Pix2Code_Pytorch_SAVE_LOAD.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Pix2Code Pytorch SAVE LOAD.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "metadata": {
        "id": "SHdua88B5vxR",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# SET GDRIVE"
      ]
    },
    {
      "metadata": {
        "id": "VASx_mpvnzHX",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "efa592a2-e4fb-4692-cc6b-0166abfa1d58"
      },
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive/')"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive/\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "n9JyQNLz3O0r",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "8c9bd22c-447a-4d9d-e609-74d48b03ddb8"
      },
      "cell_type": "code",
      "source": [
        "!ls drive"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "My Drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "GoCvEpiymwng",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# DOWNLOAD FILE"
      ]
    },
    {
      "metadata": {
        "id": "SzIHD9zUytdZ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 59704
        },
        "outputId": "96bc1a33-458d-4349-f9fb-63252accd033"
      },
      "cell_type": "code",
      "source": [
        "!rm processed_data.zip\n",
        "!curl -L -o processed_data.zip https://www.dropbox.com/s/5lmwlnejhd70jfv/processed_data.zip?dl=1\n",
        "!rm -rf processed_data\n",
        "!unzip processed_data.zip"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "rm: cannot remove 'processed_data.zip': No such file or directory\n",
            "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n",
            "                                 Dload  Upload   Total   Spent    Left  Speed\n",
            "  0     0    0     0    0     0      0      0 --:--:-- --:--:-- --:--:--     0\n",
            "  0     0    0     0    0     0      0      0 --:--:-- --:--:-- --:--:--     0\n",
            "100  296M  100  296M    0     0  18.5M      0  0:00:16  0:00:16 --:--:-- 32.7M\n",
            "Archive:  processed_data.zip\n",
            "   creating: processed_data/\n",
            "   creating: processed_data/data_dev/\n",
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          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "2m6HeyeimzQc",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        },
        "outputId": "fcb0b8b9-b37d-4e94-9a62-07b6e28ae1dc"
      },
      "cell_type": "code",
      "source": [
        "# remove filenya dulu biar ga duplicate\n",
        "!rm bootstrap.vocab\n",
        "# download folder bahan training\n",
        "!curl -O https://raw.githubusercontent.com/andrewsoohwanlee/pix2code-pytorch/master/bootstrap.vocab"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "rm: cannot remove 'bootstrap.vocab': No such file or directory\n",
            "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n",
            "                                 Dload  Upload   Total   Spent    Left  Speed\n",
            "100   124  100   124    0     0    124      0  0:00:01 --:--:--  0:00:01   320\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "cefZcl0izO8C",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# IMPORT AND INSTALL TORCH"
      ]
    },
    {
      "metadata": {
        "id": "EAbGVfhWhren",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 326
        },
        "outputId": "f066b64c-3d13-4254-dc72-34aa08b1dc74"
      },
      "cell_type": "code",
      "source": [
        "!pip3 install torch torchvision"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting torch\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/49/0e/e382bcf1a6ae8225f50b99cc26effa2d4cc6d66975ccf3fa9590efcbedce/torch-0.4.1-cp36-cp36m-manylinux1_x86_64.whl (519.5MB)\n",
            "\u001b[K    100% |████████████████████████████████| 519.5MB 34kB/s \n",
            "tcmalloc: large alloc 1073750016 bytes == 0x59fd6000 @  0x7fc1ea6941c4 0x46d6a4 0x5fcbcc 0x4c494d 0x54f3c4 0x553aaf 0x54e4c8 0x54f4f6 0x553aaf 0x54efc1 0x54f24d 0x553aaf 0x54efc1 0x54f24d 0x553aaf 0x54efc1 0x54f24d 0x551ee0 0x54e4c8 0x54f4f6 0x553aaf 0x54efc1 0x54f24d 0x551ee0 0x54efc1 0x54f24d 0x551ee0 0x54e4c8 0x54f4f6 0x553aaf 0x54e4c8\n",
            "\u001b[?25hCollecting torchvision\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/ca/0d/f00b2885711e08bd71242ebe7b96561e6f6d01fdb4b9dcf4d37e2e13c5e1/torchvision-0.2.1-py2.py3-none-any.whl (54kB)\n",
            "\u001b[K    100% |████████████████████████████████| 61kB 21.5MB/s \n",
            "\u001b[?25hCollecting pillow>=4.1.1 (from torchvision)\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/d1/24/f53ff6b61b3d728b90934bddb4f03f8ab584a7f49299bf3bde56e2952612/Pillow-5.2.0-cp36-cp36m-manylinux1_x86_64.whl (2.0MB)\n",
            "\u001b[K    100% |████████████████████████████████| 2.0MB 4.5MB/s \n",
            "\u001b[?25hRequirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from torchvision) (1.14.5)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from torchvision) (1.11.0)\n",
            "Installing collected packages: torch, pillow, torchvision\n",
            "  Found existing installation: Pillow 4.0.0\n",
            "    Uninstalling Pillow-4.0.0:\n",
            "      Successfully uninstalled Pillow-4.0.0\n",
            "Successfully installed pillow-5.2.0 torch-0.4.1 torchvision-0.2.1\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "ZxHcJ2ACf_eR",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pdb\n",
        "import os\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "import torch.optim as optim\n",
        "import torchvision\n",
        "from PIL import Image\n",
        "from torch.utils.data import Dataset, DataLoader\n",
        "from torchvision import datasets, models, transforms\n",
        "from torch.nn.utils.rnn import pack_padded_sequence\n",
        "from torch.autograd import Variable"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "Qi4Vt-1WzvYF",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# PARAMS "
      ]
    },
    {
      "metadata": {
        "id": "bx86NOFchzCL",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Hyperparams\n",
        "batch_size = 4\n",
        "embed_size = 256\n",
        "num_epochs = 1000\n",
        "learning_rate = 0.001\n",
        "hidden_size = 512\n",
        "num_layers = 1\n",
        "\n",
        "# Other params\n",
        "shuffle = True\n",
        "num_workers = 2 \n",
        "\n",
        "# Logging Variables\n",
        "save_after_x_epochs = 50\n",
        "log_step = 5\n",
        "\n",
        "# Paths\n",
        "data_dir = './processed_data/data_train/' \n",
        "model_path = './drive/My Drive/model/'\n",
        "vocab_path = './bootstrap.vocab'"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "F3A_oKCHks5Q",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Building Vocabulary"
      ]
    },
    {
      "metadata": {
        "id": "DuIOOZFphw3J",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def load_doc(filename):\n",
        "    file = open(filename, 'r')\n",
        "    text = file.read()\n",
        "    file.close()\n",
        "    return text"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "h9m6MQ_Kk4ty",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "class Vocabulary(object):\n",
        "    def __init__(self):\n",
        "        self.word2idx = {}\n",
        "        self.idx2word = {}\n",
        "        self.idx = 0\n",
        "        \n",
        "    def add_word(self, word):\n",
        "        if not word in self.word2idx:\n",
        "            self.word2idx[word] = self.idx\n",
        "            self.idx2word[self.idx] = word\n",
        "            self.idx += 1\n",
        "    \n",
        "    def __call__(self, word):\n",
        "        if not word in self.word2idx:\n",
        "            return self.word2idx['<unk>']\n",
        "        return self.word2idx[word]\n",
        "    \n",
        "    def __len__(self):\n",
        "        return len(self.word2idx)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "WhdeotyklFmz",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def build_vocab(vocab_file_path):\n",
        "    vocab = Vocabulary()\n",
        "    \n",
        "    # Load the vocab file (super basic split())\n",
        "    words_raw = load_doc(vocab_file_path)\n",
        "    words = set(words_raw.split(' '))\n",
        "    \n",
        "    for i, word in enumerate(words):\n",
        "        vocab.add_word(word)\n",
        "        \n",
        "    vocab.add_word(' ')\n",
        "    vocab.add_word('<unk>') # If we find and unknown word\n",
        "    \n",
        "    print('Created vocabulary of ' + str(len(vocab)) + ' items from ' + vocab_file_path)\n",
        "    \n",
        "    return vocab"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "whV2AIx0l23D",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "ae534b1f-1fce-46e3-82e3-8c857edd12f9"
      },
      "cell_type": "code",
      "source": [
        "# Load vocabulary\n",
        "vocab = build_vocab(vocab_path)\n",
        "\n",
        "vocab_size = len(vocab)"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Created vocabulary of 19 items from ./bootstrap.vocab\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "yae7NVNTnskL",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Build Dataset (images and captions)"
      ]
    },
    {
      "metadata": {
        "id": "QJEfJXj6l8Rz",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "class ImageHTMLDataSet(Dataset):\n",
        "    def __init__(self, data_dir, vocab, transform=None):\n",
        "        self.data_dir = data_dir\n",
        "        self.vocab = vocab\n",
        "        self.transform = transform\n",
        "        \n",
        "        self.raw_image_names = []\n",
        "        self.raw_captions = []\n",
        "        \n",
        "        # Fetch all files from our data directory\n",
        "        self.filenames = os.listdir(data_dir)\n",
        "        self.filenames.sort()\n",
        "        \n",
        "        # Sort files based on their filetype\n",
        "        # Assume associated training examples have same filenames\n",
        "        for filename in self.filenames:\n",
        "            # AMBIL 3 DIGIT TRAKHIR DARI NAMA FILENYA\n",
        "            if filename[-3:] == 'png':\n",
        "                # Store image filename\n",
        "                self.raw_image_names.append(filename)\n",
        "            elif filename[-3:] == 'gui':\n",
        "                data = load_doc(data_dir + filename)\n",
        "                self.raw_captions.append(data)\n",
        "                \n",
        "        print('Created dataset of ' + str(len(self)) + ' items from ' + data_dir)\n",
        "        \n",
        "    def __getitem__(self, idx):\n",
        "        img_path = self.raw_image_names[idx]\n",
        "        raw_caption = self.raw_captions[idx]\n",
        "        # Get image from filesystem\n",
        "        image = Image.open(os.path.join(self.data_dir, img_path)).convert('RGB')\n",
        "        if self.transform is not None:\n",
        "            image = self.transform(image)\n",
        "        \n",
        "        # Convert caption (string) to list of vocab ID's\n",
        "        caption = []\n",
        "        caption.append(self.vocab('<START>'))\n",
        "        \n",
        "        # Remove newlines, separate words with spaces\n",
        "        tokens = ' '.join(raw_caption.split())\n",
        "        \n",
        "        # Add space before each comma\n",
        "        tokens = tokens.replace(',', ' ,')\n",
        "        \n",
        "        # Split into words\n",
        "        tokens = tokens.split(' ')\n",
        "        \n",
        "        caption.extend([self.vocab(token) for token in tokens])\n",
        "        caption.append(self.vocab('<END>'))\n",
        "        \n",
        "        target = torch.Tensor(caption)\n",
        "        \n",
        "        return image, target\n",
        "      \n",
        "    def __len__(self):\n",
        "        return len(self.raw_image_names)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "DMjuU5zioTGi",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "  def collate_fn(data):\n",
        "      # sort datalist by caption length; descending order\n",
        "      data.sort(key = lambda data_pair: len(data_pair[1]), reverse=True)\n",
        "      images, captions = zip(*data)\n",
        "\n",
        "      # Merge images ( from tuple of 3D Tensor to 4D Tensor)\n",
        "      images = torch.stack(images, 0)\n",
        "\n",
        "      # Merge captions ( from tuple of 1D tensor to 2D tensor)\n",
        "      lengths = [len(caption) for caption in captions] # List of caption lengths\n",
        "      targets = torch.zeros(len(captions), max(lengths)).long()\n",
        "\n",
        "      for i, caption in enumerate(captions):\n",
        "          end = lengths[i]\n",
        "          targets[i, :end] = caption[:end]\n",
        "\n",
        "      return images, targets, lengths"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "kGUjFv8yVZjm",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "5a9a7325-7a75-4283-c739-c134bce6d058"
      },
      "cell_type": "code",
      "source": [
        "# Image preprocessing, normalization for the pretrained resnet\n",
        "transform = transforms.Compose([ \n",
        "    transforms.Resize((224,224)),\n",
        "    transforms.ToTensor(), \n",
        "    transforms.Normalize((0.485, 0.456, 0.406), \n",
        "                         (0.229, 0.224, 0.225))])\n",
        "\n",
        "# Create data loader\n",
        "img_html_dataset = ImageHTMLDataSet(data_dir=data_dir, vocab=vocab, transform=transform)\n",
        "data_loader = DataLoader(dataset=img_html_dataset,\n",
        "                         batch_size=batch_size,\n",
        "                         shuffle=shuffle,\n",
        "                         num_workers=num_workers,\n",
        "                         collate_fn=collate_fn)"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Created dataset of 1400 items from ./processed_data/data_train/\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "Q7ibVel9xOS6",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# ENCODER MODEL"
      ]
    },
    {
      "metadata": {
        "id": "fHKjji4dWM5n",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "class EncoderCNN(nn.Module):\n",
        "    def __init__(self, embed_size):\n",
        "        super(EncoderCNN, self).__init__()\n",
        "        resnet = models.resnet152(pretrained = True)\n",
        "        modules = list(resnet.children())[:-1]\n",
        "        self.resnet = nn.Sequential(*modules)\n",
        "        self.linear = nn.Linear(resnet.fc.in_features, embed_size)\n",
        "        self.bn = nn.BatchNorm1d(embed_size, momentum=0.01)\n",
        "        \n",
        "        \n",
        "    def forward(self, images):\n",
        "        with torch.no_grad():\n",
        "          features = self.resnet(images)\n",
        "        features = features.reshape(features.size(0), -1)        \n",
        "        features = self.bn(self.linear(features))        \n",
        "        return features"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "g5gEpbgjXkv2",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# DECODER MODEL"
      ]
    },
    {
      "metadata": {
        "id": "zxON50AyXi1V",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "class DecoderRNN(nn.Module):\n",
        "    def __init__(self, embed_size, hidden_size, vocab_size, num_layers, max_seq_length=100):\n",
        "        super(DecoderRNN, self).__init__()\n",
        "        self.embed = nn.Embedding(vocab_size, embed_size)\n",
        "        self.lstm = nn.LSTM(embed_size,hidden_size, num_layers, batch_first=True)        \n",
        "        self.linear = nn.Linear(hidden_size, vocab_size)\n",
        "        self.max_seg_length = max_seq_length\n",
        "    \n",
        "    def forward(self, features, captions, lengths): \n",
        "        embeddings = self.embed(captions)\n",
        "        embeddings = torch.cat((features.unsqueeze(1), embeddings), 1)\n",
        "        packed = pack_padded_sequence(embeddings, lengths, batch_first=True)\n",
        "        hiddens, _ = self.lstm(packed)\n",
        "        outputs = self.linear(hiddens[0])\n",
        "        return outputs\n",
        "      \n",
        "    def sample(self, features, states=None):\n",
        "        sampled_ids = []\n",
        "        inputs = features.unsqueeze(1)      \n",
        "        for i in range(self.max_seg_length):\n",
        "            hiddens, states = self.lstm(inputs, states)\n",
        "            outputs = self.linear(hiddens.squeeze(1))\n",
        "            _, predicted = outputs.max(1)\n",
        "            sampled_ids.append(predicted)\n",
        "            inputs = self.embed(predicted)\n",
        "            inputs = inputs.unsqueeze(1)\n",
        "        sampled_ids = torch.stack(sampled_ids, 1)\n",
        "        return sampled_ids"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "GPD3h7ZiZSYz",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Training"
      ]
    },
    {
      "metadata": {
        "id": "XxEywrqf32gZ",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Device configuration\n",
        "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "2gFEBlEvXooc",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# BUILD MODELNYA\n",
        "encoder = EncoderCNN(256).to(device)\n",
        "decoder = DecoderRNN(256, 512, len(vocab), 1).to(device)\n",
        "#  LOSS AND OPTIMIZER\n",
        "criterion = nn.CrossEntropyLoss()\n",
        "params = list(decoder.parameters()) + list(encoder.linear.parameters()) + list(encoder.bn.parameters())\n",
        "optimizer = torch.optim.Adam(params, lr=0.001)  "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "4R29DIC0sX2V",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "3d876769-41ce-49ae-f03e-5b58db18587c"
      },
      "cell_type": "code",
      "source": [
        "total_step = len(data_loader)\n",
        "total_step"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "350"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "metadata": {
        "id": "ZzEyRQmFDGBS",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def save_checkpoint(state):\n",
        "    torch.save(state, os.path.join(model_path, 'checkpoint-{}-{}.pth'.format(epoch+1, i+1)))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "90A8pzWBCdUe",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33779
        },
        "outputId": "3ce35f74-775e-4217-b7c9-f4ef870c2607"
      },
      "cell_type": "code",
      "source": [
        "for epoch in range(300):\n",
        "    for i, (images, captions, lengths) in enumerate(data_loader):\n",
        "        \n",
        "        # Set mini-batch dataset\n",
        "        images = images.to(device)\n",
        "        captions = captions.to(device)\n",
        "        targets = pack_padded_sequence(captions, lengths, batch_first=True)[0]\n",
        "        \n",
        "        # Forward, backward and optimize\n",
        "        features = encoder(images)\n",
        "        outputs = decoder(features, captions, lengths)        \n",
        "        loss = criterion(outputs, targets)\n",
        "        encoder.zero_grad()\n",
        "        decoder.zero_grad()\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "        \n",
        "        if (i+1) % 50 == 0:\n",
        "            print('Epoch [{}/{}], Step [{}/{}], Loss:{:.4f}, Perplexity: {:5.4f}'\n",
        "                  .format(epoch, 300, i+1, total_step, loss.item(), np.exp(loss.item())))\n",
        "                  \n",
        "        # Save the model checkpoints\n",
        "        if (epoch+1) % 100 == 0 and (i+1) % total_step == 0:\n",
        "            save_checkpoint({\n",
        "              'epoch': epoch + 1,\n",
        "              'encoder': encoder.state_dict(),\n",
        "              'decoder': decoder.state_dict(),\n",
        "              'optimizer' : optimizer.state_dict(),\n",
        "            })"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch [0/300], Step [50/350], Loss:0.3188, Perplexity: 1.3755\n",
            "Epoch [0/300], Step [100/350], Loss:0.2432, Perplexity: 1.2754\n",
            "Epoch [0/300], Step [150/350], Loss:0.2217, Perplexity: 1.2482\n",
            "Epoch [0/300], Step [200/350], Loss:0.2081, Perplexity: 1.2314\n",
            "Epoch [0/300], Step [250/350], Loss:0.2180, Perplexity: 1.2436\n",
            "Epoch [0/300], Step [300/350], Loss:0.1896, Perplexity: 1.2087\n",
