├── log ├── SFEW.png ├── CAER-S.png ├── FED-RO.png ├── RAF-DB.png ├── AffectNet7.png ├── AffectNet8.png ├── SFEW.txt └── RAF-DB.txt ├── checkpoint └── checkpoint.txt ├── LICENSE ├── README.md ├── model ├── attention.py └── manet.py └── main.py /log/SFEW.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/zengqunzhao/MA-Net/HEAD/log/SFEW.png -------------------------------------------------------------------------------- /log/CAER-S.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/zengqunzhao/MA-Net/HEAD/log/CAER-S.png -------------------------------------------------------------------------------- /log/FED-RO.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/zengqunzhao/MA-Net/HEAD/log/FED-RO.png -------------------------------------------------------------------------------- /log/RAF-DB.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/zengqunzhao/MA-Net/HEAD/log/RAF-DB.png -------------------------------------------------------------------------------- /log/AffectNet7.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/zengqunzhao/MA-Net/HEAD/log/AffectNet7.png -------------------------------------------------------------------------------- /log/AffectNet8.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/zengqunzhao/MA-Net/HEAD/log/AffectNet8.png -------------------------------------------------------------------------------- /checkpoint/checkpoint.txt: -------------------------------------------------------------------------------- 1 | downloading the pre-trained models from Google Drive and put it here. -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2021 Zengqun (Zeke) Zhao 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # MA-Net 2 | 3 | *Zengqun Zhao, Qingshan Liu, Shanmin Wang. "[Learning Deep Global Multi-scale and Local Attention Features 4 | for Facial Expression Recognition in the Wild](https://drive.google.com/file/d/1YvnRWzUJSO2xmI-eBFy-nc8KKOINbjxT/view?usp=sharing)". IEEE Transactions on Image Processing.* 5 | 6 | ## Requirements 7 | - Python >= 3.6 8 | - PyTorch >= 1.2 9 | - torchvision >= 0.4.0 10 | 11 | ## Training 12 | 13 | - Step 1: download basic emotions dataset of [RAF-DB](http://www.whdeng.cn/raf/model1.html), and make sure it have the structure like following: 14 | 15 | ``` 16 | ./RAF-DB/ 17 | train/ 18 | 0/ 19 | train_09748.jpg 20 | ... 21 | train_12271.jpg 22 | 1/ 23 | ... 24 | 6/ 25 | test/ 26 | 0/ 27 | ... 28 | 6/ 29 | 30 | [Note] 0: Neutral; 1: Happiness; 2: Sadness; 3: Surprise; 4: Fear; 5: Disgust; 6: Anger 31 | ``` 32 | 33 | - Step 2: download pre-trained model from 34 | [Google Drive](https://drive.google.com/file/d/1tro_RCovLKNACt4MKYp3dmIvvxiOC2pi/view?usp=sharing), 35 | and put it into ***./checkpoint***. 36 | 37 | - Step 3: change ***data_path*** in *main.py* to your path 38 | 39 | - Step 4: run ```python main.py ``` 40 | 41 | ## Citation 42 | 43 | ``` 44 | @article{zhao2021learning, 45 | title={Learning Deep Global Multi-scale and Local Attention Features for Facial Expression Recognition in the Wild}, 46 | author={Zhao, Zengqun and Liu, Qingshan and Wang, Shanmin}, 47 | journal={IEEE Transactions on Image Processing}, 48 | volume={30}, 49 | pages={6544-6556}, 50 | year={2021}, 51 | publisher={IEEE} 52 | } 53 | ``` 54 | 55 | ## Note 56 | The samples' number of CAER-S dataset employed in our work should be: all (69,982 samples), training set (48,995 samples), and test set (20,987 samples). We apologize for the typos in our paper. 57 | -------------------------------------------------------------------------------- /model/attention.py: -------------------------------------------------------------------------------- 1 | import torch 2 | import torch.nn as nn 3 | import torch.nn.functional as F 4 | 5 | 6 | class BasicConv(nn.Module): 7 | def __init__(self, in_planes, out_planes, kernel_size, stride=1, padding=0, dilation=1, groups=1, relu=True, bn=True, bias=False): 8 | super(BasicConv, self).__init__() 9 | self.out_channels = out_planes 10 | self.conv = nn.Conv2d(in_planes, out_planes, kernel_size=kernel_size, stride=stride, padding=padding, dilation=dilation, groups=groups, bias=bias) 11 | self.bn = nn.BatchNorm2d(out_planes, eps=1e-5, momentum=0.01, affine=True) if bn else None 12 | self.relu = nn.ReLU() if relu else None 13 | 14 | def forward(self, x): 15 | x = self.conv(x) 16 | if self.bn is not None: 17 | x = self.bn(x) 18 | if self.relu is not None: 19 | x = self.relu(x) 20 | return x 21 | 22 | 23 | class Flatten(nn.Module): 24 | def forward(self, x): 25 | return x.view(x.size(0), -1) 26 | 27 | 28 | class ChannelGate(nn.Module): 29 | def __init__(self, gate_channels, reduction_ratio=16, pool_types=['avg', 'max']): 30 | super(ChannelGate, self).__init__() 31 | self.gate_channels = gate_channels 32 | self.mlp = nn.Sequential(Flatten(), 33 | nn.Linear(gate_channels, gate_channels // reduction_ratio), 34 | nn.ReLU(), 35 | nn.Linear(gate_channels // reduction_ratio, gate_channels)) 36 | self.pool_types = pool_types 37 | 38 | def forward(self, x): 39 | channel_att_sum = None 40 | for pool_type in self.pool_types: 41 | if pool_type == 'avg': 42 | avg_pool = F.avg_pool2d(x, (x.size(2), x.size(3)), stride=(x.size(2), x.size(3))) 43 | channel_att_raw = self.mlp(avg_pool ) 44 | elif pool_type == 'max': 45 | max_pool = F.max_pool2d(x, (x.size(2), x.size(3)), stride=(x.size(2), x.size(3))) 46 | channel_att_raw = self.mlp(max_pool) 47 | if channel_att_sum is None: 48 | channel_att_sum = channel_att_raw 49 | else: 50 | channel_att_sum = channel_att_sum + channel_att_raw 51 | 52 | scale = torch.sigmoid(channel_att_sum).unsqueeze(2).unsqueeze(3).expand_as(x) 53 | return x * scale 54 | 55 | 56 | class ChannelPool(nn.Module): 57 | def forward(self, x): 58 | return torch.cat((torch.max(x, 1)[0].unsqueeze(1), torch.mean(x, 1).unsqueeze(1)), dim=1) 59 | 60 | 61 | class SpatialGate(nn.Module): 62 | def __init__(self): 63 | super(SpatialGate, self).__init__() 64 | kernel_size = 7 65 | self.compress = ChannelPool() 66 | self.spatial = BasicConv(2, 1, kernel_size, stride=1, padding=(kernel_size-1) // 2, relu=False) 67 | 68 | def forward(self, x): 69 | x_compress = self.compress(x) 70 | x_out = self.spatial(x_compress) 71 | scale = torch.sigmoid(x_out) 72 | return x * scale 73 | 74 | 75 | class CBAM(nn.Module): 76 | def __init__(self, gate_channels, reduction_ratio=16, pool_types=['avg', 'max']): 77 | super(CBAM, self).__init__() 78 | self.ChannelGate = ChannelGate(gate_channels, reduction_ratio, pool_types) 79 | self.SpatialGate = SpatialGate() 80 | 81 | def forward(self, x): 82 | x_out = self.ChannelGate(x) 83 | x_out = self.SpatialGate(x_out) 84 | 85 | return x_out 86 | -------------------------------------------------------------------------------- /model/manet.py: -------------------------------------------------------------------------------- 1 | from .attention import * 2 | 3 | 4 | def conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1): 5 | """3x3 convolution with padding""" 6 | return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, 7 | padding=dilation, groups=groups, bias=False, dilation=dilation) 8 | 9 | 10 | def conv1x1(in_planes, out_planes, stride=1): 11 | """1x1 convolution""" 12 | return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False) 13 | 14 | 15 | class BasicBlock(nn.Module): 16 | __constants__ = ['downsample'] 17 | 18 | def __init__(self, inplanes, planes, stride=1, downsample=None): 19 | super(BasicBlock, self).__init__() 20 | norm_layer = nn.BatchNorm2d 21 | self.conv1 = conv3x3(inplanes, planes, stride) 22 | self.bn1 = norm_layer(planes) 23 | self.relu = nn.ReLU(inplace=True) 24 | self.conv2 = conv3x3(planes, planes) 25 | self.bn2 = norm_layer(planes) 26 | self.downsample = downsample 27 | self.stride = stride 28 | 29 | def forward(self, x): 30 | identity = x 31 | 32 | out = self.conv1(x) 33 | out = self.bn1(out) 34 | out = self.relu(out) 35 | out = self.conv2(out) 36 | out = self.bn2(out) 37 | 38 | if self.downsample is not None: 39 | identity = self.downsample(x) 40 | 41 | out += identity 42 | out = self.relu(out) 43 | 44 | return out 45 | 46 | 47 | class MulScaleBlock(nn.Module): 48 | __constants__ = ['downsample'] 49 | 50 | def __init__(self, inplanes, planes, stride=1, downsample=None): 51 | super(MulScaleBlock, self).__init__() 52 | norm_layer = nn.BatchNorm2d 53 | scale_width = int(planes / 4) 54 | 55 | self.scale_width = scale_width 56 | 57 | self.conv1 = conv3x3(inplanes, planes, stride) 58 | self.bn1 = norm_layer(planes) 59 | self.relu = nn.ReLU(inplace=False) 60 | 61 | self.conv1_2_1 = conv3x3(scale_width, scale_width) 62 | self.bn1_2_1 = norm_layer(scale_width) 63 | self.conv1_2_2 = conv3x3(scale_width, scale_width) 64 | self.bn1_2_2 = norm_layer(scale_width) 65 | self.conv1_2_3 = conv3x3(scale_width, scale_width) 66 | self.bn1_2_3 = norm_layer(scale_width) 67 | self.conv1_2_4 = conv3x3(scale_width, scale_width) 68 | self.bn1_2_4 = norm_layer(scale_width) 69 | 70 | self.conv2_2_1 = conv3x3(scale_width, scale_width) 71 | self.bn2_2_1 = norm_layer(scale_width) 72 | self.conv2_2_2 = conv3x3(scale_width, scale_width) 73 | self.bn2_2_2 = norm_layer(scale_width) 74 | self.conv2_2_3 = conv3x3(scale_width, scale_width) 75 | self.bn2_2_3 = norm_layer(scale_width) 76 | self.conv2_2_4 = conv3x3(scale_width, scale_width) 77 | self.bn2_2_4 = norm_layer(scale_width) 78 | 79 | self.downsample = downsample 80 | self.stride = stride 81 | 82 | def forward(self, x): 83 | identity = x 84 | 85 | out = self.conv1(x) 86 | out = self.bn1(out) 87 | out = self.relu(out) 88 | 89 | sp_x = torch.split(out, self.scale_width, 1) 90 | 91 | out_1_1 = self.bn1_2_1(self.conv1_2_1(sp_x[0])) 92 | out_1_2 = self.bn1_2_2(self.conv1_2_2(self.relu(out_1_1) + sp_x[1])) 93 | out_1_3 = self.bn1_2_3(self.conv1_2_3(self.relu(out_1_2) + sp_x[2])) 94 | out_1_4 = self.bn1_2_4(self.conv1_2_4(self.relu(out_1_3) + sp_x[3])) 95 | out_1 = torch.cat([out_1_1, out_1_2, out_1_3, out_1_4], dim=1) 96 | 97 | out_2_4 = self.bn2_2_4(self.conv2_2_4(sp_x[3])) 98 | out_2_3 = self.bn2_2_3(self.conv2_2_3(self.relu(out_2_4) + sp_x[2])) 99 | out_2_2 = self.bn2_2_2(self.conv2_2_2(self.relu(out_2_3) + sp_x[1])) 100 | out_2_1 = self.bn2_2_1(self.conv2_2_1(self.relu(out_2_2) + sp_x[0])) 101 | out_2 = torch.cat([out_2_1, out_2_2, out_2_3, out_2_4], dim=1) 102 | 103 | out = out_1 + out_2 104 | 105 | if self.downsample is not None: 106 | identity = self.downsample(x) 107 | 108 | out += identity 109 | out = self.relu(out) 110 | 111 | return out 112 | 113 | 114 | class AttentionBlock(nn.Module): 115 | __constants__ = ['downsample'] 116 | 117 | def __init__(self, inplanes, planes, stride=1, downsample=None): 118 | super(AttentionBlock, self).__init__() 119 | norm_layer = nn.BatchNorm2d 120 | self.conv1 = conv3x3(inplanes, planes, stride) 121 | self.bn1 = norm_layer(planes) 122 | self.relu = nn.ReLU(inplace=True) 123 | self.conv2 = conv3x3(planes, planes) 124 | self.bn2 = norm_layer(planes) 125 | self.downsample = downsample 126 | self.stride = stride 127 | self.cbam = CBAM(planes, 16) 128 | 129 | def forward(self, x): 130 | identity = x 131 | 132 | out = self.conv1(x) 133 | out = self.bn1(out) 134 | out = self.relu(out) 135 | 136 | out = self.conv2(out) 137 | out = self.bn2(out) 138 | 139 | out = self.cbam(out) 140 | 141 | if self.downsample is not None: 142 | identity = self.downsample(x) 143 | 144 | out += identity 145 | out = self.relu(out) 146 | 147 | return out 148 | 149 | 150 | class MANet(nn.Module): 151 | 152 | def __init__(self, block_b, block_m, block_a, layers, num_classes=12666): 153 | super(MANet, self).__init__() 154 | norm_layer = nn.BatchNorm2d 155 | self._norm_layer = norm_layer 156 | self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False) 157 | self.bn1 = norm_layer(64) 158 | self.relu = nn.ReLU(inplace=True) 159 | self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) 160 | 161 | self.layer1 = self._make_layer(block_b, 64, 64, layers[0]) 162 | self.layer2 = self._make_layer(block_b, 64, 128, layers[1], stride=2) 163 | 164 | # In this branch, each BasicBlock replaced by AttentiveBlock. 165 | self.layer3_1_p1 = self._make_layer(block_a, 128, 256, layers[2], stride=2) 166 | self.layer4_1_p1 = self._make_layer(block_a, 256, 512, layers[3], stride=1) 167 | 168 | self.layer3_1_p2 = self._make_layer(block_a, 128, 256, layers[2], stride=2) 169 | self.layer4_1_p2 = self._make_layer(block_a, 256, 512, layers[3], stride=1) 170 | 171 | self.layer3_1_p3 = self._make_layer(block_a, 128, 256, layers[2], stride=2) 172 | self.layer4_1_p3 = self._make_layer(block_a, 256, 512, layers[3], stride=1) 173 | 174 | self.layer3_1_p4 = self._make_layer(block_a, 128, 256, layers[2], stride=2) 175 | self.layer4_1_p4 = self._make_layer(block_a, 256, 512, layers[3], stride=1) 176 | 177 | # In this branch, each BasicBlock replaced by MulScaleBlock. 178 | self.layer3_2 = self._make_layer(block_m, 128, 256, layers[2], stride=2) 179 | self.layer4_2 = self._make_layer(block_m, 256, 512, layers[3], stride=2) 180 | 181 | self.avgpool = nn.AdaptiveAvgPool2d((1, 1)) 182 | 183 | self.fc_1 = nn.Linear(512, num_classes) 184 | self.fc_2 = nn.Linear(512, num_classes) 185 | 186 | for m in self.modules(): 187 | if isinstance(m, nn.Conv2d): 188 | nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') 189 | elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)): 190 | nn.init.constant_(m.weight, 1) 191 | nn.init.constant_(m.bias, 0) 192 | 193 | def _make_layer(self, block, inplanes, planes, blocks, stride=1): 194 | norm_layer = self._norm_layer 195 | downsample = None 196 | if stride != 1 or inplanes != planes: 197 | downsample = nn.Sequential(conv1x1(inplanes, planes, stride), norm_layer(planes)) 198 | layers = [] 199 | layers.append(block(inplanes, planes, stride, downsample)) 200 | inplanes = planes 201 | for _ in range(1, blocks): 202 | layers.append(block(inplanes, planes)) 203 | return nn.Sequential(*layers) 204 | 205 | def _forward_impl(self, x): 206 | 207 | x = self.conv1(x) 208 | x = self.bn1(x) 209 | x = self.relu(x) 210 | x = self.maxpool(x) 211 | 212 | x = self.layer1(x) 213 | x = self.layer2(x) 214 | 215 | # branch 1 ############################################ 216 | patch_11 = x[:, :, 0:14, 0:14] 217 | patch_12 = x[:, :, 0:14, 14:28] 218 | patch_21 = x[:, :, 14:28, 0:14] 219 | patch_22 = x[:, :, 14:28, 14:28] 220 | 221 | branch_1_p1_out = self.layer3_1_p1(patch_11) 222 | branch_1_p1_out = self.layer4_1_p1(branch_1_p1_out) 223 | 224 | branch_1_p2_out = self.layer3_1_p2(patch_12) 225 | branch_1_p2_out = self.layer4_1_p2(branch_1_p2_out) 226 | 227 | branch_1_p3_out = self.layer3_1_p3(patch_21) 228 | branch_1_p3_out = self.layer4_1_p3(branch_1_p3_out) 229 | 230 | branch_1_p4_out = self.layer3_1_p4(patch_22) 231 | branch_1_p4_out = self.layer4_1_p4(branch_1_p4_out) 232 | 233 | branch_1_out_1 = torch.cat([branch_1_p1_out, branch_1_p2_out], dim=3) 234 | branch_1_out_2 = torch.cat([branch_1_p3_out, branch_1_p4_out], dim=3) 235 | branch_1_out = torch.cat([branch_1_out_1, branch_1_out_2], dim=2) 236 | 237 | branch_1_out = self.avgpool(branch_1_out) 238 | branch_1_out = torch.flatten(branch_1_out, 1) 239 | branch_1_out = self.fc_1(branch_1_out) 240 | 241 | # branch 2 ############################################ 242 | branch_2_out = self.layer3_2(x) 243 | branch_2_out = self.layer4_2(branch_2_out) 244 | branch_2_out = self.avgpool(branch_2_out) 245 | branch_2_out = torch.flatten(branch_2_out, 1) 246 | branch_2_out = self.fc_2(branch_2_out) 247 | 248 | return branch_1_out, branch_2_out 249 | 250 | def forward(self, x): 251 | return self._forward_impl(x) 252 | 253 | 254 | def manet(): 255 | return MANet(block_b=BasicBlock, block_m=MulScaleBlock, block_a=AttentionBlock, layers=[2, 2, 2, 2]) 256 | -------------------------------------------------------------------------------- /main.py: -------------------------------------------------------------------------------- 1 | import argparse 2 | import os 3 | import time 4 | import shutil 5 | import torch 6 | import torch.nn as nn 7 | import torch.nn.parallel 8 | import torch.backends.cudnn as cudnn 9 | import torch.optim 10 | import torch.utils.data 11 | import torch.utils.data.distributed 12 | import matplotlib.pyplot as plt 13 | import torchvision.datasets as datasets 14 | import torchvision.transforms as transforms 15 | import numpy as np 16 | import datetime 17 | from model.manet import manet 18 | 19 | now = datetime.datetime.now() 20 | time_str = now.strftime("[%m-%d]-[%H-%M]-") 21 | data_path = '/home/zhaozengqun/datasets_static/RAFDB_Face/' 22 | checkpoint_path = '' 23 | 24 | parser = argparse.ArgumentParser() 25 | parser.add_argument('--data', type=str, default=data_path) 26 | parser.add_argument('--checkpoint_path', type=str, default='./checkpoint/' + time_str + 'model.pth') 27 | parser.add_argument('--best_checkpoint_path', type=str, default='./checkpoint/'+time_str+'model_best.pth') 28 | parser.add_argument('-j', '--workers', default=4, type=int, metavar='N', help='number of data loading workers') 29 | parser.add_argument('--epochs', default=100, type=int, metavar='N', help='number of total epochs to run') 30 | parser.add_argument('--start-epoch', default=0, type=int, metavar='N', help='manual epoch number (useful on restarts)') 31 | parser.add_argument('-b', '--batch-size', default=128, type=int, metavar='N') 32 | parser.add_argument('--lr', '--learning-rate', default=0.01, type=float, metavar='LR', dest='lr') 33 | parser.add_argument('--momentum', default=0.9, type=float, metavar='M') 34 | parser.add_argument('--wd', '--weight-decay', default=1e-4, type=float, metavar='W', dest='weight_decay') 35 | parser.add_argument('-p', '--print-freq', default=10, type=int, metavar='N', help='print frequency') 36 | parser.add_argument('--resume', default=checkpoint_path, type=str, metavar='PATH', help='path to checkpoint') 37 | parser.add_argument('-e', '--evaluate', default=False, action='store_true', help='evaluate model on test set') 38 | parser.add_argument('--beta', type=float, default=0.6) 39 | parser.add_argument('--gpu', type=str, default='0') 40 | args = parser.parse_args() 41 | print('beta', args.beta) 42 | 43 | 44 | def main(): 45 | os.environ["CUDA_VISIBLE_DEVICES"] = args.gpu 46 | best_acc = 0 47 | print('Training time: ' + now.strftime("%m-%d %H:%M")) 48 | 49 | # create model 50 | model = manet() 51 | model = torch.nn.DataParallel(model).cuda() 52 | checkpoint = torch.load('./checkpoint/Pretrained_on_MSCeleb.pth.tar') 53 | pre_trained_dict = checkpoint['state_dict'] 54 | model.load_state_dict(pre_trained_dict) 55 | model.module.fc_1 = torch.nn.Linear(512, 7).cuda() 56 | model.module.fc_2 = torch.nn.Linear(512, 7).cuda() 57 | 58 | # define loss function (criterion) and optimizer 59 | criterion = nn.CrossEntropyLoss().cuda() 60 | optimizer = torch.optim.SGD(model.parameters(), args.lr, momentum=args.momentum, weight_decay=args.weight_decay) 61 | scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=15, gamma=0.1) 62 | recorder = RecorderMeter(args.epochs) 63 | 64 | # optionally resume from a checkpoint 65 | if args.resume: 66 | if os.path.isfile(args.resume): 67 | print("=> loading checkpoint '{}'".format(args.resume)) 68 | checkpoint = torch.load(args.resume) 69 | args.start_epoch = checkpoint['epoch'] 70 | best_acc = checkpoint['best_acc'] 71 | recorder = checkpoint['recorder'] 72 | best_acc = best_acc.to() 73 | model.load_state_dict(checkpoint['state_dict']) 74 | optimizer.load_state_dict(checkpoint['optimizer']) 75 | print("=> loaded checkpoint '{}' (epoch {})".format(args.resume, checkpoint['epoch'])) 76 | else: 77 | print("=> no checkpoint found at '{}'".format(args.resume)) 78 | cudnn.benchmark = True 79 | 80 | # Data loading code 81 | traindir = os.path.join(args.data, 'train') 82 | valdir = os.path.join(args.data, 'test') 83 | 84 | train_dataset = datasets.ImageFolder(traindir, 85 | transforms.Compose([transforms.RandomResizedCrop((224, 224)), 86 | transforms.RandomHorizontalFlip(), 87 | transforms.ToTensor()])) 88 | 89 | test_dataset = datasets.ImageFolder(valdir, 90 | transforms.Compose([transforms.Resize((224, 224)), 91 | transforms.ToTensor()])) 92 | 93 | train_loader = torch.utils.data.DataLoader(train_dataset, 94 | batch_size=args.batch_size, 95 | shuffle=True, 96 | num_workers=args.workers, 97 | pin_memory=True) 98 | val_loader = torch.utils.data.DataLoader(test_dataset, 