mahjongSL_vgg.ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"accelerator":"GPU","colab":{"name":"mahjongSL_vgg.ipynb","provenance":[],"collapsed_sections":[],"mount_file_id":"1RTqNO9gMvlZlHEXJPbR2T6c_XGXUw65H","authorship_tag":"ABX9TyMfU3YdY9jJxz6qghmo+Mi4"},"kernelspec":{"display_name":"Python 3","name":"python3"}},"cells":[{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"hctevwvXOU5W","executionInfo":{"status":"ok","timestamp":1607324803203,"user_tz":-480,"elapsed":49552,"user":{"displayName":"yang zeng","photoUrl":"","userId":"13995063463501692620"}},"outputId":"d5bcdc85-5f3d-435d-cbc5-9889f48a8d1c"},"source":["!pip install bloscpack"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Collecting bloscpack\n","\u001b[?25l  Downloading https://files.pythonhosted.org/packages/39/c6/ca9b5567caad38b118bf0cbd92d122b067ebc7961742793c6b4c02895bef/bloscpack-0.16.0.tar.gz (99kB)\n","\u001b[K     |████████████████████████████████| 102kB 6.0MB/s \n","\u001b[?25hCollecting blosc\n","\u001b[?25l  Downloading https://files.pythonhosted.org/packages/1c/89/004eeb307ba3f56fca9727f47fbde09f8ae3936a1cb0dd6b7708f846d3fd/blosc-1.9.2.tar.gz (816kB)\n","\u001b[K     |████████████████████████████████| 819kB 9.1MB/s \n","\u001b[?25h  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n","  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n","    Preparing wheel metadata ... \u001b[?25l\u001b[?25hdone\n","Requirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from bloscpack) (1.18.5)\n","Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from bloscpack) (1.15.0)\n","Collecting deprecated\n","  Downloading https://files.pythonhosted.org/packages/76/a1/05d7f62f956d77b23a640efc650f80ce24483aa2f85a09c03fb64f49e879/Deprecated-1.2.10-py2.py3-none-any.whl\n","Requirement already satisfied: wrapt<2,>=1.10 in /usr/local/lib/python3.6/dist-packages (from deprecated->bloscpack) (1.12.1)\n","Building wheels for collected packages: blosc\n","  Building wheel for blosc (PEP 517) ... \u001b[?25l\u001b[?25hdone\n","  Created wheel for blosc: filename=blosc-1.9.2-cp36-cp36m-linux_x86_64.whl size=476058 sha256=61757b8645aa8739e499a7d4bcc7b910783e980b232ab08e7b62999c0c894426\n","  Stored in directory: /root/.cache/pip/wheels/d4/28/7c/84ad4e27f91d46943386ad9ca3e3494184cf9cffc55d6f9973\n","Successfully built blosc\n","Building wheels for collected packages: bloscpack\n","  Building wheel for bloscpack (setup.py) ... \u001b[?25l\u001b[?25hdone\n","  Created wheel for bloscpack: filename=bloscpack-0.16.0-cp36-none-any.whl size=56335 sha256=9c4005613a90dc64b5a9a21cdaff035a190c71404c174e5d85229d76348843c8\n","  Stored in directory: /root/.cache/pip/wheels/38/07/a7/6eb1c2ee35fb49ff7376d37c00142a73751b34da37d935cfdd\n","Successfully built bloscpack\n","Installing collected packages: blosc, deprecated, bloscpack\n","Successfully installed blosc-1.9.2 bloscpack-0.16.0 deprecated-1.2.10\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"MWTid0WlOiaP"},"source":["import warnings\n","warnings.filterwarnings('ignore')\n","import torch\n","import torchvision\n","import torchvision.transforms as transforms\n","from torch.utils.data import TensorDataset, DataLoader\n","import torch.nn as nn\n","import torch.nn.functional as F\n","\n","\n","import random\n","from tqdm import tqdm\n","import numpy as np\n","import bloscpack as bp\n","from sklearn import metrics\n","import time\n","from datetime import timedelta\n","\n","from torchvision import models"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"nIDTp9B6Okrs","executionInfo":{"status":"ok","timestamp":1607324807397,"user_tz":-480,"elapsed":53738,"user":{"displayName":"yang zeng","photoUrl":"","userId":"13995063463501692620"}},"outputId":"7001e1e9-f206-46db-e378-b73252ad04eb"},"source":["device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n","print(device)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["cuda:0\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"8DIJXxk5O1Y_"},"source":["# Data"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"D8h01OG3Om1_","executionInfo":{"status":"ok","timestamp":1607324816303,"user_tz":-480,"elapsed":62640,"user":{"displayName":"yang zeng","photoUrl":"","userId":"13995063463501692620"}},"outputId":"c55bb818-9b7a-4d0d-b965-cfe51fceea12"},"source":["master_X = bp.unpack_ndarray_from_file('/content/drive/MyDrive/统计麻将/new_small_input/input_X_0_100136.nosync.blp')\n","master_Y = bp.unpack_ndarray_from_file('/content/drive/MyDrive/统计麻将/new_small_input/input_Y_0_100136.nosync.blp')\n","master_Y = np.where(master_Y==1)[1]\n","print(master_X.shape, master_Y.shape)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["(100136, 50, 4, 34) (100136,)\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"hl26AZEnOrLu","executionInfo":{"status":"ok","timestamp":1607324906526,"user_tz":-480,"elapsed":152859,"user":{"displayName":"yang zeng","photoUrl":"","userId":"13995063463501692620"}},"outputId":"2495ac9d-d557-4b16-ff9c-504e5564da2b"},"source":["# split training and validation set\n","n = len(master_X)\n","train_size = int(0.8*n)\n","train_idx = random.sample(range(n),train_size)\n","val_idx = [i for i in range(n) if i not in train_idx]\n","\n","train_X = torch.from_numpy(master_X[train_idx,:,:,:]).type(torch.FloatTensor)\n","train_Y = torch.from_numpy(master_Y[train_idx]).type(torch.LongTensor)\n","val_X = torch.from_numpy(master_X[val_idx,:,:,:]).type(torch.FloatTensor)\n","val_Y = torch.from_numpy(master_Y[val_idx]).type(torch.LongTensor)\n","print(train_X.shape,train_Y.shape,val_X.shape,val_Y.shape)\n","\n","# Transform into dataloader\n","train_dataset = TensorDataset(train_X, train_Y)\n","val_dataset = TensorDataset(val_X, val_Y)\n","\n","train_dataloader = DataLoader(train_dataset,batch_size=128,shuffle=True,num_workers=2)\n","val_dataloader = DataLoader(val_dataset,batch_size=128,shuffle=True,num_workers=2)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["torch.Size([80108, 50, 4, 34]) torch.Size([80108]) torch.Size([20028, 50, 4, 34]) torch.Size([20028])\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"RUBLUlbGOwjf"},"source":["# Model"]},{"cell_type":"code","metadata":{"id":"vUBPIT4HUZoX"},"source":["import torch\n","import torch.nn as nn\n","from typing import Union, List, Dict, Any, cast\n","\n","\n","__all__ = [\n","    'VGG', 'vgg11', 'vgg11_bn', 'vgg13', 'vgg13_bn', 'vgg16', 'vgg16_bn',\n","    'vgg19_bn', 'vgg19',\n","]\n","\n","\n","model_urls = {\n","    'vgg11': 'https://download.pytorch.org/models/vgg11-bbd30ac9.pth',\n","    'vgg13': 'https://download.pytorch.org/models/vgg13-c768596a.pth',\n","    'vgg16': 'https://download.pytorch.org/models/vgg16-397923af.pth',\n","    'vgg19': 'https://download.pytorch.org/models/vgg19-dcbb9e9d.pth',\n","    'vgg11_bn': 'https://download.pytorch.org/models/vgg11_bn-6002323d.pth',\n","    'vgg13_bn': 'https://download.pytorch.org/models/vgg13_bn-abd245e5.pth',\n","    'vgg16_bn': 'https://download.pytorch.org/models/vgg16_bn-6c64b313.pth',\n","    'vgg19_bn': 'https://download.pytorch.org/models/vgg19_bn-c79401a0.pth',\n","}\n","\n","\n","class VGG(nn.Module):\n","\n","    def __init__(\n","        self,\n","        features: nn.Module,\n","        num_classes: int = 34,\n","        init_weights: bool = True\n","    ) -> None:\n","        super(VGG, self).__init__()\n","        self.features = features\n","        self.avgpool = nn.AdaptiveAvgPool2d((4, 34))\n","        self.classifier = nn.Sequential(\n","            nn.Linear(512 * 4 * 34, 4096),\n","            nn.ReLU(True),\n","            nn.Dropout(),\n","            nn.Linear(4096, 4096),\n","            nn.ReLU(True),\n","            nn.Dropout(),\n","            nn.Linear(4096, num_classes),\n","        )\n","        if init_weights:\n","            self._initialize_weights()\n","\n","    def forward(self, x: torch.Tensor) -> torch.Tensor:\n","        x = self.features(x)\n","        x = self.avgpool(x)\n","        x = torch.flatten(x, 1)\n","        x = self.classifier(x)\n","        return x\n","\n","    def _initialize_weights(self) -> None:\n","        for m in self.modules():\n","            if isinstance(m, nn.Conv2d):\n","                nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')\n","                if m.bias is not None:\n","                    nn.init.constant_(m.bias, 0)\n","            elif isinstance(m, nn.BatchNorm2d):\n","                nn.init.constant_(m.weight, 1)\n","                nn.init.constant_(m.bias, 0)\n","            elif isinstance(m, nn.Linear):\n","                nn.init.normal_(m.weight, 0, 0.01)\n","                nn.init.constant_(m.bias, 0)\n","\n","\n","def make_layers(cfg: List[Union[str, int]], batch_norm: bool = False) -> nn.Sequential:\n","    layers: List[nn.Module] = []\n","    in_channels = 50\n","    for v in cfg:\n","        if v == 'M':\n","            continue\n","        else:\n","            v = cast(int, v)\n","            conv2d = nn.Conv2d(in_channels, v, kernel_size=3, padding=1)\n","            if batch_norm:\n","                layers += [conv2d, nn.BatchNorm2d(v), nn.ReLU(inplace=True)]\n","            else:\n","                layers += [conv2d, nn.ReLU(inplace=True)]\n","            in_channels = v\n","    return nn.Sequential(*layers)\n","\n","\n","cfgs: Dict[str, List[Union[str, int]]] = {\n","    'A': [64, 'M', 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'],\n","    'B': [64, 64, 'M', 128, 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'],\n","    'D': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 'M', 512, 512, 512, 'M', 512, 512, 512, 'M'],\n","    'E': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 256, 'M', 512, 512, 512, 512, 'M', 512, 512, 512, 512, 'M'],\n","}\n","\n","\n","def _vgg(arch: str, cfg: str, batch_norm: bool, pretrained: bool, progress: bool, **kwargs: Any) -> VGG:\n","    if pretrained:\n","        kwargs['init_weights'] = False\n","    model = VGG(make_layers(cfgs[cfg], batch_norm=batch_norm), **kwargs)\n","    if pretrained:\n","        state_dict = load_state_dict_from_url(model_urls[arch],\n","                                              progress=progress)\n","        model.load_state_dict(state_dict)\n","    return model\n","\n","def vgg16(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> VGG:\n","    r\"\"\"VGG 16-layer model (configuration \"D\")\n","    `\"Very Deep Convolutional Networks For Large-Scale Image Recognition\" <https://arxiv.org/pdf/1409.1556.pdf>`._\n","    Args:\n","        pretrained (bool): If True, returns a model pre-trained on ImageNet\n","        progress (bool): If True, displays a progress bar of the download to stderr\n","    \"\"\"\n","    return _vgg('vgg16', 'D', False, pretrained, progress, **kwargs)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"mF8wNxHUOzPk"},"source":["#define a network\n","class Majongai(nn.Module):\n","  def __init__(self):\n","    super(Majongai,self).__init__()\n","    self.model = vgg16(pretrained = False)\n","\n","  def forward(self,x):\n","    output = self.model(x)\n","    return output"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"APspY0xgRK3f","executionInfo":{"status":"ok","timestamp":1607324921783,"user_tz":-480,"elapsed":168109,"user":{"displayName":"yang zeng","photoUrl":"","userId":"13995063463501692620"}},"outputId":"485f648d-a837-40ce-c127-061368b334e1"},"source":["from torchsummary import summary\n","net = Majongai()\n","net.to(device)\n","summary(net,(50,4,34))"],"execution_count":null,"outputs":[{"output_type":"stream","text":["----------------------------------------------------------------\n","        Layer (type)               Output Shape         Param #\n","================================================================\n","            Conv2d-1            [-1, 64, 4, 34]          28,864\n","              ReLU-2            [-1, 64, 4, 34]               0\n","            Conv2d-3            [-1, 64, 4, 34]          36,928\n","              ReLU-4            [-1, 64, 4, 34]               0\n","            Conv2d-5           [-1, 128, 4, 34]          73,856\n","              ReLU-6           [-1, 128, 4, 34]               0\n","            Conv2d-7           [-1, 128, 4, 34]         147,584\n","              ReLU-8           [-1, 128, 4, 34]               0\n","            Conv2d-9           [-1, 256, 4, 34]         295,168\n","             ReLU-10           [-1, 256, 4, 34]               0\n","           Conv2d-11           [-1, 256, 4, 34]         590,080\n","             ReLU-12           [-1, 256, 4, 34]               0\n","           Conv2d-13           [-1, 256, 4, 34]         590,080\n","             ReLU-14           [-1, 256, 4, 34]               0\n","           Conv2d-15           [-1, 512, 4, 34]       1,180,160\n","             ReLU-16           [-1, 512, 4, 34]               0\n","           Conv2d-17           [-1, 512, 4, 34]       2,359,808\n","             ReLU-18           [-1, 512, 4, 34]               0\n","           Conv2d-19           [-1, 512, 4, 34]       2,359,808\n","             ReLU-20           [-1, 512, 4, 34]               0\n","           Conv2d-21           [-1, 512, 4, 34]       2,359,808\n","             ReLU-22           [-1, 512, 4, 34]               0\n","           Conv2d-23           [-1, 512, 4, 34]       2,359,808\n","             ReLU-24           [-1, 512, 4, 34]               0\n","           Conv2d-25           [-1, 512, 4, 34]       2,359,808\n","             ReLU-26           [-1, 512, 4, 34]               0\n","AdaptiveAvgPool2d-27           [-1, 512, 4, 34]               0\n","           Linear-28                 [-1, 4096]     285,216,768\n","             ReLU-29                 [-1, 4096]               0\n","          Dropout-30                 [-1, 4096]               0\n","           Linear-31                 [-1, 4096]      16,781,312\n","             ReLU-32                 [-1, 4096]               0\n","          Dropout-33                 [-1, 4096]               0\n","           Linear-34                   [-1, 34]         139,298\n","              VGG-35                   [-1, 34]               0\n","================================================================\n","Total params: 316,879,138\n","Trainable params: 316,879,138\n","Non-trainable params: 0\n","----------------------------------------------------------------\n","Input size (MB): 0.03\n","Forward/backward pass size (MB): 9.48\n","Params size (MB): 1208.80\n","Estimated Total Size (MB): 1218.31\n","----------------------------------------------------------------\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"knUHdENdQ4HK"},"source":["import torch.optim as optim\n","def get_time_dif(start_time):\n","  end_time = time.time()\n","  time_dif = end_time - start_time\n","  return timedelta(seconds=int(round(time_dif)))"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"ZuKXPK0_7krp"},"source":["def evaluate(model, data_loader):\n","  model.eval()\n","  loss_total = 0\n","  predict_all = np.array([], dtype=int)\n","  labels_all = np.array([], dtype=int)\n","  with torch.no_grad():\n","    for inputs, labels in data_loader:\n","      inputs, labels = inputs.to(device), labels.to(device)\n","      outputs = model(inputs)\n","      loss = F.cross_entropy(outputs, labels)\n","      loss_total += loss\n","      labels = labels.data.cpu().numpy()\n","      predic = torch.max(outputs.data,1)[1].cpu().numpy()\n","      labels_all = np.append(labels_all, labels)\n","      predict_all = np.append(predict_all, predic)\n","  acc = metrics.accuracy_score(labels_all, predict_all)\n","  recall = metrics.recall_score(labels_all, predict_all,average='macro')\n","  f1 = metrics.f1_score(labels_all, predict_all,average='macro')\n","  return acc, recall, f1, loss_total/len(data_loader)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"0Md6HxWm_hZG"},"source":["def train(model,train_loader,val_loader,learning_rate=0.001,\n","    num_epochs=100,save_path='/content/drive/MyDrive/统计麻将/result/best_sl.ckpt'):\n","  start_time = time.time()\n","  model.train()\n","  criterion = nn.CrossEntropyLoss()\n","  optimizer = optim.Adam(model.parameters(), lr=learning_rate)\n","  total_batch = 0\n","  val_best_loss = float('inf')\n","  last_improve = 0\n","  flag = False\n","\n","  trainloss = []\n","  trainacc = []\n","  valloss = []\n","  valacc = []\n","  valrecall = []\n","  valf1 = []\n","\n","  for epoch in range(num_epochs):\n","    running_loss = 0.0\n","    print('Epoch[{}/{}]'.format(epoch+1, num_epochs))\n","    for i,data in enumerate(train_loader,0):\n","      inputs, labels = data\n","      inputs, labels = inputs.to(device), labels.to(device)\n","      optimizer.zero_grad()\n","\n","      outputs = model(inputs)\n","      loss = criterion(outputs,labels)\n","      loss.backward()\n","      optimizer.step()\n","\n","      running_loss += loss.item()\n","      if i % 50 == 49:\n","        true = labels.data.cpu()\n","        predic = torch.max(outputs.data,1)[1].cpu()\n","        train_acc = metrics.accuracy_score(true,predic)\n","        val_acc, val_recall, val_f1, val_loss = evaluate(model,val_loader)\n","\n","        trainloss.append(running_loss / 50)\n","        trainacc.append(train_acc)\n","        valloss.append(val_loss)\n","        valacc.append(val_acc)\n","        valrecall.append(val_recall)\n","        valf1.append(val_f1)\n","\n","        if val_loss < val_best_loss:\n","          val_best_loss = val_loss\n","          torch.save(model.state_dict(),save_path)\n","          improve = '*'\n","          last_improve = total_batch\n","        else:\n","          improve = ''\n","        time_dif = get_time_dif(start_time)\n","        msg = 'Iter: {0:>6}, Train Loss:{1:>5.4}, Train Acc:{2:6.2%}, Val Loss:{3:5.4}, Val Acc:{4:6.2%}, Val Recall:{5:6.2%} Val F1:{6:6.2%}, Time:{7} {8}'\n","        print(msg.format(total_batch,running_loss / 50,train_acc,val_loss,val_acc,val_recall,val_f1,time_dif,improve))\n","        model.train()\n","        running_loss = 0.0\n","      total_batch += 1\n","      # if total_batch - last_improve > 10000:\n","      #   print(\"No optimization for a long time, auto-stopping...\")\n","      #   flag = True\n","      #   break\n","    # if flag:\n","    #   break                  "],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"lgDZI32DyvbB","executionInfo":{"status":"error","timestamp":1607339183005,"user_tz":-480,"elapsed":14429322,"user":{"displayName":"yang zeng","photoUrl":"","userId":"13995063463501692620"}},"outputId":"126d5257-9bfe-4897-85e7-75a9c494e8c3"},"source":["model = Majongai().to(device)\n","train(model, train_dataloader, val_dataloader,learning_rate=0.001)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Epoch[1/100]\n","Iter:     49, Train Loss:4.697, Train Acc: 6.25%, Val Loss:3.519, Val Acc: 4.07%, Val Recall: 2.94% Val F1: 0.23%, Time:0:00:31 *\n","Iter:     99, Train Loss:3.508, Train Acc: 2.34%, Val Loss:3.497, Val Acc: 4.40%, Val Recall: 2.94% Val F1: 0.25%, Time:0:00:59 *\n","Iter:    149, Train Loss:3.481, Train Acc: 5.47%, Val Loss: 3.48, Val Acc: 4.21%, Val Recall: 2.90% Val F1: 0.37%, Time:0:01:32 *\n","Iter:    199, Train Loss:3.458, Train Acc: 2.34%, Val Loss:3.441, Val Acc: 4.23%, Val Recall: 3.16% Val F1: 0.42%, Time:0:02:03 *\n","Iter:    249, Train Loss:3.449, Train Acc: 6.25%, Val Loss: 3.45, Val Acc: 4.38%, Val Recall: 3.56% Val F1: 0.54%, Time:0:02:21 \n","Iter:    299, Train Loss:3.452, Train Acc: 0.00%, Val Loss: 3.44, Val Acc: 4.32%, Val Recall: 3.54% Val F1: 0.54%, Time:0:02:48 *\n","Iter:    349, Train Loss:3.448, Train Acc: 4.69%, Val Loss:3.437, Val Acc: 4.74%, Val Recall: 3.53% Val F1: 0.69%, Time:0:03:18 *\n","Iter:    399, Train Loss:3.437, Train Acc: 5.47%, Val Loss:3.435, Val Acc: 4.66%, Val Recall: 3.61% Val F1: 0.94%, Time:0:03:49 *\n","Iter:    449, Train Loss:3.446, Train Acc: 2.34%, Val Loss:3.434, Val Acc: 4.66%, Val Recall: 3.87% Val F1: 0.94%, Time:0:04:19 *\n","Iter:    499, Train Loss:3.441, Train Acc: 3.12%, Val Loss:3.435, Val Acc: 4.75%, Val Recall: 3.65% Val F1: 1.21%, Time:0:04:37 \n","Iter:    549, Train Loss:3.439, Train Acc: 3.12%, Val Loss:3.437, Val Acc: 4.56%, Val Recall: 3.97% Val F1: 1.14%, Time:0:04:54 \n","Iter:    599, Train Loss:3.434, Train Acc: 4.69%, Val Loss:3.429, Val Acc: 4.94%, Val Recall: 3.91% Val F1: 1.53%, Time:0:05:21 *\n","Epoch[2/100]\n","Iter:    675, Train Loss:3.429, Train Acc: 3.12%, Val Loss:3.429, Val Acc: 4.20%, Val Recall: 3.41% Val F1: 0.90%, Time:0:05:44 \n","Iter:    725, Train Loss:3.431, Train Acc: 3.12%, Val Loss:3.425, Val Acc: 5.01%, Val Recall: 4.19% Val F1: 1.28%, Time:0:06:12 *\n","Iter:    775, Train Loss:3.435, Train Acc: 2.34%, Val Loss:3.426, Val Acc: 4.93%, Val Recall: 4.07% Val F1: 1.16%, Time:0:06:30 \n","Iter:    825, Train Loss:3.429, Train Acc: 5.47%, Val Loss:3.425, Val Acc: 5.09%, Val Recall: 4.28% Val F1: 1.44%, Time:0:06:48 \n","Iter:    875, Train Loss:3.431, Train Acc: 2.34%, Val Loss:3.425, Val Acc: 4.71%, Val Recall: 4.12% Val F1: 1.33%, Time:0:07:05 \n","Iter:    925, Train Loss:3.432, Train Acc: 3.12%, Val Loss:3.434, Val Acc: 4.46%, Val Recall: 3.40% Val F1: 0.85%, Time:0:07:22 \n","Iter:    975, Train Loss:3.428, Train Acc: 5.47%, Val Loss:3.425, Val Acc: 4.75%, Val Recall: 3.77% Val F1: 1.29%, Time:0:07:40 \n","Iter:   1025, Train Loss:3.418, Train Acc: 7.03%, Val Loss:3.429, Val Acc: 4.78%, Val Recall: 3.67% Val F1: 0.59%, Time:0:07:57 \n","Iter:   1075, Train Loss:3.427, Train Acc: 7.03%, Val Loss:3.423, Val Acc: 5.19%, Val Recall: 3.95% Val F1: 1.59%, Time:0:08:24 *\n","Iter:   1125, Train Loss:3.434, Train Acc: 3.12%, Val Loss:3.422, Val Acc: 5.04%, Val Recall: 4.18% Val F1: 1.72%, Time:0:08:52 *\n","Iter:   1175, Train Loss:3.425, Train Acc: 7.81%, Val Loss:3.425, Val Acc: 4.88%, Val Recall: 3.89% Val F1: 1.36%, Time:0:09:10 \n","Iter:   1225, Train Loss: 3.43, Train Acc: 8.59%, Val Loss:3.424, Val Acc: 5.02%, Val Recall: 3.87% Val F1: 1.78%, Time:0:09:27 \n","Epoch[3/100]\n","Iter:   1301, Train Loss:3.419, Train Acc: 4.69%, Val Loss:3.423, Val Acc: 4.84%, Val Recall: 3.75% Val F1: 1.06%, Time:0:09:50 \n","Iter:   1351, Train Loss:3.429, Train Acc: 6.25%, Val Loss:3.422, Val Acc: 4.91%, Val Recall: 3.92% Val F1: 1.30%, Time:0:10:18 *\n","Iter:   1401, Train Loss:3.419, Train Acc: 6.25%, Val Loss: 3.42, Val Acc: 5.06%, Val Recall: 4.21% Val F1: 1.50%, Time:0:10:48 *\n","Iter:   1451, Train Loss:3.423, Train Acc: 1.56%, Val Loss:3.424, Val Acc: 5.14%, Val Recall: 4.42% Val F1: 1.97%, Time:0:11:06 \n","Iter:   1501, Train Loss:3.424, Train Acc: 3.12%, Val Loss:3.418, Val Acc: 5.01%, Val Recall: 3.80% Val F1: 1.12%, Time:0:11:33 *\n","Iter:   1551, Train Loss:3.419, Train Acc: 5.47%, Val Loss:3.431, Val Acc: 4.75%, Val Recall: 3.59% Val F1: 0.97%, Time:0:11:51 \n","Iter:   1601, Train Loss:3.415, Train Acc: 3.91%, Val Loss:3.423, Val Acc: 4.75%, Val Recall: 3.66% Val F1: 1.23%, Time:0:12:08 \n","Iter:   1651, Train Loss:3.424, Train Acc: 5.47%, Val Loss:3.418, Val Acc: 4.78%, Val Recall: 3.83% Val F1: 1.04%, Time:0:12:26 \n","Iter:   1701, Train Loss:3.415, Train Acc: 8.59%, Val Loss:3.422, Val Acc: 5.29%, Val Recall: 4.44% Val F1: 1.76%, Time:0:12:43 \n","Iter:   1751, Train Loss:3.425, Train Acc: 3.91%, Val Loss:3.418, Val Acc: 4.81%, Val Recall: 4.03% Val F1: 1.42%, Time:0:13:00 \n","Iter:   1801, Train Loss:3.423, Train Acc: 4.69%, Val Loss:3.426, Val Acc: 5.08%, Val Recall: 4.24% Val F1: 1.81%, Time:0:13:18 \n","Iter:   1851, Train Loss:3.432, Train Acc: 4.69%, Val Loss:3.422, Val Acc: 5.04%, Val Recall: 4.16% Val F1: 1.68%, Time:0:13:35 \n","Epoch[4/100]\n","Iter:   1927, Train Loss:3.415, Train Acc: 6.25%, Val Loss:3.425, Val Acc: 5.04%, Val Recall: 4.43% Val F1: 2.56%, Time:0:13:58 \n","Iter:   1977, Train Loss:3.423, Train Acc: 5.47%, Val Loss:3.417, Val Acc: 5.00%, Val Recall: 3.96% Val F1: 1.69%, Time:0:14:26 *\n","Iter:   2027, Train Loss:3.411, Train Acc: 3.12%, Val Loss:3.416, Val Acc: 5.31%, Val Recall: 4.58% Val F1: 2.53%, Time:0:14:54 *\n","Iter:   2077, Train Loss:3.417, Train Acc: 7.03%, Val Loss:3.418, Val Acc: 5.27%, Val Recall: 4.52% Val F1: 1.92%, Time:0:15:11 \n","Iter:   2127, Train Loss:3.428, Train Acc: 5.47%, Val Loss:3.416, Val Acc: 5.09%, Val Recall: 4.15% Val F1: 2.04%, Time:0:15:29 \n","Iter:   2177, Train Loss:3.418, Train Acc: 4.69%, Val Loss:3.426, Val Acc: 4.96%, Val Recall: 3.99% Val F1: 2.31%, Time:0:15:46 \n","Iter:   2227, Train Loss:3.419, Train Acc: 5.47%, Val Loss:3.414, Val Acc: 5.44%, Val Recall: 4.37% Val F1: 2.34%, Time:0:16:14 *\n","Iter:   2277, Train Loss:3.411, Train Acc: 2.34%, Val Loss:3.419, Val Acc: 5.18%, Val Recall: 4.11% Val F1: 2.12%, Time:0:16:31 \n","Iter:   2327, Train Loss:3.413, Train Acc: 3.91%, Val Loss:3.412, Val Acc: 5.08%, Val Recall: 4.17% Val F1: 1.68%, Time:0:16:58 *\n","Iter:   2377, Train Loss:3.425, Train Acc: 3.12%, Val Loss: 3.42, Val Acc: 5.02%, Val Recall: 4.06% Val F1: 1.45%, Time:0:17:16 \n","Iter:   2427, Train Loss:3.419, Train Acc: 5.47%, Val Loss:3.414, Val Acc: 5.09%, Val Recall: 4.24% Val F1: 1.83%, Time:0:17:34 \n","Iter:   2477, Train Loss:3.426, Train Acc: 7.03%, Val Loss:3.423, Val Acc: 5.32%, Val Recall: 4.42% Val F1: 1.95%, Time:0:17:51 \n","Epoch[5/100]\n","Iter:   2553, Train Loss:3.404, Train Acc: 6.25%, Val Loss:3.424, Val Acc: 5.15%, Val Recall: 4.68% Val F1: 2.34%, Time:0:18:13 \n","Iter:   2603, Train Loss:3.415, Train Acc: 6.25%, Val Loss: 3.41, Val Acc: 5.65%, Val Recall: 4.78% Val F1: 2.83%, Time:0:18:42 *\n","Iter:   2653, Train Loss:3.403, Train Acc: 4.69%, Val Loss:3.413, Val Acc: 5.29%, Val Recall: 4.26% Val F1: 2.15%, Time:0:18:59 \n","Iter:   2703, Train Loss:3.414, Train Acc: 2.34%, Val Loss:3.405, Val Acc: 5.74%, Val Recall: 4.86% Val F1: 3.16%, Time:0:19:27 *\n","Iter:   2753, Train Loss:3.403, Train Acc: 7.81%, Val Loss:3.405, Val Acc: 5.76%, Val Recall: 4.94% Val F1: 3.17%, Time:0:19:44 \n","Iter:   2803, Train Loss:3.407, Train Acc: 5.47%, Val Loss:3.407, Val Acc: 5.36%, Val Recall: 4.32% Val F1: 2.25%, Time:0:20:02 \n","Iter:   2853, Train Loss:3.411, Train Acc: 6.25%, Val Loss:3.406, Val Acc: 5.37%, Val Recall: 4.51% Val F1: 2.43%, Time:0:20:19 \n","Iter:   2903, Train Loss:3.402, Train Acc: 8.59%, Val Loss:3.404, Val Acc: 5.49%, Val Recall: 4.69% Val F1: 2.41%, Time:0:20:47 *\n","Iter:   2953, Train Loss:3.413, Train Acc: 5.47%, Val Loss:3.407, Val Acc: 5.51%, Val Recall: 4.73% Val F1: 2.30%, Time:0:21:04 \n","Iter:   3003, Train Loss:3.407, Train Acc: 3.12%, Val Loss:3.404, Val Acc: 5.64%, Val Recall: 5.05% Val F1: 2.88%, Time:0:21:31 *\n","Iter:   3053, Train Loss:3.398, Train Acc: 8.59%, Val Loss:3.405, Val Acc: 5.22%, Val Recall: 4.71% Val F1: 2.55%, Time:0:21:49 \n","Iter:   3103, Train Loss:3.409, Train Acc: 5.47%, Val Loss:3.405, Val Acc: 5.77%, Val Recall: 5.15% Val F1: 3.07%, Time:0:22:07 \n","Epoch[6/100]\n","Iter:   3179, Train Loss:3.386, Train Acc: 3.91%, Val Loss:3.396, Val Acc: 5.50%, Val Recall: 5.09% Val F1: 3.21%, Time:0:22:40 *\n","Iter:   3229, Train Loss:3.396, Train Acc: 4.69%, Val Loss:3.394, Val Acc: 5.82%, Val Recall: 5.12% Val F1: 2.93%, Time:0:23:09 *\n","Iter:   3279, Train Loss:3.382, Train Acc:10.16%, Val Loss:3.386, Val Acc: 6.38%, Val Recall: 5.71% Val F1: 3.33%, Time:0:23:40 *\n","Iter:   3329, Train Loss:3.393, Train Acc: 9.38%, Val Loss:3.356, Val Acc: 7.14%, Val Recall: 6.33% Val F1: 4.65%, Time:0:24:12 *\n","Iter:   3379, Train Loss:3.357, Train Acc: 4.69%, Val Loss: 3.34, Val Acc: 9.04%, Val Recall: 8.28% Val F1: 7.37%, Time:0:24:43 *\n","Iter:   3429, Train Loss: 3.32, Train Acc:13.28%, Val Loss: 3.34, Val Acc:10.02%, Val Recall: 8.98% Val F1: 9.21%, Time:0:25:01 \n","Iter:   3479, Train Loss:3.285, Train Acc: 8.59%, Val Loss:3.272, Val Acc:12.16%, Val Recall:11.28% Val F1:11.74%, Time:0:25:29 *\n","Iter:   3529, Train Loss:3.264, Train Acc:12.50%, Val Loss:3.247, Val Acc:12.62%, Val Recall:11.73% Val F1:12.62%, Time:0:25:59 *\n","Iter:   3579, Train Loss:3.256, Train