            "Epoch [0/300], Step [350/350], Loss:0.2162, Perplexity: 1.2414\n",
            "Epoch [1/300], Step [50/350], Loss:0.1886, Perplexity: 1.2076\n",
            "Epoch [1/300], Step [100/350], Loss:0.1838, Perplexity: 1.2017\n",
            "Epoch [1/300], Step [150/350], Loss:0.1954, Perplexity: 1.2158\n",
            "Epoch [1/300], Step [200/350], Loss:0.1673, Perplexity: 1.1821\n",
            "Epoch [1/300], Step [250/350], Loss:0.1756, Perplexity: 1.1919\n",
            "Epoch [1/300], Step [300/350], Loss:0.1787, Perplexity: 1.1956\n",
            "Epoch [1/300], Step [350/350], Loss:0.1693, Perplexity: 1.1845\n",
            "Epoch [2/300], Step [50/350], Loss:0.1561, Perplexity: 1.1689\n",
            "Epoch [2/300], Step [100/350], Loss:0.1633, Perplexity: 1.1774\n",
            "Epoch [2/300], Step [150/350], Loss:0.1763, Perplexity: 1.1928\n",
            "Epoch [2/300], Step [200/350], Loss:0.1929, Perplexity: 1.2128\n",
            "Epoch [2/300], Step [250/350], Loss:0.1715, Perplexity: 1.1871\n",
            "Epoch [2/300], Step [300/350], Loss:0.1696, Perplexity: 1.1849\n",
            "Epoch [2/300], Step [350/350], Loss:0.1798, Perplexity: 1.1970\n",
            "Epoch [3/300], Step [50/350], Loss:0.1660, Perplexity: 1.1806\n",
            "Epoch [3/300], Step [100/350], Loss:0.1663, Perplexity: 1.1810\n",
            "Epoch [3/300], Step [150/350], Loss:0.1585, Perplexity: 1.1717\n",
            "Epoch [3/300], Step [200/350], Loss:0.1724, Perplexity: 1.1882\n",
            "Epoch [3/300], Step [250/350], Loss:0.1385, Perplexity: 1.1485\n",
            "Epoch [3/300], Step [300/350], Loss:0.1737, Perplexity: 1.1897\n",
            "Epoch [3/300], Step [350/350], Loss:0.1871, Perplexity: 1.2057\n",
            "Epoch [4/300], Step [50/350], Loss:0.1823, Perplexity: 1.2000\n",
            "Epoch [4/300], Step [100/350], Loss:0.1798, Perplexity: 1.1970\n",
            "Epoch [4/300], Step [150/350], Loss:0.1337, Perplexity: 1.1431\n",
            "Epoch [4/300], Step [200/350], Loss:0.1507, Perplexity: 1.1627\n",
            "Epoch [4/300], Step [250/350], Loss:0.1396, Perplexity: 1.1498\n",
            "Epoch [4/300], Step [300/350], Loss:0.1618, Perplexity: 1.1756\n",
            "Epoch [4/300], Step [350/350], Loss:0.1457, Perplexity: 1.1569\n",
            "Epoch [5/300], Step [50/350], Loss:0.1543, Perplexity: 1.1668\n",
            "Epoch [5/300], Step [100/350], Loss:0.1389, Perplexity: 1.1490\n",
            "Epoch [5/300], Step [150/350], Loss:0.1257, Perplexity: 1.1339\n",
            "Epoch [5/300], Step [200/350], Loss:0.1665, Perplexity: 1.1812\n",
            "Epoch [5/300], Step [250/350], Loss:0.1290, Perplexity: 1.1377\n",
            "Epoch [5/300], Step [300/350], Loss:0.1304, Perplexity: 1.1393\n",
            "Epoch [5/300], Step [350/350], Loss:0.1898, Perplexity: 1.2090\n",
            "Epoch [6/300], Step [50/350], Loss:0.1272, Perplexity: 1.1357\n",
            "Epoch [6/300], Step [100/350], Loss:0.1578, Perplexity: 1.1710\n",
            "Epoch [6/300], Step [150/350], Loss:0.1374, Perplexity: 1.1473\n",
            "Epoch [6/300], Step [200/350], Loss:0.1314, Perplexity: 1.1404\n",
            "Epoch [6/300], Step [250/350], Loss:0.1352, Perplexity: 1.1448\n",
            "Epoch [6/300], Step [300/350], Loss:0.1593, Perplexity: 1.1727\n",
            "Epoch [6/300], Step [350/350], Loss:0.1309, Perplexity: 1.1399\n",
            "Epoch [7/300], Step [50/350], Loss:0.1759, Perplexity: 1.1924\n",
            "Epoch [7/300], Step [100/350], Loss:0.1206, Perplexity: 1.1282\n",
            "Epoch [7/300], Step [150/350], Loss:0.1786, Perplexity: 1.1956\n",
            "Epoch [7/300], Step [200/350], Loss:0.1441, Perplexity: 1.1550\n",
            "Epoch [7/300], Step [250/350], Loss:0.1163, Perplexity: 1.1233\n",
            "Epoch [7/300], Step [300/350], Loss:0.1626, Perplexity: 1.1766\n",
            "Epoch [7/300], Step [350/350], Loss:0.1291, Perplexity: 1.1378\n",
            "Epoch [8/300], Step [50/350], Loss:0.1268, Perplexity: 1.1352\n",
            "Epoch [8/300], Step [100/350], Loss:0.1265, Perplexity: 1.1349\n",
            "Epoch [8/300], Step [150/350], Loss:0.1253, Perplexity: 1.1334\n",
            "Epoch [8/300], Step [200/350], Loss:0.1293, Perplexity: 1.1380\n",
            "Epoch [8/300], Step [250/350], Loss:0.1549, Perplexity: 1.1676\n",
            "Epoch [8/300], Step [300/350], Loss:0.1345, Perplexity: 1.1439\n",
            "Epoch [8/300], Step [350/350], Loss:0.1312, Perplexity: 1.1401\n",
            "Epoch [9/300], Step [50/350], Loss:0.1717, Perplexity: 1.1873\n",
            "Epoch [9/300], Step [100/350], Loss:0.1232, Perplexity: 1.1311\n",
            "Epoch [9/300], Step [150/350], Loss:0.1328, Perplexity: 1.1421\n",
            "Epoch [9/300], Step [200/350], Loss:0.1324, Perplexity: 1.1416\n",
            "Epoch [9/300], Step [250/350], Loss:0.1382, Perplexity: 1.1482\n",
            "Epoch [9/300], Step [300/350], Loss:0.1276, Perplexity: 1.1361\n",
            "Epoch [9/300], Step [350/350], Loss:0.1252, Perplexity: 1.1334\n",
            "Epoch [10/300], Step [50/350], Loss:0.1338, Perplexity: 1.1432\n",
            "Epoch [10/300], Step [100/350], Loss:0.1153, Perplexity: 1.1222\n",
            "Epoch [10/300], Step [150/350], Loss:0.1616, Perplexity: 1.1754\n",
            "Epoch [10/300], Step [200/350], Loss:0.1119, Perplexity: 1.1184\n",
            "Epoch [10/300], Step [250/350], Loss:0.1382, Perplexity: 1.1482\n",
            "Epoch [10/300], Step [300/350], Loss:0.1199, Perplexity: 1.1274\n",
            "Epoch [10/300], Step [350/350], Loss:0.1147, Perplexity: 1.1215\n",
            "Epoch [11/300], Step [50/350], Loss:0.1242, Perplexity: 1.1323\n",
            "Epoch [11/300], Step [100/350], Loss:0.1158, Perplexity: 1.1228\n",
            "Epoch [11/300], Step [150/350], Loss:0.1399, Perplexity: 1.1501\n",
            "Epoch [11/300], Step [200/350], Loss:0.1567, Perplexity: 1.1697\n",
            "Epoch [11/300], Step [250/350], Loss:0.1175, Perplexity: 1.1247\n",
            "Epoch [11/300], Step [300/350], Loss:0.1537, Perplexity: 1.1662\n",
            "Epoch [11/300], Step [350/350], Loss:0.1189, Perplexity: 1.1263\n",
            "Epoch [12/300], Step [50/350], Loss:0.1248, Perplexity: 1.1329\n",
            "Epoch [12/300], Step [100/350], Loss:0.1455, Perplexity: 1.1566\n",
            "Epoch [12/300], Step [150/350], Loss:0.1636, Perplexity: 1.1778\n",
            "Epoch [12/300], Step [200/350], Loss:0.1179, Perplexity: 1.1252\n",
            "Epoch [12/300], Step [250/350], Loss:0.1351, Perplexity: 1.1447\n",
            "Epoch [12/300], Step [300/350], Loss:0.1484, Perplexity: 1.1600\n",
            "Epoch [12/300], Step [350/350], Loss:0.1580, Perplexity: 1.1711\n",
            "Epoch [13/300], Step [50/350], Loss:0.1133, Perplexity: 1.1200\n",
            "Epoch [13/300], Step [100/350], Loss:0.1394, Perplexity: 1.1496\n",
            "Epoch [13/300], Step [150/350], Loss:0.1314, Perplexity: 1.1405\n",
            "Epoch [13/300], Step [200/350], Loss:0.2743, Perplexity: 1.3156\n",
            "Epoch [13/300], Step [250/350], Loss:0.1220, Perplexity: 1.1298\n",
            "Epoch [13/300], Step [300/350], Loss:0.1229, Perplexity: 1.1308\n",
            "Epoch [13/300], Step [350/350], Loss:0.1568, Perplexity: 1.1698\n",
            "Epoch [14/300], Step [50/350], Loss:0.1215, Perplexity: 1.1292\n",
            "Epoch [14/300], Step [100/350], Loss:0.1193, Perplexity: 1.1267\n",
            "Epoch [14/300], Step [150/350], Loss:0.1346, Perplexity: 1.1441\n",
            "Epoch [14/300], Step [200/350], Loss:0.1195, Perplexity: 1.1270\n",
            "Epoch [14/300], Step [250/350], Loss:0.1202, Perplexity: 1.1277\n",
            "Epoch [14/300], Step [300/350], Loss:0.1075, Perplexity: 1.1135\n",
            "Epoch [14/300], Step [350/350], Loss:0.1091, Perplexity: 1.1153\n",
            "Epoch [15/300], Step [50/350], Loss:0.1180, Perplexity: 1.1252\n",
            "Epoch [15/300], Step [100/350], Loss:0.1130, Perplexity: 1.1197\n",
            "Epoch [15/300], Step [150/350], Loss:0.1131, Perplexity: 1.1198\n",
            "Epoch [15/300], Step [200/350], Loss:0.1234, Perplexity: 1.1314\n",
            "Epoch [15/300], Step [250/350], Loss:0.1106, Perplexity: 1.1169\n",
            "Epoch [15/300], Step [300/350], Loss:0.1242, Perplexity: 1.1323\n",
            "Epoch [15/300], Step [350/350], Loss:0.1134, Perplexity: 1.1201\n",
            "Epoch [16/300], Step [50/350], Loss:0.1055, Perplexity: 1.1112\n",
            "Epoch [16/300], Step [100/350], Loss:0.1306, Perplexity: 1.1395\n",
            "Epoch [16/300], Step [150/350], Loss:0.1362, Perplexity: 1.1459\n",
            "Epoch [16/300], Step [200/350], Loss:0.1108, Perplexity: 1.1172\n",
            "Epoch [16/300], Step [250/350], Loss:0.1159, Perplexity: 1.1228\n",
            "Epoch [16/300], Step [300/350], Loss:0.1170, Perplexity: 1.1241\n",
            "Epoch [16/300], Step [350/350], Loss:0.1205, Perplexity: 1.1280\n",
            "Epoch [17/300], Step [50/350], Loss:0.1286, Perplexity: 1.1373\n",
            "Epoch [17/300], Step [100/350], Loss:0.1185, Perplexity: 1.1258\n",
            "Epoch [17/300], Step [150/350], Loss:0.1188, Perplexity: 1.1262\n",
            "Epoch [17/300], Step [200/350], Loss:0.1490, Perplexity: 1.1606\n",
            "Epoch [17/300], Step [250/350], Loss:0.1134, Perplexity: 1.1201\n",
            "Epoch [17/300], Step [300/350], Loss:0.1134, Perplexity: 1.1201\n",
            "Epoch [17/300], Step [350/350], Loss:0.1365, Perplexity: 1.1462\n",
            "Epoch [18/300], Step [50/350], Loss:0.1304, Perplexity: 1.1393\n",
            "Epoch [18/300], Step [100/350], Loss:0.1320, Perplexity: 1.1411\n",
            "Epoch [18/300], Step [150/350], Loss:0.1097, Perplexity: 1.1159\n",
            "Epoch [18/300], Step [200/350], Loss:0.1128, Perplexity: 1.1194\n",
            "Epoch [18/300], Step [250/350], Loss:0.1140, Perplexity: 1.1207\n",
            "Epoch [18/300], Step [300/350], Loss:0.1158, Perplexity: 1.1228\n",
            "Epoch [18/300], Step [350/350], Loss:0.1133, Perplexity: 1.1199\n",
            "Epoch [19/300], Step [50/350], Loss:0.1167, Perplexity: 1.1238\n",
            "Epoch [19/300], Step [100/350], Loss:0.1239, Perplexity: 1.1319\n",
            "Epoch [19/300], Step [150/350], Loss:0.1160, Perplexity: 1.1230\n",
            "Epoch [19/300], Step [200/350], Loss:0.1134, Perplexity: 1.1200\n",
            "Epoch [19/300], Step [250/350], Loss:0.1227, Perplexity: 1.1305\n",
            "Epoch [19/300], Step [300/350], Loss:0.1161, Perplexity: 1.1231\n",
            "Epoch [19/300], Step [350/350], Loss:0.1377, Perplexity: 1.1477\n",
            "Epoch [20/300], Step [50/350], Loss:0.1040, Perplexity: 1.1096\n",
            "Epoch [20/300], Step [100/350], Loss:0.1258, Perplexity: 1.1340\n",
            "Epoch [20/300], Step [150/350], Loss:0.1130, Perplexity: 1.1196\n",
            "Epoch [20/300], Step [200/350], Loss:0.1204, Perplexity: 1.1279\n",
            "Epoch [20/300], Step [250/350], Loss:0.1068, Perplexity: 1.1127\n",
            "Epoch [20/300], Step [300/350], Loss:0.1255, Perplexity: 1.1337\n",
            "Epoch [20/300], Step [350/350], Loss:0.1019, Perplexity: 1.1073\n",
            "Epoch [21/300], Step [50/350], Loss:0.1163, Perplexity: 1.1234\n",
            "Epoch [21/300], Step [100/350], Loss:0.1143, Perplexity: 1.1211\n",
            "Epoch [21/300], Step [150/350], Loss:0.1224, Perplexity: 1.1303\n",
            "Epoch [21/300], Step [200/350], Loss:0.1389, Perplexity: 1.1490\n",
            "Epoch [21/300], Step [250/350], Loss:0.1077, Perplexity: 1.1137\n",
            "Epoch [21/300], Step [300/350], Loss:0.1200, Perplexity: 1.1275\n",
            "Epoch [21/300], Step [350/350], Loss:0.1246, Perplexity: 1.1327\n",
            "Epoch [22/300], Step [50/350], Loss:0.1102, Perplexity: 1.1165\n",
            "Epoch [22/300], Step [100/350], Loss:0.1134, Perplexity: 1.1201\n",
            "Epoch [22/300], Step [150/350], Loss:0.1251, Perplexity: 1.1333\n",
            "Epoch [22/300], Step [200/350], Loss:0.1424, Perplexity: 1.1531\n",
            "Epoch [22/300], Step [250/350], Loss:0.1361, Perplexity: 1.1457\n",
            "Epoch [22/300], Step [300/350], Loss:0.1075, Perplexity: 1.1134\n",
            "Epoch [22/300], Step [350/350], Loss:0.1249, Perplexity: 1.1330\n",
            "Epoch [23/300], Step [50/350], Loss:0.1180, Perplexity: 1.1253\n",
            "Epoch [23/300], Step [100/350], Loss:0.1183, Perplexity: 1.1256\n",
            "Epoch [23/300], Step [150/350], Loss:0.1156, Perplexity: 1.1225\n",
            "Epoch [23/300], Step [200/350], Loss:0.0949, Perplexity: 1.0996\n",
            "Epoch [23/300], Step [250/350], Loss:0.1222, Perplexity: 1.1300\n",
            "Epoch [23/300], Step [300/350], Loss:0.1065, Perplexity: 1.1124\n",
            "Epoch [23/300], Step [350/350], Loss:0.1488, Perplexity: 1.1605\n",
            "Epoch [24/300], Step [50/350], Loss:0.1169, Perplexity: 1.1241\n",
            "Epoch [24/300], Step [100/350], Loss:0.1019, Perplexity: 1.1073\n",
            "Epoch [24/300], Step [150/350], Loss:0.1221, Perplexity: 1.1299\n",
            "Epoch [24/300], Step [200/350], Loss:0.1240, Perplexity: 1.1320\n",
            "Epoch [24/300], Step [250/350], Loss:0.1140, Perplexity: 1.1207\n",
            "Epoch [24/300], Step [300/350], Loss:0.1049, Perplexity: 1.1106\n",
            "Epoch [24/300], Step [350/350], Loss:0.1034, Perplexity: 1.1089\n",
            "Epoch [25/300], Step [50/350], Loss:0.1108, Perplexity: 1.1172\n",
            "Epoch [25/300], Step [100/350], Loss:0.1211, Perplexity: 1.1287\n",
            "Epoch [25/300], Step [150/350], Loss:0.1061, Perplexity: 1.1119\n",
            "Epoch [25/300], Step [200/350], Loss:0.1162, Perplexity: 1.1232\n",
            "Epoch [25/300], Step [250/350], Loss:0.0997, Perplexity: 1.1049\n",
            "Epoch [25/300], Step [300/350], Loss:0.1044, Perplexity: 1.1100\n",
            "Epoch [25/300], Step [350/350], Loss:0.1136, Perplexity: 1.1203\n",
            "Epoch [26/300], Step [50/350], Loss:0.1032, Perplexity: 1.1087\n",
            "Epoch [26/300], Step [100/350], Loss:0.1410, Perplexity: 1.1515\n",
            "Epoch [26/300], Step [150/350], Loss:0.1271, Perplexity: 1.1355\n",
            "Epoch [26/300], Step [200/350], Loss:0.1088, Perplexity: 1.1150\n",
            "Epoch [26/300], Step [250/350], Loss:0.1050, Perplexity: 1.1107\n",
            "Epoch [26/300], Step [300/350], Loss:0.1237, Perplexity: 1.1317\n",
            "Epoch [26/300], Step [350/350], Loss:0.1107, Perplexity: 1.1170\n",
            "Epoch [27/300], Step [50/350], Loss:0.1058, Perplexity: 1.1116\n",
            "Epoch [27/300], Step [100/350], Loss:0.1109, Perplexity: 1.1172\n",
            "Epoch [27/300], Step [150/350], Loss:0.1111, Perplexity: 1.1175\n",
            "Epoch [27/300], Step [200/350], Loss:0.1035, Perplexity: 1.1090\n",
            "Epoch [27/300], Step [250/350], Loss:0.1140, Perplexity: 1.1207\n",
            "Epoch [27/300], Step [300/350], Loss:0.1246, Perplexity: 1.1327\n",
            "Epoch [27/300], Step [350/350], Loss:0.1603, Perplexity: 1.1738\n",
            "Epoch [28/300], Step [50/350], Loss:0.1230, Perplexity: 1.1309\n",
            "Epoch [28/300], Step [100/350], Loss:0.1041, Perplexity: 1.1097\n",
            "Epoch [28/300], Step [150/350], Loss:0.1307, Perplexity: 1.1396\n",
            "Epoch [28/300], Step [200/350], Loss:0.1281, Perplexity: 1.1366\n",
            "Epoch [28/300], Step [250/350], Loss:0.1082, Perplexity: 1.1143\n",
            "Epoch [28/300], Step [300/350], Loss:0.1123, Perplexity: 1.1188\n",
            "Epoch [28/300], Step [350/350], Loss:0.1211, Perplexity: 1.1287\n",
            "Epoch [29/300], Step [50/350], Loss:0.1117, Perplexity: 1.1182\n",
            "Epoch [29/300], Step [100/350], Loss:0.1029, Perplexity: 1.1084\n",
            "Epoch [29/300], Step [150/350], Loss:0.0937, Perplexity: 1.0982\n",
            "Epoch [29/300], Step [200/350], Loss:0.0923, Perplexity: 1.0967\n",
            "Epoch [29/300], Step [250/350], Loss:0.0978, Perplexity: 1.1028\n",
            "Epoch [29/300], Step [300/350], Loss:0.1209, Perplexity: 1.1285\n",
            "Epoch [29/300], Step [350/350], Loss:0.1323, Perplexity: 1.1415\n",
            "Epoch [30/300], Step [50/350], Loss:0.1075, Perplexity: 1.1135\n",
            "Epoch [30/300], Step [100/350], Loss:0.0964, Perplexity: 1.1012\n",
            "Epoch [30/300], Step [150/350], Loss:0.0930, Perplexity: 1.0975\n",
            "Epoch [30/300], Step [200/350], Loss:0.1039, Perplexity: 1.1095\n",
            "Epoch [30/300], Step [250/350], Loss:0.1192, Perplexity: 1.1266\n",
            "Epoch [30/300], Step [300/350], Loss:0.1152, Perplexity: 1.1221\n",
            "Epoch [30/300], Step [350/350], Loss:0.0926, Perplexity: 1.0971\n",
            "Epoch [31/300], Step [50/350], Loss:0.1047, Perplexity: 1.1104\n",
            "Epoch [31/300], Step [100/350], Loss:0.1054, Perplexity: 1.1111\n",
            "Epoch [31/300], Step [150/350], Loss:0.0969, Perplexity: 1.1017\n",
            "Epoch [31/300], Step [200/350], Loss:0.1194, Perplexity: 1.1268\n",
            "Epoch [31/300], Step [250/350], Loss:0.1158, Perplexity: 1.1228\n",
            "Epoch [31/300], Step [300/350], Loss:0.1080, Perplexity: 1.1140\n",
            "Epoch [31/300], Step [350/350], Loss:0.1099, Perplexity: 1.1161\n",
            "Epoch [32/300], Step [50/350], Loss:0.1110, Perplexity: 1.1174\n",
            "Epoch [32/300], Step [100/350], Loss:0.0979, Perplexity: 1.1029\n",
            "Epoch [32/300], Step [150/350], Loss:0.1255, Perplexity: 1.1337\n",
            "Epoch [32/300], Step [200/350], Loss:0.1166, Perplexity: 1.1236\n",
            "Epoch [32/300], Step [250/350], Loss:0.0954, Perplexity: 1.1001\n",
            "Epoch [32/300], Step [300/350], Loss:0.0944, Perplexity: 1.0990\n",
            "Epoch [32/300], Step [350/350], Loss:0.0998, Perplexity: 1.1049\n",
            "Epoch [33/300], Step [50/350], Loss:0.0861, Perplexity: 1.0899\n",
            "Epoch [33/300], Step [100/350], Loss:0.0922, Perplexity: 1.0966\n",
            "Epoch [33/300], Step [150/350], Loss:0.1090, Perplexity: 1.1152\n",
            "Epoch [33/300], Step [200/350], Loss:0.1149, Perplexity: 1.1217\n",
            "Epoch [33/300], Step [250/350], Loss:0.0951, Perplexity: 1.0998\n",
            "Epoch [33/300], Step [300/350], Loss:0.1028, Perplexity: 1.1083\n",
            "Epoch [33/300], Step [350/350], Loss:0.1755, Perplexity: 1.1918\n",
            "Epoch [34/300], Step [50/350], Loss:0.0910, Perplexity: 1.0953\n",
            "Epoch [34/300], Step [100/350], Loss:0.1099, Perplexity: 1.1161\n",
            "Epoch [34/300], Step [150/350], Loss:0.1108, Perplexity: 1.1172\n",
            "Epoch [34/300], Step [200/350], Loss:0.1237, Perplexity: 1.1317\n",
            "Epoch [34/300], Step [250/350], Loss:0.0982, Perplexity: 1.1032\n",
            "Epoch [34/300], Step [300/350], Loss:0.1146, Perplexity: 1.1214\n",
            "Epoch [34/300], Step [350/350], Loss:0.1130, Perplexity: 1.1196\n",
            "Epoch [35/300], Step [50/350], Loss:0.1494, Perplexity: 1.1611\n",
            "Epoch [35/300], Step [100/350], Loss:0.1097, Perplexity: 1.1159\n",
            "Epoch [35/300], Step [150/350], Loss:0.1125, Perplexity: 1.1191\n",
            "Epoch [35/300], Step [200/350], Loss:0.1153, Perplexity: 1.1222\n",
            "Epoch [35/300], Step [250/350], Loss:0.0981, Perplexity: 1.1030\n",
            "Epoch [35/300], Step [300/350], Loss:0.1584, Perplexity: 1.1717\n",
            "Epoch [35/300], Step [350/350], Loss:0.1044, Perplexity: 1.1101\n",
            "Epoch [36/300], Step [50/350], Loss:0.1215, Perplexity: 1.1292\n",
            "Epoch [36/300], Step [100/350], Loss:0.1022, Perplexity: 1.1076\n",
            "Epoch [36/300], Step [150/350], Loss:0.1114, Perplexity: 1.1178\n",
            "Epoch [36/300], Step [200/350], Loss:0.0935, Perplexity: 1.0980\n",
            "Epoch [36/300], Step [250/350], Loss:0.1159, Perplexity: 1.1229\n",
            "Epoch [36/300], Step [300/350], Loss:0.1173, Perplexity: 1.1245\n",
            "Epoch [36/300], Step [350/350], Loss:0.0968, Perplexity: 1.1016\n",
            "Epoch [37/300], Step [50/350], Loss:0.0832, Perplexity: 1.0867\n",
            "Epoch [37/300], Step [100/350], Loss:0.0937, Perplexity: 1.0982\n",
            "Epoch [37/300], Step [150/350], Loss:0.1040, Perplexity: 1.1096\n",
            "Epoch [37/300], Step [200/350], Loss:0.0939, Perplexity: 1.0984\n",
            "Epoch [37/300], Step [250/350], Loss:0.0980, Perplexity: 1.1029\n",
            "Epoch [37/300], Step [300/350], Loss:0.1063, Perplexity: 1.1122\n",
            "Epoch [37/300], Step [350/350], Loss:0.1160, Perplexity: 1.1230\n",
            "Epoch [38/300], Step [50/350], Loss:0.0962, Perplexity: 1.1010\n",