99 | batch_size=args.batch_size, 100 | shuffle=False, 101 | num_workers=args.workers, 102 | pin_memory=True) 103 | 104 | if args.evaluate: 105 | validate(val_loader, model, criterion, args) 106 | return 107 | 108 | for epoch in range(args.start_epoch, args.epochs): 109 | start_time = time.time() 110 | current_learning_rate = optimizer.state_dict()['param_groups'][0]['lr'] 111 | print('Current learning rate: ', current_learning_rate) 112 | txt_name = './log/' + time_str + 'log.txt' 113 | with open(txt_name, 'a') as f: 114 | f.write('Current learning rate: ' + str(current_learning_rate) + '\n') 115 | 116 | # train for one epoch 117 | train_acc, train_los = train(train_loader, model, criterion, optimizer, epoch, args) 118 | 119 | # evaluate on validation set 120 | val_acc, val_los = validate(val_loader, model, criterion, args) 121 | 122 | scheduler.step() 123 | 124 | recorder.update(epoch, train_los, train_acc, val_los, val_acc) 125 | curve_name = time_str + 'cnn.png' 126 | recorder.plot_curve(os.path.join('./log/', curve_name)) 127 | 128 | # remember best acc and save checkpoint 129 | is_best = val_acc > best_acc 130 | best_acc = max(val_acc, best_acc) 131 | 132 | print('Current best accuracy: ', best_acc.item()) 133 | txt_name = './log/' + time_str + 'log.txt' 134 | with open(txt_name, 'a') as f: 135 | f.write('Current best accuracy: ' + str(best_acc.item()) + '\n') 136 | 137 | save_checkpoint({'epoch': epoch + 1, 138 | 'state_dict': model.state_dict(), 139 | 'best_acc': best_acc, 140 | 'optimizer': optimizer.state_dict(), 141 | 'recorder': recorder}, is_best, args) 142 | end_time = time.time() 143 | epoch_time = end_time - start_time 144 | print("An Epoch Time: ", epoch_time) 145 | txt_name = './log/' + time_str + 'log.txt' 146 | with open(txt_name, 'a') as f: 147 | f.write(str(epoch_time) + '\n') 148 | 149 | 150 | def train(train_loader, model, criterion, optimizer, epoch, args): 151 | losses = AverageMeter('Loss', ':.4f') 152 | top1 = AverageMeter('Accuracy', ':6.3f') 153 | progress = ProgressMeter(len(train_loader), 154 | [losses, top1], 155 | prefix="Epoch: [{}]".format(epoch)) 156 | 157 | # switch to train mode 158 | model.train() 159 | 160 | for i, (images, target) in enumerate(train_loader): 161 | 162 | images = images.cuda() 163 | target = target.cuda() 164 | 165 | # compute output 166 | output1, output2 = model(images) 167 | output = (args.beta * output1) + ((1-args.beta) * output2) 168 | loss = (args.beta * criterion(output1, target)) + ((1-args.beta) * criterion(output2, target)) 169 | 170 | # measure accuracy and record loss 171 | acc1, _ = accuracy(output, target, topk=(1, 5)) 172 | losses.update(loss.item(), images.size(0)) 173 | top1.update(acc1[0], images.size(0)) 174 | 175 | # compute gradient and do SGD step 176 | optimizer.zero_grad() 177 | loss.backward() 178 | optimizer.step() 179 | 180 | # print loss and accuracy 181 | if i % args.print_freq == 0: 182 | progress.display(i) 183 | 184 | return top1.avg, losses.avg 185 | 186 | 187 | def validate(val_loader, model, criterion, args): 188 | losses = AverageMeter('Loss', ':.4f') 189 | top1 = AverageMeter('Accuracy', ':6.3f') 190 | progress = ProgressMeter(len(val_loader), 191 | [losses, top1], 192 | prefix='Test: ') 193 | 194 | # switch to evaluate mode 195 | model.eval() 196 | 197 | with torch.no_grad(): 198 | for i, (images, target) in enumerate(val_loader): 199 | images = images.cuda() 200 | target = target.cuda() 201 | 202 | # compute output 203 | output1, output2 = model(images) 204 | output = (args.beta * output1) + ((1-args.beta) * output2) 205 | loss = (args.beta * criterion(output1, target)) + ((1 - args.beta) * criterion(output2, target)) 206 | 207 | # measure accuracy and record loss 208 | acc, _ = accuracy(output, target, topk=(1, 5)) 209 | losses.update(loss.item(), images.size(0)) 210 | top1.update(acc[0], images.size(0)) 211 | 212 | if i % args.print_freq == 0: 213 | progress.display(i) 214 | 215 | print(' **** Accuracy {top1.avg:.3f} *** '.format(top1=top1)) 216 | with open('./log/' + time_str + 'log.txt', 'a') as f: 217 | f.write(' * Accuracy {top1.avg:.3f}'.format(top1=top1) + '\n') 218 | return top1.avg, losses.avg 219 | 220 | 221 | def save_checkpoint(state, is_best, args): 222 | torch.save(state, args.checkpoint_path) 223 | if is_best: 224 | shutil.copyfile(args.checkpoint_path, args.best_checkpoint_path) 225 | 226 | 227 | class AverageMeter(object): 228 | """Computes and stores the average and current value""" 229 | def __init__(self, name, fmt=':f'): 230 | self.name = name 231 | self.fmt = fmt 232 | self.reset() 233 | 234 | def reset(self): 235 | self.val = 0 236 | self.avg = 0 237 | self.sum = 0 238 | self.count = 0 239 | 240 | def update(self, val, n=1): 241 | self.val = val 242 | self.sum += val * n 243 | self.count += n 244 | self.avg = self.sum / self.count 245 | 246 | def __str__(self): 247 | fmtstr = '{name} {val' + self.fmt + '} ({avg' + self.fmt + '})' 248 | return fmtstr.format(**self.__dict__) 249 | 250 | 251 | class ProgressMeter(object): 252 | def __init__(self, num_batches, meters, prefix=""): 253 | self.batch_fmtstr = self._get_batch_fmtstr(num_batches) 254 | self.meters = meters 255 | self.prefix = prefix 256 | 257 | def display(self, batch): 258 | entries = [self.prefix + self.batch_fmtstr.format(batch)] 259 | entries += [str(meter) for meter in self.meters] 260 | print_txt = '\t'.join(entries) 261 | print(print_txt) 262 | txt_name = './log/' + time_str + 'log.txt' 263 | with open(txt_name, 'a') as f: 264 | f.write(print_txt + '\n') 265 | 266 | def _get_batch_fmtstr(self, num_batches): 267 | num_digits = len(str(num_batches // 1)) 268 | fmt = '{:' + str(num_digits) + 'd}' 269 | return '[' + fmt + '/' + fmt.format(num_batches) + ']' 270 | 271 | 272 | def accuracy(output, target, topk=(1,)): 273 | """Computes the accuracy over the k top predictions for the specified values of k""" 274 | with torch.no_grad(): 275 | maxk = max(topk) 276 | batch_size = target.size(0) 277 | _, pred = output.topk(maxk, 1, True, True) 278 | pred = pred.t() 279 | correct = pred.eq(target.view(1, -1).expand_as(pred)) 280 | res = [] 281 | for k in topk: 282 | correct_k = correct[:k].view(-1).float().sum(0, keepdim=True) 283 | res.append(correct_k.mul_(100.0 / batch_size)) 284 | return res 285 | 286 | 287 | class RecorderMeter(object): 288 | """Computes and stores the minimum loss value and its epoch index""" 289 | 290 | def __init__(self, total_epoch): 291 | self.reset(total_epoch) 292 | 293 | def reset(self, total_epoch): 294 | self.total_epoch = total_epoch 295 | self.current_epoch = 0 296 | self.epoch_losses = np.zeros((self.total_epoch, 2), dtype=np.float32) # [epoch, train/val] 297 | self.epoch_accuracy = np.zeros((self.total_epoch, 2), dtype=np.float32) # [epoch, train/val] 298 | 299 | def update(self, idx, train_loss, train_acc, val_loss, val_acc): 300 | self.epoch_losses[idx, 0] = train_loss * 30 301 | self.epoch_losses[idx, 1] = val_loss * 30 302 | self.epoch_accuracy[idx, 0] = train_acc 303 | self.epoch_accuracy[idx, 1] = val_acc 304 | self.current_epoch = idx + 1 305 | 306 | def plot_curve(self, save_path): 307 | 308 | title = 'the accuracy/loss curve of train/val' 309 | dpi = 80 310 | width, height = 1800, 800 311 | legend_fontsize = 10 312 | figsize = width / float(dpi), height / float(dpi) 313 | 314 | fig = plt.figure(figsize=figsize) 315 | x_axis = np.array([i for i in range(self.total_epoch)]) # epochs 316 | y_axis = np.zeros(self.total_epoch) 317 | 318 | plt.xlim(0, self.total_epoch) 319 | plt.ylim(0, 100) 320 | interval_y = 5 321 | interval_x = 5 322 | plt.xticks(np.arange(0, self.total_epoch + interval_x, interval_x)) 323 | plt.yticks(np.arange(0, 100 + interval_y, interval_y)) 324 | plt.grid() 325 | plt.title(title, fontsize=20) 326 | plt.xlabel('the training epoch', fontsize=16) 327 | plt.ylabel('accuracy', fontsize=16) 328 | 329 | y_axis[:] = self.epoch_accuracy[:, 0] 330 | plt.plot(x_axis, y_axis, color='g', linestyle='-', label='train-accuracy', lw=2) 331 | plt.legend(loc=4, fontsize=legend_fontsize) 332 | 333 | y_axis[:] = self.epoch_accuracy[:, 1] 334 | plt.plot(x_axis, y_axis, color='y', linestyle='-', label='valid-accuracy', lw=2) 335 | plt.legend(loc=4, fontsize=legend_fontsize) 336 | 337 | y_axis[:] = self.epoch_losses[:, 0] 338 | plt.plot(x_axis, y_axis, color='g', linestyle=':', label='train-loss-x30', lw=2) 339 | plt.legend(loc=4, fontsize=legend_fontsize) 340 | 341 | y_axis[:] = self.epoch_losses[:, 1] 342 | plt.plot(x_axis, y_axis, color='y', linestyle=':', label='valid-loss-x30', lw=2) 343 | plt.legend(loc=4, fontsize=legend_fontsize) 344 | 345 | if save_path is not None: 346 | fig.savefig(save_path, dpi=dpi, bbox_inches='tight') 347 | print('Saved figure') 348 | plt.close(fig) 349 | 350 | 351 | if __name__ == '__main__': 352 | main() 353 | -------------------------------------------------------------------------------- /log/SFEW.txt: -------------------------------------------------------------------------------- 1 | Current learning rate: 0.001 2 | Epoch: [0][0/8] Loss 4.5949 (4.5949) Accuracy 17.188 (17.188) 3 | Test: [0/4] Loss 2.5761 (2.5761) Accuracy 36.719 (36.719) 4 | * Accuracy 18.119 5 | Current best accuracy: 18.119264602661133 6 | 14.153930902481079 7 | Current learning rate: 0.001 8 | Epoch: [1][0/8] Loss 3.7257 (3.7257) Accuracy 20.312 (20.312) 9 | Test: [0/4] Loss 1.8672 (1.8672) Accuracy 91.406 (91.406) 10 | * Accuracy 42.202 11 | Current best accuracy: 42.20183181762695 12 | 13.608882427215576 13 | Current learning rate: 0.001 14 | Epoch: [2][0/8] Loss 3.2437 (3.2437) Accuracy 50.000 (50.000) 15 | Test: [0/4] Loss 2.3225 (2.3225) Accuracy 67.188 (67.188) 16 | * Accuracy 50.000 17 | Current best accuracy: 49.999996185302734 18 | 12.094828367233276 19 | Current learning rate: 0.001 20 | Epoch: [3][0/8] Loss 3.1912 (3.1912) Accuracy 46.875 (46.875) 21 | Test: [0/4] Loss 2.1158 (2.1158) Accuracy 82.812 (82.812) 22 | * Accuracy 54.587 23 | Current best accuracy: 54.587154388427734 24 | 12.26271367073059 25 | Current learning rate: 0.001 26 | Epoch: [4][0/8] Loss 2.9028 (2.9028) Accuracy 50.000 (50.000) 27 | Test: [0/4] Loss 1.8266 (1.8266) Accuracy 88.281 (88.281) 28 | * Accuracy 54.587 29 | Current best accuracy: 54.587154388427734 30 | 8.444463968276978 31 | Current learning rate: 0.001 32 | Epoch: [5][0/8] Loss 2.8772 (2.8772) Accuracy 50.781 (50.781) 33 | Test: [0/4] Loss 1.6778 (1.6778) Accuracy 88.281 (88.281) 34 | * Accuracy 55.275 35 | Current best accuracy: 55.27522659301758 36 | 12.096774101257324 37 | Current learning rate: 0.001 38 | Epoch: [6][0/8] Loss 2.8065 (2.8065) Accuracy 50.000 (50.000) 39 | Test: [0/4] Loss 1.7983 (1.7983) Accuracy 84.375 (84.375) 40 | * Accuracy 58.257 41 | Current best accuracy: 58.25687789916992 42 | 12.062581300735474 43 | Current learning rate: 0.001 44 | Epoch: [7][0/8] Loss 2.5310 (2.5310) Accuracy 55.469 (55.469) 45 | Test: [0/4] Loss 1.8011 (1.8011) Accuracy 83.594 (83.594) 46 | * Accuracy 58.716 47 | Current best accuracy: 58.71559524536133 48 | 12.192733526229858 49 | Current learning rate: 0.001 50 | Epoch: [8][0/8] Loss 2.7242 (2.7242) Accuracy 61.719 (61.719) 51 | Test: [0/4] Loss 1.6097 (1.6097) Accuracy 86.719 (86.719) 52 | * Accuracy 58.257 53 | Current best accuracy: 58.71559524536133 54 | 8.520623922348022 55 | Current learning rate: 0.001 56 | Epoch: [9][0/8] Loss 2.5883 (2.5883) Accuracy 57.031 (57.031) 57 | Test: [0/4] Loss 1.6643 (1.6643) Accuracy 84.375 (84.375) 58 | * Accuracy 58.945 59 | Current best accuracy: 58.944950103759766 60 | 10.909721612930298 61 | Current learning rate: 0.0001 62 | Epoch: [10][0/8] Loss 2.6226 (2.6226) Accuracy 57.031 (57.031) 63 | Test: [0/4] Loss 1.5620 (1.5620) Accuracy 85.938 (85.938) 64 | * Accuracy 58.945 65 | Current best accuracy: 58.944950103759766 66 | 7.450170278549194 67 | Current learning rate: 0.0001 68 | Epoch: [11][0/8] Loss 2.5116 (2.5116) Accuracy 58.594 (58.594) 69 | Test: [0/4] Loss 1.5481 (1.5481) Accuracy 85.938 (85.938) 70 | * Accuracy 58.945 71 | Current best accuracy: 58.944950103759766 72 | 7.482295513153076 73 | Current learning rate: 0.0001 74 | Epoch: [12][0/8] Loss 2.4160 (2.4160) Accuracy 60.938 (60.938) 75 | Test: [0/4] Loss 1.5016 (1.5016) Accuracy 86.719 (86.719) 76 | * Accuracy 58.716 77 | Current best accuracy: 58.944950103759766 78 | 7.473083734512329 79 | Current learning rate: 0.0001 80 | Epoch: [13][0/8] Loss 2.6928 (2.6928) Accuracy 54.688 (54.688) 81 | Test: [0/4] Loss 1.4825 (1.4825) Accuracy 86.719 (86.719) 82 | * Accuracy 57.339 83 | Current best accuracy: 58.944950103759766 84 | 7.425713539123535 85 | Current learning rate: 0.0001 86 | Epoch: [14][0/8] Loss 2.3645 (2.3645) Accuracy 60.156 (60.156) 87 | Test: [0/4] Loss 1.5008 (1.5008) Accuracy 85.938 (85.938) 88 | * Accuracy 57.569 89 | Current best accuracy: 58.944950103759766 90 | 7.618582248687744 91 | Current learning rate: 0.0001 92 | Epoch: [15][0/8] Loss 2.5374 (2.5374) Accuracy 56.250 (56.250) 93 | Test: [0/4] Loss 1.5112 (1.5112) Accuracy 85.938 (85.938) 94 | * Accuracy 58.716 95 | Current best accuracy: 58.944950103759766 96 | 7.953222036361694 97 | Current learning rate: 0.0001 98 | Epoch: [16][0/8] Loss 2.6766 (2.6766) Accuracy 52.344 (52.344) 99 | Test: [0/4] Loss 1.4722 (1.4722) Accuracy 85.938 (85.938) 100 | * Accuracy 58.486 101 | Current best accuracy: 58.944950103759766 102 | 7.512688398361206 103 | Current learning rate: 0.0001 104 | Epoch: [17][0/8] Loss 2.4106 (2.4106) Accuracy 58.594 (58.594) 105 | Test: [0/4] Loss 1.5017 (1.5017) Accuracy 85.938 (85.938) 106 | * Accuracy 57.569 107 | Current best accuracy: 58.944950103759766 108 | 7.494899034500122 109 | Current learning rate: 0.0001 110 | Epoch: [18][0/8] Loss 2.4836 (2.4836) Accuracy 53.906 (53.906) 111 | Test: [0/4] Loss 1.5173 (1.5173) Accuracy 85.938 (85.938) 112 | * Accuracy 58.716 113 | Current best accuracy: 58.944950103759766 114 | 7.434387445449829 115 | Current learning rate: 0.0001 116 | Epoch: [19][0/8] Loss 2.5699 (2.5699) Accuracy 58.594 (58.594) 117 | Test: [0/4] Loss 1.4772 (1.4772) Accuracy 85.938 (85.938) 118 | * Accuracy 58.257 119 | Current best accuracy: 58.944950103759766 120 | 7.531460285186768 121 | Current learning rate: 1.0000000000000003e-05 122 | Epoch: [20][0/8] Loss 2.3373 (2.3373) Accuracy 58.594 (58.594) 123 | Test: [0/4] Loss 1.4718 (1.4718) Accuracy 85.938 (85.938) 124 | * Accuracy 58.257 125 | Current best accuracy: 58.944950103759766 126 | 7.671631574630737 127 | Current learning rate: 1.0000000000000003e-05 128 | Epoch: [21][0/8] Loss 2.7054 (2.7054) Accuracy 49.219 (49.219) 129 | Test: [0/4] Loss 1.4757 (1.4757) Accuracy 86.719 (86.719) 130 | * Accuracy 58.257 131 | Current best accuracy: 58.944950103759766 132 | 7.613374948501587 133 | Current learning rate: 1.0000000000000003e-05 134 | Epoch: [22][0/8] Loss 2.4582 (2.4582) Accuracy 60.938 (60.938) 135 | Test: [0/4] Loss 1.4700 (1.4700) Accuracy 86.719 (86.719) 136 | * Accuracy 58.257 137 | Current best accuracy: 58.944950103759766 138 | 7.212494373321533 139 | Current learning rate: 1.0000000000000003e-05 140 | Epoch: [23][0/8] Loss 2.3382 (2.3382) Accuracy 56.250 (56.250) 141 | Test: [0/4] Loss 1.4688 (1.4688) Accuracy 86.719 (86.719) 142 | * Accuracy 58.716 143 | Current best accuracy: 58.944950103759766 144 | 7.69232702255249 145 | Current learning rate: 1.0000000000000003e-05 146 | Epoch: [24][0/8] Loss 2.1503 (2.1503) Accuracy 61.719 (61.719) 147 | Test: [0/4] Loss 1.4948 (1.4948) Accuracy 85.938 (85.938) 148 | * Accuracy 58.716 149 | Current best accuracy: 58.944950103759766 150 | 7.423769474029541 151 | Current learning rate: 1.0000000000000003e-05 152 | Epoch: [25][0/8] Loss 2.5083 (2.5083) Accuracy 53.125 (53.125) 153 | Test: [0/4] Loss 1.4995 (1.4995) Accuracy 86.719 (86.719) 154 | * Accuracy 59.174 155 | Current best accuracy: 59.17430877685547 156 | 10.152883052825928 157 | Current learning rate: 1.0000000000000003e-05 158 | Epoch: [26][0/8] Loss 2.4834 (2.4834) Accuracy 55.469 (55.469) 159 | Test: [0/4] Loss 1.4404 (1.4404) Accuracy 85.938 (85.938) 160 | * Accuracy 58.486 161 | Current best accuracy: 59.17430877685547 162 | 7.575411081314087 163 | Current learning rate: 1.0000000000000003e-05 164 | Epoch: [27][0/8] Loss 2.4459 (2.4459) Accuracy 53.906 (53.906) 165 | Test: [0/4] Loss 1.4639 (1.4639) Accuracy 85.938 (85.938) 166 | * Accuracy 58.486 167 | Current best accuracy: 59.17430877685547 168 | 7.779388189315796 169 | Current learning rate: 1.0000000000000003e-05 170 | Epoch: [28][0/8] Loss 2.4486 (2.4486) Accuracy 57.812 (57.812) 171 | Test: [0/4] Loss 1.4726 (1.4726) Accuracy 86.719 (86.719) 172 | * Accuracy 58.486 173 | Current best accuracy: 59.17430877685547 174 | 7.5832359790802 175 | Current learning rate: 1.0000000000000003e-05 176 | Epoch: [29][0/8] Loss 2.3666 (2.3666) Accuracy 57.031 (57.031) 177 | Test: [0/4] Loss 1.4693 (1.4693) Accuracy 85.938 (85.938) 178 | * Accuracy 58.486 179 | Current best accuracy: 59.17430877685547 180 | 7.71010160446167 181 | Current learning rate: 1.0000000000000002e-06 182 | Epoch: [30][0/8] Loss 2.5635 (2.5635) Accuracy 54.688 (54.688) 183 | Test: [0/4] Loss 1.4816 (1.4816) Accuracy 85.938 (85.938) 184 | * Accuracy 58.716 185 | Current best accuracy: 59.17430877685547 186 | 7.778925895690918 187 | Current learning rate: 1.0000000000000002e-06 188 | Epoch: [31][0/8] Loss 2.5223 (2.5223) Accuracy 53.125 (53.125) 189 | Test: [0/4] Loss 1.4814 (1.4814) Accuracy 85.938 (85.938) 190 | * Accuracy 58.486 191 | Current best accuracy: 59.17430877685547 192 | 7.90248441696167 193 | Current learning rate: 1.0000000000000002e-06 194 | Epoch: [32][0/8] Loss 2.3704 (2.3704) Accuracy 60.938 (60.938) 195 | Test: [0/4] Loss 1.4730 (1.4730) Accuracy 85.938 (85.938) 196 | * Accuracy 58.257 197 | Current best accuracy: 59.17430877685547 198 | 8.00007963180542 199 | Current learning rate: 1.0000000000000002e-06 200 | Epoch: [33][0/8] Loss 2.5656 (2.5656) Accuracy 56.250 (56.250) 201 | Test: [0/4] Loss 1.4569 (1.4569) Accuracy 85.938 (85.938) 202 | * Accuracy 58.028 203 | Current best accuracy: 59.17430877685547 204 | 8.275448083877563 205 | Current learning rate: 1.0000000000000002e-06 206 | Epoch: [34][0/8] Loss 2.2443 (2.2443) Accuracy 58.594 (58.594) 207 | Test: [0/4] Loss 1.4781 (1.4781) Accuracy 86.719 (86.719) 208 | * Accuracy 58.716 209 | Current best accuracy: 59.17430877685547 210 | 8.25335955619812 211 | Current learning rate: 1.0000000000000002e-06 212 | Epoch: [35][0/8] Loss 2.3572 (2.3572) Accuracy 59.375 (59.375) 213 | Test: [0/4] Loss 1.4609 (1.4609) Accuracy 85.938 (85.938) 214 | * Accuracy 58.257 215 | Current best accuracy: 59.17430877685547 216 | 7.9495320320129395 217 | Current learning rate: 1.0000000000000002e-06 218 | Epoch: [36][0/8] Loss 2.1935 (2.1935) Accuracy 65.625 (65.625) 219 | Test: [0/4] Loss 1.4658 (1.4658) Accuracy 86.719 (86.719) 220 | * Accuracy 58.716 221 | Current best accuracy: 59.17430877685547 222 | 8.167771816253662 223 | Current learning rate: 1.0000000000000002e-06 224 | Epoch: [37][0/8] Loss 2.9189 (2.9189) Accuracy 46.875 (46.875) 225 | Test: [0/4] Loss 1.4638 (1.4638) Accuracy 85.938 (85.938) 226 | * Accuracy 58.716 227 | Current best accuracy: 59.17430877685547 228 | 8.176531791687012 229 | Current learning rate: 1.0000000000000002e-06 230 | Epoch: [38][0/8] Loss 2.5178 (2.5178) Accuracy 51.562 (51.562) 231 | Test: [0/4] Loss 1.4553 (1.4553) Accuracy 86.719 (86.719) 232 | * Accuracy 58.257 233 | Current best accuracy: 59.17430877685547 234 | 8.214637279510498 235 | Current learning rate: 1.0000000000000002e-06 236 | Epoch: [39][0/8] Loss 2.3598 (2.3598) Accuracy 59.375 (59.375) 237 | Test: [0/4] Loss 1.4736 (1.4736) Accuracy 86.719 (86.719) 238 | * Accuracy 58.716 239 | Current best accuracy: 59.17430877685547 240 | 8.27189326286316 241 | Current learning rate: 1.0000000000000002e-07 242 | Epoch: [40][0/8] Loss 2.5344 (2.5344) Accuracy 55.469 (55.469) 243 | Test: [0/4] Loss 1.4896 (1.4896) Accuracy 85.938 (85.938) 244 | * Accuracy 59.404 245 | Current best accuracy: 59.40366744995117 246 | 11.896949052810669 247 | Current learning rate: 1.0000000000000002e-07 248 | Epoch: [41][0/8] Loss 2.4416 (2.4416) Accuracy 60.156 (60.156) 249 | Test: [0/4] Loss 1.5027 (1.5027) Accuracy 85.938 (85.938) 250 | * Accuracy 59.174 251 | Current best accuracy: 59.40366744995117 252 | 