Acc: 9.38%, Val Loss:3.245, Val Acc:13.48%, Val Recall:12.80% Val F1:13.70%, Time:0:26:29 *\n","Iter:   3629, Train Loss:3.232, Train Acc:12.50%, Val Loss: 3.22, Val Acc:13.80%, Val Recall:13.08% Val F1:14.31%, Time:0:27:00 *\n","Iter:   3679, Train Loss:3.206, Train Acc:13.28%, Val Loss:3.193, Val Acc:14.23%, Val Recall:13.71% Val F1:15.21%, Time:0:27:33 *\n","Iter:   3729, Train Loss:3.225, Train Acc:22.66%, Val Loss:3.203, Val Acc:14.48%, Val Recall:13.89% Val F1:15.78%, Time:0:27:50 \n","Epoch[7/100]\n","Iter:   3805, Train Loss:3.167, Train Acc:13.28%, Val Loss:3.186, Val Acc:14.99%, Val Recall:14.42% Val F1:15.17%, Time:0:28:24 *\n","Iter:   3855, Train Loss:3.194, Train Acc:13.28%, Val Loss:3.179, Val Acc:14.65%, Val Recall:14.17% Val F1:15.50%, Time:0:28:52 *\n","Iter:   3905, Train Loss:3.168, Train Acc:17.97%, Val Loss: 3.18, Val Acc:15.13%, Val Recall:14.46% Val F1:15.78%, Time:0:29:10 \n","Iter:   3955, Train Loss:3.174, Train Acc:12.50%, Val Loss:3.186, Val Acc:14.78%, Val Recall:14.32% Val F1:15.88%, Time:0:29:28 \n","Iter:   4005, Train Loss:3.181, Train Acc:18.75%, Val Loss:3.184, Val Acc:15.39%, Val Recall:14.68% Val F1:15.87%, Time:0:29:45 \n","Iter:   4055, Train Loss:3.169, Train Acc:12.50%, Val Loss: 3.18, Val Acc:14.89%, Val Recall:14.18% Val F1:16.33%, Time:0:30:02 \n","Iter:   4105, Train Loss:3.175, Train Acc:17.19%, Val Loss:3.171, Val Acc:15.32%, Val Recall:14.57% Val F1:15.70%, Time:0:30:30 *\n","Iter:   4155, Train Loss:3.183, Train Acc:15.62%, Val Loss:3.178, Val Acc:15.73%, Val Recall:15.05% Val F1:15.47%, Time:0:30:47 \n","Iter:   4205, Train Loss:3.169, Train Acc:10.94%, Val Loss:3.154, Val Acc:15.76%, Val Recall:15.00% Val F1:15.80%, Time:0:31:15 *\n","Iter:   4255, Train Loss:3.177, Train Acc:14.06%, Val Loss:3.163, Val Acc:15.50%, Val Recall:14.69% Val F1:15.89%, Time:0:31:33 \n","Iter:   4305, Train Loss:3.183, Train Acc:10.94%, Val Loss:3.162, Val Acc:15.52%, Val Recall:14.95% Val F1:15.37%, Time:0:31:50 \n","Iter:   4355, Train Loss:3.174, Train Acc:16.41%, Val Loss:3.156, Val Acc:15.51%, Val Recall:14.79% Val F1:16.16%, Time:0:32:08 \n","Epoch[8/100]\n","Iter:   4431, Train Loss:3.126, Train Acc:12.50%, Val Loss:3.149, Val Acc:15.70%, Val Recall:14.80% Val F1:16.01%, Time:0:32:41 *\n","Iter:   4481, Train Loss:3.142, Train Acc:17.19%, Val Loss:3.145, Val Acc:16.09%, Val Recall:15.30% Val F1:16.01%, Time:0:33:10 *\n","Iter:   4531, Train Loss:3.146, Train Acc:15.62%, Val Loss:3.146, Val Acc:15.99%, Val Recall:15.23% Val F1:15.84%, Time:0:33:28 \n","Iter:   4581, Train Loss:3.144, Train Acc:14.84%, Val Loss:3.155, Val Acc:15.70%, Val Recall:14.91% Val F1:15.90%, Time:0:33:45 \n","Iter:   4631, Train Loss:3.148, Train Acc:14.06%, Val Loss: 3.15, Val Acc:15.89%, Val Recall:15.14% Val F1:15.69%, Time:0:34:03 \n","Iter:   4681, Train Loss: 3.14, Train Acc:21.09%, Val Loss:3.148, Val Acc:15.49%, Val Recall:14.88% Val F1:15.63%, Time:0:34:20 \n","Iter:   4731, Train Loss:3.139, Train Acc:20.31%, Val Loss:3.155, Val Acc:15.90%, Val Recall:14.96% Val F1:16.19%, Time:0:34:37 \n","Iter:   4781, Train Loss:3.167, Train Acc: 9.38%, Val Loss:3.148, Val Acc:16.21%, Val Recall:15.32% Val F1:16.13%, Time:0:34:55 \n","Iter:   4831, Train Loss:3.146, Train Acc:12.50%, Val Loss:3.158, Val Acc:15.75%, Val Recall:14.80% Val F1:16.08%, Time:0:35:12 \n","Iter:   4881, Train Loss:3.132, Train Acc:17.97%, Val Loss:3.151, Val Acc:16.00%, Val Recall:15.20% Val F1:16.31%, Time:0:35:29 \n","Iter:   4931, Train Loss:3.145, Train Acc:14.06%, Val Loss:3.156, Val Acc:15.88%, Val Recall:15.08% Val F1:15.77%, Time:0:35:47 \n","Iter:   4981, Train Loss:3.177, Train Acc:14.06%, Val Loss:3.141, Val Acc:16.43%, Val Recall:15.55% Val F1:16.32%, Time:0:36:14 *\n","Epoch[9/100]\n","Iter:   5057, Train Loss:3.103, Train Acc:14.84%, Val Loss:3.162, Val Acc:16.56%, Val Recall:15.61% Val F1:16.24%, Time:0:36:37 \n","Iter:   5107, Train Loss:3.121, Train Acc:17.19%, Val Loss: 3.15, Val Acc:16.20%, Val Recall:15.40% Val F1:15.90%, Time:0:36:54 \n","Iter:   5157, Train Loss:3.128, Train Acc:14.84%, Val Loss:3.135, Val Acc:16.64%, Val Recall:15.58% Val F1:16.37%, Time:0:37:22 *\n","Iter:   5207, Train Loss:3.129, Train Acc:21.88%, Val Loss:3.133, Val Acc:16.50%, Val Recall:15.80% Val F1:16.30%, Time:0:37:51 *\n","Iter:   5257, Train Loss:3.141, Train Acc:13.28%, Val Loss:3.137, Val Acc:16.20%, Val Recall:15.36% Val F1:16.77%, Time:0:38:09 \n","Iter:   5307, Train Loss:3.097, Train Acc:15.62%, Val Loss:3.131, Val Acc:16.49%, Val Recall:15.57% Val F1:16.33%, Time:0:38:36 *\n","Iter:   5357, Train Loss:3.136, Train Acc:14.84%, Val Loss:3.137, Val Acc:16.58%, Val Recall:15.88% Val F1:16.08%, Time:0:38:54 \n","Iter:   5407, Train Loss:3.106, Train Acc:15.62%, Val Loss:3.135, Val Acc:16.49%, Val Recall:15.46% Val F1:16.22%, Time:0:39:12 \n","Iter:   5457, Train Loss:3.106, Train Acc:18.75%, Val Loss: 3.13, Val Acc:16.71%, Val Recall:15.81% Val F1:16.69%, Time:0:39:39 *\n","Iter:   5507, Train Loss: 3.11, Train Acc:20.31%, Val Loss:3.142, Val Acc:16.38%, Val Recall:15.60% Val F1:15.92%, Time:0:39:56 \n","Iter:   5557, Train Loss:3.124, Train Acc:11.72%, Val Loss:3.135, Val Acc:16.39%, Val Recall:15.44% Val F1:16.47%, Time:0:40:14 \n","Iter:   5607, Train Loss:3.112, Train Acc:13.28%, Val Loss:3.132, Val Acc:16.57%, Val Recall:15.83% Val F1:16.08%, Time:0:40:31 \n","Epoch[10/100]\n","Iter:   5683, Train Loss:3.086, Train Acc:15.62%, Val Loss:3.137, Val Acc:16.48%, Val Recall:15.74% Val F1:16.09%, Time:0:40:54 \n","Iter:   5733, Train Loss:3.091, Train Acc:17.19%, Val Loss:3.135, Val Acc:16.71%, Val Recall:15.74% Val F1:16.25%, Time:0:41:11 \n","Iter:   5783, Train Loss:3.088, Train Acc:18.75%, Val Loss:3.126, Val Acc:16.66%, Val Recall:15.76% Val F1:16.28%, Time:0:41:40 *\n","Iter:   5833, Train Loss: 3.09, Train Acc:17.97%, Val Loss:3.136, Val Acc:16.72%, Val Recall:15.84% Val F1:16.23%, Time:0:41:57 \n","Iter:   5883, Train Loss:3.101, Train Acc:10.16%, Val Loss:3.131, Val Acc:16.80%, Val Recall:15.96% Val F1:16.15%, Time:0:42:15 \n","Iter:   5933, Train Loss:3.077, Train Acc:16.41%, Val Loss:3.127, Val Acc:16.65%, Val Recall:15.81% Val F1:16.36%, Time:0:42:32 \n","Iter:   5983, Train Loss:3.074, Train Acc:18.75%, Val Loss:3.126, Val Acc:16.80%, Val Recall:15.92% Val F1:16.60%, Time:0:42:59 *\n","Iter:   6033, Train Loss:3.091, Train Acc:17.97%, Val Loss:3.119, Val Acc:16.86%, Val Recall:16.03% Val F1:16.45%, Time:0:43:28 *\n","Iter:   6083, Train Loss:3.082, Train Acc:14.84%, Val Loss: 3.12, Val Acc:17.10%, Val Recall:16.29% Val F1:16.63%, Time:0:43:46 \n","Iter:   6133, Train Loss:3.102, Train Acc:17.19%, Val Loss: 3.12, Val Acc:16.85%, Val Recall:16.02% Val F1:16.33%, Time:0:44:04 \n","Iter:   6183, Train Loss:3.077, Train Acc:12.50%, Val Loss:3.137, Val Acc:16.78%, Val Recall:16.11% Val F1:16.35%, Time:0:44:21 \n","Iter:   6233, Train Loss:3.092, Train Acc:14.84%, Val Loss:3.121, Val Acc:17.21%, Val Recall:16.29% Val F1:16.56%, Time:0:44:38 \n","Epoch[11/100]\n","Iter:   6309, Train Loss: 3.04, Train Acc:26.56%, Val Loss:3.125, Val Acc:17.27%, Val Recall:16.51% Val F1:16.70%, Time:0:45:01 \n","Iter:   6359, Train Loss:3.059, Train Acc:17.19%, Val Loss: 3.12, Val Acc:17.24%, Val Recall:16.47% Val F1:16.68%, Time:0:45:18 \n","Iter:   6409, Train Loss:3.045, Train Acc:19.53%, Val Loss:3.117, Val Acc:17.35%, Val Recall:16.34% Val F1:17.10%, Time:0:45:46 *\n","Iter:   6459, Train Loss:3.039, Train Acc:21.88%, Val Loss: 3.12, Val Acc:17.17%, Val Recall:16.21% Val F1:16.53%, Time:0:46:04 \n","Iter:   6509, Train Loss: 3.06, Train Acc:21.09%, Val Loss:3.135, Val Acc:16.91%, Val Recall:15.80% Val F1:16.29%, Time:0:46:22 \n","Iter:   6559, Train Loss:3.051, Train Acc:17.97%, Val Loss:3.116, Val Acc:17.16%, Val Recall:16.31% Val F1:17.02%, Time:0:46:49 *\n","Iter:   6609, Train Loss:3.075, Train Acc:15.62%, Val Loss:3.109, Val Acc:17.53%, Val Recall:16.72% Val F1:16.95%, Time:0:47:19 *\n","Iter:   6659, Train Loss:3.076, Train Acc:20.31%, Val Loss:3.115, Val Acc:17.05%, Val Recall:16.09% Val F1:16.84%, Time:0:47:36 \n","Iter:   6709, Train Loss:3.088, Train Acc:14.84%, Val Loss:3.111, Val Acc:17.33%, Val Recall:16.56% Val F1:16.87%, Time:0:47:54 \n","Iter:   6759, Train Loss:3.028, Train Acc:13.28%, Val Loss:3.105, Val Acc:17.75%, Val Recall:16.92% Val F1:17.31%, Time:0:48:21 *\n","Iter:   6809, Train Loss:3.076, Train Acc:19.53%, Val Loss:3.104, Val Acc:17.74%, Val Recall:16.83% Val F1:17.02%, Time:0:48:51 *\n","Iter:   6859, Train Loss:3.059, Train Acc:16.41%, Val Loss:3.105, Val Acc:17.72%, Val Recall:16.66% Val F1:16.93%, Time:0:49:09 \n","Epoch[12/100]\n","Iter:   6935, Train Loss:3.007, Train Acc:19.53%, Val Loss:3.116, Val Acc:17.61%, Val Recall:16.81% Val F1:16.93%, Time:0:49:31 \n","Iter:   6985, Train Loss:3.013, Train Acc:22.66%, Val Loss:3.115, Val Acc:17.55%, Val Recall:16.52% Val F1:16.77%, Time:0:49:49 \n","Iter:   7035, Train Loss:3.009, Train Acc:19.53%, Val Loss:3.107, Val Acc:17.61%, Val Recall:16.69% Val F1:16.97%, Time:0:50:06 \n","Iter:   7085, Train Loss:3.018, Train Acc:23.44%, Val Loss:3.111, Val Acc:17.29%, Val Recall:16.35% Val F1:17.17%, Time:0:50:24 \n","Iter:   7135, Train Loss:3.039, Train Acc:21.09%, Val Loss:3.111, Val Acc:17.62%, Val Recall:16.80% Val F1:17.01%, Time:0:50:41 \n","Iter:   7185, Train Loss:3.002, Train Acc:18.75%, Val Loss:3.096, Val Acc:18.18%, Val Recall:17.24% Val F1:17.57%, Time:0:51:09 *\n","Iter:   7235, Train Loss:3.008, Train Acc:16.41%, Val Loss:3.112, Val Acc:18.04%, Val Recall:17.26% Val F1:17.33%, Time:0:51:27 \n","Iter:   7285, Train Loss:3.018, Train Acc:25.78%, Val Loss:3.092, Val Acc:18.08%, Val Recall:17.29% Val F1:17.50%, Time:0:51:54 *\n","Iter:   7335, Train Loss:3.016, Train Acc:25.78%, Val Loss:3.088, Val Acc:17.99%, Val Recall:17.11% Val F1:17.41%, Time:0:52:23 *\n","Iter:   7385, Train Loss:2.987, Train Acc:19.53%, Val Loss:3.087, Val Acc:18.06%, Val Recall:17.16% Val F1:17.41%, Time:0:52:54 *\n","Iter:   7435, Train Loss:3.019, Train Acc:11.72%, Val Loss:3.082, Val Acc:18.40%, Val Recall:17.49% Val F1:17.84%, Time:0:53:27 *\n","Iter:   7485, Train Loss: 3.02, Train Acc:27.34%, Val Loss:3.071, Val Acc:18.68%, Val Recall:17.79% Val F1:18.04%, Time:0:53:58 *\n","Epoch[13/100]\n","Iter:   7561, Train Loss:2.941, Train Acc:21.88%, Val Loss:3.075, Val Acc:18.67%, Val Recall:17.76% Val F1:18.07%, Time:0:54:21 \n","Iter:   7611, Train Loss: 2.94, Train Acc:15.62%, Val Loss:3.077, Val Acc:18.66%, Val Recall:17.68% Val F1:17.88%, Time:0:54:38 \n","Iter:   7661, Train Loss:2.986, Train Acc:26.56%, Val Loss:3.074, Val Acc:18.44%, Val Recall:17.39% Val F1:17.93%, Time:0:54:56 \n","Iter:   7711, Train Loss:2.955, Train Acc:23.44%, Val Loss:3.079, Val Acc:18.57%, Val Recall:17.66% Val F1:18.04%, Time:0:55:13 \n","Iter:   7761, Train Loss: 2.97, Train Acc:30.47%, Val Loss:3.088, Val Acc:18.36%, Val Recall:17.54% Val F1:17.71%, Time:0:55:31 \n","Iter:   7811, Train Loss:2.978, Train Acc:21.09%, Val Loss:3.069, Val Acc:18.96%, Val Recall:18.03% Val F1:18.32%, Time:0:55:59 *\n","Iter:   7861, Train Loss:2.966, Train Acc:21.09%, Val Loss:3.068, Val Acc:19.06%, Val Recall:18.15% Val F1:18.36%, Time:0:56:28 *\n","Iter:   7911, Train Loss:2.951, Train Acc:14.06%, Val Loss:3.064, Val Acc:19.22%, Val Recall:18.22% Val F1:18.56%, Time:0:57:00 *\n","Iter:   7961, Train Loss:2.962, Train Acc:15.62%, Val Loss:3.068, Val Acc:18.77%, Val Recall:17.88% Val F1:18.13%, Time:0:57:18 \n","Iter:   8011, Train Loss:2.964, Train Acc:17.97%, Val Loss:3.065, Val Acc:18.92%, Val Recall:17.84% Val F1:18.36%, Time:0:57:35 \n","Iter:   8061, Train Loss: 2.96, Train Acc:20.31%, Val Loss:3.065, Val Acc:19.19%, Val Recall:18.26% Val F1:18.51%, Time:0:57:53 \n","Iter:   8111, Train Loss:2.937, Train Acc:25.78%, Val Loss:3.073, Val Acc:19.12%, Val Recall:18.01% Val F1:18.30%, Time:0:58:10 \n","Epoch[14/100]\n","Iter:   8187, Train Loss:2.863, Train Acc:26.56%, Val Loss:3.065, Val Acc:19.31%, Val Recall:18.39% Val F1:18.55%, Time:0:58:32 \n","Iter:   8237, Train Loss:2.894, Train Acc:27.34%, Val Loss:3.064, Val Acc:19.36%, Val Recall:18.26% Val F1:18.54%, Time:0:59:01 *\n","Iter:   8287, Train Loss:2.889, Train Acc:22.66%, Val Loss:3.048, Val Acc:19.75%, Val Recall:18.80% Val F1:19.02%, Time:0:59:31 *\n","Iter:   8337, Train Loss: 2.89, Train Acc:22.66%, Val Loss:3.063, Val Acc:19.72%, Val Recall:18.78% Val F1:18.94%, Time:0:59:48 \n","Iter:   8387, Train Loss:2.873, Train Acc:21.09%, Val Loss:3.056, Val Acc:19.54%, Val Recall:18.54% Val F1:18.81%, Time:1:00:06 \n","Iter:   8437, Train Loss:2.897, Train Acc:21.09%, Val Loss:3.043, Val Acc:19.92%, Val Recall:18.87% Val F1:19.07%, Time:1:00:33 *\n","Iter:   8487, Train Loss:2.909, Train Acc:24.22%, Val Loss:3.045, Val Acc:19.85%, Val Recall:18.84% Val F1:18.99%, Time:1:00:51 \n","Iter:   8537, Train Loss:2.915, Train Acc:15.62%, Val Loss:3.038, Val Acc:20.16%, Val Recall:19.16% Val F1:19.44%, Time:1:01:18 *\n","Iter:   8587, Train Loss:2.874, Train Acc:23.44%, Val Loss:3.029, Val Acc:20.02%, Val Recall:18.96% Val F1:19.27%, Time:1:01:48 *\n","Iter:   8637, Train Loss:2.864, Train Acc:28.12%, Val Loss: 3.03, Val Acc:20.40%, Val Recall:19.34% Val F1:19.53%, Time:1:02:05 \n","Iter:   8687, Train Loss:2.885, Train Acc:29.69%, Val Loss:3.028, Val Acc:20.45%, Val Recall:19.40% Val F1:19.57%, Time:1:02:33 *\n","Iter:   8737, Train Loss:2.893, Train Acc:19.53%, Val Loss:3.025, Val Acc:20.28%, Val Recall:19.45% Val F1:19.67%, Time:1:03:02 *\n","Epoch[15/100]\n","Iter:   8813, Train Loss:2.759, Train Acc:30.47%, Val Loss:3.017, Val Acc:20.67%, Val Recall:19.68% Val F1:19.73%, Time:1:03:36 *\n","Iter:   8863, Train Loss:2.785, Train Acc:26.56%, Val Loss:3.026, Val Acc:20.60%, Val Recall:19.55% Val F1:19.81%, Time:1:03:54 \n","Iter:   8913, Train Loss:2.817, Train Acc:28.12%, Val Loss: 3.02, Val Acc:20.16%, Val Recall:19.13% Val F1:19.38%, Time:1:04:11 \n","Iter:   8963, Train Loss:2.763, Train Acc:25.78%, Val Loss:3.011, Val Acc:20.47%, Val Recall:19.41% Val F1:19.76%, Time:1:04:39 *\n","Iter:   9013, Train Loss:2.796, Train Acc:23.44%, Val Loss:3.014, Val Acc:20.96%, Val Recall:19.90% Val F1:20.26%, Time:1:04:56 \n","Iter:   9063, Train Loss:2.788, Train Acc:21.09%, Val Loss:3.023, Val Acc:21.18%, Val Recall:20.15% Val F1:20.48%, Time:1:05:14 \n","Iter:   9113, Train Loss: 2.83, Train Acc:28.91%, Val Loss:3.005, Val Acc:20.43%, Val Recall:19.40% Val F1:19.53%, Time:1:05:41 *\n","Iter:   9163, Train Loss: 2.85, Train Acc:20.31%, Val Loss: 2.99, Val Acc:21.13%, Val Recall:20.03% Val F1:20.27%, Time:1:06:11 *\n","Iter:   9213, Train Loss:2.812, Train Acc:28.91%, Val Loss:2.987, Val Acc:21.42%, Val Recall:20.43% Val F1:20.78%, Time:1:06:41 *\n","Iter:   9263, Train Loss:2.822, Train Acc:23.44%, Val Loss:2.992, Val Acc:21.06%, Val Recall:20.12% Val F1:20.26%, Time:1:06:59 \n","Iter:   9313, Train Loss:2.786, Train Acc:23.44%, Val Loss:2.963, Val Acc:21.52%, Val Recall:20.27% Val F1:20.69%, Time:1:07:26 *\n","Iter:   9363, Train Loss:2.788, Train Acc:25.00%, Val Loss:2.974, Val Acc:21.76%, Val Recall:20.61% Val F1:20.91%, Time:1:07:44 \n","Epoch[16/100]\n","Iter:   9439, Train Loss:2.674, Train Acc:25.00%, Val Loss:2.973, Val Acc:21.78%, Val Recall:20.65% Val F1:20.94%, Time:1:08:07 \n","Iter:   9489, Train Loss:2.713, Train Acc:29.69%, Val Loss:2.958, Val Acc:21.71%, Val Recall:20.50% Val F1:20.76%, Time:1:08:35 *\n","Iter:   9539, Train Loss:2.684, Train Acc:26.56%, Val Loss:2.954, Val Acc:22.12%, Val Recall:21.08% Val F1:21.29%, Time:1:09:03 *\n","Iter:   9589, Train Loss:2.676, Train Acc:35.16%, Val Loss:2.977, Val Acc:22.24%, Val Recall:21.35% Val F1:21.58%, Time:1:09:21 \n","Iter:   9639, Train Loss:2.687, Train Acc:24.22%, Val Loss:2.966, Val Acc:22.66%, Val Recall:21.51% Val F1:21.85%, Time:1:09:39 \n","Iter:   9689, Train Loss:2.716, Train Acc:25.00%, Val Loss:2.952, Val Acc:22.56%, Val Recall:21.47% Val F1:21.74%, Time:1:10:06 *\n","Iter:   9739, Train Loss:2.707, Train Acc:27.34%, Val Loss:2.941, Val Acc:22.83%, Val Recall:21.58% Val F1:21.93%, Time:1:10:36 *\n","Iter:   9789, Train Loss:2.717, Train Acc:27.34%, Val Loss:2.945, Val Acc:23.25%, Val Recall:22.05% Val F1:22.26%, Time:1:10:54 \n","Iter:   9839, Train Loss: 2.69, Train Acc:29.69%, Val Loss:2.922, Val Acc:22.79%, Val Recall:21.59% Val F1:21.92%, Time:1:11:22 *\n","Iter:   9889, Train Loss:2.708, Train Acc:29.69%, Val Loss:2.925, Val Acc:23.50%, Val Recall:22.24% Val F1:22.55%, Time:1:11:39 \n","Iter:   9939, Train Loss:2.703, Train Acc:29.69%, Val Loss:2.928, Val Acc:23.40%, Val Recall:22.40% Val F1:22.69%, Time:1:11:57 \n","Iter:   9989, Train Loss:2.662, Train Acc:29.69%, Val Loss:2.911, Val Acc:23.70%, Val Recall:22.66% Val F1:22.74%, Time:1:12:24 *\n","Epoch[17/100]\n","Iter:  10065, Train Loss:2.536, Train Acc:30.47%, Val Loss:2.899, Val Acc:23.88%, Val Recall:22.70% Val F1:22.86%, Time:1:12:58 *\n","Iter:  10115, Train Loss: 2.57, Train Acc:29.69%, Val Loss:2.902, Val Acc:23.84%, Val Recall:22.67% Val F1:23.05%, Time:1:13:16 \n","Iter:  10165, Train Loss: 2.62, Train Acc:28.91%, Val Loss:2.888, Val Acc:24.41%, Val Recall:23.29% Val F1:23.53%, Time:1:13:43 *\n","Iter:  10215, Train Loss:2.575, Train Acc:29.69%, Val Loss:2.899, Val Acc:23.85%, Val Recall:22.62% Val F1:22.90%, Time:1:14:01 \n","Iter:  10265, Train Loss:2.586, Train Acc:29.69%, Val Loss:2.892, Val Acc:24.59%, Val Recall:23.44% Val F1:23.57%, Time:1:14:19 \n","Iter:  10315, Train Loss:2.568, Train Acc:29.69%, Val Loss:2.871, Val Acc:24.73%, Val Recall:23.57% Val F1:23.73%, Time:1:14:46 *\n","Iter:  10365, Train Loss:2.572, Train Acc:28.91%, Val Loss:2.863, Val Acc:24.89%, Val Recall:23.78% Val F1:23.93%, Time:1:15:16 *\n","Iter:  10415, Train Loss:2.572, Train Acc:34.38%, Val Loss:2.869, Val Acc:25.05%, Val Recall:23.93% Val F1:24.12%, Time:1:15:33 \n","Iter:  10465, Train Loss:2.542, Train Acc:36.72%, Val Loss:2.838, Val Acc:25.23%, Val Recall:24.09% Val F1:24.41%, Time:1:16:01 *\n","Iter:  10515, Train Loss:  2.6, Train Acc:28.91%, Val Loss: 2.85, Val Acc:25.42%, Val Recall:24.32% Val F1:24.58%, Time:1:16:18 \n","Iter:  10565, Train Loss: 2.58, Train Acc:42.19%, Val Loss:2.813, Val Acc:25.95%, Val Recall:24.70% Val F1:24.98%, Time:1:16:45 *\n","Iter:  10615, Train Loss:2.573, Train Acc:37.50%, Val Loss:2.817, Val Acc:25.94%, Val Recall:24.78% Val F1:25.02%, Time:1:17:03 \n","Epoch[18/100]\n","Iter:  10691, Train Loss:2.419, Train Acc:34.38%, Val Loss:2.812, Val Acc:26.64%, Val Recall:25.57% Val F1:25.75%, Time:1:17:37 *\n","Iter:  10741, Train Loss:2.411, Train Acc:42.19%, Val Loss:2.819, Val Acc:26.40%, Val Recall:25.46% Val F1:25.58%, Time:1:17:55 \n","Iter:  10791, Train Loss:2.437, Train Acc:35.16%, Val Loss:2.794, Val Acc:26.75%, Val Recall:25.46% Val F1:25.63%, Time:1:18:22 *\n","Iter:  10841, Train Loss:2.417, Train Acc:35.16%, Val Loss:2.777, Val Acc:27.22%, Val Recall:25.95% Val F1:26.28%, Time:1:18:51 *\n","Iter:  10891, Train Loss:2.444, Train Acc:42.97%, Val Loss:2.797, Val Acc:27.10%, Val Recall:25.87% Val F1:26.23%, Time:1:19:08 \n","Iter:  10941, Train Loss:2.451, Train Acc:28.12%, Val Loss:2.795, Val Acc:27.12%, Val Recall:25.97% Val F1:26.13%, Time:1:19:26 \n","Iter:  10991, Train Loss:2.421, Train Acc:38.28%, Val Loss:2.782, Val Acc:27.50%, Val Recall:26.27% Val F1:26.43%, Time:1:19:43 \n","Iter:  11041, Train Loss:2.451, Train Acc:35.16%, Val Loss:2.758, Val Acc:27.63%, Val Recall:26.45% Val F1:26.85%, Time:1:20:11 *\n","Iter:  11091, Train Loss: 2.42, Train Acc:38.28%, Val Loss:2.726, Val Acc:27.97%, Val Recall:26.72% Val F1:27.06%, Time:1:20:41 *\n","Iter:  11141, Train Loss:2.453, Train Acc:33.59%, Val Loss: 2.73, Val Acc:28.42%, Val Recall:27.23% Val F1:27.30%, Time:1:20:58 \n","Iter:  11191, Train Loss:2.398, Train Acc:36.72%, Val Loss:2.737, Val Acc:27.64%, Val Recall:26.45% Val F1:26.74%, Time:1:21:16 \n","Iter:  11241, Train Loss:2.416, Train Acc:35.94%, Val Loss:2.705, Val Acc:28.39%, Val Recall:27.13% Val F1:27.28%, Time:1:21:43 *\n","Epoch[19/100]\n","Iter:  11317, Train Loss:2.284, Train Acc:34.38%, Val Loss:  2.7, Val Acc:28.24%, Val Recall:27.34% Val F1:27.56%, Time:1:22:17 *\n","Iter:  11367, Train Loss: 2.26, Train Acc:41.41%, Val Loss:2.706, Val Acc:29.00%, Val Recall:27.91% Val F1:28.11%, Time:1:22:35 \n","Iter:  11417, Train Loss:2.271, Train Acc:37.50%, Val Loss:2.696, Val Acc:29.30%, Val Recall:28.13% Val F1:28.33%, Time:1:23:02 *\n","Iter:  11467, Train Loss:2.294, Train Acc:35.16%, Val Loss:2.712, Val Acc:29.62%, Val Recall:28.60% Val F1:28.61%, Time:1:23:20 \n","Iter:  11517, Train Loss:2.277, Train Acc:42.19%, Val Loss:2.713, Val Acc:30.09%, Val Recall:28.88% Val F1:29.04%, Time:1:23:38 \n","Iter:  11567, Train Loss:2.249, Train Acc:45.31%, Val Loss: 2.69, Val Acc:30.26%, Val Recall:29.11% Val F1:29.40%, Time:1:24:05 *\n","Iter:  11617, Train Loss:2.304, Train Acc:35.94%, Val Loss:2.699, Val Acc:29.74%, Val Recall:28.40% Val F1:28.81%, Time:1:24:23 \n","Iter:  11667, Train Loss:2.292, Train Acc:39.84%, Val Loss:2.643, Val Acc:30.51%, Val Recall:29.36% Val F1:29.47%, Time:1:24:51 *\n","Iter:  11717, Train Loss:2.288, Train Acc:34.38%, Val Loss:2.636, Val Acc:30.75%, Val Recall:29.58% Val F1:29.83%, Time:1:25:20 *\n","Iter:  11767, Train Loss:2.269, Train Acc:31.25%, Val Loss:2.622, Val Acc:31.10%, Val Recall:30.12% Val F1:30.29%, Time:1:25:51 *\n","Iter:  11817, Train Loss:2.267, Train Acc:43.75%, Val Loss:2.605, Val Acc:31.18%, Val Recall:30.01% Val F1:30.23%, Time:1:26:24 *\n","Iter:  11867, Train Loss:2.241, Train Acc:31.25%, Val Loss:2.581, Val Acc:31.44%, Val Recall:30.26% Val F1:30.45%, Time:1:26:55 *\n","Epoch[20/100]\n","Iter:  11943, Train Loss:2.051, Train Acc:42.97%, Val Loss: 2.59, Val Acc:32.12%, Val Recall:31.12% Val F1:31.23%, Time:1:27:17 \n","Iter:  11993, Train Loss:2.114, Train Acc:42.97%, Val Loss:2.589, Val Acc:32.17%, Val Recall:31.28% Val F1:31.30%, Time:1:27:35 \n","Iter:  12043, Train Loss:2.108, Train Acc:38.28%, Val Loss: 2.59, Val Acc:32.45%, Val Recall:31.53% Val F1:31.58%, Time:1:27:53 \n","Iter:  12093, Train Loss: 2.11, Train Acc:48.44%, Val Loss:2.583, Val Acc:32.66%, Val Recall:31.66% Val F1:31.73%, Time:1:28:10 \n","Iter:  12143, Train Loss:2.087, Train Acc:40.62%, Val Loss:2.573, Val Acc:32.53%, Val Recall:31.20% Val F1:31.65%, Time:1:28:39 *\n","Iter:  12193, Train Loss:2.164, Train Acc:46.09%, Val Loss:2.568, Val Acc:33.07%, Val Recall:32.07% Val F1:32.37%, Time:1:29:07 *\n","Iter:  12243, Train Loss: 2.12, Train Acc:40.62%, Val Loss:2.552, Val Acc:32.76%, Val Recall:31.75% Val F1:31.92%, Time:1:29:40 *\n","Iter:  12293, Train Loss:2.162, Train Acc:40.62%, Val Loss:  2.5, Val Acc:34.19%, Val Recall:33.27% Val F1:33.49%, Time:1:30:11 *\n","Iter:  12343, Train Loss:2.078, Train Acc:45.31%, Val Loss:2.523, Val Acc:33.81%, Val Recall:32.67% Val F1:32.84%, Time:1:30:29 \n","Iter:  12393, Train Loss:2.106, Train Acc:43.75%, Val Loss:2.522, Val Acc:34.15%, Val Recall:32.98% Val F1:33.30%, Time:1:30:46 \n","Iter:  12443, Train Loss:2.102, Train Acc:47.66%, Val Loss:2.483, Val Acc:34.69%, Val Recall:33.53% Val F1:33.77%, Time:1:31:14 *\n","Iter:  12493, Train Loss:2.117, Train Acc:44.53%, Val Loss:2.458, Val Acc:35.28%, Val Recall:33.99% Val F1:34.40%, Time:1:31:44 *\n","Epoch[21/100]\n","Iter:  12569, Train Loss:1.894, Train Acc:44.53%, Val Loss:2.451, Val Acc:35.64%, Val Recall:34.54% Val F1:34.78%, Time:1:32:19 *\n","Iter:  12619, Train Loss:1.941, Train Acc:53.91%, Val Loss:2.451, Val Acc:35.47%, Val Recall:34.48% Val F1:34.74%, Time:1:32:37 \n","Iter:  12669, Train Loss: 1.95, Train Acc:50.78%, Val Loss: 2.44, Val Acc:36.23%, Val Recall:35.08% Val F1:35.35%, Time:1:33:04 *\n","Iter:  12719, Train Loss:1.949, Train Acc:35.16%, Val Loss:2.434, Val Acc:36.29%, Val Recall:35.16% Val F1:35.49%, Time:1:33:33 *\n","Iter:  12769, Train Loss: 1.94, Train Acc:46.09%, Val Loss:2.423, Val Acc:36.13%, Val Recall:35.08% Val F1:35.31%, Time:1:34:04 *\n","Iter:  12819, Train Loss: 1.95, Train Acc:37.50%, Val Loss:2.436, Val Acc:36.25%, Val Recall:35.09% Val F1:35.49%, Time:1:34:22 \n","Iter:  12869, Train Loss:1.906, Train Acc:47.66%, Val Loss:2.431, Val Acc:37.02%, Val Recall:36.20% Val F1:36.22%, Time:1:34:40 \n","Iter:  12919, Train Loss:1.987, Train Acc:47.66%, Val Loss:2.359, Val Acc:38.25%, Val Recall:37.21% Val F1:37.51%, Time:1:35:07 *\n","Iter:  12969, Train Loss:1.928, Train Acc:44.53%, Val Loss:2.346, Val Acc:38.35%, Val Recall:37.44% Val F1:37.60%, Time:1:35:38 *\n","Iter:  13019, Train Loss:1.926, Train Acc:53.12%, Val Loss:2.352, Val Acc:38.22%, Val Recall:37.30% Val F1:37.34%, Time:1:35:55 \n","Iter:  13069, Train Loss:1.961, Train Acc:48.44%, Val Loss:2.338, Val Acc:38.36%, Val Recall:37.50% Val F1:37.66%, Time:1:36:24 *\n","Iter:  13119, Train Loss:1.927, Train Acc:50.78%, Val Loss:2.298, Val Acc:38.98%, Val Recall:38.04% Val F1:38.28%, Time:1:36:52 *\n","Epoch[22/100]\n","Iter:  13195, Train Loss:1.697, Train Acc:55.47%, Val Loss:2.291, Val Acc:39.84%, Val Recall:38.89% Val F1:39.01%, Time:1:37:26 *\n","Iter:  13245, Train Loss:1.748, Train Acc:56.25%, Val Loss:2.298, Val Acc:39.95%, Val Recall:39.08% Val F1:39.36%, Time:1:37:44 \n","Iter:  13295, Train Loss:1.787, Train Acc:45.31%, Val Loss:2.336, Val Acc:40.11%, Val Recall:39.23% Val F1:39.41%, Time:1:38:02 \n","Iter:  13345, Train Loss:1.759, Train Acc:54.69%, Val Loss:2.249, Val Acc:40.56%, Val Recall:39.65% Val F1:39.95%, Time:1:38:29 *\n","Iter:  13395, Train Loss:1.778, Train Acc:45.31%, Val Loss:2.284, Val Acc:40.30%, Val Recall:39.41% Val F1:39.85%, Time:1:38:47 \n","Iter:  13445, Train Loss:1.743, Train Acc:49.22%, Val Loss:2.225, Val Acc:41.51%, Val Recall:40.41% Val F1:40.88%, Time:1:39:15 *\n","Iter:  13495, Train Loss:1.769, Train Acc:48.44%, Val Loss:2.235, Val Acc:41.16%, Val Recall:40.19% Val F1:40.52%, Time:1:39:33 \n","Iter:  13545, Train Loss:1.751, Train Acc:50.00%, Val Loss:2.227, Val Acc:41.83%, Val Recall:41.07% Val F1:41.22%, Time:1:39:50 \n","Iter:  13595, Train Loss:1.752, Train Acc:56.25%, Val Loss:2.203, Val Acc:42.54%, Val Recall:41.58% Val F1:41.87%, Time:1:40:17 *\n","Iter:  13645, Train Loss:1.751, Train Acc:46.09%, Val Loss:2.188, Val Acc:42.04%, Val Recall:41.02% Val F1:41.55%, Time:1:40:47 *\n","Iter:  13695, Train Loss:1.771, Train Acc:50.78%, Val Loss:2.155, Val Acc:42.96%, Val Recall:42.00% Val F1:42.28%, Time:1:41:17 *\n","Iter:  13745, Train Loss:1.738, Train Acc:50.78%, Val Loss:2.174, Val Acc:43.11%, Val Recall:42.12% Val F1:42.41%, Time:1:41:35 \n","Epoch[23/100]\n","Iter:  13821, Train Loss: 1.51, Train Acc:60.94%, Val Loss: 2.15, Val Acc:44.19%, Val Recall:43.47% Val F1:43.67%, Time:1:42:09 *\n","Iter:  13871, Train Loss:1.573, Train Acc:60.94%, Val Loss:2.162, Val Acc:43.56%, Val Recall:42.74% Val F1:43.03%, Time:1:42:27 \n","Iter:  13921, Train Loss:1.579, Train Acc:53.91%, Val Loss:2.086, Val Acc:44.56%, Val Recall:43.63% Val F1:44.01%, Time:1:42:54 *\n","Iter:  13971, Train Loss:1.578, Train Acc:54.69%, Val Loss:2.139, Val Acc:43.99%, Val Recall:43.09% Val F1:43.39%, Time:1:43:12 \n","Iter:  14021, Train Loss:1.603, Train Acc:52.34%, Val Loss:2.109, Val Acc:45.11%, Val Recall:44.05% Val F1:44.51%, Time:1:43:29 \n","Iter:  14071, Train Loss:1.578, Train Acc:57.81%, Val Loss:2.101, Val Acc:45.32%, Val Recall:44.43% Val F1:44.83%, Time:1:43:47 \n","Iter:  14121, Train Loss:1.617, Train Acc:53.91%, Val Loss:2.105, Val Acc:45.98%, Val Recall:45.20% Val F1:45.52%, Time:1:44:04 \n","Iter:  14171, Train Loss: 1.63, Train Acc:51.56%, Val Loss:2.083, Val Acc:45.29%, Val Recall:44.50% Val F1:44.93%, Time:1:44:31 *\n","Iter:  14221, Train Loss:1.594, Train Acc:58.59%, Val Loss:2.065, Val Acc:45.85%, Val Recall:45.09% Val F1:45.41%, Time:1:45:01 *\n","Iter:  14271, Train Loss:1.555, Train Acc:53.91%, Val Loss:2.007, Val Acc:47.07%, Val Recall:46.35% Val F1:46.80%, Time:1:45:34 *\n","Iter:  14321, Train Loss:  1.6, Train Acc:59.38%, Val Loss:2.004, Val Acc:47.66%, Val Recall:46.69% Val F1:47.19%, Time:1:46:04 *\n","Iter:  14371, Train Loss:1.559, Train Acc:59.38%, Val Loss: 1.95, Val