            "Epoch [38/300], Step [100/350], Loss:0.1057, Perplexity: 1.1115\n",
            "Epoch [38/300], Step [150/350], Loss:0.1139, Perplexity: 1.1206\n",
            "Epoch [38/300], Step [200/350], Loss:0.0977, Perplexity: 1.1026\n",
            "Epoch [38/300], Step [250/350], Loss:0.1495, Perplexity: 1.1613\n",
            "Epoch [38/300], Step [300/350], Loss:0.0945, Perplexity: 1.0991\n",
            "Epoch [38/300], Step [350/350], Loss:0.0985, Perplexity: 1.1036\n",
            "Epoch [39/300], Step [50/350], Loss:0.1133, Perplexity: 1.1200\n",
            "Epoch [39/300], Step [100/350], Loss:0.1308, Perplexity: 1.1398\n",
            "Epoch [39/300], Step [150/350], Loss:0.0974, Perplexity: 1.1023\n",
            "Epoch [39/300], Step [200/350], Loss:0.0934, Perplexity: 1.0979\n",
            "Epoch [39/300], Step [250/350], Loss:0.0983, Perplexity: 1.1033\n",
            "Epoch [39/300], Step [300/350], Loss:0.1099, Perplexity: 1.1162\n",
            "Epoch [39/300], Step [350/350], Loss:0.0952, Perplexity: 1.0999\n",
            "Epoch [40/300], Step [50/350], Loss:0.0914, Perplexity: 1.0957\n",
            "Epoch [40/300], Step [100/350], Loss:0.0857, Perplexity: 1.0894\n",
            "Epoch [40/300], Step [150/350], Loss:0.1234, Perplexity: 1.1313\n",
            "Epoch [40/300], Step [200/350], Loss:0.1157, Perplexity: 1.1227\n",
            "Epoch [40/300], Step [250/350], Loss:0.1026, Perplexity: 1.1081\n",
            "Epoch [40/300], Step [300/350], Loss:0.1235, Perplexity: 1.1314\n",
            "Epoch [40/300], Step [350/350], Loss:0.0981, Perplexity: 1.1031\n",
            "Epoch [41/300], Step [50/350], Loss:0.0866, Perplexity: 1.0905\n",
            "Epoch [41/300], Step [100/350], Loss:0.1071, Perplexity: 1.1130\n",
            "Epoch [41/300], Step [150/350], Loss:0.1062, Perplexity: 1.1121\n",
            "Epoch [41/300], Step [200/350], Loss:0.1020, Perplexity: 1.1074\n",
            "Epoch [41/300], Step [250/350], Loss:0.0982, Perplexity: 1.1032\n",
            "Epoch [41/300], Step [300/350], Loss:0.0965, Perplexity: 1.1013\n",
            "Epoch [41/300], Step [350/350], Loss:0.0841, Perplexity: 1.0878\n",
            "Epoch [42/300], Step [50/350], Loss:0.0966, Perplexity: 1.1014\n",
            "Epoch [42/300], Step [100/350], Loss:0.0893, Perplexity: 1.0934\n",
            "Epoch [42/300], Step [150/350], Loss:0.1056, Perplexity: 1.1114\n",
            "Epoch [42/300], Step [200/350], Loss:0.1044, Perplexity: 1.1101\n",
            "Epoch [42/300], Step [250/350], Loss:0.1702, Perplexity: 1.1855\n",
            "Epoch [42/300], Step [300/350], Loss:0.1149, Perplexity: 1.1218\n",
            "Epoch [42/300], Step [350/350], Loss:0.1365, Perplexity: 1.1463\n",
            "Epoch [43/300], Step [50/350], Loss:0.1252, Perplexity: 1.1334\n",
            "Epoch [43/300], Step [100/350], Loss:0.1113, Perplexity: 1.1177\n",
            "Epoch [43/300], Step [150/350], Loss:0.1229, Perplexity: 1.1308\n",
            "Epoch [43/300], Step [200/350], Loss:0.1170, Perplexity: 1.1241\n",
            "Epoch [43/300], Step [250/350], Loss:0.0993, Perplexity: 1.1044\n",
            "Epoch [43/300], Step [300/350], Loss:0.0832, Perplexity: 1.0867\n",
            "Epoch [43/300], Step [350/350], Loss:0.1008, Perplexity: 1.1061\n",
            "Epoch [44/300], Step [50/350], Loss:0.1110, Perplexity: 1.1174\n",
            "Epoch [44/300], Step [100/350], Loss:0.0918, Perplexity: 1.0962\n",
            "Epoch [44/300], Step [150/350], Loss:0.0998, Perplexity: 1.1050\n",
            "Epoch [44/300], Step [200/350], Loss:0.0808, Perplexity: 1.0841\n",
            "Epoch [44/300], Step [250/350], Loss:0.0980, Perplexity: 1.1029\n",
            "Epoch [44/300], Step [300/350], Loss:0.1260, Perplexity: 1.1343\n",
            "Epoch [44/300], Step [350/350], Loss:0.0919, Perplexity: 1.0962\n",
            "Epoch [45/300], Step [50/350], Loss:0.0997, Perplexity: 1.1048\n",
            "Epoch [45/300], Step [100/350], Loss:0.1070, Perplexity: 1.1130\n",
            "Epoch [45/300], Step [150/350], Loss:0.1121, Perplexity: 1.1186\n",
            "Epoch [45/300], Step [200/350], Loss:0.0976, Perplexity: 1.1025\n",
            "Epoch [45/300], Step [250/350], Loss:0.1046, Perplexity: 1.1102\n",
            "Epoch [45/300], Step [300/350], Loss:0.1214, Perplexity: 1.1291\n",
            "Epoch [45/300], Step [350/350], Loss:0.0909, Perplexity: 1.0952\n",
            "Epoch [46/300], Step [50/350], Loss:0.1248, Perplexity: 1.1330\n",
            "Epoch [46/300], Step [100/350], Loss:0.1269, Perplexity: 1.1353\n",
            "Epoch [46/300], Step [150/350], Loss:0.1292, Perplexity: 1.1379\n",
            "Epoch [46/300], Step [200/350], Loss:0.0939, Perplexity: 1.0985\n",
            "Epoch [46/300], Step [250/350], Loss:0.1052, Perplexity: 1.1109\n",
            "Epoch [46/300], Step [300/350], Loss:0.0932, Perplexity: 1.0977\n",
            "Epoch [46/300], Step [350/350], Loss:0.0898, Perplexity: 1.0940\n",
            "Epoch [47/300], Step [50/350], Loss:0.0960, Perplexity: 1.1008\n",
            "Epoch [47/300], Step [100/350], Loss:0.1064, Perplexity: 1.1123\n",
            "Epoch [47/300], Step [150/350], Loss:0.1117, Perplexity: 1.1182\n",
            "Epoch [47/300], Step [200/350], Loss:0.0973, Perplexity: 1.1022\n",
            "Epoch [47/300], Step [250/350], Loss:0.1058, Perplexity: 1.1116\n",
            "Epoch [47/300], Step [300/350], Loss:0.0886, Perplexity: 1.0926\n",
            "Epoch [47/300], Step [350/350], Loss:0.0966, Perplexity: 1.1014\n",
            "Epoch [48/300], Step [50/350], Loss:0.1076, Perplexity: 1.1136\n",
            "Epoch [48/300], Step [100/350], Loss:0.0856, Perplexity: 1.0894\n",
            "Epoch [48/300], Step [150/350], Loss:0.0938, Perplexity: 1.0983\n",
            "Epoch [48/300], Step [200/350], Loss:0.1080, Perplexity: 1.1140\n",
            "Epoch [48/300], Step [250/350], Loss:0.1064, Perplexity: 1.1123\n",
            "Epoch [48/300], Step [300/350], Loss:0.0915, Perplexity: 1.0958\n",
            "Epoch [48/300], Step [350/350], Loss:0.0978, Perplexity: 1.1028\n",
            "Epoch [49/300], Step [50/350], Loss:0.1081, Perplexity: 1.1142\n",
            "Epoch [49/300], Step [100/350], Loss:0.1127, Perplexity: 1.1193\n",
            "Epoch [49/300], Step [150/350], Loss:0.0824, Perplexity: 1.0859\n",
            "Epoch [49/300], Step [200/350], Loss:0.0899, Perplexity: 1.0941\n",
            "Epoch [49/300], Step [250/350], Loss:0.1002, Perplexity: 1.1053\n",
            "Epoch [49/300], Step [300/350], Loss:0.0898, Perplexity: 1.0939\n",
            "Epoch [49/300], Step [350/350], Loss:0.0983, Perplexity: 1.1033\n",
            "Epoch [50/300], Step [50/350], Loss:0.1056, Perplexity: 1.1114\n",
            "Epoch [50/300], Step [100/350], Loss:0.0966, Perplexity: 1.1014\n",
            "Epoch [50/300], Step [150/350], Loss:0.1126, Perplexity: 1.1192\n",
            "Epoch [50/300], Step [200/350], Loss:0.1584, Perplexity: 1.1716\n",
            "Epoch [50/300], Step [250/350], Loss:0.1129, Perplexity: 1.1196\n",
            "Epoch [50/300], Step [300/350], Loss:0.0988, Perplexity: 1.1039\n",
            "Epoch [50/300], Step [350/350], Loss:0.0947, Perplexity: 1.0993\n",
            "Epoch [51/300], Step [50/350], Loss:0.0865, Perplexity: 1.0903\n",
            "Epoch [51/300], Step [100/350], Loss:0.1064, Perplexity: 1.1122\n",
            "Epoch [51/300], Step [150/350], Loss:0.0848, Perplexity: 1.0885\n",
            "Epoch [51/300], Step [200/350], Loss:0.0938, Perplexity: 1.0984\n",
            "Epoch [51/300], Step [250/350], Loss:0.0899, Perplexity: 1.0940\n",
            "Epoch [51/300], Step [300/350], Loss:0.1027, Perplexity: 1.1081\n",
            "Epoch [51/300], Step [350/350], Loss:0.1161, Perplexity: 1.1231\n",
            "Epoch [52/300], Step [50/350], Loss:0.1070, Perplexity: 1.1129\n",
            "Epoch [52/300], Step [100/350], Loss:0.0938, Perplexity: 1.0984\n",
            "Epoch [52/300], Step [150/350], Loss:0.0774, Perplexity: 1.0805\n",
            "Epoch [52/300], Step [200/350], Loss:0.1058, Perplexity: 1.1116\n",
            "Epoch [52/300], Step [250/350], Loss:0.0931, Perplexity: 1.0976\n",
            "Epoch [52/300], Step [300/350], Loss:0.0747, Perplexity: 1.0775\n",
            "Epoch [52/300], Step [350/350], Loss:0.0978, Perplexity: 1.1028\n",
            "Epoch [53/300], Step [50/350], Loss:0.1002, Perplexity: 1.1053\n",
            "Epoch [53/300], Step [100/350], Loss:0.1059, Perplexity: 1.1117\n",
            "Epoch [53/300], Step [150/350], Loss:0.0934, Perplexity: 1.0979\n",
            "Epoch [53/300], Step [200/350], Loss:0.0768, Perplexity: 1.0798\n",
            "Epoch [53/300], Step [250/350], Loss:0.0886, Perplexity: 1.0927\n",
            "Epoch [53/300], Step [300/350], Loss:0.0914, Perplexity: 1.0957\n",
            "Epoch [53/300], Step [350/350], Loss:0.0761, Perplexity: 1.0791\n",
            "Epoch [54/300], Step [50/350], Loss:0.0760, Perplexity: 1.0790\n",
            "Epoch [54/300], Step [100/350], Loss:0.0867, Perplexity: 1.0906\n",
            "Epoch [54/300], Step [150/350], Loss:0.0873, Perplexity: 1.0912\n",
            "Epoch [54/300], Step [200/350], Loss:0.1318, Perplexity: 1.1409\n",
            "Epoch [54/300], Step [250/350], Loss:0.0786, Perplexity: 1.0818\n",
            "Epoch [54/300], Step [300/350], Loss:0.0949, Perplexity: 1.0995\n",
            "Epoch [54/300], Step [350/350], Loss:0.1122, Perplexity: 1.1188\n",
            "Epoch [55/300], Step [50/350], Loss:0.0989, Perplexity: 1.1040\n",
            "Epoch [55/300], Step [100/350], Loss:0.0949, Perplexity: 1.0995\n",
            "Epoch [55/300], Step [150/350], Loss:0.0951, Perplexity: 1.0997\n",
            "Epoch [55/300], Step [200/350], Loss:0.1181, Perplexity: 1.1253\n",
            "Epoch [55/300], Step [250/350], Loss:0.1023, Perplexity: 1.1078\n",
            "Epoch [55/300], Step [300/350], Loss:0.0893, Perplexity: 1.0934\n",
            "Epoch [55/300], Step [350/350], Loss:0.1109, Perplexity: 1.1173\n",
            "Epoch [56/300], Step [50/350], Loss:0.0812, Perplexity: 1.0846\n",
            "Epoch [56/300], Step [100/350], Loss:0.0838, Perplexity: 1.0874\n",
            "Epoch [56/300], Step [150/350], Loss:0.0966, Perplexity: 1.1014\n",
            "Epoch [56/300], Step [200/350], Loss:0.0986, Perplexity: 1.1036\n",
            "Epoch [56/300], Step [250/350], Loss:0.1055, Perplexity: 1.1113\n",
            "Epoch [56/300], Step [300/350], Loss:0.0902, Perplexity: 1.0944\n",
            "Epoch [56/300], Step [350/350], Loss:0.0916, Perplexity: 1.0960\n",
            "Epoch [57/300], Step [50/350], Loss:0.1198, Perplexity: 1.1273\n",
            "Epoch [57/300], Step [100/350], Loss:0.0844, Perplexity: 1.0880\n",
            "Epoch [57/300], Step [150/350], Loss:0.0930, Perplexity: 1.0975\n",
            "Epoch [57/300], Step [200/350], Loss:0.1019, Perplexity: 1.1072\n",
            "Epoch [57/300], Step [250/350], Loss:0.1132, Perplexity: 1.1198\n",
            "Epoch [57/300], Step [300/350], Loss:0.0967, Perplexity: 1.1015\n",
            "Epoch [57/300], Step [350/350], Loss:0.1175, Perplexity: 1.1247\n",
            "Epoch [58/300], Step [50/350], Loss:0.1010, Perplexity: 1.1062\n",
            "Epoch [58/300], Step [100/350], Loss:0.0895, Perplexity: 1.0936\n",
            "Epoch [58/300], Step [150/350], Loss:0.1177, Perplexity: 1.1249\n",
            "Epoch [58/300], Step [200/350], Loss:0.1138, Perplexity: 1.1205\n",
            "Epoch [58/300], Step [250/350], Loss:0.0868, Perplexity: 1.0907\n",
            "Epoch [58/300], Step [300/350], Loss:0.1129, Perplexity: 1.1195\n",
            "Epoch [58/300], Step [350/350], Loss:0.0718, Perplexity: 1.0744\n",
            "Epoch [59/300], Step [50/350], Loss:0.0912, Perplexity: 1.0955\n",
            "Epoch [59/300], Step [100/350], Loss:0.1131, Perplexity: 1.1197\n",
            "Epoch [59/300], Step [150/350], Loss:0.1140, Perplexity: 1.1208\n",
            "Epoch [59/300], Step [200/350], Loss:0.0941, Perplexity: 1.0987\n",
            "Epoch [59/300], Step [250/350], Loss:0.1058, Perplexity: 1.1116\n",
            "Epoch [59/300], Step [300/350], Loss:0.1185, Perplexity: 1.1258\n",
            "Epoch [59/300], Step [350/350], Loss:0.1084, Perplexity: 1.1145\n",
            "Epoch [60/300], Step [50/350], Loss:0.0911, Perplexity: 1.0954\n",
            "Epoch [60/300], Step [100/350], Loss:0.1013, Perplexity: 1.1067\n",
            "Epoch [60/300], Step [150/350], Loss:0.0866, Perplexity: 1.0905\n",
            "Epoch [60/300], Step [200/350], Loss:0.1152, Perplexity: 1.1221\n",
            "Epoch [60/300], Step [250/350], Loss:0.0947, Perplexity: 1.0993\n",
            "Epoch [60/300], Step [300/350], Loss:0.0811, Perplexity: 1.0845\n",
            "Epoch [60/300], Step [350/350], Loss:0.1268, Perplexity: 1.1352\n",
            "Epoch [61/300], Step [50/350], Loss:0.0753, Perplexity: 1.0782\n",
            "Epoch [61/300], Step [100/350], Loss:0.0917, Perplexity: 1.0961\n",
            "Epoch [61/300], Step [150/350], Loss:0.0876, Perplexity: 1.0916\n",
            "Epoch [61/300], Step [200/350], Loss:0.0979, Perplexity: 1.1029\n",
            "Epoch [61/300], Step [250/350], Loss:0.0844, Perplexity: 1.0881\n",
            "Epoch [61/300], Step [300/350], Loss:0.1014, Perplexity: 1.1067\n",
            "Epoch [61/300], Step [350/350], Loss:0.0799, Perplexity: 1.0831\n",
            "Epoch [62/300], Step [50/350], Loss:0.0869, Perplexity: 1.0908\n",
            "Epoch [62/300], Step [100/350], Loss:0.1101, Perplexity: 1.1164\n",
            "Epoch [62/300], Step [150/350], Loss:0.0836, Perplexity: 1.0872\n",
            "Epoch [62/300], Step [200/350], Loss:0.1022, Perplexity: 1.1077\n",
            "Epoch [62/300], Step [250/350], Loss:0.0816, Perplexity: 1.0850\n",
            "Epoch [62/300], Step [300/350], Loss:0.1066, Perplexity: 1.1125\n",
            "Epoch [62/300], Step [350/350], Loss:0.1050, Perplexity: 1.1107\n",
            "Epoch [63/300], Step [50/350], Loss:0.1105, Perplexity: 1.1168\n",
            "Epoch [63/300], Step [100/350], Loss:0.0910, Perplexity: 1.0953\n",
            "Epoch [63/300], Step [150/350], Loss:0.0921, Perplexity: 1.0965\n",
            "Epoch [63/300], Step [200/350], Loss:0.0841, Perplexity: 1.0878\n",
            "Epoch [63/300], Step [250/350], Loss:0.1274, Perplexity: 1.1359\n",
            "Epoch [63/300], Step [300/350], Loss:0.0871, Perplexity: 1.0910\n",
            "Epoch [63/300], Step [350/350], Loss:0.1431, Perplexity: 1.1539\n",
            "Epoch [64/300], Step [50/350], Loss:0.0917, Perplexity: 1.0960\n",
            "Epoch [64/300], Step [100/350], Loss:0.1148, Perplexity: 1.1216\n",
            "Epoch [64/300], Step [150/350], Loss:0.1276, Perplexity: 1.1361\n",
            "Epoch [64/300], Step [200/350], Loss:0.0989, Perplexity: 1.1039\n",
            "Epoch [64/300], Step [250/350], Loss:0.0989, Perplexity: 1.1040\n",
            "Epoch [64/300], Step [300/350], Loss:0.0923, Perplexity: 1.0967\n",
            "Epoch [64/300], Step [350/350], Loss:0.1151, Perplexity: 1.1220\n",
            "Epoch [65/300], Step [50/350], Loss:0.1046, Perplexity: 1.1102\n",
            "Epoch [65/300], Step [100/350], Loss:0.0697, Perplexity: 1.0721\n",
            "Epoch [65/300], Step [150/350], Loss:0.0677, Perplexity: 1.0701\n",
            "Epoch [65/300], Step [200/350], Loss:0.1056, Perplexity: 1.1114\n",
            "Epoch [65/300], Step [250/350], Loss:0.1037, Perplexity: 1.1093\n",
            "Epoch [65/300], Step [300/350], Loss:0.0932, Perplexity: 1.0977\n",
            "Epoch [65/300], Step [350/350], Loss:0.1298, Perplexity: 1.1386\n",
            "Epoch [66/300], Step [50/350], Loss:0.0907, Perplexity: 1.0949\n",
            "Epoch [66/300], Step [100/350], Loss:0.0872, Perplexity: 1.0911\n",
            "Epoch [66/300], Step [150/350], Loss:0.0907, Perplexity: 1.0950\n",
            "Epoch [66/300], Step [200/350], Loss:0.0780, Perplexity: 1.0811\n",
            "Epoch [66/300], Step [250/350], Loss:0.1006, Perplexity: 1.1058\n",
            "Epoch [66/300], Step [300/350], Loss:0.1116, Perplexity: 1.1181\n",
            "Epoch [66/300], Step [350/350], Loss:0.0853, Perplexity: 1.0891\n",
            "Epoch [67/300], Step [50/350], Loss:0.0843, Perplexity: 1.0880\n",
            "Epoch [67/300], Step [100/350], Loss:0.0861, Perplexity: 1.0899\n",
            "Epoch [67/300], Step [150/350], Loss:0.0942, Perplexity: 1.0987\n",
            "Epoch [67/300], Step [200/350], Loss:0.1595, Perplexity: 1.1729\n",
            "Epoch [67/300], Step [250/350], Loss:0.1012, Perplexity: 1.1065\n",
            "Epoch [67/300], Step [300/350], Loss:0.1006, Perplexity: 1.1059\n",
            "Epoch [67/300], Step [350/350], Loss:0.0764, Perplexity: 1.0794\n",
            "Epoch [68/300], Step [50/350], Loss:0.0787, Perplexity: 1.0819\n",
            "Epoch [68/300], Step [100/350], Loss:0.0832, Perplexity: 1.0868\n",
            "Epoch [68/300], Step [150/350], Loss:0.1056, Perplexity: 1.1114\n",
            "Epoch [68/300], Step [200/350], Loss:0.1046, Perplexity: 1.1102\n",
            "Epoch [68/300], Step [250/350], Loss:0.1221, Perplexity: 1.1299\n",
            "Epoch [68/300], Step [300/350], Loss:0.1349, Perplexity: 1.1445\n",
            "Epoch [68/300], Step [350/350], Loss:0.0842, Perplexity: 1.0878\n",
            "Epoch [69/300], Step [50/350], Loss:0.1166, Perplexity: 1.1237\n",
            "Epoch [69/300], Step [100/350], Loss:0.1108, Perplexity: 1.1172\n",
            "Epoch [69/300], Step [150/350], Loss:0.0748, Perplexity: 1.0777\n",
            "Epoch [69/300], Step [200/350], Loss:0.1266, Perplexity: 1.1349\n",
            "Epoch [69/300], Step [250/350], Loss:0.0878, Perplexity: 1.0918\n",
            "Epoch [69/300], Step [300/350], Loss:0.0691, Perplexity: 1.0716\n",
            "Epoch [69/300], Step [350/350], Loss:0.0717, Perplexity: 1.0743\n",
            "Epoch [70/300], Step [50/350], Loss:0.0982, Perplexity: 1.1031\n",
            "Epoch [70/300], Step [100/350], Loss:0.0903, Perplexity: 1.0944\n",
            "Epoch [70/300], Step [150/350], Loss:0.0922, Perplexity: 1.0966\n",
            "Epoch [70/300], Step [200/350], Loss:0.0904, Perplexity: 1.0946\n",
            "Epoch [70/300], Step [250/350], Loss:0.0794, Perplexity: 1.0826\n",
            "Epoch [70/300], Step [300/350], Loss:0.0919, Perplexity: 1.0963\n",
            "Epoch [70/300], Step [350/350], Loss:0.0661, Perplexity: 1.0683\n",
            "Epoch [71/300], Step [50/350], Loss:0.0960, Perplexity: 1.1008\n",
            "Epoch [71/300], Step [100/350], Loss:0.1008, Perplexity: 1.1061\n",
            "Epoch [71/300], Step [150/350], Loss:0.1608, Perplexity: 1.1745\n",
            "Epoch [71/300], Step [200/350], Loss:0.1141, Perplexity: 1.1208\n",
            "Epoch [71/300], Step [250/350], Loss:0.1063, Perplexity: 1.1122\n",
            "Epoch [71/300], Step [300/350], Loss:0.0982, Perplexity: 1.1032\n",
            "Epoch [71/300], Step [350/350], Loss:0.1560, Perplexity: 1.1688\n",
            "Epoch [72/300], Step [50/350], Loss:0.1065, Perplexity: 1.1124\n",
            "Epoch [72/300], Step [100/350], Loss:0.0827, Perplexity: 1.0862\n",
            "Epoch [72/300], Step [150/350], Loss:0.1238, Perplexity: 1.1318\n",
            "Epoch [72/300], Step [200/350], Loss:0.0923, Perplexity: 1.0966\n",
            "Epoch [72/300], Step [250/350], Loss:0.1131, Perplexity: 1.1198\n",
            "Epoch [72/300], Step [300/350], Loss:0.1065, Perplexity: 1.1124\n",
            "Epoch [72/300], Step [350/350], Loss:0.0891, Perplexity: 1.0932\n",
            "Epoch [73/300], Step [50/350], Loss:0.1078, Perplexity: 1.1138\n",
            "Epoch [73/300], Step [100/350], Loss:0.0997, Perplexity: 1.1048\n",
            "Epoch [73/300], Step [150/350], Loss:0.0921, Perplexity: 1.0965\n",
            "Epoch [73/300], Step [200/350], Loss:0.1330, Perplexity: 1.1423\n",
            "Epoch [73/300], Step [250/350], Loss:0.0983, Perplexity: 1.1033\n",
            "Epoch [73/300], Step [300/350], Loss:0.1140, Perplexity: 1.1207\n",
            "Epoch [73/300], Step [350/350], Loss:0.0851, Perplexity: 1.0888\n",
            "Epoch [74/300], Step [50/350], Loss:0.1129, Perplexity: 1.1195\n",
            "Epoch [74/300], Step [100/350], Loss:0.1068, Perplexity: 1.1127\n",
            "Epoch [74/300], Step [150/350], Loss:0.0891, Perplexity: 1.0932\n",
            "Epoch [74/300], Step [200/350], Loss:0.0969, Perplexity: 1.1017\n",
            "Epoch [74/300], Step [250/350], Loss:0.1029, Perplexity: 1.1084\n",
            "Epoch [74/300], Step [300/350], Loss:0.0778, Perplexity: 1.0809\n",
            "Epoch [74/300], Step [350/350], Loss:0.0912, Perplexity: 1.0955\n",
            "Epoch [75/300], Step [50/350], Loss:0.0885, Perplexity: 1.0925\n",
            "Epoch [75/300], Step [100/350], Loss:0.1126, Perplexity: 1.1191\n",
            "Epoch [75/300], Step [150/350], Loss:0.0808, Perplexity: 1.0842\n",
