████████▍        | 672/1000 [00:48<00:21, 15.52it/s]\u001b[A\u001b[A\n",
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      "\n",
      "\n",
      "Current loss: 0.1378077268600464:  68%|█████████████████▊        | 683/1000 [00:48<00:20, 15.44it/s]\u001b[A\u001b[A\u001b[A\n",
      "\n",
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      "\n",
      "Current loss: 0.13998731970787048:  67%|████████████████▊        | 674/1000 [00:48<00:21, 14.89it/s]\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "Current loss: 0.13849316537380219:  67%|████████████████▊        | 674/1000 [00:48<00:21, 15.13it/s]\u001b[A\u001b[A\u001b[A\n",
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      "\n",
      "\n",
      "Current loss: 0.1379646360874176:  68%|█████████████████▊        | 683/1000 [00:48<00:20, 15.44it/s]\u001b[A\u001b[A\u001b[A\n",
      "\n",
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      "\n",
      "\n",
      "Current loss: 0.1386571228504181:  68%|█████████████████▌        | 676/1000 [00:48<00:21, 14.85it/s]\u001b[A\u001b[A\u001b[A\n",
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      "\n",
      "\n",
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      "\n",
      "\n",
      "Current loss: 0.13848218321800232:  68%|████████████████▉        | 678/1000 [00:48<00:21, 15.24it/s]\u001b[A\u001b[A\u001b[A\n",
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      "\n",
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      "\n",
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      "\n",
      "\n",
      "Current loss: 0.13854995369911194:  68%|████████████████▉        | 678/1000 [00:48<00:21, 15.24it/s]\u001b[A\u001b[A\u001b[A\n",
      "\n",
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      "\n",
      "\n",
      "Current loss: 0.13852694630622864:  68%|████████████████▉        | 678/1000 [00:48<00:21, 15.24it/s]\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "Current loss: 0.13852694630622864:  68%|█████████████████        | 680/1000 [00:48<00:20, 15.42it/s]\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "Current loss: 0.13989973068237305:  68%|████████████████▉        | 676/1000 [00:48<00:21, 14.80it/s]\u001b[A\u001b[A\n",
      "\n",
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      "\n",
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      "\n",
      "\n",
      "Current loss: 0.1376500427722931:  69%|█████████████████▉        | 689/1000 [00:48<00:18, 16.67it/s]\u001b[A\u001b[A\u001b[A\n",
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      "\n",
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      "\n",
      "Current loss: 0.13765451312065125:  69%|█████████████████▏       | 689/1000 [00:48<00:18, 16.67it/s]\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "Current loss: 0.13857482373714447:  68%|█████████████████        | 680/1000 [00:48<00:20, 15.42it/s]\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "Current loss: 0.13857482373714447:  68%|█████████████████        | 682/1000 [00:48<00:21, 14.89it/s]\u001b[A\u001b[A\u001b[A\n",
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      "\n",
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      "\n",
      "\n",
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      "\n",
      "Current loss: 0.13757112622261047:  69%|█████████████████▎       | 691/1000 [00:48<00:19, 15.91it/s]\u001b[A\u001b[A\n",
      "\n",
      "Current loss: 0.13981309533119202:  68%|█████████████████        | 682/1000 [00:48<00:21, 14.77it/s]\u001b[A\u001b[A\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Current loss: 0.14907479286193848:  68%|█████████████████        | 683/1000 [00:48<00:21, 15.05it/s]\u001b[A\n",
      "\n",
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      "\n",
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      "\n",
      "\n",
      "Current loss: 0.1381015032529831:  69%|██████████████████        | 694/1000 [00:49<00:19, 15.95it/s]\u001b[A\u001b[A\u001b[A\n",
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      "\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Current loss: 0.14848703145980835:  70%|█████████████████▍       | 699/1000 [00:49<00:19, 15.77it/s]\u001b[A\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "Current loss: 0.1478825807571411:  72%|██████████████████▋       | 717/1000 [00:50<00:17, 15.78it/s]\u001b[A\n",
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      "Current loss: 0.13753420114517212:  72%|██████████████████       | 720/1000 [00:51<00:17, 15.76it/s]\u001b[A\u001b[A\u001b[A\n",
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      "\n",
      "\n",