Acc:48.60%, Val Recall:47.77% Val F1:48.12%, Time:1:46:35 *\n","Epoch[24/100]\n","Iter:  14447, Train Loss:1.371, Train Acc:60.16%, Val Loss:1.959, Val Acc:48.99%, Val Recall:48.20% Val F1:48.56%, Time:1:46:57 \n","Iter:  14497, Train Loss:1.379, Train Acc:67.19%, Val Loss: 1.98, Val Acc:49.34%, Val Recall:48.69% Val F1:48.96%, Time:1:47:15 \n","Iter:  14547, Train Loss:1.417, Train Acc:60.94%, Val Loss:1.967, Val Acc:48.97%, Val Recall:48.45% Val F1:48.56%, Time:1:47:32 \n","Iter:  14597, Train Loss:1.451, Train Acc:64.06%, Val Loss:1.977, Val Acc:48.87%, Val Recall:48.08% Val F1:48.60%, Time:1:47:50 \n","Iter:  14647, Train Loss:1.446, Train Acc:58.59%, Val Loss:1.962, Val Acc:49.03%, Val Recall:48.27% Val F1:48.73%, Time:1:48:07 \n","Iter:  14697, Train Loss:1.421, Train Acc:59.38%, Val Loss:1.926, Val Acc:49.85%, Val Recall:49.04% Val F1:49.52%, Time:1:48:35 *\n","Iter:  14747, Train Loss:1.449, Train Acc:62.50%, Val Loss:1.929, Val Acc:49.97%, Val Recall:49.34% Val F1:49.65%, Time:1:48:53 \n","Iter:  14797, Train Loss:1.424, Train Acc:64.06%, Val Loss:1.913, Val Acc:50.09%, Val Recall:49.21% Val F1:49.88%, Time:1:49:20 *\n","Iter:  14847, Train Loss:1.411, Train Acc:53.91%, Val Loss:1.922, Val Acc:50.85%, Val Recall:50.29% Val F1:50.55%, Time:1:49:38 \n","Iter:  14897, Train Loss:1.448, Train Acc:53.12%, Val Loss:1.881, Val Acc:51.43%, Val Recall:50.60% Val F1:51.33%, Time:1:50:06 *\n","Iter:  14947, Train Loss:1.426, Train Acc:59.38%, Val Loss:1.853, Val Acc:52.77%, Val Recall:52.20% Val F1:52.58%, Time:1:50:34 *\n","Iter:  14997, Train Loss:1.423, Train Acc:51.56%, Val Loss:1.845, Val Acc:51.64%, Val Recall:51.15% Val F1:51.34%, Time:1:51:05 *\n","Epoch[25/100]\n","Iter:  15073, Train Loss: 1.21, Train Acc:64.06%, Val Loss:1.899, Val Acc:50.90%, Val Recall:50.37% Val F1:50.67%, Time:1:51:28 \n","Iter:  15123, Train Loss:1.222, Train Acc:61.72%, Val Loss:1.789, Val Acc:52.91%, Val Recall:52.33% Val F1:52.85%, Time:1:51:57 *\n","Iter:  15173, Train Loss:1.245, Train Acc:67.97%, Val Loss:1.814, Val Acc:53.27%, Val Recall:52.96% Val F1:53.16%, Time:1:52:15 \n","Iter:  15223, Train Loss:1.243, Train Acc:68.75%, Val Loss:1.815, Val Acc:54.08%, Val Recall:53.61% Val F1:53.94%, Time:1:52:32 \n","Iter:  15273, Train Loss:1.284, Train Acc:60.16%, Val Loss:1.807, Val Acc:53.50%, Val Recall:52.99% Val F1:53.36%, Time:1:52:50 \n","Iter:  15323, Train Loss:1.294, Train Acc:60.16%, Val Loss: 1.79, Val Acc:54.12%, Val Recall:53.60% Val F1:53.88%, Time:1:53:07 \n","Iter:  15373, Train Loss:1.272, Train Acc:61.72%, Val Loss:1.773, Val Acc:54.20%, Val Recall:53.80% Val F1:54.02%, Time:1:53:35 *\n","Iter:  15423, Train Loss:1.306, Train Acc:66.41%, Val Loss:1.756, Val Acc:54.56%, Val Recall:54.44% Val F1:54.53%, Time:1:54:04 *\n","Iter:  15473, Train Loss:1.323, Train Acc:59.38%, Val Loss:1.762, Val Acc:54.30%, Val Recall:53.80% Val F1:54.17%, Time:1:54:22 \n","Iter:  15523, Train Loss:1.293, Train Acc:58.59%, Val Loss:1.755, Val Acc:54.68%, Val Recall:54.25% Val F1:54.59%, Time:1:54:50 *\n","Iter:  15573, Train Loss:1.316, Train Acc:56.25%, Val Loss:1.709, Val Acc:54.91%, Val Recall:54.47% Val F1:54.66%, Time:1:55:20 *\n","Iter:  15623, Train Loss:1.299, Train Acc:68.75%, Val Loss:1.717, Val Acc:55.35%, Val Recall:54.84% Val F1:55.07%, Time:1:55:37 \n","Epoch[26/100]\n","Iter:  15699, Train Loss:1.069, Train Acc:64.06%, Val Loss:1.695, Val Acc:55.97%, Val Recall:55.52% Val F1:55.83%, Time:1:56:11 *\n","Iter:  15749, Train Loss:1.129, Train Acc:67.97%, Val Loss:1.721, Val Acc:56.25%, Val Recall:55.84% Val F1:56.24%, Time:1:56:29 \n","Iter:  15799, Train Loss: 1.09, Train Acc:71.09%, Val Loss:1.665, Val Acc:56.88%, Val Recall:56.57% Val F1:56.93%, Time:1:56:57 *\n","Iter:  15849, Train Loss: 1.15, Train Acc:67.97%, Val Loss:1.675, Val Acc:57.16%, Val Recall:56.80% Val F1:57.11%, Time:1:57:14 \n","Iter:  15899, Train Loss:1.149, Train Acc:68.75%, Val Loss:1.644, Val Acc:57.12%, Val Recall:56.71% Val F1:57.21%, Time:1:57:42 *\n","Iter:  15949, Train Loss:1.159, Train Acc:62.50%, Val Loss:1.658, Val Acc:57.30%, Val Recall:57.05% Val F1:57.20%, Time:1:58:00 \n","Iter:  15999, Train Loss:1.142, Train Acc:59.38%, Val Loss:1.648, Val Acc:57.45%, Val Recall:57.14% Val F1:57.44%, Time:1:58:18 \n","Iter:  16049, Train Loss:1.183, Train Acc:61.72%, Val Loss:1.648, Val Acc:58.37%, Val Recall:58.09% Val F1:58.33%, Time:1:58:35 \n","Iter:  16099, Train Loss:1.155, Train Acc:69.53%, Val Loss: 1.62, Val Acc:58.34%, Val Recall:58.07% Val F1:58.35%, Time:1:59:02 *\n","Iter:  16149, Train Loss: 1.15, Train Acc:62.50%, Val Loss:1.588, Val Acc:59.20%, Val Recall:58.81% Val F1:59.20%, Time:1:59:31 *\n","Iter:  16199, Train Loss:1.159, Train Acc:76.56%, Val Loss:1.594, Val Acc:58.75%, Val Recall:58.42% Val F1:58.84%, Time:1:59:49 \n","Iter:  16249, Train Loss: 1.18, Train Acc:67.19%, Val Loss:1.539, Val Acc:59.97%, Val Recall:59.62% Val F1:60.01%, Time:2:00:16 *\n","Epoch[27/100]\n","Iter:  16325, Train Loss:0.9774, Train Acc:68.75%, Val Loss: 1.55, Val Acc:60.02%, Val Recall:60.08% Val F1:60.11%, Time:2:00:39 \n","Iter:  16375, Train Loss:0.9885, Train Acc:82.03%, Val Loss:1.588, Val Acc:59.24%, Val Recall:58.93% Val F1:59.29%, Time:2:00:57 \n","Iter:  16425, Train Loss:1.021, Train Acc:69.53%, Val Loss:1.621, Val Acc:58.51%, Val Recall:58.01% Val F1:58.51%, Time:2:01:14 \n","Iter:  16475, Train Loss:1.041, Train Acc:71.88%, Val Loss:1.549, Val Acc:60.21%, Val Recall:59.89% Val F1:60.34%, Time:2:01:32 \n","Iter:  16525, Train Loss: 1.03, Train Acc:72.66%, Val Loss:1.505, Val Acc:60.93%, Val Recall:60.54% Val F1:61.08%, Time:2:02:00 *\n","Iter:  16575, Train Loss:1.094, Train Acc:68.75%, Val Loss: 1.54, Val Acc:61.23%, Val Recall:60.92% Val F1:61.35%, Time:2:02:18 \n","Iter:  16625, Train Loss:1.051, Train Acc:61.72%, Val Loss:1.503, Val Acc:61.28%, Val Recall:60.92% Val F1:61.41%, Time:2:02:46 *\n","Iter:  16675, Train Loss:1.068, Train Acc:70.31%, Val Loss:1.516, Val Acc:61.24%, Val Recall:60.96% Val F1:61.31%, Time:2:03:03 \n","Iter:  16725, Train Loss:1.044, Train Acc:70.31%, Val Loss:1.493, Val Acc:61.81%, Val Recall:61.71% Val F1:61.90%, Time:2:03:31 *\n","Iter:  16775, Train Loss: 1.04, Train Acc:65.62%, Val Loss: 1.46, Val Acc:62.27%, Val Recall:61.88% Val F1:62.49%, Time:2:04:01 *\n","Iter:  16825, Train Loss:1.088, Train Acc:74.22%, Val Loss:1.495, Val Acc:61.61%, Val Recall:61.38% Val F1:61.70%, Time:2:04:18 \n","Iter:  16875, Train Loss:1.034, Train Acc:71.88%, Val Loss:1.428, Val Acc:63.19%, Val Recall:63.10% Val F1:63.33%, Time:2:04:46 *\n","Epoch[28/100]\n","Iter:  16951, Train Loss:0.8861, Train Acc:69.53%, Val Loss: 1.44, Val Acc:63.46%, Val Recall:63.37% Val F1:63.64%, Time:2:05:08 \n","Iter:  17001, Train Loss:0.9024, Train Acc:71.09%, Val Loss:1.442, Val Acc:62.82%, Val Recall:62.59% Val F1:62.76%, Time:2:05:26 \n","Iter:  17051, Train Loss:0.9656, Train Acc:75.00%, Val Loss:1.453, Val Acc:63.60%, Val Recall:63.47% Val F1:63.71%, Time:2:05:44 \n","Iter:  17101, Train Loss:0.9132, Train Acc:66.41%, Val Loss:1.431, Val Acc:63.65%, Val Recall:63.46% Val F1:63.84%, Time:2:06:01 \n","Iter:  17151, Train Loss:0.9334, Train Acc:75.00%, Val Loss:1.477, Val Acc:62.96%, Val Recall:62.72% Val F1:63.08%, Time:2:06:18 \n","Iter:  17201, Train Loss:0.9279, Train Acc:66.41%, Val Loss:1.411, Val Acc:63.67%, Val Recall:63.29% Val F1:63.96%, Time:2:06:47 *\n","Iter:  17251, Train Loss:0.9363, Train Acc:67.97%, Val Loss:1.442, Val Acc:63.73%, Val Recall:63.44% Val F1:63.94%, Time:2:07:04 \n","Iter:  17301, Train Loss:0.8987, Train Acc:78.91%, Val Loss:1.406, Val Acc:65.52%, Val Recall:65.43% Val F1:65.65%, Time:2:07:32 *\n","Iter:  17351, Train Loss:0.9476, Train Acc:64.84%, Val Loss:1.363, Val Acc:65.15%, Val Recall:64.97% Val F1:65.50%, Time:2:08:01 *\n","Iter:  17401, Train Loss:0.9398, Train Acc:69.53%, Val Loss: 1.36, Val Acc:65.16%, Val Recall:64.99% Val F1:65.33%, Time:2:08:33 *\n","Iter:  17451, Train Loss:0.9504, Train Acc:74.22%, Val Loss:1.408, Val Acc:65.35%, Val Recall:65.35% Val F1:65.52%, Time:2:08:51 \n","Iter:  17501, Train Loss:0.9138, Train Acc:76.56%, Val Loss: 1.37, Val Acc:65.80%, Val Recall:65.63% Val F1:66.03%, Time:2:09:09 \n","Epoch[29/100]\n","Iter:  17577, Train Loss:0.8058, Train Acc:67.97%, Val Loss:1.366, Val Acc:66.19%, Val Recall:66.10% Val F1:66.40%, Time:2:09:31 \n","Iter:  17627, Train Loss:0.8281, Train Acc:73.44%, Val Loss:1.333, Val Acc:66.33%, Val Recall:66.13% Val F1:66.60%, Time:2:09:59 *\n","Iter:  17677, Train Loss:0.8476, Train Acc:87.50%, Val Loss: 1.32, Val Acc:66.48%, Val Recall:66.26% Val F1:66.90%, Time:2:10:28 *\n","Iter:  17727, Train Loss:0.8597, Train Acc:79.69%, Val Loss:1.341, Val Acc:66.68%, Val Recall:66.70% Val F1:66.96%, Time:2:10:46 \n","Iter:  17777, Train Loss:0.8655, Train Acc:70.31%, Val Loss:1.315, Val Acc:65.76%, Val Recall:65.83% Val F1:66.12%, Time:2:11:13 *\n","Iter:  17827, Train Loss:0.8597, Train Acc:75.78%, Val Loss:1.268, Val Acc:67.65%, Val Recall:67.76% Val F1:67.93%, Time:2:11:42 *\n","Iter:  17877, Train Loss:0.876, Train Acc:71.88%, Val Loss:1.317, Val Acc:66.80%, Val Recall:66.66% Val F1:67.05%, Time:2:12:00 \n","Iter:  17927, Train Loss:0.8765, Train Acc:72.66%, Val Loss:1.309, Val Acc:66.86%, Val Recall:66.75% Val F1:67.08%, Time:2:12:18 \n","Iter:  17977, Train Loss:0.8652, Train Acc:76.56%, Val Loss:1.327, Val Acc:66.61%, Val Recall:66.67% Val F1:66.88%, Time:2:12:35 \n","Iter:  18027, Train Loss:0.8978, Train Acc:79.69%, Val Loss:1.253, Val Acc:68.22%, Val Recall:68.31% Val F1:68.57%, Time:2:13:02 *\n","Iter:  18077, Train Loss:0.8996, Train Acc:75.00%, Val Loss:1.262, Val Acc:67.95%, Val Recall:67.94% Val F1:68.34%, Time:2:13:20 \n","Iter:  18127, Train Loss:0.8516, Train Acc:71.88%, Val Loss:1.313, Val Acc:67.62%, Val Recall:67.64% Val F1:68.02%, Time:2:13:38 \n","Epoch[30/100]\n","Iter:  18203, Train Loss:0.6998, Train Acc:78.91%, Val Loss:1.231, Val Acc:68.69%, Val Recall:68.82% Val F1:69.04%, Time:2:14:11 *\n","Iter:  18253, Train Loss:0.7482, Train Acc:71.88%, Val Loss:1.258, Val Acc:68.41%, Val Recall:68.57% Val F1:68.67%, Time:2:14:28 \n","Iter:  18303, Train Loss:0.7638, Train Acc:72.66%, Val Loss:1.247, Val Acc:68.74%, Val Recall:68.71% Val F1:69.21%, Time:2:14:46 \n","Iter:  18353, Train Loss:0.7837, Train Acc:72.66%, Val Loss:1.246, Val Acc:68.38%, Val Recall:68.55% Val F1:68.71%, Time:2:15:03 \n","Iter:  18403, Train Loss:0.7753, Train Acc:82.03%, Val Loss:1.249, Val Acc:68.79%, Val Recall:68.91% Val F1:69.25%, Time:2:15:21 \n","Iter:  18453, Train Loss:0.7805, Train Acc:75.00%, Val Loss:1.254, Val Acc:69.08%, Val Recall:69.29% Val F1:69.47%, Time:2:15:38 \n","Iter:  18503, Train Loss:0.7938, Train Acc:79.69%, Val Loss:1.234, Val Acc:69.63%, Val Recall:69.80% Val F1:70.09%, Time:2:15:55 \n","Iter:  18553, Train Loss:0.793, Train Acc:68.75%, Val Loss:1.205, Val Acc:69.72%, Val Recall:69.76% Val F1:70.10%, Time:2:16:23 *\n","Iter:  18603, Train Loss:0.7984, Train Acc:75.00%, Val Loss:1.237, Val Acc:68.97%, Val Recall:69.10% Val F1:69.39%, Time:2:16:40 \n","Iter:  18653, Train Loss:0.7889, Train Acc:80.47%, Val Loss:1.167, Val Acc:70.36%, Val Recall:70.55% Val F1:70.76%, Time:2:17:08 *\n","Iter:  18703, Train Loss:0.8011, Train Acc:74.22%, Val Loss:1.195, Val Acc:69.91%, Val Recall:70.03% Val F1:70.32%, Time:2:17:26 \n","Iter:  18753, Train Loss:0.7711, Train Acc:75.78%, Val Loss:1.163, Val Acc:70.68%, Val Recall:70.85% Val F1:71.11%, Time:2:17:53 *\n","Epoch[31/100]\n","Iter:  18829, Train Loss:0.6598, Train Acc:78.12%, Val Loss:1.175, Val Acc:70.37%, Val Recall:70.44% Val F1:70.89%, Time:2:18:16 \n","Iter:  18879, Train Loss:0.6802, Train Acc:77.34%, Val Loss:1.153, Val Acc:70.82%, Val Recall:71.03% Val F1:71.18%, Time:2:18:44 *\n","Iter:  18929, Train Loss:0.7019, Train Acc:82.03%, Val Loss:1.186, Val Acc:70.39%, Val Recall:70.54% Val F1:70.86%, Time:2:19:02 \n","Iter:  18979, Train Loss:0.7672, Train Acc:74.22%, Val Loss:1.185, Val Acc:69.38%, Val Recall:69.38% Val F1:69.78%, Time:2:19:20 \n","Iter:  19029, Train Loss:0.7259, Train Acc:76.56%, Val Loss:1.147, Val Acc:70.86%, Val Recall:70.80% Val F1:71.17%, Time:2:19:47 *\n","Iter:  19079, Train Loss:0.7255, Train Acc:82.03%, Val Loss:1.152, Val Acc:71.19%, Val Recall:71.15% Val F1:71.56%, Time:2:20:05 \n","Iter:  19129, Train Loss:0.7453, Train Acc:78.12%, Val Loss:1.163, Val Acc:70.82%, Val Recall:70.68% Val F1:71.06%, Time:2:20:22 \n","Iter:  19179, Train Loss:0.7407, Train Acc:75.00%, Val Loss:1.156, Val Acc:71.43%, Val Recall:71.53% Val F1:71.74%, Time:2:20:40 \n","Iter:  19229, Train Loss:0.743, Train Acc:82.03%, Val Loss:1.143, Val Acc:71.52%, Val Recall:71.49% Val F1:71.98%, Time:2:21:07 *\n","Iter:  19279, Train Loss:0.7394, Train Acc:80.47%, Val Loss:1.148, Val Acc:71.50%, Val Recall:71.79% Val F1:71.88%, Time:2:21:25 \n","Iter:  19329, Train Loss:0.7613, Train Acc:80.47%, Val Loss: 1.11, Val Acc:72.18%, Val Recall:72.28% Val F1:72.50%, Time:2:21:52 *\n","Iter:  19379, Train Loss:0.7319, Train Acc:74.22%, Val Loss:1.092, Val Acc:72.40%, Val Recall:72.57% Val F1:72.83%, Time:2:22:22 *\n","Epoch[32/100]\n","Iter:  19455, Train Loss:0.6135, Train Acc:85.16%, Val Loss:1.081, Val Acc:73.32%, Val Recall:73.54% Val F1:73.78%, Time:2:22:57 *\n","Iter:  19505, Train Loss:0.626, Train Acc:83.59%, Val Loss: 1.12, Val Acc:72.72%, Val Recall:72.97% Val F1:73.33%, Time:2:23:14 \n","Iter:  19555, Train Loss:0.6736, Train Acc:78.91%, Val Loss:1.148, Val Acc:71.78%, Val Recall:71.98% Val F1:72.16%, Time:2:23:32 \n","Iter:  19605, Train Loss:0.6862, Train Acc:76.56%, Val Loss:1.101, Val Acc:72.21%, Val Recall:72.52% Val F1:72.68%, Time:2:23:49 \n","Iter:  19655, Train Loss:0.6898, Train Acc:77.34%, Val Loss:1.127, Val Acc:71.49%, Val Recall:71.76% Val F1:71.93%, Time:2:24:07 \n","Iter:  19705, Train Loss:0.7121, Train Acc:86.72%, Val Loss:1.122, Val Acc:72.65%, Val Recall:72.71% Val F1:73.05%, Time:2:24:24 \n","Iter:  19755, Train Loss:0.6558, Train Acc:81.25%, Val Loss:1.105, Val Acc:72.68%, Val Recall:72.69% Val