            "Epoch [75/300], Step [200/350], Loss:0.1012, Perplexity: 1.1065\n",
            "Epoch [75/300], Step [250/350], Loss:0.1151, Perplexity: 1.1220\n",
            "Epoch [75/300], Step [300/350], Loss:0.1066, Perplexity: 1.1125\n",
            "Epoch [75/300], Step [350/350], Loss:0.0931, Perplexity: 1.0976\n",
            "Epoch [76/300], Step [50/350], Loss:0.1057, Perplexity: 1.1115\n",
            "Epoch [76/300], Step [100/350], Loss:0.0842, Perplexity: 1.0879\n",
            "Epoch [76/300], Step [150/350], Loss:0.0832, Perplexity: 1.0868\n",
            "Epoch [76/300], Step [200/350], Loss:0.0725, Perplexity: 1.0752\n",
            "Epoch [76/300], Step [250/350], Loss:0.0976, Perplexity: 1.1026\n",
            "Epoch [76/300], Step [300/350], Loss:0.0833, Perplexity: 1.0869\n",
            "Epoch [76/300], Step [350/350], Loss:0.0839, Perplexity: 1.0875\n",
            "Epoch [77/300], Step [50/350], Loss:0.0846, Perplexity: 1.0883\n",
            "Epoch [77/300], Step [100/350], Loss:0.0759, Perplexity: 1.0788\n",
            "Epoch [77/300], Step [150/350], Loss:0.1246, Perplexity: 1.1327\n",
            "Epoch [77/300], Step [200/350], Loss:0.0618, Perplexity: 1.0638\n",
            "Epoch [77/300], Step [250/350], Loss:0.0645, Perplexity: 1.0666\n",
            "Epoch [77/300], Step [300/350], Loss:0.0761, Perplexity: 1.0791\n",
            "Epoch [77/300], Step [350/350], Loss:0.0962, Perplexity: 1.1010\n",
            "Epoch [78/300], Step [50/350], Loss:0.0842, Perplexity: 1.0878\n",
            "Epoch [78/300], Step [100/350], Loss:0.0845, Perplexity: 1.0881\n",
            "Epoch [78/300], Step [150/350], Loss:0.0869, Perplexity: 1.0908\n",
            "Epoch [78/300], Step [200/350], Loss:0.0918, Perplexity: 1.0961\n",
            "Epoch [78/300], Step [250/350], Loss:0.0969, Perplexity: 1.1018\n",
            "Epoch [78/300], Step [300/350], Loss:0.0849, Perplexity: 1.0886\n",
            "Epoch [78/300], Step [350/350], Loss:0.0964, Perplexity: 1.1012\n",
            "Epoch [79/300], Step [50/350], Loss:0.1155, Perplexity: 1.1224\n",
            "Epoch [79/300], Step [100/350], Loss:0.0750, Perplexity: 1.0779\n",
            "Epoch [79/300], Step [150/350], Loss:0.0869, Perplexity: 1.0908\n",
            "Epoch [79/300], Step [200/350], Loss:0.0849, Perplexity: 1.0886\n",
            "Epoch [79/300], Step [250/350], Loss:0.0832, Perplexity: 1.0868\n",
            "Epoch [79/300], Step [300/350], Loss:0.1213, Perplexity: 1.1290\n",
            "Epoch [79/300], Step [350/350], Loss:0.0730, Perplexity: 1.0757\n",
            "Epoch [80/300], Step [50/350], Loss:0.1064, Perplexity: 1.1123\n",
            "Epoch [80/300], Step [100/350], Loss:0.0754, Perplexity: 1.0783\n",
            "Epoch [80/300], Step [150/350], Loss:0.0883, Perplexity: 1.0923\n",
            "Epoch [80/300], Step [200/350], Loss:0.0672, Perplexity: 1.0695\n",
            "Epoch [80/300], Step [250/350], Loss:0.0763, Perplexity: 1.0793\n",
            "Epoch [80/300], Step [300/350], Loss:0.1072, Perplexity: 1.1131\n",
            "Epoch [80/300], Step [350/350], Loss:0.0999, Perplexity: 1.1050\n",
            "Epoch [81/300], Step [50/350], Loss:0.0806, Perplexity: 1.0839\n",
            "Epoch [81/300], Step [100/350], Loss:0.0973, Perplexity: 1.1022\n",
            "Epoch [81/300], Step [150/350], Loss:0.1093, Perplexity: 1.1156\n",
            "Epoch [81/300], Step [200/350], Loss:0.0878, Perplexity: 1.0918\n",
            "Epoch [81/300], Step [250/350], Loss:0.0846, Perplexity: 1.0883\n",
            "Epoch [81/300], Step [300/350], Loss:0.0925, Perplexity: 1.0969\n",
            "Epoch [81/300], Step [350/350], Loss:0.0843, Perplexity: 1.0880\n",
            "Epoch [82/300], Step [50/350], Loss:0.0854, Perplexity: 1.0892\n",
            "Epoch [82/300], Step [100/350], Loss:0.0670, Perplexity: 1.0693\n",
            "Epoch [82/300], Step [150/350], Loss:0.1206, Perplexity: 1.1281\n",
            "Epoch [82/300], Step [200/350], Loss:0.0720, Perplexity: 1.0747\n",
            "Epoch [82/300], Step [250/350], Loss:0.0762, Perplexity: 1.0792\n",
            "Epoch [82/300], Step [300/350], Loss:0.0842, Perplexity: 1.0879\n",
            "Epoch [82/300], Step [350/350], Loss:0.0924, Perplexity: 1.0968\n",
            "Epoch [83/300], Step [50/350], Loss:0.0776, Perplexity: 1.0807\n",
            "Epoch [83/300], Step [100/350], Loss:0.0852, Perplexity: 1.0889\n",
            "Epoch [83/300], Step [150/350], Loss:0.0742, Perplexity: 1.0771\n",
            "Epoch [83/300], Step [200/350], Loss:0.1010, Perplexity: 1.1063\n",
            "Epoch [83/300], Step [250/350], Loss:0.1068, Perplexity: 1.1127\n",
            "Epoch [83/300], Step [300/350], Loss:0.0783, Perplexity: 1.0815\n",
            "Epoch [83/300], Step [350/350], Loss:0.0689, Perplexity: 1.0713\n",
            "Epoch [84/300], Step [50/350], Loss:0.1143, Perplexity: 1.1211\n",
            "Epoch [84/300], Step [100/350], Loss:0.0832, Perplexity: 1.0867\n",
            "Epoch [84/300], Step [150/350], Loss:0.0841, Perplexity: 1.0877\n",
            "Epoch [84/300], Step [200/350], Loss:0.0793, Perplexity: 1.0826\n",
            "Epoch [84/300], Step [250/350], Loss:0.0693, Perplexity: 1.0718\n",
            "Epoch [84/300], Step [300/350], Loss:0.0987, Perplexity: 1.1037\n",
            "Epoch [84/300], Step [350/350], Loss:0.0811, Perplexity: 1.0844\n",
            "Epoch [85/300], Step [50/350], Loss:0.0597, Perplexity: 1.0615\n",
            "Epoch [85/300], Step [100/350], Loss:0.0852, Perplexity: 1.0890\n",
            "Epoch [85/300], Step [150/350], Loss:0.0628, Perplexity: 1.0648\n",
            "Epoch [85/300], Step [200/350], Loss:0.0777, Perplexity: 1.0808\n",
            "Epoch [85/300], Step [250/350], Loss:0.0882, Perplexity: 1.0922\n",
            "Epoch [85/300], Step [300/350], Loss:0.0775, Perplexity: 1.0806\n",
            "Epoch [85/300], Step [350/350], Loss:0.0800, Perplexity: 1.0833\n",
            "Epoch [86/300], Step [50/350], Loss:0.0876, Perplexity: 1.0915\n",
            "Epoch [86/300], Step [100/350], Loss:0.0986, Perplexity: 1.1036\n",
            "Epoch [86/300], Step [150/350], Loss:0.1075, Perplexity: 1.1135\n",
            "Epoch [86/300], Step [200/350], Loss:0.1098, Perplexity: 1.1160\n",
            "Epoch [86/300], Step [250/350], Loss:0.0804, Perplexity: 1.0837\n",
            "Epoch [86/300], Step [300/350], Loss:0.1171, Perplexity: 1.1242\n",
            "Epoch [86/300], Step [350/350], Loss:0.1078, Perplexity: 1.1138\n",
            "Epoch [87/300], Step [50/350], Loss:0.0872, Perplexity: 1.0912\n",
            "Epoch [87/300], Step [100/350], Loss:0.1119, Perplexity: 1.1184\n",
            "Epoch [87/300], Step [150/350], Loss:0.0651, Perplexity: 1.0672\n",
            "Epoch [87/300], Step [200/350], Loss:0.0900, Perplexity: 1.0942\n",
            "Epoch [87/300], Step [250/350], Loss:0.1131, Perplexity: 1.1198\n",
            "Epoch [87/300], Step [300/350], Loss:0.1070, Perplexity: 1.1129\n",
            "Epoch [87/300], Step [350/350], Loss:0.1021, Perplexity: 1.1074\n",
            "Epoch [88/300], Step [50/350], Loss:0.0684, Perplexity: 1.0707\n",
            "Epoch [88/300], Step [100/350], Loss:0.0868, Perplexity: 1.0907\n",
            "Epoch [88/300], Step [150/350], Loss:0.0793, Perplexity: 1.0825\n",
            "Epoch [88/300], Step [200/350], Loss:0.1028, Perplexity: 1.1083\n",
            "Epoch [88/300], Step [250/350], Loss:0.0900, Perplexity: 1.0942\n",
            "Epoch [88/300], Step [300/350], Loss:0.0679, Perplexity: 1.0702\n",
            "Epoch [88/300], Step [350/350], Loss:0.0772, Perplexity: 1.0803\n",
            "Epoch [89/300], Step [50/350], Loss:0.0778, Perplexity: 1.0809\n",
            "Epoch [89/300], Step [100/350], Loss:0.1558, Perplexity: 1.1686\n",
            "Epoch [89/300], Step [150/350], Loss:0.0944, Perplexity: 1.0990\n",
            "Epoch [89/300], Step [200/350], Loss:0.0866, Perplexity: 1.0904\n",
            "Epoch [89/300], Step [250/350], Loss:0.0796, Perplexity: 1.0829\n",
            "Epoch [89/300], Step [300/350], Loss:0.0675, Perplexity: 1.0698\n",
            "Epoch [89/300], Step [350/350], Loss:0.0655, Perplexity: 1.0677\n",
            "Epoch [90/300], Step [50/350], Loss:0.0629, Perplexity: 1.0649\n",
            "Epoch [90/300], Step [100/350], Loss:0.0672, Perplexity: 1.0696\n",
            "Epoch [90/300], Step [150/350], Loss:0.1030, Perplexity: 1.1085\n",
            "Epoch [90/300], Step [200/350], Loss:0.0657, Perplexity: 1.0679\n",
            "Epoch [90/300], Step [250/350], Loss:0.0619, Perplexity: 1.0639\n",
            "Epoch [90/300], Step [300/350], Loss:0.0636, Perplexity: 1.0657\n",
            "Epoch [90/300], Step [350/350], Loss:0.1434, Perplexity: 1.1542\n",
            "Epoch [91/300], Step [50/350], Loss:0.0771, Perplexity: 1.0801\n",
            "Epoch [91/300], Step [100/350], Loss:0.0912, Perplexity: 1.0955\n",
            "Epoch [91/300], Step [150/350], Loss:0.0848, Perplexity: 1.0885\n",
            "Epoch [91/300], Step [200/350], Loss:0.0941, Perplexity: 1.0987\n",
            "Epoch [91/300], Step [250/350], Loss:0.0795, Perplexity: 1.0827\n",
            "Epoch [91/300], Step [300/350], Loss:0.0826, Perplexity: 1.0861\n",
            "Epoch [91/300], Step [350/350], Loss:0.0909, Perplexity: 1.0952\n",
            "Epoch [92/300], Step [50/350], Loss:0.0905, Perplexity: 1.0948\n",
            "Epoch [92/300], Step [100/350], Loss:0.0633, Perplexity: 1.0653\n",
            "Epoch [92/300], Step [150/350], Loss:0.0866, Perplexity: 1.0904\n",
            "Epoch [92/300], Step [200/350], Loss:0.0842, Perplexity: 1.0878\n",
            "Epoch [92/300], Step [250/350], Loss:0.1105, Perplexity: 1.1168\n",
            "Epoch [92/300], Step [300/350], Loss:0.0925, Perplexity: 1.0969\n",
            "Epoch [92/300], Step [350/350], Loss:0.0891, Perplexity: 1.0932\n",
            "Epoch [93/300], Step [50/350], Loss:0.0768, Perplexity: 1.0798\n",
            "Epoch [93/300], Step [100/350], Loss:0.0520, Perplexity: 1.0534\n",
            "Epoch [93/300], Step [150/350], Loss:0.0885, Perplexity: 1.0925\n",
            "Epoch [93/300], Step [200/350], Loss:0.0723, Perplexity: 1.0749\n",
            "Epoch [93/300], Step [250/350], Loss:0.0479, Perplexity: 1.0490\n",
            "Epoch [93/300], Step [300/350], Loss:0.0758, Perplexity: 1.0788\n",
            "Epoch [93/300], Step [350/350], Loss:0.0818, Perplexity: 1.0853\n",
            "Epoch [94/300], Step [50/350], Loss:0.0799, Perplexity: 1.0832\n",
            "Epoch [94/300], Step [100/350], Loss:0.0920, Perplexity: 1.0963\n",
            "Epoch [94/300], Step [150/350], Loss:0.0679, Perplexity: 1.0702\n",
            "Epoch [94/300], Step [200/350], Loss:0.0858, Perplexity: 1.0896\n",
            "Epoch [94/300], Step [250/350], Loss:0.0909, Perplexity: 1.0952\n",
            "Epoch [94/300], Step [300/350], Loss:0.1122, Perplexity: 1.1188\n",
            "Epoch [94/300], Step [350/350], Loss:0.0758, Perplexity: 1.0787\n",
            "Epoch [95/300], Step [50/350], Loss:0.0802, Perplexity: 1.0835\n",
            "Epoch [95/300], Step [100/350], Loss:0.0706, Perplexity: 1.0731\n",
            "Epoch [95/300], Step [150/350], Loss:0.0882, Perplexity: 1.0922\n",
            "Epoch [95/300], Step [200/350], Loss:0.0845, Perplexity: 1.0882\n",
            "Epoch [95/300], Step [250/350], Loss:0.1068, Perplexity: 1.1128\n",
            "Epoch [95/300], Step [300/350], Loss:0.0736, Perplexity: 1.0764\n",
            "Epoch [95/300], Step [350/350], Loss:0.0570, Perplexity: 1.0586\n",
            "Epoch [96/300], Step [50/350], Loss:0.0767, Perplexity: 1.0797\n",
            "Epoch [96/300], Step [100/350], Loss:0.0793, Perplexity: 1.0825\n",
            "Epoch [96/300], Step [150/350], Loss:0.0953, Perplexity: 1.1000\n",
            "Epoch [96/300], Step [200/350], Loss:0.0758, Perplexity: 1.0788\n",
            "Epoch [96/300], Step [250/350], Loss:0.0792, Perplexity: 1.0825\n",
            "Epoch [96/300], Step [300/350], Loss:0.1241, Perplexity: 1.1321\n",
            "Epoch [96/300], Step [350/350], Loss:0.0762, Perplexity: 1.0792\n",
            "Epoch [97/300], Step [50/350], Loss:0.0863, Perplexity: 1.0901\n",
            "Epoch [97/300], Step [100/350], Loss:0.0690, Perplexity: 1.0714\n",
            "Epoch [97/300], Step [150/350], Loss:0.0755, Perplexity: 1.0784\n",
            "Epoch [97/300], Step [200/350], Loss:0.0785, Perplexity: 1.0817\n",
            "Epoch [97/300], Step [250/350], Loss:0.1041, Perplexity: 1.1097\n",
            "Epoch [97/300], Step [300/350], Loss:0.0996, Perplexity: 1.1047\n",
            "Epoch [97/300], Step [350/350], Loss:0.0798, Perplexity: 1.0831\n",
            "Epoch [98/300], Step [50/350], Loss:0.0729, Perplexity: 1.0757\n",
            "Epoch [98/300], Step [100/350], Loss:0.0794, Perplexity: 1.0827\n",
            "Epoch [98/300], Step [150/350], Loss:0.0548, Perplexity: 1.0563\n",
            "Epoch [98/300], Step [200/350], Loss:0.0560, Perplexity: 1.0576\n",
            "Epoch [98/300], Step [250/350], Loss:0.0887, Perplexity: 1.0927\n",
            "Epoch [98/300], Step [300/350], Loss:0.0683, Perplexity: 1.0707\n",
            "Epoch [98/300], Step [350/350], Loss:0.0782, Perplexity: 1.0813\n",
            "Epoch [99/300], Step [50/350], Loss:0.0950, Perplexity: 1.0997\n",
            "Epoch [99/300], Step [100/350], Loss:0.0788, Perplexity: 1.0820\n",
            "Epoch [99/300], Step [150/350], Loss:0.0883, Perplexity: 1.0924\n",
            "Epoch [99/300], Step [200/350], Loss:0.0938, Perplexity: 1.0984\n",
            "Epoch [99/300], Step [250/350], Loss:0.0742, Perplexity: 1.0770\n",
            "Epoch [99/300], Step [300/350], Loss:0.0820, Perplexity: 1.0855\n",
            "Epoch [99/300], Step [350/350], Loss:0.1015, Perplexity: 1.1068\n",
            "Epoch [100/300], Step [50/350], Loss:0.0624, Perplexity: 1.0644\n",
            "Epoch [100/300], Step [100/350], Loss:0.0772, Perplexity: 1.0803\n",
            "Epoch [100/300], Step [150/350], Loss:0.0677, Perplexity: 1.0700\n",
            "Epoch [100/300], Step [200/350], Loss:0.1157, Perplexity: 1.1226\n",
            "Epoch [100/300], Step [250/350], Loss:0.0781, Perplexity: 1.0812\n",
            "Epoch [100/300], Step [300/350], Loss:0.0740, Perplexity: 1.0768\n",
            "Epoch [100/300], Step [350/350], Loss:0.0870, Perplexity: 1.0909\n",
            "Epoch [101/300], Step [50/350], Loss:0.0800, Perplexity: 1.0832\n",
            "Epoch [101/300], Step [100/350], Loss:0.1527, Perplexity: 1.1650\n",
            "Epoch [101/300], Step [150/350], Loss:0.0711, Perplexity: 1.0737\n",
            "Epoch [101/300], Step [200/350], Loss:0.0769, Perplexity: 1.0800\n",
            "Epoch [101/300], Step [250/350], Loss:0.0918, Perplexity: 1.0961\n",
            "Epoch [101/300], Step [300/350], Loss:0.0742, Perplexity: 1.0770\n",
            "Epoch [101/300], Step [350/350], Loss:0.0849, Perplexity: 1.0886\n",
            "Epoch [102/300], Step [50/350], Loss:0.0810, Perplexity: 1.0844\n",
            "Epoch [102/300], Step [100/350], Loss:0.0758, Perplexity: 1.0788\n",
            "Epoch [102/300], Step [150/350], Loss:0.0618, Perplexity: 1.0638\n",
            "Epoch [102/300], Step [200/350], Loss:0.0869, Perplexity: 1.0908\n",
            "Epoch [102/300], Step [250/350], Loss:0.0638, Perplexity: 1.0659\n",
            "Epoch [102/300], Step [300/350], Loss:0.0684, Perplexity: 1.0708\n",
            "Epoch [102/300], Step [350/350], Loss:0.1104, Perplexity: 1.1168\n",
            "Epoch [103/300], Step [50/350], Loss:0.0933, Perplexity: 1.0978\n",
            "Epoch [103/300], Step [100/350], Loss:0.0722, Perplexity: 1.0749\n",
            "Epoch [103/300], Step [150/350], Loss:0.0882, Perplexity: 1.0922\n",
            "Epoch [103/300], Step [200/350], Loss:0.0675, Perplexity: 1.0699\n",
            "Epoch [103/300], Step [250/350], Loss:0.0976, Perplexity: 1.1025\n",
            "Epoch [103/300], Step [300/350], Loss:0.0557, Perplexity: 1.0573\n",
            "Epoch [103/300], Step [350/350], Loss:0.0870, Perplexity: 1.0909\n",
            "Epoch [104/300], Step [50/350], Loss:0.0942, Perplexity: 1.0988\n",
            "Epoch [104/300], Step [100/350], Loss:0.0645, Perplexity: 1.0667\n",
            "Epoch [104/300], Step [150/350], Loss:0.0600, Perplexity: 1.0618\n",
            "Epoch [104/300], Step [200/350], Loss:0.0702, Perplexity: 1.0727\n",
            "Epoch [104/300], Step [250/350], Loss:0.0889, Perplexity: 1.0930\n",
            "Epoch [104/300], Step [300/350], Loss:0.0924, Perplexity: 1.0967\n",
            "Epoch [104/300], Step [350/350], Loss:0.0541, Perplexity: 1.0556\n",
            "Epoch [105/300], Step [50/350], Loss:0.0732, Perplexity: 1.0759\n",
            "Epoch [105/300], Step [100/350], Loss:0.0893, Perplexity: 1.0935\n",
            "Epoch [105/300], Step [150/350], Loss:0.0814, Perplexity: 1.0848\n",
            "Epoch [105/300], Step [200/350], Loss:0.0653, Perplexity: 1.0674\n",
            "Epoch [105/300], Step [250/350], Loss:0.0717, Perplexity: 1.0744\n",
            "Epoch [105/300], Step [300/350], Loss:0.0782, Perplexity: 1.0814\n",
            "Epoch [105/300], Step [350/350], Loss:0.0602, Perplexity: 1.0620\n",
            "Epoch [106/300], Step [50/350], Loss:0.0964, Perplexity: 1.1012\n",
            "Epoch [106/300], Step [100/350], Loss:0.0865, Perplexity: 1.0903\n",
            "Epoch [106/300], Step [150/350], Loss:0.0721, Perplexity: 1.0748\n",
            "Epoch [106/300], Step [200/350], Loss:0.0787, Perplexity: 1.0819\n",
            "Epoch [106/300], Step [250/350], Loss:0.0889, Perplexity: 1.0929\n",
            "Epoch [106/300], Step [300/350], Loss:0.0952, Perplexity: 1.0999\n",
            "Epoch [106/300], Step [350/350], Loss:0.0926, Perplexity: 1.0971\n",
            "Epoch [107/300], Step [50/350], Loss:0.1193, Perplexity: 1.1267\n",
            "Epoch [107/300], Step [100/350], Loss:0.0757, Perplexity: 1.0787\n",
            "Epoch [107/300], Step [150/350], Loss:0.0882, Perplexity: 1.0922\n",
            "Epoch [107/300], Step [200/350], Loss:0.0720, Perplexity: 1.0746\n",
            "Epoch [107/300], Step [250/350], Loss:0.0602, Perplexity: 1.0620\n",
            "Epoch [107/300], Step [300/350], Loss:0.1179, Perplexity: 1.1251\n",
            "Epoch [107/300], Step [350/350], Loss:0.0977, Perplexity: 1.1026\n",
            "Epoch [108/300], Step [50/350], Loss:0.0855, Perplexity: 1.0892\n",
            "Epoch [108/300], Step [100/350], Loss:0.0631, Perplexity: 1.0652\n",
            "Epoch [108/300], Step [150/350], Loss:0.0780, Perplexity: 1.0811\n",
            "Epoch [108/300], Step [200/350], Loss:0.0744, Perplexity: 1.0773\n",
            "Epoch [108/300], Step [250/350], Loss:0.0855, Perplexity: 1.0893\n",
            "Epoch [108/300], Step [300/350], Loss:0.0699, Perplexity: 1.0724\n",
            "Epoch [108/300], Step [350/350], Loss:0.1170, Perplexity: 1.1241\n",
            "Epoch [109/300], Step [50/350], Loss:0.0910, Perplexity: 1.0953\n",
            "Epoch [109/300], Step [100/350], Loss:0.0835, Perplexity: 1.0871\n",
            "Epoch [109/300], Step [150/350], Loss:0.0672, Perplexity: 1.0695\n",
            "Epoch [109/300], Step [200/350], Loss:0.0995, Perplexity: 1.1047\n",
            "Epoch [109/300], Step [250/350], Loss:0.0664, Perplexity: 1.0686\n",
            "Epoch [109/300], Step [300/350], Loss:0.0825, Perplexity: 1.0860\n",
            "Epoch [109/300], Step [350/350], Loss:0.0736, Perplexity: 1.0764\n",
            "Epoch [110/300], Step [50/350], Loss:0.0692, Perplexity: 1.0717\n",
            "Epoch [110/300], Step [100/350], Loss:0.0671, Perplexity: 1.0694\n",
            "Epoch [110/300], Step [150/350], Loss:0.0662, Perplexity: 1.0684\n",
            "Epoch [110/300], Step [200/350], Loss:0.0647, Perplexity: 1.0668\n",
            "Epoch [110/300], Step [250/350], Loss:0.0967, Perplexity: 1.1015\n",
            "Epoch [110/300], Step [300/350], Loss:0.0682, Perplexity: 1.0705\n",
            "Epoch [110/300], Step [350/350], Loss:0.0748, Perplexity: 1.0776\n",
            "Epoch [111/300], Step [50/350], Loss:0.0840, Perplexity: 1.0876\n",
            "Epoch [111/300], Step [100/350], Loss:0.0759, Perplexity: 1.0789\n",
            "Epoch [111/300], Step [150/350], Loss:0.0652, Perplexity: 1.0674\n",
            "Epoch [111/300], Step [200/350], Loss:0.0795, Perplexity: 1.0827\n",
            "Epoch [111/300], Step [250/350], Loss:0.0832, Perplexity: 1.0867\n",
            "Epoch [111/300], Step [300/350], Loss:0.0892, Perplexity: 1.0933\n",
            "Epoch [111/300], Step [350/350], Loss:0.0786, Perplexity: 1.0818\n",
            "Epoch [112/300], Step [50/350], Loss:0.0962, Perplexity: 1.1010\n",
            "Epoch [112/300], Step [100/350], Loss:0.0845, Perplexity: 1.0882\n",
            "Epoch [112/300], Step [150/350], Loss:0.1289, Perplexity: 1.1376\n",
            "Epoch [112/300], Step [200/350], Loss:0.1033, Perplexity: 1.1089\n",