8.56544828414917 253 | Current learning rate: 1.0000000000000002e-07 254 | Epoch: [42][0/8] Loss 2.5486 (2.5486) Accuracy 52.344 (52.344) 255 | Test: [0/4] Loss 1.4696 (1.4696) Accuracy 85.938 (85.938) 256 | * Accuracy 58.945 257 | Current best accuracy: 59.40366744995117 258 | 8.881960153579712 259 | Current learning rate: 1.0000000000000002e-07 260 | Epoch: [43][0/8] Loss 2.4375 (2.4375) Accuracy 56.250 (56.250) 261 | Test: [0/4] Loss 1.4655 (1.4655) Accuracy 85.938 (85.938) 262 | * Accuracy 58.486 263 | Current best accuracy: 59.40366744995117 264 | 8.68686819076538 265 | Current learning rate: 1.0000000000000002e-07 266 | Epoch: [44][0/8] Loss 2.2972 (2.2972) Accuracy 64.062 (64.062) 267 | Test: [0/4] Loss 1.4557 (1.4557) Accuracy 86.719 (86.719) 268 | * Accuracy 58.486 269 | Current best accuracy: 59.40366744995117 270 | 8.951219320297241 271 | Current learning rate: 1.0000000000000002e-07 272 | Epoch: [45][0/8] Loss 2.5830 (2.5830) Accuracy 52.344 (52.344) 273 | Test: [0/4] Loss 1.4669 (1.4669) Accuracy 85.938 (85.938) 274 | * Accuracy 58.716 275 | Current best accuracy: 59.40366744995117 276 | 8.529399871826172 277 | Current learning rate: 1.0000000000000002e-07 278 | Epoch: [46][0/8] Loss 2.4132 (2.4132) Accuracy 58.594 (58.594) 279 | Test: [0/4] Loss 1.4739 (1.4739) Accuracy 85.938 (85.938) 280 | * Accuracy 58.716 281 | Current best accuracy: 59.40366744995117 282 | 8.52416706085205 283 | Current learning rate: 1.0000000000000002e-07 284 | Epoch: [47][0/8] Loss 2.4237 (2.4237) Accuracy 59.375 (59.375) 285 | Test: [0/4] Loss 1.4516 (1.4516) Accuracy 86.719 (86.719) 286 | * Accuracy 58.716 287 | Current best accuracy: 59.40366744995117 288 | 8.637136697769165 289 | Current learning rate: 1.0000000000000002e-07 290 | Epoch: [48][0/8] Loss 2.2917 (2.2917) Accuracy 60.938 (60.938) 291 | Test: [0/4] Loss 1.4533 (1.4533) Accuracy 85.938 (85.938) 292 | * Accuracy 58.257 293 | Current best accuracy: 59.40366744995117 294 | 8.66755723953247 295 | Current learning rate: 1.0000000000000002e-07 296 | Epoch: [49][0/8] Loss 2.2642 (2.2642) Accuracy 64.844 (64.844) 297 | Test: [0/4] Loss 1.4617 (1.4617) Accuracy 86.719 (86.719) 298 | * Accuracy 58.257 299 | Current best accuracy: 59.40366744995117 300 | 8.845441341400146 301 | Current learning rate: 1.0000000000000004e-08 302 | Epoch: [50][0/8] Loss 2.3174 (2.3174) Accuracy 60.156 (60.156) 303 | Test: [0/4] Loss 1.4429 (1.4429) Accuracy 86.719 (86.719) 304 | * Accuracy 58.716 305 | Current best accuracy: 59.40366744995117 306 | 8.855155229568481 307 | Current learning rate: 1.0000000000000004e-08 308 | Epoch: [51][0/8] Loss 2.4019 (2.4019) Accuracy 59.375 (59.375) 309 | Test: [0/4] Loss 1.4526 (1.4526) Accuracy 86.719 (86.719) 310 | * Accuracy 58.028 311 | Current best accuracy: 59.40366744995117 312 | 8.625126838684082 313 | Current learning rate: 1.0000000000000004e-08 314 | Epoch: [52][0/8] Loss 2.4966 (2.4966) Accuracy 53.906 (53.906) 315 | Test: [0/4] Loss 1.4400 (1.4400) Accuracy 86.719 (86.719) 316 | * Accuracy 57.798 317 | Current best accuracy: 59.40366744995117 318 | 8.725281000137329 319 | Current learning rate: 1.0000000000000004e-08 320 | Epoch: [53][0/8] Loss 2.5464 (2.5464) Accuracy 52.344 (52.344) 321 | Test: [0/4] Loss 1.4653 (1.4653) Accuracy 85.938 (85.938) 322 | * Accuracy 58.028 323 | Current best accuracy: 59.40366744995117 324 | 8.677398443222046 325 | Current learning rate: 1.0000000000000004e-08 326 | Epoch: [54][0/8] Loss 2.5940 (2.5940) Accuracy 53.125 (53.125) 327 | Test: [0/4] Loss 1.4560 (1.4560) Accuracy 85.938 (85.938) 328 | * Accuracy 58.028 329 | Current best accuracy: 59.40366744995117 330 | 8.468147039413452 331 | Current learning rate: 1.0000000000000004e-08 332 | Epoch: [55][0/8] Loss 2.4599 (2.4599) Accuracy 57.812 (57.812) 333 | Test: [0/4] Loss 1.4449 (1.4449) Accuracy 85.938 (85.938) 334 | * Accuracy 58.028 335 | Current best accuracy: 59.40366744995117 336 | 8.407550573348999 337 | Current learning rate: 1.0000000000000004e-08 338 | Epoch: [56][0/8] Loss 2.3656 (2.3656) Accuracy 58.594 (58.594) 339 | Test: [0/4] Loss 1.4667 (1.4667) Accuracy 85.938 (85.938) 340 | * Accuracy 58.028 341 | Current best accuracy: 59.40366744995117 342 | 8.39966893196106 343 | Current learning rate: 1.0000000000000004e-08 344 | Epoch: [57][0/8] Loss 2.5482 (2.5482) Accuracy 58.594 (58.594) 345 | Test: [0/4] Loss 1.4662 (1.4662) Accuracy 87.500 (87.500) 346 | * Accuracy 59.404 347 | Current best accuracy: 59.40366744995117 348 | 8.402401447296143 349 | Current learning rate: 1.0000000000000004e-08 350 | Epoch: [58][0/8] Loss 2.5270 (2.5270) Accuracy 51.562 (51.562) 351 | Test: [0/4] Loss 1.4744 (1.4744) Accuracy 85.938 (85.938) 352 | * Accuracy 58.945 353 | Current best accuracy: 59.40366744995117 354 | 8.517487049102783 355 | Current learning rate: 1.0000000000000004e-08 356 | Epoch: [59][0/8] Loss 2.5277 (2.5277) Accuracy 63.281 (63.281) 357 | Test: [0/4] Loss 1.4802 (1.4802) Accuracy 85.938 (85.938) 358 | * Accuracy 58.945 359 | Current best accuracy: 59.40366744995117 360 | 8.586340188980103 361 | Current learning rate: 1.0000000000000005e-09 362 | Epoch: [60][0/8] Loss 2.4549 (2.4549) Accuracy 56.250 (56.250) 363 | Test: [0/4] Loss 1.4573 (1.4573) Accuracy 85.938 (85.938) 364 | * Accuracy 58.486 365 | Current best accuracy: 59.40366744995117 366 | 8.409189939498901 367 | Current learning rate: 1.0000000000000005e-09 368 | Epoch: [61][0/8] Loss 2.7610 (2.7610) Accuracy 49.219 (49.219) 369 | Test: [0/4] Loss 1.4617 (1.4617) Accuracy 85.938 (85.938) 370 | * Accuracy 58.486 371 | Current best accuracy: 59.40366744995117 372 | 8.433232545852661 373 | Current learning rate: 1.0000000000000005e-09 374 | Epoch: [62][0/8] Loss 2.4323 (2.4323) Accuracy 60.938 (60.938) 375 | Test: [0/4] Loss 1.4877 (1.4877) Accuracy 86.719 (86.719) 376 | * Accuracy 58.945 377 | Current best accuracy: 59.40366744995117 378 | 8.397521734237671 379 | Current learning rate: 1.0000000000000005e-09 380 | Epoch: [63][0/8] Loss 2.5470 (2.5470) Accuracy 53.906 (53.906) 381 | Test: [0/4] Loss 1.4685 (1.4685) Accuracy 86.719 (86.719) 382 | * Accuracy 58.716 383 | Current best accuracy: 59.40366744995117 384 | 7.482413291931152 385 | Current learning rate: 1.0000000000000005e-09 386 | Epoch: [64][0/8] Loss 2.3363 (2.3363) Accuracy 58.594 (58.594) 387 | Test: [0/4] Loss 1.4510 (1.4510) Accuracy 85.938 (85.938) 388 | * Accuracy 57.798 389 | Current best accuracy: 59.40366744995117 390 | 7.530675411224365 391 | Current learning rate: 1.0000000000000005e-09 392 | Epoch: [65][0/8] Loss 2.6933 (2.6933) Accuracy 46.094 (46.094) 393 | Test: [0/4] Loss 1.4816 (1.4816) Accuracy 85.938 (85.938) 394 | * Accuracy 58.257 395 | Current best accuracy: 59.40366744995117 396 | 7.378602504730225 397 | Current learning rate: 1.0000000000000005e-09 398 | Epoch: [66][0/8] Loss 2.4226 (2.4226) Accuracy 53.906 (53.906) 399 | Test: [0/4] Loss 1.4800 (1.4800) Accuracy 86.719 (86.719) 400 | * Accuracy 58.945 401 | Current best accuracy: 59.40366744995117 402 | 7.0807459354400635 403 | Current learning rate: 1.0000000000000005e-09 404 | Epoch: [67][0/8] Loss 2.4649 (2.4649) Accuracy 59.375 (59.375) 405 | Test: [0/4] Loss 1.4722 (1.4722) Accuracy 86.719 (86.719) 406 | * Accuracy 58.716 407 | Current best accuracy: 59.40366744995117 408 | 7.367851257324219 409 | Current learning rate: 1.0000000000000005e-09 410 | Epoch: [68][0/8] Loss 2.3834 (2.3834) Accuracy 57.031 (57.031) 411 | Test: [0/4] Loss 1.4616 (1.4616) Accuracy 85.938 (85.938) 412 | * Accuracy 58.028 413 | Current best accuracy: 59.40366744995117 414 | 7.458495616912842 415 | Current learning rate: 1.0000000000000005e-09 416 | Epoch: [69][0/8] Loss 2.5636 (2.5636) Accuracy 53.906 (53.906) 417 | Test: [0/4] Loss 1.4625 (1.4625) Accuracy 85.938 (85.938) 418 | * Accuracy 58.716 419 | Current best accuracy: 59.40366744995117 420 | 7.381686687469482 421 | Current learning rate: 1.0000000000000004e-10 422 | Epoch: [70][0/8] Loss 2.4365 (2.4365) Accuracy 58.594 (58.594) 423 | Test: [0/4] Loss 1.4809 (1.4809) Accuracy 85.938 (85.938) 424 | * Accuracy 58.486 425 | Current best accuracy: 59.40366744995117 426 | 7.62793493270874 427 | Current learning rate: 1.0000000000000004e-10 428 | Epoch: [71][0/8] Loss 2.3981 (2.3981) Accuracy 59.375 (59.375) 429 | Test: [0/4] Loss 1.4662 (1.4662) Accuracy 85.938 (85.938) 430 | * Accuracy 58.716 431 | Current best accuracy: 59.40366744995117 432 | 7.403401851654053 433 | Current learning rate: 1.0000000000000004e-10 434 | Epoch: [72][0/8] Loss 2.4989 (2.4989) Accuracy 58.594 (58.594) 435 | Test: [0/4] Loss 1.4811 (1.4811) Accuracy 85.938 (85.938) 436 | * Accuracy 58.486 437 | Current best accuracy: 59.40366744995117 438 | 7.423985958099365 439 | Current learning rate: 1.0000000000000004e-10 440 | Epoch: [73][0/8] Loss 2.8913 (2.8913) Accuracy 42.969 (42.969) 441 | Test: [0/4] Loss 1.5037 (1.5037) Accuracy 86.719 (86.719) 442 | * Accuracy 58.716 443 | Current best accuracy: 59.40366744995117 444 | 7.499053478240967 445 | Current learning rate: 1.0000000000000004e-10 446 | Epoch: [74][0/8] Loss 2.4361 (2.4361) Accuracy 59.375 (59.375) 447 | Test: [0/4] Loss 1.5023 (1.5023) Accuracy 86.719 (86.719) 448 | * Accuracy 58.945 449 | Current best accuracy: 59.40366744995117 450 | 7.381526947021484 451 | Current learning rate: 1.0000000000000004e-10 452 | Epoch: [75][0/8] Loss 2.4280 (2.4280) Accuracy 59.375 (59.375) 453 | Test: [0/4] Loss 1.4848 (1.4848) Accuracy 86.719 (86.719) 454 | * Accuracy 58.716 455 | Current best accuracy: 59.40366744995117 456 | 7.644371271133423 457 | Current learning rate: 1.0000000000000004e-10 458 | Epoch: [76][0/8] Loss 2.4162 (2.4162) Accuracy 56.250 (56.250) 459 | Test: [0/4] Loss 1.4607 (1.4607) Accuracy 86.719 (86.719) 460 | * Accuracy 58.486 461 | Current best accuracy: 59.40366744995117 462 | 7.487860679626465 463 | Current learning rate: 1.0000000000000004e-10 464 | Epoch: [77][0/8] Loss 2.5856 (2.5856) Accuracy 53.125 (53.125) 465 | Test: [0/4] Loss 1.4687 (1.4687) Accuracy 85.938 (85.938) 466 | * Accuracy 58.257 467 | Current best accuracy: 59.40366744995117 468 | 7.53557014465332 469 | Current learning rate: 1.0000000000000004e-10 470 | Epoch: [78][0/8] Loss 2.5481 (2.5481) Accuracy 49.219 (49.219) 471 | Test: [0/4] Loss 1.4503 (1.4503) Accuracy 85.938 (85.938) 472 | * Accuracy 58.257 473 | Current best accuracy: 59.40366744995117 474 | 7.520495653152466 475 | Current learning rate: 1.0000000000000004e-10 476 | Epoch: [79][0/8] Loss 2.5457 (2.5457) Accuracy 54.688 (54.688) 477 | Test: [0/4] Loss 1.4621 (1.4621) Accuracy 85.938 (85.938) 478 | * Accuracy 58.716 479 | Current best accuracy: 59.40366744995117 480 | 7.646473169326782 481 | Current learning rate: 1.0000000000000006e-11 482 | Epoch: [80][0/8] Loss 2.3990 (2.3990) Accuracy 56.250 (56.250) 483 | Test: [0/4] Loss 1.4860 (1.4860) Accuracy 85.938 (85.938) 484 | * Accuracy 58.486 485 | Current best accuracy: 59.40366744995117 486 | 7.5310447216033936 487 | Current learning rate: 1.0000000000000006e-11 488 | Epoch: [81][0/8] Loss 2.4911 (2.4911) Accuracy 57.812 (57.812) 489 | Test: [0/4] Loss 1.4991 (1.4991) Accuracy 85.938 (85.938) 490 | * Accuracy 58.716 491 | Current best accuracy: 59.40366744995117 492 | 7.447022438049316 493 | Current learning rate: 1.0000000000000006e-11 494 | Epoch: [82][0/8] Loss 2.5744 (2.5744) Accuracy 50.781 (50.781) 495 | Test: [0/4] Loss 1.4844 (1.4844) Accuracy 85.938 (85.938) 496 | * Accuracy 58.486 497 | Current best accuracy: 59.40366744995117 498 | 7.484816312789917 499 | Current learning rate: 1.0000000000000006e-11 500 | Epoch: [83][0/8] Loss 2.5239 (2.5239) Accuracy 52.344 (52.344) 501 | Test: [0/4] Loss 1.4737 (1.4737) Accuracy 86.719 (86.719) 502 | * Accuracy 58.486 503 | Current best accuracy: 59.40366744995117 504 | 7.465449810028076 505 | Current learning rate: 1.0000000000000006e-11 506 | Epoch: [84][0/8] Loss 2.3664 (2.3664) Accuracy 57.812 (57.812) 507 | Test: [0/4] Loss 1.4805 (1.4805) Accuracy 86.719 (86.719) 508 | * Accuracy 58.257 509 | Current best accuracy: 59.40366744995117 510 | 7.8608434200286865 511 | Current learning rate: 1.0000000000000006e-11 512 | Epoch: [85][0/8] Loss 2.7595 (2.7595) Accuracy 52.344 (52.344) 513 | Test: [0/4] Loss 1.5004 (1.5004) Accuracy 86.719 (86.719) 514 | * Accuracy 58.945 515 | Current best accuracy: 59.40366744995117 516 | 7.865482568740845 517 | Current learning rate: 1.0000000000000006e-11 518 | Epoch: [86][0/8] Loss 2.2585 (2.2585) Accuracy 66.406 (66.406) 519 | Test: [0/4] Loss 1.4728 (1.4728) Accuracy 85.938 (85.938) 520 | * Accuracy 58.257 521 | Current best accuracy: 59.40366744995117 522 | 7.794929265975952 523 | Current learning rate: 1.0000000000000006e-11 524 | Epoch: [87][0/8] Loss 2.6336 (2.6336) Accuracy 53.906 (53.906) 525 | Test: [0/4] Loss 1.4536 (1.4536) Accuracy 85.938 (85.938) 526 | * Accuracy 58.257 527 | Current best accuracy: 59.40366744995117 528 | 7.74241828918457 529 | Current learning rate: 1.0000000000000006e-11 530 | Epoch: [88][0/8] Loss 2.4542 (2.4542) Accuracy 57.031 (57.031) 531 | Test: [0/4] Loss 1.4770 (1.4770) Accuracy 86.719 (86.719) 532 | * Accuracy 58.028 533 | Current best accuracy: 59.40366744995117 534 | 8.211538076400757 535 | Current learning rate: 1.0000000000000006e-11 536 | Epoch: [89][0/8] Loss 2.4599 (2.4599) Accuracy 57.812 (57.812) 537 | Test: [0/4] Loss 1.4731 (1.4731) Accuracy 85.938 (85.938) 538 | * Accuracy 58.028 539 | Current best accuracy: 59.40366744995117 540 | 8.091779947280884 541 | Current learning rate: 1.0000000000000006e-12 542 | Epoch: [90][0/8] Loss 2.4754 (2.4754) Accuracy 55.469 (55.469) 543 | Test: [0/4] Loss 1.4672 (1.4672) Accuracy 85.938 (85.938) 544 | * Accuracy 58.028 545 | Current best accuracy: 59.40366744995117 546 | 8.16722297668457 547 | Current learning rate: 1.0000000000000006e-12 548 | Epoch: [91][0/8] Loss 2.5501 (2.5501) Accuracy 53.906 (53.906) 549 | Test: [0/4] Loss 1.4682 (1.4682) Accuracy 86.719 (86.719) 550 | * Accuracy 58.257 551 | Current best accuracy: 59.40366744995117 552 | 7.984006404876709 553 | Current learning rate: 1.0000000000000006e-12 554 | Epoch: [92][0/8] Loss 2.4476 (2.4476) Accuracy 57.812 (57.812) 555 | Test: [0/4] Loss 1.4644 (1.4644) Accuracy 85.938 (85.938) 556 | * Accuracy 58.486 557 | Current best accuracy: 59.40366744995117 558 | 8.434601783752441 559 | Current learning rate: 1.0000000000000006e-12 560 | Epoch: [93][0/8] Loss 2.8011 (2.8011) Accuracy 47.656 (47.656) 561 | Test: [0/4] Loss 1.4397 (1.4397) Accuracy 85.938 (85.938) 562 | * Accuracy 58.028 563 | Current best accuracy: 59.40366744995117 564 | 8.233031272888184 565 | Current learning rate: 1.0000000000000006e-12 566 | Epoch: [94][0/8] Loss 2.6245 (2.6245) Accuracy 55.469 (55.469) 567 | Test: [0/4] Loss 1.4535 (1.4535) Accuracy 85.938 (85.938) 568 | * Accuracy 58.257 569 | Current best accuracy: 59.40366744995117 570 | 8.570972919464111 571 | Current learning rate: 1.0000000000000006e-12 572 | Epoch: [95][0/8] Loss 2.2135 (2.2135) Accuracy 64.844 (64.844) 573 | Test: [0/4] Loss 1.4700 (1.4700) Accuracy 85.938 (85.938) 574 | * Accuracy 57.798 575 | Current best accuracy: 59.40366744995117 576 | 8.570459842681885 577 | Current learning rate: 1.0000000000000006e-12 578 | Epoch: [96][0/8] Loss 2.7373 (2.7373) Accuracy 50.781 (50.781) 579 | Test: [0/4] Loss 1.4706 (1.4706) Accuracy 85.938 (85.938) 580 | * Accuracy 58.486 581 | Current best accuracy: 59.40366744995117 582 | 8.450437784194946 583 | Current learning rate: 1.0000000000000006e-12 584 | Epoch: [97][0/8] Loss 2.4910 (2.4910) Accuracy 56.250 (56.250) 585 | Test: [0/4] Loss 1.4927 (1.4927) Accuracy 85.938 (85.938) 586 | * Accuracy 58.945 587 | Current best accuracy: 59.40366744995117 588 | 10.736592769622803 589 | Current learning rate: 1.0000000000000006e-12 590 | Epoch: [98][0/8] Loss 2.5507 (2.5507) Accuracy 53.125 (53.125) 591 | Test: [0/4] Loss 1.4730 (1.4730) Accuracy 85.938 (85.938) 592 | * Accuracy 58.028 593 | Current best accuracy: 59.40366744995117 594 | 8.383886575698853 595 | Current learning rate: 1.0000000000000006e-12 596 | Epoch: [99][0/8] Loss 2.3635 (2.3635) Accuracy 58.594 (58.594) 597 | Test: [0/4] Loss 1.4388 (1.4388) Accuracy 85.938 (85.938) 598 | * Accuracy 57.569 599 | Current best accuracy: 59.40366744995117 600 | 8.601725816726685 601 | -------------------------------------------------------------------------------- /log/RAF-DB.txt: -------------------------------------------------------------------------------- 1 | Current learning rate: 0.01 2 | Epoch: [0][ 0/96] Loss 2.7088 (2.7088) Accuracy 10.156 (10.156) 3 | Epoch: [0][10/96] Loss 1.7269 (1.9157) Accuracy 53.906 (42.259) 4 | Epoch: [0][20/96] Loss 1.1512 (1.6757) Accuracy 64.062 (49.070) 5 | Epoch: [0][30/96] Loss 1.2351 (1.5576) Accuracy 60.156 (52.218) 6 | Epoch: [0][40/96] Loss 1.6036 (1.4939) Accuracy 56.250 (53.468) 7 | Epoch: [0][50/96] Loss 1.2241 (1.4206) Accuracy 61.719 (54.963) 8 | Epoch: [0][60/96] Loss 0.9108 (1.3614) Accuracy 68.750 (56.545) 9 | Epoch: [0][70/96] Loss 1.0299 (1.3148) Accuracy 64.844 (57.614) 10 | Epoch: [0][80/96] Loss 1.0493 (1.2741) Accuracy 67.188 (58.738) 11 | Epoch: [0][90/96] Loss 0.9313 (1.2422) Accuracy 66.406 (59.598) 12 | Test: [ 0/24] Loss 0.5033 (0.5033) Accuracy 86.719 (86.719) 13 | Test: [10/24] Loss 0.5120 (0.5172) Accuracy 82.031 (84.801) 14 | Test: [20/24] Loss 0.6487 (0.7374) Accuracy 80.469 (76.079) 15 | * Accuracy 71.871 16 | Current best accuracy: 71.87092590332031 17 | 40.106828927993774 18 | Current learning rate: 0.01 19 | Epoch: [1][ 0/96] Loss 0.9466 (0.9466) Accuracy 67.969 (67.969) 20 | Epoch: [1][10/96] Loss 0.8868 (0.9193) Accuracy 70.312 (69.247) 21 | Epoch: [1][20/96] Loss 0.9390 (0.9192) Accuracy 72.656 (69.234) 22 | Epoch: [1][30/96] Loss 1.0193 (0.9249) Accuracy 67.969 (69.178) 23 | Epoch: [1][40/96] Loss 0.8185 (0.9109) Accuracy 73.438 (69.741) 24 | Epoch: [1][50/96] Loss 0.8844 (0.9096) Accuracy 71.094 (69.700) 25 | Epoch: [1][60/96] Loss 1.0390 (0.9045) Accuracy 60.938 (69.595) 26 | Epoch: [1][70/96] Loss 0.8081 (0.9078) Accuracy 68.750 (69.520) 27 | Epoch: [1][80/96] Loss 0.8047 (0.8975) Accuracy 74.219 (69.821) 28 | Epoch: [1][90/96] Loss 0.7882 (0.8916) Accuracy 74.219 (69.952) 29 | Test: [ 0/24] Loss 0.2131 (0.2131) Accuracy 96.094 (96.094) 30 | Test: [10/24] Loss 0.3748 (0.3178) Accuracy 87.500 (91.477) 31 | Test: [20/24] Loss 0.7178 (0.4817) Accuracy 77.344 (83.780) 32 | * Accuracy 77.705 33 | Current best accuracy: 77.7053451538086 34 | 45.5964560508728 35 | Current learning rate: 0.01 36 | Epoch: [2][ 0/96] Loss 0.7850 (0.7850) Accuracy 76.562 (76.562) 37 | Epoch: [2][10/96] Loss 0.9932 (0.8203) Accuracy 71.094 (73.935) 38 | Epoch: [2][20/96] Loss 1.0082 (0.8514) Accuracy 68.750 (71.949) 39 | Epoch: [2][30/96] Loss 0.8642 (0.8353) Accuracy 71.875 (72.379) 40 | Epoch: [2][40/96] Loss 0.8172 (0.8201) Accuracy 71.094 (72.771) 41 | Epoch: [2][50/96] Loss 0.5889 (0.8223) Accuracy 78.906 (72.794) 42 | Epoch: [2][60/96] Loss 0.6635 (0.8188) Accuracy 79.688 (72.695) 43 | Epoch: [2][70/96] Loss 0.9936 (0.8141) Accuracy 64.062 (72.964) 44 | Epoch: [2][80/96] Loss 0.9348 (0.8225) Accuracy 72.656 (72.801) 45 | Epoch: [2][90/96] Loss 0.9237 (0.8253) Accuracy 66.406 (72.553) 46 | Test: [ 0/24] Loss 0.2917 (0.2917) Accuracy 94.531 (94.531) 47 | Test: [10/24] Loss 0.1966 (0.3418) Accuracy 91.406 (89.915) 48 | Test: [20/24] Loss 1.0997 (0.6470) Accuracy 64.844 (77.976) 49 | * Accuracy 76.923 50 | Current best accuracy: 77.7053451538086 51 | 36.424731492996216 52 | Current learning rate: 0.01 53 | Epoch: [3][ 0/96] Loss 0.8161 (0.8161) Accuracy 71.875 (71.875) 54 | Epoch: [3][10/96] Loss 0.5913 (0.8079) Accuracy 81.250 (73.295) 55 | Epoch: [3][20/96] Loss 0.7822 (0.7877) Accuracy 74.219 (74.702) 56 | Epoch: [3][30/96] Loss 0.7207 (0.7768) Accuracy 76.562 (74.723) 57 | Epoch: [3][40/96] Loss 0.7287 (0.7727) Accuracy 73.438 (74.543) 58 | Epoch: [3][50/96] Loss 0.7312 (0.7688) Accuracy 72.656 (74.510) 59 | Epoch: [3][60/96] Loss 0.7213 (0.7678) Accuracy 75.781 (74.449) 60 | Epoch: [3][70/96] Loss 0.7278 (0.7547) Accuracy 72.656 (74.813) 61 | Epoch: [3][80/96] Loss 0.7065 (0.7587) Accuracy 76.562 (74.643) 62 | Epoch: [3][90/96] Loss 0.6534 (0.7582) Accuracy 79.688 (74.734) 63 | Test: [ 0/24] Loss 0.3635 (0.3635) Accuracy 90.625 (90.625) 64 | Test: [10/24] Loss 0.2480 (0.3802) Accuracy 90.625 (89.276) 65 | Test: [20/24] Loss 