      "Current loss: 0.13773047924041748:  72%|██████████████████       | 720/1000 [00:51<00:17, 15.76it/s]\u001b[A\u001b[A\u001b[A\n",
      "\n",
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      "\n",
      "Current loss: 0.13884198665618896:  72%|█████████████████▉       | 718/1000 [00:51<00:17, 16.39it/s]\u001b[A\u001b[A\n",
      "\n",
      "Current loss: 0.13574345409870148:  73%|██████████████████▏      | 729/1000 [00:51<00:16, 16.59it/s]\u001b[A\u001b[A\n",
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      "\n",
      "\n",
      "Current loss: 0.13749201595783234:  72%|██████████████████       | 720/1000 [00:51<00:17, 15.76it/s]\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "Current loss: 0.13749201595783234:  72%|██████████████████       | 722/1000 [00:51<00:17, 15.90it/s]\u001b[A\u001b[A\u001b[A\n",
      "\n",
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      "\n",
      "\n",
      "Current loss: 0.13762541115283966:  72%|██████████████████       | 722/1000 [00:51<00:17, 15.90it/s]\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "Current loss: 0.1387583166360855:  72%|██████████████████▋       | 720/1000 [00:51<00:17, 16.02it/s]\u001b[A\u001b[A\n",
      "\n",
      "Current loss: 0.1387583166360855:  72%|██████████████████▊       | 722/1000 [00:51<00:17, 16.09it/s]\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "Current loss: 0.13572511076927185:  73%|██████████████████▎      | 731/1000 [00:51<00:16, 16.22it/s]\u001b[A\u001b[A\u001b[A\n",
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      "\n",
      "\n",
      "Current loss: 0.13572511076927185:  73%|██████████████████▎      | 733/1000 [00:51<00:16, 16.12it/s]\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "Current loss: 0.13888847827911377:  72%|██████████████████       | 722/1000 [00:51<00:17, 16.09it/s]\u001b[A\u001b[A\n",
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      "\n",
      "\n",
      "Current loss: 0.13751864433288574:  72%|██████████████████       | 724/1000 [00:51<00:17, 16.16it/s]\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "Current loss: 0.1374027132987976:  72%|██████████████████▊       | 724/1000 [00:51<00:17, 16.16it/s]\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "Current loss: 0.1374027132987976:  73%|██████████████████▉       | 726/1000 [00:51<00:16, 16.30it/s]\u001b[A\u001b[A\u001b[A\n",
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      "\n",
      "Current loss: 0.13868597149848938:  72%|██████████████████       | 722/1000 [00:51<00:17, 16.09it/s]\u001b[A\u001b[A\n",
      "\n",
      "Current loss: 0.13868597149848938:  72%|██████████████████       | 724/1000 [00:51<00:17, 15.61it/s]\u001b[A\u001b[A\n",
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      "\n",
      "\n",
      "Current loss: 0.1374460607767105:  73%|██████████████████▉       | 726/1000 [00:51<00:16, 16.30it/s]\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "Current loss: 0.1388915777206421:  72%|██████████████████▊       | 724/1000 [00:51<00:17, 15.61it/s]\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "Current loss: 0.13745227456092834:  73%|██████████████████▏      | 726/1000 [00:51<00:16, 16.30it/s]\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "Current loss: 0.13745227456092834:  73%|██████████████████▏      | 728/1000 [00:51<00:16, 16.44it/s]\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "Current loss: 0.13582322001457214:  74%|██████████████████▍      | 735/1000 [00:51<00:16, 16.01it/s]\u001b[A\u001b[A\n",
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      "\n",
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      "\n",
      "\n",
      "Current loss: 0.1373695731163025:  73%|██████████████████▉       | 728/1000 [00:51<00:16, 16.44it/s]\u001b[A\u001b[A\u001b[A\n",
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      "\n",
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      "\n",
      "Current loss: 0.13860076665878296:  73%|██████████████████▏      | 726/1000 [00:51<00:16, 16.19it/s]\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "Current loss: 0.1375012993812561:  73%|██████████████████▉       | 728/1000 [00:51<00:16, 16.44it/s]\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "Current loss: 0.13860076665878296:  73%|██████████████████▏      | 728/1000 [00:51<00:16, 16.44it/s]\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "Current loss: 0.1375012993812561:  73%|██████████████████▉       | 730/1000 [00:51<00:16, 16.36it/s]\u001b[A\u001b[A\u001b[A\n",
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      "\n",
      "\n",
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      "\n",
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      "\n",
