Namespace(accumulate=1, batch_norm=False, batch_size=8, clip_grad=0, crop_ratio=0.875, data_dir='/home/ubuntu/yizhu/data/kinetics400/kinetics400/train_256', dataset='kinetics400', dtype='float32', eval=False, fast_temporal_stride=2, hard_weight=0.5, hashtag='', input_5d=False, input_size=224, kvstore=None, label_smoothing=False, last_gamma=False, log_interval=50, logging_file='i3d_res101_seg1_kinetics400_b8_g8_inflate311_f32s2_step_video_cleanv3.txt', lr=0.01, lr_decay=0.1, lr_decay_epoch='40,80,100', lr_decay_period=0, lr_mode='step', mixup=False, mixup_alpha=0.2, mixup_off_epoch=0, mode='hybrid', model='i3d_resnet101_v1_kinetics400', momentum=0.9, new_height=256, new_length=32, new_step=2, new_width=340, no_wd=False, num_classes=400, num_crop=1, num_epochs=100, num_gpus=8, num_segments=1, num_workers=32, partial_bn=False, prefetch_ratio=1.0, resume_epoch=0, resume_params='', resume_states='', save_dir='/home/ubuntu/yizhu/logs/mxnet/kinetics400/i3d/i3d_res101_seg1_kinetics400_b8_g8_inflate311_f32s2_step_video_cleanv3', save_frequency=20, scale_ratios='1.0,0.8', slow_temporal_stride=16, slowfast=False, teacher=None, temperature=20, train_list='/home/ubuntu/yizhu/data/kinetics400/kinetics400/k400_train_240618.txt', use_amp=False, use_decord=True, use_gn=False, use_pretrained=False, use_se=False, use_tsn=False, val_data_dir='/home/ubuntu/yizhu/data/kinetics400/kinetics400/val_256', val_list='/home/ubuntu/yizhu/code/gluon-cv/extra/kinetics400/k400_val_19761_cleanv3.txt', video_loader=True, warmup_epochs=0, warmup_lr=0.0, wd=0.0001) Total batch size is set to 64 on 8 GPUs I3D_ResNetV1( (first_stage): HybridSequential( (0): Conv3D(3 -> 64, kernel_size=(5, 7, 7), stride=(2, 2, 2), padding=(2, 3, 3), bias=False) (1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=64) (2): Activation(relu) (3): MaxPool3D(size=(1, 3, 3), stride=(2, 2, 2), padding=(0, 1, 1), ceil_mode=False, global_pool=False, pool_type=max, layout=NCDHW) ) (pool2): MaxPool3D(size=(2, 1, 1), stride=(2, 1, 1), padding=(0, 0, 0), ceil_mode=False, global_pool=False, pool_type=max, layout=NCDHW) (res_layers): HybridSequential( (0): HybridSequential( (0): Bottleneck( (bottleneck): HybridSequential( (0): Conv3D(64 -> 64, kernel_size=(3, 1, 1), stride=(1, 1, 1), padding=(1, 0, 0), bias=False) (1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=64) (2): Activation(relu) (3): Conv3D(64 -> 64, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1), bias=False) (4): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=64) (5): Activation(relu) (6): Conv3D(64 -> 256, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False) (7): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=256) ) (conv1): Conv3D(64 -> 64, kernel_size=(3, 1, 1), stride=(1, 1, 1), padding=(1, 0, 0), bias=False) (conv2): Conv3D(64 -> 64, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1), bias=False) (bn1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=64) (bn2): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=64) (conv3): Conv3D(64 -> 256, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False) (bn3): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=256) (relu): Activation(relu) (downsample): HybridSequential( (0): Conv3D(64 -> 256, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False) (1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=256) ) ) (1): Bottleneck( (bottleneck): HybridSequential( (0): Conv3D(256 -> 64, kernel_size=(3, 1, 1), stride=(1, 1, 1), padding=(1, 0, 0), bias=False) (1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=64) (2): Activation(relu) (3): Conv3D(64 -> 64, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1), bias=False) (4): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=64) (5): Activation(relu) (6): Conv3D(64 -> 256, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False) (7): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=256) ) (conv1): Conv3D(256 -> 64, kernel_size=(3, 1, 1), stride=(1, 1, 1), padding=(1, 0, 0), bias=False) (conv2): Conv3D(64 -> 64, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1), bias=False) (bn1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=64) (bn2): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=64) (conv3): Conv3D(64 -> 256, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False) (bn3): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=256) (relu): Activation(relu) ) (2): Bottleneck( (bottleneck): HybridSequential( (0): Conv3D(256 -> 64, kernel_size=(3, 1, 1), stride=(1, 1, 1), padding=(1, 0, 0), bias=False) (1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=64) (2): Activation(relu) (3): Conv3D(64 -> 64, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1), bias=False) (4): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=64) (5): Activation(relu) (6): Conv3D(64 -> 256, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False) (7): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=256) ) (conv1): Conv3D(256 -> 64, kernel_size=(3, 1, 1), stride=(1, 1, 1), padding=(1, 0, 0), bias=False) (conv2): Conv3D(64 -> 64, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1), bias=False) (bn1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=64) (bn2): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=64) (conv3): Conv3D(64 -> 256, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False) (bn3): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=256) (relu): Activation(relu) ) ) (1): HybridSequential( (0): Bottleneck( (bottleneck): HybridSequential( (0): Conv3D(256 -> 128, kernel_size=(3, 1, 1), stride=(1, 1, 1), padding=(1, 0, 0), bias=False) (1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=128) (2): Activation(relu) (3): Conv3D(128 -> 128, kernel_size=(1, 3, 3), stride=(1, 2, 2), padding=(0, 1, 1), bias=False) (4): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=128) (5): Activation(relu) (6): Conv3D(128 -> 512, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False) (7): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=512) ) (conv1): Conv3D(256 -> 128, kernel_size=(3, 1, 1), stride=(1, 1, 1), padding=(1, 0, 0), bias=False) (conv2): Conv3D(128 -> 128, kernel_size=(1, 3, 3), stride=(1, 2, 2), padding=(0, 1, 1), bias=False) (bn1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=128) (bn2): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=128) (conv3): Conv3D(128 -> 512, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False) (bn3): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=512) (relu): Activation(relu) (downsample): HybridSequential( (0): Conv3D(256 -> 512, kernel_size=(1, 1, 1), stride=(1, 2, 2), bias=False) (1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=512) ) ) (1): Bottleneck( (bottleneck): HybridSequential( (0): Conv3D(512 -> 128, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False) (1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=128) (2): Activation(relu) (3): Conv3D(128 -> 128, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1), bias=False) (4): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=128) (5): Activation(relu) (6): Conv3D(128 -> 512, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False) (7): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=512) ) (conv1): Conv3D(512 -> 128, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False) (conv2): Conv3D(128 -> 128, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1), bias=False) (bn1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=128) (bn2): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=128) (conv3): Conv3D(128 -> 512, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False) (bn3): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=512) (relu): Activation(relu) ) (2): Bottleneck( (bottleneck): HybridSequential( (0): Conv3D(512 -> 128, kernel_size=(3, 1, 1), stride=(1, 1, 1), padding=(1, 0, 0), bias=False) (1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=128) (2): Activation(relu) (3): Conv3D(128 -> 128, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1), bias=False) (4): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=128) (5): Activation(relu) (6): Conv3D(128 -> 512, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False) (7): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=512) ) (conv1): Conv3D(512 -> 128, kernel_size=(3, 1, 1), stride=(1, 1, 1), padding=(1, 0, 0), bias=False) (conv2): Conv3D(128 -> 128, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1), bias=False) (bn1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=128) (bn2): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=128) (conv3): Conv3D(128 -> 512, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False) (bn3): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=512) (relu): Activation(relu) ) (3): Bottleneck( (bottleneck): HybridSequential( (0): Conv3D(512 -> 128, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False) (1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=128) (2): Activation(relu) (3): Conv3D(128 -> 128, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1), bias=False) (4): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=128) (5): Activation(relu) (6): Conv3D(128 -> 512, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False) (7): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=512) ) (conv1): Conv3D(512 -> 128, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False) (conv2): Conv3D(128 -> 128, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1), bias=False) (bn1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=128) (bn2): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=128) (conv3): Conv3D(128 -> 512, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False) (bn3): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=512) (relu): Activation(relu) ) ) (2): HybridSequential( (0): Bottleneck( (bottleneck): HybridSequential( (0): Conv3D(512 -> 256, kernel_size=(3, 1, 1), stride=(1, 1, 1), padding=(1, 0, 0), bias=False) (1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=256) (2): Activation(relu) (3): Conv3D(256 -> 256, kernel_size=(1, 3, 3), stride=(1, 2, 2), padding=(0, 1, 1), bias=False) (4): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=256) (5): Activation(relu) (6): Conv3D(256 -> 1024, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False) (7): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=1024) ) (conv1): Conv3D(512 -> 256, kernel_size=(3, 1, 1), stride=(1, 1, 1), padding=(1, 0, 0), bias=False) (conv2): Conv3D(256 -> 256, kernel_size=(1, 3, 3), stride=(1, 2, 2), padding=(0, 1, 1), bias=False) (bn1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=256) (bn2): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=256) (conv3): Conv3D(256 -> 1024, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False) (bn3): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=1024) (relu): Activation(relu) (downsample): HybridSequential( (0): Conv3D(512 -> 1024, kernel_size=(1, 1, 1), stride=(1, 2, 2), bias=False) (1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=1024) ) ) (1): Bottleneck( (bottleneck): HybridSequential( (0): Conv3D(1024 -> 256, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False) (1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=256) (2): Activation(relu) (3): Conv3D(256 -> 256, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1), bias=False) (4): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=256) (5): Activation(relu) (6): Conv3D(256 -> 1024, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False) (7): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=1024) ) (conv1): Conv3D(1024 -> 256, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False) (conv2): Conv3D(256 -> 256, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1), bias=False) (bn1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=256) (bn2): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=256) (conv3): Conv3D(256 -> 1024, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False) (bn3): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=1024) (relu): Activation(relu) ) (2): Bottleneck( (bottleneck): HybridSequential( (0): Conv3D(1024 -> 256, kernel_size=(3, 1, 1), stride=(1, 1, 1), padding=(1, 0, 0), bias=False) (1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=256) (2): Activation(relu) (3): Conv3D(256 -> 256, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1), bias=False) (4): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=256) (5): Activation(relu) (6): Conv3D(256 -> 1024, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False) (7): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=1024) ) (conv1): Conv3D(1024 -> 256, kernel_size=(3, 1, 1), stride=(1, 1, 1), padding=(1, 0, 0), bias=False) (conv2): Conv3D(256 -> 256, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1), bias=False) (bn1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=256) (bn2): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=256) (conv3): Conv3D(256 -> 1024, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False) (bn3): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=1024) (relu): Activation(relu) ) (3): Bottleneck( (bottleneck): HybridSequential( (0): Conv3D(1024 -> 256, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False) (1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=256) (2): Activation(relu) (3): Conv3D(256 -> 256, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1), bias=False) (4): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=256) (5): Activation(relu) (6): Conv3D(256 -> 1024, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False) (7): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=1024) ) (conv1): Conv3D(1024 -> 256, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False) (conv2): Conv3D(256 -> 256, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1), bias=False) (bn1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=256) (bn2): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=256) (conv3): Conv3D(256 -> 1024, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False) (bn3): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=1024) (relu): Activation(relu) ) (4): Bottleneck( (bottleneck): HybridSequential( (0): Conv3D(1024 -> 256, kernel_size=(3, 1, 1), stride=(1, 1, 1), padding=(1, 0, 0), bias=False) (1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=256) (2): Activation(relu) (3): Conv3D(256 -> 256, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1), bias=False) (4): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=256) (5): Activation(relu) (6): Conv3D(256 -> 1024, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False) (7): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=1024) ) (conv1): Conv3D(1024 -> 256, kernel_size=(3, 1, 1), stride=(1, 1, 1), padding=(1, 0, 0), bias=False) (conv2): Conv3D(256 -> 256, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1), bias=False) (bn1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=256) (bn2): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=256) (conv3): Conv3D(256 -> 1024, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False) (bn3): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=1024) (relu): Activation(relu) ) (5): Bottleneck( (bottleneck): HybridSequential( (0): Conv3D(1024 -> 256, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False) (1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=256) (2): Activation(relu) (3): Conv3D(256 -> 256, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1), bias=False) (4): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=256) (5): Activation(relu) (6): Conv3D(256 -> 1024, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False) (7): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=1024) ) (conv1): Conv3D(1024 -> 256, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False) (conv2): Conv3D(256 -> 256, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1), bias=False) (bn1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=256) (bn2): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=256) (conv3): Conv3D(256 -> 1024, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False) (bn3): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=1024) (relu): Activation(relu) ) (6): Bottleneck( (bottleneck): HybridSequential( (0): Conv3D(1024 -> 256, kernel_size=(3, 1, 1), stride=(1, 1, 1), padding=(1, 0, 0), bias=False) (1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=256) (2): Activation(relu) (3): Conv3D(256 -> 256, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1), bias=False) (4): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=256) (5): Activation(relu) (6): Conv3D(256 -> 1024, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False) (7): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=1024) ) (conv1): Conv3D(1024 -> 256, kernel_size=(3, 1, 1), stride=(1, 1, 1), padding=(1, 0, 0), bias=False) (conv2): Conv3D(256 -> 256, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1), bias=False) (bn1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=256) (bn2): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=256) (conv3): Conv3D(256 -> 1024, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False) (bn3): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=1024) (relu): Activation(relu) ) (7): Bottleneck( (bottleneck): HybridSequential( (0): Conv3D(1024 -> 256, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False) (1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=256) (2): Activation(relu) (3): Conv3D(256 -> 256, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1), bias=False) (4): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=256) (5): Activation(relu) (6): Conv3D(256 -> 1024, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False) (7): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=1024) ) (conv1): Conv3D(1024 -> 256, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False) (conv2): Conv3D(256 -> 256, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1), bias=False) (bn1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=256) (bn2): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=256) (conv3): Conv3D(256 -> 1024, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False) (bn3): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=1024) (relu): Activation(relu) ) (8): Bottleneck( (bottleneck): HybridSequential( (0): Conv3D(1024 -> 256, kernel_size=(3, 1, 1), stride=(1, 1, 1), padding=(1, 0, 0), bias=False) (1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=256) (2): Activation(relu) (3): Conv3D(256 -> 256, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1), bias=False) (4): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=256) (5): Activation(relu) (6): Conv3D(256 -> 1024, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False) (7): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=1024) ) (conv1): Conv3D(1024 -> 256, kernel_size=(3, 1, 1), stride=(1, 1, 1), padding=(1, 0, 0), bias=False) (conv2): Conv3D(256 -> 256, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1), bias=False) (bn1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=256) (bn2): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=256) (conv3): Conv3D(256 -> 1024, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False) (bn3): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=1024) (relu): Activation(relu) ) (9): Bottleneck( (bottleneck): HybridSequential( (0): Conv3D(1024 -> 256, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False) (1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=256) (2): Activation(relu) (3): Conv3D(256 -> 256, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1), bias=False) (4): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=256) (5): Activation(relu) (6): Conv3D(256 -> 1024, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False) (7): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=1024) ) (conv1): Conv3D(1024 -> 256, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False) (conv2): Conv3D(256 -> 256, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1), bias=False) (bn1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=256) (bn2): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=256) (conv3): Conv3D(256 -> 1024, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False) (bn3): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=1024) (relu): Activation(relu) ) (10): Bottleneck( (bottleneck): HybridSequential( (0): Conv3D(1024 -> 256, kernel_size=(3, 1, 1), stride=(1, 1, 1), padding=(1, 0, 0), bias=False) (1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=256) (2): Activation(relu) (3): Conv3D(256 -> 256, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1), bias=False) (4): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=256) (5): Activation(relu) (6): Conv3D(256 -> 1024, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False) (7): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=1024) ) (conv1): Conv3D(1024 -> 256, kernel_size=(3, 1, 1), stride=(1, 1, 1), padding=(1, 0, 0), bias=False) (conv2): Conv3D(256 -> 256, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1), bias=False) (bn1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=256) (bn2): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=256) (conv3): Conv3D(256 -> 1024, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False) (bn3): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=1024) (relu): Activation(relu) ) (11): Bottleneck( (bottleneck): HybridSequential( (0): Conv3D(1024 -> 256, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False) (1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=256) (2): Activation(relu) (3): Conv3D(256 -> 256, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1), bias=False) (4): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=256) (5): Activation(relu) (6): Conv3D(256 -> 1024, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False) (7): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=1024) ) (conv1): Conv3D(1024 -> 256, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False) (conv2): Conv3D(256 -> 256, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1), bias=False) (bn1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=256) (bn2): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=256) (conv3): Conv3D(256 -> 1024, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False) (bn3): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=1024) (relu): Activation(relu) ) (12): Bottleneck( (bottleneck): HybridSequential( (0): Conv3D(1024 -> 256, kernel_size=(3, 1, 1), stride=(1, 1, 1), padding=(1, 0, 0), bias=False) (1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=256) (2): Activation(relu) (3): Conv3D(256 -> 256, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1), bias=False) (4): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=256) (5): Activation(relu) (6): Conv3D(256 -> 1024, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False) (7): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=1024) ) (conv1): Conv3D(1024 -> 256, kernel_size=(3, 1, 1), stride=(1, 1, 1), padding=(1, 0, 0), bias=False) (conv2): Conv3D(256 -> 256, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1), bias=False) (bn1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=256) (bn2): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=256) (conv3): Conv3D(256 -> 1024, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False) (bn3): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=1024) (relu): Activation(relu) ) (13): Bottleneck( (bottleneck): HybridSequential( (0): Conv3D(1024 -> 256, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False) (1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=256) (2): Activation(relu) (3): Conv3D(256 -> 256, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1), bias=False) (4): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=256) (5): Activation(relu) (6): Conv3D(256 -> 1024, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False) (7): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=1024) ) (conv1): Conv3D(1024 -> 256, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False) (conv2): Conv3D(256 -> 256, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1), bias=False) (bn1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=256) (bn2): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=256) (conv3): Conv3D(256 -> 1024, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False) (bn3): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=1024) (relu): Activation(relu) ) (14): Bottleneck( (bottleneck): HybridSequential( (0): Conv3D(1024 -> 256, kernel_size=(3, 1, 1), stride=(1, 1, 1), padding=(1, 0, 0), bias=False) (1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=256) (2): Activation(relu) (3): Conv3D(256 -> 256, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1), bias=False) (4): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=256) (5): Activation(relu) (6): Conv3D(256 -> 1024, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False) (7): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=1024) ) (conv1): Conv3D(1024 -> 256, kernel_size=(3, 1, 1), stride=(1, 1, 1), padding=(1, 0, 0), bias=False) (conv2): Conv3D(256 -> 256, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1), bias=False) (bn1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=256) (bn2): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=256) (conv3): Conv3D(256 -> 1024, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False) (bn3): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=1024) (relu): Activation(relu) ) (15): Bottleneck( (bottleneck): HybridSequential( (0): Conv3D(1024 -> 256, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False) (1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=256) (2): Activation(relu) (3): Conv3D(256 -> 256, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1), bias=False) (4): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=256) (5): Activation(relu) (6): Conv3D(256 -> 1024, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False) (7): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=1024) ) (conv1): Conv3D(1024 -> 256, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False) (conv2): Conv3D(256 -> 256, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1), bias=False) (bn1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=256) (bn2): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=256) (conv3): Conv3D(256 -> 1024, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False) (bn3): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=1024) (relu): Activation(relu) ) (16): Bottleneck( (bottleneck): HybridSequential( (0): Conv3D(1024 -> 256, kernel_size=(3, 1, 1), stride=(1, 1, 1), padding=(1, 0, 0), bias=False) (1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=256) (2): Activation(relu) (3): Conv3D(256 -> 256, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1), bias=False) (4): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=256) (5): Activation(relu) (6): Conv3D(256 -> 1024, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False) (7): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=1024) ) (conv1): Conv3D(1024 -> 256, kernel_size=(3, 1, 1), stride=(1, 1, 1), padding=(1, 0, 0), bias=False) (conv2): Conv3D(256 -> 256, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1), bias=False) (bn1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=256) (bn2): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=256) (conv3): Conv3D(256 -> 1024, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False) (bn3): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=1024) (relu): Activation(relu) ) (17): Bottleneck( (bottleneck): HybridSequential( (0): Conv3D(1024 -> 256, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False) (1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=256) (2): Activation(relu) (3): Conv3D(256 -> 256, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1), bias=False) (4): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=256) (5): Activation(relu) (6): Conv3D(256 -> 1024, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False) (7): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=1024) ) (conv1): Conv3D(1024 -> 256, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False) (conv2): Conv3D(256 -> 256, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1), bias=False) (bn1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=256) (bn2): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=256) (conv3): Conv3D(256 -> 1024, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False) (bn3): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=1024) (relu): Activation(relu) ) (18): Bottleneck( (bottleneck): HybridSequential( (0): Conv3D(1024 -> 256, kernel_size=(3, 1, 1), stride=(1, 1, 1), padding=(1, 0, 0), bias=False) (1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=256) (2): Activation(relu) (3): Conv3D(256 -> 256, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1), bias=False) (4): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=256) (5): Activation(relu) (6): Conv3D(256 -> 1024, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False) (7): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=1024) ) (conv1): Conv3D(1024 -> 256, kernel_size=(3, 1, 1), stride=(1, 1, 1), padding=(1, 0, 0), bias=False) (conv2): Conv3D(256 -> 256, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1), bias=False) (bn1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=256) (bn2): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=256) (conv3): Conv3D(256 -> 1024, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False) (bn3): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=1024) (relu): Activation(relu) ) (19): Bottleneck( (bottleneck): HybridSequential( (0): Conv3D(1024 -> 256, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False) (1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=256) (2): Activation(relu) (3): Conv3D(256 -> 256, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1), bias=False) (4): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=256) (5): Activation(relu) (6): Conv3D(256 -> 1024, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False) (7): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=1024) ) (conv1): Conv3D(1024 -> 256, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False) (conv2): Conv3D(256 -> 256, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1), bias=False) (bn1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=256) (bn2): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=256) (conv3): Conv3D(256 -> 1024, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False) (bn3): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=1024) (relu): Activation(relu) ) (20): Bottleneck( (bottleneck): HybridSequential( (0): Conv3D(1024 -> 256, kernel_size=(3, 1, 1), stride=(1, 1, 1), padding=(1, 0, 0), bias=False) (1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=256) (2): Activation(relu) (3): Conv3D(256 -> 256, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1), bias=False) (4): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=256) (5): Activation(relu) (6): Conv3D(256 -> 1024, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False) (7): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=1024) ) (conv1): Conv3D(1024 -> 256, kernel_size=(3, 1, 1), stride=(1, 1, 1), padding=(1, 0, 0), bias=False) (conv2): Conv3D(256 -> 256, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1), bias=False) (bn1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=256) (bn2): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=256) (conv3): Conv3D(256 -> 1024, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False) (bn3): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=1024) (relu): Activation(relu) ) (21): Bottleneck( (bottleneck): HybridSequential( (0): Conv3D(1024 -> 256, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False) (1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=256) (2): Activation(relu) (3): Conv3D(256 -> 256, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1), bias=False) (4): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=256) (5): Activation(relu) (6): Conv3D(256 -> 1024, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False) (7): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=1024) ) (conv1): Conv3D(1024 -> 256, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False) (conv2): Conv3D(256 -> 256, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1), bias=False) (bn1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=256) (bn2): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=256) (conv3): Conv3D(256 -> 1024, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False) (bn3): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=1024) (relu): Activation(relu) ) (22): Bottleneck( (bottleneck): HybridSequential( (0): Conv3D(1024 -> 256, kernel_size=(3, 1, 1), stride=(1, 1, 1), padding=(1, 0, 0), bias=False) (1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=256) (2): Activation(relu) (3): Conv3D(256 -> 256, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1), bias=False) (4): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=256) (5): Activation(relu) (6): Conv3D(256 -> 1024, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False) (7): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=1024) ) (conv1): Conv3D(1024 -> 256, kernel_size=(3, 1, 1), stride=(1, 1, 1), padding=(1, 0, 0), bias=False) (conv2): Conv3D(256 -> 256, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1), bias=False) (bn1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=256) (bn2): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=256) (conv3): Conv3D(256 -> 1024, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False) (bn3): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=1024) (relu): Activation(relu) ) ) (3): HybridSequential( (0): Bottleneck( (bottleneck): HybridSequential( (0): Conv3D(1024 -> 512, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False) (1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=512) (2): Activation(relu) (3): Conv3D(512 -> 512, kernel_size=(1, 3, 3), stride=(1, 2, 2), padding=(0, 1, 1), bias=False) (4): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=512) (5): Activation(relu) (6): Conv3D(512 -> 2048, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False) (7): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=2048) ) (conv1): Conv3D(1024 -> 512, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False) (conv2): Conv3D(512 -> 512, kernel_size=(1, 3, 3), stride=(1, 2, 2), padding=(0, 1, 1), bias=False) (bn1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=512) (bn2): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=512) (conv3): Conv3D(512 -> 2048, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False) (bn3): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=2048) (relu): Activation(relu) (downsample): HybridSequential( (0): Conv3D(1024 -> 2048, kernel_size=(1, 1, 1), stride=(1, 2, 2), bias=False) (1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=2048) ) ) (1): Bottleneck( (bottleneck): HybridSequential( (0): Conv3D(2048 -> 512, kernel_size=(3, 1, 1), stride=(1, 1, 1), padding=(1, 0, 0), bias=False) (1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=512) (2): Activation(relu) (3): Conv3D(512 -> 512, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1), bias=False) (4): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=512) (5): Activation(relu) (6): Conv3D(512 -> 2048, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False) (7): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=2048) ) (conv1): Conv3D(2048 -> 512, kernel_size=(3, 1, 1), stride=(1, 1, 1), padding=(1, 0, 0), bias=False) (conv2): Conv3D(512 -> 512, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1), bias=False) (bn1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=512) (bn2): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=512) (conv3): Conv3D(512 -> 2048, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False) (bn3): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=2048) (relu): Activation(relu) ) (2): Bottleneck( (bottleneck): HybridSequential( (0): Conv3D(2048 -> 512, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False) (1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=512) (2): Activation(relu) (3): Conv3D(512 -> 512, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1), bias=False) (4): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=512) (5): Activation(relu) (6): Conv3D(512 -> 2048, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False) (7): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=2048) ) (conv1): Conv3D(2048 -> 512, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False) (conv2): Conv3D(512 -> 512, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1), bias=False) (bn1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=512) (bn2): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=512) (conv3): Conv3D(512 -> 2048, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False) (bn3): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=2048) (relu): Activation(relu) ) ) ) (st_avg): GlobalAvgPool3D(size=(1, 1, 1), stride=(1, 1, 1), padding=(0, 0, 0), ceil_mode=True, global_pool=True, pool_type=avg, layout=NCDHW) (head): HybridSequential( (0): Dropout(p = 0.5, axes=()) (1): Dense(2048 -> 400, linear) ) (fc): Dense(2048 -> 400, linear) ) Load 240618 training samples and 19404 validation samples. Epoch[000] Batch [0049]/[3759] Speed: 32.352335 samples/sec accuracy=0.843750 loss=5.936220 lr=0.010000 Epoch[000] Batch [0099]/[3759] Speed: 66.006655 samples/sec accuracy=1.515625 loss=5.791428 lr=0.010000 Epoch[000] Batch [0149]/[3759] Speed: 65.721990 samples/sec accuracy=2.437500 loss=5.636109 lr=0.010000 Epoch[000] Batch [0199]/[3759] Speed: 67.113218 samples/sec accuracy=3.320312 loss=5.495221 lr=0.010000 Epoch[000] Batch [0249]/[3759] Speed: 65.967171 samples/sec accuracy=4.425000 loss=5.356683 lr=0.010000 Epoch[000] Batch [0299]/[3759] Speed: 66.625685 samples/sec accuracy=5.114583 loss=5.251387 lr=0.010000 Epoch[000] Batch [0349]/[3759] Speed: 65.917531 samples/sec accuracy=6.000000 loss=5.147693 lr=0.010000 Epoch[000] Batch [0399]/[3759] Speed: 66.708167 samples/sec accuracy=6.671875 loss=5.065093 lr=0.010000 Epoch[000] Batch [0449]/[3759] Speed: 66.477574 samples/sec accuracy=7.461806 loss=4.981861 lr=0.010000 Epoch[000] Batch [0499]/[3759] Speed: 66.729592 