0.6506 (0.4475) Accuracy 75.781 (86.235) 66 | * Accuracy 83.540 67 | Current best accuracy: 83.53976440429688 68 | 40.670286655426025 69 | Current learning rate: 0.01 70 | Epoch: [4][ 0/96] Loss 0.6729 (0.6729) Accuracy 75.000 (75.000) 71 | Epoch: [4][10/96] Loss 0.6984 (0.7089) Accuracy 76.562 (75.355) 72 | Epoch: [4][20/96] Loss 0.6182 (0.7012) Accuracy 81.250 (76.265) 73 | Epoch: [4][30/96] Loss 0.6006 (0.7152) Accuracy 81.250 (75.932) 74 | Epoch: [4][40/96] Loss 0.7650 (0.7216) Accuracy 74.219 (75.819) 75 | Epoch: [4][50/96] Loss 0.6506 (0.7242) Accuracy 78.125 (75.551) 76 | Epoch: [4][60/96] Loss 0.5895 (0.7279) Accuracy 78.906 (75.474) 77 | Epoch: [4][70/96] Loss 0.6324 (0.7260) Accuracy 73.438 (75.484) 78 | Epoch: [4][80/96] Loss 0.6654 (0.7252) Accuracy 82.031 (75.463) 79 | Epoch: [4][90/96] Loss 0.9670 (0.7279) Accuracy 67.188 (75.404) 80 | Test: [ 0/24] Loss 0.5765 (0.5765) Accuracy 81.250 (81.250) 81 | Test: [10/24] Loss 0.4129 (0.6199) Accuracy 85.156 (81.250) 82 | Test: [20/24] Loss 0.7516 (0.6143) Accuracy 71.875 (80.990) 83 | * Accuracy 80.052 84 | Current best accuracy: 83.53976440429688 85 | 35.17909646034241 86 | Current learning rate: 0.01 87 | Epoch: [5][ 0/96] Loss 0.8163 (0.8163) Accuracy 75.781 (75.781) 88 | Epoch: [5][10/96] Loss 0.7552 (0.7064) Accuracy 79.688 (77.344) 89 | Epoch: [5][20/96] Loss 0.7367 (0.6919) Accuracy 73.438 (77.158) 90 | Epoch: [5][30/96] Loss 0.8442 (0.6954) Accuracy 73.438 (76.890) 91 | Epoch: [5][40/96] Loss 0.7456 (0.7102) Accuracy 78.906 (76.658) 92 | Epoch: [5][50/96] Loss 0.5709 (0.7178) Accuracy 84.375 (76.639) 93 | Epoch: [5][60/96] Loss 0.8458 (0.7212) Accuracy 74.219 (76.562) 94 | Epoch: [5][70/96] Loss 0.6468 (0.7148) Accuracy 75.781 (76.640) 95 | Epoch: [5][80/96] Loss 0.6728 (0.7163) Accuracy 79.688 (76.543) 96 | Epoch: [5][90/96] Loss 0.8580 (0.7155) Accuracy 72.656 (76.580) 97 | Test: [ 0/24] Loss 0.1217 (0.1217) Accuracy 99.219 (99.219) 98 | Test: [10/24] Loss 0.5101 (0.3317) Accuracy 82.812 (89.844) 99 | Test: [20/24] Loss 0.6660 (0.6496) Accuracy 75.781 (77.790) 100 | * Accuracy 74.250 101 | Current best accuracy: 83.53976440429688 102 | 35.357388973236084 103 | Current learning rate: 0.01 104 | Epoch: [6][ 0/96] Loss 0.7325 (0.7325) Accuracy 72.656 (72.656) 105 | Epoch: [6][10/96] Loss 0.5900 (0.7420) Accuracy 78.906 (75.852) 106 | Epoch: [6][20/96] Loss 0.6650 (0.7236) Accuracy 78.906 (75.818) 107 | Epoch: [6][30/96] Loss 0.7437 (0.6986) Accuracy 74.219 (76.865) 108 | Epoch: [6][40/96] Loss 0.7199 (0.6903) Accuracy 76.562 (77.268) 109 | Epoch: [6][50/96] Loss 0.6548 (0.6837) Accuracy 78.125 (77.405) 110 | Epoch: [6][60/96] Loss 0.6493 (0.6864) Accuracy 81.250 (77.357) 111 | Epoch: [6][70/96] Loss 0.7460 (0.6923) Accuracy 75.000 (77.003) 112 | Epoch: [6][80/96] Loss 0.7118 (0.6934) Accuracy 76.562 (76.939) 113 | Epoch: [6][90/96] Loss 0.7977 (0.6931) Accuracy 73.438 (76.880) 114 | Test: [ 0/24] Loss 0.1489 (0.1489) Accuracy 96.875 (96.875) 115 | Test: [10/24] Loss 0.2116 (0.2417) Accuracy 91.406 (92.614) 116 | Test: [20/24] Loss 0.8423 (0.4653) Accuracy 70.312 (83.780) 117 | * Accuracy 80.867 118 | Current best accuracy: 83.53976440429688 119 | 35.426268100738525 120 | Current learning rate: 0.01 121 | Epoch: [7][ 0/96] Loss 0.6869 (0.6869) Accuracy 77.344 (77.344) 122 | Epoch: [7][10/96] Loss 0.6621 (0.6535) Accuracy 78.906 (77.983) 123 | Epoch: [7][20/96] Loss 0.8128 (0.6457) Accuracy 71.875 (78.423) 124 | Epoch: [7][30/96] Loss 0.6366 (0.6499) Accuracy 74.219 (78.100) 125 | Epoch: [7][40/96] Loss 0.5321 (0.6382) Accuracy 85.938 (78.620) 126 | Epoch: [7][50/96] Loss 0.6008 (0.6420) Accuracy 82.031 (78.447) 127 | Epoch: [7][60/96] Loss 0.6478 (0.6479) Accuracy 78.906 (78.368) 128 | Epoch: [7][70/96] Loss 0.6821 (0.6493) Accuracy 75.000 (78.301) 129 | Epoch: [7][80/96] Loss 0.5845 (0.6467) Accuracy 81.250 (78.424) 130 | Epoch: [7][90/96] Loss 0.5402 (0.6475) Accuracy 80.469 (78.391) 131 | Test: [ 0/24] Loss 0.1986 (0.1986) Accuracy 97.656 (97.656) 132 | Test: [10/24] Loss 0.1533 (0.2647) Accuracy 93.750 (91.974) 133 | Test: [20/24] Loss 0.8335 (0.3856) Accuracy 74.219 (87.351) 134 | * Accuracy 84.257 135 | Current best accuracy: 84.25684356689453 136 | 40.07467794418335 137 | Current learning rate: 0.01 138 | Epoch: [8][ 0/96] Loss 0.6251 (0.6251) Accuracy 78.125 (78.125) 139 | Epoch: [8][10/96] Loss 0.6457 (0.6359) Accuracy 77.344 (78.196) 140 | Epoch: [8][20/96] Loss 0.5937 (0.6290) Accuracy 76.562 (78.646) 141 | Epoch: [8][30/96] Loss 0.6760 (0.6360) Accuracy 73.438 (78.453) 142 | Epoch: [8][40/96] Loss 0.8443 (0.6523) Accuracy 71.094 (77.782) 143 | Epoch: [8][50/96] Loss 0.7823 (0.6613) Accuracy 70.312 (77.650) 144 | Epoch: [8][60/96] Loss 0.6883 (0.6660) Accuracy 72.656 (77.472) 145 | Epoch: [8][70/96] Loss 0.6800 (0.6687) Accuracy 75.000 (77.311) 146 | Epoch: [8][80/96] Loss 0.6289 (0.6609) Accuracy 78.906 (77.701) 147 | Epoch: [8][90/96] Loss 0.7934 (0.6607) Accuracy 73.438 (77.687) 148 | Test: [ 0/24] Loss 0.1494 (0.1494) Accuracy 96.875 (96.875) 149 | Test: [10/24] Loss 0.1760 (0.2199) Accuracy 92.969 (93.324) 150 | Test: [20/24] Loss 0.9491 (0.5116) Accuracy 67.969 (82.366) 151 | * Accuracy 81.095 152 | Current best accuracy: 84.25684356689453 153 | 34.86864686012268 154 | Current learning rate: 0.01 155 | Epoch: [9][ 0/96] Loss 0.6685 (0.6685) Accuracy 76.562 (76.562) 156 | Epoch: [9][10/96] Loss 0.6240 (0.6656) Accuracy 78.125 (77.983) 157 | Epoch: [9][20/96] Loss 0.8189 (0.6820) Accuracy 69.531 (77.307) 158 | Epoch: [9][30/96] Loss 0.7179 (0.6839) Accuracy 78.906 (77.117) 159 | Epoch: [9][40/96] Loss 0.6850 (0.6775) Accuracy 75.781 (77.496) 160 | Epoch: [9][50/96] Loss 0.5912 (0.6850) Accuracy 82.812 (77.374) 161 | Epoch: [9][60/96] Loss 0.6354 (0.6802) Accuracy 78.125 (77.472) 162 | Epoch: [9][70/96] Loss 0.5464 (0.6768) Accuracy 84.375 (77.509) 163 | Epoch: [9][80/96] Loss 0.6756 (0.6742) Accuracy 77.344 (77.643) 164 | Epoch: [9][90/96] Loss 0.7523 (0.6690) Accuracy 73.438 (77.825) 165 | Test: [ 0/24] Loss 0.3994 (0.3994) Accuracy 87.500 (87.500) 166 | Test: [10/24] Loss 0.2689 (0.4489) Accuracy 89.844 (85.653) 167 | Test: [20/24] Loss 0.4639 (0.4348) Accuracy 86.719 (86.570) 168 | * Accuracy 83.833 169 | Current best accuracy: 84.25684356689453 170 | 34.30891251564026 171 | Current learning rate: 0.01 172 | Epoch: [10][ 0/96] Loss 0.6532 (0.6532) Accuracy 80.469 (80.469) 173 | Epoch: [10][10/96] Loss 0.6749 (0.6257) Accuracy 74.219 (80.114) 174 | Epoch: [10][20/96] Loss 0.7492 (0.6278) Accuracy 73.438 (79.725) 175 | Epoch: [10][30/96] Loss 0.5283 (0.6262) Accuracy 81.250 (79.360) 176 | Epoch: [10][40/96] Loss 0.6733 (0.6282) Accuracy 74.219 (79.173) 177 | Epoch: [10][50/96] Loss 0.6825 (0.6367) Accuracy 79.688 (78.830) 178 | Epoch: [10][60/96] Loss 0.6243 (0.6484) Accuracy 82.031 (78.548) 179 | Epoch: [10][70/96] Loss 0.5210 (0.6464) Accuracy 81.250 (78.697) 180 | Epoch: [10][80/96] Loss 0.5871 (0.6439) Accuracy 79.688 (78.877) 181 | Epoch: [10][90/96] Loss 0.6588 (0.6468) Accuracy 75.781 (78.709) 182 | Test: [ 0/24] Loss 0.3605 (0.3605) Accuracy 90.625 (90.625) 183 | Test: [10/24] Loss 0.2580 (0.4065) Accuracy 91.406 (87.713) 184 | Test: [20/24] Loss 0.6233 (0.4191) Accuracy 77.344 (86.942) 185 | * Accuracy 84.583 186 | Current best accuracy: 84.5827865600586 187 | 36.617589712142944 188 | Current learning rate: 0.01 189 | Epoch: [11][ 0/96] Loss 0.6025 (0.6025) Accuracy 76.562 (76.562) 190 | Epoch: [11][10/96] Loss 0.5881 (0.5991) Accuracy 82.812 (80.611) 191 | Epoch: [11][20/96] Loss 0.6176 (0.6024) Accuracy 80.469 (79.836) 192 | Epoch: [11][30/96] Loss 0.7666 (0.6064) Accuracy 72.656 (79.662) 193 | Epoch: [11][40/96] Loss 0.6066 (0.6010) Accuracy 82.812 (80.088) 194 | Epoch: [11][50/96] Loss 0.5388 (0.6074) Accuracy 79.688 (79.810) 195 | Epoch: [11][60/96] Loss 0.6085 (0.6083) Accuracy 79.688 (80.046) 196 | Epoch: [11][70/96] Loss 0.5554 (0.6018) Accuracy 82.031 (80.183) 197 | Epoch: [11][80/96] Loss 0.6898 (0.6010) Accuracy 75.000 (80.179) 198 | Epoch: [11][90/96] Loss 0.5674 (0.6012) Accuracy 80.469 (80.168) 199 | Test: [ 0/24] Loss 0.3174 (0.3174) Accuracy 92.188 (92.188) 200 | Test: [10/24] Loss 0.3536 (0.3769) Accuracy 86.719 (88.920) 201 | Test: [20/24] Loss 0.8254 (0.4858) Accuracy 72.656 (84.747) 202 | * Accuracy 83.735 203 | Current best accuracy: 84.5827865600586 204 | 34.37671613693237 205 | Current learning rate: 0.01 206 | Epoch: [12][ 0/96] Loss 0.6303 (0.6303) Accuracy 76.562 (76.562) 207 | Epoch: [12][10/96] Loss 0.7887 (0.5754) Accuracy 74.219 (80.753) 208 | Epoch: [12][20/96] Loss 0.5933 (0.5520) Accuracy 79.688 (81.510) 209 | Epoch: [12][30/96] Loss 0.5463 (0.5531) Accuracy 81.250 (81.300) 210 | Epoch: [12][40/96] Loss 0.5765 (0.5716) Accuracy 80.469 (80.545) 211 | Epoch: [12][50/96] Loss 0.5448 (0.5763) Accuracy 82.031 (80.285) 212 | Epoch: [12][60/96] Loss 0.6742 (0.5826) Accuracy 79.688 (80.366) 213 | Epoch: [12][70/96] Loss 0.6206 (0.5834) Accuracy 74.219 (80.150) 214 | Epoch: [12][80/96] Loss 0.5667 (0.5855) Accuracy 78.906 (80.160) 215 | Epoch: [12][90/96] Loss 0.6558 (0.5850) Accuracy 77.344 (80.194) 216 | Test: [ 0/24] Loss 0.2510 (0.2510) Accuracy 95.312 (95.312) 217 | Test: [10/24] Loss 0.1315 (0.2813) Accuracy 94.531 (91.548) 218 | Test: [20/24] Loss 0.4543 (0.3421) Accuracy 88.281 (89.397) 219 | * Accuracy 84.713 220 | Current best accuracy: 84.71316528320312 221 | 36.34793043136597 222 | Current learning rate: 0.01 223 | Epoch: [13][ 0/96] Loss 0.5864 (0.5864) Accuracy 78.125 (78.125) 224 | Epoch: [13][10/96] Loss 0.7170 (0.5716) Accuracy 76.562 (80.256) 225 | Epoch: [13][20/96] Loss 0.4907 (0.5563) Accuracy 83.594 (80.990) 226 | Epoch: [13][30/96] Loss 0.5305 (0.5501) Accuracy 81.250 (81.326) 227 | Epoch: [13][40/96] Loss 0.6081 (0.5554) Accuracy 80.469 (81.345) 228 | Epoch: [13][50/96] Loss 0.7536 (0.5603) Accuracy 72.656 (81.373) 229 | Epoch: [13][60/96] Loss 0.7164 (0.5719) Accuracy 75.000 (81.096) 230 | Epoch: [13][70/96] Loss 0.5898 (0.5728) Accuracy 81.250 (81.052) 231 | Epoch: [13][80/96] Loss 0.5738 (0.5732) Accuracy 81.250 (80.999) 232 | Epoch: [13][90/96] Loss 0.5645 (0.5718) Accuracy 81.250 (80.984) 233 | Test: [ 0/24] Loss 0.3382 (0.3382) Accuracy 86.719 (86.719) 234 | Test: [10/24] Loss 0.2176 (0.3750) Accuracy 92.188 (87.500) 235 | Test: [20/24] Loss 0.4878 (0.4439) Accuracy 84.375 (85.491) 236 | * Accuracy 83.572 237 | Current best accuracy: 84.71316528320312 238 | 34.252110958099365 239 | Current learning rate: 0.01 240 | Epoch: [14][ 0/96] Loss 0.5208 (0.5208) Accuracy 85.938 (85.938) 241 | Epoch: [14][10/96] Loss 0.6700 (0.5513) Accuracy 75.781 (81.605) 242 | Epoch: [14][20/96] Loss 0.3677 (0.5377) Accuracy 92.188 (82.403) 243 | Epoch: [14][30/96] Loss 0.6751 (0.5559) Accuracy 76.562 (81.628) 244 | Epoch: [14][40/96] Loss 0.4915 (0.5531) Accuracy 83.594 (81.498) 245 | Epoch: [14][50/96] Loss 0.5344 (0.5460) Accuracy 82.031 (81.648) 246 | Epoch: [14][60/96] Loss 0.5696 (0.5485) Accuracy 82.031 (81.570) 247 | Epoch: [14][70/96] Loss 0.7322 (0.5572) Accuracy 78.125 (81.294) 248 | Epoch: [14][80/96] Loss 0.6429 (0.5644) Accuracy 78.125 (81.192) 249 | Epoch: [14][90/96] Loss 0.4298 (0.5603) Accuracy 85.156 (81.344) 250 | Test: [ 0/24] Loss 0.2538 (0.2538) Accuracy 91.406 (91.406) 251 | Test: [10/24] Loss 0.1493 (0.3029) Accuracy 95.312 (90.412) 252 | Test: [20/24] Loss 1.1176 (0.4120) Accuracy 67.969 (87.351) 253 | * Accuracy 83.703 254 | Current best accuracy: 84.71316528320312 255 | 33.92336821556091 256 | Current learning rate: 0.001 257 | Epoch: [15][ 0/96] Loss 0.6325 (0.6325) Accuracy 78.906 (78.906) 258 | Epoch: [15][10/96] Loss 0.3990 (0.5349) Accuracy 85.938 (81.676) 259 | Epoch: [15][20/96] Loss 0.4506 (0.5141) Accuracy 88.281 (82.850) 260 | Epoch: [15][30/96] Loss 0.5330 (0.5090) Accuracy 82.812 (83.014) 261 | Epoch: [15][40/96] Loss 0.4725 (0.5053) Accuracy 82.812 (83.194) 262 | Epoch: [15][50/96] Loss 0.6886 (0.4990) Accuracy 76.562 (83.349) 263 | Epoch: [15][60/96] Loss 0.5576 (0.5028) Accuracy 82.812 (83.197) 264 | Epoch: [15][70/96] Loss 0.5074 (0.4984) Accuracy 84.375 (83.572) 265 | Epoch: [15][80/96] Loss 0.5106 (0.4980) Accuracy 84.375 (83.594) 266 | Epoch: [15][90/96] Loss 0.4571 (0.4994) Accuracy 86.719 (83.568) 267 | Test: [ 0/24] Loss 0.1782 (0.1782) Accuracy 95.312 (95.312) 268 | Test: [10/24] Loss 0.1700 (0.2597) Accuracy 92.969 (91.974) 269 | Test: [20/24] Loss 0.6565 (0.3336) Accuracy 80.469 (89.360) 270 | * Accuracy 87.060 271 | Current best accuracy: 87.05997467041016 272 | 36.87536358833313 273 | Current learning rate: 0.001 274 | Epoch: [16][ 0/96] Loss 0.5497 (0.5497) Accuracy 82.031 (82.031) 275 | Epoch: [16][10/96] Loss 0.5529 (0.4500) Accuracy 84.375 (84.943) 276 | Epoch: [16][20/96] Loss 0.5621 (0.4756) Accuracy 80.469 (84.301) 277 | Epoch: [16][30/96] Loss 0.5978 (0.4834) Accuracy 78.906 (84.022) 278 | Epoch: [16][40/96] Loss 0.5327 (0.4721) Accuracy 85.938 (84.413) 279 | Epoch: [16][50/96] Loss 0.3905 (0.4693) Accuracy 85.938 (84.589) 280 | Epoch: [16][60/96] Loss 0.4159 (0.4643) Accuracy 83.594 (84.721) 281 | Epoch: [16][70/96] Loss 0.4720 (0.4717) Accuracy 79.688 (84.309) 282 | Epoch: [16][80/96] Loss 0.3806 (0.4690) Accuracy 86.719 (84.346) 283 | Epoch: [16][90/96] Loss 0.4456 (0.4678) Accuracy 85.156 (84.332) 284 | Test: [ 0/24] Loss 0.2071 (0.2071) Accuracy 96.875 (96.875) 285 | Test: [10/24] Loss 0.1244 (0.2820) Accuracy 93.750 (91.264) 286 | Test: [20/24] Loss 0.5434 (0.3201) Accuracy 85.938 (89.807) 287 | * Accuracy 87.744 288 | Current best accuracy: 87.74446105957031 289 | 36.901938676834106 290 | Current learning rate: 0.001 291 | Epoch: [17][ 0/96] Loss 0.4169 (0.4169) Accuracy 85.156 (85.156) 292 | Epoch: [17][10/96] Loss 0.3852 (0.4439) Accuracy 87.500 (85.582) 293 | Epoch: [17][20/96] Loss 0.5670 (0.4487) Accuracy 80.469 (85.528) 294 | Epoch: [17][30/96] Loss 0.5212 (0.4580) Accuracy 80.469 (84.904) 295 | Epoch: [17][40/96] Loss 0.3911 (0.4628) Accuracy 87.500 (84.585) 296 | Epoch: [17][50/96] Loss 0.3482 (0.4506) Accuracy 88.281 (85.064) 297 | Epoch: [17][60/96] Loss 0.5279 (0.4494) Accuracy 83.594 (85.143) 298 | Epoch: [17][70/96] Loss 0.4608 (0.4512) Accuracy 85.156 (85.189) 299 | Epoch: [17][80/96] Loss 0.5131 (0.4536) Accuracy 86.719 (85.137) 300 | Epoch: [17][90/96] Loss 0.3660 (0.4543) Accuracy 89.062 (85.165) 301 | Test: [ 0/24] Loss 0.1612 (0.1612) Accuracy 96.094 (96.094) 302 | Test: [10/24] Loss 0.1159 (0.2423) Accuracy 94.531 (92.116) 303 | Test: [20/24] Loss 0.5876 (0.3101) Accuracy 84.375 (90.216) 304 | * Accuracy 87.907 305 | Current best accuracy: 87.90743255615234 306 | 36.907596588134766 307 | Current learning rate: 0.001 308 | Epoch: [18][ 0/96] Loss 0.3632 (0.3632) Accuracy 91.406 (91.406) 309 | Epoch: [18][10/96] Loss 0.3348 (0.4532) Accuracy 87.500 (85.227) 310 | Epoch: [18][20/96] Loss 0.4662 (0.4414) Accuracy 81.250 (85.379) 311 | Epoch: [18][30/96] Loss 0.3385 (0.4417) Accuracy 89.844 (85.433) 312 | Epoch: [18][40/96] Loss 0.4424 (0.4418) Accuracy 85.938 (85.480) 313 | Epoch: [18][50/96] Loss 0.5420 (0.4381) Accuracy 78.906 (85.524) 314 | Epoch: [18][60/96] Loss 0.3848 (0.4435) Accuracy 86.719 (85.400) 315 | Epoch: [18][70/96] Loss 0.4988 (0.4433) Accuracy 87.500 (85.420) 316 | Epoch: [18][80/96] Loss 0.3950 (0.4374) Accuracy 85.938 (85.619) 317 | Epoch: [18][90/96] Loss 0.4183 (0.4413) Accuracy 87.500 (85.465) 318 | Test: [ 0/24] Loss 0.1764 (0.1764) Accuracy 96.875 (96.875) 319 | Test: [10/24] Loss 0.1099 (0.2495) Accuracy 94.531 (91.974) 320 | Test: [20/24] Loss 0.6135 (0.3070) Accuracy 85.156 (90.476) 321 | * Accuracy 88.070 322 | Current best accuracy: 88.07040405273438 323 | 36.80367708206177 324 | Current learning rate: 0.001 325 | Epoch: [19][ 0/96] Loss 0.3125 (0.3125) Accuracy 91.406 (91.406) 326 | Epoch: [19][10/96] Loss 0.4516 (0.4049) Accuracy 82.812 (86.009) 327 | Epoch: [19][20/96] Loss 0.4286 (0.4252) Accuracy 86.719 (85.454) 328 | Epoch: [19][30/96] Loss 0.4930 (0.4309) Accuracy 85.156 (85.685) 329 | Epoch: [19][40/96] Loss 0.4612 (0.4337) Accuracy 82.812 (85.595) 330 | Epoch: [19][50/96] Loss 0.4130 (0.4355) Accuracy 85.938 (85.646) 331 | Epoch: [19][60/96] Loss 0.3670 (0.4317) Accuracy 90.625 (85.809) 332 | Epoch: [19][70/96] Loss 0.4332 (0.4318) Accuracy 88.281 (85.805) 333 | Epoch: [19][80/96] Loss 0.3864 (0.4289) Accuracy 83.594 (85.909) 334 | Epoch: [19][90/96] Loss 0.3112 (0.4308) Accuracy 92.188 (85.800) 335 | Test: [ 0/24] Loss 0.1492 (0.1492) Accuracy 97.656 (97.656) 336 | Test: [10/24] Loss 0.1698 (0.2583) Accuracy 92.969 (91.477) 337 | Test: [20/24] Loss 0.5836 (0.3157) Accuracy 84.375 (89.695) 338 | * Accuracy 87.451 339 | Current best accuracy: 88.07040405273438 340 | 34.60780715942383 341 | Current learning rate: 0.001 342 | Epoch: [20][ 0/96] Loss 0.4472 (0.4472) Accuracy 84.375 (84.375) 343 | Epoch: [20][10/96] Loss 0.2854 (0.3893) Accuracy 91.406 (87.287) 344 | Epoch: [20][20/96] Loss 0.3370 (0.4053) Accuracy 90.625 (86.942) 345 | Epoch: [20][30/96] Loss 0.3357 (0.4055) Accuracy 89.062 (86.870) 346 | Epoch: [20][40/96] Loss 0.4113 (0.4150) Accuracy 86.719 (86.643) 347 | Epoch: [20][50/96] Loss 0.4923 (0.4149) Accuracy 85.156 (86.581) 348 | Epoch: [20][60/96] Loss 0.3271 (0.4154) Accuracy 89.844 (86.693) 349 | Epoch: [20][70/96] Loss 0.4194 (0.4208) Accuracy 81.250 (86.543) 350 | Epoch: [20][80/96] Loss 0.4355 (0.4248) Accuracy 85.938 (86.275) 351 | Epoch: [20][90/96] Loss 0.5592 (0.4264) Accuracy 78.906 (86.135) 352 | Test: [ 0/24] Loss 0.1853 (0.1853) Accuracy 96.875 (96.875) 353 | Test: [10/24] Loss 0.1101 (0.2641) Accuracy 96.875 (91.832) 354 | Test: [20/24] Loss 0.6745 (0.3227) Accuracy 82.812 (89.993) 355 | * Accuracy 87.875 356 | Current best accuracy: 88.07040405273438 357 | 34.81442594528198 358 | Current learning rate: 0.001 359 | Epoch: [21][ 0/96] Loss 0.4650 (0.4650) Accuracy 81.250 (81.250) 360 | Epoch: [21][10/96] Loss 0.2852 (0.4078) Accuracy 92.188 (87.145) 361 | Epoch: [21][20/96] Loss 0.3299 (0.4282) Accuracy 91.406 (85.826) 362 | Epoch: [21][30/96] Loss 0.3246 (0.4119) Accuracy 89.062 (86.568) 363 | Epoch: [21][40/96] Loss 0.4346 (0.4157) Accuracy 85.156 (86.528) 364 | Epoch: [21][50/96] Loss 0.3760 (0.4177) Accuracy 89.062 (86.596) 365 | Epoch: [21][60/96] Loss 0.4541 (0.4160) Accuracy 85.938 (86.591) 366 | Epoch: [21][70/96] Loss 0.4544 (0.4213) Accuracy 84.375 (86.466) 367 | Epoch: [21][80/96] Loss 0.3914 (0.4223) Accuracy 87.500 (86.304) 368 | Epoch: [21][90/96] Loss 0.3667 (0.4221) Accuracy 89.062 (86.324) 369 | Test: [ 0/24] Loss 0.1571 (0.1571) Accuracy 97.656 (97.656) 370 | Test: [10/24] Loss 0.1445 (0.2540) Accuracy 93.750 (91.761) 371 | Test: [20/24] Loss 0.6227 (0.3203) Accuracy 84.375 (89.844) 372 | * Accuracy 87.842 373 | Current best accuracy: 88.07040405273438 374 | 34.25020980834961 375 | Current learning rate: 0.001 376 | Epoch: [22][ 0/96] Loss 0.4850 (0.4850) Accuracy 85.938 (85.938) 377 | Epoch: [22][10/96] Loss 0.6915 (0.4280) Accuracy 75.000 (85.298) 378 | Epoch: [22][20/96] Loss 0.4074 (0.4211) Accuracy 87.500 (86.049) 379 | Epoch: [22][30/96] Loss 0.3110 (0.4145) Accuracy 89.062 (86.643) 380 | Epoch: [22][40/96] Loss 0.3918 (0.4152) Accuracy 86.719 (86.547) 381 | Epoch: [22][50/96] Loss 0.4252 (0.4123) Accuracy 83.594 (86.489) 382 | Epoch: [22][60/96] Loss 0.4888 (0.4057) Accuracy 85.938 (86.796) 383 | Epoch: [22][70/96] Loss 0.4028 (0.4094) Accuracy 85.938 (86.620) 384 | Epoch: [22][80/96] Loss 0.4205 (0.4120) Accuracy 89.062 (86.574) 385 | Epoch: [22][90/96] Loss 0.3671 (0.4134) Accuracy 89.062 (86.607) 386 | Test: [ 0/24] Loss 0.1637 (0.1637) Accuracy 97.656 (97.656) 387 | Test: [10/24] Loss 0.1346 (0.2539) Accuracy 93.750 (92.045) 388 | Test: [20/24] Loss 0.6845 (0.3219) Accuracy 83.594 (90.067) 389 | * Accuracy 87.907 390 | Current best accuracy: 88.07040405273438 391 | 35.06278967857361 392 | Current learning rate: 0.001 393 | Epoch: [23][ 0/96] Loss 0.2831 (0.2831) Accuracy 89.844 (89.844) 394 | Epoch: [23][10/96] Loss 0.3513 (0.3900) Accuracy 86.719 (87.855) 395 | Epoch: [23][20/96] Loss 0.3713 (0.4011) Accuracy 89.062 (87.240) 396 | Epoch: [23][30/96] Loss 0.3640 (0.4022) Accuracy 89.844 (87.198) 397 | Epoch: [23][40/96] Loss 0.4576 (0.4058) Accuracy 85.156 (87.214) 398 | Epoch: [23][50/96] Loss 0.3606 (0.4030) Accuracy 87.500 (87.040) 399 | Epoch: [23][60/96] Loss 0.4568 (0.4066) Accuracy 84.375 (86.936) 400 | Epoch: [23][70/96] Loss 0.2976 (0.4047) Accuracy 