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      "\n",
      "\n",
      "Current loss: 0.13746938109397888:  73%|██████████████████▎      | 730/1000 [00:51<00:16, 16.36it/s]\u001b[A\u001b[A\u001b[A\n",
      "\n",
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      "\n",
      "\n",
      "Current loss: 0.1357269585132599:  74%|███████████████████▎      | 741/1000 [00:51<00:15, 16.34it/s]\u001b[A\u001b[A\u001b[A\n",
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      "\n",
      "\n",
      "Current loss: 0.13578322529792786:  74%|██████████████████▌      | 741/1000 [00:51<00:15, 16.34it/s]\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "Current loss: 0.1387862265110016:  73%|██████████████████▉       | 730/1000 [00:51<00:16, 16.59it/s]\u001b[A\u001b[A\n",
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      "\n",
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      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Current loss: 0.13844987750053406:  73%|██████████████████▎      | 732/1000 [00:52<00:16, 16.07it/s]\u001b[A\u001b[A\n",
      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Current loss: 0.14700168371200562:  75%|██████████████████▋      | 749/1000 [00:53<00:16, 15.49it/s]\u001b[A\n",
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      "\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Current loss: 0.1349918246269226:  78%|████████████████████▏     | 777/1000 [00:54<00:14, 15.13it/s]\u001b[A\u001b[A\u001b[A\n",
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      "\n",
      "\n",
      "Current loss: 0.13655009865760803:  77%|███████████████████▏     | 768/1000 [00:54<00:15, 14.89it/s]\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "Current loss: 0.13777250051498413:  77%|███████████████████▏     | 766/1000 [00:54<00:15, 14.93it/s]\u001b[A\u001b[A\n",
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      "\n",
      "Current loss: 0.13791120052337646:  77%|███████████████████▏     | 766/1000 [00:54<00:15, 14.93it/s]\u001b[A\u001b[A\n",
      "\n",
      "Current loss: 0.13791120052337646:  77%|███████████████████▏     | 768/1000 [00:54<00:15, 15.01it/s]\u001b[A\u001b[A\n",
      "Current loss: 0.14647114276885986:  77%|███████████████████▏     | 769/1000 [00:54<00:15, 14.83it/s]\u001b[A\n",
      "\n",
      "\n",
      "Current loss: 0.13679522275924683:  77%|███████████████████▏     | 768/1000 [00:54<00:15, 14.89it/s]\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "Current loss: 0.134335458278656:  78%|█████████████████████      | 779/1000 [00:54<00:14, 15.06it/s]\u001b[A\u001b[A\u001b[A\n",
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      "\n",
      "\n",
      "Current loss: 0.13653123378753662:  77%|███████████████████▎     | 770/1000 [00:54<00:15, 14.84it/s]\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "Current loss: 0.13475650548934937:  78%|███████████████████▌     | 781/1000 [00:54<00:14, 15.18it/s]\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "Current loss: 0.1367994248867035:  77%|████████████████████      | 770/1000 [00:54<00:15, 14.84it/s]\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "Current loss: 0.13785025477409363:  77%|███████████████████▏     | 768/1000 [00:54<00:15, 15.01it/s]\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "Current loss: 0.1367994248867035:  77%|████████████████████      | 772/1000 [00:54<00:14, 15.26it/s]\u001b[A\u001b[A\u001b[A\n",
      "Current loss: 0.1464383900165558:  77%|████████████████████      | 771/1000 [00:54<00:15, 15.11it/s]\u001b[A\n",
      "\n",
      "Current loss: 0.13430407643318176:  78%|███████████████████▌     | 781/1000 [00:54<00:14, 15.18it/s]\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "Current loss: 0.13653190433979034:  77%|███████████████████▎     | 772/1000 [00:54<00:14, 15.26it/s]\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "Current loss: 0.13770900666713715:  77%|███████████████████▎     | 770/1000 [00:54<00:15, 15.09it/s]\u001b[A\u001b[A\n",
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      "\n",
      "\n",
      "Current loss: 0.13673588633537292:  77%|███████████████████▎     | 772/1000 [00:54<00:14, 15.26it/s]\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "Current loss: 0.13673588633537292:  77%|███████████████████▎     | 774/1000 [00:54<00:14, 15.60it/s]\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "Current loss: 0.1377636194229126:  77%|████████████████████      | 770/1000 [00:54<00:15, 15.09it/s]\u001b[A\u001b[A\n",
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      "\n",