samples/sec 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accuracy=23.177705 loss=3.675512 lr=0.010000 Epoch[000] Batch [3399]/[3759] Speed: 67.196959 samples/sec accuracy=23.322610 loss=3.665633 lr=0.010000 Epoch[000] Batch [3449]/[3759] Speed: 66.793939 samples/sec accuracy=23.448822 loss=3.657212 lr=0.010000 Epoch[000] Batch [3499]/[3759] Speed: 66.733144 samples/sec accuracy=23.592411 loss=3.647918 lr=0.010000 Epoch[000] Batch [3549]/[3759] Speed: 66.946879 samples/sec accuracy=23.720511 loss=3.639271 lr=0.010000 Epoch[000] Batch [3599]/[3759] Speed: 66.385701 samples/sec accuracy=23.857639 loss=3.630033 lr=0.010000 Epoch[000] Batch [3649]/[3759] Speed: 66.816777 samples/sec accuracy=23.977740 loss=3.621324 lr=0.010000 Epoch[000] Batch [3699]/[3759] Speed: 65.727468 samples/sec accuracy=24.105152 loss=3.612645 lr=0.010000 Epoch[000] Batch [3749]/[3759] Speed: 76.088815 samples/sec accuracy=24.222500 loss=3.604783 lr=0.010000 Batch [0049]/[0303]: acc-top1=35.812500 acc-top5=63.625000 Batch [0099]/[0303]: acc-top1=36.109375 acc-top5=64.109375 Batch [0149]/[0303]: acc-top1=36.052083 acc-top5=63.666667 Batch [0199]/[0303]: acc-top1=36.187500 acc-top5=63.640625 Batch [0249]/[0303]: acc-top1=36.350000 acc-top5=63.875000 Batch [0299]/[0303]: acc-top1=36.125000 acc-top5=63.630208 [Epoch 000] training: accuracy=24.251796 loss=3.603044 [Epoch 000] speed: 65 samples/sec time cost: 3965.292185 [Epoch 000] validation: acc-top1=36.107673 acc-top5=63.608705 loss=2.898537 Epoch[001] Batch [0049]/[3760] Speed: 45.557661 samples/sec accuracy=34.468750 loss=2.859760 lr=0.010000 Epoch[001] Batch [0099]/[3760] Speed: 64.964966 samples/sec accuracy=34.484375 loss=2.886078 lr=0.010000 Epoch[001] Batch [0149]/[3760] Speed: 66.481622 samples/sec accuracy=34.760417 loss=2.883446 lr=0.010000 Epoch[001] Batch [0199]/[3760] Speed: 66.554672 samples/sec accuracy=35.109375 loss=2.867411 lr=0.010000 Epoch[001] Batch [0249]/[3760] Speed: 65.919656 samples/sec accuracy=35.337500 loss=2.860693 lr=0.010000 Epoch[001] Batch [0299]/[3760] 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accuracy=36.865809 loss=2.795097 lr=0.010000 Epoch[001] Batch [1749]/[3760] Speed: 65.788531 samples/sec accuracy=36.913393 loss=2.792504 lr=0.010000 Epoch[001] Batch [1799]/[3760] Speed: 65.969008 samples/sec accuracy=36.974826 loss=2.789086 lr=0.010000 Epoch[001] Batch [1849]/[3760] Speed: 66.250388 samples/sec accuracy=37.026182 loss=2.787607 lr=0.010000 Epoch[001] Batch [1899]/[3760] Speed: 66.618640 samples/sec accuracy=37.079770 loss=2.784685 lr=0.010000 Epoch[001] Batch [1949]/[3760] Speed: 66.639571 samples/sec accuracy=37.144231 loss=2.782144 lr=0.010000 Epoch[001] Batch [1999]/[3760] Speed: 65.365343 samples/sec accuracy=37.177344 loss=2.779426 lr=0.010000 Epoch[001] Batch [2049]/[3760] Speed: 66.253403 samples/sec accuracy=37.226372 loss=2.777136 lr=0.010000 Epoch[001] Batch [2099]/[3760] Speed: 67.288201 samples/sec accuracy=37.250000 loss=2.774840 lr=0.010000 Epoch[001] Batch [2149]/[3760] Speed: 65.945206 samples/sec accuracy=37.303779 loss=2.772729 lr=0.010000 Epoch[001] 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accuracy=37.672759 loss=2.751519 lr=0.010000 Epoch[001] Batch [2699]/[3760] Speed: 66.613515 samples/sec accuracy=37.671875 loss=2.750541 lr=0.010000 Epoch[001] Batch [2749]/[3760] Speed: 66.219045 samples/sec accuracy=37.707955 loss=2.747366 lr=0.010000 Epoch[001] Batch [2799]/[3760] Speed: 66.172951 samples/sec accuracy=37.728237 loss=2.745894 lr=0.010000 Epoch[001] Batch [2849]/[3760] Speed: 65.997615 samples/sec accuracy=37.801535 loss=2.742510 lr=0.010000 Epoch[001] Batch [2899]/[3760] Speed: 67.043866 samples/sec accuracy=37.851832 loss=2.739894 lr=0.010000 Epoch[001] Batch [2949]/[3760] Speed: 65.946776 samples/sec accuracy=37.888242 loss=2.738467 lr=0.010000 Epoch[001] Batch [2999]/[3760] Speed: 66.570863 samples/sec accuracy=37.942188 loss=2.736252 lr=0.010000 Epoch[001] Batch [3049]/[3760] Speed: 65.670810 samples/sec accuracy=38.003586 loss=2.733684 lr=0.010000 Epoch[001] Batch [3099]/[3760] Speed: 66.428745 samples/sec accuracy=38.035786 loss=2.731729 lr=0.010000 Epoch[001] 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accuracy=38.438368 loss=2.709194 lr=0.010000 Epoch[001] Batch [3649]/[3760] Speed: 65.573873 samples/sec accuracy=38.464897 loss=2.707716 lr=0.010000 Epoch[001] Batch [3699]/[3760] Speed: 66.202008 samples/sec accuracy=38.521537 loss=2.705189 lr=0.010000 Epoch[001] Batch [3749]/[3760] Speed: 74.964843 samples/sec accuracy=38.547917 loss=2.703388 lr=0.010000 Batch [0049]/[0303]: acc-top1=44.750000 acc-top5=70.531250 Batch [0099]/[0303]: acc-top1=45.218750 acc-top5=71.343750 Batch [0149]/[0303]: acc-top1=44.833333 acc-top5=71.500000 Batch [0199]/[0303]: acc-top1=44.648438 acc-top5=71.343750 Batch [0249]/[0303]: acc-top1=44.637500 acc-top5=71.687500 Batch [0299]/[0303]: acc-top1=44.322917 acc-top5=71.255208 [Epoch 001] training: accuracy=38.556350 loss=2.702910 [Epoch 001] speed: 65 samples/sec time cost: 3951.747867 [Epoch 001] validation: acc-top1=44.291460 acc-top5=71.266502 loss=2.483962 Epoch[002] Batch [0049]/[3759] Speed: 45.832035 samples/sec accuracy=42.218750 loss=2.456731 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acc-top1=47.812500 acc-top5=73.968750 Batch [0249]/[0303]: acc-top1=47.993750 acc-top5=74.200000 Batch [0299]/[0303]: acc-top1=47.661458 acc-top5=73.968750 [Epoch 002] training: accuracy=44.046788 loss=2.418063 [Epoch 002] speed: 65 samples/sec time cost: 3955.622792 [Epoch 002] validation: acc-top1=47.674299 acc-top5=73.963490 loss=2.307702 Epoch[003] Batch [0049]/[3760] Speed: 45.513287 samples/sec accuracy=46.968750 loss=2.261303 lr=0.010000 Epoch[003] Batch [0099]/[3760] Speed: 64.726975 samples/sec accuracy=46.578125 loss=2.268571 lr=0.010000 Epoch[003] Batch [0149]/[3760] Speed: 66.773013 samples/sec accuracy=47.177083 loss=2.243888 lr=0.010000 Epoch[003] Batch [0199]/[3760] Speed: 66.468889 samples/sec accuracy=47.320312 loss=2.242370 lr=0.010000 Epoch[003] Batch [0249]/[3760] Speed: 66.109519 samples/sec accuracy=47.031250 loss=2.264283 lr=0.010000 Epoch[003] Batch [0299]/[3760] Speed: 66.604200 samples/sec accuracy=46.973958 loss=2.268483 lr=0.010000 Epoch[003] Batch 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accuracy=47.503853 loss=2.244334 lr=0.010000 Epoch[003] Batch [3699]/[3760] Speed: 66.315329 samples/sec accuracy=47.523649 loss=2.243426 lr=0.010000 Epoch[003] Batch [3749]/[3760] Speed: 74.461728 samples/sec accuracy=47.523333 loss=2.243003 lr=0.010000 Batch [0049]/[0303]: acc-top1=51.531250 acc-top5=77.750000 Batch [0099]/[0303]: acc-top1=51.671875 acc-top5=77.468750 Batch [0149]/[0303]: acc-top1=51.677083 acc-top5=77.208333 Batch [0199]/[0303]: acc-top1=51.851562 acc-top5=77.054688 Batch [0249]/[0303]: acc-top1=51.756250 acc-top5=77.131250 Batch [0299]/[0303]: acc-top1=51.375000 acc-top5=76.890625 [Epoch 003] training: accuracy=47.526596 loss=2.242821 [Epoch 003] speed: 65 samples/sec time cost: 3950.722290 [Epoch 003] validation: acc-top1=51.382013 acc-top5=76.882219 loss=2.116655 Epoch[004] Batch [0049]/[3760] Speed: 45.969726 samples/sec accuracy=49.125000 loss=2.123583 lr=0.010000 Epoch[004] Batch [0099]/[3760] Speed: 65.486574 samples/sec accuracy=49.781250 loss=2.115058 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[0299]/[0303]: acc-top1=52.145833 acc-top5=77.333333 [Epoch 004] training: accuracy=49.949717 loss=2.130481 [Epoch 004] speed: 66 samples/sec time cost: 3946.118530 [Epoch 004] validation: acc-top1=52.165842 acc-top5=77.320545 loss=2.135731 Epoch[005] Batch [0049]/[3759] Speed: 46.020791 samples/sec accuracy=50.625000 loss=2.051025 lr=0.010000 Epoch[005] Batch [0099]/[3759] Speed: 65.413810 samples/sec accuracy=51.234375 loss=2.054140 lr=0.010000 Epoch[005] Batch [0149]/[3759] Speed: 66.036486 samples/sec accuracy=51.687500 loss=2.046076 lr=0.010000 Epoch[005] Batch [0199]/[3759] Speed: 65.950050 samples/sec accuracy=51.523438 loss=2.044403 lr=0.010000 Epoch[005] Batch [0249]/[3759] Speed: 66.556727 samples/sec accuracy=51.275000 loss=2.060819 lr=0.010000 Epoch[005] Batch [0299]/[3759] Speed: 66.706490 samples/sec accuracy=51.286458 loss=2.056150 lr=0.010000 Epoch[005] Batch [0349]/[3759] Speed: 66.303882 samples/sec accuracy=51.334821 loss=2.055794 lr=0.010000 Epoch[005] Batch 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accuracy=51.485243 loss=2.046638 lr=0.010000 Epoch[005] Batch [1849]/[3759] Speed: 65.869616 samples/sec accuracy=51.477196 loss=2.045958 lr=0.010000 Epoch[005] Batch [1899]/[3759] Speed: 65.972343 samples/sec accuracy=51.497533 loss=2.045181 lr=0.010000 Epoch[005] Batch [1949]/[3759] Speed: 66.820883 samples/sec accuracy=51.499199 loss=2.045153 lr=0.010000 Epoch[005] Batch [1999]/[3759] Speed: 66.282482 samples/sec accuracy=51.470313 loss=2.047071 lr=0.010000 Epoch[005] Batch [2049]/[3759] Speed: 65.930181 samples/sec accuracy=51.461128 loss=2.047327 lr=0.010000 Epoch[005] Batch [2099]/[3759] Speed: 66.755779 samples/sec accuracy=51.483631 loss=2.046318 lr=0.010000 Epoch[005] Batch [2149]/[3759] Speed: 66.135763 samples/sec accuracy=51.497093 loss=2.046417 lr=0.010000 Epoch[005] Batch [2199]/[3759] Speed: 65.680147 samples/sec accuracy=51.477983 loss=2.046603 lr=0.010000 Epoch[005] Batch [2249]/[3759] Speed: 66.284081 samples/sec accuracy=51.499306 loss=2.047124 lr=0.010000 Epoch[005] 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accuracy=51.610227 loss=2.044836 lr=0.010000 Epoch[005] Batch [2799]/[3759] Speed: 67.453511 samples/sec accuracy=51.619978 loss=2.045270 lr=0.010000 Epoch[005] Batch [2849]/[3759] Speed: 66.367052 samples/sec accuracy=51.643640 loss=2.045015 lr=0.010000 Epoch[005] Batch [2899]/[3759] Speed: 66.819248 samples/sec accuracy=51.668642 loss=2.043208 lr=0.010000 Epoch[005] Batch [2949]/[3759] Speed: 66.568183 samples/sec accuracy=51.645127 loss=2.043215 lr=0.010000 Epoch[005] Batch [2999]/[3759] Speed: 66.684526 samples/sec accuracy=51.661979 loss=2.042001 lr=0.010000 Epoch[005] Batch [3049]/[3759] Speed: 66.292543 samples/sec accuracy=51.675717 loss=2.041593 lr=0.010000 Epoch[005] Batch [3099]/[3759] Speed: 65.865307 samples/sec accuracy=51.694556 loss=2.041154 lr=0.010000 Epoch[005] Batch [3149]/[3759] Speed: 66.185258 samples/sec accuracy=51.695437 loss=2.040679 lr=0.010000 Epoch[005] Batch [3199]/[3759] Speed: 66.490525 samples/sec accuracy=51.683105 loss=2.040886 lr=0.010000 Epoch[005] 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accuracy=51.706081 loss=2.040377 lr=0.010000 Epoch[005] Batch [3749]/[3759] Speed: 75.604015 samples/sec accuracy=51.687917 loss=2.040817 lr=0.010000 Batch [0049]/[0303]: acc-top1=54.375000 acc-top5=78.906250 Batch [0099]/[0303]: acc-top1=54.187500 acc-top5=78.578125 Batch [0149]/[0303]: acc-top1=53.687500 acc-top5=78.302083 Batch [0199]/[0303]: acc-top1=53.484375 acc-top5=77.875000 Batch [0249]/[0303]: acc-top1=53.593750 acc-top5=78.112500 Batch [0299]/[0303]: acc-top1=53.312500 acc-top5=77.869792 [Epoch 005] training: accuracy=51.690942 loss=2.040658 [Epoch 005] speed: 66 samples/sec time cost: 3942.227420 [Epoch 005] validation: acc-top1=53.295173 acc-top5=77.836221 loss=2.085282 Epoch[006] Batch [0049]/[3760] Speed: 45.405101 samples/sec accuracy=52.031250 loss=1.946429 lr=0.010000 Epoch[006] Batch [0099]/[3760] Speed: 66.562935 samples/sec accuracy=53.156250 loss=1.915684 lr=0.010000 Epoch[006] Batch [0149]/[3760] Speed: 66.547687 samples/sec accuracy=53.291667 loss=1.926903 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lr=0.010000 Epoch[006] Batch [3049]/[3760] Speed: 66.038127 samples/sec accuracy=53.010246 loss=1.971768 lr=0.010000 Epoch[006] Batch [3099]/[3760] Speed: 66.468509 samples/sec accuracy=53.014113 loss=1.971984 lr=0.010000 Epoch[006] Batch [3149]/[3760] Speed: 66.591777 samples/sec accuracy=53.002480 loss=1.972686 lr=0.010000 Epoch[006] Batch [3199]/[3760] Speed: 66.967702 samples/sec accuracy=52.979980 loss=1.973440 lr=0.010000 Epoch[006] Batch [3249]/[3760] Speed: 66.246230 samples/sec accuracy=52.984615 loss=1.973400 lr=0.010000 Epoch[006] Batch [3299]/[3760] Speed: 66.535707 samples/sec accuracy=53.004735 loss=1.972887 lr=0.010000 Epoch[006] Batch [3349]/[3760] Speed: 66.303219 samples/sec accuracy=53.010261 loss=1.972108 lr=0.010000 Epoch[006] Batch [3399]/[3760] Speed: 66.694557 samples/sec accuracy=53.005974 loss=1.972327 lr=0.010000 Epoch[006] Batch [3449]/[3760] Speed: 66.812076 samples/sec accuracy=53.047101 loss=1.971442 lr=0.010000 Epoch[006] Batch [3499]/[3760] Speed: 66.166232 samples/sec accuracy=53.056696 loss=1.971112 lr=0.010000 Epoch[006] Batch [3549]/[3760] Speed: 66.932193 samples/sec accuracy=53.047975 loss=1.971233 lr=0.010000 Epoch[006] Batch [3599]/[3760] Speed: 66.225159 samples/sec accuracy=53.046441 loss=1.970772 lr=0.010000 Epoch[006] Batch [3649]/[3760] Speed: 66.823911 samples/sec accuracy=53.055651 loss=1.970260 lr=0.010000 Epoch[006] Batch [3699]/[3760] Speed: 66.482135 samples/sec accuracy=53.052787 loss=1.970615 lr=0.010000 Epoch[006] Batch [3749]/[3760] Speed: 75.734735 samples/sec accuracy=53.045833 loss=1.971177 lr=0.010000 Batch [0049]/[0303]: acc-top1=55.718750 acc-top5=79.531250 Batch [0099]/[0303]: acc-top1=55.625000 acc-top5=79.609375 Batch [0149]/[0303]: acc-top1=55.052083 acc-top5=79.552083 Batch [0199]/[0303]: acc-top1=55.132812 acc-top5=79.148438 Batch [0249]/[0303]: acc-top1=55.250000 acc-top5=79.225000 Batch [0299]/[0303]: acc-top1=55.015625 acc-top5=78.989583 [Epoch 006] training: accuracy=53.039811 loss=1.971319 [Epoch 006] speed: 66 samples/sec time cost: 3931.415099 [Epoch 006] validation: acc-top1=55.027847 acc-top5=78.960396 loss=1.955154 Epoch[007] Batch [0049]/[3760] Speed: 45.806671 samples/sec accuracy=54.062500 loss=1.882678 lr=0.010000 Epoch[007] Batch [0099]/[3760] Speed: 66.725626 samples/sec accuracy=53.937500 loss=1.893177 lr=0.010000 Epoch[007] Batch [0149]/[3760] Speed: 67.053228 samples/sec accuracy=54.677083 loss=1.884195 lr=0.010000 Epoch[007] Batch [0199]/[3760] Speed: 66.493771 samples/sec accuracy=55.023438 loss=1.889314 lr=0.010000 Epoch[007] Batch [0249]/[3760] Speed: 67.036977 samples/sec accuracy=54.843750 loss=1.890264 lr=0.010000 Epoch[007] Batch [0299]/[3760] Speed: 66.368295 samples/sec accuracy=54.838542 loss=1.890108 lr=0.010000 Epoch[007] Batch [0349]/[3760] Speed: 66.960098 samples/sec accuracy=54.424107 loss=1.906239 lr=0.010000 Epoch[007] Batch [0399]/[3760] Speed: 66.699554 samples/sec accuracy=54.371094 loss=1.911876 lr=0.010000 Epoch[007] Batch 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accuracy=54.673611 loss=1.903881 lr=0.010000 Epoch[007] Batch [0949]/[3760] Speed: 66.107742 samples/sec accuracy=54.666118 loss=1.903141 lr=0.010000 Epoch[007] Batch [0999]/[3760] Speed: 66.992178 samples/sec accuracy=54.635937 loss=1.903562 lr=0.010000 Epoch[007] Batch [1049]/[3760] Speed: 66.177969 samples/sec accuracy=54.694940 loss=1.901267 lr=0.010000 Epoch[007] Batch [1099]/[3760] Speed: 67.027053 samples/sec accuracy=54.691761 loss=1.900682 lr=0.010000 Epoch[007] Batch [1149]/[3760] Speed: 66.346459 samples/sec accuracy=54.706522 loss=1.897864 lr=0.010000 Epoch[007] Batch [1199]/[3760] Speed: 67.099399 samples/sec accuracy=54.697917 loss=1.898250 lr=0.010000 Epoch[007] Batch [1249]/[3760] Speed: 66.300975 samples/sec accuracy=54.730000 loss=1.897105 lr=0.010000 Epoch[007] Batch [1299]/[3760] Speed: 66.437676 samples/sec accuracy=54.722356 loss=1.897190 lr=0.010000 Epoch[007] Batch [1349]/[3760] Speed: 66.650229 samples/sec accuracy=54.709491 loss=1.898596 lr=0.010000 Epoch[007] 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accuracy=54.559966 loss=1.901696 lr=0.010000 Epoch[007] Batch [1899]/[3760] Speed: 66.169510 samples/sec accuracy=54.560033 loss=1.902376 lr=0.010000 Epoch[007] Batch [1949]/[3760] Speed: 66.931860 samples/sec accuracy=54.528045 loss=1.902694 lr=0.010000 Epoch[007] Batch [1999]/[3760] Speed: 65.901120 samples/sec accuracy=54.510156 loss=1.904108 lr=0.010000 Epoch[007] Batch [2049]/[3760] Speed: 67.202664 samples/sec accuracy=54.541159 loss=1.903896 lr=0.010000 Epoch[007] Batch [2099]/[3760] Speed: 66.619232 samples/sec accuracy=54.517857 loss=1.904474 lr=0.010000 Epoch[007] Batch [2149]/[3760] Speed: 66.003223 samples/sec accuracy=54.498547 loss=1.905500 lr=0.010000 Epoch[007] Batch [2199]/[3760] Speed: 66.846057 samples/sec accuracy=54.509943 loss=1.904493 lr=0.010000 Epoch[007] Batch [2249]/[3760] Speed: 66.009138 samples/sec accuracy=54.509028 loss=1.904913 lr=0.010000 Epoch[007] Batch [2299]/[3760] Speed: 66.785557 samples/sec accuracy=54.493886 loss=1.905212 lr=0.010000 Epoch[007] 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accuracy=54.486049 loss=1.908446 lr=0.010000 Epoch[007] Batch [2849]/[3760] Speed: 66.278010 samples/sec accuracy=54.473136 loss=1.909511 lr=0.010000 Epoch[007] Batch [2899]/[3760] Speed: 66.703167 samples/sec accuracy=54.463901 loss=1.909780 lr=0.010000 Epoch[007] Batch [2949]/[3760] Speed: 66.583862 samples/sec accuracy=54.471398 loss=1.909215 lr=0.010000 Epoch[007] Batch [2999]/[3760] Speed: 66.690610 samples/sec accuracy=54.463542 loss=1.909076 lr=0.010000 Epoch[007] Batch [3049]/[3760] Speed: 66.714202 samples/sec accuracy=54.443648 loss=1.909198 lr=0.010000 Epoch[007] Batch [3099]/[3760] Speed: 66.445737 samples/sec accuracy=54.443044 loss=1.909344 lr=0.010000 Epoch[007] Batch [3149]/[3760] Speed: 67.204309 samples/sec accuracy=54.443452 loss=1.908970 lr=0.010000 Epoch[007] Batch [3199]/[3760] Speed: 66.463354 samples/sec accuracy=54.437012 loss=1.908928 lr=0.010000 Epoch[007] Batch [3249]/[3760] Speed: 66.744732 samples/sec accuracy=54.434135 loss=1.909661 lr=0.010000 Epoch[007] 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accuracy=54.389167 loss=1.911041 lr=0.010000 Batch [0049]/[0303]: acc-top1=55.687500 acc-top5=80.218750 Batch [0099]/[0303]: acc-top1=55.812500 acc-top5=79.906250 Batch [0149]/[0303]: acc-top1=55.770833 acc-top5=80.052083 Batch [0199]/[0303]: acc-top1=55.664062 acc-top5=79.781250 Batch [0249]/[0303]: acc-top1=55.975000 acc-top5=79.900000 Batch [0299]/[0303]: acc-top1=55.713542 acc-top5=79.828125 [Epoch 007] training: accuracy=54.389545 loss=1.910986 [Epoch 007] speed: 66 samples/sec time cost: 3926.426211 [Epoch 007] validation: acc-top1=55.739480 acc-top5=79.826733 loss=1.972666 Epoch[008] Batch [0049]/[3759] Speed: 45.636773 samples/sec accuracy=54.312500 loss=1.916523 lr=0.010000 Epoch[008] Batch [0099]/[3759] Speed: 65.431986 samples/sec accuracy=55.593750 loss=1.869053 lr=0.010000 Epoch[008] Batch [0149]/[3759] Speed: 67.184933 samples/sec accuracy=55.291667 loss=1.875052 lr=0.010000 Epoch[008] Batch [0199]/[3759] Speed: 66.638348 samples/sec accuracy=55.140625 loss=1.874430 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66.631576 samples/sec accuracy=55.344190 loss=1.867156 lr=0.010000 Epoch[008] Batch [3599]/[3759] Speed: 66.690830 samples/sec accuracy=55.347222 loss=1.866824 lr=0.010000 Epoch[008] Batch [3649]/[3759] Speed: 67.035524 samples/sec accuracy=55.326627 loss=1.867099 lr=0.010000 Epoch[008] Batch [3699]/[3759] Speed: 66.636709 samples/sec accuracy=55.320946 loss=1.866926 lr=0.010000 Epoch[008] Batch [3749]/[3759] Speed: 75.726709 samples/sec accuracy=55.333750 loss=1.865857 lr=0.010000 Batch [0049]/[0303]: acc-top1=56.718750 acc-top5=80.468750 Batch [0099]/[0303]: acc-top1=56.562500 acc-top5=80.531250 Batch [0149]/[0303]: acc-top1=56.510417 acc-top5=80.385417 Batch [0199]/[0303]: acc-top1=56.453125 acc-top5=80.250000 Batch [0249]/[0303]: acc-top1=56.712500 acc-top5=80.350000 Batch [0299]/[0303]: acc-top1=56.437500 acc-top5=80.067708 [Epoch 008] training: accuracy=55.336775 loss=1.865761 [Epoch 008] speed: 66 samples/sec time cost: 3919.297224 [Epoch 008] validation: acc-top1=56.420173 acc-top5=80.048474 loss=1.957455 Epoch[009] Batch [0049]/[3760] Speed: 45.422253 samples/sec accuracy=57.218750 loss=1.803990 lr=0.010000 Epoch[009] Batch [0099]/[3760] Speed: 66.721056 samples/sec accuracy=56.750000 loss=1.794364 lr=0.010000 Epoch[009] Batch [0149]/[3760] Speed: 66.272545 samples/sec accuracy=55.802083 loss=1.839691 lr=0.010000 Epoch[009] Batch [0199]/[3760] Speed: 67.111140 samples/sec accuracy=56.312500 loss=1.817165 lr=0.010000 Epoch[009] Batch [0249]/[3760] Speed: 67.166622 samples/sec accuracy=56.443750 loss=1.813783 lr=0.010000 Epoch[009] Batch [0299]/[3760] Speed: 66.831080 samples/sec accuracy=56.520833 loss=1.806108 lr=0.010000 Epoch[009] Batch [0349]/[3760] Speed: 67.072723 samples/sec accuracy=56.379464 loss=1.814411 lr=0.010000 Epoch[009] Batch [0399]/[3760] Speed: 66.946567 samples/sec accuracy=56.578125 loss=1.811236 lr=0.010000 Epoch[009] Batch [0449]/[3760] Speed: 66.584398 samples/sec accuracy=56.635417 loss=1.808233 lr=0.010000 Epoch[009] Batch 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accuracy=56.300987 loss=1.818232 lr=0.010000 Epoch[009] Batch [1949]/[3760] Speed: 66.526663 samples/sec accuracy=56.264423 loss=1.820007 lr=0.010000 Epoch[009] Batch [1999]/[3760] Speed: 67.235822 samples/sec accuracy=56.263281 loss=1.819620 lr=0.010000 Epoch[009] Batch [2049]/[3760] Speed: 66.750237 samples/sec accuracy=56.237805 loss=1.820147 lr=0.010000 Epoch[009] Batch [2099]/[3760] Speed: 67.163475 samples/sec accuracy=56.222470 loss=1.820514 lr=0.010000 Epoch[009] Batch [2149]/[3760] Speed: 66.946742 samples/sec accuracy=56.226744 loss=1.820108 lr=0.010000 Epoch[009] Batch [2199]/[3760] Speed: 66.593247 samples/sec accuracy=56.232244 loss=1.820028 lr=0.010000 Epoch[009] Batch [2249]/[3760] Speed: 67.088619 samples/sec accuracy=56.261111 loss=1.819182 lr=0.010000 Epoch[009] Batch [2299]/[3760] Speed: 66.630616 samples/sec accuracy=56.250000 loss=1.819817 lr=0.010000 Epoch[009] Batch [2349]/[3760] Speed: 66.754422 samples/sec accuracy=56.242021 loss=1.819986 lr=0.010000 Epoch[009] 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accuracy=56.207785 loss=1.820780 lr=0.010000 Epoch[009] Batch [2899]/[3760] Speed: 67.199655 samples/sec accuracy=56.238685 loss=1.819618 lr=0.010000 Epoch[009] Batch [2949]/[3760] Speed: 67.225467 samples/sec accuracy=56.229873 loss=1.820132 lr=0.010000 Epoch[009] Batch [2999]/[3760] Speed: 66.946065 samples/sec accuracy=56.228125 loss=1.820081 lr=0.010000 Epoch[009] Batch [3049]/[3760] Speed: 66.953033 samples/sec accuracy=56.218750 loss=1.820188 lr=0.010000 Epoch[009] Batch [3099]/[3760] Speed: 67.450381 samples/sec accuracy=56.224294 loss=1.820908 lr=0.010000 Epoch[009] Batch [3149]/[3760] Speed: 66.484819 samples/sec accuracy=56.184524 loss=1.822270 lr=0.010000 Epoch[009] Batch [3199]/[3760] Speed: 67.473532 samples/sec accuracy=56.187500 loss=1.822023 lr=0.010000 Epoch[009] Batch [3249]/[3760] Speed: 66.772289 samples/sec accuracy=56.193750 loss=1.821984 lr=0.010000 Epoch[009] Batch [3299]/[3760] Speed: 67.102477 samples/sec accuracy=56.189394 loss=1.822187 lr=0.010000 Epoch[009] 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[0099]/[0303]: acc-top1=56.921875 acc-top5=80.546875 Batch [0149]/[0303]: acc-top1=56.510417 acc-top5=80.541667 Batch [0199]/[0303]: acc-top1=56.343750 acc-top5=80.531250 Batch [0249]/[0303]: acc-top1=56.693750 acc-top5=80.762500 Batch [0299]/[0303]: acc-top1=56.302083 acc-top5=80.401042 [Epoch 009] training: accuracy=56.168966 loss=1.823637 [Epoch 009] speed: 66 samples/sec time cost: 3911.009840 [Epoch 009] validation: acc-top1=56.317038 acc-top5=80.388820 loss=1.934952 Epoch[010] Batch [0049]/[3760] Speed: 45.762798 samples/sec accuracy=55.781250 loss=1.814889 lr=0.010000 Epoch[010] Batch [0099]/[3760] Speed: 65.160217 samples/sec accuracy=56.203125 loss=1.777547 lr=0.010000 Epoch[010] Batch [0149]/[3760] Speed: 66.883126 samples/sec accuracy=56.791667 loss=1.767618 lr=0.010000 Epoch[010] Batch [0199]/[3760] Speed: 66.789445 samples/sec accuracy=56.625000 loss=1.773813 lr=0.010000 Epoch[010] Batch [0249]/[3760] Speed: 67.101069 samples/sec accuracy=56.668750 loss=1.776078 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accuracy=57.531250 loss=1.770092 lr=0.010000 Epoch[011] Batch [0099]/[3759] Speed: 66.234693 samples/sec accuracy=58.656250 loss=1.718082 lr=0.010000 Epoch[011] Batch [0149]/[3759] Speed: 66.842397 samples/sec accuracy=58.437500 loss=1.719755 lr=0.010000 Epoch[011] Batch [0199]/[3759] Speed: 66.930320 samples/sec accuracy=58.445312 loss=1.719288 lr=0.010000 Epoch[011] Batch [0249]/[3759] Speed: 67.143500 samples/sec accuracy=58.512500 loss=1.716732 lr=0.010000 Epoch[011] Batch [0299]/[3759] Speed: 67.409569 samples/sec accuracy=58.515625 loss=1.728134 lr=0.010000 Epoch[011] Batch [0349]/[3759] Speed: 66.311852 samples/sec accuracy=58.468750 loss=1.727626 lr=0.010000 Epoch[011] Batch [0399]/[3759] Speed: 67.532077 samples/sec accuracy=58.355469 loss=1.732094 lr=0.010000 Epoch[011] Batch [0449]/[3759] Speed: 67.584235 samples/sec accuracy=58.111111 loss=1.738908 lr=0.010000 Epoch[011] Batch [0499]/[3759] Speed: 67.200170 samples/sec accuracy=58.103125 loss=1.742379 lr=0.010000 Epoch[011] 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accuracy=58.131250 loss=1.736987 lr=0.010000 Epoch[011] Batch [1049]/[3759] Speed: 67.334508 samples/sec accuracy=58.068452 loss=1.739713 lr=0.010000 Epoch[011] Batch [1099]/[3759] Speed: 66.512924 samples/sec accuracy=58.147727 loss=1.737979 lr=0.010000 Epoch[011] Batch [1149]/[3759] Speed: 66.528669 samples/sec accuracy=58.096467 loss=1.740068 lr=0.010000 Epoch[011] Batch [1199]/[3759] Speed: 66.457874 samples/sec accuracy=58.078125 loss=1.739682 lr=0.010000 Epoch[011] Batch [1249]/[3759] Speed: 67.539712 samples/sec accuracy=58.116250 loss=1.738023 lr=0.010000 Epoch[011] Batch [1299]/[3759] Speed: 66.289091 samples/sec accuracy=58.056490 loss=1.738859 lr=0.010000 Epoch[011] Batch [1349]/[3759] Speed: 67.612866 samples/sec accuracy=58.031250 loss=1.739938 lr=0.010000 Epoch[011] Batch [1399]/[3759] Speed: 67.168340 samples/sec accuracy=57.988839 