92.188 (87.093) 401 | Epoch: [23][80/96] Loss 0.3753 (0.4095) Accuracy 83.594 (86.834) 402 | Epoch: [23][90/96] Loss 0.6554 (0.4137) Accuracy 77.344 (86.710) 403 | Test: [ 0/24] Loss 0.1884 (0.1884) Accuracy 96.094 (96.094) 404 | Test: [10/24] Loss 0.1258 (0.2658) Accuracy 96.875 (91.335) 405 | Test: [20/24] Loss 0.5643 (0.3124) Accuracy 85.938 (89.993) 406 | * Accuracy 87.744 407 | Current best accuracy: 88.07040405273438 408 | 34.261555194854736 409 | Current learning rate: 0.001 410 | Epoch: [24][ 0/96] Loss 0.4658 (0.4658) Accuracy 84.375 (84.375) 411 | Epoch: [24][10/96] Loss 0.5277 (0.4305) Accuracy 81.250 (85.298) 412 | Epoch: [24][20/96] Loss 0.5484 (0.4232) Accuracy 83.594 (86.086) 413 | Epoch: [24][30/96] Loss 0.3710 (0.4276) Accuracy 88.281 (85.786) 414 | Epoch: [24][40/96] Loss 0.2422 (0.4263) Accuracy 96.094 (85.918) 415 | Epoch: [24][50/96] Loss 0.3602 (0.4137) Accuracy 88.281 (86.336) 416 | Epoch: [24][60/96] Loss 0.3758 (0.4133) Accuracy 87.500 (86.424) 417 | Epoch: [24][70/96] Loss 0.4393 (0.4120) Accuracy 89.844 (86.345) 418 | Epoch: [24][80/96] Loss 0.4697 (0.4063) Accuracy 85.156 (86.593) 419 | Epoch: [24][90/96] Loss 0.4255 (0.4074) Accuracy 85.938 (86.599) 420 | Test: [ 0/24] Loss 0.1958 (0.1958) Accuracy 96.094 (96.094) 421 | Test: [10/24] Loss 0.1415 (0.2721) Accuracy 95.312 (91.690) 422 | Test: [20/24] Loss 0.5933 (0.3247) Accuracy 82.031 (89.881) 423 | * Accuracy 87.842 424 | Current best accuracy: 88.07040405273438 425 | 33.91360688209534 426 | Current learning rate: 0.001 427 | Epoch: [25][ 0/96] Loss 0.3599 (0.3599) Accuracy 88.281 (88.281) 428 | Epoch: [25][10/96] Loss 0.4911 (0.4394) Accuracy 84.375 (85.582) 429 | Epoch: [25][20/96] Loss 0.3899 (0.4061) Accuracy 85.156 (87.202) 430 | Epoch: [25][30/96] Loss 0.4398 (0.3974) Accuracy 84.375 (87.752) 431 | Epoch: [25][40/96] Loss 0.5324 (0.4015) Accuracy 84.375 (87.557) 432 | Epoch: [25][50/96] Loss 0.5114 (0.4078) Accuracy 82.812 (87.316) 433 | Epoch: [25][60/96] Loss 0.3961 (0.4091) Accuracy 89.062 (87.295) 434 | Epoch: [25][70/96] Loss 0.3085 (0.4093) Accuracy 89.844 (87.247) 435 | Epoch: [25][80/96] Loss 0.4008 (0.4057) Accuracy 85.938 (87.249) 436 | Epoch: [25][90/96] Loss 0.3680 (0.4040) Accuracy 85.938 (87.328) 437 | Test: [ 0/24] Loss 0.1747 (0.1747) Accuracy 96.875 (96.875) 438 | Test: [10/24] Loss 0.1434 (0.2764) Accuracy 94.531 (91.690) 439 | Test: [20/24] Loss 0.6067 (0.3306) Accuracy 84.375 (89.881) 440 | * Accuracy 87.875 441 | Current best accuracy: 88.07040405273438 442 | 34.82909107208252 443 | Current learning rate: 0.001 444 | Epoch: [26][ 0/96] Loss 0.5046 (0.5046) Accuracy 83.594 (83.594) 445 | Epoch: [26][10/96] Loss 0.3608 (0.4106) Accuracy 88.281 (86.719) 446 | Epoch: [26][20/96] Loss 0.3786 (0.3990) Accuracy 87.500 (86.905) 447 | Epoch: [26][30/96] Loss 0.4501 (0.3906) Accuracy 83.594 (87.500) 448 | Epoch: [26][40/96] Loss 0.4252 (0.3881) Accuracy 84.375 (87.633) 449 | Epoch: [26][50/96] Loss 0.3444 (0.3939) Accuracy 86.719 (87.531) 450 | Epoch: [26][60/96] Loss 0.5002 (0.3917) Accuracy 85.938 (87.692) 451 | Epoch: [26][70/96] Loss 0.3030 (0.3936) Accuracy 92.188 (87.643) 452 | Epoch: [26][80/96] Loss 0.3121 (0.3941) Accuracy 91.406 (87.568) 453 | Epoch: [26][90/96] Loss 0.4082 (0.3935) Accuracy 86.719 (87.680) 454 | Test: [ 0/24] Loss 0.2040 (0.2040) Accuracy 96.094 (96.094) 455 | Test: [10/24] Loss 0.1248 (0.2972) Accuracy 96.094 (90.980) 456 | Test: [20/24] Loss 0.5510 (0.3333) Accuracy 85.938 (89.881) 457 | * Accuracy 88.005 458 | Current best accuracy: 88.07040405273438 459 | 34.61080741882324 460 | Current learning rate: 0.001 461 | Epoch: [27][ 0/96] Loss 0.3676 (0.3676) Accuracy 89.062 (89.062) 462 | Epoch: [27][10/96] Loss 0.3354 (0.3541) Accuracy 86.719 (88.636) 463 | Epoch: [27][20/96] Loss 0.2957 (0.3775) Accuracy 90.625 (87.984) 464 | Epoch: [27][30/96] Loss 0.3270 (0.3772) Accuracy 88.281 (88.105) 465 | Epoch: [27][40/96] Loss 0.3674 (0.3785) Accuracy 89.062 (87.881) 466 | Epoch: [27][50/96] Loss 0.3450 (0.3824) Accuracy 88.281 (87.837) 467 | Epoch: [27][60/96] Loss 0.3448 (0.3815) Accuracy 88.281 (87.743) 468 | Epoch: [27][70/96] Loss 0.4013 (0.3855) Accuracy 82.031 (87.533) 469 | Epoch: [27][80/96] Loss 0.3380 (0.3850) Accuracy 89.062 (87.606) 470 | Epoch: [27][90/96] Loss 0.3853 (0.3881) Accuracy 89.062 (87.440) 471 | Test: [ 0/24] Loss 0.2193 (0.2193) Accuracy 93.750 (93.750) 472 | Test: [10/24] Loss 0.1414 (0.3161) Accuracy 96.094 (90.483) 473 | Test: [20/24] Loss 0.5765 (0.3423) Accuracy 82.812 (89.621) 474 | * Accuracy 87.744 475 | Current best accuracy: 88.07040405273438 476 | 34.76277732849121 477 | Current learning rate: 0.001 478 | Epoch: [28][ 0/96] Loss 0.3920 (0.3920) Accuracy 85.156 (85.156) 479 | Epoch: [28][10/96] Loss 0.4562 (0.3732) Accuracy 86.719 (88.281) 480 | Epoch: [28][20/96] Loss 0.3806 (0.3794) Accuracy 89.844 (88.132) 481 | Epoch: [28][30/96] Loss 0.4930 (0.3794) Accuracy 82.031 (87.954) 482 | Epoch: [28][40/96] Loss 0.5700 (0.3796) Accuracy 82.812 (87.843) 483 | Epoch: [28][50/96] Loss 0.4637 (0.3793) Accuracy 85.156 (87.868) 484 | Epoch: [28][60/96] Loss 0.4004 (0.3889) Accuracy 83.594 (87.346) 485 | Epoch: [28][70/96] Loss 0.3798 (0.3886) Accuracy 92.188 (87.390) 486 | Epoch: [28][80/96] Loss 0.4226 (0.3913) Accuracy 86.719 (87.355) 487 | Epoch: [28][90/96] Loss 0.3971 (0.3945) Accuracy 85.156 (87.320) 488 | Test: [ 0/24] Loss 0.1847 (0.1847) Accuracy 96.094 (96.094) 489 | Test: [10/24] Loss 0.1166 (0.2712) Accuracy 96.875 (91.477) 490 | Test: [20/24] Loss 0.6736 (0.3311) Accuracy 81.250 (89.844) 491 | * Accuracy 87.712 492 | Current best accuracy: 88.07040405273438 493 | 33.946534872055054 494 | Current learning rate: 0.001 495 | Epoch: [29][ 0/96] Loss 0.4668 (0.4668) Accuracy 85.156 (85.156) 496 | Epoch: [29][10/96] Loss 0.4023 (0.3846) Accuracy 88.281 (87.784) 497 | Epoch: [29][20/96] Loss 0.4300 (0.3878) Accuracy 86.719 (87.835) 498 | Epoch: [29][30/96] Loss 0.2273 (0.3829) Accuracy 92.188 (88.029) 499 | Epoch: [29][40/96] Loss 0.4322 (0.3868) Accuracy 86.719 (87.710) 500 | Epoch: [29][50/96] Loss 0.4009 (0.3870) Accuracy 84.375 (87.607) 501 | Epoch: [29][60/96] Loss 0.4258 (0.3898) Accuracy 82.812 (87.167) 502 | Epoch: [29][70/96] Loss 0.4064 (0.3915) Accuracy 89.062 (87.159) 503 | Epoch: [29][80/96] Loss 0.4146 (0.3945) Accuracy 89.062 (87.162) 504 | Epoch: [29][90/96] Loss 0.4529 (0.3948) Accuracy 86.719 (87.174) 505 | Test: [ 0/24] Loss 0.2075 (0.2075) Accuracy 95.312 (95.312) 506 | Test: [10/24] Loss 0.1104 (0.2913) Accuracy 96.875 (91.193) 507 | Test: [20/24] Loss 0.5782 (0.3235) Accuracy 86.719 (90.551) 508 | * Accuracy 88.396 509 | Current best accuracy: 88.39634704589844 510 | 38.894869327545166 511 | Current learning rate: 0.00010000000000000002 512 | Epoch: [30][ 0/96] Loss 0.4650 (0.4650) Accuracy 83.594 (83.594) 513 | Epoch: [30][10/96] Loss 0.6492 (0.4156) Accuracy 78.125 (86.222) 514 | Epoch: [30][20/96] Loss 0.4315 (0.3868) Accuracy 82.812 (87.054) 515 | Epoch: [30][30/96] Loss 0.3469 (0.3875) Accuracy 88.281 (87.147) 516 | Epoch: [30][40/96] Loss 0.3516 (0.3859) Accuracy 87.500 (87.252) 517 | Epoch: [30][50/96] Loss 0.3356 (0.3814) Accuracy 88.281 (87.592) 518 | Epoch: [30][60/96] Loss 0.3614 (0.3845) Accuracy 89.062 (87.474) 519 | Epoch: [30][70/96] Loss 0.3428 (0.3833) Accuracy 88.281 (87.456) 520 | Epoch: [30][80/96] Loss 0.3698 (0.3843) Accuracy 88.281 (87.432) 521 | Epoch: [30][90/96] Loss 0.3662 (0.3837) Accuracy 90.625 (87.431) 522 | Test: [ 0/24] Loss 0.1912 (0.1912) Accuracy 95.312 (95.312) 523 | Test: [10/24] Loss 0.1016 (0.2739) Accuracy 98.438 (91.619) 524 | Test: [20/24] Loss 0.6302 (0.3194) Accuracy 83.594 (90.365) 525 | * Accuracy 88.005 526 | Current best accuracy: 88.39634704589844 527 | 34.03004598617554 528 | Current learning rate: 0.00010000000000000002 529 | Epoch: [31][ 0/96] Loss 0.4110 (0.4110) Accuracy 86.719 (86.719) 530 | Epoch: [31][10/96] Loss 0.3590 (0.3737) Accuracy 89.062 (88.707) 531 | Epoch: [31][20/96] Loss 0.3617 (0.3694) Accuracy 84.375 (88.430) 532 | Epoch: [31][30/96] Loss 0.4008 (0.3738) Accuracy 86.719 (88.054) 533 | Epoch: [31][40/96] Loss 0.3311 (0.3719) Accuracy 90.625 (88.110) 534 | Epoch: [31][50/96] Loss 0.5026 (0.3761) Accuracy 82.812 (87.944) 535 | Epoch: [31][60/96] Loss 0.4309 (0.3814) Accuracy 82.812 (87.820) 536 | Epoch: [31][70/96] Loss 0.6104 (0.3798) Accuracy 77.344 (87.918) 537 | Epoch: [31][80/96] Loss 0.3829 (0.3772) Accuracy 87.500 (87.876) 538 | Epoch: [31][90/96] Loss 0.3147 (0.3731) Accuracy 90.625 (88.049) 539 | Test: [ 0/24] Loss 0.1810 (0.1810) Accuracy 96.094 (96.094) 540 | Test: [10/24] Loss 0.1328 (0.2824) Accuracy 95.312 (91.548) 541 | Test: [20/24] Loss 0.5750 (0.3242) Accuracy 85.938 (90.290) 542 | * Accuracy 88.136 543 | Current best accuracy: 88.39634704589844 544 | 34.30001211166382 545 | Current learning rate: 0.00010000000000000002 546 | Epoch: [32][ 0/96] Loss 0.3372 (0.3372) Accuracy 88.281 (88.281) 547 | Epoch: [32][10/96] Loss 0.2991 (0.3685) Accuracy 92.188 (88.068) 548 | Epoch: [32][20/96] Loss 0.3115 (0.3571) Accuracy 91.406 (88.616) 549 | Epoch: [32][30/96] Loss 0.3736 (0.3616) Accuracy 88.281 (88.584) 550 | Epoch: [32][40/96] Loss 0.4282 (0.3702) Accuracy 85.156 (88.129) 551 | Epoch: [32][50/96] Loss 0.4760 (0.3757) Accuracy 83.594 (87.806) 552 | Epoch: [32][60/96] Loss 0.3982 (0.3751) Accuracy 88.281 (87.807) 553 | Epoch: [32][70/96] Loss 0.4975 (0.3754) Accuracy 85.156 (87.830) 554 | Epoch: [32][80/96] Loss 0.2679 (0.3719) Accuracy 92.188 (87.953) 555 | Epoch: [32][90/96] Loss 0.3835 (0.3747) Accuracy 87.500 (87.835) 556 | Test: [ 0/24] Loss 0.1686 (0.1686) Accuracy 96.094 (96.094) 557 | Test: [10/24] Loss 0.1333 (0.2689) Accuracy 96.094 (91.761) 558 | Test: [20/24] Loss 0.6317 (0.3272) Accuracy 84.375 (90.290) 559 | * Accuracy 88.136 560 | Current best accuracy: 88.39634704589844 561 | 34.90234875679016 562 | Current learning rate: 0.00010000000000000002 563 | Epoch: [33][ 0/96] Loss 0.4028 (0.4028) Accuracy 87.500 (87.500) 564 | Epoch: [33][10/96] Loss 0.4250 (0.3540) Accuracy 89.062 (88.565) 565 | Epoch: [33][20/96] Loss 0.3443 (0.3661) Accuracy 89.844 (88.281) 566 | Epoch: [33][30/96] Loss 0.2990 (0.3715) Accuracy 89.844 (88.180) 567 | Epoch: [33][40/96] Loss 0.2971 (0.3647) Accuracy 91.406 (88.510) 568 | Epoch: [33][50/96] Loss 0.3074 (0.3643) Accuracy 89.062 (88.649) 569 | Epoch: [33][60/96] Loss 0.3604 (0.3679) Accuracy 89.062 (88.537) 570 | Epoch: [33][70/96] Loss 0.4783 (0.3705) Accuracy 82.812 (88.182) 571 | Epoch: [33][80/96] Loss 0.3858 (0.3716) Accuracy 87.500 (88.204) 572 | Epoch: [33][90/96] Loss 0.2915 (0.3701) Accuracy 92.188 (88.264) 573 | Test: [ 0/24] Loss 0.1765 (0.1765) Accuracy 96.094 (96.094) 574 | Test: [10/24] Loss 0.1178 (0.2702) Accuracy 96.094 (91.406) 575 | Test: [20/24] Loss 0.6290 (0.3271) Accuracy 84.375 (90.067) 576 | * Accuracy 87.940 577 | Current best accuracy: 88.39634704589844 578 | 34.52114939689636 579 | Current learning rate: 0.00010000000000000002 580 | Epoch: [34][ 0/96] Loss 0.3632 (0.3632) Accuracy 91.406 (91.406) 581 | Epoch: [34][10/96] Loss 0.3111 (0.3456) Accuracy 91.406 (89.276) 582 | Epoch: [34][20/96] Loss 0.4012 (0.3693) Accuracy 88.281 (88.690) 583 | Epoch: [34][30/96] Loss 0.3989 (0.3692) Accuracy 90.625 (88.609) 584 | Epoch: [34][40/96] Loss 0.3901 (0.3724) Accuracy 89.062 (88.319) 585 | Epoch: [34][50/96] Loss 0.3020 (0.3739) Accuracy 89.062 (88.021) 586 | Epoch: [34][60/96] Loss 0.4156 (0.3756) Accuracy 85.938 (87.935) 587 | Epoch: [34][70/96] Loss 0.3257 (0.3793) Accuracy 88.281 (87.918) 588 | Epoch: [34][80/96] Loss 0.3975 (0.3829) Accuracy 85.938 (87.876) 589 | Epoch: [34][90/96] Loss 0.4712 (0.3843) Accuracy 82.812 (87.629) 590 | Test: [ 0/24] Loss 0.1736 (0.1736) Accuracy 96.094 (96.094) 591 | Test: [10/24] Loss 0.1363 (0.2751) Accuracy 95.312 (91.406) 592 | Test: [20/24] Loss 0.6208 (0.3311) Accuracy 83.594 (90.104) 593 | * Accuracy 88.038 594 | Current best accuracy: 88.39634704589844 595 | 34.292564153671265 596 | Current learning rate: 0.00010000000000000002 597 | Epoch: [35][ 0/96] Loss 0.4446 (0.4446) Accuracy 86.719 (86.719) 598 | Epoch: [35][10/96] Loss 0.4480 (0.4186) Accuracy 82.812 (86.293) 599 | Epoch: [35][20/96] Loss 0.4282 (0.4071) Accuracy 89.062 (86.719) 600 | Epoch: [35][30/96] Loss 0.2474 (0.4042) Accuracy 96.094 (87.122) 601 | Epoch: [35][40/96] Loss 0.3857 (0.4061) Accuracy 83.594 (86.719) 602 | Epoch: [35][50/96] Loss 0.4162 (0.3973) Accuracy 85.938 (87.025) 603 | Epoch: [35][60/96] Loss 0.4244 (0.3900) Accuracy 85.156 (87.231) 604 | Epoch: [35][70/96] Loss 0.3802 (0.3869) Accuracy 88.281 (87.302) 605 | Epoch: [35][80/96] Loss 0.3185 (0.3858) Accuracy 92.188 (87.307) 606 | Epoch: [35][90/96] Loss 0.4058 (0.3866) Accuracy 84.375 (87.294) 607 | Test: [ 0/24] Loss 0.1827 (0.1827) Accuracy 96.094 (96.094) 608 | Test: [10/24] Loss 0.1295 (0.2793) Accuracy 96.094 (91.335) 609 | Test: [20/24] Loss 0.6201 (0.3311) Accuracy 83.594 (90.104) 610 | * Accuracy 88.038 611 | Current best accuracy: 88.39634704589844 612 | 34.48656249046326 613 | Current learning rate: 0.00010000000000000002 614 | Epoch: [36][ 0/96] Loss 0.4087 (0.4087) Accuracy 88.281 (88.281) 615 | Epoch: [36][10/96] Loss 0.3008 (0.3675) Accuracy 86.719 (88.352) 616 | Epoch: [36][20/96] Loss 0.3569 (0.3848) Accuracy 90.625 (88.021) 617 | Epoch: [36][30/96] Loss 0.3512 (0.3843) Accuracy 89.844 (88.155) 618 | Epoch: [36][40/96] Loss 0.5339 (0.3847) Accuracy 81.250 (87.957) 619 | Epoch: [36][50/96] Loss 0.3837 (0.3832) Accuracy 86.719 (87.791) 620 | Epoch: [36][60/96] Loss 0.2786 (0.3764) Accuracy 91.406 (88.025) 621 | Epoch: [36][70/96] Loss 0.2851 (0.3789) Accuracy 93.750 (87.984) 622 | Epoch: [36][80/96] Loss 0.3390 (0.3802) Accuracy 90.625 (87.924) 623 | Epoch: [36][90/96] Loss 0.3171 (0.3793) Accuracy 91.406 (88.032) 624 | Test: [ 0/24] Loss 0.1939 (0.1939) Accuracy 96.094 (96.094) 625 | Test: [10/24] Loss 0.1289 (0.2892) Accuracy 96.875 (91.406) 626 | Test: [20/24] Loss 0.5975 (0.3301) Accuracy 85.156 (90.179) 627 | * Accuracy 88.136 628 | Current best accuracy: 88.39634704589844 629 | 34.5140540599823 630 | Current learning rate: 0.00010000000000000002 631 | Epoch: [37][ 0/96] Loss 0.4272 (0.4272) Accuracy 85.938 (85.938) 632 | Epoch: [37][10/96] Loss 0.4657 (0.3612) Accuracy 85.938 (88.494) 633 | Epoch: [37][20/96] Loss 0.2965 (0.3659) Accuracy 92.188 (88.765) 634 | Epoch: [37][30/96] Loss 0.4070 (0.3705) Accuracy 87.500 (88.357) 635 | Epoch: [37][40/96] Loss 0.4729 (0.3743) Accuracy 82.812 (88.224) 636 | Epoch: [37][50/96] Loss 0.3809 (0.3831) Accuracy 89.062 (87.760) 637 | Epoch: [37][60/96] Loss 0.3509 (0.3852) Accuracy 91.406 (87.679) 638 | Epoch: [37][70/96] Loss 0.3939 (0.3774) Accuracy 89.844 (87.951) 639 | Epoch: [37][80/96] Loss 0.3417 (0.3768) Accuracy 86.719 (87.915) 640 | Epoch: [37][90/96] Loss 0.3587 (0.3788) Accuracy 89.062 (87.912) 641 | Test: [ 0/24] Loss 0.1723 (0.1723) Accuracy 96.094 (96.094) 642 | Test: [10/24] Loss 0.1450 (0.2797) Accuracy 95.312 (91.477) 643 | Test: [20/24] Loss 0.6200 (0.3329) Accuracy 84.375 (90.067) 644 | * Accuracy 88.038 645 | Current best accuracy: 88.39634704589844 646 | 34.595879793167114 647 | Current learning rate: 0.00010000000000000002 648 | Epoch: [38][ 0/96] Loss 0.3379 (0.3379) Accuracy 89.062 (89.062) 649 | Epoch: [38][10/96] Loss 0.3975 (0.3402) Accuracy 85.938 (88.991) 650 | Epoch: [38][20/96] Loss 0.2906 (0.3578) Accuracy 92.188 (88.356) 651 | Epoch: [38][30/96] Loss 0.4129 (0.3542) Accuracy 85.938 (88.281) 652 | Epoch: [38][40/96] Loss 0.4441 (0.3552) Accuracy 84.375 (88.472) 653 | Epoch: [38][50/96] Loss 0.5205 (0.3629) Accuracy 85.156 (88.067) 654 | Epoch: [38][60/96] Loss 0.3163 (0.3610) Accuracy 91.406 (88.115) 655 | Epoch: [38][70/96] Loss 0.3078 (0.3616) Accuracy 90.625 (88.116) 656 | Epoch: [38][80/96] Loss 0.3761 (0.3590) Accuracy 87.500 (88.175) 657 | Epoch: [38][90/96] Loss 0.4081 (0.3639) Accuracy 85.156 (88.007) 658 | Test: [ 0/24] Loss 0.1747 (0.1747) Accuracy 96.094 (96.094) 659 | Test: [10/24] Loss 0.1359 (0.2762) Accuracy 96.094 (91.477) 660 | Test: [20/24] Loss 0.5706 (0.3248) Accuracy 85.938 (90.179) 661 | * Accuracy 88.136 662 | Current best accuracy: 88.39634704589844 663 | 34.76978778839111 664 | Current learning rate: 0.00010000000000000002 665 | Epoch: [39][ 0/96] Loss 0.4259 (0.4259) Accuracy 87.500 (87.500) 666 | Epoch: [39][10/96] Loss 0.3412 (0.3792) Accuracy 86.719 (87.500) 667 | Epoch: [39][20/96] Loss 0.3618 (0.3736) Accuracy 85.156 (87.426) 668 | Epoch: [39][30/96] Loss 0.3525 (0.3680) Accuracy 89.062 (87.853) 669 | Epoch: [39][40/96] Loss 0.4388 (0.3664) Accuracy 85.156 (87.938) 670 | Epoch: [39][50/96] Loss 0.3734 (0.3625) Accuracy 89.062 (88.082) 671 | Epoch: [39][60/96] Loss 0.3965 (0.3619) Accuracy 85.156 (88.153) 672 | Epoch: [39][70/96] Loss 0.4227 (0.3608) Accuracy 85.938 (88.281) 673 | Epoch: [39][80/96] Loss 0.4047 (0.3658) Accuracy 85.938 (88.069) 674 | Epoch: [39][90/96] Loss 0.3555 (0.3671) Accuracy 87.500 (87.989) 675 | Test: [ 0/24] Loss 0.1913 (0.1913) Accuracy 96.094 (96.094) 676 | Test: [10/24] Loss 0.1110 (0.2774) Accuracy 96.094 (91.193) 677 | Test: [20/24] Loss 0.5775 (0.3230) Accuracy 85.156 (90.104) 678 | * Accuracy 87.940 679 | Current best accuracy: 88.39634704589844 680 | 34.17898106575012 681 | Current learning rate: 0.00010000000000000002 682 | Epoch: [40][ 0/96] Loss 0.2916 (0.2916) Accuracy 91.406 (91.406) 683 | Epoch: [40][10/96] Loss 0.3394 (0.3393) Accuracy 88.281 (88.849) 684 | Epoch: [40][20/96] Loss 0.3953 (0.3579) Accuracy 88.281 (88.467) 685 | Epoch: [40][30/96] Loss 0.4016 (0.3634) Accuracy 88.281 (88.206) 686 | Epoch: [40][40/96] Loss 0.3702 (0.3636) Accuracy 89.062 (88.300) 687 | Epoch: [40][50/96] Loss 0.4423 (0.3667) Accuracy 85.156 (88.220) 688 | Epoch: [40][60/96] Loss 0.2848 (0.3613) Accuracy 92.188 (88.294) 689 | Epoch: [40][70/96] Loss 0.3571 (0.3665) Accuracy 89.844 (88.083) 690 | Epoch: [40][80/96] Loss 0.3966 (0.3635) Accuracy 83.594 (88.214) 691 | Epoch: [40][90/96] Loss 0.2714 (0.3643) Accuracy 91.406 (88.238) 692 | Test: [ 0/24] Loss 0.1567 (0.1567) Accuracy 96.094 (96.094) 693 | Test: [10/24] Loss 0.1451 (0.2615) Accuracy 94.531 (91.264) 694 | Test: [20/24] Loss 0.6239 (0.3247) Accuracy 84.375 (89.918) 695 | * Accuracy 87.744 696 | Current best accuracy: 88.39634704589844 697 | 34.56089186668396 698 | Current learning rate: 0.00010000000000000002 699 | Epoch: [41][ 0/96] Loss 0.2482 (0.2482) Accuracy 92.188 (92.188) 700 | Epoch: [41][10/96] Loss 0.3485 (0.3660) Accuracy 85.156 (87.926) 701 | Epoch: [41][20/96] Loss 0.5583 (0.3783) Accuracy 82.812 (87.351) 702 | Epoch: [41][30/96] Loss 0.3289 (0.3840) Accuracy 89.062 (87.097) 703 | Epoch: [41][40/96] Loss 0.3809 (0.3826) Accuracy 89.062 (87.271) 704 | Epoch: [41][50/96] Loss 0.3419 (0.3790) Accuracy 88.281 (87.255) 705 | Epoch: [41][60/96] Loss 0.3701 (0.3809) Accuracy 89.844 (87.103) 706 | Epoch: [41][70/96] Loss 0.3720 (0.3793) Accuracy 87.500 (87.291) 707 | Epoch: [41][80/96] Loss 0.2820 (0.3758) Accuracy 91.406 (87.355) 708 | Epoch: [41][90/96] Loss 0.4333 (0.3784) Accuracy 85.156 (87.466) 709 | Test: [ 0/24] Loss 0.1862 (0.1862) Accuracy 96.094 (96.094) 710 | Test: [10/24] Loss 0.1400 (0.2923) Accuracy 94.531 (91.122) 711 | Test: [20/24] Loss 0.5992 (0.3328) Accuracy 84.375 (89.918) 712 | * Accuracy 87.842 713 | Current best accuracy: 88.39634704589844 714 | 34.585673570632935 715 | Current learning rate: 0.00010000000000000002 716 | Epoch: [42][ 0/96] Loss 0.3007 (0.3007) Accuracy 90.625 (90.625) 717 | Epoch: [42][10/96] Loss 0.2894 (0.3459) Accuracy 92.188 (89.134) 718 | Epoch: [42][20/96] Loss 0.3605 (0.3566) Accuracy 89.844 (88.728) 719 | Epoch: [42][30/96] Loss 0.3083 (0.3625) Accuracy 88.281 (88.458) 720 | Epoch: [42][40/96] Loss 0.3449 (0.3586) Accuracy 87.500 (88.529) 721 | Epoch: [42][50/96] Loss 0.4650 (0.3615) Accuracy 84.375 (88.496) 722 | Epoch: [42][60/96] Loss 0.4013 (0.3635) Accuracy 86.719 (88.409) 723 | Epoch: [42][70/96] Loss 0.3653 (0.3631) Accuracy 88.281 (88.479) 724 | Epoch: [42][80/96] Loss 0.3300 (0.3610) Accuracy 89.062 (88.628) 725 | Epoch: [42][90/96] Loss 0.3198 (0.3603) Accuracy 90.625 (88.753) 726 | Test: [ 0/24] Loss 0.1614 (0.1614) Accuracy 96.094 (96.094) 727 | Test: [10/24] Loss 0.1471 (0.2718) Accuracy 95.312 (91.619) 728 | Test: [20/24] Loss 0.6177 (0.3346) Accuracy 84.375 (90.030) 729 | * Accuracy 88.038 