      "Current loss: 0.13425889611244202:  78%|███████████████████▌     | 783/1000 [00:54<00:13, 15.51it/s]\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "Current loss: 0.1365307867527008:  77%|████████████████████      | 774/1000 [00:54<00:14, 15.60it/s]\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "Current loss: 0.13767316937446594:  77%|███████████████████▎     | 772/1000 [00:54<00:14, 15.32it/s]\u001b[A\u001b[A\n",
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      "\n",
      "\n",
      "Current loss: 0.13666945695877075:  77%|███████████████████▎     | 774/1000 [00:54<00:14, 15.60it/s]\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "Current loss: 0.13666945695877075:  78%|███████████████████▍     | 776/1000 [00:54<00:14, 15.82it/s]\u001b[A\u001b[A\u001b[A\n",
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      "\n",
      "Current loss: 0.13771362602710724:  77%|███████████████████▎     | 772/1000 [00:54<00:14, 15.32it/s]\u001b[A\u001b[A\n",
      "\n",
      "Current loss: 0.13771362602710724:  77%|███████████████████▎     | 774/1000 [00:54<00:14, 15.50it/s]\u001b[A\u001b[A\n",
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      "\n",
      "\n",
      "Current loss: 0.13649201393127441:  78%|███████████████████▍     | 776/1000 [00:54<00:14, 15.82it/s]\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "Current loss: 0.13766281306743622:  77%|███████████████████▎     | 774/1000 [00:54<00:14, 15.50it/s]\u001b[A\u001b[A\n",
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      "\n",
      "\n",
      "Current loss: 0.13665416836738586:  78%|███████████████████▍     | 776/1000 [00:54<00:14, 15.82it/s]\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "Current loss: 0.13441409170627594:  79%|███████████████████▋     | 787/1000 [00:54<00:13, 15.43it/s]\u001b[A\u001b[A\u001b[A\n",
      "\n",
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      "\n",
      "Current loss: 0.1341610699892044:  79%|████████████████████▍     | 787/1000 [00:54<00:13, 15.43it/s]\u001b[A\u001b[A\n",
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      "\n",
      "\n",
      "Current loss: 0.13644784688949585:  78%|███████████████████▍     | 778/1000 [00:54<00:14, 15.74it/s]\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "Current loss: 0.13434651494026184:  79%|███████████████████▋     | 787/1000 [00:54<00:13, 15.43it/s]\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "Current loss: 0.13434651494026184:  79%|███████████████████▋     | 789/1000 [00:54<00:13, 15.64it/s]\u001b[A\u001b[A\u001b[A\n",
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      "\n",
      "\n",
      "Current loss: 0.1366634964942932:  78%|████████████████████▎     | 780/1000 [00:54<00:14, 15.69it/s]\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "Current loss: 0.13767895102500916:  78%|███████████████████▍     | 776/1000 [00:54<00:14, 15.57it/s]\u001b[A\u001b[A\n",
      "\n",
      "Current loss: 0.13767895102500916:  78%|███████████████████▍     | 778/1000 [00:54<00:14, 15.41it/s]\u001b[A\u001b[A\n",
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      "\n",
      "\n",
      "Current loss: 0.1364147961139679:  78%|████████████████████▎     | 780/1000 [00:55<00:14, 15.69it/s]\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "Current loss: 0.13429546356201172:  79%|███████████████████▊     | 791/1000 [00:55<00:13, 15.71it/s]\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "Current loss: 0.13665826618671417:  78%|███████████████████▌     | 780/1000 [00:55<00:14, 15.69it/s]\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "Current loss: 0.13665826618671417:  78%|███████████████████▌     | 782/1000 [00:55<00:13, 15.69it/s]\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "Current loss: 0.1376245617866516:  78%|████████████████████▏     | 778/1000 [00:55<00:14, 15.41it/s]\u001b[A\u001b[A\n",
      "\n",
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      "\n",
      "\n",
      "Current loss: 0.1363445520401001:  78%|████████████████████▎     | 782/1000 [00:55<00:13, 15.69it/s]\u001b[A\u001b[A\u001b[A\n",
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      "\n",
      "Current loss: 0.13757051527500153:  78%|███████████████████▌     | 780/1000 [00:55<00:13, 15.77it/s]\u001b[A\u001b[A\n",
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      "\n",
      "\n",
      "Current loss: 0.1366787999868393:  78%|████████████████████▎     | 782/1000 [00:55<00:13, 15.69it/s]\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "Current loss: 0.1366787999868393:  78%|████████████████████▍     | 784/1000 [00:55<00:13, 15.73it/s]\u001b[A\u001b[A\u001b[A\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Current loss: 0.1462969034910202:  78%|████████████████████▎     | 783/1000 [00:55<00:13, 15.69it/s]\u001b[A\n",