            "Epoch [112/300], Step [250/350], Loss:0.0628, Perplexity: 1.0648\n",
            "Epoch [112/300], Step [300/350], Loss:0.0613, Perplexity: 1.0632\n",
            "Epoch [112/300], Step [350/350], Loss:0.0917, Perplexity: 1.0960\n",
            "Epoch [113/300], Step [50/350], Loss:0.0672, Perplexity: 1.0695\n",
            "Epoch [113/300], Step [100/350], Loss:0.0941, Perplexity: 1.0987\n",
            "Epoch [113/300], Step [150/350], Loss:0.0994, Perplexity: 1.1045\n",
            "Epoch [113/300], Step [200/350], Loss:0.0617, Perplexity: 1.0636\n",
            "Epoch [113/300], Step [250/350], Loss:0.0998, Perplexity: 1.1049\n",
            "Epoch [113/300], Step [300/350], Loss:0.0624, Perplexity: 1.0643\n",
            "Epoch [113/300], Step [350/350], Loss:0.0562, Perplexity: 1.0578\n",
            "Epoch [114/300], Step [50/350], Loss:0.0789, Perplexity: 1.0821\n",
            "Epoch [114/300], Step [100/350], Loss:0.0816, Perplexity: 1.0850\n",
            "Epoch [114/300], Step [150/350], Loss:0.1054, Perplexity: 1.1112\n",
            "Epoch [114/300], Step [200/350], Loss:0.0898, Perplexity: 1.0940\n",
            "Epoch [114/300], Step [250/350], Loss:0.0689, Perplexity: 1.0713\n",
            "Epoch [114/300], Step [300/350], Loss:0.1205, Perplexity: 1.1280\n",
            "Epoch [114/300], Step [350/350], Loss:0.0639, Perplexity: 1.0660\n",
            "Epoch [115/300], Step [50/350], Loss:0.0947, Perplexity: 1.0993\n",
            "Epoch [115/300], Step [100/350], Loss:0.0755, Perplexity: 1.0784\n",
            "Epoch [115/300], Step [150/350], Loss:0.0952, Perplexity: 1.0998\n",
            "Epoch [115/300], Step [200/350], Loss:0.0585, Perplexity: 1.0603\n",
            "Epoch [115/300], Step [250/350], Loss:0.0702, Perplexity: 1.0727\n",
            "Epoch [115/300], Step [300/350], Loss:0.0743, Perplexity: 1.0771\n",
            "Epoch [115/300], Step [350/350], Loss:0.0668, Perplexity: 1.0691\n",
            "Epoch [116/300], Step [50/350], Loss:0.0649, Perplexity: 1.0670\n",
            "Epoch [116/300], Step [100/350], Loss:0.0706, Perplexity: 1.0732\n",
            "Epoch [116/300], Step [150/350], Loss:0.0898, Perplexity: 1.0939\n",
            "Epoch [116/300], Step [200/350], Loss:0.0956, Perplexity: 1.1003\n",
            "Epoch [116/300], Step [250/350], Loss:0.0737, Perplexity: 1.0765\n",
            "Epoch [116/300], Step [300/350], Loss:0.0854, Perplexity: 1.0892\n",
            "Epoch [116/300], Step [350/350], Loss:0.0697, Perplexity: 1.0722\n",
            "Epoch [117/300], Step [50/350], Loss:0.0559, Perplexity: 1.0574\n",
            "Epoch [117/300], Step [100/350], Loss:0.0559, Perplexity: 1.0575\n",
            "Epoch [117/300], Step [150/350], Loss:0.0782, Perplexity: 1.0813\n",
            "Epoch [117/300], Step [200/350], Loss:0.0543, Perplexity: 1.0558\n",
            "Epoch [117/300], Step [250/350], Loss:0.0572, Perplexity: 1.0589\n",
            "Epoch [117/300], Step [300/350], Loss:0.0705, Perplexity: 1.0731\n",
            "Epoch [117/300], Step [350/350], Loss:0.0639, Perplexity: 1.0660\n",
            "Epoch [118/300], Step [50/350], Loss:0.0800, Perplexity: 1.0833\n",
            "Epoch [118/300], Step [100/350], Loss:0.0741, Perplexity: 1.0769\n",
            "Epoch [118/300], Step [150/350], Loss:0.0858, Perplexity: 1.0896\n",
            "Epoch [118/300], Step [200/350], Loss:0.0691, Perplexity: 1.0715\n",
            "Epoch [118/300], Step [250/350], Loss:0.0598, Perplexity: 1.0616\n",
            "Epoch [118/300], Step [300/350], Loss:0.0707, Perplexity: 1.0733\n",
            "Epoch [118/300], Step [350/350], Loss:0.0892, Perplexity: 1.0932\n",
            "Epoch [119/300], Step [50/350], Loss:0.0598, Perplexity: 1.0617\n",
            "Epoch [119/300], Step [100/350], Loss:0.0684, Perplexity: 1.0708\n",
            "Epoch [119/300], Step [150/350], Loss:0.1571, Perplexity: 1.1701\n",
            "Epoch [119/300], Step [200/350], Loss:0.0728, Perplexity: 1.0756\n",
            "Epoch [119/300], Step [250/350], Loss:0.0859, Perplexity: 1.0897\n",
            "Epoch [119/300], Step [300/350], Loss:0.0544, Perplexity: 1.0559\n",
            "Epoch [119/300], Step [350/350], Loss:0.0652, Perplexity: 1.0674\n",
            "Epoch [120/300], Step [50/350], Loss:0.0885, Perplexity: 1.0926\n",
            "Epoch [120/300], Step [100/350], Loss:0.1013, Perplexity: 1.1066\n",
            "Epoch [120/300], Step [150/350], Loss:0.0633, Perplexity: 1.0654\n",
            "Epoch [120/300], Step [200/350], Loss:0.0729, Perplexity: 1.0756\n",
            "Epoch [120/300], Step [250/350], Loss:0.0668, Perplexity: 1.0690\n",
            "Epoch [120/300], Step [300/350], Loss:0.0914, Perplexity: 1.0957\n",
            "Epoch [120/300], Step [350/350], Loss:0.0654, Perplexity: 1.0676\n",
            "Epoch [121/300], Step [50/350], Loss:0.0762, Perplexity: 1.0792\n",
            "Epoch [121/300], Step [100/350], Loss:0.0530, Perplexity: 1.0544\n",
            "Epoch [121/300], Step [150/350], Loss:0.0556, Perplexity: 1.0571\n",
            "Epoch [121/300], Step [200/350], Loss:0.1129, Perplexity: 1.1195\n",
            "Epoch [121/300], Step [250/350], Loss:0.0908, Perplexity: 1.0951\n",
            "Epoch [121/300], Step [300/350], Loss:0.0831, Perplexity: 1.0867\n",
            "Epoch [121/300], Step [350/350], Loss:0.0884, Perplexity: 1.0925\n",
            "Epoch [122/300], Step [50/350], Loss:0.0794, Perplexity: 1.0826\n",
            "Epoch [122/300], Step [100/350], Loss:0.0782, Perplexity: 1.0813\n",
            "Epoch [122/300], Step [150/350], Loss:0.0704, Perplexity: 1.0730\n",
            "Epoch [122/300], Step [200/350], Loss:0.1344, Perplexity: 1.1438\n",
            "Epoch [122/300], Step [250/350], Loss:0.0657, Perplexity: 1.0679\n",
            "Epoch [122/300], Step [300/350], Loss:0.0643, Perplexity: 1.0664\n",
            "Epoch [122/300], Step [350/350], Loss:0.0586, Perplexity: 1.0603\n",
            "Epoch [123/300], Step [50/350], Loss:0.0810, Perplexity: 1.0844\n",
            "Epoch [123/300], Step [100/350], Loss:0.0585, Perplexity: 1.0602\n",
            "Epoch [123/300], Step [150/350], Loss:0.0726, Perplexity: 1.0753\n",
            "Epoch [123/300], Step [200/350], Loss:0.0479, Perplexity: 1.0491\n",
            "Epoch [123/300], Step [250/350], Loss:0.0767, Perplexity: 1.0797\n",
            "Epoch [123/300], Step [300/350], Loss:0.0687, Perplexity: 1.0711\n",
            "Epoch [123/300], Step [350/350], Loss:0.0792, Perplexity: 1.0824\n",
            "Epoch [124/300], Step [50/350], Loss:0.1302, Perplexity: 1.1391\n",
            "Epoch [124/300], Step [100/350], Loss:0.0704, Perplexity: 1.0729\n",
            "Epoch [124/300], Step [150/350], Loss:0.0703, Perplexity: 1.0728\n",
            "Epoch [124/300], Step [200/350], Loss:0.1176, Perplexity: 1.1248\n",
            "Epoch [124/300], Step [250/350], Loss:0.0615, Perplexity: 1.0634\n",
            "Epoch [124/300], Step [300/350], Loss:0.1042, Perplexity: 1.1098\n",
            "Epoch [124/300], Step [350/350], Loss:0.0670, Perplexity: 1.0693\n",
            "Epoch [125/300], Step [50/350], Loss:0.0652, Perplexity: 1.0673\n",
            "Epoch [125/300], Step [100/350], Loss:0.0580, Perplexity: 1.0597\n",
            "Epoch [125/300], Step [150/350], Loss:0.1162, Perplexity: 1.1232\n",
            "Epoch [125/300], Step [200/350], Loss:0.0616, Perplexity: 1.0636\n",
            "Epoch [125/300], Step [250/350], Loss:0.0651, Perplexity: 1.0673\n",
            "Epoch [125/300], Step [300/350], Loss:0.0589, Perplexity: 1.0607\n",
            "Epoch [125/300], Step [350/350], Loss:0.0602, Perplexity: 1.0620\n",
            "Epoch [126/300], Step [50/350], Loss:0.0724, Perplexity: 1.0751\n",
            "Epoch [126/300], Step [100/350], Loss:0.0652, Perplexity: 1.0674\n",
            "Epoch [126/300], Step [150/350], Loss:0.0867, Perplexity: 1.0906\n",
            "Epoch [126/300], Step [200/350], Loss:0.0904, Perplexity: 1.0946\n",
            "Epoch [126/300], Step [250/350], Loss:0.1133, Perplexity: 1.1200\n",
            "Epoch [126/300], Step [300/350], Loss:0.0553, Perplexity: 1.0569\n",
            "Epoch [126/300], Step [350/350], Loss:0.0492, Perplexity: 1.0505\n",
            "Epoch [127/300], Step [50/350], Loss:0.0440, Perplexity: 1.0450\n",
            "Epoch [127/300], Step [100/350], Loss:0.0621, Perplexity: 1.0641\n",
            "Epoch [127/300], Step [150/350], Loss:0.0723, Perplexity: 1.0749\n",
            "Epoch [127/300], Step [200/350], Loss:0.0885, Perplexity: 1.0925\n",
            "Epoch [127/300], Step [250/350], Loss:0.0728, Perplexity: 1.0755\n",
            "Epoch [127/300], Step [300/350], Loss:0.0851, Perplexity: 1.0889\n",
            "Epoch [127/300], Step [350/350], Loss:0.0481, Perplexity: 1.0492\n",
            "Epoch [128/300], Step [50/350], Loss:0.1043, Perplexity: 1.1099\n",
            "Epoch [128/300], Step [100/350], Loss:0.0692, Perplexity: 1.0716\n",
            "Epoch [128/300], Step [150/350], Loss:0.1026, Perplexity: 1.1080\n",
            "Epoch [128/300], Step [200/350], Loss:0.1221, Perplexity: 1.1299\n",
            "Epoch [128/300], Step [250/350], Loss:0.0637, Perplexity: 1.0657\n",
            "Epoch [128/300], Step [300/350], Loss:0.0705, Perplexity: 1.0730\n",
            "Epoch [128/300], Step [350/350], Loss:0.0553, Perplexity: 1.0568\n",
            "Epoch [129/300], Step [50/350], Loss:0.0498, Perplexity: 1.0510\n",
            "Epoch [129/300], Step [100/350], Loss:0.0655, Perplexity: 1.0677\n",
            "Epoch [129/300], Step [150/350], Loss:0.0615, Perplexity: 1.0635\n",
            "Epoch [129/300], Step [200/350], Loss:0.0421, Perplexity: 1.0430\n",
            "Epoch [129/300], Step [250/350], Loss:0.0500, Perplexity: 1.0513\n",
            "Epoch [129/300], Step [300/350], Loss:0.0624, Perplexity: 1.0644\n",
            "Epoch [129/300], Step [350/350], Loss:0.0759, Perplexity: 1.0788\n",
            "Epoch [130/300], Step [50/350], Loss:0.0654, Perplexity: 1.0675\n",
            "Epoch [130/300], Step [100/350], Loss:0.0844, Perplexity: 1.0881\n",
            "Epoch [130/300], Step [150/350], Loss:0.0528, Perplexity: 1.0542\n",
            "Epoch [130/300], Step [200/350], Loss:0.0876, Perplexity: 1.0915\n",
            "Epoch [130/300], Step [250/350], Loss:0.0885, Perplexity: 1.0925\n",
            "Epoch [130/300], Step [300/350], Loss:0.0649, Perplexity: 1.0670\n",
            "Epoch [130/300], Step [350/350], Loss:0.0898, Perplexity: 1.0940\n",
            "Epoch [131/300], Step [50/350], Loss:0.0749, Perplexity: 1.0778\n",
            "Epoch [131/300], Step [100/350], Loss:0.0710, Perplexity: 1.0736\n",
            "Epoch [131/300], Step [150/350], Loss:0.0625, Perplexity: 1.0645\n",
            "Epoch [131/300], Step [200/350], Loss:0.0653, Perplexity: 1.0675\n",
            "Epoch [131/300], Step [250/350], Loss:0.0752, Perplexity: 1.0781\n",
            "Epoch [131/300], Step [300/350], Loss:0.0700, Perplexity: 1.0725\n",
            "Epoch [131/300], Step [350/350], Loss:0.0517, Perplexity: 1.0530\n",
            "Epoch [132/300], Step [50/350], Loss:0.0763, Perplexity: 1.0793\n",
            "Epoch [132/300], Step [100/350], Loss:0.0559, Perplexity: 1.0575\n",
            "Epoch [132/300], Step [150/350], Loss:0.0619, Perplexity: 1.0638\n",
            "Epoch [132/300], Step [200/350], Loss:0.0604, Perplexity: 1.0622\n",
            "Epoch [132/300], Step [250/350], Loss:0.0928, Perplexity: 1.0972\n",
            "Epoch [132/300], Step [300/350], Loss:0.0701, Perplexity: 1.0726\n",
            "Epoch [132/300], Step [350/350], Loss:0.0829, Perplexity: 1.0864\n",
            "Epoch [133/300], Step [50/350], Loss:0.0934, Perplexity: 1.0979\n",
            "Epoch [133/300], Step [100/350], Loss:0.0681, Perplexity: 1.0705\n",
            "Epoch [133/300], Step [150/350], Loss:0.0461, Perplexity: 1.0472\n",
            "Epoch [133/300], Step [200/350], Loss:0.0738, Perplexity: 1.0766\n",
            "Epoch [133/300], Step [250/350], Loss:0.0695, Perplexity: 1.0719\n",
            "Epoch [133/300], Step [300/350], Loss:0.0805, Perplexity: 1.0839\n",
            "Epoch [133/300], Step [350/350], Loss:0.0871, Perplexity: 1.0910\n",
            "Epoch [134/300], Step [50/350], Loss:0.0646, Perplexity: 1.0668\n",
            "Epoch [134/300], Step [100/350], Loss:0.0728, Perplexity: 1.0755\n",
            "Epoch [134/300], Step [150/350], Loss:0.0542, Perplexity: 1.0557\n",
            "Epoch [134/300], Step [200/350], Loss:0.0671, Perplexity: 1.0694\n",
            "Epoch [134/300], Step [250/350], Loss:0.0647, Perplexity: 1.0669\n",
            "Epoch [134/300], Step [300/350], Loss:0.0755, Perplexity: 1.0784\n",
            "Epoch [134/300], Step [350/350], Loss:0.0645, Perplexity: 1.0666\n",
            "Epoch [135/300], Step [50/350], Loss:0.0756, Perplexity: 1.0785\n",
            "Epoch [135/300], Step [100/350], Loss:0.0632, Perplexity: 1.0653\n",
            "Epoch [135/300], Step [150/350], Loss:0.0656, Perplexity: 1.0678\n",
            "Epoch [135/300], Step [200/350], Loss:0.0891, Perplexity: 1.0932\n",
            "Epoch [135/300], Step [250/350], Loss:0.0589, Perplexity: 1.0607\n",
            "Epoch [135/300], Step [300/350], Loss:0.0730, Perplexity: 1.0757\n",
            "Epoch [135/300], Step [350/350], Loss:0.0815, Perplexity: 1.0849\n",
            "Epoch [136/300], Step [50/350], Loss:0.0782, Perplexity: 1.0813\n",
            "Epoch [136/300], Step [100/350], Loss:0.0476, Perplexity: 1.0488\n",
            "Epoch [136/300], Step [150/350], Loss:0.0579, Perplexity: 1.0596\n",
            "Epoch [136/300], Step [200/350], Loss:0.0784, Perplexity: 1.0815\n",
            "Epoch [136/300], Step [250/350], Loss:0.0659, Perplexity: 1.0681\n",
            "Epoch [136/300], Step [300/350], Loss:0.0776, Perplexity: 1.0807\n",
            "Epoch [136/300], Step [350/350], Loss:0.0531, Perplexity: 1.0546\n",
            "Epoch [137/300], Step [50/350], Loss:0.0578, Perplexity: 1.0595\n",
            "Epoch [137/300], Step [100/350], Loss:0.0661, Perplexity: 1.0683\n",
            "Epoch [137/300], Step [150/350], Loss:0.0861, Perplexity: 1.0899\n",
            "Epoch [137/300], Step [200/350], Loss:0.0373, Perplexity: 1.0380\n",
            "Epoch [137/300], Step [250/350], Loss:0.0622, Perplexity: 1.0642\n",
            "Epoch [137/300], Step [300/350], Loss:0.0574, Perplexity: 1.0590\n",
            "Epoch [137/300], Step [350/350], Loss:0.0522, Perplexity: 1.0535\n",
            "Epoch [138/300], Step [50/350], Loss:0.0548, Perplexity: 1.0563\n",
            "Epoch [138/300], Step [100/350], Loss:0.0570, Perplexity: 1.0587\n",
            "Epoch [138/300], Step [150/350], Loss:0.0610, Perplexity: 1.0629\n",
            "Epoch [138/300], Step [200/350], Loss:0.0703, Perplexity: 1.0728\n",
            "Epoch [138/300], Step [250/350], Loss:0.0639, Perplexity: 1.0659\n",
            "Epoch [138/300], Step [300/350], Loss:0.0584, Perplexity: 1.0602\n",
            "Epoch [138/300], Step [350/350], Loss:0.0531, Perplexity: 1.0545\n",
            "Epoch [139/300], Step [50/350], Loss:0.0507, Perplexity: 1.0521\n",
            "Epoch [139/300], Step [100/350], Loss:0.0892, Perplexity: 1.0933\n",
            "Epoch [139/300], Step [150/350], Loss:0.0707, Perplexity: 1.0733\n",
            "Epoch [139/300], Step [200/350], Loss:0.0582, Perplexity: 1.0599\n",
            "Epoch [139/300], Step [250/350], Loss:0.0456, Perplexity: 1.0467\n",
            "Epoch [139/300], Step [300/350], Loss:0.0840, Perplexity: 1.0876\n",
            "Epoch [139/300], Step [350/350], Loss:0.0693, Perplexity: 1.0718\n",
            "Epoch [140/300], Step [50/350], Loss:0.0735, Perplexity: 1.0762\n",
            "Epoch [140/300], Step [100/350], Loss:0.0421, Perplexity: 1.0430\n",
            "Epoch [140/300], Step [150/350], Loss:0.0608, Perplexity: 1.0627\n",
            "Epoch [140/300], Step [200/350], Loss:0.0551, Perplexity: 1.0567\n",
            "Epoch [140/300], Step [250/350], Loss:0.0828, Perplexity: 1.0863\n",
            "Epoch [140/300], Step [300/350], Loss:0.1467, Perplexity: 1.1580\n",
            "Epoch [140/300], Step [350/350], Loss:0.0901, Perplexity: 1.0942\n",
            "Epoch [141/300], Step [50/350], Loss:0.0695, Perplexity: 1.0719\n",
            "Epoch [141/300], Step [100/350], Loss:0.0973, Perplexity: 1.1022\n",
            "Epoch [141/300], Step [150/350], Loss:0.0484, Perplexity: 1.0496\n",
            "Epoch [141/300], Step [200/350], Loss:0.0672, Perplexity: 1.0695\n",
            "Epoch [141/300], Step [250/350], Loss:0.0722, Perplexity: 1.0748\n",
            "Epoch [141/300], Step [300/350], Loss:0.1053, Perplexity: 1.1110\n",
            "Epoch [141/300], Step [350/350], Loss:0.0538, Perplexity: 1.0553\n",
            "Epoch [142/300], Step [50/350], Loss:0.0953, Perplexity: 1.1000\n",
            "Epoch [142/300], Step [100/350], Loss:0.0678, Perplexity: 1.0701\n",
            "Epoch [142/300], Step [150/350], Loss:0.0603, Perplexity: 1.0621\n",
            "Epoch [142/300], Step [200/350], Loss:0.0511, Perplexity: 1.0524\n",
            "Epoch [142/300], Step [250/350], Loss:0.0827, Perplexity: 1.0862\n",
            "Epoch [142/300], Step [300/350], Loss:0.0493, Perplexity: 1.0505\n",
            "Epoch [142/300], Step [350/350], Loss:0.0491, Perplexity: 1.0503\n",
            "Epoch [143/300], Step [50/350], Loss:0.0597, Perplexity: 1.0615\n",
            "Epoch [143/300], Step [100/350], Loss:0.0494, Perplexity: 1.0506\n",
            "Epoch [143/300], Step [150/350], Loss:0.0657, Perplexity: 1.0679\n",
            "Epoch [143/300], Step [200/350], Loss:0.0947, Perplexity: 1.0993\n",
            "Epoch [143/300], Step [250/350], Loss:0.0913, Perplexity: 1.0956\n",
            "Epoch [143/300], Step [300/350], Loss:0.0643, Perplexity: 1.0664\n",
            "Epoch [143/300], Step [350/350], Loss:0.0612, Perplexity: 1.0631\n",
            "Epoch [144/300], Step [50/350], Loss:0.0581, Perplexity: 1.0598\n",
            "Epoch [144/300], Step [100/350], Loss:0.0461, Perplexity: 1.0471\n",
            "Epoch [144/300], Step [150/350], Loss:0.0915, Perplexity: 1.0959\n",
            "Epoch [144/300], Step [200/350], Loss:0.1053, Perplexity: 1.1110\n",
            "Epoch [144/300], Step [250/350], Loss:0.0396, Perplexity: 1.0404\n",
            "Epoch [144/300], Step [300/350], Loss:0.0612, Perplexity: 1.0631\n",
            "Epoch [144/300], Step [350/350], Loss:0.0778, Perplexity: 1.0809\n",
            "Epoch [145/300], Step [50/350], Loss:0.0912, Perplexity: 1.0955\n",
            "Epoch [145/300], Step [100/350], Loss:0.0656, Perplexity: 1.0678\n",
            "Epoch [145/300], Step [150/350], Loss:0.0609, Perplexity: 1.0628\n",
            "Epoch [145/300], Step [200/350], Loss:0.1028, Perplexity: 1.1082\n",
            "Epoch [145/300], Step [250/350], Loss:0.1135, Perplexity: 1.1201\n",
            "Epoch [145/300], Step [300/350], Loss:0.0750, Perplexity: 1.0779\n",
            "Epoch [145/300], Step [350/350], Loss:0.0660, Perplexity: 1.0682\n",
            "Epoch [146/300], Step [50/350], Loss:0.0758, Perplexity: 1.0787\n",
            "Epoch [146/300], Step [100/350], Loss:0.0629, Perplexity: 1.0649\n",
            "Epoch [146/300], Step [150/350], Loss:0.0597, Perplexity: 1.0615\n",
            "Epoch [146/300], Step [200/350], Loss:0.0417, Perplexity: 1.0426\n",
            "Epoch [146/300], Step [250/350], Loss:0.0876, Perplexity: 1.0916\n",
            "Epoch [146/300], Step [300/350], Loss:0.0710, Perplexity: 1.0735\n",
            "Epoch [146/300], Step [350/350], Loss:0.0579, Perplexity: 1.0596\n",
            "Epoch [147/300], Step [50/350], Loss:0.0640, Perplexity: 1.0661\n",
            "Epoch [147/300], Step [100/350], Loss:0.0453, Perplexity: 1.0464\n",
            "Epoch [147/300], Step [150/350], Loss:0.0708, Perplexity: 1.0734\n",
            "Epoch [147/300], Step [200/350], Loss:0.0760, Perplexity: 1.0789\n",
            "Epoch [147/300], Step [250/350], Loss:0.0787, Perplexity: 1.0818\n",
            "Epoch [147/300], Step [300/350], Loss:0.0569, Perplexity: 1.0586\n",
            "Epoch [147/300], Step [350/350], Loss:0.0594, Perplexity: 1.0612\n",
            "Epoch [148/300], Step [50/350], Loss:0.0545, Perplexity: 1.0560\n",
            "Epoch [148/300], Step [100/350], Loss:0.0550, Perplexity: 1.0565\n",
            "Epoch [148/300], Step [150/350], Loss:0.0510, Perplexity: 1.0523\n",
            "Epoch [148/300], Step [200/350], Loss:0.0849, Perplexity: 1.0886\n",
            "Epoch [148/300], Step [250/350], Loss:0.0618, Perplexity: 1.0638\n",
            "Epoch [148/300], Step [300/350], Loss:0.0479, Perplexity: 1.0491\n",
            "Epoch [148/300], Step [350/350], Loss:0.0417, Perplexity: 1.0426\n",
            "Epoch [149/300], Step [50/350], Loss:0.0956, Perplexity: 1.1003\n",
            "Epoch [149/300], Step [100/350], Loss:0.0524, Perplexity: 1.0538\n",