730 | Current best accuracy: 88.39634704589844 731 | 35.40381193161011 732 | Current learning rate: 0.00010000000000000002 733 | Epoch: [43][ 0/96] Loss 0.3225 (0.3225) Accuracy 89.844 (89.844) 734 | Epoch: [43][10/96] Loss 0.3892 (0.3562) Accuracy 87.500 (88.991) 735 | Epoch: [43][20/96] Loss 0.3618 (0.3719) Accuracy 88.281 (88.653) 736 | Epoch: [43][30/96] Loss 0.3820 (0.3699) Accuracy 88.281 (88.659) 737 | Epoch: [43][40/96] Loss 0.4155 (0.3689) Accuracy 86.719 (88.739) 738 | Epoch: [43][50/96] Loss 0.3934 (0.3701) Accuracy 87.500 (88.388) 739 | Epoch: [43][60/96] Loss 0.3506 (0.3735) Accuracy 88.281 (88.179) 740 | Epoch: [43][70/96] Loss 0.4469 (0.3736) Accuracy 87.500 (88.160) 741 | Epoch: [43][80/96] Loss 0.3549 (0.3715) Accuracy 89.844 (88.194) 742 | Epoch: [43][90/96] Loss 0.3627 (0.3698) Accuracy 85.938 (88.230) 743 | Test: [ 0/24] Loss 0.1783 (0.1783) Accuracy 96.094 (96.094) 744 | Test: [10/24] Loss 0.1337 (0.2798) Accuracy 95.312 (91.406) 745 | Test: [20/24] Loss 0.5928 (0.3321) Accuracy 84.375 (90.067) 746 | * Accuracy 88.103 747 | Current best accuracy: 88.39634704589844 748 | 34.72577452659607 749 | Current learning rate: 0.00010000000000000002 750 | Epoch: [44][ 0/96] Loss 0.3284 (0.3284) Accuracy 85.938 (85.938) 751 | Epoch: [44][10/96] Loss 0.4504 (0.3659) Accuracy 87.500 (88.281) 752 | Epoch: [44][20/96] Loss 0.4565 (0.3755) Accuracy 86.719 (87.946) 753 | Epoch: [44][30/96] Loss 0.3967 (0.3739) Accuracy 86.719 (87.828) 754 | Epoch: [44][40/96] Loss 0.2969 (0.3756) Accuracy 89.844 (87.729) 755 | Epoch: [44][50/96] Loss 0.4473 (0.3794) Accuracy 82.812 (87.623) 756 | Epoch: [44][60/96] Loss 0.4003 (0.3831) Accuracy 89.062 (87.731) 757 | Epoch: [44][70/96] Loss 0.3546 (0.3801) Accuracy 89.062 (87.775) 758 | Epoch: [44][80/96] Loss 0.2753 (0.3719) Accuracy 91.406 (87.944) 759 | Epoch: [44][90/96] Loss 0.3792 (0.3707) Accuracy 84.375 (87.904) 760 | Test: [ 0/24] Loss 0.1843 (0.1843) Accuracy 96.094 (96.094) 761 | Test: [10/24] Loss 0.1296 (0.2839) Accuracy 96.094 (91.548) 762 | Test: [20/24] Loss 0.6069 (0.3290) Accuracy 85.156 (90.216) 763 | * Accuracy 88.136 764 | Current best accuracy: 88.39634704589844 765 | 34.980387687683105 766 | Current learning rate: 1.0000000000000003e-05 767 | Epoch: [45][ 0/96] Loss 0.4800 (0.4800) Accuracy 85.156 (85.156) 768 | Epoch: [45][10/96] Loss 0.2853 (0.3957) Accuracy 89.844 (87.287) 769 | Epoch: [45][20/96] Loss 0.2897 (0.3863) Accuracy 88.281 (87.202) 770 | Epoch: [45][30/96] Loss 0.2883 (0.3796) Accuracy 90.625 (87.752) 771 | Epoch: [45][40/96] Loss 0.4360 (0.3776) Accuracy 85.938 (87.843) 772 | Epoch: [45][50/96] Loss 0.4760 (0.3792) Accuracy 85.938 (87.776) 773 | Epoch: [45][60/96] Loss 0.4550 (0.3789) Accuracy 85.156 (87.846) 774 | Epoch: [45][70/96] Loss 0.2868 (0.3799) Accuracy 91.406 (87.940) 775 | Epoch: [45][80/96] Loss 0.3452 (0.3760) Accuracy 88.281 (88.088) 776 | Epoch: [45][90/96] Loss 0.3133 (0.3750) Accuracy 90.625 (88.152) 777 | Test: [ 0/24] Loss 0.1594 (0.1594) Accuracy 96.094 (96.094) 778 | Test: [10/24] Loss 0.1338 (0.2621) Accuracy 96.094 (91.619) 779 | Test: [20/24] Loss 0.6467 (0.3321) Accuracy 83.594 (89.918) 780 | * Accuracy 87.973 781 | Current best accuracy: 88.39634704589844 782 | 34.52375292778015 783 | Current learning rate: 1.0000000000000003e-05 784 | Epoch: [46][ 0/96] Loss 0.3358 (0.3358) Accuracy 85.938 (85.938) 785 | Epoch: [46][10/96] Loss 0.4485 (0.3783) Accuracy 85.938 (88.210) 786 | Epoch: [46][20/96] Loss 0.3355 (0.3635) Accuracy 88.281 (88.690) 787 | Epoch: [46][30/96] Loss 0.3475 (0.3529) Accuracy 89.844 (88.810) 788 | Epoch: [46][40/96] Loss 0.4095 (0.3714) Accuracy 84.375 (87.900) 789 | Epoch: [46][50/96] Loss 0.3489 (0.3734) Accuracy 89.062 (87.730) 790 | Epoch: [46][60/96] Loss 0.5403 (0.3725) Accuracy 81.250 (87.769) 791 | Epoch: [46][70/96] Loss 0.3175 (0.3747) Accuracy 87.500 (87.577) 792 | Epoch: [46][80/96] Loss 0.3832 (0.3714) Accuracy 86.719 (87.635) 793 | Epoch: [46][90/96] Loss 0.2930 (0.3692) Accuracy 92.188 (87.843) 794 | Test: [ 0/24] Loss 0.1618 (0.1618) Accuracy 96.094 (96.094) 795 | Test: [10/24] Loss 0.1474 (0.2722) Accuracy 94.531 (91.193) 796 | Test: [20/24] Loss 0.6509 (0.3324) Accuracy 82.812 (89.695) 797 | * Accuracy 87.679 798 | Current best accuracy: 88.39634704589844 799 | 35.3451361656189 800 | Current learning rate: 1.0000000000000003e-05 801 | Epoch: [47][ 0/96] Loss 0.3603 (0.3603) Accuracy 86.719 (86.719) 802 | Epoch: [47][10/96] Loss 0.2790 (0.3490) Accuracy 91.406 (89.347) 803 | Epoch: [47][20/96] Loss 0.3277 (0.3533) Accuracy 90.625 (88.951) 804 | Epoch: [47][30/96] Loss 0.3817 (0.3523) Accuracy 88.281 (88.987) 805 | Epoch: [47][40/96] Loss 0.3306 (0.3516) Accuracy 90.625 (89.139) 806 | Epoch: [47][50/96] Loss 0.5028 (0.3566) Accuracy 84.375 (88.940) 807 | Epoch: [47][60/96] Loss 0.3745 (0.3616) Accuracy 89.844 (88.768) 808 | Epoch: [47][70/96] Loss 0.4122 (0.3652) Accuracy 85.156 (88.545) 809 | Epoch: [47][80/96] Loss 0.4801 (0.3672) Accuracy 84.375 (88.329) 810 | Epoch: [47][90/96] Loss 0.3468 (0.3662) Accuracy 87.500 (88.316) 811 | Test: [ 0/24] Loss 0.1818 (0.1818) Accuracy 96.094 (96.094) 812 | Test: [10/24] Loss 0.1397 (0.2849) Accuracy 95.312 (91.406) 813 | Test: [20/24] Loss 0.6121 (0.3361) Accuracy 83.594 (89.955) 814 | * Accuracy 88.005 815 | Current best accuracy: 88.39634704589844 816 | 34.77374625205994 817 | Current learning rate: 1.0000000000000003e-05 818 | Epoch: [48][ 0/96] Loss 0.2425 (0.2425) Accuracy 92.188 (92.188) 819 | Epoch: [48][10/96] Loss 0.3314 (0.3478) Accuracy 89.062 (88.636) 820 | Epoch: [48][20/96] Loss 0.3404 (0.3472) Accuracy 89.062 (89.025) 821 | Epoch: [48][30/96] Loss 0.2920 (0.3612) Accuracy 89.062 (88.256) 822 | Epoch: [48][40/96] Loss 0.3394 (0.3690) Accuracy 86.719 (87.938) 823 | Epoch: [48][50/96] Loss 0.3761 (0.3725) Accuracy 85.938 (87.745) 824 | Epoch: [48][60/96] Loss 0.3771 (0.3695) Accuracy 89.844 (87.884) 825 | Epoch: [48][70/96] Loss 0.4932 (0.3693) Accuracy 85.156 (87.918) 826 | Epoch: [48][80/96] Loss 0.3474 (0.3706) Accuracy 89.062 (87.953) 827 | Epoch: [48][90/96] Loss 0.3345 (0.3728) Accuracy 92.188 (87.835) 828 | Test: [ 0/24] Loss 0.1615 (0.1615) Accuracy 96.094 (96.094) 829 | Test: [10/24] Loss 0.1530 (0.2745) Accuracy 94.531 (91.335) 830 | Test: [20/24] Loss 0.6120 (0.3356) Accuracy 84.375 (89.769) 831 | * Accuracy 87.777 832 | Current best accuracy: 88.39634704589844 833 | 34.945650577545166 834 | Current learning rate: 1.0000000000000003e-05 835 | Epoch: [49][ 0/96] Loss 0.4197 (0.4197) Accuracy 85.156 (85.156) 836 | Epoch: [49][10/96] Loss 0.4885 (0.3786) Accuracy 85.156 (87.784) 837 | Epoch: [49][20/96] Loss 0.3898 (0.3806) Accuracy 85.938 (87.426) 838 | Epoch: [49][30/96] Loss 0.4285 (0.3716) Accuracy 89.844 (88.054) 839 | Epoch: [49][40/96] Loss 0.3145 (0.3729) Accuracy 89.844 (87.805) 840 | Epoch: [49][50/96] Loss 0.3666 (0.3754) Accuracy 86.719 (87.592) 841 | Epoch: [49][60/96] Loss 0.3778 (0.3728) Accuracy 89.844 (87.769) 842 | Epoch: [49][70/96] Loss 0.3335 (0.3728) Accuracy 88.281 (87.775) 843 | Epoch: [49][80/96] Loss 0.4713 (0.3765) Accuracy 85.938 (87.703) 844 | Epoch: [49][90/96] Loss 0.4087 (0.3768) Accuracy 84.375 (87.646) 845 | Test: [ 0/24] Loss 0.1820 (0.1820) Accuracy 96.094 (96.094) 846 | Test: [10/24] Loss 0.1242 (0.2816) Accuracy 96.094 (91.477) 847 | Test: [20/24] Loss 0.6487 (0.3354) Accuracy 83.594 (89.993) 848 | * Accuracy 88.103 849 | Current best accuracy: 88.39634704589844 850 | 35.32496523857117 851 | Current learning rate: 1.0000000000000003e-05 852 | Epoch: [50][ 0/96] Loss 0.3424 (0.3424) Accuracy 89.062 (89.062) 853 | Epoch: [50][10/96] Loss 0.4353 (0.3628) Accuracy 86.719 (88.920) 854 | Epoch: [50][20/96] Loss 0.3396 (0.3598) Accuracy 89.844 (88.653) 855 | Epoch: [50][30/96] Loss 0.4573 (0.3637) Accuracy 82.031 (88.458) 856 | Epoch: [50][40/96] Loss 0.4325 (0.3724) Accuracy 85.156 (88.091) 857 | Epoch: [50][50/96] Loss 0.3934 (0.3795) Accuracy 87.500 (87.714) 858 | Epoch: [50][60/96] Loss 0.3106 (0.3800) Accuracy 89.844 (87.679) 859 | Epoch: [50][70/96] Loss 0.2875 (0.3785) Accuracy 90.625 (87.775) 860 | Epoch: [50][80/96] Loss 0.3549 (0.3806) Accuracy 88.281 (87.751) 861 | Epoch: [50][90/96] Loss 0.3531 (0.3791) Accuracy 87.500 (87.715) 862 | Test: [ 0/24] Loss 0.1682 (0.1682) Accuracy 96.094 (96.094) 863 | Test: [10/24] Loss 0.1395 (0.2741) Accuracy 96.094 (91.690) 864 | Test: [20/24] Loss 0.6344 (0.3300) Accuracy 83.594 (90.067) 865 | * Accuracy 87.973 866 | Current best accuracy: 88.39634704589844 867 | 34.96661567687988 868 | Current learning rate: 1.0000000000000003e-05 869 | Epoch: [51][ 0/96] Loss 0.3346 (0.3346) Accuracy 91.406 (91.406) 870 | Epoch: [51][10/96] Loss 0.3359 (0.3719) Accuracy 88.281 (87.997) 871 | Epoch: [51][20/96] Loss 0.2995 (0.3793) Accuracy 89.062 (87.909) 872 | Epoch: [51][30/96] Loss 0.3262 (0.3801) Accuracy 88.281 (87.576) 873 | Epoch: [51][40/96] Loss 0.3190 (0.3721) Accuracy 89.062 (87.748) 874 | Epoch: [51][50/96] Loss 0.3325 (0.3676) Accuracy 90.625 (87.975) 875 | Epoch: [51][60/96] Loss 0.3365 (0.3696) Accuracy 86.719 (87.974) 876 | Epoch: [51][70/96] Loss 0.3824 (0.3723) Accuracy 87.500 (87.797) 877 | Epoch: [51][80/96] Loss 0.3841 (0.3678) Accuracy 88.281 (88.079) 878 | Epoch: [51][90/96] Loss 0.2906 (0.3672) Accuracy 89.062 (88.007) 879 | Test: [ 0/24] Loss 0.1883 (0.1883) Accuracy 96.094 (96.094) 880 | Test: [10/24] Loss 0.1340 (0.2872) Accuracy 96.094 (91.335) 881 | Test: [20/24] Loss 0.5635 (0.3315) Accuracy 86.719 (90.067) 882 | * Accuracy 88.103 883 | Current best accuracy: 88.39634704589844 884 | 35.32732558250427 885 | Current learning rate: 1.0000000000000003e-05 886 | Epoch: [52][ 0/96] Loss 0.4270 (0.4270) Accuracy 86.719 (86.719) 887 | Epoch: [52][10/96] Loss 0.2574 (0.3716) Accuracy 90.625 (87.926) 888 | Epoch: [52][20/96] Loss 0.3882 (0.3786) Accuracy 88.281 (87.946) 889 | Epoch: [52][30/96] Loss 0.3660 (0.3906) Accuracy 87.500 (87.525) 890 | Epoch: [52][40/96] Loss 0.4319 (0.3862) Accuracy 85.938 (87.424) 891 | Epoch: [52][50/96] Loss 0.3191 (0.3774) Accuracy 92.188 (87.546) 892 | Epoch: [52][60/96] Loss 0.4815 (0.3774) Accuracy 83.594 (87.615) 893 | Epoch: [52][70/96] Loss 0.3598 (0.3776) Accuracy 89.062 (87.654) 894 | Epoch: [52][80/96] Loss 0.3485 (0.3770) Accuracy 89.844 (87.703) 895 | Epoch: [52][90/96] Loss 0.3048 (0.3737) Accuracy 90.625 (87.835) 896 | Test: [ 0/24] Loss 0.1651 (0.1651) Accuracy 96.094 (96.094) 897 | Test: [10/24] Loss 0.1328 (0.2664) Accuracy 96.094 (91.761) 898 | Test: [20/24] Loss 0.6345 (0.3290) Accuracy 83.594 (90.141) 899 | * Accuracy 88.038 900 | Current best accuracy: 88.39634704589844 901 | 39.801589012145996 902 | Current learning rate: 1.0000000000000003e-05 903 | Epoch: [53][ 0/96] Loss 0.2627 (0.2627) Accuracy 92.969 (92.969) 904 | Epoch: [53][10/96] Loss 0.4061 (0.3742) Accuracy 86.719 (87.926) 905 | Epoch: [53][20/96] Loss 0.3070 (0.3814) Accuracy 90.625 (87.649) 906 | Epoch: [53][30/96] Loss 0.2769 (0.3785) Accuracy 91.406 (87.828) 907 | Epoch: [53][40/96] Loss 0.3911 (0.3771) Accuracy 85.156 (87.862) 908 | Epoch: [53][50/96] Loss 0.4584 (0.3729) Accuracy 83.594 (88.174) 909 | Epoch: [53][60/96] Loss 0.4169 (0.3734) Accuracy 86.719 (88.051) 910 | Epoch: [53][70/96] Loss 0.3935 (0.3744) Accuracy 87.500 (87.973) 911 | Epoch: [53][80/96] Loss 0.2744 (0.3708) Accuracy 92.188 (88.146) 912 | Epoch: [53][90/96] Loss 0.3805 (0.3688) Accuracy 86.719 (88.290) 913 | Test: [ 0/24] Loss 0.1907 (0.1907) Accuracy 96.094 (96.094) 914 | Test: [10/24] Loss 0.1268 (0.2888) Accuracy 96.094 (91.193) 915 | Test: [20/24] Loss 0.5832 (0.3305) Accuracy 84.375 (89.993) 916 | * Accuracy 88.005 917 | Current best accuracy: 88.39634704589844 918 | 35.29306507110596 919 | Current learning rate: 1.0000000000000003e-05 920 | Epoch: [54][ 0/96] Loss 0.4191 (0.4191) Accuracy 84.375 (84.375) 921 | Epoch: [54][10/96] Loss 0.5686 (0.3879) Accuracy 78.906 (87.358) 922 | Epoch: [54][20/96] Loss 0.5263 (0.3686) Accuracy 84.375 (87.909) 923 | Epoch: [54][30/96] Loss 0.4539 (0.3862) Accuracy 85.156 (87.550) 924 | Epoch: [54][40/96] Loss 0.3922 (0.3908) Accuracy 89.062 (87.443) 925 | Epoch: [54][50/96] Loss 0.4900 (0.3849) Accuracy 85.156 (87.669) 926 | Epoch: [54][60/96] Loss 0.3827 (0.3826) Accuracy 88.281 (87.666) 927 | Epoch: [54][70/96] Loss 0.4367 (0.3840) Accuracy 85.156 (87.676) 928 | Epoch: [54][80/96] Loss 0.3217 (0.3819) Accuracy 91.406 (87.654) 929 | Epoch: [54][90/96] Loss 0.3428 (0.3799) Accuracy 88.281 (87.723) 930 | Test: [ 0/24] Loss 0.1705 (0.1705) Accuracy 96.094 (96.094) 931 | Test: [10/24] Loss 0.1359 (0.2745) Accuracy 96.094 (91.548) 932 | Test: [20/24] Loss 0.6176 (0.3306) Accuracy 83.594 (90.067) 933 | * Accuracy 88.103 934 | Current best accuracy: 88.39634704589844 935 | 35.62822198867798 936 | Current learning rate: 1.0000000000000003e-05 937 | Epoch: [55][ 0/96] Loss 0.4198 (0.4198) Accuracy 85.938 (85.938) 938 | Epoch: [55][10/96] Loss 0.4256 (0.3618) Accuracy 87.500 (87.642) 939 | Epoch: [55][20/96] Loss 0.3168 (0.3654) Accuracy 89.844 (87.909) 940 | Epoch: [55][30/96] Loss 0.3953 (0.3789) Accuracy 89.062 (87.626) 941 | Epoch: [55][40/96] Loss 0.3808 (0.3794) Accuracy 86.719 (87.576) 942 | Epoch: [55][50/96] Loss 0.3135 (0.3748) Accuracy 88.281 (87.776) 943 | Epoch: [55][60/96] Loss 0.5146 (0.3830) Accuracy 84.375 (87.487) 944 | Epoch: [55][70/96] Loss 0.3592 (0.3795) Accuracy 87.500 (87.654) 945 | Epoch: [55][80/96] Loss 0.5669 (0.3799) Accuracy 79.688 (87.645) 946 | Epoch: [55][90/96] Loss 0.2896 (0.3783) Accuracy 90.625 (87.758) 947 | Test: [ 0/24] Loss 0.1755 (0.1755) Accuracy 96.094 (96.094) 948 | Test: [10/24] Loss 0.1412 (0.2839) Accuracy 96.094 (91.477) 949 | Test: [20/24] Loss 0.6218 (0.3319) Accuracy 83.594 (90.104) 950 | * Accuracy 88.038 951 | Current best accuracy: 88.39634704589844 952 | 35.3572096824646 953 | Current learning rate: 1.0000000000000003e-05 954 | Epoch: [56][ 0/96] Loss 0.4311 (0.4311) Accuracy 83.594 (83.594) 955 | Epoch: [56][10/96] Loss 0.4593 (0.3666) Accuracy 85.156 (87.926) 956 | Epoch: [56][20/96] Loss 0.5176 (0.3752) Accuracy 84.375 (87.946) 957 | Epoch: [56][30/96] Loss 0.3321 (0.3781) Accuracy 88.281 (87.878) 958 | Epoch: [56][40/96] Loss 0.3863 (0.3753) Accuracy 88.281 (88.034) 959 | Epoch: [56][50/96] Loss 0.3890 (0.3715) Accuracy 87.500 (88.281) 960 | Epoch: [56][60/96] Loss 0.3331 (0.3702) Accuracy 90.625 (88.332) 961 | Epoch: [56][70/96] Loss 0.3956 (0.3735) Accuracy 90.625 (88.193) 962 | Epoch: [56][80/96] Loss 0.3828 (0.3753) Accuracy 86.719 (88.108) 963 | Epoch: [56][90/96] Loss 0.4164 (0.3765) Accuracy 85.938 (87.998) 964 | Test: [ 0/24] Loss 0.1802 (0.1802) Accuracy 96.094 (96.094) 965 | Test: [10/24] Loss 0.1517 (0.2908) Accuracy 94.531 (91.122) 966 | Test: [20/24] Loss 0.5931 (0.3374) Accuracy 84.375 (89.844) 967 | * Accuracy 87.875 968 | Current best accuracy: 88.39634704589844 969 | 35.537593364715576 970 | Current learning rate: 1.0000000000000003e-05 971 | Epoch: [57][ 0/96] Loss 0.3129 (0.3129) Accuracy 91.406 (91.406) 972 | Epoch: [57][10/96] Loss 0.4185 (0.3588) Accuracy 84.375 (89.276) 973 | Epoch: [57][20/96] Loss 0.3514 (0.3514) Accuracy 87.500 (89.397) 974 | Epoch: [57][30/96] Loss 0.3926 (0.3520) Accuracy 87.500 (89.088) 975 | Epoch: [57][40/96] Loss 0.3574 (0.3563) Accuracy 89.844 (88.910) 976 | Epoch: [57][50/96] Loss 0.3762 (0.3539) Accuracy 85.938 (88.955) 977 | Epoch: [57][60/96] Loss 0.3284 (0.3529) Accuracy 92.188 (88.922) 978 | Epoch: [57][70/96] Loss 0.3896 (0.3504) Accuracy 85.938 (89.029) 979 | Epoch: [57][80/96] Loss 0.3963 (0.3572) Accuracy 87.500 (88.850) 980 | Epoch: [57][90/96] Loss 0.3567 (0.3588) Accuracy 88.281 (88.711) 981 | Test: [ 0/24] Loss 0.1815 (0.1815) Accuracy 96.094 (96.094) 982 | Test: [10/24] Loss 0.1187 (0.2754) Accuracy 96.094 (91.335) 983 | Test: [20/24] Loss 0.6591 (0.3336) Accuracy 83.594 (89.955) 984 | * Accuracy 87.907 985 | Current best accuracy: 88.39634704589844 986 | 35.454386472702026 987 | Current learning rate: 1.0000000000000003e-05 988 | Epoch: [58][ 0/96] Loss 0.2800 (0.2800) Accuracy 92.188 (92.188) 989 | Epoch: [58][10/96] Loss 0.2177 (0.3570) Accuracy 92.969 (88.636) 990 | Epoch: [58][20/96] Loss 0.2735 (0.3531) Accuracy 92.969 (88.802) 991 | Epoch: [58][30/96] Loss 0.3070 (0.3539) Accuracy 88.281 (88.533) 992 | Epoch: [58][40/96] Loss 0.3420 (0.3556) Accuracy 86.719 (88.300) 993 | Epoch: [58][50/96] Loss 0.4024 (0.3566) Accuracy 83.594 (88.128) 994 | Epoch: [58][60/96] Loss 0.3030 (0.3603) Accuracy 91.406 (87.923) 995 | Epoch: [58][70/96] Loss 0.3440 (0.3615) Accuracy 89.844 (87.984) 996 | Epoch: [58][80/96] Loss 0.2699 (0.3589) Accuracy 90.625 (88.127) 997 | Epoch: [58][90/96] Loss 0.3104 (0.3598) Accuracy 88.281 (88.161) 998 | Test: [ 0/24] Loss 0.1908 (0.1908) Accuracy 96.094 (96.094) 999 | Test: [10/24] Loss 0.1327 (0.2946) Accuracy 96.094 (91.122) 1000 | Test: [20/24] Loss 0.6136 (0.3380) Accuracy 83.594 (89.844) 1001 | * Accuracy 87.875 1002 | Current best accuracy: 88.39634704589844 1003 | 35.45718431472778 1004 | Current learning rate: 1.0000000000000003e-05 1005 | Epoch: [59][ 0/96] Loss 0.3210 (0.3210) Accuracy 89.062 (89.062) 1006 | Epoch: [59][10/96] Loss 0.2643 (0.3581) Accuracy 92.969 (89.347) 1007 | Epoch: [59][20/96] Loss 0.5530 (0.3921) Accuracy 83.594 (87.760) 1008 | Epoch: [59][30/96] Loss 0.3307 (0.3900) Accuracy 89.844 (87.828) 1009 | Epoch: [59][40/96] Loss 0.4330 (0.3892) Accuracy 86.719 (87.862) 1010 | Epoch: [59][50/96] Loss 0.5042 (0.3872) Accuracy 83.594 (87.944) 1011 | Epoch: [59][60/96] Loss 0.3431 (0.3793) Accuracy 89.844 (88.089) 1012 | Epoch: [59][70/96] Loss 0.3033 (0.3779) Accuracy 92.188 (88.061) 1013 | Epoch: [59][80/96] Loss 0.3942 (0.3760) Accuracy 88.281 (87.982) 1014 | Epoch: [59][90/96] Loss 0.4296 (0.3761) Accuracy 84.375 (87.955) 1015 | Test: [ 0/24] Loss 0.1822 (0.1822) Accuracy 96.094 (96.094) 1016 | Test: [10/24] Loss 0.1632 (0.2966) Accuracy 92.969 (90.838) 1017 | Test: [20/24] Loss 0.6085 (0.3457) Accuracy 84.375 (89.546) 1018 | * Accuracy 87.647 1019 | Current best accuracy: 88.39634704589844 1020 | 36.395347595214844 1021 | Current learning rate: 1.0000000000000002e-06 1022 | Epoch: [60][ 0/96] Loss 0.4494 (0.4494) Accuracy 87.500 (87.500) 1023 | Epoch: [60][10/96] Loss 0.2487 (0.3654) Accuracy 93.750 (88.352) 1024 | Epoch: [60][20/96] Loss 0.3125 (0.3550) Accuracy 90.625 (88.542) 1025 | Epoch: [60][30/96] Loss 0.3560 (0.3558) Accuracy 88.281 (88.407) 1026 | Epoch: [60][40/96] Loss 0.4195 (0.3713) Accuracy 86.719 (88.014) 1027 | Epoch: [60][50/96] Loss 0.2646 (0.3733) Accuracy 92.969 (88.036) 1028 | Epoch: [60][60/96] Loss 0.3726 (0.3684) Accuracy 89.062 (88.153) 1029 | Epoch: [60][70/96] Loss 0.2982 (0.3654) Accuracy 89.062 (88.248) 1030 | Epoch: [60][80/96] Loss 0.4183 (0.3689) Accuracy 83.594 (88.088) 1031 | Epoch: [60][90/96] Loss 0.3448 (0.3690) Accuracy 88.281 (88.101) 1032 | Test: [ 0/24] Loss 0.1858 (0.1858) Accuracy 96.094 (96.094) 1033 | Test: [10/24] Loss 0.1398 (0.2902) Accuracy 96.094 (91.477) 1034 | Test: [20/24] Loss 0.5965 (0.3333) Accuracy 84.375 (90.141) 1035 | * Accuracy 88.136 1036 | Current best accuracy: 88.39634704589844 1037 | 35.2281448841095 1038 | Current learning rate: 1.0000000000000002e-06 1039 | Epoch: [61][ 0/96] Loss 0.2909 (0.2909) Accuracy 92.969 (92.969) 1040 | Epoch: [61][10/96] Loss 0.3467 (0.3288) Accuracy 88.281 (89.205) 1041 | Epoch: [61][20/96] Loss 0.3883 (0.3500) Accuracy 87.500 (88.765) 1042 | Epoch: [61][30/96] Loss 0.3851 (0.3636) Accuracy 88.281 (88.458) 1043 | Epoch: [61][40/96] Loss 0.3204 (0.3716) Accuracy 86.719 (87.938) 1044 | Epoch: [61][50/96] Loss 0.4125 (0.3682) Accuracy 89.844 (88.281) 1045 | Epoch: [61][60/96] Loss 0.4587 (0.3700) Accuracy 84.375 (88.243) 1046 | Epoch: [61][70/96] Loss 0.4463 (0.3725) Accuracy 85.938 (88.017) 1047 | Epoch: [61][80/96] Loss 0.3058 (0.3705) Accuracy 89.844 (88.088) 1048 | Epoch: [61][90/96] Loss 0.3661 (0.3715) Accuracy 90.625 (88.075) 1049 | Test: [ 0/24] Loss 0.1813 (0.1813) Accuracy 96.094 (96.094) 1050 | Test: [10/24] Loss 0.1343 (0.2815) Accuracy 95.312 (91.335) 1051 | Test: [20/24] Loss 0.6009 (0.3306) Accuracy 84.375 (89.955) 1052 | * Accuracy 87.973 1053 | Current best accuracy: 88.39634704589844 1054 | 35.54773259162903 1055 | Current learning rate: 1.0000000000000002e-06 1056 | Epoch: [62][ 0/96] Loss 0.3762 (0.3762) Accuracy 89.062 (89.062) 1057 | Epoch: [62][10/96] Loss 0.3457 (0.3815) Accuracy 89.844 (87.926) 1058 | Epoch: [62][20/96] Loss 0.3005 (0.3695) Accuracy 90.625 (88.579) 1059 | Epoch: [62][30/96] Loss 0.3448 (0.3600) Accuracy 88.281 (88.785) 1060 | Epoch: [62][40/96] Loss 0.4498 (0.3673) Accuracy 83.594 (88.396) 1061 | Epoch: [62][50/96] Loss 0.3033 (0.3737) Accuracy 91.406 (88.235) 1062 | Epoch: [62][60/96] Loss 0.3642 (0.3706) Accuracy 88.281 (88.435) 1063 | Epoch: [62][70/96] Loss 0.4701 (0.3699) Accuracy 80.469 (88.413) 1064 | Epoch: [62][80/96] Loss 0.3355 (0.3708) Accuracy 90.625 (88.378) 1065 | Epoch: [62][90/96] Loss 0.3677 (0.3694) Accuracy 89.062 (88.410) 1066 | Test: [ 0/24] Loss 0.1678 (0.1678) Accuracy 96.094 (96.094) 1067 | Test: [10/24] Loss 0.1459 (0.2765) Accuracy 94.531 (91.264) 1068 | Test: [20/24] Loss 0.5805 (0.3293) Accuracy 85.938 (89.955) 1069 | * Accuracy 88.038 1070 | Current best accuracy: 88.39634704589844 1071 | 35.80559945106506 1072 | Current learning rate: 1.0000000000000002e-06 1073 | Epoch: [63][ 0/96] Loss 0.3399 (0.3399) Accuracy 91.406 (91.406) 1074 | Epoch: [63][10/96] Loss 0.3753 (0.3524) Accuracy 88.281 (88.920) 1075 | Epoch: [63][20/96] Loss 0.3316 (0.3653) Accuracy 85.156 (88.132) 1076 | Epoch: [63][30/96] Loss 0.3641 (0.3746) Accuracy 89.062 (88.029) 1077 | Epoch: [63][40/96] Loss 0.4119 (0.3684) Accuracy 83.594 (88.300) 1078 | Epoch: [63][50/96] Loss 0.3357 (0.3669) Accuracy 89.844 (88.189) 1079 | Epoch: [63][60/96] Loss 0.2633 (0.3698) Accuracy 89.844 (88.102) 1080 | Epoch: [63][70/96] Loss 0.3972 (0.3678) Accuracy 84.375 (88.193) 1081 | Epoch: [63][80/96] Loss 0.3454 (0.3666) Accuracy 89.844 (88.262) 1082 | Epoch: [63][90/96] Loss 0.3399 (0.3678) Accuracy 89.062 (88.238) 1083 | Test: [ 0/24] Loss 0.1770 (0.1770) Accuracy 96.094 (96.094) 1084 | Test: [10/24] Loss 0.1431 (0.2798) Accuracy 95.312 (91.548) 1085 | Test: [20/24] Loss 0.5822 (0.3297) Accuracy 86.719 (90.216) 1086 | * Accuracy 88.168 1087 | Current best accuracy: 88.39634704589844 1088 | 34.968414545059204 1089 | Current learning rate: 1.0000000000000002e-06 1090 | Epoch: [64][ 0/96] Loss 0.2382 (0.2382) Accuracy 93.750 (93.750) 1091 | Epoch: [64][10/96] Loss 0.2190 (0.3411) Accuracy 95.312 (90.483) 1092 | Epoch: [64][20/96] Loss 0.4402 (0.3709) Accuracy 88.281 (89.360) 1093 | Epoch: [64][30/96] Loss 0.4889 (0.3824) Accuracy 86.719 (88.609) 1094 | Epoch: [64][40/96] Loss 0.3695 (0.3847) Accuracy 85.938 (88.357) 1095 | Epoch: [64][50/96] Loss 0.3678 (0.3799) Accuracy 86.719 (88.281) 1096 | Epoch: [64][60/96] Loss 0.4307 (0.3922) Accuracy 82.812 (87.705) 1097 | Epoch: [64][70/96] Loss 0.4221 (0.3886) Accuracy 84.375 (87.742) 1098 | Epoch: [64][80/96] Loss 0.4236 (0.3840) Accuracy 85.938 (87.944) 1099 | Epoch: [64][90/96] Loss 0.3129 (0.3799) Accuracy 91.406 (88.118) 1100 | Test: [ 0/24] Loss 0.1751 (0.1751) Accuracy 96.094 (96.094) 1101 | Test: [10/24] Loss 0.1589 (0.2880) Accuracy 93.750 (91.122) 1102 | Test: [20/24] Loss 0.5927 (0.3383) Accuracy 84.375 (89.769) 1103 | * Accuracy 87.842 1104 | Current best accuracy: 88.39634704589844 1105 | 35.782134771347046 1106 | Current learning rate: 1.0000000000000002e-06 1107 | Epoch: [65][ 0/96] Loss 0.3874 (0.3874) Accuracy 88.281 (88.281) 1108 | Epoch: [65][10/96] Loss 0.3184 (0.3748) Accuracy 91.406 (87.855) 1109 | Epoch: [65][20/96] Loss 0.3299 (0.3640) Accuracy 91.406 (88.467) 1110 | Epoch: [65][30/96] Loss 0.4047 (0.3537) Accuracy 88.281 (88.962) 1111 | Epoch: [65][40/96] Loss 0.3173 (0.3596) Accuracy 90.625 (88.662) 1112 | Epoch: [65][50/96] Loss 0.3182 (0.3676) Accuracy 89.062 (88.434) 1113 | Epoch: [65][60/96] Loss 0.3615 (0.3707) Accuracy 87.500 (88.268) 1114 | Epoch: [65][70/96] Loss 0.3370 (0.3735) Accuracy 89.844 (88.259) 1115 | Epoch: [65][80/96] Loss 0.4510 (0.3730) Accuracy 81.250 (88.088) 1116 | Epoch: [65][90/96] Loss 0.4054 (0.3737) Accuracy 87.500 (88.084) 1117 | Test: [ 0/24] Loss 0.1776 (0.1776) Accuracy 96.094 (96.094) 1118 | Test: [10/24] Loss 0.1322 (0.2778) Accuracy 96.094 (91.406) 1119 | Test: [20/24] Loss 0.6601 (0.3359) Accuracy 83.594 (89.993) 1120 | * Accuracy 88.070 1121 | Current best accuracy: 88.39634704589844 1122 | 35.962197065353394 1123 | Current learning rate: 1.0000000000000002e-06 1124 | Epoch: [66][ 0/96] Loss 0.3143 (0.3143) Accuracy 90.625 (90.625) 1125 | Epoch: [66][10/96] Loss 0.4131 (0.3423) Accuracy 85.938 (89.276) 1126 | Epoch: [66][20/96] Loss 0.3252 (0.3562) Accuracy 92.188 (88.542) 1127 | Epoch: [66][30/96] Loss 0.3187 (0.3495) Accuracy 89.844 (88.861) 1128 | Epoch: [66][40/96] Loss 0.5068 (0.3643) Accuracy 79.688 (88.319) 1129 | Epoch: [66][50/96] Loss 0.4362 (0.3691) Accuracy 83.594 (88.174) 1130 | Epoch: [66][60/96] Loss 0.3043 (0.3701) Accuracy 89.844 (88.038) 1131 | Epoch: [66][70/96] Loss 0.2962 (0.3697) Accuracy 92.969 (87.973) 1132 | Epoch: [66][80/96] Loss 0.3423 (0.3729) Accuracy 87.500 (87.934) 1133 | Epoch: [66][90/96] Loss 0.3659 (0.3705) Accuracy 89.062 (87.981) 1134 | Test: [ 0/24] Loss 0.1697 (0.1697) Accuracy 96.094 (96.094) 1135 | Test: [10/24] Loss 0.1326 (0.2710) Accuracy 96.094 (91.619) 1136 | Test: [20/24] Loss 0.6361 (0.3289) Accuracy 84.375 (90.141) 1137 | * Accuracy 88.070 1138 | Current best accuracy: 88.39634704589844 1139 | 35.01353073120117 1140 | Current learning rate: 1.0000000000000002e-06 1141 | Epoch: [67][ 0/96] Loss 0.4106 (0.4106) Accuracy 89.062 (89.062) 1142 | Epoch: [67][10/96] Loss 0.3558 (0.3479) Accuracy 88.281 (88.849) 1143 | Epoch: [67][20/96] Loss 0.3141 (0.3424) Accuracy 90.625 (89.174) 1144 | Epoch: [67][30/96] Loss 0.4212 (0.3489) Accuracy 85.156 (88.609) 1145 | Epoch: [67][40/96] Loss 0.3769 (0.3553) Accuracy 88.281 (88.529) 1146 | Epoch: [67][50/96] Loss 0.3408 (0.3605) Accuracy 89.062 (88.404) 1147 | Epoch: [67][60/96] Loss 0.4299 (0.3597) Accuracy 90.625 (88.665) 1148 | Epoch: [67][70/96] Loss 0.4603 (0.3660) Accuracy 85.938 (88.413) 1149 | Epoch: [67][80/96] Loss 0.4096 (0.3662) Accuracy 86.719 (88.320) 1150 | Epoch: [67][90/96] Loss 0.4125 (0.3668) Accuracy 85.156 (88.333) 1151 | Test: [ 0/24] Loss 0.1912 (0.1912) Accuracy 96.094 (96.094) 1152 | Test: [10/24] Loss 0.1271 (0.2888) Accuracy 96.094 (91.477) 1153 | Test: [20/24] Loss 0.6076 (0.3293) Accuracy 84.375 (90.253) 1154 | * Accuracy 88.201 1155 | Current best accuracy: 88.39634704589844 1156 | 35.353535890579224 1157 | Current learning rate: 1.0000000000000002e-06 1158 | Epoch: [68][ 0/96] Loss 0.4178 (0.4178) Accuracy 85.156 (85.156) 1159 | Epoch: [68][10/96] Loss 0.3254 (0.3723) Accuracy 91.406 (87.926) 1160 | Epoch: [68][20/96] Loss 0.3256 (0.3713) Accuracy 89.062 (88.021) 1161 | Epoch: [68][30/96] Loss 0.3409 (0.3735) Accuracy 88.281 (88.004) 1162 | Epoch: [68][40/96] Loss 0.3754 (0.3687) Accuracy 89.062 (88.224) 1163 | Epoch: [68][50/96] Loss 0.3931 (0.3725) Accuracy 87.500 (88.051) 1164 | Epoch: [68][60/96] Loss 0.3281 (0.3736) Accuracy 86.719 (88.051) 1165 | Epoch: [68][70/96] Loss 0.4690 (0.3745) Accuracy 82.812 (87.918) 1166 | Epoch: [68][80/96] Loss 0.3607 (0.3735) Accuracy 89.062 (87.953) 1167 | Epoch: [68][90/96] Loss 0.4777 (0.3759) Accuracy 85.938 (87.835) 1168 | Test: [ 0/24] Loss 0.1660 (0.1660) Accuracy 96.094 (96.094) 1169 | Test: [10/24] Loss 0.1454 (0.2734) Accuracy 95.312 (91.548) 1170 | Test: [20/24] Loss 0.5794 (0.3257) Accuracy 85.938 (90.104) 1171 | * Accuracy 88.005 1172 | Current best accuracy: 88.39634704589844 1173 | 34.83409833908081 1174 | Current learning rate: 1.0000000000000002e-06 1175 | Epoch: [69][ 0/96] Loss 0.3377 (0.3377) Accuracy 89.062 (89.062) 1176 | Epoch: [69][10/96] Loss 0.3408 (0.3368) Accuracy 89.062 (89.702) 1177 | Epoch: [69][20/96] Loss 0.4074 (0.3524) Accuracy 85.156 (89.025) 1178 | Epoch: [69][30/96] Loss 0.3688 (0.3644) Accuracy 88.281 (88.609) 1179 | Epoch: [69][40/96] Loss 0.3077 (0.3621) Accuracy 88.281 (88.548) 1180 | Epoch: [69][50/96] Loss 0.3179 (0.3623) Accuracy 88.281 (88.465) 1181 | Epoch: [69][60/96] Loss 0.3265 (0.3607) Accuracy 89.062 (88.448) 1182 | Epoch: [69][70/96] Loss 0.4586 (0.3593) Accuracy 87.500 (88.600) 1183 | Epoch: [69][80/96] Loss 0.3120 (0.3613) Accuracy 89.062 (88.551) 1184 | Epoch: [69][90/96] Loss 0.3641 (0.3608) Accuracy 87.500 (88.470) 1185 | Test: [ 0/24] Loss 0.1694 (0.1694) Accuracy 96.094 (96.094) 1186 | Test: [10/24] Loss 0.1510 (0.2761) Accuracy 94.531 (91.406) 1187 | Test: [20/24] Loss 0.6076 (0.3349) Accuracy 85.156 (89.807) 1188 | * Accuracy 87.842 1189 | Current best accuracy: 88.39634704589844 1190 | 35.46050190925598 1191 | Current learning rate: 1.0000000000000002e-06 1192 | Epoch: [70][ 0/96] Loss 0.4012 (0.4012) Accuracy 85.938 (85.938) 1193 | Epoch: [70][10/96] Loss 0.4810 (0.3655) Accuracy 83.594 (88.423) 1194 | Epoch: [70][20/96] Loss 0.2902 (0.3603) Accuracy 88.281 (88.765) 1195 | Epoch: [70][30/96] Loss 0.2820 (0.3694) Accuracy 91.406 (88.281) 1196 | Epoch: [70][40/96] Loss 0.3625 (0.3646) Accuracy 89.844 (88.434) 1197 | Epoch: [70][50/96] Loss 0.3093 (0.3710) Accuracy 91.406 (87.990) 1198 | Epoch: [70][60/96] Loss 0.3310 (0.3716) Accuracy 89.844 (88.051) 1199 | Epoch: [70][70/96] Loss 0.3874 (0.3721) Accuracy 85.938 (87.973) 1200 | Epoch: [70][80/96] Loss 0.4882 (0.3734) Accuracy 83.594 (87.886) 1201 | Epoch: [70][90/96] Loss 0.3838 (0.3738) Accuracy 87.500 (87.869) 1202 | Test: [ 0/24] Loss 0.1793 (0.1793) Accuracy 96.094 (96.094) 1203 | Test: [10/24] Loss 0.1415 (0.2848) Accuracy 96.094 (91.335) 1204 | Test: [20/24] Loss 0.5946 (0.3333) Accuracy 84.375 (89.918) 1205 | * Accuracy 87.940 1206 | Current best accuracy: 88.39634704589844 1207 | 35.26735520362854 1208 | Current learning rate: 1.0000000000000002e-06 1209 | Epoch: [71][ 0/96] Loss 0.3449 (0.3449) Accuracy 90.625 (90.625) 1210 | Epoch: [71][10/96] Loss 0.3556 (0.3992) Accuracy 88.281 (87.287) 1211 | Epoch: [71][20/96] Loss 0.4984 (0.3810) Accuracy 84.375 (87.798) 1212 | Epoch: [71][30/96] Loss 0.4254 (0.3825) Accuracy 84.375 (87.802) 1213 | Epoch: [71][40/96] Loss 0.3234 (0.3847) Accuracy 90.625 (87.786) 1214 | Epoch: [71][50/96] Loss 0.2928 (0.3877) Accuracy 92.969 (87.699) 1215 | Epoch: [71][60/96] Loss 0.3971 (0.3823) Accuracy 85.938 (87.871) 1216 | Epoch: [71][70/96] Loss 0.3310 (0.3799) Accuracy 87.500 (87.918) 1217 | Epoch: [71][80/96] Loss 0.2679 (0.3800) Accuracy 92.188 (87.876) 1218 | Epoch: [71][90/96] Loss 0.4987 (0.3828) Accuracy 85.938 (87.869) 1219 | Test: [ 0/24] Loss 0.1738 (0.1738) Accuracy 96.094 (96.094) 1220 | Test: [10/24] Loss 0.1442 (0.2800) Accuracy 94.531 (91.406) 1221 | Test: [20/24] Loss 0.5842 (0.3311) Accuracy 85.156 (89.955) 1222 | * Accuracy 87.973 1223 | Current best accuracy: 88.39634704589844 1224 | 35.25314545631409 1225 | Current learning rate: 1.0000000000000002e-06 1226 | Epoch: [72][ 0/96] Loss 0.4213 (0.4213) Accuracy 83.594 (83.594) 1227 | Epoch: [72][10/96] Loss 0.4202 (0.3959) Accuracy 88.281 (87.145) 1228 | Epoch: [72][20/96] Loss 0.3124 (0.3760) Accuracy 89.844 (87.946) 1229 | Epoch: [72][30/96] Loss 0.4630 (0.3794) Accuracy 85.156 (88.004) 1230 | Epoch: [72][40/96] Loss 0.4037 (0.3728) Accuracy 90.625 (88.357) 1231 | Epoch: [72][50/96] Loss 0.4073 (0.3669) Accuracy 85.938 (88.465) 1232 | Epoch: [72][60/96] Loss 0.4571 (0.3677) Accuracy 88.281 (88.448) 1233 | Epoch: [72][70/96] Loss 0.3043 (0.3662) Accuracy 92.188 (88.534) 1234 | Epoch: [72][80/96] Loss 0.3868 (0.3701) Accuracy 86.719 (88.416) 1235 | Epoch: [72][90/96] Loss 0.3539 (0.3703) Accuracy 89.062 (88.350) 1236 | Test: [ 0/24] Loss 0.1650 (0.1650) Accuracy 96.094 (96.094) 1237 | Test: [10/24] Loss 0.1460 (0.2755) Accuracy 95.312 (91.406) 1238 | Test: [20/24] Loss 0.6132 (0.3330) Accuracy 83.594 (89.769) 1239 | * Accuracy 87.777 1240 | Current best accuracy: 88.39634704589844 1241 | 35.268794298172 1242 | Current learning rate: 1.0000000000000002e-06 1243 | Epoch: [73][ 0/96] Loss 0.4691 (0.4691) Accuracy 85.938 (85.938) 1244 | Epoch: [73][10/96] Loss 0.3753 (0.3446) Accuracy 88.281 (89.560) 1245 | Epoch: [73][20/96] Loss 0.4006 (0.3613) Accuracy 90.625 (88.690) 1246 | Epoch: [73][30/96] Loss 0.4414 (0.3708) Accuracy 83.594 (88.105) 1247 | Epoch: [73][40/96] Loss 0.3250 (0.3739) Accuracy 88.281 (87.614) 1248 | Epoch: [73][50/96] Loss 0.4576 (0.3741) Accuracy 83.594 (87.669) 1249 | Epoch: [73][60/96] Loss 0.2934 (0.3794) Accuracy 92.188 (87.449) 1250 | Epoch: [73][70/96] Loss 0.4836 (0.3799) Accuracy 85.156 (87.544) 1251 | Epoch: [73][80/96] Loss 0.3227 (0.3798) Accuracy 88.281 (87.481) 1252 | Epoch: [73][90/96] Loss 0.3159 (0.3815) Accuracy 91.406 (87.543) 1253 | Test: [ 0/24] Loss 0.1659 (0.1659) Accuracy 96.094 (96.094) 1254 | Test: [10/24] Loss 0.1472 (0.2757) Accuracy 94.531 (91.477) 1255 | Test: [20/24] Loss 0.6332 (0.3310) Accuracy 85.156 (89.993) 1256 | * Accuracy 87.907 1257 | Current best accuracy: 88.39634704589844 1258 | 35.29379630088806 1259 | Current learning rate: 1.0000000000000002e-06 1260 | Epoch: [74][ 0/96] Loss 0.3568 (0.3568) Accuracy 87.500 (87.500) 1261 | Epoch: [74][10/96] Loss 0.2501 (0.3524) Accuracy 93.750 (88.423) 1262 | Epoch: [74][20/96] Loss 0.2865 (0.3591) Accuracy 91.406 (88.430) 1263 | Epoch: [74][30/96] Loss 0.2105 (0.3573) Accuracy 94.531 (88.458) 1264 | Epoch: [74][40/96] Loss 0.2918 (0.3521) Accuracy 91.406 (88.681) 1265 | Epoch: [74][50/96] Loss 0.4446 (0.3599) Accuracy 82.812 (88.327) 1266 | Epoch: [74][60/96] Loss 0.3529 (0.3583) Accuracy 88.281 (88.384) 1267 | Epoch: [74][70/96] Loss 0.3520 (0.3612) Accuracy 87.500 (88.358) 1268 | Epoch: [74][80/96] Loss 0.3782 (0.3620) Accuracy 89.062 (88.387) 1269 | Epoch: [74][90/96] Loss 0.3914 (0.3674) Accuracy 85.938 (88.213) 1270 | Test: [ 0/24] Loss 0.1646 (0.1646) Accuracy 96.094 (96.094) 1271 | Test: [10/24] Loss 0.1359 (0.2706) Accuracy 96.094 (91.619) 1272 | Test: [20/24] Loss 0.6321 (0.3264) Accuracy 84.375 (90.179) 1273 | * Accuracy 88.103 1274 | Current best accuracy: 88.39634704589844 1275 | 35.14426875114441 1276 | Current learning rate: 1.0000000000000002e-07 1277 | Epoch: [75][ 0/96] Loss 0.4110 (0.4110) Accuracy 89.062 (89.062) 1278 | Epoch: [75][10/96] Loss 0.3261 (0.3645) Accuracy 91.406 (88.352) 1279 | Epoch: [75][20/96] Loss 0.3844 (0.3669) Accuracy 86.719 (88.281) 1280 | Epoch: [75][30/96] Loss 0.3881 (0.3777) Accuracy 90.625 (87.752) 1281 | Epoch: [75][40/96] Loss 0.3443 (0.3779) Accuracy 89.062 (87.710) 1282 | Epoch: [75][50/96] Loss 0.5437 (0.3761) Accuracy 82.031 (87.868) 1283 | Epoch: [75][60/96] Loss 0.2284 (0.3731) Accuracy 93.750 (87.987) 1284 | Epoch: [75][70/96] Loss 0.3261 (0.3736) Accuracy 89.844 (87.918) 1285 | Epoch: [75][80/96] Loss 0.3483 (0.3701) Accuracy 89.844 (88.088) 1286 | Epoch: [75][90/96] Loss 0.2571 (0.3647) Accuracy 91.406 (88.307) 1287 | Test: [ 0/24] Loss 0.1778 (0.1778) Accuracy 96.094 (96.094) 1288 | Test: [10/24] Loss 0.1467 (0.2886) Accuracy 95.312 (91.264) 1289 | Test: [20/24] Loss 0.6338 (0.3404) Accuracy 83.594 (89.769) 1290 | * Accuracy 87.842 1291 | Current best accuracy: 88.39634704589844 1292 | 35.062323808670044 1293 | Current learning rate: 1.0000000000000002e-07 1294 | Epoch: [76][ 0/96] Loss 0.3901 (0.3901) Accuracy 86.719 (86.719) 1295 | Epoch: [76][10/96] Loss 0.3920 (0.3503) Accuracy 88.281 (88.778) 1296 | Epoch: [76][20/96] Loss 0.4850 (0.3612) Accuracy 82.812 (88.467) 1297 | Epoch: [76][30/96] Loss 0.4103 (0.3606) Accuracy 87.500 (88.508) 1298 | Epoch: [76][40/96] Loss 0.4217 (0.3620) Accuracy 90.625 (88.662) 1299 | Epoch: [76][50/96] Loss 0.3388 (0.3703) Accuracy 88.281 (88.251) 1300 | Epoch: [76][60/96] Loss 0.4766 (0.3735) Accuracy 87.500 (88.076) 1301 | Epoch: [76][70/96] Loss 0.2405 (0.3709) Accuracy 92.188 (88.204) 1302 | Epoch: [76][80/96] Loss 0.3770 (0.3705) Accuracy 89.844 (88.156) 1303 | Epoch: [76][90/96] Loss 0.2767 (0.3698) Accuracy 92.969 (88.187) 1304 | Test: [ 0/24] Loss 0.1671 (0.1671) Accuracy 96.094 (96.094) 1305 | Test: [10/24] Loss 0.1439 (0.2749) Accuracy 94.531 (91.548) 1306 | Test: [20/24] Loss 0.6256 (0.3348) Accuracy 83.594 (90.030) 1307 | * Accuracy 88.136 1308 | Current best accuracy: 88.39634704589844 1309 | 34.12614297866821 1310 | Current learning rate: 1.0000000000000002e-07 1311 | Epoch: [77][ 0/96] Loss 0.3961 (0.3961) Accuracy 89.062 (89.062) 1312 | Epoch: [77][10/96] Loss 0.2888 (0.3608) Accuracy 89.844 (88.139) 1313 | Epoch: [77][20/96] Loss 0.4713 (0.3774) Accuracy 85.938 (87.872) 1314 | Epoch: [77][30/96] Loss 0.2705 (0.3716) Accuracy 92.969 (88.483) 1315 | Epoch: [77][40/96] Loss 0.3558 (0.3625) Accuracy 85.938 (88.777) 1316 | Epoch: [77][50/96] Loss 0.4170 (0.3645) Accuracy 83.594 (88.649) 1317 | Epoch: [77][60/96] Loss 0.4836 (0.3622) Accuracy 82.031 (88.691) 1318 | Epoch: [77][70/96] Loss 0.4421 (0.3645) Accuracy 85.156 (88.633) 1319 | Epoch: [77][80/96] Loss 0.4118 (0.3618) Accuracy 86.719 (88.715) 1320 | Epoch: [77][90/96] Loss 0.4721 (0.3634) Accuracy 86.719 (88.573) 1321 | Test: [ 0/24] Loss 0.1799 (0.1799) Accuracy 96.094 (96.094) 1322 | Test: [10/24] Loss 0.1420 (0.2841) Accuracy 96.094 (91.548) 1323 | Test: [20/24] Loss 0.6062 (0.3334) Accuracy 84.375 (90.104) 1324 | * Accuracy 88.103 1325 | Current best accuracy: 88.39634704589844 1326 | 34.48247671127319 1327 | Current learning rate: 1.0000000000000002e-07 1328 | Epoch: [78][ 0/96] Loss 0.2551 (0.2551) Accuracy 94.531 (94.531) 1329 | Epoch: [78][10/96] Loss 0.3629 (0.3341) Accuracy 89.844 (89.986) 1330 | Epoch: [78][20/96] Loss 0.3170 (0.3631) Accuracy 89.062 (88.467) 1331 | Epoch: [78][30/96] Loss 0.3000 (0.3844) Accuracy 91.406 (87.702) 1332 | Epoch: [78][40/96] Loss 0.3615 (0.3756) Accuracy 88.281 (87.938) 1333 | Epoch: [78][50/96] Loss 0.3058 (0.3769) Accuracy 92.188 (87.914) 1334 | Epoch: [78][60/96] Loss 0.2924 (0.3753) Accuracy 90.625 (87.923) 1335 | Epoch: [78][70/96] Loss 0.3857 (0.3745) Accuracy 89.844 (88.039) 1336 | Epoch: [78][80/96] Loss 0.2902 (0.3733) Accuracy 89.844 (88.156) 1337 | Epoch: [78][90/96] Loss 0.3700 (0.3757) Accuracy 85.938 (87.981) 1338 | Test: [ 0/24] Loss 0.1872 (0.1872) Accuracy 96.094 (96.094) 1339 | Test: [10/24] Loss 0.1234 (0.2819) Accuracy 96.094 (91.477) 1340 | Test: [20/24] Loss 0.5672 (0.3256) Accuracy 85.156 (90.253) 1341 | * Accuracy 88.168 1342 | Current best accuracy: 88.39634704589844 1343 | 34.14760446548462 1344 | Current learning rate: 1.0000000000000002e-07 1345 | Epoch: [79][ 0/96] Loss 0.4057 (0.4057) Accuracy 87.500 (87.500) 1346 | Epoch: [79][10/96] Loss 0.4902 (0.4068) Accuracy 82.812 (87.074) 1347 | Epoch: [79][20/96] Loss 0.3327 (0.3751) Accuracy 88.281 (88.318) 1348 | Epoch: [79][30/96] Loss 0.4528 (0.3761) Accuracy 87.500 (88.130) 1349 | Epoch: [79][40/96] Loss 0.4447 (0.3795) Accuracy 85.938 (87.900) 1350 | Epoch: [79][50/96] Loss 0.3750 (0.3728) Accuracy 86.719 (88.189) 1351 | Epoch: [79][60/96] Loss 0.4246 (0.3761) Accuracy 88.281 (88.012) 1352 | Epoch: [79][70/96] Loss 0.3421 (0.3783) Accuracy 90.625 (87.984) 1353 | Epoch: [79][80/96] Loss 0.3256 (0.3741) Accuracy 89.844 (88.185) 1354 | Epoch: [79][90/96] Loss 0.3739 (0.3777) Accuracy 89.844 (88.075) 1355 | Test: [ 0/24] Loss 0.1821 (0.1821) Accuracy 96.094 (96.094) 1356 | Test: [10/24] Loss 0.1330 (0.2807) Accuracy 96.094 (91.406) 1357 | Test: [20/24] Loss 0.6119 (0.3346) Accuracy 83.594 (89.918) 1358 | * Accuracy 87.973 1359 | Current best accuracy: 88.39634704589844 1360 | 34.65243744850159 1361 | Current learning rate: 1.0000000000000002e-07 1362 | Epoch: [80][ 0/96] Loss 0.2628 (0.2628) Accuracy 92.969 (92.969) 1363 | Epoch: [80][10/96] Loss 0.3585 (0.3536) Accuracy 85.938 (88.423) 1364 | Epoch: [80][20/96] Loss 0.3364 (0.3541) Accuracy 89.844 (88.504) 1365 | Epoch: [80][30/96] Loss 0.5077 (0.3649) Accuracy 83.594 (88.004) 1366 | Epoch: [80][40/96] Loss 0.3674 (0.3688) Accuracy 89.062 (88.072) 1367 | Epoch: [80][50/96] Loss 0.3635 (0.3734) Accuracy 87.500 (87.714) 1368 | Epoch: [80][60/96] Loss 0.3580 (0.3712) Accuracy 88.281 (87.743) 1369 | Epoch: [80][70/96] Loss 0.4276 (0.3687) Accuracy 88.281 (87.918) 1370 | Epoch: [80][80/96] Loss 0.2873 (0.3716) Accuracy 90.625 (87.770) 1371 | Epoch: [80][90/96] Loss 0.4948 (0.3708) Accuracy 79.688 (87.809) 1372 | Test: [ 0/24] Loss 0.1558 (0.1558) Accuracy 96.094 (96.094) 1373 | Test: [10/24] Loss 0.1437 (0.2635) Accuracy 94.531 (91.619) 1374 | Test: [20/24] Loss 0.6486 (0.3312) Accuracy 83.594 (89.955) 1375 | * Accuracy 87.842 1376 | Current best accuracy: 88.39634704589844 1377 | 34.309731245040894 1378 | Current learning rate: 1.0000000000000002e-07 1379 | Epoch: [81][ 0/96] Loss 0.3082 (0.3082) Accuracy 91.406 (91.406) 