      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
      "\n",
      "Current loss: 0.13592684268951416:  79%|███████████████████▊     | 792/1000 [00:55<00:12, 16.26it/s]\u001b[A\u001b[A\u001b[A\n",
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      "\n",
      "Current loss: 0.13385449349880219:  80%|████████████████████     | 803/1000 [00:55<00:11, 16.87it/s]\u001b[A\u001b[A\n",
      "\n",
      "Current loss: 0.13725318014621735:  79%|███████████████████▊     | 790/1000 [00:55<00:13, 16.15it/s]\u001b[A\u001b[A\n",
      "\n",
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      "\n",
      "\n",
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      "\n",
      "\n",
      "Current loss: 0.13397178053855896:  80%|████████████████████     | 803/1000 [00:55<00:11, 16.87it/s]\u001b[A\u001b[A\u001b[A\n",
      "\n",
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      "\n",
      "\n",
      "Current loss: 0.13593333959579468:  79%|███████████████████▊     | 794/1000 [00:55<00:12, 16.13it/s]\u001b[A\u001b[A\u001b[A\n",
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      "\n",
      "\n",
      "Current loss: 0.13637448847293854:  79%|███████████████████▊     | 794/1000 [00:55<00:12, 16.13it/s]\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
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      "\n",
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      "\n",
      "Current loss: 0.13722212612628937:  79%|███████████████████▊     | 794/1000 [00:55<00:12, 16.05it/s]\u001b[A\u001b[A\n",
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      "\n",
      "\n",
      "Current loss: 0.136036679148674:  80%|█████████████████████▍     | 796/1000 [00:56<00:12, 16.27it/s]\u001b[A\u001b[A\u001b[A\n",
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      "\n",
      "Current loss: 0.13725398480892181:  79%|███████████████████▊     | 794/1000 [00:56<00:12, 16.05it/s]\u001b[A\u001b[A\n",
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      "\n",
      "\n",
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      "\n",
      "\n",
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      "\n",
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      "\n",
      "Current loss: 0.13384708762168884:  81%|████████████████████▏    | 808/1000 [00:56<00:11, 16.21it/s]\u001b[A\u001b[A\n",
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      "\n",
      "\n",
      "Current loss: 0.13608667254447937:  80%|███████████████████▉     | 798/1000 [00:56<00:13, 15.35it/s]\u001b[A\u001b[A\u001b[A\n",
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      "\n",
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      "\n",
      "\n",
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      "\n",
      "\n",
      "Current loss: 0.13378041982650757:  81%|████████████████████▏    | 808/1000 [00:56<00:11, 16.21it/s]\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "Current loss: 0.13378041982650757:  81%|████████████████████▎    | 810/1000 [00:56<00:11, 16.19it/s]\u001b[A\u001b[A\n",
      "\n",
      "Current loss: 0.13718023896217346:  80%|███████████████████▉     | 798/1000 [00:56<00:13, 15.42it/s]\u001b[A\u001b[A\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Current loss: 0.14567908644676208:  80%|███████████████████▉     | 799/1000 [00:56<00:13, 15.39it/s]\u001b[A\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Current loss: 0.14482498168945312:  82%|████████████████████▍    | 817/1000 [00:57<00:11, 15.66it/s]\u001b[A\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Current loss: 0.13272584974765778:  84%|█████████████████████    | 844/1000 [00:58<00:10, 15.15it/s]\u001b[A\n",
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      "Current loss: 0.13559091091156006:  84%|█████████████████████    | 840/1000 [00:58<00:10, 14.65it/s]\u001b[A\u001b[A\u001b[A\n",
      "\n",
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      "\n",
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      "\n",
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      "\n",
      "\n",
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      "\n",
      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Current loss: 0.13267837464809418:  86%|█████████████████████▌   | 860/1000 [00:59<00:08, 15.78it/s]\u001b[A\u001b[A\u001b[A\n",
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      "Current loss: 0.13186445832252502:  87%|█████████████████████▊   | 872/1000 [01:00<00:08, 15.33it/s]\u001b[A\u001b[A\u001b[A\n",