            "Epoch [149/300], Step [150/350], Loss:0.0706, Perplexity: 1.0732\n",
            "Epoch [149/300], Step [200/350], Loss:0.0396, Perplexity: 1.0404\n",
            "Epoch [149/300], Step [250/350], Loss:0.0502, Perplexity: 1.0515\n",
            "Epoch [149/300], Step [300/350], Loss:0.0474, Perplexity: 1.0486\n",
            "Epoch [149/300], Step [350/350], Loss:0.0595, Perplexity: 1.0613\n",
            "Epoch [150/300], Step [50/350], Loss:0.0942, Perplexity: 1.0988\n",
            "Epoch [150/300], Step [100/350], Loss:0.0555, Perplexity: 1.0571\n",
            "Epoch [150/300], Step [150/350], Loss:0.0931, Perplexity: 1.0975\n",
            "Epoch [150/300], Step [200/350], Loss:0.0342, Perplexity: 1.0348\n",
            "Epoch [150/300], Step [250/350], Loss:0.0613, Perplexity: 1.0632\n",
            "Epoch [150/300], Step [300/350], Loss:0.1091, Perplexity: 1.1153\n",
            "Epoch [150/300], Step [350/350], Loss:0.0518, Perplexity: 1.0531\n",
            "Epoch [151/300], Step [50/350], Loss:0.0432, Perplexity: 1.0441\n",
            "Epoch [151/300], Step [100/350], Loss:0.0531, Perplexity: 1.0545\n",
            "Epoch [151/300], Step [150/350], Loss:0.0736, Perplexity: 1.0764\n",
            "Epoch [151/300], Step [200/350], Loss:0.0591, Perplexity: 1.0608\n",
            "Epoch [151/300], Step [250/350], Loss:0.0714, Perplexity: 1.0740\n",
            "Epoch [151/300], Step [300/350], Loss:0.0697, Perplexity: 1.0721\n",
            "Epoch [151/300], Step [350/350], Loss:0.0551, Perplexity: 1.0567\n",
            "Epoch [152/300], Step [50/350], Loss:0.0667, Perplexity: 1.0690\n",
            "Epoch [152/300], Step [100/350], Loss:0.0488, Perplexity: 1.0500\n",
            "Epoch [152/300], Step [150/350], Loss:0.0408, Perplexity: 1.0416\n",
            "Epoch [152/300], Step [200/350], Loss:0.0612, Perplexity: 1.0631\n",
            "Epoch [152/300], Step [250/350], Loss:0.0864, Perplexity: 1.0902\n",
            "Epoch [152/300], Step [300/350], Loss:0.0927, Perplexity: 1.0971\n",
            "Epoch [152/300], Step [350/350], Loss:0.0592, Perplexity: 1.0609\n",
            "Epoch [153/300], Step [50/350], Loss:0.0632, Perplexity: 1.0652\n",
            "Epoch [153/300], Step [100/350], Loss:0.0558, Perplexity: 1.0574\n",
            "Epoch [153/300], Step [150/350], Loss:0.0748, Perplexity: 1.0777\n",
            "Epoch [153/300], Step [200/350], Loss:0.0624, Perplexity: 1.0644\n",
            "Epoch [153/300], Step [250/350], Loss:0.0448, Perplexity: 1.0458\n",
            "Epoch [153/300], Step [300/350], Loss:0.0948, Perplexity: 1.0995\n",
            "Epoch [153/300], Step [350/350], Loss:0.0472, Perplexity: 1.0483\n",
            "Epoch [154/300], Step [50/350], Loss:0.1420, Perplexity: 1.1525\n",
            "Epoch [154/300], Step [100/350], Loss:0.0338, Perplexity: 1.0344\n",
            "Epoch [154/300], Step [150/350], Loss:0.0439, Perplexity: 1.0449\n",
            "Epoch [154/300], Step [200/350], Loss:0.0625, Perplexity: 1.0645\n",
            "Epoch [154/300], Step [250/350], Loss:0.0294, Perplexity: 1.0299\n",
            "Epoch [154/300], Step [300/350], Loss:0.0505, Perplexity: 1.0518\n",
            "Epoch [154/300], Step [350/350], Loss:0.0617, Perplexity: 1.0637\n",
            "Epoch [155/300], Step [50/350], Loss:0.0637, Perplexity: 1.0658\n",
            "Epoch [155/300], Step [100/350], Loss:0.0699, Perplexity: 1.0724\n",
            "Epoch [155/300], Step [150/350], Loss:0.0547, Perplexity: 1.0562\n",
            "Epoch [155/300], Step [200/350], Loss:0.0502, Perplexity: 1.0515\n",
            "Epoch [155/300], Step [250/350], Loss:0.0619, Perplexity: 1.0639\n",
            "Epoch [155/300], Step [300/350], Loss:0.0711, Perplexity: 1.0737\n",
            "Epoch [155/300], Step [350/350], Loss:0.1109, Perplexity: 1.1173\n",
            "Epoch [156/300], Step [50/350], Loss:0.0442, Perplexity: 1.0452\n",
            "Epoch [156/300], Step [100/350], Loss:0.0562, Perplexity: 1.0578\n",
            "Epoch [156/300], Step [150/350], Loss:0.0529, Perplexity: 1.0544\n",
            "Epoch [156/300], Step [200/350], Loss:0.0647, Perplexity: 1.0668\n",
            "Epoch [156/300], Step [250/350], Loss:0.0592, Perplexity: 1.0610\n",
            "Epoch [156/300], Step [300/350], Loss:0.0656, Perplexity: 1.0678\n",
            "Epoch [156/300], Step [350/350], Loss:0.1343, Perplexity: 1.1437\n",
            "Epoch [157/300], Step [50/350], Loss:0.0548, Perplexity: 1.0563\n",
            "Epoch [157/300], Step [100/350], Loss:0.0557, Perplexity: 1.0573\n",
            "Epoch [157/300], Step [150/350], Loss:0.0559, Perplexity: 1.0575\n",
            "Epoch [157/300], Step [200/350], Loss:0.0710, Perplexity: 1.0736\n",
            "Epoch [157/300], Step [250/350], Loss:0.0550, Perplexity: 1.0565\n",
            "Epoch [157/300], Step [300/350], Loss:0.0929, Perplexity: 1.0974\n",
            "Epoch [157/300], Step [350/350], Loss:0.0480, Perplexity: 1.0492\n",
            "Epoch [158/300], Step [50/350], Loss:0.0298, Perplexity: 1.0302\n",
            "Epoch [158/300], Step [100/350], Loss:0.0311, Perplexity: 1.0316\n",
            "Epoch [158/300], Step [150/350], Loss:0.1588, Perplexity: 1.1721\n",
            "Epoch [158/300], Step [200/350], Loss:0.0701, Perplexity: 1.0726\n",
            "Epoch [158/300], Step [250/350], Loss:0.0650, Perplexity: 1.0672\n",
            "Epoch [158/300], Step [300/350], Loss:0.0748, Perplexity: 1.0777\n",
            "Epoch [158/300], Step [350/350], Loss:0.0668, Perplexity: 1.0691\n",
            "Epoch [159/300], Step [50/350], Loss:0.0611, Perplexity: 1.0630\n",
            "Epoch [159/300], Step [100/350], Loss:0.0576, Perplexity: 1.0593\n",
            "Epoch [159/300], Step [150/350], Loss:0.0741, Perplexity: 1.0769\n",
            "Epoch [159/300], Step [200/350], Loss:0.0917, Perplexity: 1.0960\n",
            "Epoch [159/300], Step [250/350], Loss:0.0464, Perplexity: 1.0475\n",
            "Epoch [159/300], Step [300/350], Loss:0.0428, Perplexity: 1.0438\n",
            "Epoch [159/300], Step [350/350], Loss:0.0565, Perplexity: 1.0581\n",
            "Epoch [160/300], Step [50/350], Loss:0.0455, Perplexity: 1.0465\n",
            "Epoch [160/300], Step [100/350], Loss:0.0356, Perplexity: 1.0362\n",
            "Epoch [160/300], Step [150/350], Loss:0.0550, Perplexity: 1.0565\n",
            "Epoch [160/300], Step [200/350], Loss:0.0462, Perplexity: 1.0473\n",
            "Epoch [160/300], Step [250/350], Loss:0.0716, Perplexity: 1.0742\n",
            "Epoch [160/300], Step [300/350], Loss:0.0483, Perplexity: 1.0495\n",
            "Epoch [160/300], Step [350/350], Loss:0.0697, Perplexity: 1.0722\n",
            "Epoch [161/300], Step [50/350], Loss:0.0413, Perplexity: 1.0422\n",
            "Epoch [161/300], Step [100/350], Loss:0.0541, Perplexity: 1.0556\n",
            "Epoch [161/300], Step [150/350], Loss:0.0407, Perplexity: 1.0415\n",
            "Epoch [161/300], Step [200/350], Loss:0.0755, Perplexity: 1.0785\n",
            "Epoch [161/300], Step [250/350], Loss:0.0946, Perplexity: 1.0993\n",
            "Epoch [161/300], Step [300/350], Loss:0.0498, Perplexity: 1.0511\n",
            "Epoch [161/300], Step [350/350], Loss:0.0493, Perplexity: 1.0505\n",
            "Epoch [162/300], Step [50/350], Loss:0.0783, Perplexity: 1.0815\n",
            "Epoch [162/300], Step [100/350], Loss:0.0456, Perplexity: 1.0466\n",
            "Epoch [162/300], Step [150/350], Loss:0.0891, Perplexity: 1.0932\n",
            "Epoch [162/300], Step [200/350], Loss:0.0306, Perplexity: 1.0311\n",
            "Epoch [162/300], Step [250/350], Loss:0.0841, Perplexity: 1.0878\n",
            "Epoch [162/300], Step [300/350], Loss:0.0506, Perplexity: 1.0519\n",
            "Epoch [162/300], Step [350/350], Loss:0.0400, Perplexity: 1.0408\n",
            "Epoch [163/300], Step [50/350], Loss:0.0420, Perplexity: 1.0429\n",
            "Epoch [163/300], Step [100/350], Loss:0.0783, Perplexity: 1.0815\n",
            "Epoch [163/300], Step [150/350], Loss:0.0566, Perplexity: 1.0583\n",
            "Epoch [163/300], Step [200/350], Loss:0.0401, Perplexity: 1.0409\n",
            "Epoch [163/300], Step [250/350], Loss:0.0570, Perplexity: 1.0587\n",
            "Epoch [163/300], Step [300/350], Loss:0.0468, Perplexity: 1.0479\n",
            "Epoch [163/300], Step [350/350], Loss:0.0686, Perplexity: 1.0710\n",
            "Epoch [164/300], Step [50/350], Loss:0.0403, Perplexity: 1.0411\n",
            "Epoch [164/300], Step [100/350], Loss:0.0746, Perplexity: 1.0774\n",
            "Epoch [164/300], Step [150/350], Loss:0.0308, Perplexity: 1.0313\n",
            "Epoch [164/300], Step [200/350], Loss:0.0881, Perplexity: 1.0921\n",
            "Epoch [164/300], Step [250/350], Loss:0.0379, Perplexity: 1.0386\n",
            "Epoch [164/300], Step [300/350], Loss:0.0991, Perplexity: 1.1042\n",
            "Epoch [164/300], Step [350/350], Loss:0.0375, Perplexity: 1.0382\n",
            "Epoch [165/300], Step [50/350], Loss:0.0487, Perplexity: 1.0500\n",
            "Epoch [165/300], Step [100/350], Loss:0.0666, Perplexity: 1.0688\n",
            "Epoch [165/300], Step [150/350], Loss:0.0662, Perplexity: 1.0684\n",
            "Epoch [165/300], Step [200/350], Loss:0.0506, Perplexity: 1.0519\n",
            "Epoch [165/300], Step [250/350], Loss:0.0443, Perplexity: 1.0453\n",
            "Epoch [165/300], Step [300/350], Loss:0.0619, Perplexity: 1.0638\n",
            "Epoch [165/300], Step [350/350], Loss:0.0652, Perplexity: 1.0674\n",
            "Epoch [166/300], Step [50/350], Loss:0.0536, Perplexity: 1.0551\n",
            "Epoch [166/300], Step [100/350], Loss:0.0489, Perplexity: 1.0501\n",
            "Epoch [166/300], Step [150/350], Loss:0.0439, Perplexity: 1.0449\n",
            "Epoch [166/300], Step [200/350], Loss:0.0433, Perplexity: 1.0442\n",
            "Epoch [166/300], Step [250/350], Loss:0.1114, Perplexity: 1.1178\n",
            "Epoch [166/300], Step [300/350], Loss:0.0702, Perplexity: 1.0727\n",
            "Epoch [166/300], Step [350/350], Loss:0.0430, Perplexity: 1.0439\n",
            "Epoch [167/300], Step [50/350], Loss:0.0972, Perplexity: 1.1021\n",
            "Epoch [167/300], Step [100/350], Loss:0.0779, Perplexity: 1.0810\n",
            "Epoch [167/300], Step [150/350], Loss:0.0495, Perplexity: 1.0507\n",
            "Epoch [167/300], Step [200/350], Loss:0.0553, Perplexity: 1.0569\n",
            "Epoch [167/300], Step [250/350], Loss:0.0894, Perplexity: 1.0935\n",
            "Epoch [167/300], Step [300/350], Loss:0.0487, Perplexity: 1.0499\n",
            "Epoch [167/300], Step [350/350], Loss:0.0472, Perplexity: 1.0483\n",
            "Epoch [168/300], Step [50/350], Loss:0.0577, Perplexity: 1.0594\n",
            "Epoch [168/300], Step [100/350], Loss:0.0635, Perplexity: 1.0655\n",
            "Epoch [168/300], Step [150/350], Loss:0.0539, Perplexity: 1.0554\n",
            "Epoch [168/300], Step [200/350], Loss:0.0474, Perplexity: 1.0485\n",
            "Epoch [168/300], Step [250/350], Loss:0.0480, Perplexity: 1.0492\n",
            "Epoch [168/300], Step [300/350], Loss:0.0437, Perplexity: 1.0447\n",
            "Epoch [168/300], Step [350/350], Loss:0.0430, Perplexity: 1.0439\n",
            "Epoch [169/300], Step [50/350], Loss:0.0413, Perplexity: 1.0421\n",
            "Epoch [169/300], Step [100/350], Loss:0.1092, Perplexity: 1.1153\n",
            "Epoch [169/300], Step [150/350], Loss:0.0459, Perplexity: 1.0470\n",
            "Epoch [169/300], Step [200/350], Loss:0.0280, Perplexity: 1.0284\n",
            "Epoch [169/300], Step [250/350], Loss:0.0413, Perplexity: 1.0422\n",
            "Epoch [169/300], Step [300/350], Loss:0.0558, Perplexity: 1.0574\n",
            "Epoch [169/300], Step [350/350], Loss:0.0491, Perplexity: 1.0503\n",
            "Epoch [170/300], Step [50/350], Loss:0.0333, Perplexity: 1.0339\n",
            "Epoch [170/300], Step [100/350], Loss:0.0574, Perplexity: 1.0591\n",
            "Epoch [170/300], Step [150/350], Loss:0.0453, Perplexity: 1.0463\n",
            "Epoch [170/300], Step [200/350], Loss:0.0590, Perplexity: 1.0607\n",
            "Epoch [170/300], Step [250/350], Loss:0.0592, Perplexity: 1.0609\n",
            "Epoch [170/300], Step [300/350], Loss:0.0362, Perplexity: 1.0369\n",
            "Epoch [170/300], Step [350/350], Loss:0.0529, Perplexity: 1.0543\n",
            "Epoch [171/300], Step [50/350], Loss:0.0781, Perplexity: 1.0813\n",
            "Epoch [171/300], Step [100/350], Loss:0.0622, Perplexity: 1.0642\n",
            "Epoch [171/300], Step [150/350], Loss:0.0353, Perplexity: 1.0359\n",
            "Epoch [171/300], Step [200/350], Loss:0.0513, Perplexity: 1.0526\n",
            "Epoch [171/300], Step [250/350], Loss:0.0635, Perplexity: 1.0655\n",
            "Epoch [171/300], Step [300/350], Loss:0.0855, Perplexity: 1.0892\n",
            "Epoch [171/300], Step [350/350], Loss:0.0674, Perplexity: 1.0697\n",
            "Epoch [172/300], Step [50/350], Loss:0.0340, Perplexity: 1.0346\n",
            "Epoch [172/300], Step [100/350], Loss:0.0524, Perplexity: 1.0538\n",
            "Epoch [172/300], Step [150/350], Loss:0.0411, Perplexity: 1.0420\n",
            "Epoch [172/300], Step [200/350], Loss:0.1097, Perplexity: 1.1160\n",
            "Epoch [172/300], Step [250/350], Loss:0.0455, Perplexity: 1.0466\n",
            "Epoch [172/300], Step [300/350], Loss:0.0452, Perplexity: 1.0462\n",
            "Epoch [172/300], Step [350/350], Loss:0.0810, Perplexity: 1.0844\n",
            "Epoch [173/300], Step [50/350], Loss:0.0630, Perplexity: 1.0650\n",
            "Epoch [173/300], Step [100/350], Loss:0.0297, Perplexity: 1.0302\n",
            "Epoch [173/300], Step [150/350], Loss:0.0690, Perplexity: 1.0714\n",
            "Epoch [173/300], Step [200/350], Loss:0.0366, Perplexity: 1.0373\n",
            "Epoch [173/300], Step [250/350], Loss:0.0685, Perplexity: 1.0709\n",
            "Epoch [173/300], Step [300/350], Loss:0.0855, Perplexity: 1.0892\n",
            "Epoch [173/300], Step [350/350], Loss:0.0575, Perplexity: 1.0592\n",
            "Epoch [174/300], Step [50/350], Loss:0.0421, Perplexity: 1.0430\n",
            "Epoch [174/300], Step [100/350], Loss:0.0444, Perplexity: 1.0454\n",
            "Epoch [174/300], Step [150/350], Loss:0.0467, Perplexity: 1.0478\n",
            "Epoch [174/300], Step [200/350], Loss:0.0465, Perplexity: 1.0476\n",
            "Epoch [174/300], Step [250/350], Loss:0.0427, Perplexity: 1.0437\n",
            "Epoch [174/300], Step [300/350], Loss:0.0402, Perplexity: 1.0410\n",
            "Epoch [174/300], Step [350/350], Loss:0.0483, Perplexity: 1.0495\n",
            "Epoch [175/300], Step [50/350], Loss:0.0489, Perplexity: 1.0501\n",
            "Epoch [175/300], Step [100/350], Loss:0.0597, Perplexity: 1.0615\n",
            "Epoch [175/300], Step [150/350], Loss:0.0370, Perplexity: 1.0377\n",
            "Epoch [175/300], Step [200/350], Loss:0.0642, Perplexity: 1.0663\n",
            "Epoch [175/300], Step [250/350], Loss:0.0682, Perplexity: 1.0705\n",
            "Epoch [175/300], Step [300/350], Loss:0.0424, Perplexity: 1.0433\n",
            "Epoch [175/300], Step [350/350], Loss:0.0404, Perplexity: 1.0413\n",
            "Epoch [176/300], Step [50/350], Loss:0.0570, Perplexity: 1.0587\n",
            "Epoch [176/300], Step [100/350], Loss:0.0505, Perplexity: 1.0518\n",
            "Epoch [176/300], Step [150/350], Loss:0.0360, Perplexity: 1.0367\n",
            "Epoch [176/300], Step [200/350], Loss:0.0412, Perplexity: 1.0421\n",
            "Epoch [176/300], Step [250/350], Loss:0.0627, Perplexity: 1.0647\n",
            "Epoch [176/300], Step [300/350], Loss:0.0454, Perplexity: 1.0465\n",
            "Epoch [176/300], Step [350/350], Loss:0.1128, Perplexity: 1.1195\n",
            "Epoch [177/300], Step [50/350], Loss:0.0531, Perplexity: 1.0545\n",
            "Epoch [177/300], Step [100/350], Loss:0.0782, Perplexity: 1.0813\n",
            "Epoch [177/300], Step [150/350], Loss:0.0323, Perplexity: 1.0329\n",
            "Epoch [177/300], Step [200/350], Loss:0.0589, Perplexity: 1.0607\n",
            "Epoch [177/300], Step [250/350], Loss:0.0456, Perplexity: 1.0466\n",
            "Epoch [177/300], Step [300/350], Loss:0.0553, Perplexity: 1.0569\n",
            "Epoch [177/300], Step [350/350], Loss:0.0661, Perplexity: 1.0684\n",
            "Epoch [178/300], Step [50/350], Loss:0.0470, Perplexity: 1.0481\n",
            "Epoch [178/300], Step [100/350], Loss:0.0525, Perplexity: 1.0539\n",
            "Epoch [178/300], Step [150/350], Loss:0.0250, Perplexity: 1.0253\n",
            "Epoch [178/300], Step [200/350], Loss:0.0711, Perplexity: 1.0737\n",
            "Epoch [178/300], Step [250/350], Loss:0.0596, Perplexity: 1.0614\n",
            "Epoch [178/300], Step [300/350], Loss:0.0593, Perplexity: 1.0611\n",
            "Epoch [178/300], Step [350/350], Loss:0.0431, Perplexity: 1.0440\n",
            "Epoch [179/300], Step [50/350], Loss:0.0393, Perplexity: 1.0401\n",
            "Epoch [179/300], Step [100/350], Loss:0.0652, Perplexity: 1.0674\n",
            "Epoch [179/300], Step [150/350], Loss:0.0470, Perplexity: 1.0481\n",
            "Epoch [179/300], Step [200/350], Loss:0.0489, Perplexity: 1.0501\n",
            "Epoch [179/300], Step [250/350], Loss:0.0297, Perplexity: 1.0302\n",
            "Epoch [179/300], Step [300/350], Loss:0.0384, Perplexity: 1.0392\n",
            "Epoch [179/300], Step [350/350], Loss:0.0479, Perplexity: 1.0491\n",
            "Epoch [180/300], Step [50/350], Loss:0.0547, Perplexity: 1.0563\n",
            "Epoch [180/300], Step [100/350], Loss:0.0449, Perplexity: 1.0460\n",
            "Epoch [180/300], Step [150/350], Loss:0.0736, Perplexity: 1.0764\n",
            "Epoch [180/300], Step [200/350], Loss:0.0457, Perplexity: 1.0468\n",
            "Epoch [180/300], Step [250/350], Loss:0.0660, Perplexity: 1.0682\n",
            "Epoch [180/300], Step [300/350], Loss:0.0521, Perplexity: 1.0535\n",
            "Epoch [180/300], Step [350/350], Loss:0.0576, Perplexity: 1.0593\n",
            "Epoch [181/300], Step [50/350], Loss:0.0719, Perplexity: 1.0746\n",
            "Epoch [181/300], Step [100/350], Loss:0.1200, Perplexity: 1.1275\n",
            "Epoch [181/300], Step [150/350], Loss:0.0678, Perplexity: 1.0702\n",
            "Epoch [181/300], Step [200/350], Loss:0.0617, Perplexity: 1.0636\n",
            "Epoch [181/300], Step [250/350], Loss:0.0577, Perplexity: 1.0594\n",
            "Epoch [181/300], Step [300/350], Loss:0.0551, Perplexity: 1.0566\n",
            "Epoch [181/300], Step [350/350], Loss:0.0419, Perplexity: 1.0428\n",
            "Epoch [182/300], Step [50/350], Loss:0.0598, Perplexity: 1.0616\n",
            "Epoch [182/300], Step [100/350], Loss:0.0679, Perplexity: 1.0702\n",
            "Epoch [182/300], Step [150/350], Loss:0.0549, Perplexity: 1.0564\n",
            "Epoch [182/300], Step [200/350], Loss:0.0469, Perplexity: 1.0480\n",
            "Epoch [182/300], Step [250/350], Loss:0.0473, Perplexity: 1.0484\n",
            "Epoch [182/300], Step [300/350], Loss:0.0644, Perplexity: 1.0666\n",
            "Epoch [182/300], Step [350/350], Loss:0.0412, Perplexity: 1.0420\n",
            "Epoch [183/300], Step [50/350], Loss:0.0584, Perplexity: 1.0602\n",
            "Epoch [183/300], Step [100/350], Loss:0.0671, Perplexity: 1.0694\n",
            "Epoch [183/300], Step [150/350], Loss:0.0308, Perplexity: 1.0313\n",
            "Epoch [183/300], Step [200/350], Loss:0.0453, Perplexity: 1.0464\n",
            "Epoch [183/300], Step [250/350], Loss:0.0501, Perplexity: 1.0514\n",
            "Epoch [183/300], Step [300/350], Loss:0.0360, Perplexity: 1.0367\n",
            "Epoch [183/300], Step [350/350], Loss:0.0712, Perplexity: 1.0738\n",
            "Epoch [184/300], Step [50/350], Loss:0.0601, Perplexity: 1.0620\n",
            "Epoch [184/300], Step [100/350], Loss:0.0266, Perplexity: 1.0269\n",
            "Epoch [184/300], Step [150/350], Loss:0.0764, Perplexity: 1.0794\n",
            "Epoch [184/300], Step [200/350], Loss:0.0582, Perplexity: 1.0599\n",
            "Epoch [184/300], Step [250/350], Loss:0.0519, Perplexity: 1.0533\n",
            "Epoch [184/300], Step [300/350], Loss:0.0374, Perplexity: 1.0381\n",
            "Epoch [184/300], Step [350/350], Loss:0.0190, Perplexity: 1.0192\n",
            "Epoch [185/300], Step [50/350], Loss:0.0738, Perplexity: 1.0766\n",
            "Epoch [185/300], Step [100/350], Loss:0.0274, Perplexity: 1.0278\n",
            "Epoch [185/300], Step [150/350], Loss:0.0783, Perplexity: 1.0815\n",
            "Epoch [185/300], Step [200/350], Loss:0.0338, Perplexity: 1.0344\n",
            "Epoch [185/300], Step [250/350], Loss:0.0580, Perplexity: 1.0597\n",
            "Epoch [185/300], Step [300/350], Loss:0.0867, Perplexity: 1.0906\n",
            "Epoch [185/300], Step [350/350], Loss:0.0628, Perplexity: 1.0648\n",