F1:73.07%, Time:2:24:42 \n","Iter:  19805, Train Loss:0.6792, Train Acc:79.69%, Val Loss:1.094, Val Acc:73.59%, Val Recall:73.59% Val F1:73.85%, Time:2:24:59 \n","Iter:  19855, Train Loss:0.6825, Train Acc:81.25%, Val Loss:1.061, Val Acc:73.18%, Val Recall:73.38% Val F1:73.52%, Time:2:25:26 *\n","Iter:  19905, Train Loss:0.6951, Train Acc:87.50%, Val Loss: 1.08, Val Acc:73.34%, Val Recall:73.52% Val F1:73.81%, Time:2:25:44 \n","Iter:  19955, Train Loss:0.7155, Train Acc:81.25%, Val Loss:1.041, Val Acc:73.61%, Val Recall:73.68% Val F1:74.11%, Time:2:26:11 *\n","Iter:  20005, Train Loss:0.6874, Train Acc:88.28%, Val Loss: 1.03, Val Acc:73.93%, Val Recall:74.09% Val F1:74.46%, Time:2:26:41 *\n","Epoch[33/100]\n","Iter:  20081, Train Loss:0.5602, Train Acc:77.34%, Val Loss:1.055, Val Acc:73.54%, Val Recall:73.68% Val F1:74.02%, Time:2:27:04 \n","Iter:  20131, Train Loss:0.6361, Train Acc:84.38%, Val Loss:1.031, Val Acc:74.33%, Val Recall:74.41% Val F1:74.67%, Time:2:27:21 \n","Iter:  20181, Train Loss:0.5899, Train Acc:82.81%, Val Loss:1.044, Val Acc:74.49%, Val Recall:74.64% Val F1:74.90%, Time:2:27:39 \n","Iter:  20231, Train Loss:0.6601, Train Acc:79.69%, Val Loss:1.042, Val Acc:74.14%, Val Recall:74.31% Val F1:74.59%, Time:2:27:56 \n","Iter:  20281, Train Loss:0.6344, Train Acc:85.16%, Val Loss:1.023, Val Acc:74.44%, Val Recall:74.65% Val F1:74.93%, Time:2:28:25 *\n","Iter:  20331, Train Loss:0.5985, Train Acc:86.72%, Val Loss:1.016, Val Acc:75.08%, Val Recall:75.30% Val F1:75.54%, Time:2:28:53 *\n","Iter:  20381, Train Loss:0.6383, Train Acc:79.69%, Val Loss:1.057, Val Acc:74.76%, Val Recall:75.05% Val F1:75.26%, Time:2:29:11 \n","Iter:  20431, Train Loss:0.6486, Train Acc:78.12%, Val Loss:1.016, Val Acc:75.28%, Val Recall:75.67% Val F1:75.90%, Time:2:29:29 \n","Iter:  20481, Train Loss:0.6487, Train Acc:80.47%, Val Loss:1.005, Val Acc:75.52%, Val Recall:75.82% Val F1:76.03%, Time:2:29:56 *\n","Iter:  20531, Train Loss:0.6418, Train Acc:78.91%, Val Loss:0.991, Val Acc:75.44%, Val Recall:75.74% Val F1:75.98%, Time:2:30:26 *\n","Iter:  20581, Train Loss: 0.64, Train Acc:81.25%, Val Loss:1.015, Val Acc:75.68%, Val Recall:76.00% Val F1:76.19%, Time:2:30:44 \n","Iter:  20631, Train Loss:0.6561, Train Acc:82.03%, Val Loss:1.042, Val Acc:74.50%, Val Recall:74.64% Val F1:75.12%, Time:2:31:02 \n","Epoch[34/100]\n","Iter:  20707, Train Loss:0.5376, Train Acc:81.25%, Val Loss:1.049, Val Acc:75.34%, Val Recall:75.62% Val F1:75.85%, Time:2:31:24 \n","Iter:  20757, Train Loss:0.5619, Train Acc:84.38%, Val Loss:1.021, Val Acc:75.13%, Val Recall:75.29% Val F1:75.72%, Time:2:31:42 \n","Iter:  20807, Train Loss:0.5863, Train Acc:82.81%, Val Loss:1.001, Val Acc:75.21%, Val Recall:75.48% Val F1:75.65%, Time:2:31:59 \n","Iter:  20857, Train Loss:0.5909, Train Acc:86.72%, Val Loss:1.021, Val Acc:75.13%, Val Recall:75.37% Val F1:75.61%, Time:2:32:17 \n","Iter:  20907, Train Loss:0.6061, Train Acc:91.41%, Val Loss:0.9906, Val Acc:75.64%, Val Recall:75.94% Val F1:76.20%, Time:2:32:45 *\n","Iter:  20957, Train Loss:0.5954, Train Acc:78.91%, Val Loss:0.9746, Val Acc:76.34%, Val Recall:76.80% Val F1:76.95%, Time:2:33:13 *\n","Iter:  21007, Train Loss:0.6342, Train Acc:74.22%, Val Loss:0.941, Val Acc:76.42%, Val Recall:76.95% Val F1:76.97%, Time:2:33:45 *\n","Iter:  21057, Train Loss:0.5997, Train Acc:82.03%, Val Loss:0.9598, Val Acc:76.07%, Val Recall:76.35% Val F1:76.61%, Time:2:34:02 \n","Iter:  21107, Train Loss:0.6118, Train Acc:77.34%, Val Loss:0.9142, Val Acc:76.87%, Val Recall:77.18% Val F1:77.40%, Time:2:34:30 *\n","Iter:  21157, Train Loss:0.5831, Train Acc:75.78%, Val Loss:0.9569, Val Acc:76.56%, Val Recall:76.84% Val F1:77.22%, Time:2:34:48 \n","Iter:  21207, Train Loss:0.6058, Train Acc:84.38%, Val Loss:0.9557, Val Acc:76.86%, Val Recall:76.97% Val F1:77.39%, Time:2:35:06 \n","Iter:  21257, Train Loss:0.6158, Train Acc:79.69%, Val Loss:0.9378, Val Acc:77.59%, Val Recall:77.91% Val F1:78.21%, Time:2:35:23 \n","Epoch[35/100]\n","Iter:  21333, Train Loss:0.5005, Train Acc:80.47%, Val Loss:0.959, Val Acc:77.10%, Val Recall:77.48% Val F1:77.64%, Time:2:35:45 \n","Iter:  21383, Train Loss:0.5421, Train Acc:73.44%, Val Loss:0.9278, Val Acc:77.20%, Val Recall:77.49% Val F1:77.84%, Time:2:36:03 \n","Iter:  21433, Train Loss:0.5495, Train Acc:82.81%, Val Loss:1.001, Val Acc:76.67%, Val Recall:77.14% Val F1:77.17%, Time:2:36:20 \n","Iter:  21483, Train Loss:0.5547, Train Acc:78.12%, Val Loss:0.9359, Val Acc:77.05%, Val Recall:77.46% Val F1:77.61%, Time:2:36:38 \n","Iter:  21533, Train Loss:0.5849, Train Acc:82.03%, Val Loss:0.9339, Val Acc:76.94%, Val Recall:77.30% Val F1:77.41%, Time:2:36:55 \n","Iter:  21583, Train Loss:0.5809, Train Acc:85.94%, Val Loss:0.9542, Val Acc:76.66%, Val Recall:77.13% Val F1:77.09%, Time:2:37:13 \n","Iter:  21633, Train Loss:0.5971, Train Acc:73.44%, Val Loss:0.9247, Val Acc:77.24%, Val Recall:77.54% Val F1:77.81%, Time:2:37:30 \n","Iter:  21683, Train Loss:0.5571, Train Acc:86.72%, Val Loss:0.9025, Val Acc:77.75%, Val Recall:78.34% Val F1:78.22%, Time:2:37:59 *\n","Iter:  21733, Train Loss:0.592, Train Acc:86.72%, Val Loss:0.9035, Val Acc:77.90%, Val Recall:78.37% Val F1:78.37%, Time:2:38:17 \n","Iter:  21783, Train Loss:0.6019, Train Acc:76.56%, Val Loss:0.874, Val Acc:78.22%, Val Recall:78.60% Val F1:78.71%, Time:2:38:44 *\n","Iter:  21833, Train Loss:0.5821, Train Acc:78.91%, Val Loss:0.8789, Val Acc:78.35%, Val Recall:78.76% Val F1:78.83%, Time:2:39:02 \n","Iter:  21883, Train Loss:0.5647, Train Acc:84.38%, Val Loss:0.853, Val Acc:78.84%, Val Recall:79.11% Val F1:79.50%, Time:2:39:30 *\n","Epoch[36/100]\n","Iter:  21959, Train Loss:0.4872, Train Acc:87.50%, Val Loss:0.8681, Val Acc:78.61%, Val Recall:79.16% Val F1:79.14%, Time:2:39:52 \n","Iter:  22009, Train Loss:0.4875, Train Acc:84.38%, Val Loss:0.8619, Val Acc:78.87%, Val Recall:79.22% Val F1:79.38%, Time:2:40:10 \n","Iter:  22059, Train Loss:0.5021, Train Acc:86.72%, Val Loss:0.916, Val Acc:78.02%, Val Recall:78.51% Val F1:78.57%, Time:2:40:28 \n","Iter:  22109, Train Loss:0.5431, Train Acc:83.59%, Val Loss:0.9023, Val Acc:78.50%, Val Recall:78.81% Val F1:79.13%, Time:2:40:45 \n","Iter:  22159, Train Loss:0.4984, Train Acc:82.03%, Val Loss:0.9257, Val Acc:77.69%, Val Recall:77.89% Val F1:78.32%, Time:2:41:03 \n","Iter:  22209, Train Loss:0.5579, Train Acc:80.47%, Val Loss:0.9285, Val Acc:77.77%, Val Recall:78.17% Val F1:78.29%, Time:2:41:20 \n","Iter:  22259, Train Loss:0.5504, Train Acc:81.25%, Val Loss:0.9088, Val Acc:77.35%, Val Recall:77.73% Val F1:77.85%, Time:2:41:38 \n","Iter:  22309, Train Loss:0.5598, Train Acc:83.59%, Val Loss:0.9216, Val Acc:77.36%, Val Recall:77.56% Val F1:77.87%, Time:2:41:55 \n","Iter:  22359, Train Loss:0.5798, Train Acc:91.41%, Val Loss:0.884, Val Acc:78.66%, Val Recall:79.07% Val F1:79.21%, Time:2:42:13 \n","Iter:  22409, Train Loss:0.5742, Train Acc:84.38%, Val Loss:0.8656, Val Acc:78.49%, Val Recall:78.73% Val F1:79.13%, Time:2:42:30 \n","Iter:  22459, Train Loss:0.5933, Train Acc:86.72%, Val Loss:0.8157, Val Acc:79.69%, Val Recall:80.08% Val F1:80.28%, Time:2:42:59 *\n","Iter:  22509, Train Loss:0.5676, Train Acc:87.50%, Val Loss:0.882, Val Acc:78.53%, Val Recall:78.88% Val F1:79.11%, Time:2:43:17 \n","Epoch[37/100]\n","Iter:  22585, Train Loss:0.4564, Train Acc:85.94%, Val Loss:0.822, Val Acc:79.34%, Val Recall:79.79% Val F1:79.97%, Time:2:43:40 \n","Iter:  22635, Train Loss:0.4664, Train Acc:85.94%, Val Loss:0.8683, Val Acc:79.60%, Val Recall:80.10% Val F1:80.29%, Time:2:43:57 \n","Iter:  22685, Train Loss:0.4524, Train Acc:82.03%, Val Loss:0.8492, Val Acc:78.75%, Val Recall:79.14% Val F1:79.37%, Time:2:44:15 \n","Iter:  22735, Train Loss:0.5116, Train Acc:86.72%, Val Loss:0.9024, Val Acc:78.70%, Val Recall:78.93% Val F1:79.18%, Time:2:44:32 \n","Iter:  22785, Train Loss: 0.54, Train Acc:89.06%, Val Loss:0.8382, Val Acc:79.67%, Val Recall:80.13% Val F1:80.24%, Time:2:44:50 \n","Iter:  22835, Train Loss:0.475, Train Acc:86.72%, Val Loss:0.8699, Val Acc:78.88%, Val Recall:79.11% Val F1:79.47%, Time:2:45:07 \n","Iter:  22885, Train Loss:0.4909, Train Acc:83.59%, Val Loss:0.8929, Val Acc:79.47%, Val Recall:79.78% Val F1:80.00%, Time:2:45:25 \n","Iter:  22935, Train Loss:0.5051, Train Acc:87.50%, Val Loss:0.8329, Val Acc:79.35%, Val Recall:79.76% Val F1:79.95%, Time:2:45:42 \n","Iter:  22985, Train Loss:0.5198, Train Acc:82.03%, Val Loss:0.8372, Val Acc:79.84%, Val Recall:80.23% Val F1:80.41%, Time:2:46:00 \n","Iter:  23035, Train Loss:0.5147, Train Acc:85.16%, Val Loss:0.8415, Val Acc:79.86%, Val Recall:80.37% Val F1:80.45%, Time:2:46:17 \n","Iter:  23085, Train Loss:0.5413, Train Acc:80.47%, Val Loss:0.8474, Val Acc:79.56%, Val Recall:79.97% Val F1:80.14%, Time:2:46:35 \n","Iter:  23135, Train Loss:0.532, Train Acc:83.59%, Val Loss:0.809, Val Acc:80.03%, Val Recall:80.50% Val F1:80.58%, Time:2:47:02 *\n","Epoch[38/100]\n","Iter:  23211, Train Loss:0.4265, Train Acc:84.38%, Val Loss:0.8519, Val Acc:79.57%, Val Recall:80.02% Val F1:80.08%, Time:2:47:25 \n","Iter:  23261, Train Loss:0.4429, Train Acc:87.50%, Val Loss:0.804, Val Acc:80.37%, Val Recall:80.89% Val F1:80.95%, Time:2:47:53 *\n","Iter:  23311, Train Loss:0.4669, Train Acc:85.16%, Val Loss:0.8508, Val Acc:80.14%, Val Recall:80.46% Val F1:80.74%, Time:2:48:11 \n","Iter:  23361, Train Loss:0.4604, Train Acc:83.59%, Val Loss:0.8582, Val Acc:79.91%, Val Recall:80.37% Val F1:80.44%, Time:2:48:29 \n","Iter:  23411, Train Loss:0.4878, Train Acc:79.69%, Val Loss:0.8325, Val Acc:79.74%, Val Recall:80.05% Val F1:80.31%, Time:2:48:46 \n","Iter:  23461, Train Loss:0.4919, Train Acc:81.25%, Val Loss:0.8519, Val Acc:79.32%, Val Recall:79.60% Val F1:80.03%, Time:2:49:04 \n","Iter:  23511, Train Loss:0.4901, Train Acc:82.81%, Val Loss:0.8707, Val Acc:79.45%, Val Recall:79.71% Val F1:79.96%, Time:2:49:21 \n","Iter:  23561, Train Loss:0.5325, Train Acc:82.81%, Val Loss:0.8482, Val Acc:79.63%, Val Recall:79.83% Val F1:80.22%, Time:2:49:39 \n","Iter:  23611, Train Loss:0.5344, Train Acc:85.16%, Val Loss:0.852, Val Acc:79.79%, Val Recall:80.16% Val F1:80.40%, Time:2:49:56 \n","Iter:  23661, Train Loss:0.489, Train Acc:79.69%, Val Loss:0.821, Val Acc:80.40%, Val Recall:80.85% Val F1:81.02%, Time:2:50:14 \n","Iter:  23711, Train Loss:0.5404, Train Acc:82.81%, Val Loss:0.8374, Val Acc:80.40%, Val Recall:80.87% Val F1:80.96%, Time:2:50:31 \n","Iter:  23761, Train Loss:0.5044, Train Acc:82.03%, Val Loss:0.767, Val Acc:81.44%, Val Recall:81.84% Val F1:82.06%, Time:2:50:58 *\n","Epoch[39/100]\n","Iter:  23837, Train Loss:0.4211, Train Acc:90.62%, Val Loss:0.8228, Val Acc:80.80%, Val Recall:81.36% Val F1:81.39%, Time:2:51:21 \n","Iter:  23887, Train Loss: 0.42, Train Acc:85.16%, Val Loss:0.8363, Val Acc:81.07%, Val Recall:81.42% Val F1:81.68%, Time:2:51:39 \n","Iter:  23937, Train Loss:0.419, Train Acc:87.50%, Val Loss:0.8586, Val Acc:81.53%, Val Recall:82.02% Val F1:82.06%, Time:2:51:56 \n","Iter:  23987, Train Loss:0.4519, Train Acc:90.62%, Val Loss:0.7971, Val Acc:81.36%, Val Recall:81.80% Val F1:81.96%, Time:2:52:14 \n","Iter:  24037, Train Loss:0.4377, Train Acc:90.62%, Val Loss:0.8356, Val Acc:80.42%, Val Recall:80.82% Val F1:80.98%, Time:2:52:31 \n","Iter:  24087, Train Loss:0.4942, Train Acc:87.50%, Val Loss:0.792, Val Acc:81.14%, Val Recall:81.52% Val F1:81.73%, Time:2:52:49 \n","Iter:  24137, Train Loss:0.4748, Train Acc:87.50%, Val Loss:0.823, Val Acc:80.89%, Val Recall:81.18% Val F1:81.55%, Time:2:53:06 \n","Iter:  24187, Train Loss:0.4719, Train Acc:89.06%, Val Loss:0.8018, Val Acc:80.90%, Val Recall:81.31% Val F1:81.51%, Time:2:53:23 \n","Iter:  24237, Train Loss:0.4642, Train Acc:88.28%, Val Loss:0.7746, Val Acc:81.16%, Val Recall:81.62% Val F1:81.82%, Time:2:53:41 \n","Iter:  24287, Train Loss:0.4859, Train Acc:85.94%, Val Loss:0.8041, Val Acc:80.95%, Val Recall:81.43% Val F1:81.57%, Time:2:53:58 \n","Iter:  24337, Train Loss:0.5068, Train Acc:89.06%, Val Loss:0.8164, Val Acc:81.11%, Val Recall:81.48% Val F1:81.64%, Time:2:54:16 \n","Iter:  24387, Train Loss:0.4996, Train Acc:85.94%, Val Loss:0.7733, Val Acc:81.67%, Val Recall:82.14% Val F1:82.33%, Time:2:54:33 \n","Epoch[40/100]\n","Iter:  24463, Train Loss:0.4317, Train Acc:83.59%, Val Loss:0.8035, Val Acc:80.01%, Val Recall:80.23% Val F1:80.66%, Time:2:54:55 \n","Iter:  24513, Train Loss:0.4533, Train Acc:90.62%, Val Loss:0.8148, Val Acc:81.49%, Val Recall:81.90% Val F1:82.11%, Time:2:55:13 \n","Iter:  24563, Train Loss:0.4353, Train Acc:86.72%, Val Loss:0.7478, Val Acc:82.08%, Val Recall:82.57% Val F1:82.67%, Time:2:55:41 *\n","Iter:  24613, Train Loss:0.4429, Train Acc:88.28%, Val Loss:0.7811, Val Acc:81.44%, Val Recall:81.89% Val F1:81.97%, Time:2:55:59 \n","Iter:  24663, Train Loss:0.4848, Train Acc:87.50%, Val Loss:0.7695, Val Acc:81.41%, Val Recall:81.91% Val F1:82.04%, Time:2:56:16 \n","Iter:  24713, Train Loss:0.4825, Train Acc:89.06%, Val Loss:0.7764, Val Acc:81.79%, Val Recall:82.30% Val F1:82.40%, Time:2:56:34 \n","Iter:  24763, Train Loss:0.4581, Train Acc:90.62%, Val Loss:0.7935, Val Acc:80.72%, Val Recall:81.11% Val F1:81.35%, Time:2:56:51 \n","Iter:  24813, Train Loss:0.4625, Train Acc:86.72%, Val Loss:0.763, Val Acc:81.70%, Val Recall:82.27% Val F1:82.27%, Time:2:57:09 \n","Iter:  24863, Train Loss:0.4858, Train Acc:87.50%, Val Loss:0.7283, Val Acc:81.64%, Val Recall:82.00% Val F1:82.37%, Time:2:57:36 *\n","Iter:  24913, Train Loss:0.4741, Train Acc:85.94%, Val Loss:0.7375, Val Acc:82.22%, Val Recall:82.69% Val F1:82.93%, Time:2:57:54 \n","Iter:  24963, Train Loss:0.4527, Train Acc:85.16%, Val Loss:0.7253, Val Acc:82.60%, Val Recall:83.05% Val F1:83.27%, Time:2:58:21 *\n","Iter:  25013, Train Loss:0.4309, Train Acc:84.38%, Val Loss:0.7302, Val Acc:82.10%, Val Recall:82.56% Val F1:82.74%, Time:2:58:39 \n","Epoch[41/100]\n","Iter:  25089, Train Loss:0.3545, Train Acc:86.72%, Val Loss:0.727, Val Acc:82.74%, Val Recall:83.31% Val F1:83.46%, Time:2:59:01 \n","Iter:  25139, Train Loss:0.3762, Train Acc:92.19%, Val