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      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Current loss: 0.13589483499526978:  86%|█████████████████████▌   | 864/1000 [01:00<00:08, 15.67it/s]\u001b[A\u001b[A\n",
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      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Current loss: 0.13555343449115753:  88%|██████████████████████   | 880/1000 [01:01<00:07, 15.96it/s]\u001b[A\u001b[A\n",
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      "\n",
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      "\n",
      "Current loss: 0.13547036051750183:  89%|██████████████████████▏  | 886/1000 [01:01<00:07, 15.67it/s]\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "Current loss: 0.1349511742591858:  89%|███████████████████████   | 888/1000 [01:01<00:07, 15.23it/s]\u001b[A\u001b[A\u001b[A\n",
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      "\n",
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      "\n",
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      "\n",
      "\n",
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      "\n",
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      "Current loss: 0.13159793615341187:  90%|██████████████████████▌  | 900/1000 [01:02<00:06, 15.12it/s]\u001b[A\u001b[A\u001b[A\n",
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      "\n",
      "Current loss: 0.13110606372356415:  90%|██████████████████████▌  | 900/1000 [01:02<00:06, 15.12it/s]\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "Current loss: 0.13489177823066711:  89%|██████████████████████▎  | 890/1000 [01:02<00:07, 15.03it/s]\u001b[A\u001b[A\u001b[A\n",
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      "\n",
      "Current loss: 0.13548392057418823:  89%|██████████████████████▏  | 888/1000 [01:02<00:07, 15.66it/s]\u001b[A\u001b[A\n",
      "\n",
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      "\n",
      "\n",
      "Current loss: 0.13465094566345215:  89%|██████████████████████▎  | 890/1000 [01:02<00:07, 15.03it/s]\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
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      "\n",
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      "\n",
      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
      "\n",
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      "\n",
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      "\n",
      "\n",
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      "\n",
      "\n",
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      "\n",
      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
      "\n",
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      "\n",
      "\n",
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      "\n",
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      "\n",
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      "\n",
      "\n",
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      "\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Current loss: 0.13446921110153198:  90%|██████████████████████▌  | 900/1000 [01:02<00:06, 14.79it/s]\u001b[A\u001b[A\u001b[A\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Current loss: 0.13054850697517395:  93%|███████████████████████▏ | 926/1000 [01:03<00:05, 13.58it/s]\u001b[A\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "Current loss: 0.12995758652687073:  94%|███████████████████████▌ | 942/1000 [01:05<00:04, 13.91it/s]\u001b[A\n",
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      "\n",
      "Current loss: 0.13498768210411072:  93%|███████████████████████▎ | 932/1000 [01:05<00:04, 14.36it/s]\u001b[A\u001b[A\n",
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      "\n",
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      "Current loss: 0.1305137723684311:  94%|████████████████████████▌ | 944/1000 [01:05<00:04, 13.93it/s]\u001b[A\u001b[A\u001b[A\n",
      "\n",
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      "\n",
      "Current loss: 0.12992994487285614:  94%|███████████████████████▌ | 944/1000 [01:05<00:04, 13.93it/s]\u001b[A\u001b[A\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
      "\n",
      "Current loss: 0.13004083931446075:  95%|███████████████████████▋ | 948/1000 [01:05<00:03, 14.37it/s]\u001b[A\u001b[A\u001b[A\n",
      "\n",
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      "\n",
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      "\n",
      "\n",
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      "\n",
      "\n",
      "Current loss: 0.13030916452407837:  95%|███████████████████████▋ | 948/1000 [01:05<00:03, 14.37it/s]\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "Current loss: 0.13030916452407837:  95%|███████████████████████▊ | 950/1000 [01:05<00:03, 14.35it/s]\u001b[A\u001b[A\n",