            "Epoch [186/300], Step [50/350], Loss:0.0859, Perplexity: 1.0897\n",
            "Epoch [186/300], Step [100/350], Loss:0.0522, Perplexity: 1.0536\n",
            "Epoch [186/300], Step [150/350], Loss:0.0782, Perplexity: 1.0813\n",
            "Epoch [186/300], Step [200/350], Loss:0.0598, Perplexity: 1.0617\n",
            "Epoch [186/300], Step [250/350], Loss:0.0569, Perplexity: 1.0585\n",
            "Epoch [186/300], Step [300/350], Loss:0.0406, Perplexity: 1.0415\n",
            "Epoch [186/300], Step [350/350], Loss:0.0659, Perplexity: 1.0681\n",
            "Epoch [187/300], Step [50/350], Loss:0.0508, Perplexity: 1.0521\n",
            "Epoch [187/300], Step [100/350], Loss:0.0622, Perplexity: 1.0642\n",
            "Epoch [187/300], Step [150/350], Loss:0.0355, Perplexity: 1.0361\n",
            "Epoch [187/300], Step [200/350], Loss:0.0437, Perplexity: 1.0447\n",
            "Epoch [187/300], Step [250/350], Loss:0.0429, Perplexity: 1.0438\n",
            "Epoch [187/300], Step [300/350], Loss:0.0401, Perplexity: 1.0409\n",
            "Epoch [187/300], Step [350/350], Loss:0.0528, Perplexity: 1.0542\n",
            "Epoch [188/300], Step [50/350], Loss:0.0402, Perplexity: 1.0410\n",
            "Epoch [188/300], Step [100/350], Loss:0.0296, Perplexity: 1.0301\n",
            "Epoch [188/300], Step [150/350], Loss:0.0335, Perplexity: 1.0341\n",
            "Epoch [188/300], Step [200/350], Loss:0.0429, Perplexity: 1.0438\n",
            "Epoch [188/300], Step [250/350], Loss:0.0571, Perplexity: 1.0588\n",
            "Epoch [188/300], Step [300/350], Loss:0.0505, Perplexity: 1.0518\n",
            "Epoch [188/300], Step [350/350], Loss:0.0467, Perplexity: 1.0479\n",
            "Epoch [189/300], Step [50/350], Loss:0.0497, Perplexity: 1.0509\n",
            "Epoch [189/300], Step [100/350], Loss:0.1099, Perplexity: 1.1162\n",
            "Epoch [189/300], Step [150/350], Loss:0.0960, Perplexity: 1.1008\n",
            "Epoch [189/300], Step [200/350], Loss:0.0471, Perplexity: 1.0482\n",
            "Epoch [189/300], Step [250/350], Loss:0.0623, Perplexity: 1.0643\n",
            "Epoch [189/300], Step [300/350], Loss:0.0489, Perplexity: 1.0502\n",
            "Epoch [189/300], Step [350/350], Loss:0.0757, Perplexity: 1.0787\n",
            "Epoch [190/300], Step [50/350], Loss:0.0440, Perplexity: 1.0450\n",
            "Epoch [190/300], Step [100/350], Loss:0.0434, Perplexity: 1.0444\n",
            "Epoch [190/300], Step [150/350], Loss:0.0400, Perplexity: 1.0408\n",
            "Epoch [190/300], Step [200/350], Loss:0.0459, Perplexity: 1.0470\n",
            "Epoch [190/300], Step [250/350], Loss:0.0355, Perplexity: 1.0361\n",
            "Epoch [190/300], Step [300/350], Loss:0.0384, Perplexity: 1.0392\n",
            "Epoch [190/300], Step [350/350], Loss:0.0392, Perplexity: 1.0400\n",
            "Epoch [191/300], Step [50/350], Loss:0.0448, Perplexity: 1.0459\n",
            "Epoch [191/300], Step [100/350], Loss:0.0417, Perplexity: 1.0426\n",
            "Epoch [191/300], Step [150/350], Loss:0.0503, Perplexity: 1.0516\n",
            "Epoch [191/300], Step [200/350], Loss:0.0275, Perplexity: 1.0279\n",
            "Epoch [191/300], Step [250/350], Loss:0.0688, Perplexity: 1.0712\n",
            "Epoch [191/300], Step [300/350], Loss:0.0505, Perplexity: 1.0518\n",
            "Epoch [191/300], Step [350/350], Loss:0.0460, Perplexity: 1.0471\n",
            "Epoch [192/300], Step [50/350], Loss:0.0642, Perplexity: 1.0663\n",
            "Epoch [192/300], Step [100/350], Loss:0.0317, Perplexity: 1.0322\n",
            "Epoch [192/300], Step [150/350], Loss:0.0360, Perplexity: 1.0366\n",
            "Epoch [192/300], Step [200/350], Loss:0.0623, Perplexity: 1.0643\n",
            "Epoch [192/300], Step [250/350], Loss:0.0392, Perplexity: 1.0400\n",
            "Epoch [192/300], Step [300/350], Loss:0.0375, Perplexity: 1.0382\n",
            "Epoch [192/300], Step [350/350], Loss:0.0362, Perplexity: 1.0369\n",
            "Epoch [193/300], Step [50/350], Loss:0.1102, Perplexity: 1.1165\n",
            "Epoch [193/300], Step [100/350], Loss:0.0719, Perplexity: 1.0746\n",
            "Epoch [193/300], Step [150/350], Loss:0.0391, Perplexity: 1.0399\n",
            "Epoch [193/300], Step [200/350], Loss:0.0347, Perplexity: 1.0353\n",
            "Epoch [193/300], Step [250/350], Loss:0.0680, Perplexity: 1.0703\n",
            "Epoch [193/300], Step [300/350], Loss:0.0483, Perplexity: 1.0494\n",
            "Epoch [193/300], Step [350/350], Loss:0.0364, Perplexity: 1.0371\n",
            "Epoch [194/300], Step [50/350], Loss:0.0582, Perplexity: 1.0599\n",
            "Epoch [194/300], Step [100/350], Loss:0.0533, Perplexity: 1.0547\n",
            "Epoch [194/300], Step [150/350], Loss:0.0334, Perplexity: 1.0340\n",
            "Epoch [194/300], Step [200/350], Loss:0.0605, Perplexity: 1.0623\n",
            "Epoch [194/300], Step [250/350], Loss:0.0486, Perplexity: 1.0498\n",
            "Epoch [194/300], Step [300/350], Loss:0.0589, Perplexity: 1.0606\n",
            "Epoch [194/300], Step [350/350], Loss:0.0791, Perplexity: 1.0823\n",
            "Epoch [195/300], Step [50/350], Loss:0.0564, Perplexity: 1.0580\n",
            "Epoch [195/300], Step [100/350], Loss:0.0383, Perplexity: 1.0390\n",
            "Epoch [195/300], Step [150/350], Loss:0.0202, Perplexity: 1.0204\n",
            "Epoch [195/300], Step [200/350], Loss:0.0448, Perplexity: 1.0458\n",
            "Epoch [195/300], Step [250/350], Loss:0.0376, Perplexity: 1.0383\n",
            "Epoch [195/300], Step [300/350], Loss:0.1016, Perplexity: 1.1070\n",
            "Epoch [195/300], Step [350/350], Loss:0.0550, Perplexity: 1.0565\n",
            "Epoch [196/300], Step [50/350], Loss:0.0421, Perplexity: 1.0430\n",
            "Epoch [196/300], Step [100/350], Loss:0.0547, Perplexity: 1.0562\n",
            "Epoch [196/300], Step [150/350], Loss:0.0382, Perplexity: 1.0389\n",
            "Epoch [196/300], Step [200/350], Loss:0.0417, Perplexity: 1.0425\n",
            "Epoch [196/300], Step [250/350], Loss:0.0303, Perplexity: 1.0308\n",
            "Epoch [196/300], Step [300/350], Loss:0.1242, Perplexity: 1.1322\n",
            "Epoch [196/300], Step [350/350], Loss:0.0764, Perplexity: 1.0794\n",
            "Epoch [197/300], Step [50/350], Loss:0.0314, Perplexity: 1.0319\n",
            "Epoch [197/300], Step [100/350], Loss:0.0476, Perplexity: 1.0488\n",
            "Epoch [197/300], Step [150/350], Loss:0.0444, Perplexity: 1.0454\n",
            "Epoch [197/300], Step [200/350], Loss:0.0773, Perplexity: 1.0804\n",
            "Epoch [197/300], Step [250/350], Loss:0.0860, Perplexity: 1.0898\n",
            "Epoch [197/300], Step [300/350], Loss:0.0651, Perplexity: 1.0673\n",
            "Epoch [197/300], Step [350/350], Loss:0.0331, Perplexity: 1.0337\n",
            "Epoch [198/300], Step [50/350], Loss:0.0530, Perplexity: 1.0544\n",
            "Epoch [198/300], Step [100/350], Loss:0.0281, Perplexity: 1.0285\n",
            "Epoch [198/300], Step [150/350], Loss:0.0168, Perplexity: 1.0169\n",
            "Epoch [198/300], Step [200/350], Loss:0.0576, Perplexity: 1.0593\n",
            "Epoch [198/300], Step [250/350], Loss:0.0383, Perplexity: 1.0390\n",
            "Epoch [198/300], Step [300/350], Loss:0.0311, Perplexity: 1.0316\n",
            "Epoch [198/300], Step [350/350], Loss:0.0469, Perplexity: 1.0480\n",
            "Epoch [199/300], Step [50/350], Loss:0.0559, Perplexity: 1.0575\n",
            "Epoch [199/300], Step [100/350], Loss:0.0570, Perplexity: 1.0586\n",
            "Epoch [199/300], Step [150/350], Loss:0.0518, Perplexity: 1.0532\n",
            "Epoch [199/300], Step [200/350], Loss:0.0401, Perplexity: 1.0409\n",
            "Epoch [199/300], Step [250/350], Loss:0.0539, Perplexity: 1.0554\n",
            "Epoch [199/300], Step [300/350], Loss:0.0393, Perplexity: 1.0401\n",
            "Epoch [199/300], Step [350/350], Loss:0.0511, Perplexity: 1.0525\n",
            "Epoch [200/300], Step [50/350], Loss:0.0402, Perplexity: 1.0410\n",
            "Epoch [200/300], Step [100/350], Loss:0.0324, Perplexity: 1.0330\n",
            "Epoch [200/300], Step [150/350], Loss:0.0647, Perplexity: 1.0668\n",
            "Epoch [200/300], Step [200/350], Loss:0.0322, Perplexity: 1.0327\n",
            "Epoch [200/300], Step [250/350], Loss:0.0350, Perplexity: 1.0356\n",
            "Epoch [200/300], Step [300/350], Loss:0.0287, Perplexity: 1.0291\n",
            "Epoch [200/300], Step [350/350], Loss:0.0638, Perplexity: 1.0658\n",
            "Epoch [201/300], Step [50/350], Loss:0.0587, Perplexity: 1.0604\n",
            "Epoch [201/300], Step [100/350], Loss:0.0242, Perplexity: 1.0245\n",
            "Epoch [201/300], Step [150/350], Loss:0.0426, Perplexity: 1.0435\n",
            "Epoch [201/300], Step [200/350], Loss:0.0685, Perplexity: 1.0709\n",
            "Epoch [201/300], Step [250/350], Loss:0.0439, Perplexity: 1.0448\n",
            "Epoch [201/300], Step [300/350], Loss:0.0168, Perplexity: 1.0169\n",
            "Epoch [201/300], Step [350/350], Loss:0.0553, Perplexity: 1.0569\n",
            "Epoch [202/300], Step [50/350], Loss:0.0357, Perplexity: 1.0363\n",
            "Epoch [202/300], Step [100/350], Loss:0.0475, Perplexity: 1.0486\n",
            "Epoch [202/300], Step [150/350], Loss:0.0364, Perplexity: 1.0371\n",
            "Epoch [202/300], Step [200/350], Loss:0.0419, Perplexity: 1.0427\n",
            "Epoch [202/300], Step [250/350], Loss:0.0500, Perplexity: 1.0513\n",
            "Epoch [202/300], Step [300/350], Loss:0.0280, Perplexity: 1.0284\n",
            "Epoch [202/300], Step [350/350], Loss:0.0663, Perplexity: 1.0686\n",
            "Epoch [203/300], Step [50/350], Loss:0.0282, Perplexity: 1.0286\n",
            "Epoch [203/300], Step [100/350], Loss:0.0380, Perplexity: 1.0387\n",
            "Epoch [203/300], Step [150/350], Loss:0.0488, Perplexity: 1.0500\n",
            "Epoch [203/300], Step [200/350], Loss:0.0218, Perplexity: 1.0221\n",
            "Epoch [203/300], Step [250/350], Loss:0.0164, Perplexity: 1.0165\n",
            "Epoch [203/300], Step [300/350], Loss:0.0428, Perplexity: 1.0437\n",
            "Epoch [203/300], Step [350/350], Loss:0.0537, Perplexity: 1.0551\n",
            "Epoch [204/300], Step [50/350], Loss:0.0354, Perplexity: 1.0360\n",
            "Epoch [204/300], Step [100/350], Loss:0.0431, Perplexity: 1.0440\n",
            "Epoch [204/300], Step [150/350], Loss:0.0317, Perplexity: 1.0322\n",
            "Epoch [204/300], Step [200/350], Loss:0.0234, Perplexity: 1.0237\n",
            "Epoch [204/300], Step [250/350], Loss:0.0278, Perplexity: 1.0282\n",
            "Epoch [204/300], Step [300/350], Loss:0.0302, Perplexity: 1.0307\n",
            "Epoch [204/300], Step [350/350], Loss:0.0351, Perplexity: 1.0358\n",
            "Epoch [205/300], Step [50/350], Loss:0.0333, Perplexity: 1.0338\n",
            "Epoch [205/300], Step [100/350], Loss:0.0513, Perplexity: 1.0526\n",
            "Epoch [205/300], Step [150/350], Loss:0.0590, Perplexity: 1.0607\n",
            "Epoch [205/300], Step [200/350], Loss:0.0389, Perplexity: 1.0397\n",
            "Epoch [205/300], Step [250/350], Loss:0.0620, Perplexity: 1.0639\n",
            "Epoch [205/300], Step [300/350], Loss:0.0617, Perplexity: 1.0636\n",
            "Epoch [205/300], Step [350/350], Loss:0.1068, Perplexity: 1.1128\n",
            "Epoch [206/300], Step [50/350], Loss:0.0353, Perplexity: 1.0360\n",
            "Epoch [206/300], Step [100/350], Loss:0.0210, Perplexity: 1.0212\n",
            "Epoch [206/300], Step [150/350], Loss:0.0266, Perplexity: 1.0270\n",
            "Epoch [206/300], Step [200/350], Loss:0.0160, Perplexity: 1.0161\n",
            "Epoch [206/300], Step [250/350], Loss:0.0352, Perplexity: 1.0358\n",
            "Epoch [206/300], Step [300/350], Loss:0.0589, Perplexity: 1.0607\n",
            "Epoch [206/300], Step [350/350], Loss:0.0215, Perplexity: 1.0217\n",
            "Epoch [207/300], Step [50/350], Loss:0.0392, Perplexity: 1.0399\n",
            "Epoch [207/300], Step [100/350], Loss:0.0600, Perplexity: 1.0618\n",
            "Epoch [207/300], Step [150/350], Loss:0.0511, Perplexity: 1.0524\n",
            "Epoch [207/300], Step [200/350], Loss:0.0353, Perplexity: 1.0359\n",
            "Epoch [207/300], Step [250/350], Loss:0.0484, Perplexity: 1.0496\n",
            "Epoch [207/300], Step [300/350], Loss:0.0328, Perplexity: 1.0333\n",
            "Epoch [207/300], Step [350/350], Loss:0.0380, Perplexity: 1.0388\n",
            "Epoch [208/300], Step [50/350], Loss:0.0377, Perplexity: 1.0385\n",
            "Epoch [208/300], Step [100/350], Loss:0.0350, Perplexity: 1.0357\n",
            "Epoch [208/300], Step [150/350], Loss:0.0726, Perplexity: 1.0753\n",
            "Epoch [208/300], Step [200/350], Loss:0.0282, Perplexity: 1.0286\n",
            "Epoch [208/300], Step [250/350], Loss:0.0476, Perplexity: 1.0487\n",
            "Epoch [208/300], Step [300/350], Loss:0.0481, Perplexity: 1.0493\n",
            "Epoch [208/300], Step [350/350], Loss:0.0437, Perplexity: 1.0447\n",
            "Epoch [209/300], Step [50/350], Loss:0.0528, Perplexity: 1.0542\n",
            "Epoch [209/300], Step [100/350], Loss:0.0314, Perplexity: 1.0319\n",
            "Epoch [209/300], Step [150/350], Loss:0.1125, Perplexity: 1.1191\n",
            "Epoch [209/300], Step [200/350], Loss:0.0483, Perplexity: 1.0495\n",
            "Epoch [209/300], Step [250/350], Loss:0.0428, Perplexity: 1.0437\n",
            "Epoch [209/300], Step [300/350], Loss:0.0147, Perplexity: 1.0148\n",
            "Epoch [209/300], Step [350/350], Loss:0.0557, Perplexity: 1.0573\n",
            "Epoch [210/300], Step [50/350], Loss:0.0311, Perplexity: 1.0316\n",
            "Epoch [210/300], Step [100/350], Loss:0.0377, Perplexity: 1.0384\n",
            "Epoch [210/300], Step [150/350], Loss:0.0396, Perplexity: 1.0404\n",
            "Epoch [210/300], Step [200/350], Loss:0.0386, Perplexity: 1.0394\n",
            "Epoch [210/300], Step [250/350], Loss:0.0359, Perplexity: 1.0365\n",
            "Epoch [210/300], Step [300/350], Loss:0.0458, Perplexity: 1.0469\n",
            "Epoch [210/300], Step [350/350], Loss:0.0498, Perplexity: 1.0510\n",
            "Epoch [211/300], Step [50/350], Loss:0.0657, Perplexity: 1.0679\n",
            "Epoch [211/300], Step [100/350], Loss:0.1321, Perplexity: 1.1412\n",
            "Epoch [211/300], Step [150/350], Loss:0.0845, Perplexity: 1.0882\n",
            "Epoch [211/300], Step [200/350], Loss:0.0375, Perplexity: 1.0382\n",
            "Epoch [211/300], Step [250/350], Loss:0.0459, Perplexity: 1.0470\n",
            "Epoch [211/300], Step [300/350], Loss:0.0350, Perplexity: 1.0357\n",
            "Epoch [211/300], Step [350/350], Loss:0.0486, Perplexity: 1.0498\n",
            "Epoch [212/300], Step [50/350], Loss:0.0466, Perplexity: 1.0477\n",
            "Epoch [212/300], Step [100/350], Loss:0.0406, Perplexity: 1.0414\n",
            "Epoch [212/300], Step [150/350], Loss:0.0222, Perplexity: 1.0225\n",
            "Epoch [212/300], Step [200/350], Loss:0.0276, Perplexity: 1.0280\n",
            "Epoch [212/300], Step [250/350], Loss:0.0273, Perplexity: 1.0277\n",
            "Epoch [212/300], Step [300/350], Loss:0.0477, Perplexity: 1.0489\n",
            "Epoch [212/300], Step [350/350], Loss:0.0632, Perplexity: 1.0652\n",
            "Epoch [213/300], Step [50/350], Loss:0.0223, Perplexity: 1.0225\n",
            "Epoch [213/300], Step [100/350], Loss:0.0307, Perplexity: 1.0312\n",
            "Epoch [213/300], Step [150/350], Loss:0.0691, Perplexity: 1.0715\n",
            "Epoch [213/300], Step [200/350], Loss:0.0670, Perplexity: 1.0693\n",
            "Epoch [213/300], Step [250/350], Loss:0.0270, Perplexity: 1.0273\n",
            "Epoch [213/300], Step [300/350], Loss:0.0301, Perplexity: 1.0305\n",
            "Epoch [213/300], Step [350/350], Loss:0.0394, Perplexity: 1.0402\n",
            "Epoch [214/300], Step [50/350], Loss:0.0381, Perplexity: 1.0389\n",
            "Epoch [214/300], Step [100/350], Loss:0.0225, Perplexity: 1.0227\n",
            "Epoch [214/300], Step [150/350], Loss:0.0297, Perplexity: 1.0301\n",
            "Epoch [214/300], Step [200/350], Loss:0.0404, Perplexity: 1.0413\n",
            "Epoch [214/300], Step [250/350], Loss:0.0270, Perplexity: 1.0274\n",
            "Epoch [214/300], Step [300/350], Loss:0.0708, Perplexity: 1.0733\n",
            "Epoch [214/300], Step [350/350], Loss:0.0626, Perplexity: 1.0646\n",
            "Epoch [215/300], Step [50/350], Loss:0.0323, Perplexity: 1.0328\n",
            "Epoch [215/300], Step [100/350], Loss:0.0461, Perplexity: 1.0472\n",
            "Epoch [215/300], Step [150/350], Loss:0.0346, Perplexity: 1.0352\n",
            "Epoch [215/300], Step [200/350], Loss:0.0425, Perplexity: 1.0435\n",
            "Epoch [215/300], Step [250/350], Loss:0.0518, Perplexity: 1.0532\n",
            "Epoch [215/300], Step [300/350], Loss:0.0420, Perplexity: 1.0429\n",
            "Epoch [215/300], Step [350/350], Loss:0.0241, Perplexity: 1.0244\n",
            "Epoch [216/300], Step [50/350], Loss:0.0535, Perplexity: 1.0549\n",
            "Epoch [216/300], Step [100/350], Loss:0.0646, Perplexity: 1.0667\n",
            "Epoch [216/300], Step [150/350], Loss:0.0129, Perplexity: 1.0130\n",
            "Epoch [216/300], Step [200/350], Loss:0.0371, Perplexity: 1.0378\n",
            "Epoch [216/300], Step [250/350], Loss:0.0961, Perplexity: 1.1009\n",
            "Epoch [216/300], Step [300/350], Loss:0.0421, Perplexity: 1.0430\n",
            "Epoch [216/300], Step [350/350], Loss:0.0521, Perplexity: 1.0535\n",
            "Epoch [217/300], Step [50/350], Loss:0.0308, Perplexity: 1.0313\n",
            "Epoch [217/300], Step [100/350], Loss:0.0337, Perplexity: 1.0343\n",
            "Epoch [217/300], Step [150/350], Loss:0.0302, Perplexity: 1.0307\n",
            "Epoch [217/300], Step [200/350], Loss:0.0612, Perplexity: 1.0631\n",
            "Epoch [217/300], Step [250/350], Loss:0.0478, Perplexity: 1.0490\n",
            "Epoch [217/300], Step [300/350], Loss:0.0628, Perplexity: 1.0648\n",
            "Epoch [217/300], Step [350/350], Loss:0.0326, Perplexity: 1.0332\n",
            "Epoch [218/300], Step [50/350], Loss:0.0564, Perplexity: 1.0580\n",
            "Epoch [218/300], Step [100/350], Loss:0.0443, Perplexity: 1.0453\n",
            "Epoch [218/300], Step [150/350], Loss:0.0482, Perplexity: 1.0493\n",
            "Epoch [218/300], Step [200/350], Loss:0.0477, Perplexity: 1.0488\n",
            "Epoch [218/300], Step [250/350], Loss:0.0263, Perplexity: 1.0267\n",
            "Epoch [218/300], Step [300/350], Loss:0.0207, Perplexity: 1.0209\n",
            "Epoch [218/300], Step [350/350], Loss:0.0412, Perplexity: 1.0420\n",
            "Epoch [219/300], Step [50/350], Loss:0.0398, Perplexity: 1.0406\n",
            "Epoch [219/300], Step [100/350], Loss:0.0208, Perplexity: 1.0211\n",
            "Epoch [219/300], Step [150/350], Loss:0.0275, Perplexity: 1.0279\n",
            "Epoch [219/300], Step [200/350], Loss:0.0414, Perplexity: 1.0422\n",
            "Epoch [219/300], Step [250/350], Loss:0.0319, Perplexity: 1.0324\n",
            "Epoch [219/300], Step [300/350], Loss:0.0223, Perplexity: 1.0226\n",
            "Epoch [219/300], Step [350/350], Loss:0.0232, Perplexity: 1.0235\n",
            "Epoch [220/300], Step [50/350], Loss:0.0308, Perplexity: 1.0313\n",
            "Epoch [220/300], Step [100/350], Loss:0.0473, Perplexity: 1.0484\n",
            "Epoch [220/300], Step [150/350], Loss:0.0218, Perplexity: 1.0220\n",
            "Epoch [220/300], Step [200/350], Loss:0.0190, Perplexity: 1.0192\n",
            "Epoch [220/300], Step [250/350], Loss:0.0434, Perplexity: 1.0443\n",
            "Epoch [220/300], Step [300/350], Loss:0.0316, Perplexity: 1.0321\n",
            "Epoch [220/300], Step [350/350], Loss:0.0678, Perplexity: 1.0701\n",
            "Epoch [221/300], Step [50/350], Loss:0.0549, Perplexity: 1.0564\n",
            "Epoch [221/300], Step [100/350], Loss:0.0223, Perplexity: 1.0226\n",
            "Epoch [221/300], Step [150/350], Loss:0.0792, Perplexity: 1.0824\n",
            "Epoch [221/300], Step [200/350], Loss:0.0274, Perplexity: 1.0278\n",
            "Epoch [221/300], Step [250/350], Loss:0.0578, Perplexity: 1.0595\n",
            "Epoch [221/300], Step [300/350], Loss:0.0829, Perplexity: 1.0865\n",
            "Epoch [221/300], Step [350/350], Loss:0.0218, Perplexity: 1.0220\n",
            "Epoch [222/300], Step [50/350], Loss:0.0442, Perplexity: 1.0452\n",
            "Epoch [222/300], Step [100/350], Loss:0.0258, Perplexity: 1.0261\n",
            "Epoch [222/300], Step [150/350], Loss:0.0585, Perplexity: 1.0603\n",
            "Epoch [222/300], Step [200/350], Loss:0.0281, Perplexity: 1.0285\n",
            "Epoch [222/300], Step [250/350], Loss:0.0499, Perplexity: 1.0511\n",
            "Epoch [222/300], Step [300/350], Loss:0.0685, Perplexity: 1.0709\n",