loss=1.739807 lr=0.010000 Epoch[011] Batch [1449]/[3759] Speed: 66.738223 samples/sec accuracy=57.971983 loss=1.740714 lr=0.010000 Epoch[011] 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accuracy=57.696121 loss=1.752641 lr=0.010000 Epoch[011] Batch [2949]/[3759] Speed: 67.644047 samples/sec accuracy=57.692797 loss=1.752576 lr=0.010000 Epoch[011] Batch [2999]/[3759] Speed: 66.983601 samples/sec accuracy=57.692708 loss=1.752435 lr=0.010000 Epoch[011] Batch [3049]/[3759] Speed: 66.812662 samples/sec accuracy=57.697234 loss=1.752658 lr=0.010000 Epoch[011] Batch [3099]/[3759] Speed: 67.002719 samples/sec accuracy=57.674899 loss=1.754071 lr=0.010000 Epoch[011] Batch [3149]/[3759] Speed: 67.429595 samples/sec accuracy=57.677083 loss=1.753716 lr=0.010000 Epoch[011] Batch [3199]/[3759] Speed: 66.900114 samples/sec accuracy=57.673340 loss=1.754084 lr=0.010000 Epoch[011] Batch [3249]/[3759] Speed: 66.737363 samples/sec accuracy=57.653846 loss=1.754102 lr=0.010000 Epoch[011] Batch [3299]/[3759] Speed: 67.321603 samples/sec accuracy=57.645833 loss=1.754901 lr=0.010000 Epoch[011] Batch [3349]/[3759] Speed: 67.268815 samples/sec accuracy=57.638993 loss=1.755778 lr=0.010000 Epoch[011] 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acc-top5=81.510417 Batch [0199]/[0303]: acc-top1=57.671875 acc-top5=81.296875 Batch [0249]/[0303]: acc-top1=57.787500 acc-top5=81.418750 Batch [0299]/[0303]: acc-top1=57.578125 acc-top5=81.135417 [Epoch 011] training: accuracy=57.598846 loss=1.757306 [Epoch 011] speed: 66 samples/sec time cost: 3897.976308 [Epoch 011] validation: acc-top1=57.539191 acc-top5=81.126238 loss=1.910513 Epoch[012] Batch [0049]/[3760] Speed: 46.136883 samples/sec accuracy=58.500000 loss=1.716373 lr=0.010000 Epoch[012] Batch [0099]/[3760] Speed: 66.332576 samples/sec accuracy=58.671875 loss=1.694689 lr=0.010000 Epoch[012] Batch [0149]/[3760] Speed: 67.343563 samples/sec accuracy=58.979167 loss=1.693731 lr=0.010000 Epoch[012] Batch [0199]/[3760] Speed: 67.299006 samples/sec accuracy=59.078125 loss=1.686092 lr=0.010000 Epoch[012] Batch [0249]/[3760] Speed: 67.174829 samples/sec accuracy=58.962500 loss=1.684237 lr=0.010000 Epoch[012] Batch [0299]/[3760] Speed: 66.410352 samples/sec accuracy=59.020833 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[0249]/[0303]: acc-top1=58.743750 acc-top5=81.437500 Batch [0299]/[0303]: acc-top1=58.536458 acc-top5=81.145833 [Epoch 013] training: accuracy=58.854721 loss=1.699160 [Epoch 013] speed: 66 samples/sec time cost: 3905.254154 [Epoch 013] validation: acc-top1=58.539604 acc-top5=81.131394 loss=1.865028 Epoch[014] Batch [0049]/[3759] Speed: 45.618796 samples/sec accuracy=61.500000 loss=1.573214 lr=0.010000 Epoch[014] Batch [0099]/[3759] Speed: 66.083040 samples/sec accuracy=60.406250 loss=1.614733 lr=0.010000 Epoch[014] Batch [0149]/[3759] Speed: 67.134352 samples/sec accuracy=60.281250 loss=1.624142 lr=0.010000 Epoch[014] Batch [0199]/[3759] Speed: 66.824101 samples/sec accuracy=60.351562 loss=1.617981 lr=0.010000 Epoch[014] Batch [0249]/[3759] Speed: 66.945675 samples/sec accuracy=60.225000 loss=1.635586 lr=0.010000 Epoch[014] Batch [0299]/[3759] Speed: 67.049081 samples/sec accuracy=59.875000 loss=1.651701 lr=0.010000 Epoch[014] Batch [0349]/[3759] Speed: 67.365722 samples/sec 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accuracy=59.176270 loss=1.685049 lr=0.010000 Epoch[014] Batch [3249]/[3759] Speed: 66.886307 samples/sec accuracy=59.183654 loss=1.684372 lr=0.010000 Epoch[014] Batch [3299]/[3759] Speed: 67.134901 samples/sec accuracy=59.180398 loss=1.685095 lr=0.010000 Epoch[014] Batch [3349]/[3759] Speed: 67.170879 samples/sec accuracy=59.188433 loss=1.685094 lr=0.010000 Epoch[014] Batch [3399]/[3759] Speed: 66.727739 samples/sec accuracy=59.187500 loss=1.685784 lr=0.010000 Epoch[014] Batch [3449]/[3759] Speed: 67.402802 samples/sec accuracy=59.181159 loss=1.686363 lr=0.010000 Epoch[014] Batch [3499]/[3759] Speed: 67.043937 samples/sec accuracy=59.178571 loss=1.686274 lr=0.010000 Epoch[014] Batch [3549]/[3759] Speed: 66.980386 samples/sec accuracy=59.163292 loss=1.686534 lr=0.010000 Epoch[014] Batch [3599]/[3759] Speed: 67.783394 samples/sec accuracy=59.163194 loss=1.686324 lr=0.010000 Epoch[014] Batch [3649]/[3759] Speed: 66.934525 samples/sec accuracy=59.176370 loss=1.686434 lr=0.010000 Epoch[014] Batch [3699]/[3759] Speed: 67.294734 samples/sec accuracy=59.198902 loss=1.685247 lr=0.010000 Epoch[014] Batch [3749]/[3759] Speed: 75.716342 samples/sec accuracy=59.212917 loss=1.685141 lr=0.010000 Batch [0049]/[0303]: acc-top1=60.093750 acc-top5=82.406250 Batch [0099]/[0303]: acc-top1=59.765625 acc-top5=82.234375 Batch [0149]/[0303]: acc-top1=59.625000 acc-top5=82.302083 Batch [0199]/[0303]: acc-top1=59.429687 acc-top5=82.320312 Batch [0249]/[0303]: acc-top1=59.368750 acc-top5=82.343750 Batch [0299]/[0303]: acc-top1=59.052083 acc-top5=82.156250 [Epoch 014] training: accuracy=59.214136 loss=1.685097 [Epoch 014] speed: 66 samples/sec time cost: 3896.988483 [Epoch 014] validation: acc-top1=59.039810 acc-top5=82.142120 loss=1.853514 Epoch[015] Batch [0049]/[3760] Speed: 45.598600 samples/sec accuracy=61.187500 loss=1.578649 lr=0.010000 Epoch[015] Batch [0099]/[3760] Speed: 66.258273 samples/sec accuracy=61.187500 loss=1.574980 lr=0.010000 Epoch[015] Batch [0149]/[3760] Speed: 66.950465 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lr=0.010000 Epoch[015] Batch [3499]/[3760] Speed: 66.333997 samples/sec accuracy=59.750446 loss=1.657224 lr=0.010000 Epoch[015] Batch [3549]/[3760] Speed: 67.530600 samples/sec accuracy=59.741637 loss=1.657380 lr=0.010000 Epoch[015] Batch [3599]/[3760] Speed: 66.943923 samples/sec accuracy=59.730035 loss=1.657787 lr=0.010000 Epoch[015] Batch [3649]/[3760] Speed: 67.517888 samples/sec accuracy=59.726884 loss=1.657737 lr=0.010000 Epoch[015] Batch [3699]/[3760] Speed: 66.513825 samples/sec accuracy=59.733953 loss=1.658180 lr=0.010000 Epoch[015] Batch [3749]/[3760] Speed: 75.813325 samples/sec accuracy=59.716667 loss=1.658638 lr=0.010000 Batch [0049]/[0303]: acc-top1=59.875000 acc-top5=82.687500 Batch [0099]/[0303]: acc-top1=60.093750 acc-top5=82.796875 Batch [0149]/[0303]: acc-top1=59.718750 acc-top5=82.770833 Batch [0199]/[0303]: acc-top1=59.640625 acc-top5=82.468750 Batch [0249]/[0303]: acc-top1=59.806250 acc-top5=82.568750 Batch [0299]/[0303]: acc-top1=59.359375 acc-top5=82.265625 [Epoch 015] training: accuracy=59.712434 loss=1.658717 [Epoch 015] speed: 66 samples/sec time cost: 3900.274959 [Epoch 015] validation: acc-top1=59.375000 acc-top5=82.265883 loss=1.830291 Epoch[016] Batch [0049]/[3760] Speed: 45.859871 samples/sec accuracy=60.750000 loss=1.602370 lr=0.010000 Epoch[016] Batch [0099]/[3760] Speed: 65.528309 samples/sec accuracy=60.906250 loss=1.609697 lr=0.010000 Epoch[016] Batch [0149]/[3760] Speed: 67.314190 samples/sec accuracy=60.958333 loss=1.615437 lr=0.010000 Epoch[016] Batch [0199]/[3760] Speed: 66.950843 samples/sec accuracy=61.132812 loss=1.604189 lr=0.010000 Epoch[016] Batch [0249]/[3760] Speed: 66.224573 samples/sec accuracy=61.087500 loss=1.591378 lr=0.010000 Epoch[016] Batch [0299]/[3760] Speed: 67.238215 samples/sec accuracy=61.130208 loss=1.590732 lr=0.010000 Epoch[016] Batch [0349]/[3760] Speed: 67.514347 samples/sec accuracy=60.946429 loss=1.597238 lr=0.010000 Epoch[016] Batch [0399]/[3760] Speed: 66.179183 samples/sec 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accuracy=60.395833 loss=1.622295 lr=0.010000 Epoch[016] Batch [1399]/[3760] Speed: 66.917364 samples/sec accuracy=60.360491 loss=1.623720 lr=0.010000 Epoch[016] Batch [1449]/[3760] Speed: 66.941823 samples/sec accuracy=60.329741 loss=1.624957 lr=0.010000 Epoch[016] Batch [1499]/[3760] Speed: 66.944826 samples/sec accuracy=60.315625 loss=1.625851 lr=0.010000 Epoch[016] Batch [1549]/[3760] Speed: 67.413897 samples/sec accuracy=60.277218 loss=1.628173 lr=0.010000 Epoch[016] Batch [1599]/[3760] Speed: 66.783308 samples/sec accuracy=60.273438 loss=1.628873 lr=0.010000 Epoch[016] Batch [1649]/[3760] Speed: 66.790811 samples/sec accuracy=60.313447 loss=1.628122 lr=0.010000 Epoch[016] Batch [1699]/[3760] Speed: 66.334250 samples/sec accuracy=60.310662 loss=1.628182 lr=0.010000 Epoch[016] Batch [1749]/[3760] Speed: 66.926839 samples/sec accuracy=60.276786 loss=1.629659 lr=0.010000 Epoch[016] Batch [1799]/[3760] Speed: 67.228687 samples/sec accuracy=60.279514 loss=1.630563 lr=0.010000 Epoch[016] 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Batch [3749]/[3760] Speed: 76.348379 samples/sec accuracy=59.978750 loss=1.644747 lr=0.010000 Batch [0049]/[0303]: acc-top1=60.093750 acc-top5=82.687500 Batch [0099]/[0303]: acc-top1=59.500000 acc-top5=82.937500 Batch [0149]/[0303]: acc-top1=59.645833 acc-top5=82.833333 Batch [0199]/[0303]: acc-top1=59.523438 acc-top5=82.554688 Batch [0249]/[0303]: acc-top1=59.656250 acc-top5=82.600000 Batch [0299]/[0303]: acc-top1=59.375000 acc-top5=82.286458 [Epoch 016] training: accuracy=59.981300 loss=1.644720 [Epoch 016] speed: 66 samples/sec time cost: 3899.284635 [Epoch 016] validation: acc-top1=59.364686 acc-top5=82.271040 loss=1.799352 Epoch[017] Batch [0049]/[3759] Speed: 45.654228 samples/sec accuracy=61.218750 loss=1.576354 lr=0.010000 Epoch[017] Batch [0099]/[3759] Speed: 65.420289 samples/sec accuracy=61.140625 loss=1.585504 lr=0.010000 Epoch[017] Batch [0149]/[3759] Speed: 67.156023 samples/sec accuracy=61.760417 loss=1.565139 lr=0.010000 Epoch[017] Batch [0199]/[3759] Speed: 66.934679 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67.183635 samples/sec accuracy=60.483094 loss=1.623535 lr=0.010000 Epoch[017] Batch [3099]/[3759] Speed: 67.141132 samples/sec accuracy=60.481351 loss=1.623568 lr=0.010000 Epoch[017] Batch [3149]/[3759] Speed: 67.299617 samples/sec accuracy=60.482143 loss=1.623537 lr=0.010000 Epoch[017] Batch [3199]/[3759] Speed: 66.682112 samples/sec accuracy=60.459473 loss=1.624234 lr=0.010000 Epoch[017] Batch [3249]/[3759] Speed: 67.133866 samples/sec accuracy=60.451923 loss=1.624607 lr=0.010000 Epoch[017] Batch [3299]/[3759] Speed: 67.155024 samples/sec accuracy=60.457386 loss=1.624848 lr=0.010000 Epoch[017] Batch [3349]/[3759] Speed: 66.431108 samples/sec accuracy=60.466884 loss=1.625166 lr=0.010000 Epoch[017] Batch [3399]/[3759] Speed: 66.524190 samples/sec accuracy=60.465993 loss=1.625223 lr=0.010000 Epoch[017] Batch [3449]/[3759] Speed: 67.178179 samples/sec accuracy=60.459239 loss=1.625850 lr=0.010000 Epoch[017] Batch [3499]/[3759] Speed: 67.196759 samples/sec accuracy=60.469196 loss=1.626135 lr=0.010000 Epoch[017] Batch [3549]/[3759] Speed: 66.799536 samples/sec accuracy=60.444102 loss=1.626963 lr=0.010000 Epoch[017] Batch [3599]/[3759] Speed: 67.282162 samples/sec accuracy=60.442708 loss=1.626669 lr=0.010000 Epoch[017] Batch [3649]/[3759] Speed: 66.922429 samples/sec accuracy=60.456336 loss=1.626446 lr=0.010000 Epoch[017] Batch [3699]/[3759] Speed: 66.707333 samples/sec accuracy=60.442568 loss=1.626748 lr=0.010000 Epoch[017] Batch [3749]/[3759] Speed: 76.576437 samples/sec accuracy=60.433333 loss=1.627397 lr=0.010000 Batch [0049]/[0303]: acc-top1=58.343750 acc-top5=82.093750 Batch [0099]/[0303]: acc-top1=58.562500 acc-top5=82.062500 Batch [0149]/[0303]: acc-top1=58.447917 acc-top5=82.229167 Batch [0199]/[0303]: acc-top1=58.648437 acc-top5=81.812500 Batch [0249]/[0303]: acc-top1=58.893750 acc-top5=82.018750 Batch [0299]/[0303]: acc-top1=58.593750 acc-top5=81.682292 [Epoch 017] training: accuracy=60.427474 loss=1.627537 [Epoch 017] speed: 66 samples/sec time cost: 3898.061157 [Epoch 017] validation: acc-top1=58.601485 acc-top5=81.688325 loss=1.885895 Epoch[018] Batch [0049]/[3760] Speed: 45.403424 samples/sec accuracy=61.312500 loss=1.577063 lr=0.010000 Epoch[018] Batch [0099]/[3760] Speed: 66.468698 samples/sec accuracy=61.296875 loss=1.558480 lr=0.010000 Epoch[018] Batch [0149]/[3760] Speed: 67.173988 samples/sec accuracy=61.510417 loss=1.546538 lr=0.010000 Epoch[018] Batch [0199]/[3760] Speed: 66.692537 samples/sec accuracy=61.578125 loss=1.550622 lr=0.010000 Epoch[018] Batch [0249]/[3760] Speed: 67.416125 samples/sec accuracy=61.881250 loss=1.545001 lr=0.010000 Epoch[018] Batch [0299]/[3760] Speed: 67.593748 samples/sec accuracy=61.885417 loss=1.547382 lr=0.010000 Epoch[018] Batch [0349]/[3760] Speed: 66.829096 samples/sec accuracy=61.714286 loss=1.551530 lr=0.010000 Epoch[018] Batch [0399]/[3760] Speed: 66.753934 samples/sec accuracy=61.640625 loss=1.559770 lr=0.010000 Epoch[018] Batch [0449]/[3760] Speed: 67.461639 samples/sec 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accuracy=60.677083 loss=1.614989 lr=0.010000 Epoch[018] Batch [3349]/[3760] Speed: 67.480786 samples/sec accuracy=60.684235 loss=1.614898 lr=0.010000 Epoch[018] Batch [3399]/[3760] Speed: 67.241500 samples/sec accuracy=60.676011 loss=1.614826 lr=0.010000 Epoch[018] Batch [3449]/[3760] Speed: 68.004667 samples/sec accuracy=60.681159 loss=1.614255 lr=0.010000 Epoch[018] Batch [3499]/[3760] Speed: 66.591872 samples/sec accuracy=60.682143 loss=1.614405 lr=0.010000 Epoch[018] Batch [3549]/[3760] Speed: 67.104147 samples/sec accuracy=60.681338 loss=1.614611 lr=0.010000 Epoch[018] Batch [3599]/[3760] Speed: 67.069292 samples/sec accuracy=60.680556 loss=1.614802 lr=0.010000 Epoch[018] Batch [3649]/[3760] Speed: 67.390801 samples/sec accuracy=60.651541 loss=1.616052 lr=0.010000 Epoch[018] Batch [3699]/[3760] Speed: 66.954168 samples/sec accuracy=60.644848 loss=1.615854 lr=0.010000 Epoch[018] Batch [3749]/[3760] Speed: 75.347083 samples/sec accuracy=60.648333 loss=1.615296 lr=0.010000 Batch [0049]/[0303]: acc-top1=59.406250 acc-top5=82.562500 Batch [0099]/[0303]: acc-top1=59.218750 acc-top5=82.312500 Batch [0149]/[0303]: acc-top1=59.177083 acc-top5=82.145833 Batch [0199]/[0303]: acc-top1=59.218750 acc-top5=81.898438 Batch [0249]/[0303]: acc-top1=59.600000 acc-top5=82.087500 Batch [0299]/[0303]: acc-top1=59.395833 acc-top5=81.812500 [Epoch 018] training: accuracy=60.651596 loss=1.615260 [Epoch 018] speed: 66 samples/sec time cost: 3900.153011 [Epoch 018] validation: acc-top1=59.421411 acc-top5=81.775990 loss=1.874456 Epoch[019] Batch [0049]/[3760] Speed: 46.132881 samples/sec accuracy=62.031250 loss=1.520048 lr=0.010000 Epoch[019] Batch [0099]/[3760] Speed: 66.384957 samples/sec accuracy=62.203125 loss=1.525619 lr=0.010000 Epoch[019] Batch [0149]/[3760] Speed: 67.065820 samples/sec accuracy=62.072917 loss=1.534051 lr=0.010000 Epoch[019] Batch [0199]/[3760] Speed: 67.080885 samples/sec accuracy=61.882812 loss=1.554397 lr=0.010000 Epoch[019] Batch [0249]/[3760] Speed: 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lr=0.010000 Epoch[019] Batch [3599]/[3760] Speed: 67.429110 samples/sec accuracy=61.109375 loss=1.593988 lr=0.010000 Epoch[019] Batch [3649]/[3760] Speed: 67.580379 samples/sec accuracy=61.093322 loss=1.595277 lr=0.010000 Epoch[019] Batch [3699]/[3760] Speed: 67.775609 samples/sec accuracy=61.090372 loss=1.595270 lr=0.010000 Epoch[019] Batch [3749]/[3760] Speed: 74.844823 samples/sec accuracy=61.086250 loss=1.595776 lr=0.010000 Batch [0049]/[0303]: acc-top1=59.687500 acc-top5=82.000000 Batch [0099]/[0303]: acc-top1=59.312500 acc-top5=82.203125 Batch [0149]/[0303]: acc-top1=59.572917 acc-top5=82.458333 Batch [0199]/[0303]: acc-top1=59.546875 acc-top5=82.250000 Batch [0249]/[0303]: acc-top1=59.475000 acc-top5=82.268750 Batch [0299]/[0303]: acc-top1=59.067708 acc-top5=81.906250 [Epoch 019] training: accuracy=61.082114 loss=1.595795 [Epoch 019] speed: 66 samples/sec time cost: 3892.312012 [Epoch 019] validation: acc-top1=59.075908 acc-top5=81.884282 loss=1.903058 Epoch[020] Batch 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accuracy=61.416045 loss=1.585140 lr=0.010000 Epoch[020] Batch [3399]/[3759] Speed: 67.700228 samples/sec accuracy=61.405331 loss=1.585278 lr=0.010000 Epoch[020] Batch [3449]/[3759] Speed: 66.946940 samples/sec accuracy=61.392210 loss=1.585491 lr=0.010000 Epoch[020] Batch [3499]/[3759] Speed: 66.871616 samples/sec accuracy=61.410268 loss=1.584858 lr=0.010000 Epoch[020] Batch [3549]/[3759] Speed: 67.380360 samples/sec accuracy=61.414173 loss=1.584410 lr=0.010000 Epoch[020] Batch [3599]/[3759] Speed: 67.463574 samples/sec accuracy=61.415799 loss=1.584526 lr=0.010000 Epoch[020] Batch [3649]/[3759] Speed: 66.969871 samples/sec accuracy=61.431935 loss=1.584326 lr=0.010000 Epoch[020] Batch [3699]/[3759] Speed: 67.140458 samples/sec accuracy=61.421453 loss=1.584934 lr=0.010000 Epoch[020] Batch [3749]/[3759] Speed: 75.664822 samples/sec accuracy=61.397500 loss=1.585581 lr=0.010000 Batch [0049]/[0303]: acc-top1=60.656250 acc-top5=82.562500 Batch [0099]/[0303]: acc-top1=60.234375 acc-top5=82.562500 Batch [0149]/[0303]: acc-top1=60.114583 acc-top5=82.447917 Batch [0199]/[0303]: acc-top1=59.929687 acc-top5=82.406250 Batch [0249]/[0303]: acc-top1=59.956250 acc-top5=82.456250 Batch [0299]/[0303]: acc-top1=59.583333 acc-top5=82.203125 [Epoch 020] training: accuracy=61.401387 loss=1.585412 [Epoch 020] speed: 66 samples/sec time cost: 3894.997457 [Epoch 020] validation: acc-top1=59.576114 acc-top5=82.219472 loss=1.823427 Epoch[021] Batch [0049]/[3760] Speed: 46.738674 samples/sec accuracy=63.218750 loss=1.492424 lr=0.010000 Epoch[021] Batch [0099]/[3760] Speed: 66.535800 samples/sec accuracy=63.593750 loss=1.503324 lr=0.010000 Epoch[021] Batch [0149]/[3760] Speed: 67.302066 samples/sec accuracy=62.833333 loss=1.521608 lr=0.010000 Epoch[021] Batch [0199]/[3760] Speed: 66.992206 samples/sec accuracy=62.671875 loss=1.534065 lr=0.010000 Epoch[021] Batch [0249]/[3760] Speed: 66.926206 samples/sec accuracy=62.493750 loss=1.536197 lr=0.010000 Epoch[021] Batch [0299]/[3760] 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accuracy=61.853860 loss=1.557119 lr=0.010000 Epoch[021] Batch [1749]/[3760] Speed: 67.961998 samples/sec accuracy=61.868750 loss=1.556260 lr=0.010000 Epoch[021] Batch [1799]/[3760] Speed: 66.913696 samples/sec accuracy=61.826389 loss=1.557191 lr=0.010000 Epoch[021] Batch [1849]/[3760] Speed: 66.768625 samples/sec accuracy=61.780405 loss=1.560186 lr=0.010000 Epoch[021] Batch [1899]/[3760] Speed: 66.876758 samples/sec accuracy=61.745066 loss=1.562315 lr=0.010000 Epoch[021] Batch [1949]/[3760] Speed: 67.094773 samples/sec accuracy=61.722756 loss=1.563408 lr=0.010000 Epoch[021] Batch [1999]/[3760] Speed: 67.820033 samples/sec accuracy=61.702344 loss=1.564408 lr=0.010000 Epoch[021] Batch [2049]/[3760] Speed: 66.670343 samples/sec accuracy=61.698933 loss=1.564535 lr=0.010000 Epoch[021] Batch [2099]/[3760] Speed: 66.925807 samples/sec accuracy=61.682292 loss=1.564338 lr=0.010000 Epoch[021] Batch [2149]/[3760] Speed: 67.383532 samples/sec accuracy=61.674419 loss=1.564416 lr=0.010000 Epoch[021] 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accuracy=61.716981 loss=1.564071 lr=0.010000 Epoch[021] Batch [2699]/[3760] Speed: 66.689178 samples/sec accuracy=61.701968 loss=1.564140 lr=0.010000 Epoch[021] Batch [2749]/[3760] Speed: 67.233378 samples/sec accuracy=61.687500 loss=1.564574 lr=0.010000 Epoch[021] Batch [2799]/[3760] Speed: 67.673575 samples/sec accuracy=61.690848 loss=1.565036 lr=0.010000 Epoch[021] Batch [2849]/[3760] Speed: 67.392068 samples/sec accuracy=61.683114 loss=1.565282 lr=0.010000 Epoch[021] Batch [2899]/[3760] Speed: 67.803573 samples/sec accuracy=61.682112 loss=1.565999 lr=0.010000 Epoch[021] Batch [2949]/[3760] Speed: 66.734251 samples/sec accuracy=61.688559 loss=1.567025 lr=0.010000 Epoch[021] Batch [2999]/[3760] Speed: 67.344212 samples/sec accuracy=61.682812 loss=1.566939 lr=0.010000 Epoch[021] Batch [3049]/[3760] Speed: 67.798609 samples/sec accuracy=61.676742 loss=1.567318 lr=0.010000 Epoch[021] Batch [3099]/[3760] Speed: 66.714666 samples/sec accuracy=61.638105 loss=1.568825 lr=0.010000 Epoch[021] Batch [3149]/[3760] Speed: 67.631391 samples/sec accuracy=61.637401 loss=1.569217 lr=0.010000 Epoch[021] Batch [3199]/[3760] Speed: 67.126641 samples/sec accuracy=61.620605 loss=1.569772 lr=0.010000 Epoch[021] Batch [3249]/[3760] Speed: 67.532254 samples/sec accuracy=61.599519 loss=1.570480 lr=0.010000 Epoch[021] Batch [3299]/[3760] Speed: 67.281142 samples/sec accuracy=61.598011 loss=1.570360 lr=0.010000 Epoch[021] Batch [3349]/[3760] Speed: 66.998726 samples/sec accuracy=61.590485 loss=1.570259 lr=0.010000 Epoch[021] Batch [3399]/[3760] Speed: 67.497681 samples/sec accuracy=61.540901 loss=1.571819 lr=0.010000 Epoch[021] Batch [3449]/[3760] Speed: 66.772744 samples/sec accuracy=61.533514 loss=1.571821 lr=0.010000 Epoch[021] Batch [3499]/[3760] Speed: 67.894437 samples/sec accuracy=61.524107 loss=1.572319 lr=0.010000 Epoch[021] Batch [3549]/[3760] Speed: 67.099363 samples/sec accuracy=61.530370 loss=1.572240 lr=0.010000 Epoch[021] Batch [3599]/[3760] Speed: 67.283312 samples/sec accuracy=61.533420 loss=1.572351 lr=0.010000 Epoch[021] Batch [3649]/[3760] Speed: 66.478200 samples/sec accuracy=61.519264 loss=1.572891 lr=0.010000 Epoch[021] Batch [3699]/[3760] Speed: 66.805043 samples/sec accuracy=61.510557 loss=1.573361 lr=0.010000 Epoch[021] Batch [3749]/[3760] Speed: 75.913433 samples/sec accuracy=61.518750 loss=1.573458 lr=0.010000 Batch [0049]/[0303]: acc-top1=59.312500 acc-top5=83.375000 Batch [0099]/[0303]: acc-top1=58.953125 acc-top5=83.062500 Batch [0149]/[0303]: acc-top1=58.833333 acc-top5=82.739583 Batch [0199]/[0303]: acc-top1=58.976562 acc-top5=82.421875 Batch [0249]/[0303]: acc-top1=59.237500 acc-top5=82.550000 Batch [0299]/[0303]: acc-top1=59.005208 acc-top5=82.208333 [Epoch 021] training: accuracy=61.518866 loss=1.573548 [Epoch 021] speed: 66 samples/sec time cost: 3893.401043 [Epoch 021] validation: acc-top1=59.003713 acc-top5=82.204002 loss=1.857288 Epoch[022] Batch [0049]/[3760] Speed: 46.834709 samples/sec accuracy=62.625000 loss=1.522327 lr=0.010000 Epoch[022] Batch [0099]/[3760] Speed: 66.453189 samples/sec accuracy=62.062500 loss=1.544819 lr=0.010000 Epoch[022] Batch [0149]/[3760] Speed: 68.046127 samples/sec accuracy=62.166667 loss=1.553343 lr=0.010000 Epoch[022] Batch [0199]/[3760] Speed: 67.507932 samples/sec accuracy=62.132812 loss=1.547584 lr=0.010000 Epoch[022] Batch [0249]/[3760] Speed: 68.265519 samples/sec accuracy=62.262500 loss=1.542633 lr=0.010000 Epoch[022] Batch [0299]/[3760] Speed: 67.722745 samples/sec accuracy=62.385417 loss=1.535775 lr=0.010000 Epoch[022] Batch [0349]/[3760] Speed: 67.506965 samples/sec accuracy=62.263393 loss=1.542711 lr=0.010000 Epoch[022] Batch [0399]/[3760] Speed: 68.049280 samples/sec accuracy=62.355469 loss=1.537451 lr=0.010000 Epoch[022] Batch [0449]/[3760] Speed: 67.861565 samples/sec accuracy=62.357639 loss=1.537285 lr=0.010000 Epoch[022] Batch [0499]/[3760] Speed: 68.820535 samples/sec accuracy=62.359375 loss=1.538421 lr=0.010000 Epoch[022] Batch [0549]/[3760] Speed: 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acc-top1=59.875000 acc-top5=82.781250 Batch [0249]/[0303]: acc-top1=60.075000 acc-top5=82.912500 Batch [0299]/[0303]: acc-top1=59.875000 acc-top5=82.645833 [Epoch 022] training: accuracy=61.899102 loss=1.560796 [Epoch 022] speed: 67 samples/sec time cost: 3857.496042 [Epoch 022] validation: acc-top1=59.854579 acc-top5=82.611386 loss=1.839492 Epoch[023] Batch [0049]/[3759] Speed: 46.108670 samples/sec accuracy=62.562500 loss=1.526804 lr=0.010000 Epoch[023] Batch [0099]/[3759] Speed: 66.864504 samples/sec accuracy=62.062500 loss=1.536134 lr=0.010000 Epoch[023] Batch [0149]/[3759] Speed: 67.690476 samples/sec accuracy=61.750000 loss=1.559645 lr=0.010000 Epoch[023] Batch [0199]/[3759] Speed: 67.400079 samples/sec accuracy=61.671875 loss=1.560317 lr=0.010000 Epoch[023] Batch [0249]/[3759] Speed: 67.510658 samples/sec accuracy=61.725000 loss=1.557225 lr=0.010000 Epoch[023] Batch [0299]/[3759] Speed: 68.189272 samples/sec accuracy=61.812500 loss=1.556658 lr=0.010000 Epoch[023] Batch 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accuracy=62.366319 loss=1.543231 lr=0.010000 Epoch[023] Batch [2749]/[3759] Speed: 68.219310 samples/sec accuracy=62.336364 loss=1.543830 lr=0.010000 Epoch[023] Batch [2799]/[3759] Speed: 67.726698 samples/sec accuracy=62.320871 loss=1.544309 lr=0.010000 Epoch[023] Batch [2849]/[3759] Speed: 68.374261 samples/sec accuracy=62.332237 loss=1.543333 lr=0.010000 Epoch[023] Batch [2899]/[3759] Speed: 67.624796 samples/sec accuracy=62.334591 loss=1.543085 lr=0.010000 Epoch[023] Batch [2949]/[3759] Speed: 67.793302 samples/sec accuracy=62.306674 loss=1.544383 lr=0.010000 Epoch[023] Batch [2999]/[3759] Speed: 67.480247 samples/sec accuracy=62.299479 loss=1.544705 lr=0.010000 Epoch[023] Batch [3049]/[3759] Speed: 67.824825 samples/sec accuracy=62.293545 loss=1.545178 lr=0.010000 Epoch[023] Batch [3099]/[3759] Speed: 68.193501 samples/sec accuracy=62.283770 loss=1.545778 lr=0.010000 Epoch[023] Batch [3149]/[3759] Speed: 67.609965 samples/sec accuracy=62.283730 loss=1.545382 lr=0.010000 Epoch[023] 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accuracy=62.169092 loss=1.550644 lr=0.010000 Epoch[023] Batch [3699]/[3759] Speed: 67.901070 samples/sec accuracy=62.171030 loss=1.550721 lr=0.010000 Epoch[023] Batch [3749]/[3759] Speed: 77.449138 samples/sec accuracy=62.182500 loss=1.550319 lr=0.010000 Batch [0049]/[0303]: acc-top1=60.281250 acc-top5=82.843750 Batch [0099]/[0303]: acc-top1=60.312500 acc-top5=82.656250 Batch [0149]/[0303]: acc-top1=60.104167 acc-top5=82.708333 Batch [0199]/[0303]: acc-top1=59.937500 acc-top5=82.414062 Batch [0249]/[0303]: acc-top1=59.968750 acc-top5=82.600000 Batch [0299]/[0303]: acc-top1=59.697917 acc-top5=82.265625 [Epoch 023] training: accuracy=62.185754 loss=1.550366 [Epoch 023] speed: 67 samples/sec time cost: 3856.458219 [Epoch 023] validation: acc-top1=59.679249 acc-top5=82.240099 loss=1.865250 Epoch[024] Batch [0049]/[3760] Speed: 46.607351 samples/sec accuracy=64.125000 loss=1.458958 lr=0.010000 Epoch[024] Batch [0099]/[3760] Speed: 65.489525 samples/sec accuracy=63.500000 loss=1.455740 