Loss:0.7215, Val Acc:82.85%, Val Recall:83.24% Val F1:83.51%, Time:2:59:29 *\n","Iter:  25189, Train Loss:0.4255, Train Acc:84.38%, Val Loss:0.7361, Val Acc:81.96%, Val Recall:82.46% Val F1:82.61%, Time:2:59:47 \n","Iter:  25239, Train Loss:0.4145, Train Acc:80.47%, Val Loss:0.7144, Val Acc:83.25%, Val Recall:83.81% Val F1:83.93%, Time:3:00:15 *\n","Iter:  25289, Train Loss:0.383, Train Acc:89.84%, Val Loss:0.7277, Val Acc:82.51%, Val Recall:83.08% Val F1:83.09%, Time:3:00:33 \n","Iter:  25339, Train Loss:0.4415, Train Acc:88.28%, Val Loss:0.7258, Val Acc:82.50%, Val Recall:82.86% Val F1:83.22%, Time:3:00:50 \n","Iter:  25389, Train Loss:0.4492, Train Acc:89.06%, Val Loss:0.7596, Val Acc:81.58%, Val Recall:82.06% Val F1:82.12%, Time:3:01:08 \n","Iter:  25439, Train Loss:0.4419, Train Acc:87.50%, Val Loss:0.773, Val Acc:81.73%, Val Recall:82.19% Val F1:82.31%, Time:3:01:25 \n","Iter:  25489, Train Loss:0.4611, Train Acc:88.28%, Val Loss:0.7153, Val Acc:82.34%, Val Recall:82.72% Val F1:82.99%, Time:3:01:42 \n","Iter:  25539, Train Loss:0.4351, Train Acc:87.50%, Val Loss:0.7388, Val Acc:82.63%, Val Recall:83.19% Val F1:83.30%, Time:3:02:00 \n","Iter:  25589, Train Loss:0.433, Train Acc:92.19%, Val Loss:0.7845, Val Acc:82.03%, Val Recall:82.54% Val F1:82.64%, Time:3:02:17 \n","Iter:  25639, Train Loss:0.4484, Train Acc:79.69%, Val Loss:0.7024, Val Acc:83.16%, Val Recall:83.86% Val F1:83.85%, Time:3:02:44 *\n","Epoch[42/100]\n","Iter:  25715, Train Loss:0.3946, Train Acc:85.94%, Val Loss:0.7473, Val Acc:82.53%, Val Recall:82.96% Val F1:83.20%, Time:3:03:07 \n","Iter:  25765, Train Loss:0.3943, Train Acc:85.94%, Val Loss:0.7405, Val Acc:82.25%, Val Recall:82.53% Val F1:82.93%, Time:3:03:25 \n","Iter:  25815, Train Loss:0.3732, Train Acc:89.84%, Val Loss:0.7144, Val Acc:83.35%, Val Recall:83.92% Val F1:84.01%, Time:3:03:42 \n","Iter:  25865, Train Loss:0.4221, Train Acc:85.16%, Val Loss:0.7352, Val Acc:82.13%, Val Recall:82.40% Val F1:82.82%, Time:3:04:00 \n","Iter:  25915, Train Loss:0.3918, Train Acc:86.72%, Val Loss:0.7504, Val Acc:82.50%, Val Recall:82.93% Val F1:83.09%, Time:3:04:17 \n","Iter:  25965, Train Loss:0.4032, Train Acc:85.16%, Val Loss:0.694, Val Acc:83.55%, Val Recall:84.07% Val F1:84.07%, Time:3:04:46 *\n","Iter:  26015, Train Loss:0.4074, Train Acc:88.28%, Val Loss:0.7385, Val Acc:82.80%, Val Recall:83.27% Val F1:83.40%, Time:3:05:03 \n","Iter:  26065, Train Loss:0.4358, Train Acc:91.41%, Val Loss:0.7446, Val Acc:82.82%, Val Recall:83.20% Val F1:83.45%, Time:3:05:21 \n","Iter:  26115, Train Loss:0.4296, Train Acc:88.28%, Val Loss:0.7402, Val Acc:82.82%, Val Recall:83.27% Val F1:83.41%, Time:3:05:39 \n","Iter:  26165, Train Loss:0.4304, Train Acc:89.06%, Val Loss:0.6952, Val Acc:82.56%, Val Recall:82.87% Val F1:83.18%, Time:3:05:56 \n","Iter:  26215, Train Loss:0.4009, Train Acc:83.59%, Val Loss:0.716, Val Acc:83.36%, Val Recall:83.84% Val F1:83.91%, Time:3:06:14 \n","Iter:  26265, Train Loss:0.4262, Train Acc:85.94%, Val Loss:0.6981, Val Acc:83.90%, Val Recall:84.36% Val F1:84.55%, Time:3:06:31 \n","Epoch[43/100]\n","Iter:  26341, Train Loss:0.3745, Train Acc:83.59%, Val Loss:0.7258, Val Acc:83.03%, Val Recall:83.41% Val F1:83.73%, Time:3:06:53 \n","Iter:  26391, Train Loss:0.3711, Train Acc:93.75%, Val Loss: 0.71, Val Acc:82.75%, Val Recall:83.25% Val F1:83.32%, Time:3:07:11 \n","Iter:  26441, Train Loss:0.3862, Train Acc:87.50%, Val Loss:0.7476, Val Acc:82.69%, Val Recall:83.12% Val F1:83.26%, Time:3:07:28 \n","Iter:  26491, Train Loss:0.4074, Train Acc:85.16%, Val Loss:0.7437, Val Acc:82.38%, Val Recall:82.65% Val F1:82.92%, Time:3:07:46 \n","Iter:  26541, Train Loss:0.3959, Train Acc:90.62%, Val Loss:0.7276, Val Acc:82.63%, Val Recall:83.15% Val F1:83.23%, Time:3:08:03 \n","Iter:  26591, Train Loss:0.4012, Train Acc:90.62%, Val Loss:0.697, Val Acc:83.50%, Val Recall:84.01% Val F1:84.07%, Time:3:08:21 \n","Iter:  26641, Train Loss:0.4042, Train Acc:89.84%, Val Loss:0.6893, Val Acc:83.75%, Val Recall:84.09% Val F1:84.38%, Time:3:08:49 *\n","Iter:  26691, Train Loss:0.4026, Train Acc:87.50%, Val Loss:0.7183, Val Acc:83.15%, Val Recall:83.55% Val F1:83.83%, Time:3:09:06 \n","Iter:  26741, Train Loss:0.4047, Train Acc:86.72%, Val Loss:0.7682, Val Acc:83.44%, Val Recall:83.85% Val F1:84.07%, Time:3:09:24 \n","Iter:  26791, Train Loss:0.4331, Train Acc:87.50%, Val Loss:0.6873, Val Acc:83.06%, Val Recall:83.56% Val F1:83.77%, Time:3:09:51 *\n","Iter:  26841, Train Loss:0.4096, Train Acc:89.84%, Val Loss:0.6435, Val Acc:84.52%, Val Recall:84.96% Val F1:85.28%, Time:3:10:20 *\n","Iter:  26891, Train Loss:0.4239, Train Acc:87.50%, Val Loss:0.6974, Val Acc:83.84%, Val Recall:84.36% Val F1:84.45%, Time:3:10:38 \n","Epoch[44/100]\n","Iter:  26967, Train Loss:0.3694, Train Acc:91.41%, Val Loss:0.6975, Val Acc:83.73%, Val Recall:84.25% Val F1:84.43%, Time:3:11:00 \n","Iter:  27017, Train Loss:0.3566, Train Acc:94.53%, Val Loss:0.711, Val Acc:83.65%, Val Recall:84.14% Val F1:84.30%, Time:3:11:18 \n","Iter:  27067, Train Loss: 0.35, Train Acc:89.06%, Val Loss:0.6813, Val Acc:84.13%, Val Recall:84.72% Val F1:84.84%, Time:3:11:35 \n","Iter:  27117, Train Loss:0.3571, Train Acc:92.97%, Val Loss:0.7391, Val Acc:83.32%, Val Recall:83.83% Val F1:84.00%, Time:3:11:53 \n","Iter:  27167, Train Loss:0.3572, Train Acc:86.72%, Val Loss:0.6841, Val Acc:84.25%, Val Recall:84.65% Val F1:84.97%, Time:3:12:10 \n","Iter:  27217, Train Loss:0.366, Train Acc:90.62%, Val Loss:0.7399, Val Acc:82.81%, Val Recall:83.24% Val F1:83.59%, Time:3:12:28 \n","Iter:  27267, Train Loss:0.405, Train Acc:85.16%, Val Loss:0.6889, Val Acc:83.40%, Val Recall:83.95% Val F1:84.12%, Time:3:12:45 \n","Iter:  27317, Train Loss:0.412, Train Acc:85.94%, Val Loss:0.6763, Val Acc:84.07%, Val Recall:84.66% Val F1:84.72%, Time:3:13:03 \n","Iter:  27367, Train Loss:0.4073, Train Acc:88.28%, Val Loss:0.6675, Val Acc:84.40%, Val Recall:84.86% Val F1:85.17%, Time:3:13:20 \n","Iter:  27417, Train Loss:0.3863, Train Acc:85.16%, Val Loss:0.642, Val Acc:84.68%, Val Recall:85.20% Val F1:85.36%, Time:3:13:49 *\n","Iter:  27467, Train Loss:0.3817, Train Acc:88.28%, Val Loss:0.6808, Val Acc:84.39%, Val Recall:84.83% Val F1:85.09%, Time:3:14:07 \n","Iter:  27517, Train Loss:0.3927, Train Acc:92.19%, Val Loss:0.7364, Val Acc:83.92%, Val Recall:84.31% Val F1:84.53%, Time:3:14:24 \n","Epoch[45/100]\n","Iter:  27593, Train Loss:0.3502, Train Acc:89.84%, Val Loss:0.7114, Val Acc:84.64%, Val Recall:85.17% Val F1:85.25%, Time:3:14:47 \n","Iter:  27643, Train Loss:0.3469, Train Acc:91.41%, Val Loss:0.7345, Val Acc:83.60%, Val Recall:84.21% Val F1:84.44%, Time:3:15:04 \n","Iter:  27693, Train Loss:0.3841, Train Acc:89.06%, Val Loss:0.663, Val Acc:84.36%, Val Recall:84.87% Val F1:84.96%, Time:3:15:22 \n","Iter:  27743, Train Loss:0.3639, Train Acc:87.50%, Val Loss:0.6927, Val Acc:83.71%, Val Recall:84.18% Val F1:84.28%, Time:3:15:39 \n","Iter:  27793, Train Loss:0.3988, Train Acc:87.50%, Val Loss:0.6895, Val Acc:83.99%, Val Recall:84.60% Val F1:84.57%, Time:3:15:57 \n","Iter:  27843, Train Loss:0.3604, Train Acc:89.06%, Val Loss:0.6576, Val Acc:84.44%, Val Recall:85.01% Val F1:84.99%, Time:3:16:14 \n","Iter:  27893, Train Loss:0.397, Train Acc:89.84%, Val Loss:0.6771, Val Acc:84.52%, Val Recall:84.95% Val F1:85.08%, Time:3:16:31 \n","Iter:  27943, Train Loss:0.3796, Train Acc:89.06%, Val Loss:0.6539, Val Acc:84.18%, Val Recall:84.57% Val F1:84.74%, Time:3:16:49 \n","Iter:  27993, Train Loss:0.3986, Train Acc:87.50%, Val Loss:0.6579, Val Acc:84.17%, Val Recall:84.59% Val F1:84.71%, Time:3:17:06 \n","Iter:  28043, Train Loss:0.4035, Train Acc:85.94%, Val Loss:0.6554, Val Acc:83.95%, Val Recall:84.32% Val F1:84.55%, Time:3:17:24 \n","Iter:  28093, Train Loss:0.3824, Train Acc:87.50%, Val Loss:0.6901, Val Acc:84.21%, Val Recall:84.65% Val F1:84.81%, Time:3:17:41 \n","Iter:  28143, Train Loss:0.363, Train Acc:82.81%, Val Loss:0.6592, Val Acc:84.32%, Val Recall:84.65% Val F1:84.96%, Time:3:17:59 \n","Epoch[46/100]\n","Iter:  28219, Train Loss:0.3213, Train Acc:88.28%, Val Loss:0.6561, Val Acc:84.50%, Val Recall:85.08% Val F1:85.17%, Time:3:18:21 \n","Iter:  28269, Train Loss:0.3367, Train Acc:89.84%, Val Loss:0.6492, Val Acc:84.50%, Val Recall:84.97% Val F1:85.31%, Time:3:18:38 \n","Iter:  28319, Train Loss:0.3667, Train Acc:92.19%, Val Loss:0.653, Val Acc:84.11%, Val Recall:84.56% Val F1:84.73%, Time:3:18:56 \n","Iter:  28369, Train Loss:0.3711, Train Acc:82.03%, Val Loss:0.697, Val Acc:84.13%, Val Recall:84.62% Val F1:84.86%, Time:3:19:13 \n","Iter:  28419, Train Loss:0.3711, Train Acc:88.28%, Val Loss:0.7222, Val Acc:84.41%, Val Recall:84.79% Val F1:85.14%, Time:3:19:31 \n","Iter:  28469, Train Loss:0.3748, Train Acc:82.81%, Val Loss:0.6855, Val Acc:84.57%, Val Recall:85.16% Val F1:85.32%, Time:3:19:48 \n","Iter:  28519, Train Loss:0.3768, Train Acc:87.50%, Val Loss:0.648, Val Acc:84.18%, Val Recall:84.63% Val F1:85.03%, Time:3:20:06 \n","Iter:  28569, Train Loss:0.3765, Train Acc:89.06%, Val Loss:0.653, Val Acc:84.46%, Val Recall:85.09% Val F1:85.20%, Time:3:20:23 \n","Iter:  28619, Train Loss:0.3707, Train Acc:91.41%, Val Loss:0.6478, Val Acc:84.56%, Val Recall:85.01% Val F1:85.25%, Time:3:20:40 \n","Iter:  28669, Train Loss:0.3753, Train Acc:91.41%, Val Loss:0.675, Val Acc:85.69%, Val Recall:86.19% Val F1:86.38%, Time:3:20:58 \n","Iter:  28719, Train Loss:0.3996, Train Acc:85.94%, Val Loss:0.6772, Val Acc:84.54%, Val Recall:85.08% Val F1:85.25%, Time:3:21:15 \n","Iter:  28769, Train Loss:0.4102, Train Acc:92.19%, Val Loss:0.6847, Val Acc:83.80%, Val Recall:84.33% Val F1:84.46%, Time:3:21:33 \n","Epoch[47/100]\n","Iter:  28845, Train Loss:0.3115, Train Acc:89.06%, Val Loss:0.6302, Val Acc:84.98%, Val Recall:85.59% Val F1:85.59%, Time:3:22:05 *\n","Iter:  28895, Train Loss:0.3279, Train Acc:89.06%, Val Loss:0.6186, Val Acc:86.15%, Val Recall:86.71% Val F1:86.87%, Time:3:22:35 *\n","Iter:  28945, Train Loss:0.3175, Train Acc:86.72%, Val Loss:0.6573, Val Acc:85.51%, Val Recall:86.04% Val F1:86.22%, Time:3:22:52 \n","Iter:  28995, Train Loss:0.3309, Train Acc:91.41%, Val Loss:0.629, Val Acc:86.07%, Val Recall:86.56% Val F1:86.76%, Time:3:23:10 \n","Iter:  29045, Train Loss:0.351, Train Acc:87.50%, Val Loss:0.614, Val Acc:85.19%, Val Recall:85.71% Val F1:85.88%, Time:3:23:37 *\n","Iter:  29095, Train Loss:0.3414, Train Acc:97.66%, Val Loss:0.6656, Val Acc:85.01%, Val Recall:85.48% Val F1:85.74%, Time:3:23:55 \n","Iter:  29145, Train Loss:0.3695, Train Acc:88.28%, Val Loss:0.6566, Val Acc:84.69%, Val Recall:85.13% Val F1:85.39%, Time:3:24:13 \n","Iter:  29195, Train Loss:0.3694, Train Acc:89.84%, Val Loss:0.6824, Val Acc:84.74%, Val Recall:85.24% Val F1:85.41%, Time:3:24:30 \n","Iter:  29245, Train Loss:0.3714, Train Acc:89.84%, Val Loss:0.623, Val Acc:85.46%, Val Recall:85.96% Val F1:86.09%, Time:3:24:48 \n","Iter:  29295, Train Loss:0.3641, Train Acc:94.53%, Val Loss:0.6388, Val Acc:85.15%, Val Recall:85.71% Val F1:85.89%, Time:3:25:05 \n","Iter:  29345, Train Loss:0.3926, Train Acc:89.84%, Val Loss:0.651, Val Acc:84.62%, Val Recall:85.16% Val F1:85.38%, Time:3:25:22 \n","Iter:  29395, Train Loss:0.3934, Train Acc:86.72%, Val Loss:0.5975, Val Acc:85.47%, Val Recall:85.99% Val F1:86.13%, Time:3:25:49 *\n","Epoch[48/100]\n","Iter:  29471, Train Loss:0.3361, Train Acc:87.50%, Val Loss:0.5941, Val Acc:85.53%, Val Recall:86.07% Val F1:86.16%, Time:3:26:23 *\n","Iter:  29521, Train Loss:0.3536, Train Acc:90.62%, Val Loss:0.6031, Val Acc:85.92%, Val Recall:86.43% Val F1:86.66%, Time:3:26:41 \n","Iter:  29571, Train Loss:0.2982, Train Acc:89.84%, Val Loss:0.6094, Val Acc:85.84%, Val Recall:86.31% Val F1:86.61%, Time:3:26:59 \n","Iter:  29621, Train Loss:0.3393, Train Acc:91.41%, Val Loss:0.6555, Val Acc:85.20%, Val Recall:85.81% Val F1:85.92%, Time:3:27:16 \n","Iter:  29671, Train Loss:0.3545, Train Acc:85.94%, Val Loss:0.6436, Val Acc:84.81%, Val Recall:85.23% Val F1:85.56%, Time:3:27:34 \n","Iter:  29721, Train Loss:0.3587, Train Acc:90.62%, Val Loss:0.6269, Val Acc:85.32%, Val Recall:85.81% Val F1:86.05%, Time:3:27:51 \n","Iter:  29771, Train Loss:0.355, Train Acc:82.03%, Val Loss:0.5874, Val Acc:85.74%, Val Recall:86.29% Val F1:86.46%, Time:3:28:19 *\n","Iter:  29821, Train Loss:0.3255, Train Acc:95.31%, Val Loss:0.6244, Val Acc:85.34%, Val Recall:85.84% Val F1:86.04%, Time:3:28:36 \n","Iter:  29871, Train Loss:0.3625, Train Acc:87.50%, Val Loss:0.6552, Val Acc:85.00%, Val Recall:85.56% Val F1:85.66%, Time:3:28:54 \n","Iter:  29921, Train Loss:0.398, Train Acc:89.06%, Val Loss:0.6481, Val Acc:84.85%, Val Recall:85.37% Val F1:85.50%, Time:3:29:12 \n","Iter:  29971, Train Loss:0.3643, Train Acc:88.28%, Val Loss:0.6242, Val Acc:85.38%, Val Recall:85.91% Val F1:86.23%, Time:3:29:29 \n","Iter:  30021, Train Loss:0.3772, Train Acc:88.28%, Val Loss:0.6089, Val Acc:86.22%, Val Recall:86.87% Val F1:87.00%, Time:3:29:46 \n","Epoch[49/100]\n","Iter:  30097, Train Loss:0.3111, Train Acc:88.28%, Val Loss:0.6087, Val Acc:85.34%, Val Recall:85.87% Val F1:86.05%, Time:3:30:09 \n","Iter:  30147, Train Loss:0.3258, Train Acc:86.72%, Val Loss:0.6151, Val Acc:85.71%, Val Recall:86.45% Val F1:86.42%, Time:3:30:26 \n","Iter:  30197, Train Loss:0.2964, Train Acc:83.59%, Val Loss:0.6185, Val Acc:85.56%, Val Recall:86.22% Val F1:86.29%, Time:3:30:44 \n","Iter:  30247, Train Loss:0.3377, Train Acc:91.41%, Val Loss:0.6442, Val Acc:85.33%, Val Recall:85.83% Val F1:86.05%, Time:3:31:01 \n","Iter:  30297, Train Loss:0.3358, Train Acc:84.38%, Val Loss:0.6481, Val Acc:85.52%, Val Recall:86.04% Val F1:86.27%, Time:3:31:18 \n","Iter:  30347, Train Loss:0.3289, Train Acc:93.75%, Val Loss:0.6523, Val Acc:85.49%, Val Recall:85.81% Val F1:86.15%, Time:3:31:36 \n","Iter:  30397, Train Loss:0.3668, Train Acc:92.19%, Val Loss:0.6427, Val Acc:86.20%, Val Recall:86.82% Val F1:86.88%, Time:3:31:54 \n","Iter:  30447, Train Loss:0.3551, Train Acc:93.75%, Val Loss:0.6234, Val Acc:86.19%, Val Recall:86.80% Val F1:86.92%, Time:3:32:11 \n","Iter:  30497, Train Loss:0.3288, Train Acc:96.09%, Val Loss:0.5861, Val Acc:86.22%, Val Recall:86.77% Val F1:86.97%, Time:3:32:39 *\n","Iter:  30547, Train Loss:0.3408, Train Acc:92.19%, Val Loss: 0.59, Val Acc:86.43%, Val Recall:87.05% Val F1:87.14%, Time:3:32:57 \n","Iter:  30597, Train Loss:0.3372, Train Acc:90.62%, Val Loss:0.607, Val Acc:85.74%, Val Recall:86.28% Val F1:86.48%, Time:3:33:14 \n","Iter:  30647, Train Loss:0.3637, Train Acc:86.72%, Val Loss:0.6706, Val Acc:84.66%, Val Recall:85.12% Val F1:85.34%, Time:3:33:32 \n","Epoch[50/100]\n","Iter:  30723, Train Loss:0.3122, Train Acc:91.41%, Val Loss:0.6239, Val Acc:85.67%, Val Recall:86.15% Val F1:86.46%, Time:3:33:54 \n","Iter:  30773, Train Loss:0.2967, Train Acc:91.41%, Val Loss:0.6032, Val Acc:86.51%, Val Recall:87.05% Val F1:87.36%, Time:3:34:12 \n","Iter:  30823, Train Loss:0.3291, Train Acc:89.06%, Val Loss:0.5977, Val Acc:86.55%, Val Recall:87.06% Val F1:87.30%, Time:3:34:29 \n","Iter:  30873, Train Loss:0.3143, Train Acc:89.06%, Val Loss:0.613, Val Acc:86.32%, Val Recall:86.81% Val F1:87.14%, Time:3:34:47 \n","Iter:  30923, Train Loss:0.3247, Train Acc:91.41%, Val Loss:0.6408, Val Acc:86.00%, Val Recall:86.71% Val F1:86.80%, Time:3:35:04 \n","Iter:  30973, Train Loss:0.3453, Train Acc:90.62%, Val Loss:0.5801, Val Acc:86.75%, Val Recall:87.27% Val F1:87.53%, Time:3:35:32 *\n","Iter:  31023, Train Loss:0.335, Train Acc:91.41%, Val Loss:0.634, Val Acc:85.89%, Val Recall:86.46% Val F1:86.63%, Time:3:35:50 \n","Iter:  31073, Train Loss:0.3591, Train Acc:92.19%, Val Loss:0.5882, Val Acc:85.73%, Val Recall:86.24% Val F1:86.52%, Time:3:36:07 \n","Iter:  31123, Train Loss:0.3373, Train Acc:89.84%, Val Loss:0.5993, Val Acc:85.91%, Val Recall:86.20% Val F1:86.65%, Time:3:36:25 \n","Iter:  31173, Train Loss:0.3271, Train Acc:97.66%, Val Loss:0.6048, Val Acc:86.09%, Val Recall:86.43% Val F1:86.83%, Time:3:36:42 \n","Iter:  31223, Train Loss:0.343, Train Acc:89.06%, Val Loss:0.5732, Val Acc:86.62%, Val Recall:87.16% Val F1:87.32%, Time:3:37:10 *\n","Iter:  31273, Train Loss:0.3177, Train Acc:92.97%, Val Loss:0.6033, Val Acc:86.53%, Val Recall:87.21% Val F1:87.25%, Time:3:37:27 \n","Epoch[51/100]\n","Iter:  31349, Train Loss:0.2882, Train Acc:90.62%, Val Loss:0.5828, Val Acc:86.65%, Val Recall:87.11% Val F1:87.37%, Time:3:37:50 \n","Iter:  31399, Train Loss:0.283, Train Acc:92.97%, Val Loss:0.5918, Val Acc:86.85%, Val Recall:87.49% Val F1:87.57%, Time:3:38:07 \n","Iter:  31449, Train Loss:0.2961, Train Acc:89.84%, Val Loss:0.5847, Val Acc:86.73%, Val Recall:87.18% Val F1:87.49%, Time:3:38:25 \n","Iter:  31499, Train Loss:0.295, Train Acc:88.28%, Val Loss:0.6441, Val Acc:85.89%, Val Recall:86.33% Val F1:86.58%, Time:3:38:42 \n","Iter:  31549, Train Loss:0.3358, Train Acc:88.28%, Val Loss:0.5943, Val Acc:86.38%, Val Recall:86.95% Val F1:87.04%, Time:3:39:00 \n","Iter:  31599, Train Loss:0.3308, Train Acc:91.41%, Val Loss:0.5996, Val Acc:86.25%, Val Recall:86.65% Val F1:86.90%, Time:3:39:17 \n","Iter:  31649, Train Loss:0.3369, Train Acc:92.97%, Val Loss:0.6326, Val Acc:85.59%, Val Recall:86.18% Val F1:86.19%, Time:3:39:35 \n","Iter:  31699, Train Loss:0.3586, Train Acc:84.38%, Val Loss:0.5961, Val Acc:85.96%, Val Recall:86.53% Val F1:86.65%, Time:3:39:52 \n","Iter:  31749, Train Loss:0.3334, Train Acc:85.16%, Val Loss:0.5925, Val Acc:86.27%, Val Recall:86.78% Val F1:86.92%, Time:3:40:09 \n","Iter:  31799, Train Loss:0.3143, Train Acc:92.97%, Val Loss:0.5827, Val Acc:86.79%, Val Recall:87.36% Val F1:87.45%, Time:3:40:27 \n","Iter:  31849, Train Loss:0.356, Train Acc:88.28%, Val Loss:0.5667, Val Acc:87.01%, Val Recall:87.48% Val F1:87.74%, Time:3:40:55 *\n","Iter:  31899, Train Loss:0.3384, Train Acc:92.19%, Val Loss:0.6085, Val Acc:86.75%, Val Recall:87.34% Val F1:87.42%, Time:3:41:13 \n","Epoch[52/100]\n","Iter:  31975, Train Loss:0.2746, Train Acc:89.84%, Val Loss:0.6405, Val Acc:85.82%, Val Recall:86.35% Val F1:86.52%, Time:3:41:36 \n","Iter:  32025, Train Loss:0.2771, Train Acc:90.62%, Val Loss:0.594, Val Acc:86.97%, Val Recall:87.55% Val F1:87.61%, Time:3:41:53 \n","Iter:  32075, Train Loss:0.279, Train Acc:94.53%, Val Loss:0.5634, Val Acc:86.89%, Val Recall:87.43% Val F1:87.60%, Time:3:42:21 *\n","Iter:  32125, Train Loss:0.3082, Train Acc:92.19%, Val Loss:0.5314, Val Acc:87.35%, Val Recall:87.84% Val F1:88.06%, Time:3:42:51 *\n","Iter:  32175, Train Loss:0.336, Train Acc:92.97%, Val Loss:0.5543, Val Acc:86.50%, Val Recall:87.07% Val F1:87.21%, Time:3:43:09 \n","Iter:  32225, Train Loss:0.295, Train Acc:92.97%, Val Loss:0.5716, Val Acc:86.99%, Val Recall:87.59% Val F1:87.65%, Time:3:43:26 \n","Iter:  32275, Train Loss:0.3062, Train Acc:88.28%, Val Loss:0.5703, Val Acc:86.51%, Val Recall:87.08% Val F1:87.15%, Time:3:43:44 \n","Iter:  32325, Train Loss:0.3145, Train Acc:88.28%, Val Loss:0.5639, Val Acc:86.72%, Val Recall:87.22% Val F1:87.48%, Time:3:44:01 \n","Iter:  32375, Train Loss:0.3346, Train Acc:91.41%, Val Loss:0.5684, Val Acc:86.51%, Val Recall:86.95% Val F1:87.20%, Time:3:44:19 \n","Iter:  32425, Train Loss:0.3383, Train Acc:90.62%, Val Loss:0.5822, Val Acc:86.33%, Val Recall:86.82% Val F1:87.09%, Time:3:44:36 \n","Iter:  32475, Train Loss:0.3224, Train Acc:89.84%, Val Loss:0.6024, Val Acc:86.49%, Val Recall:86.99% Val F1:87.15%, Time:3:44:54 \n","Iter:  32525, Train Loss:0.3127, Train Acc:89.84%, Val Loss:0.5446, Val Acc:87.36%, Val Recall:87.85% Val F1:88.14%, Time:3:45:11 \n","Epoch[53/100]\n","Iter:  32601, Train Loss:0.2736, Train Acc:92.19%, Val Loss:0.5513, Val Acc:87.83%, Val Recall:88.49% Val F1:88.62%, Time:3:45:33 \n","Iter:  32651, Train Loss:0.2836, Train Acc:94.53%, Val Loss:0.5283, Val Acc:87.43%, Val Recall:88.14% Val F1:88.21%, Time:3:46:01 *\n","Iter:  32701, Train Loss:0.3493, Train Acc:93.75%, Val Loss:0.6015, Val Acc:86.79%, Val Recall:87.27% Val F1:87.58%, Time:3:46:19 \n","Iter:  32751, Train Loss:0.3226, Train Acc:85.16%, Val Loss:0.5703, Val Acc:86.60%, Val Recall:87.14% Val F1:87.30%, Time:3:46:37 \n","Iter:  32801, Train Loss:0.2976, Train Acc:94.53%, Val Loss:0.5592, Val Acc:87.32%, Val Recall:87.90% Val F1:87.99%, Time:3:46:54 \n","Iter:  32851, Train Loss:0.2871, Train Acc:91.41%, Val Loss:0.6385, Val Acc:86.66%, Val Recall:87.14% Val F1:87.35%, Time:3:47:12 \n","Iter:  32901, Train Loss:0.3188, Train Acc:91.41%, Val Loss:0.5708, Val Acc:87.06%, Val Recall:87.46% Val F1:87.77%, Time:3:47:29 \n","Iter:  32951, Train Loss:0.3104, Train Acc:88.28%, Val Loss:0.5666, Val Acc:86.54%, Val Recall:87.07% Val F1:87.22%, Time:3:47:47 \n","Iter:  33001, Train Loss:0.3053, Train Acc:85.94%, Val Loss:0.5551, Val Acc:87.37%, Val Recall:88.05% Val F1:88.09%, Time:3:48:04 \n","Iter:  33051, Train Loss:0.3223, Train Acc:89.06%, Val Loss:0.5481, Val Acc:87.43%, Val Recall:88.10% Val F1:88.20%, Time:3:48:22 \n","Iter:  33101, Train Loss:0.3218, Train Acc:90.62%, Val Loss:0.5581, Val Acc:87.95%, Val Recall:88.58% Val F1:88.75%, Time:3:48:39 \n","Iter:  33151, Train Loss:0.3293, Train Acc:92.19%, Val Loss:0.5857, Val Acc:86.91%, Val Recall:87.47% Val F1:87.67%, Time:3:48:56 \n","Epoch[54/100]\n","Iter:  33227, Train Loss:0.2699, Train Acc:92.19%, Val Loss:0.5307, Val Acc:87.89%, Val Recall:88.49% Val F1:88.62%, Time:3:49:19 \n","Iter:  33277, Train Loss:0.2675, Train Acc:91.41%, Val Loss:0.5191, Val Acc:88.18%, Val Recall:88.75% Val F1:88.99%, Time:3:49:46 *\n","Iter:  33327, Train Loss:0.2545, Train Acc:93.75%, Val Loss:0.5692, Val Acc:87.11%, Val Recall:87.73% Val F1:87.89%, Time:3:50:04 \n","Iter:  33377, Train Loss:0.297, Train Acc:87.50%, Val Loss:0.597, Val Acc:87.22%, Val Recall:87.74% Val F1:88.06%, Time:3:50:22 \n","Iter:  33427, Train Loss:0.2996, Train Acc:85.16%, Val Loss:0.5616, Val Acc:87.17%, Val Recall:87.74% Val F1:88.00%, Time:3:50:39 \n","Iter:  33477, Train Loss:0.2808, Train Acc:88.28%, Val Loss:0.5512, Val Acc:87.59%, Val Recall:88.19% Val F1:88.26%, Time:3:50:57 \n","Iter:  33527, Train Loss:0.2956, Train Acc:92.97%, Val Loss:0.5557, Val Acc:87.64%, Val Recall:88.19% Val F1:88.36%, Time:3:51:14 \n","Iter:  33577, Train Loss:0.2929, Train Acc:91.41%, Val Loss:0.5597, Val Acc:87.72%, Val Recall:88.34% Val F1:88.46%, Time:3:51:32 \n","Iter:  33627, Train Loss:0.302, Train Acc:92.97%, Val Loss:0.5602, Val Acc:87.61%, Val Recall:88.18% Val F1:88.31%, Time:3:51:49 \n","Iter:  33677, Train Loss:0.2983, Train Acc:95.31%, Val Loss:0.5796, Val Acc:86.64%, Val Recall:87.08% Val F1:87.40%, Time:3:52:06 \n","Iter:  33727, Train Loss:0.3253, Train Acc:89.84%, Val Loss:0.5745, Val Acc:86.73%, Val Recall:87.39% Val F1:87.49%, Time:3:52:24 \n","Iter:  33777, Train Loss:0.3098, Train Acc:92.97%, Val Loss:0.5239, Val Acc:87.54%, Val Recall:88.24% Val F1:88.27%, Time:3:52:41 \n","Epoch[55/100]\n","Iter:  33853, Train Loss:0.2608, Train Acc:92.97%, Val Loss:0.5756, Val Acc:87.33%, Val Recall:87.96% Val F1:88.04%, Time:3:53:04 \n","Iter:  33903, Train Loss:0.2973, Train Acc:92.19%, Val Loss:0.5616, Val Acc:86.64%, Val Recall:87.19% Val F1:87.43%, Time:3:53:21 \n","Iter:  33953, Train Loss:0.2818, Train Acc:92.19%, Val Loss:0.5329, Val Acc:87.73%, Val Recall:88.41% Val F1:88.49%, Time:3:53:38 \n","Iter:  34003, Train Loss:0.3098, Train Acc:92.19%, Val Loss:0.5837, Val Acc:86.89%, Val Recall:87.46% Val F1:87.69%, Time:3:53:56 \n","Iter:  34053, Train Loss:0.3035, Train Acc:94.53%, Val Loss:0.5572, Val Acc:87.34%, Val Recall:87.87% Val F1:88.06%, Time:3:54:13 \n","Iter:  34103, Train Loss:0.3175, Train Acc:92.97%, Val Loss:0.5503, Val Acc:87.05%, Val Recall:87.64% Val F1:87.73%, Time:3:54:31 \n","Iter:  34153, Train Loss:0.2833, Train Acc:86.72%, Val Loss:0.5694, Val Acc:87.36%, Val Recall:87.91% Val F1:88.11%, Time:3:54:48 \n","Iter:  34203, Train Loss:0.3915, Train Acc:90.62%, Val Loss:0.5838, Val Acc:86.34%, Val Recall:86.84% Val F1:86.99%, Time:3:55:05 \n","Iter:  34253, Train Loss:0.3479, Train Acc:92.19%, Val Loss:0.5728, Val Acc:86.57%, Val Recall:87.16% Val F1:87.20%, Time:3:55:23 \n","Iter:  34303, Train Loss:0.3476, Train Acc:85.94%, Val Loss:0.5677, Val Acc:87.26%, Val Recall:87.81% Val F1:87.99%, Time:3:55:40 \n","Iter:  34353, Train Loss:0.3489, Train Acc:89.84%, Val Loss:0.5499, Val Acc:87.42%, Val Recall:87.94% Val F1:88.14%, Time:3:55:58 \n","Iter:  34403, Train Loss:0.3227, Train Acc:92.19%, Val Loss:0.5253, Val Acc:87.32%, Val Recall:87.77% Val F1:88.04%, Time:3:56:15 \n","Epoch[56/100]\n","Iter:  34479, Train Loss:0.2721, Train Acc:90.62%, Val Loss:0.546, Val Acc:87.35%, Val Recall:87.82% Val F1:88.06%, Time:3:56:37 \n","Iter:  34529, Train Loss:0.2827, Train Acc:89.84%, Val Loss:0.5856, Val Acc:86.74%, Val Recall:87.21% Val F1:87.43%, Time:3:56:55 \n","Iter:  34579, Train Loss:0.2631, Train Acc:89.84%, Val Loss:0.5752, Val Acc:87.91%, Val Recall:88.37% Val F1:88.56%, Time:3:57:12 \n","Iter:  34629, Train Loss: 0.28, Train Acc:93.75%, Val Loss:0.6052, Val Acc:87.11%, Val Recall:87.54% Val F1:87.74%, Time:3:57:30 \n"],"name":"stdout"},{"output_type":"error","ename":"KeyboardInterrupt","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)","\u001b[0;32m<ipython-input-12-84625a1d4ba7>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mMajongai\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mtrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrain_dataloader\u001b[0m\u001b[0;34m,\u001b[0m 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\u001b[0;34m%\u001b[0m \u001b[0;36m50\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m49\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     34\u001b[0m         \u001b[0mtrue\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlabels\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcpu\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mKeyboardInterrupt\u001b[0m: "]}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":237},"id":"rm7IP4XEEFa2","executionInfo":{"status":"error","timestamp":1607339418732,"user_tz":-480,"elapsed":817,"user":{"displayName":"yang zeng","photoUrl":"","userId":"13995063463501692620"}},"outputId":"a6405fc4-e108-4ad0-b6bd-2205288a00ef"},"source":["import numpy as np\n","np.save('/content/drive/MyDrive/统计麻将/result/trainloss.npy',np.array(trainloss)) # 保存为.npy格式\n","np.save('/content/drive/MyDrive/统计麻将/result/trainacc.npy,np'.array(trainacc))\n","np.save('/content/drive/MyDrive/统计麻将/result/valloss.npy',np.array(valloss))\n","np.save('/content/drive/MyDrive/统计麻将/result/valacc.npy',np.array(valacc))\n","np.save('/content/drive/MyDrive/统计麻将/result/valrecall.npy',np.array(valrecall))\n","np.save('/content/drive/MyDrive/统计麻将/result/valf1.npy',np.array(valf1))"],"execution_count":null,"outputs":[{"output_type":"error","ename":"NameError","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)","\u001b[0;32m<ipython-input-17-5c259c0342f9>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mnumpy\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msave\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'/content/drive/MyDrive/统计麻将/result/trainloss.npy'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrainloss\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# 保存为.npy格式\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msave\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'/content/drive/MyDrive/统计麻将/result/trainacc.npy,np'\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrainacc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msave\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'/content/drive/MyDrive/统计麻将/result/valloss.npy'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalloss\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msave\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'/content/drive/MyDrive/统计麻将/result/valacc.npy'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalacc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mNameError\u001b[0m: name 'trainloss' is not defined"]}]},{"cell_type":"code","metadata":{"id":"ozqYTZvc5XLr"},"source":[""],"execution_count":null,"outputs":[]}]}