            "Epoch [222/300], Step [350/350], Loss:0.0722, Perplexity: 1.0749\n",
            "Epoch [223/300], Step [50/350], Loss:0.0401, Perplexity: 1.0410\n",
            "Epoch [223/300], Step [100/350], Loss:0.0383, Perplexity: 1.0391\n",
            "Epoch [223/300], Step [150/350], Loss:0.0198, Perplexity: 1.0200\n",
            "Epoch [223/300], Step [200/350], Loss:0.0757, Perplexity: 1.0787\n",
            "Epoch [223/300], Step [250/350], Loss:0.0448, Perplexity: 1.0458\n",
            "Epoch [223/300], Step [300/350], Loss:0.0391, Perplexity: 1.0399\n",
            "Epoch [223/300], Step [350/350], Loss:0.1345, Perplexity: 1.1439\n",
            "Epoch [224/300], Step [50/350], Loss:0.0297, Perplexity: 1.0302\n",
            "Epoch [224/300], Step [100/350], Loss:0.0387, Perplexity: 1.0395\n",
            "Epoch [224/300], Step [150/350], Loss:0.0244, Perplexity: 1.0247\n",
            "Epoch [224/300], Step [200/350], Loss:0.0585, Perplexity: 1.0603\n",
            "Epoch [224/300], Step [250/350], Loss:0.0483, Perplexity: 1.0495\n",
            "Epoch [224/300], Step [300/350], Loss:0.0474, Perplexity: 1.0486\n",
            "Epoch [224/300], Step [350/350], Loss:0.0212, Perplexity: 1.0214\n",
            "Epoch [225/300], Step [50/350], Loss:0.0263, Perplexity: 1.0267\n",
            "Epoch [225/300], Step [100/350], Loss:0.0631, Perplexity: 1.0652\n",
            "Epoch [225/300], Step [150/350], Loss:0.0240, Perplexity: 1.0243\n",
            "Epoch [225/300], Step [200/350], Loss:0.0399, Perplexity: 1.0407\n",
            "Epoch [225/300], Step [250/350], Loss:0.0326, Perplexity: 1.0332\n",
            "Epoch [225/300], Step [300/350], Loss:0.0566, Perplexity: 1.0582\n",
            "Epoch [225/300], Step [350/350], Loss:0.0292, Perplexity: 1.0296\n",
            "Epoch [226/300], Step [50/350], Loss:0.0529, Perplexity: 1.0543\n",
            "Epoch [226/300], Step [100/350], Loss:0.0440, Perplexity: 1.0449\n",
            "Epoch [226/300], Step [150/350], Loss:0.0234, Perplexity: 1.0237\n",
            "Epoch [226/300], Step [200/350], Loss:0.0205, Perplexity: 1.0207\n",
            "Epoch [226/300], Step [250/350], Loss:0.0391, Perplexity: 1.0399\n",
            "Epoch [226/300], Step [300/350], Loss:0.0377, Perplexity: 1.0384\n",
            "Epoch [226/300], Step [350/350], Loss:0.0420, Perplexity: 1.0429\n",
            "Epoch [227/300], Step [50/350], Loss:0.0351, Perplexity: 1.0358\n",
            "Epoch [227/300], Step [100/350], Loss:0.0374, Perplexity: 1.0381\n",
            "Epoch [227/300], Step [150/350], Loss:0.0436, Perplexity: 1.0446\n",
            "Epoch [227/300], Step [200/350], Loss:0.0419, Perplexity: 1.0428\n",
            "Epoch [227/300], Step [250/350], Loss:0.0230, Perplexity: 1.0233\n",
            "Epoch [227/300], Step [300/350], Loss:0.0268, Perplexity: 1.0272\n",
            "Epoch [227/300], Step [350/350], Loss:0.0453, Perplexity: 1.0464\n",
            "Epoch [228/300], Step [50/350], Loss:0.0382, Perplexity: 1.0390\n",
            "Epoch [228/300], Step [100/350], Loss:0.0248, Perplexity: 1.0251\n",
            "Epoch [228/300], Step [150/350], Loss:0.0571, Perplexity: 1.0588\n",
            "Epoch [228/300], Step [200/350], Loss:0.0485, Perplexity: 1.0497\n",
            "Epoch [228/300], Step [250/350], Loss:0.0167, Perplexity: 1.0168\n",
            "Epoch [228/300], Step [300/350], Loss:0.0397, Perplexity: 1.0405\n",
            "Epoch [228/300], Step [350/350], Loss:0.1130, Perplexity: 1.1196\n",
            "Epoch [229/300], Step [50/350], Loss:0.0444, Perplexity: 1.0454\n",
            "Epoch [229/300], Step [100/350], Loss:0.0268, Perplexity: 1.0272\n",
            "Epoch [229/300], Step [150/350], Loss:0.0448, Perplexity: 1.0458\n",
            "Epoch [229/300], Step [200/350], Loss:0.0408, Perplexity: 1.0417\n",
            "Epoch [229/300], Step [250/350], Loss:0.0622, Perplexity: 1.0642\n",
            "Epoch [229/300], Step [300/350], Loss:0.0541, Perplexity: 1.0555\n",
            "Epoch [229/300], Step [350/350], Loss:0.0409, Perplexity: 1.0417\n",
            "Epoch [230/300], Step [50/350], Loss:0.0206, Perplexity: 1.0208\n",
            "Epoch [230/300], Step [100/350], Loss:0.0492, Perplexity: 1.0504\n",
            "Epoch [230/300], Step [150/350], Loss:0.0333, Perplexity: 1.0339\n",
            "Epoch [230/300], Step [200/350], Loss:0.0424, Perplexity: 1.0433\n",
            "Epoch [230/300], Step [250/350], Loss:0.0283, Perplexity: 1.0287\n",
            "Epoch [230/300], Step [300/350], Loss:0.0464, Perplexity: 1.0475\n",
            "Epoch [230/300], Step [350/350], Loss:0.0230, Perplexity: 1.0232\n",
            "Epoch [231/300], Step [50/350], Loss:0.0185, Perplexity: 1.0187\n",
            "Epoch [231/300], Step [100/350], Loss:0.0314, Perplexity: 1.0319\n",
            "Epoch [231/300], Step [150/350], Loss:0.0261, Perplexity: 1.0264\n",
            "Epoch [231/300], Step [200/350], Loss:0.0649, Perplexity: 1.0670\n",
            "Epoch [231/300], Step [250/350], Loss:0.0453, Perplexity: 1.0463\n",
            "Epoch [231/300], Step [300/350], Loss:0.0408, Perplexity: 1.0416\n",
            "Epoch [231/300], Step [350/350], Loss:0.0539, Perplexity: 1.0554\n",
            "Epoch [232/300], Step [50/350], Loss:0.0548, Perplexity: 1.0563\n",
            "Epoch [232/300], Step [100/350], Loss:0.0497, Perplexity: 1.0510\n",
            "Epoch [232/300], Step [150/350], Loss:0.0350, Perplexity: 1.0357\n",
            "Epoch [232/300], Step [200/350], Loss:0.0277, Perplexity: 1.0281\n",
            "Epoch [232/300], Step [250/350], Loss:0.0293, Perplexity: 1.0297\n",
            "Epoch [232/300], Step [300/350], Loss:0.0421, Perplexity: 1.0430\n",
            "Epoch [232/300], Step [350/350], Loss:0.0462, Perplexity: 1.0473\n",
            "Epoch [233/300], Step [50/350], Loss:0.0340, Perplexity: 1.0345\n",
            "Epoch [233/300], Step [100/350], Loss:0.0452, Perplexity: 1.0462\n",
            "Epoch [233/300], Step [150/350], Loss:0.0483, Perplexity: 1.0495\n",
            "Epoch [233/300], Step [200/350], Loss:0.0466, Perplexity: 1.0477\n",
            "Epoch [233/300], Step [250/350], Loss:0.0278, Perplexity: 1.0282\n",
            "Epoch [233/300], Step [300/350], Loss:0.0366, Perplexity: 1.0373\n",
            "Epoch [233/300], Step [350/350], Loss:0.0345, Perplexity: 1.0351\n",
            "Epoch [234/300], Step [50/350], Loss:0.0166, Perplexity: 1.0167\n",
            "Epoch [234/300], Step [100/350], Loss:0.0439, Perplexity: 1.0448\n",
            "Epoch [234/300], Step [150/350], Loss:0.0436, Perplexity: 1.0445\n",
            "Epoch [234/300], Step [200/350], Loss:0.0287, Perplexity: 1.0291\n",
            "Epoch [234/300], Step [250/350], Loss:0.0256, Perplexity: 1.0260\n",
            "Epoch [234/300], Step [300/350], Loss:0.0359, Perplexity: 1.0365\n",
            "Epoch [234/300], Step [350/350], Loss:0.0441, Perplexity: 1.0451\n",
            "Epoch [235/300], Step [50/350], Loss:0.0478, Perplexity: 1.0489\n",
            "Epoch [235/300], Step [100/350], Loss:0.0214, Perplexity: 1.0216\n",
            "Epoch [235/300], Step [150/350], Loss:0.0185, Perplexity: 1.0187\n",
            "Epoch [235/300], Step [200/350], Loss:0.1391, Perplexity: 1.1493\n",
            "Epoch [235/300], Step [250/350], Loss:0.0247, Perplexity: 1.0250\n",
            "Epoch [235/300], Step [300/350], Loss:0.1580, Perplexity: 1.1712\n",
            "Epoch [235/300], Step [350/350], Loss:0.0606, Perplexity: 1.0625\n",
            "Epoch [236/300], Step [50/350], Loss:0.0279, Perplexity: 1.0283\n",
            "Epoch [236/300], Step [100/350], Loss:0.0279, Perplexity: 1.0283\n",
            "Epoch [236/300], Step [150/350], Loss:0.0387, Perplexity: 1.0395\n",
            "Epoch [236/300], Step [200/350], Loss:0.0490, Perplexity: 1.0502\n",
            "Epoch [236/300], Step [250/350], Loss:0.0382, Perplexity: 1.0389\n",
            "Epoch [236/300], Step [300/350], Loss:0.0342, Perplexity: 1.0348\n",
            "Epoch [236/300], Step [350/350], Loss:0.0513, Perplexity: 1.0527\n",
            "Epoch [237/300], Step [50/350], Loss:0.0423, Perplexity: 1.0432\n",
            "Epoch [237/300], Step [100/350], Loss:0.0191, Perplexity: 1.0193\n",
            "Epoch [237/300], Step [150/350], Loss:0.0413, Perplexity: 1.0422\n",
            "Epoch [237/300], Step [200/350], Loss:0.0398, Perplexity: 1.0407\n",
            "Epoch [237/300], Step [250/350], Loss:0.0512, Perplexity: 1.0525\n",
            "Epoch [237/300], Step [300/350], Loss:0.0335, Perplexity: 1.0340\n",
            "Epoch [237/300], Step [350/350], Loss:0.0702, Perplexity: 1.0727\n",
            "Epoch [238/300], Step [50/350], Loss:0.0347, Perplexity: 1.0353\n",
            "Epoch [238/300], Step [100/350], Loss:0.0964, Perplexity: 1.1012\n",
            "Epoch [238/300], Step [150/350], Loss:0.0660, Perplexity: 1.0682\n",
            "Epoch [238/300], Step [200/350], Loss:0.0291, Perplexity: 1.0296\n",
            "Epoch [238/300], Step [250/350], Loss:0.0201, Perplexity: 1.0203\n",
            "Epoch [238/300], Step [300/350], Loss:0.0234, Perplexity: 1.0237\n",
            "Epoch [238/300], Step [350/350], Loss:0.0169, Perplexity: 1.0171\n",
            "Epoch [239/300], Step [50/350], Loss:0.0310, Perplexity: 1.0314\n",
            "Epoch [239/300], Step [100/350], Loss:0.0538, Perplexity: 1.0553\n",
            "Epoch [239/300], Step [150/350], Loss:0.0356, Perplexity: 1.0362\n",
            "Epoch [239/300], Step [200/350], Loss:0.0326, Perplexity: 1.0332\n",
            "Epoch [239/300], Step [250/350], Loss:0.0303, Perplexity: 1.0308\n",
            "Epoch [239/300], Step [300/350], Loss:0.0388, Perplexity: 1.0396\n",
            "Epoch [239/300], Step [350/350], Loss:0.0546, Perplexity: 1.0561\n",
            "Epoch [240/300], Step [50/350], Loss:0.0800, Perplexity: 1.0833\n",
            "Epoch [240/300], Step [100/350], Loss:0.0378, Perplexity: 1.0385\n",
            "Epoch [240/300], Step [150/350], Loss:0.0405, Perplexity: 1.0413\n",
            "Epoch [240/300], Step [200/350], Loss:0.0242, Perplexity: 1.0245\n",
            "Epoch [240/300], Step [250/350], Loss:0.0279, Perplexity: 1.0283\n",
            "Epoch [240/300], Step [300/350], Loss:0.1005, Perplexity: 1.1058\n",
            "Epoch [240/300], Step [350/350], Loss:0.0373, Perplexity: 1.0380\n",
            "Epoch [241/300], Step [50/350], Loss:0.0330, Perplexity: 1.0336\n",
            "Epoch [241/300], Step [100/350], Loss:0.0206, Perplexity: 1.0208\n",
            "Epoch [241/300], Step [150/350], Loss:0.0321, Perplexity: 1.0326\n",
            "Epoch [241/300], Step [200/350], Loss:0.0715, Perplexity: 1.0741\n",
            "Epoch [241/300], Step [250/350], Loss:0.0575, Perplexity: 1.0592\n",
            "Epoch [241/300], Step [300/350], Loss:0.0141, Perplexity: 1.0142\n",
            "Epoch [241/300], Step [350/350], Loss:0.0577, Perplexity: 1.0594\n",
            "Epoch [242/300], Step [50/350], Loss:0.0160, Perplexity: 1.0161\n",
            "Epoch [242/300], Step [100/350], Loss:0.0253, Perplexity: 1.0256\n",
            "Epoch [242/300], Step [150/350], Loss:0.0443, Perplexity: 1.0453\n",
            "Epoch [242/300], Step [200/350], Loss:0.0461, Perplexity: 1.0472\n",
            "Epoch [242/300], Step [250/350], Loss:0.0279, Perplexity: 1.0283\n",
            "Epoch [242/300], Step [300/350], Loss:0.0155, Perplexity: 1.0156\n",
            "Epoch [242/300], Step [350/350], Loss:0.0149, Perplexity: 1.0150\n",
            "Epoch [243/300], Step [50/350], Loss:0.0423, Perplexity: 1.0432\n",
            "Epoch [243/300], Step [100/350], Loss:0.0267, Perplexity: 1.0271\n",
            "Epoch [243/300], Step [150/350], Loss:0.0375, Perplexity: 1.0383\n",
            "Epoch [243/300], Step [200/350], Loss:0.0363, Perplexity: 1.0370\n",
            "Epoch [243/300], Step [250/350], Loss:0.0266, Perplexity: 1.0270\n",
            "Epoch [243/300], Step [300/350], Loss:0.0252, Perplexity: 1.0255\n",
            "Epoch [243/300], Step [350/350], Loss:0.0551, Perplexity: 1.0567\n",
            "Epoch [244/300], Step [50/350], Loss:0.0205, Perplexity: 1.0208\n",
            "Epoch [244/300], Step [100/350], Loss:0.0183, Perplexity: 1.0184\n",
            "Epoch [244/300], Step [150/350], Loss:0.0350, Perplexity: 1.0357\n",
            "Epoch [244/300], Step [200/350], Loss:0.0180, Perplexity: 1.0182\n",
            "Epoch [244/300], Step [250/350], Loss:0.0256, Perplexity: 1.0260\n",
            "Epoch [244/300], Step [300/350], Loss:0.0494, Perplexity: 1.0507\n",
            "Epoch [244/300], Step [350/350], Loss:0.0339, Perplexity: 1.0344\n",
            "Epoch [245/300], Step [50/350], Loss:0.0617, Perplexity: 1.0637\n",
            "Epoch [245/300], Step [100/350], Loss:0.0334, Perplexity: 1.0340\n",
            "Epoch [245/300], Step [150/350], Loss:0.0338, Perplexity: 1.0344\n",
            "Epoch [245/300], Step [200/350], Loss:0.0302, Perplexity: 1.0307\n",
            "Epoch [245/300], Step [250/350], Loss:0.0329, Perplexity: 1.0335\n",
            "Epoch [245/300], Step [300/350], Loss:0.0129, Perplexity: 1.0129\n",
            "Epoch [245/300], Step [350/350], Loss:0.0549, Perplexity: 1.0564\n",
            "Epoch [246/300], Step [50/350], Loss:0.0447, Perplexity: 1.0457\n",
            "Epoch [246/300], Step [100/350], Loss:0.0161, Perplexity: 1.0162\n",
            "Epoch [246/300], Step [150/350], Loss:0.0386, Perplexity: 1.0394\n",
            "Epoch [246/300], Step [200/350], Loss:0.0313, Perplexity: 1.0317\n",
            "Epoch [246/300], Step [250/350], Loss:0.0292, Perplexity: 1.0296\n",
            "Epoch [246/300], Step [300/350], Loss:0.0595, Perplexity: 1.0613\n",
            "Epoch [246/300], Step [350/350], Loss:0.0295, Perplexity: 1.0300\n",
            "Epoch [247/300], Step [50/350], Loss:0.0423, Perplexity: 1.0432\n",
            "Epoch [247/300], Step [100/350], Loss:0.0315, Perplexity: 1.0320\n",
            "Epoch [247/300], Step [150/350], Loss:0.0605, Perplexity: 1.0624\n",
            "Epoch [247/300], Step [200/350], Loss:0.0172, Perplexity: 1.0174\n",
            "Epoch [247/300], Step [250/350], Loss:0.0538, Perplexity: 1.0553\n",
            "Epoch [247/300], Step [300/350], Loss:0.0454, Perplexity: 1.0465\n",
            "Epoch [247/300], Step [350/350], Loss:0.0333, Perplexity: 1.0339\n",
            "Epoch [248/300], Step [50/350], Loss:0.0180, Perplexity: 1.0182\n",
            "Epoch [248/300], Step [100/350], Loss:0.0472, Perplexity: 1.0483\n",
            "Epoch [248/300], Step [150/350], Loss:0.0273, Perplexity: 1.0277\n",
            "Epoch [248/300], Step [200/350], Loss:0.0609, Perplexity: 1.0628\n",
            "Epoch [248/300], Step [250/350], Loss:0.0207, Perplexity: 1.0209\n",
            "Epoch [248/300], Step [300/350], Loss:0.0372, Perplexity: 1.0379\n",
            "Epoch [248/300], Step [350/350], Loss:0.0328, Perplexity: 1.0334\n",
            "Epoch [249/300], Step [50/350], Loss:0.0172, Perplexity: 1.0174\n",
            "Epoch [249/300], Step [100/350], Loss:0.0293, Perplexity: 1.0297\n",
            "Epoch [249/300], Step [150/350], Loss:0.0279, Perplexity: 1.0282\n",
            "Epoch [249/300], Step [200/350], Loss:0.0097, Perplexity: 1.0097\n",
            "Epoch [249/300], Step [250/350], Loss:0.0613, Perplexity: 1.0632\n",
            "Epoch [249/300], Step [300/350], Loss:0.0201, Perplexity: 1.0203\n",
            "Epoch [249/300], Step [350/350], Loss:0.0403, Perplexity: 1.0412\n",
            "Epoch [250/300], Step [50/350], Loss:0.0414, Perplexity: 1.0422\n",
            "Epoch [250/300], Step [100/350], Loss:0.0448, Perplexity: 1.0458\n",
            "Epoch [250/300], Step [150/350], Loss:0.0333, Perplexity: 1.0338\n",
            "Epoch [250/300], Step [200/350], Loss:0.0476, Perplexity: 1.0487\n",
            "Epoch [250/300], Step [250/350], Loss:0.0698, Perplexity: 1.0723\n",
            "Epoch [250/300], Step [300/350], Loss:0.0653, Perplexity: 1.0675\n",
            "Epoch [250/300], Step [350/350], Loss:0.0483, Perplexity: 1.0495\n",
            "Epoch [251/300], Step [50/350], Loss:0.0398, Perplexity: 1.0406\n",
            "Epoch [251/300], Step [100/350], Loss:0.0222, Perplexity: 1.0224\n",
            "Epoch [251/300], Step [150/350], Loss:0.0168, Perplexity: 1.0169\n",
            "Epoch [251/300], Step [200/350], Loss:0.0369, Perplexity: 1.0376\n",
            "Epoch [251/300], Step [250/350], Loss:0.0391, Perplexity: 1.0398\n",
            "Epoch [251/300], Step [300/350], Loss:0.0310, Perplexity: 1.0315\n",
            "Epoch [251/300], Step [350/350], Loss:0.0184, Perplexity: 1.0186\n",
            "Epoch [252/300], Step [50/350], Loss:0.0595, Perplexity: 1.0613\n",
            "Epoch [252/300], Step [100/350], Loss:0.0490, Perplexity: 1.0502\n",
            "Epoch [252/300], Step [150/350], Loss:0.0383, Perplexity: 1.0391\n",
            "Epoch [252/300], Step [200/350], Loss:0.0368, Perplexity: 1.0375\n",
            "Epoch [252/300], Step [250/350], Loss:0.0156, Perplexity: 1.0157\n",
            "Epoch [252/300], Step [300/350], Loss:0.0416, Perplexity: 1.0425\n",
            "Epoch [252/300], Step [350/350], Loss:0.0261, Perplexity: 1.0265\n",
            "Epoch [253/300], Step [50/350], Loss:0.0419, Perplexity: 1.0428\n",
            "Epoch [253/300], Step [100/350], Loss:0.0106, Perplexity: 1.0106\n",
            "Epoch [253/300], Step [150/350], Loss:0.0411, Perplexity: 1.0419\n",
            "Epoch [253/300], Step [200/350], Loss:0.0728, Perplexity: 1.0755\n",
            "Epoch [253/300], Step [250/350], Loss:0.0145, Perplexity: 1.0146\n",
            "Epoch [253/300], Step [300/350], Loss:0.0173, Perplexity: 1.0175\n",
            "Epoch [253/300], Step [350/350], Loss:0.0309, Perplexity: 1.0314\n",
            "Epoch [254/300], Step [50/350], Loss:0.0323, Perplexity: 1.0328\n",
            "Epoch [254/300], Step [100/350], Loss:0.0292, Perplexity: 1.0296\n",
            "Epoch [254/300], Step [150/350], Loss:0.0642, Perplexity: 1.0663\n",
            "Epoch [254/300], Step [200/350], Loss:0.0772, Perplexity: 1.0802\n",
            "Epoch [254/300], Step [250/350], Loss:0.0399, Perplexity: 1.0407\n",
            "Epoch [254/300], Step [300/350], Loss:0.0363, Perplexity: 1.0370\n",
            "Epoch [254/300], Step [350/350], Loss:0.0284, Perplexity: 1.0288\n",
            "Epoch [255/300], Step [50/350], Loss:0.0374, Perplexity: 1.0381\n",
            "Epoch [255/300], Step [100/350], Loss:0.0190, Perplexity: 1.0192\n",
            "Epoch [255/300], Step [150/350], Loss:0.0483, Perplexity: 1.0494\n",
            "Epoch [255/300], Step [200/350], Loss:0.0263, Perplexity: 1.0266\n",
            "Epoch [255/300], Step [250/350], Loss:0.0342, Perplexity: 1.0348\n",
            "Epoch [255/300], Step [300/350], Loss:0.0198, Perplexity: 1.0200\n",
            "Epoch [255/300], Step [350/350], Loss:0.0377, Perplexity: 1.0384\n",
            "Epoch [256/300], Step [50/350], Loss:0.0345, Perplexity: 1.0351\n",
            "Epoch [256/300], Step [100/350], Loss:0.0344, Perplexity: 1.0350\n",
            "Epoch [256/300], Step [150/350], Loss:0.0420, Perplexity: 1.0429\n",
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            "Epoch [261/300], Step [50/350], Loss:0.0256, Perplexity: 1.0259\n",
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            "Epoch [280/300], Step [300/350], Loss:0.0351, Perplexity: 1.0357\n",
            "Epoch [280/300], Step [350/350], Loss:0.0399, Perplexity: 1.0407\n",
            "Epoch [281/300], Step [50/350], Loss:0.0432, Perplexity: 1.0442\n",
            "Epoch [281/300], Step [100/350], Loss:0.0564, Perplexity: 1.0580\n",
            "Epoch [281/300], Step [150/350], Loss:0.0810, Perplexity: 1.0843\n",
            "Epoch [281/300], Step [200/350], Loss:0.0229, Perplexity: 1.0232\n",
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            "Epoch [281/300], Step [300/350], Loss:0.0092, Perplexity: 1.0093\n",
            "Epoch [281/300], Step [350/350], Loss:0.0385, Perplexity: 1.0393\n",
            "Epoch [282/300], Step [50/350], Loss:0.0435, Perplexity: 1.0445\n",
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            "Epoch [282/300], Step [200/350], Loss:0.0227, Perplexity: 1.0229\n",
            "Epoch [282/300], Step [250/350], Loss:0.0951, Perplexity: 1.0997\n",
            "Epoch [282/300], Step [300/350], Loss:0.0135, Perplexity: 1.0136\n",
            "Epoch [282/300], Step [350/350], Loss:0.0248, Perplexity: 1.0252\n",
            "Epoch [283/300], Step [50/350], Loss:0.0396, Perplexity: 1.0403\n",
            "Epoch [283/300], Step [100/350], Loss:0.0525, Perplexity: 1.0539\n",
            "Epoch [283/300], Step [150/350], Loss:0.0754, Perplexity: 1.0783\n",
            "Epoch [283/300], Step [200/350], Loss:0.0367, Perplexity: 1.0374\n",
            "Epoch [283/300], Step [250/350], Loss:0.0180, Perplexity: 1.0182\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}