      "\n",
      "\n",
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      "\n",
      "\n",
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      "\n",
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      "\n",
      "\n",
      "Current loss: 0.13003647327423096:  95%|███████████████████████▊ | 950/1000 [01:05<00:03, 14.35it/s]\u001b[A\u001b[A\u001b[A\n",
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      "\n",
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      "\n",
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      "\n",
      "\n",
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      "\n",
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      "\n",
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      "\n",
      "\n",
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      "\n",
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      "\n",
      "\n",
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      "\n",
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      "\n",
      "\n",
      "Current loss: 0.13390818238258362:  94%|███████████████████████▌ | 944/1000 [01:05<00:03, 14.14it/s]\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
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      "\n",
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      "\n",
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      "\n",
      "\n",
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      "\n",
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      "\n",
      "\n",
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      "\n",
      "\n",
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      "\n",
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      "\n",
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      "\n",
      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
      "\n",
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      "\n",
      "\n",
      "Current loss: 0.13380426168441772:  95%|███████████████████████▊ | 950/1000 [01:06<00:03, 15.07it/s]\u001b[A\u001b[A\u001b[A\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Current loss: 0.14168089628219604:  95%|███████████████████████▋ | 949/1000 [01:06<00:03, 15.38it/s]\u001b[A\n",
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      "\n",
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      "\n",
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      "\n",
      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
      "\n",
      "Current loss: 0.13391786813735962:  96%|████████████████████████ | 960/1000 [01:06<00:02, 15.01it/s]\u001b[A\u001b[A\u001b[A\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
      "\n",
      "Current loss: 0.13398894667625427:  96%|████████████████████████ | 960/1000 [01:06<00:02, 15.01it/s]\u001b[A\u001b[A\u001b[A\n",
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      "\n",
      "\n",
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      "\n",
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      "\n",
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      "\n",
      "\n",
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      "\n",
      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Current loss: 0.1297009289264679:  98%|█████████████████████████▍| 977/1000 [01:07<00:01, 16.85it/s]\u001b[A\u001b[A\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Current loss: 0.14108361303806305:  98%|████████████████████████▌| 983/1000 [01:08<00:01, 15.65it/s]\u001b[A\n",
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      "\u001b[A\u001b[A                                                                                              \n",
      "\u001b[A                                                                                                 \n",
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     ]
    }
   ],
   "source": [
    "im_n = torch.randn_like(im) / NOISE_SCALE + 0.5 # Seed for inversion (x_0)\n",
    "_, xadv = model(im_n.cuda(), rep.clone(), make_adv=True, **kwargs) # Image inversion using PGD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x540 with 24 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize inversion\n",
    "show_image_row([im.cpu(), im_n.cpu(), xadv.detach().cpu()], \n",
    "               [\"Original\", r\"Seed ($x_0$)\", \"Result\"], \n",
    "               fontsize=22)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:root] *",
   "language": "python",
   "name": "conda-root-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}