1380 | Epoch: [81][10/96] Loss 0.2565 (0.3654) Accuracy 90.625 (87.713) 1381 | Epoch: [81][20/96] Loss 0.3718 (0.3889) Accuracy 89.844 (87.686) 1382 | Epoch: [81][30/96] Loss 0.5078 (0.3730) Accuracy 86.719 (88.256) 1383 | Epoch: [81][40/96] Loss 0.3001 (0.3747) Accuracy 89.062 (87.900) 1384 | Epoch: [81][50/96] Loss 0.3453 (0.3680) Accuracy 86.719 (88.220) 1385 | Epoch: [81][60/96] Loss 0.3298 (0.3690) Accuracy 89.062 (88.192) 1386 | Epoch: [81][70/96] Loss 0.3350 (0.3711) Accuracy 87.500 (87.973) 1387 | Epoch: [81][80/96] Loss 0.3609 (0.3732) Accuracy 88.281 (87.809) 1388 | Epoch: [81][90/96] Loss 0.3481 (0.3719) Accuracy 89.844 (87.921) 1389 | Test: [ 0/24] Loss 0.1749 (0.1749) Accuracy 96.094 (96.094) 1390 | Test: [10/24] Loss 0.1449 (0.2843) Accuracy 95.312 (91.335) 1391 | Test: [20/24] Loss 0.5903 (0.3322) Accuracy 85.938 (89.993) 1392 | * Accuracy 88.005 1393 | Current best accuracy: 88.39634704589844 1394 | 34.0607328414917 1395 | Current learning rate: 1.0000000000000002e-07 1396 | Epoch: [82][ 0/96] Loss 0.3858 (0.3858) Accuracy 87.500 (87.500) 1397 | Epoch: [82][10/96] Loss 0.2532 (0.3784) Accuracy 91.406 (87.500) 1398 | Epoch: [82][20/96] Loss 0.4674 (0.3979) Accuracy 85.156 (87.388) 1399 | Epoch: [82][30/96] Loss 0.3061 (0.3732) Accuracy 91.406 (88.407) 1400 | Epoch: [82][40/96] Loss 0.3947 (0.3729) Accuracy 86.719 (88.338) 1401 | Epoch: [82][50/96] Loss 0.3625 (0.3694) Accuracy 85.938 (88.388) 1402 | Epoch: [82][60/96] Loss 0.3103 (0.3719) Accuracy 92.188 (88.307) 1403 | Epoch: [82][70/96] Loss 0.3846 (0.3720) Accuracy 87.500 (88.259) 1404 | Epoch: [82][80/96] Loss 0.2800 (0.3683) Accuracy 92.969 (88.329) 1405 | Epoch: [82][90/96] Loss 0.4010 (0.3700) Accuracy 84.375 (88.230) 1406 | Test: [ 0/24] Loss 0.1544 (0.1544) Accuracy 96.094 (96.094) 1407 | Test: [10/24] Loss 0.1529 (0.2663) Accuracy 93.750 (91.548) 1408 | Test: [20/24] Loss 0.6179 (0.3314) Accuracy 85.156 (89.918) 1409 | * Accuracy 87.875 1410 | Current best accuracy: 88.39634704589844 1411 | 34.62765336036682 1412 | Current learning rate: 1.0000000000000002e-07 1413 | Epoch: [83][ 0/96] Loss 0.3334 (0.3334) Accuracy 92.969 (92.969) 1414 | Epoch: [83][10/96] Loss 0.3761 (0.3531) Accuracy 88.281 (89.844) 1415 | Epoch: [83][20/96] Loss 0.4387 (0.3746) Accuracy 88.281 (88.616) 1416 | Epoch: [83][30/96] Loss 0.3554 (0.3720) Accuracy 87.500 (88.710) 1417 | Epoch: [83][40/96] Loss 0.3404 (0.3688) Accuracy 88.281 (88.700) 1418 | Epoch: [83][50/96] Loss 0.3443 (0.3663) Accuracy 87.500 (88.695) 1419 | Epoch: [83][60/96] Loss 0.2651 (0.3669) Accuracy 94.531 (88.653) 1420 | Epoch: [83][70/96] Loss 0.4045 (0.3673) Accuracy 90.625 (88.710) 1421 | Epoch: [83][80/96] Loss 0.6096 (0.3715) Accuracy 78.906 (88.436) 1422 | Epoch: [83][90/96] Loss 0.4955 (0.3684) Accuracy 82.031 (88.504) 1423 | Test: [ 0/24] Loss 0.1702 (0.1702) Accuracy 96.094 (96.094) 1424 | Test: [10/24] Loss 0.1530 (0.2823) Accuracy 93.750 (91.051) 1425 | Test: [20/24] Loss 0.6064 (0.3345) Accuracy 85.156 (89.807) 1426 | * Accuracy 87.842 1427 | Current best accuracy: 88.39634704589844 1428 | 33.02280354499817 1429 | Current learning rate: 1.0000000000000002e-07 1430 | Epoch: [84][ 0/96] Loss 0.3102 (0.3102) Accuracy 89.062 (89.062) 1431 | Epoch: [84][10/96] Loss 0.4864 (0.3729) Accuracy 85.156 (88.352) 1432 | Epoch: [84][20/96] Loss 0.4101 (0.3677) Accuracy 88.281 (88.579) 1433 | Epoch: [84][30/96] Loss 0.5405 (0.3809) Accuracy 82.031 (88.004) 1434 | Epoch: [84][40/96] Loss 0.4935 (0.3697) Accuracy 85.156 (88.377) 1435 | Epoch: [84][50/96] Loss 0.4736 (0.3663) Accuracy 84.375 (88.450) 1436 | Epoch: [84][60/96] Loss 0.3140 (0.3698) Accuracy 93.750 (88.332) 1437 | Epoch: [84][70/96] Loss 0.3104 (0.3698) Accuracy 91.406 (88.248) 1438 | Epoch: [84][80/96] Loss 0.4353 (0.3732) Accuracy 83.594 (88.088) 1439 | Epoch: [84][90/96] Loss 0.3190 (0.3735) Accuracy 90.625 (88.049) 1440 | Test: [ 0/24] Loss 0.1635 (0.1635) Accuracy 96.094 (96.094) 1441 | Test: [10/24] Loss 0.1624 (0.2789) Accuracy 92.969 (91.193) 1442 | Test: [20/24] Loss 0.6250 (0.3374) Accuracy 85.156 (89.844) 1443 | * Accuracy 87.875 1444 | Current best accuracy: 88.39634704589844 1445 | 32.992656230926514 1446 | Current learning rate: 1.0000000000000002e-07 1447 | Epoch: [85][ 0/96] Loss 0.3698 (0.3698) Accuracy 90.625 (90.625) 1448 | Epoch: [85][10/96] Loss 0.4460 (0.3661) Accuracy 86.719 (88.849) 1449 | Epoch: [85][20/96] Loss 0.3683 (0.3578) Accuracy 88.281 (88.765) 1450 | Epoch: [85][30/96] Loss 0.3331 (0.3533) Accuracy 91.406 (88.987) 1451 | Epoch: [85][40/96] Loss 0.2895 (0.3595) Accuracy 90.625 (88.720) 1452 | Epoch: [85][50/96] Loss 0.3099 (0.3592) Accuracy 91.406 (88.879) 1453 | Epoch: [85][60/96] Loss 0.3812 (0.3593) Accuracy 88.281 (89.011) 1454 | Epoch: [85][70/96] Loss 0.4057 (0.3594) Accuracy 88.281 (88.941) 1455 | Epoch: [85][80/96] Loss 0.3441 (0.3624) Accuracy 86.719 (88.696) 1456 | Epoch: [85][90/96] Loss 0.4401 (0.3643) Accuracy 86.719 (88.556) 1457 | Test: [ 0/24] Loss 0.1728 (0.1728) Accuracy 96.094 (96.094) 1458 | Test: [10/24] Loss 0.1380 (0.2780) Accuracy 95.312 (91.335) 1459 | Test: [20/24] Loss 0.6143 (0.3310) Accuracy 83.594 (89.993) 1460 | * Accuracy 88.005 1461 | Current best accuracy: 88.39634704589844 1462 | 32.93463492393494 1463 | Current learning rate: 1.0000000000000002e-07 1464 | Epoch: [86][ 0/96] Loss 0.2988 (0.2988) Accuracy 89.062 (89.062) 1465 | Epoch: [86][10/96] Loss 0.4447 (0.3588) Accuracy 84.375 (88.494) 1466 | Epoch: [86][20/96] Loss 0.3877 (0.3443) Accuracy 90.625 (89.323) 1467 | Epoch: [86][30/96] Loss 0.4773 (0.3592) Accuracy 84.375 (88.584) 1468 | Epoch: [86][40/96] Loss 0.4448 (0.3672) Accuracy 85.938 (88.148) 1469 | Epoch: [86][50/96] Loss 0.3236 (0.3692) Accuracy 88.281 (88.113) 1470 | Epoch: [86][60/96] Loss 0.3781 (0.3666) Accuracy 85.938 (88.268) 1471 | Epoch: [86][70/96] Loss 0.3581 (0.3659) Accuracy 87.500 (88.204) 1472 | Epoch: [86][80/96] Loss 0.4167 (0.3647) Accuracy 88.281 (88.387) 1473 | Epoch: [86][90/96] Loss 0.3664 (0.3660) Accuracy 85.156 (88.281) 1474 | Test: [ 0/24] Loss 0.1713 (0.1713) Accuracy 96.094 (96.094) 1475 | Test: [10/24] Loss 0.1341 (0.2731) Accuracy 95.312 (91.477) 1476 | Test: [20/24] Loss 0.6167 (0.3290) Accuracy 84.375 (89.993) 1477 | * Accuracy 87.907 1478 | Current best accuracy: 88.39634704589844 1479 | 32.687652587890625 1480 | Current learning rate: 1.0000000000000002e-07 1481 | Epoch: [87][ 0/96] Loss 0.3730 (0.3730) Accuracy 87.500 (87.500) 1482 | Epoch: [87][10/96] Loss 0.2368 (0.3295) Accuracy 92.969 (89.347) 1483 | Epoch: [87][20/96] Loss 0.4393 (0.3671) Accuracy 85.156 (88.393) 1484 | Epoch: [87][30/96] Loss 0.4073 (0.3694) Accuracy 85.156 (88.130) 1485 | Epoch: [87][40/96] Loss 0.3860 (0.3717) Accuracy 86.719 (87.938) 1486 | Epoch: [87][50/96] Loss 0.3086 (0.3670) Accuracy 91.406 (88.051) 1487 | Epoch: [87][60/96] Loss 0.3784 (0.3692) Accuracy 89.062 (87.948) 1488 | Epoch: [87][70/96] Loss 0.3501 (0.3656) Accuracy 89.062 (88.105) 1489 | Epoch: [87][80/96] Loss 0.3814 (0.3659) Accuracy 89.062 (88.166) 1490 | Epoch: [87][90/96] Loss 0.3790 (0.3659) Accuracy 87.500 (88.152) 1491 | Test: [ 0/24] Loss 0.1644 (0.1644) Accuracy 96.094 (96.094) 1492 | Test: [10/24] Loss 0.1206 (0.2595) Accuracy 96.094 (91.690) 1493 | Test: [20/24] Loss 0.6491 (0.3281) Accuracy 82.812 (89.993) 1494 | * Accuracy 87.973 1495 | Current best accuracy: 88.39634704589844 1496 | 32.82159757614136 1497 | Current learning rate: 1.0000000000000002e-07 1498 | Epoch: [88][ 0/96] Loss 0.3334 (0.3334) Accuracy 92.188 (92.188) 1499 | Epoch: [88][10/96] Loss 0.3581 (0.3542) Accuracy 87.500 (89.062) 1500 | Epoch: [88][20/96] Loss 0.4599 (0.3658) Accuracy 85.156 (88.504) 1501 | Epoch: [88][30/96] Loss 0.4696 (0.3703) Accuracy 86.719 (88.256) 1502 | Epoch: [88][40/96] Loss 0.4656 (0.3767) Accuracy 86.719 (87.976) 1503 | Epoch: [88][50/96] Loss 0.3615 (0.3784) Accuracy 89.062 (87.975) 1504 | Epoch: [88][60/96] Loss 0.2755 (0.3724) Accuracy 93.750 (88.397) 1505 | Epoch: [88][70/96] Loss 0.3364 (0.3682) Accuracy 89.062 (88.479) 1506 | Epoch: [88][80/96] Loss 0.2891 (0.3650) Accuracy 90.625 (88.580) 1507 | Epoch: [88][90/96] Loss 0.4263 (0.3704) Accuracy 86.719 (88.504) 1508 | Test: [ 0/24] Loss 0.1589 (0.1589) Accuracy 96.094 (96.094) 1509 | Test: [10/24] Loss 0.1437 (0.2697) Accuracy 95.312 (91.477) 1510 | Test: [20/24] Loss 0.6400 (0.3336) Accuracy 83.594 (89.918) 1511 | * Accuracy 87.940 1512 | Current best accuracy: 88.39634704589844 1513 | 32.76275372505188 1514 | Current learning rate: 1.0000000000000002e-07 1515 | Epoch: [89][ 0/96] Loss 0.3661 (0.3661) Accuracy 92.188 (92.188) 1516 | Epoch: [89][10/96] Loss 0.4585 (0.3665) Accuracy 86.719 (88.494) 1517 | Epoch: [89][20/96] Loss 0.3366 (0.3569) Accuracy 88.281 (88.281) 1518 | Epoch: [89][30/96] Loss 0.4060 (0.3654) Accuracy 87.500 (88.508) 1519 | Epoch: [89][40/96] Loss 0.4500 (0.3699) Accuracy 89.062 (88.510) 1520 | Epoch: [89][50/96] Loss 0.4860 (0.3732) Accuracy 81.250 (88.251) 1521 | Epoch: [89][60/96] Loss 0.4078 (0.3755) Accuracy 82.812 (88.102) 1522 | Epoch: [89][70/96] Loss 0.3429 (0.3751) Accuracy 87.500 (88.094) 1523 | Epoch: [89][80/96] Loss 0.2380 (0.3737) Accuracy 95.312 (88.146) 1524 | Epoch: [89][90/96] Loss 0.4006 (0.3721) Accuracy 85.938 (88.221) 1525 | Test: [ 0/24] Loss 0.1726 (0.1726) Accuracy 96.094 (96.094) 1526 | Test: [10/24] Loss 0.1291 (0.2685) Accuracy 96.094 (91.619) 1527 | Test: [20/24] Loss 0.5845 (0.3249) Accuracy 85.156 (90.104) 1528 | * Accuracy 88.038 1529 | Current best accuracy: 88.39634704589844 1530 | 33.04051089286804 1531 | Current learning rate: 1.0000000000000004e-08 1532 | Epoch: [90][ 0/96] Loss 0.3386 (0.3386) Accuracy 88.281 (88.281) 1533 | Epoch: [90][10/96] Loss 0.4156 (0.3828) Accuracy 87.500 (87.926) 1534 | Epoch: [90][20/96] Loss 0.3473 (0.3885) Accuracy 90.625 (88.281) 1535 | Epoch: [90][30/96] Loss 0.3454 (0.3843) Accuracy 90.625 (88.256) 1536 | Epoch: [90][40/96] Loss 0.2832 (0.3835) Accuracy 92.969 (88.034) 1537 | Epoch: [90][50/96] Loss 0.3693 (0.3827) Accuracy 84.375 (87.776) 1538 | Epoch: [90][60/96] Loss 0.4215 (0.3762) Accuracy 85.156 (87.897) 1539 | Epoch: [90][70/96] Loss 0.3312 (0.3780) Accuracy 89.844 (87.830) 1540 | Epoch: [90][80/96] Loss 0.4063 (0.3801) Accuracy 89.062 (87.693) 1541 | Epoch: [90][90/96] Loss 0.4130 (0.3819) Accuracy 90.625 (87.663) 1542 | Test: [ 0/24] Loss 0.1633 (0.1633) Accuracy 96.094 (96.094) 1543 | Test: [10/24] Loss 0.1492 (0.2753) Accuracy 95.312 (91.406) 1544 | Test: [20/24] Loss 0.6210 (0.3324) Accuracy 85.156 (89.993) 1545 | * Accuracy 87.875 1546 | Current best accuracy: 88.39634704589844 1547 | 33.35747480392456 1548 | Current learning rate: 1.0000000000000004e-08 1549 | Epoch: [91][ 0/96] Loss 0.3700 (0.3700) Accuracy 89.062 (89.062) 1550 | Epoch: [91][10/96] Loss 0.4560 (0.3683) Accuracy 88.281 (88.849) 1551 | Epoch: [91][20/96] Loss 0.3704 (0.3673) Accuracy 89.844 (88.504) 1552 | Epoch: [91][30/96] Loss 0.4396 (0.3733) Accuracy 85.156 (88.029) 1553 | Epoch: [91][40/96] Loss 0.4100 (0.3741) Accuracy 87.500 (88.014) 1554 | Epoch: [91][50/96] Loss 0.3388 (0.3711) Accuracy 88.281 (88.036) 1555 | Epoch: [91][60/96] Loss 0.3038 (0.3655) Accuracy 89.062 (88.294) 1556 | Epoch: [91][70/96] Loss 0.4335 (0.3681) Accuracy 85.156 (88.149) 1557 | Epoch: [91][80/96] Loss 0.3960 (0.3709) Accuracy 87.500 (88.108) 1558 | Epoch: [91][90/96] Loss 0.3793 (0.3706) Accuracy 85.938 (88.084) 1559 | Test: [ 0/24] Loss 0.1679 (0.1679) Accuracy 96.094 (96.094) 1560 | Test: [10/24] Loss 0.1538 (0.2814) Accuracy 94.531 (91.264) 1561 | Test: [20/24] Loss 0.6260 (0.3371) Accuracy 84.375 (89.881) 1562 | * Accuracy 87.973 1563 | Current best accuracy: 88.39634704589844 1564 | 32.88239097595215 1565 | Current learning rate: 1.0000000000000004e-08 1566 | Epoch: [92][ 0/96] Loss 0.4103 (0.4103) Accuracy 89.062 (89.062) 1567 | Epoch: [92][10/96] Loss 0.4547 (0.3872) Accuracy 81.250 (87.003) 1568 | Epoch: [92][20/96] Loss 0.2706 (0.3719) Accuracy 93.750 (88.021) 1569 | Epoch: [92][30/96] Loss 0.4449 (0.3780) Accuracy 86.719 (88.105) 1570 | Epoch: [92][40/96] Loss 0.4210 (0.3770) Accuracy 86.719 (88.129) 1571 | Epoch: [92][50/96] Loss 0.3138 (0.3711) Accuracy 87.500 (88.388) 1572 | Epoch: [92][60/96] Loss 0.3657 (0.3739) Accuracy 85.156 (88.358) 1573 | Epoch: [92][70/96] Loss 0.3123 (0.3740) Accuracy 89.844 (88.325) 1574 | Epoch: [92][80/96] Loss 0.3499 (0.3673) Accuracy 89.844 (88.484) 1575 | Epoch: [92][90/96] Loss 0.3616 (0.3670) Accuracy 89.844 (88.582) 1576 | Test: [ 0/24] Loss 0.1906 (0.1906) Accuracy 96.094 (96.094) 1577 | Test: [10/24] Loss 0.1238 (0.2885) Accuracy 96.094 (91.193) 1578 | Test: [20/24] Loss 0.6278 (0.3335) Accuracy 83.594 (89.918) 1579 | * Accuracy 87.940 1580 | Current best accuracy: 88.39634704589844 1581 | 33.28434419631958 1582 | Current learning rate: 1.0000000000000004e-08 1583 | Epoch: [93][ 0/96] Loss 0.4103 (0.4103) Accuracy 88.281 (88.281) 1584 | Epoch: [93][10/96] Loss 0.2940 (0.3423) Accuracy 90.625 (90.057) 1585 | Epoch: [93][20/96] Loss 0.3875 (0.3482) Accuracy 88.281 (89.807) 1586 | Epoch: [93][30/96] Loss 0.3393 (0.3435) Accuracy 87.500 (89.592) 1587 | Epoch: [93][40/96] Loss 0.3030 (0.3481) Accuracy 90.625 (89.215) 1588 | Epoch: [93][50/96] Loss 0.2467 (0.3487) Accuracy 92.969 (89.124) 1589 | Epoch: [93][60/96] Loss 0.2817 (0.3562) Accuracy 90.625 (88.794) 1590 | Epoch: [93][70/96] Loss 0.4205 (0.3602) Accuracy 85.938 (88.534) 1591 | Epoch: [93][80/96] Loss 0.4389 (0.3648) Accuracy 85.938 (88.339) 1592 | Epoch: [93][90/96] Loss 0.3691 (0.3636) Accuracy 85.938 (88.384) 1593 | Test: [ 0/24] Loss 0.1789 (0.1789) Accuracy 96.094 (96.094) 1594 | Test: [10/24] Loss 0.1420 (0.2845) Accuracy 95.312 (91.193) 1595 | Test: [20/24] Loss 0.5940 (0.3341) Accuracy 85.156 (89.918) 1596 | * Accuracy 87.973 1597 | Current best accuracy: 88.39634704589844 1598 | 32.912354946136475 1599 | Current learning rate: 1.0000000000000004e-08 1600 | Epoch: [94][ 0/96] Loss 0.3177 (0.3177) Accuracy 89.062 (89.062) 1601 | Epoch: [94][10/96] Loss 0.3549 (0.3483) Accuracy 90.625 (88.423) 1602 | Epoch: [94][20/96] Loss 0.4138 (0.3647) Accuracy 87.500 (88.058) 1603 | Epoch: [94][30/96] Loss 0.3246 (0.3670) Accuracy 89.062 (88.357) 1604 | Epoch: [94][40/96] Loss 0.3200 (0.3676) Accuracy 89.062 (88.319) 1605 | Epoch: [94][50/96] Loss 0.2686 (0.3588) Accuracy 92.188 (88.725) 1606 | Epoch: [94][60/96] Loss 0.3951 (0.3574) Accuracy 85.938 (88.653) 1607 | Epoch: [94][70/96] Loss 0.4014 (0.3592) Accuracy 87.500 (88.666) 1608 | Epoch: [94][80/96] Loss 0.4361 (0.3616) Accuracy 87.500 (88.484) 1609 | Epoch: [94][90/96] Loss 0.2995 (0.3621) Accuracy 91.406 (88.401) 1610 | Test: [ 0/24] Loss 0.1809 (0.1809) Accuracy 96.094 (96.094) 1611 | Test: [10/24] Loss 0.1257 (0.2789) Accuracy 96.094 (91.406) 1612 | Test: [20/24] Loss 0.6055 (0.3328) Accuracy 83.594 (89.881) 1613 | * Accuracy 87.940 1614 | Current best accuracy: 88.39634704589844 1615 | 33.04438829421997 1616 | Current learning rate: 1.0000000000000004e-08 1617 | Epoch: [95][ 0/96] Loss 0.4807 (0.4807) Accuracy 84.375 (84.375) 1618 | Epoch: [95][10/96] Loss 0.3317 (0.3823) Accuracy 89.844 (87.500) 1619 | Epoch: [95][20/96] Loss 0.4753 (0.3911) Accuracy 85.938 (87.574) 1620 | Epoch: [95][30/96] Loss 0.3624 (0.3896) Accuracy 85.156 (87.399) 1621 | Epoch: [95][40/96] Loss 0.3391 (0.3845) Accuracy 90.625 (87.710) 1622 | Epoch: [95][50/96] Loss 0.4505 (0.3796) Accuracy 87.500 (87.975) 1623 | Epoch: [95][60/96] Loss 0.2649 (0.3797) Accuracy 92.188 (87.795) 1624 | Epoch: [95][70/96] Loss 0.3716 (0.3743) Accuracy 88.281 (88.006) 1625 | Epoch: [95][80/96] Loss 0.2992 (0.3727) Accuracy 89.844 (88.166) 1626 | Epoch: [95][90/96] Loss 0.3301 (0.3723) Accuracy 89.062 (88.118) 1627 | Test: [ 0/24] Loss 0.1871 (0.1871) Accuracy 96.094 (96.094) 1628 | Test: [10/24] Loss 0.1401 (0.2931) Accuracy 96.094 (91.335) 1629 | Test: [20/24] Loss 0.5989 (0.3365) Accuracy 84.375 (90.030) 1630 | * Accuracy 88.038 1631 | Current best accuracy: 88.39634704589844 1632 | 33.89265489578247 1633 | Current learning rate: 1.0000000000000004e-08 1634 | Epoch: [96][ 0/96] Loss 0.3620 (0.3620) Accuracy 89.844 (89.844) 1635 | Epoch: [96][10/96] Loss 0.3070 (0.3914) Accuracy 91.406 (87.784) 1636 | Epoch: [96][20/96] Loss 0.3393 (0.3762) Accuracy 89.844 (88.170) 1637 | Epoch: [96][30/96] Loss 0.3143 (0.3776) Accuracy 89.062 (88.080) 1638 | Epoch: [96][40/96] Loss 0.5448 (0.3802) Accuracy 82.812 (87.881) 1639 | Epoch: [96][50/96] Loss 0.2855 (0.3809) Accuracy 90.625 (87.868) 1640 | Epoch: [96][60/96] Loss 0.3853 (0.3788) Accuracy 87.500 (87.974) 1641 | Epoch: [96][70/96] Loss 0.5023 (0.3816) Accuracy 84.375 (87.852) 1642 | Epoch: [96][80/96] Loss 0.3874 (0.3793) Accuracy 85.156 (87.828) 1643 | Epoch: [96][90/96] Loss 0.4717 (0.3813) Accuracy 84.375 (87.792) 1644 | Test: [ 0/24] Loss 0.1734 (0.1734) Accuracy 96.094 (96.094) 1645 | Test: [10/24] Loss 0.1411 (0.2783) Accuracy 94.531 (91.264) 1646 | Test: [20/24] Loss 0.6162 (0.3360) Accuracy 84.375 (89.732) 1647 | * Accuracy 87.777 1648 | Current best accuracy: 88.39634704589844 1649 | 33.03491425514221 1650 | Current learning rate: 1.0000000000000004e-08 1651 | Epoch: [97][ 0/96] Loss 0.3388 (0.3388) Accuracy 91.406 (91.406) 1652 | Epoch: [97][10/96] Loss 0.2887 (0.3516) Accuracy 90.625 (89.205) 1653 | Epoch: [97][20/96] Loss 0.2692 (0.3449) Accuracy 89.844 (89.025) 1654 | Epoch: [97][30/96] Loss 0.3811 (0.3583) Accuracy 88.281 (88.659) 1655 | Epoch: [97][40/96] Loss 0.4197 (0.3648) Accuracy 85.938 (88.205) 1656 | Epoch: [97][50/96] Loss 0.3627 (0.3638) Accuracy 88.281 (88.205) 1657 | Epoch: [97][60/96] Loss 0.4150 (0.3696) Accuracy 87.500 (88.089) 1658 | Epoch: [97][70/96] Loss 0.3230 (0.3728) Accuracy 92.969 (88.006) 1659 | Epoch: [97][80/96] Loss 0.4082 (0.3751) Accuracy 87.500 (87.934) 1660 | Epoch: [97][90/96] Loss 0.4404 (0.3746) Accuracy 84.375 (87.964) 1661 | Test: [ 0/24] Loss 0.1963 (0.1963) Accuracy 96.094 (96.094) 1662 | Test: [10/24] Loss 0.1201 (0.2885) Accuracy 96.094 (91.264) 1663 | Test: [20/24] Loss 0.5975 (0.3313) Accuracy 84.375 (90.030) 1664 | * Accuracy 88.038 1665 | Current best accuracy: 88.39634704589844 1666 | 33.408926010131836 1667 | Current learning rate: 1.0000000000000004e-08 1668 | Epoch: [98][ 0/96] Loss 0.3060 (0.3060) Accuracy 89.844 (89.844) 1669 | Epoch: [98][10/96] Loss 0.3496 (0.3746) Accuracy 89.844 (87.713) 1670 | Epoch: [98][20/96] Loss 0.3099 (0.3578) Accuracy 90.625 (88.504) 1671 | Epoch: [98][30/96] Loss 0.3352 (0.3629) Accuracy 85.156 (88.080) 1672 | Epoch: [98][40/96] Loss 0.3891 (0.3579) Accuracy 89.062 (88.357) 1673 | Epoch: [98][50/96] Loss 0.3759 (0.3627) Accuracy 88.281 (88.097) 1674 | Epoch: [98][60/96] Loss 0.3220 (0.3661) Accuracy 92.969 (88.140) 1675 | Epoch: [98][70/96] Loss 0.3795 (0.3694) Accuracy 87.500 (88.072) 1676 | Epoch: [98][80/96] Loss 0.2372 (0.3667) Accuracy 93.750 (88.175) 1677 | Epoch: [98][90/96] Loss 0.4191 (0.3705) Accuracy 85.938 (88.024) 1678 | Test: [ 0/24] Loss 0.1844 (0.1844) Accuracy 96.094 (96.094) 1679 | Test: [10/24] Loss 0.1246 (0.2808) Accuracy 96.094 (91.335) 1680 | Test: [20/24] Loss 0.6124 (0.3302) Accuracy 85.156 (90.141) 1681 | * Accuracy 88.103 1682 | Current best accuracy: 88.39634704589844 1683 | 32.6613929271698 1684 | Current learning rate: 1.0000000000000004e-08 1685 | Epoch: [99][ 0/96] Loss 0.2952 (0.2952) Accuracy 89.844 (89.844) 1686 | Epoch: [99][10/96] Loss 0.2996 (0.3698) Accuracy 89.062 (87.429) 1687 | Epoch: [99][20/96] Loss 0.4116 (0.3693) Accuracy 85.156 (87.723) 1688 | Epoch: [99][30/96] Loss 0.2674 (0.3666) Accuracy 89.062 (87.777) 1689 | Epoch: [99][40/96] Loss 0.3539 (0.3715) Accuracy 88.281 (87.729) 1690 | Epoch: [99][50/96] Loss 0.3708 (0.3750) Accuracy 89.062 (87.684) 1691 | Epoch: [99][60/96] Loss 0.3597 (0.3758) Accuracy 89.062 (87.615) 1692 | Epoch: [99][70/96] Loss 0.4745 (0.3758) Accuracy 82.031 (87.577) 1693 | Epoch: [99][80/96] Loss 0.2511 (0.3757) Accuracy 92.188 (87.712) 1694 | Epoch: [99][90/96] Loss 0.3946 (0.3736) Accuracy 84.375 (87.835) 1695 | Test: [ 0/24] Loss 0.1834 (0.1834) Accuracy 96.094 (96.094) 1696 | Test: [10/24] Loss 0.1206 (0.2802) Accuracy 96.094 (91.335) 1697 | Test: [20/24] Loss 0.6357 (0.3298) Accuracy 83.594 (90.030) 1698 | * Accuracy 88.005 1699 | Current best accuracy: 88.39634704589844 1700 | 32.60729146003723 1701 | --------------------------------------------------------------------------------