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[0299]/[0303]: acc-top1=59.911458 acc-top5=82.484375 [Epoch 024] training: accuracy=62.252743 loss=1.541421 [Epoch 024] speed: 67 samples/sec time cost: 3856.996816 [Epoch 024] validation: acc-top1=59.906147 acc-top5=82.482467 loss=1.837335 Epoch[025] Batch [0049]/[3760] Speed: 45.625307 samples/sec accuracy=62.562500 loss=1.564241 lr=0.010000 Epoch[025] Batch [0099]/[3760] Speed: 66.562446 samples/sec accuracy=63.703125 loss=1.514664 lr=0.010000 Epoch[025] Batch [0149]/[3760] Speed: 67.666252 samples/sec accuracy=63.416667 loss=1.511784 lr=0.010000 Epoch[025] Batch [0199]/[3760] Speed: 67.766371 samples/sec accuracy=63.414062 loss=1.506538 lr=0.010000 Epoch[025] Batch [0249]/[3760] Speed: 68.040773 samples/sec accuracy=63.543750 loss=1.493940 lr=0.010000 Epoch[025] Batch [0299]/[3760] Speed: 68.519662 samples/sec accuracy=63.411458 loss=1.497039 lr=0.010000 Epoch[025] Batch [0349]/[3760] Speed: 67.481401 samples/sec accuracy=63.205357 loss=1.505965 lr=0.010000 Epoch[025] Batch 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accuracy=62.680556 loss=1.528326 lr=0.010000 Epoch[025] Batch [1849]/[3760] Speed: 67.746406 samples/sec accuracy=62.668074 loss=1.529386 lr=0.010000 Epoch[025] Batch [1899]/[3760] Speed: 68.158693 samples/sec accuracy=62.673520 loss=1.529577 lr=0.010000 Epoch[025] Batch [1949]/[3760] Speed: 67.624299 samples/sec accuracy=62.653045 loss=1.529771 lr=0.010000 Epoch[025] Batch [1999]/[3760] Speed: 67.556790 samples/sec accuracy=62.662500 loss=1.528802 lr=0.010000 Epoch[025] Batch [2049]/[3760] Speed: 68.325476 samples/sec accuracy=62.640244 loss=1.530146 lr=0.010000 Epoch[025] Batch [2099]/[3760] Speed: 67.567423 samples/sec accuracy=62.649554 loss=1.530799 lr=0.010000 Epoch[025] Batch [2149]/[3760] Speed: 67.706481 samples/sec accuracy=62.633721 loss=1.531505 lr=0.010000 Epoch[025] Batch [2199]/[3760] Speed: 67.856603 samples/sec accuracy=62.631392 loss=1.531533 lr=0.010000 Epoch[025] Batch [2249]/[3760] Speed: 68.200129 samples/sec accuracy=62.648611 loss=1.530843 lr=0.010000 Epoch[025] 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accuracy=62.550000 loss=1.534174 lr=0.010000 Epoch[025] Batch [2799]/[3760] Speed: 68.039245 samples/sec accuracy=62.554687 loss=1.533419 lr=0.010000 Epoch[025] Batch [2849]/[3760] Speed: 68.107337 samples/sec accuracy=62.554825 loss=1.533778 lr=0.010000 Epoch[025] Batch [2899]/[3760] Speed: 68.847276 samples/sec accuracy=62.548491 loss=1.534526 lr=0.010000 Epoch[025] Batch [2949]/[3760] Speed: 67.215636 samples/sec accuracy=62.527542 loss=1.534952 lr=0.010000 Epoch[025] Batch [2999]/[3760] Speed: 67.206120 samples/sec accuracy=62.531771 loss=1.535113 lr=0.010000 Epoch[025] Batch [3049]/[3760] Speed: 67.937856 samples/sec accuracy=62.517418 loss=1.535906 lr=0.010000 Epoch[025] Batch [3099]/[3760] Speed: 68.133953 samples/sec accuracy=62.514113 loss=1.536147 lr=0.010000 Epoch[025] Batch [3149]/[3760] Speed: 67.755796 samples/sec accuracy=62.512401 loss=1.536065 lr=0.010000 Epoch[025] Batch [3199]/[3760] Speed: 68.297595 samples/sec accuracy=62.496582 loss=1.536612 lr=0.010000 Epoch[025] 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accuracy=62.401605 loss=1.540006 lr=0.010000 Epoch[025] Batch [3749]/[3760] Speed: 76.783303 samples/sec accuracy=62.415000 loss=1.540058 lr=0.010000 Batch [0049]/[0303]: acc-top1=60.718750 acc-top5=82.968750 Batch [0099]/[0303]: acc-top1=60.546875 acc-top5=82.265625 Batch [0149]/[0303]: acc-top1=60.510417 acc-top5=82.468750 Batch [0199]/[0303]: acc-top1=60.320312 acc-top5=82.359375 Batch [0249]/[0303]: acc-top1=60.356250 acc-top5=82.543750 Batch [0299]/[0303]: acc-top1=60.005208 acc-top5=82.250000 [Epoch 025] training: accuracy=62.413979 loss=1.540135 [Epoch 025] speed: 67 samples/sec time cost: 3855.999745 [Epoch 025] validation: acc-top1=60.004125 acc-top5=82.234942 loss=1.853393 Epoch[026] Batch [0049]/[3759] Speed: 46.082948 samples/sec accuracy=63.781250 loss=1.454324 lr=0.010000 Epoch[026] Batch [0099]/[3759] Speed: 66.497300 samples/sec accuracy=64.562500 loss=1.434601 lr=0.010000 Epoch[026] Batch [0149]/[3759] Speed: 67.829774 samples/sec accuracy=64.666667 loss=1.433253 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68.441580 samples/sec accuracy=62.711607 loss=1.523649 lr=0.010000 Epoch[026] Batch [3549]/[3759] Speed: 67.333217 samples/sec accuracy=62.702465 loss=1.523829 lr=0.010000 Epoch[026] Batch [3599]/[3759] Speed: 67.130589 samples/sec accuracy=62.687066 loss=1.524493 lr=0.010000 Epoch[026] Batch [3649]/[3759] Speed: 67.859390 samples/sec accuracy=62.676370 loss=1.524869 lr=0.010000 Epoch[026] Batch [3699]/[3759] Speed: 68.084328 samples/sec accuracy=62.639780 loss=1.526152 lr=0.010000 Epoch[026] Batch [3749]/[3759] Speed: 76.948645 samples/sec accuracy=62.651250 loss=1.525688 lr=0.010000 Batch [0049]/[0303]: acc-top1=60.687500 acc-top5=83.031250 Batch [0099]/[0303]: acc-top1=60.796875 acc-top5=82.781250 Batch [0149]/[0303]: acc-top1=60.677083 acc-top5=82.770833 Batch [0199]/[0303]: acc-top1=60.664063 acc-top5=82.687500 Batch [0249]/[0303]: acc-top1=60.662500 acc-top5=82.868750 Batch [0299]/[0303]: acc-top1=60.494792 acc-top5=82.572917 [Epoch 026] training: accuracy=62.645484 loss=1.525799 [Epoch 026] speed: 67 samples/sec time cost: 3855.541588 [Epoch 026] validation: acc-top1=60.478548 acc-top5=82.559818 loss=1.834002 Epoch[027] Batch [0049]/[3760] Speed: 46.594595 samples/sec accuracy=62.156250 loss=1.537525 lr=0.010000 Epoch[027] Batch [0099]/[3760] Speed: 66.683339 samples/sec accuracy=63.062500 loss=1.491130 lr=0.010000 Epoch[027] Batch [0149]/[3760] Speed: 67.125843 samples/sec accuracy=63.375000 loss=1.491907 lr=0.010000 Epoch[027] Batch [0199]/[3760] Speed: 68.220230 samples/sec accuracy=63.140625 loss=1.494161 lr=0.010000 Epoch[027] Batch [0249]/[3760] Speed: 67.661993 samples/sec accuracy=63.262500 loss=1.495396 lr=0.010000 Epoch[027] Batch [0299]/[3760] Speed: 68.191711 samples/sec accuracy=63.317708 loss=1.494871 lr=0.010000 Epoch[027] Batch [0349]/[3760] Speed: 67.717464 samples/sec accuracy=63.316964 loss=1.490052 lr=0.010000 Epoch[027] Batch [0399]/[3760] Speed: 68.159937 samples/sec accuracy=63.238281 loss=1.493540 lr=0.010000 Epoch[027] Batch 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accuracy=63.315972 loss=1.497807 lr=0.010000 Epoch[027] Batch [0949]/[3760] Speed: 67.601773 samples/sec accuracy=63.358553 loss=1.497371 lr=0.010000 Epoch[027] Batch [0999]/[3760] Speed: 67.994318 samples/sec accuracy=63.384375 loss=1.495756 lr=0.010000 Epoch[027] Batch [1049]/[3760] Speed: 68.512507 samples/sec accuracy=63.428571 loss=1.492640 lr=0.010000 Epoch[027] Batch [1099]/[3760] Speed: 67.296475 samples/sec accuracy=63.321023 loss=1.496147 lr=0.010000 Epoch[027] Batch [1149]/[3760] Speed: 68.124977 samples/sec accuracy=63.317935 loss=1.495297 lr=0.010000 Epoch[027] Batch [1199]/[3760] Speed: 68.100459 samples/sec accuracy=63.264323 loss=1.497696 lr=0.010000 Epoch[027] Batch [1249]/[3760] Speed: 68.182566 samples/sec accuracy=63.232500 loss=1.498103 lr=0.010000 Epoch[027] Batch [1299]/[3760] Speed: 67.759227 samples/sec accuracy=63.237981 loss=1.497618 lr=0.010000 Epoch[027] Batch [1349]/[3760] Speed: 68.031110 samples/sec accuracy=63.238426 loss=1.496853 lr=0.010000 Epoch[027] 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accuracy=63.040541 loss=1.505963 lr=0.010000 Epoch[027] Batch [1899]/[3760] Speed: 68.080694 samples/sec accuracy=63.055921 loss=1.504765 lr=0.010000 Epoch[027] Batch [1949]/[3760] Speed: 68.220156 samples/sec accuracy=63.057692 loss=1.506254 lr=0.010000 Epoch[027] Batch [1999]/[3760] Speed: 67.351121 samples/sec accuracy=63.046094 loss=1.507247 lr=0.010000 Epoch[027] Batch [2049]/[3760] Speed: 67.928290 samples/sec accuracy=63.041921 loss=1.506921 lr=0.010000 Epoch[027] Batch [2099]/[3760] Speed: 67.530997 samples/sec accuracy=63.013393 loss=1.508435 lr=0.010000 Epoch[027] Batch [2149]/[3760] Speed: 67.828944 samples/sec accuracy=62.985465 loss=1.509117 lr=0.010000 Epoch[027] Batch [2199]/[3760] Speed: 68.159439 samples/sec accuracy=62.989347 loss=1.508969 lr=0.010000 Epoch[027] Batch [2249]/[3760] Speed: 68.357800 samples/sec accuracy=62.969444 loss=1.509801 lr=0.010000 Epoch[027] Batch [2299]/[3760] Speed: 67.919941 samples/sec accuracy=62.945652 loss=1.511414 lr=0.010000 Epoch[027] 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accuracy=62.776786 loss=1.517578 lr=0.010000 Epoch[027] Batch [2849]/[3760] Speed: 67.702982 samples/sec accuracy=62.750548 loss=1.517881 lr=0.010000 Epoch[027] Batch [2899]/[3760] Speed: 68.198420 samples/sec accuracy=62.738685 loss=1.518730 lr=0.010000 Epoch[027] Batch [2949]/[3760] Speed: 67.399322 samples/sec accuracy=62.751059 loss=1.518603 lr=0.010000 Epoch[027] Batch [2999]/[3760] Speed: 67.821810 samples/sec accuracy=62.743750 loss=1.518827 lr=0.010000 Epoch[027] Batch [3049]/[3760] Speed: 67.848264 samples/sec accuracy=62.746414 loss=1.518509 lr=0.010000 Epoch[027] Batch [3099]/[3760] Speed: 68.191737 samples/sec accuracy=62.739415 loss=1.518766 lr=0.010000 Epoch[027] Batch [3149]/[3760] Speed: 66.974510 samples/sec accuracy=62.746528 loss=1.518952 lr=0.010000 Epoch[027] Batch [3199]/[3760] Speed: 67.914089 samples/sec accuracy=62.739746 loss=1.519636 lr=0.010000 Epoch[027] Batch [3249]/[3760] Speed: 67.440027 samples/sec accuracy=62.758654 loss=1.518919 lr=0.010000 Epoch[027] Batch [3299]/[3760] Speed: 68.537919 samples/sec accuracy=62.778409 loss=1.518617 lr=0.010000 Epoch[027] Batch [3349]/[3760] Speed: 67.669240 samples/sec accuracy=62.783116 loss=1.518337 lr=0.010000 Epoch[027] Batch [3399]/[3760] Speed: 68.414240 samples/sec accuracy=62.772518 loss=1.518809 lr=0.010000 Epoch[027] Batch [3449]/[3760] Speed: 67.787786 samples/sec accuracy=62.759058 loss=1.519216 lr=0.010000 Epoch[027] Batch [3499]/[3760] Speed: 68.274503 samples/sec accuracy=62.758929 loss=1.519606 lr=0.010000 Epoch[027] Batch [3549]/[3760] Speed: 67.976647 samples/sec accuracy=62.752641 loss=1.519890 lr=0.010000 Epoch[027] Batch [3599]/[3760] Speed: 68.110180 samples/sec accuracy=62.720052 loss=1.520553 lr=0.010000 Epoch[027] Batch [3649]/[3760] Speed: 68.039276 samples/sec accuracy=62.724743 loss=1.520333 lr=0.010000 Epoch[027] Batch [3699]/[3760] Speed: 68.051249 samples/sec accuracy=62.732264 loss=1.520286 lr=0.010000 Epoch[027] Batch [3749]/[3760] Speed: 75.425031 samples/sec accuracy=62.740000 loss=1.520533 lr=0.010000 Batch [0049]/[0303]: acc-top1=59.812500 acc-top5=83.218750 Batch [0099]/[0303]: acc-top1=59.921875 acc-top5=82.593750 Batch [0149]/[0303]: acc-top1=59.885417 acc-top5=82.739583 Batch [0199]/[0303]: acc-top1=59.914062 acc-top5=82.679688 Batch [0249]/[0303]: acc-top1=60.031250 acc-top5=82.875000 Batch [0299]/[0303]: acc-top1=59.703125 acc-top5=82.583333 [Epoch 027] training: accuracy=62.742686 loss=1.520558 [Epoch 027] speed: 67 samples/sec time cost: 3854.091733 [Epoch 027] validation: acc-top1=59.689563 acc-top5=82.570132 loss=1.871157 Epoch[028] Batch [0049]/[3760] Speed: 46.508320 samples/sec accuracy=63.718750 loss=1.474331 lr=0.010000 Epoch[028] Batch [0099]/[3760] Speed: 66.506006 samples/sec accuracy=63.562500 loss=1.467029 lr=0.010000 Epoch[028] Batch [0149]/[3760] Speed: 68.455197 samples/sec accuracy=63.625000 loss=1.478604 lr=0.010000 Epoch[028] Batch [0199]/[3760] Speed: 66.799900 samples/sec accuracy=63.679688 loss=1.471708 lr=0.010000 Epoch[028] Batch [0249]/[3760] Speed: 67.813264 samples/sec accuracy=63.675000 loss=1.475385 lr=0.010000 Epoch[028] Batch [0299]/[3760] Speed: 67.547029 samples/sec accuracy=63.666667 loss=1.479732 lr=0.010000 Epoch[028] Batch [0349]/[3760] Speed: 68.056729 samples/sec accuracy=63.741071 loss=1.474820 lr=0.010000 Epoch[028] Batch [0399]/[3760] Speed: 68.026265 samples/sec accuracy=63.640625 loss=1.483377 lr=0.010000 Epoch[028] Batch [0449]/[3760] Speed: 67.259910 samples/sec accuracy=63.770833 loss=1.479731 lr=0.010000 Epoch[028] Batch [0499]/[3760] Speed: 67.877259 samples/sec accuracy=63.715625 loss=1.481669 lr=0.010000 Epoch[028] Batch [0549]/[3760] Speed: 68.250056 samples/sec accuracy=63.673295 loss=1.482554 lr=0.010000 Epoch[028] Batch [0599]/[3760] Speed: 67.550162 samples/sec accuracy=63.554687 loss=1.483588 lr=0.010000 Epoch[028] Batch [0649]/[3760] Speed: 68.191931 samples/sec accuracy=63.478365 loss=1.487772 lr=0.010000 Epoch[028] Batch [0699]/[3760] Speed: 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acc-top5=82.827970 loss=1.780856 Epoch[029] Batch [0049]/[3759] Speed: 46.885688 samples/sec accuracy=64.281250 loss=1.442180 lr=0.010000 Epoch[029] Batch [0099]/[3759] Speed: 66.228023 samples/sec accuracy=64.687500 loss=1.439315 lr=0.010000 Epoch[029] Batch [0149]/[3759] Speed: 67.756628 samples/sec accuracy=63.916667 loss=1.460625 lr=0.010000 Epoch[029] Batch [0199]/[3759] Speed: 68.250309 samples/sec accuracy=63.953125 loss=1.459436 lr=0.010000 Epoch[029] Batch [0249]/[3759] Speed: 68.036527 samples/sec accuracy=63.893750 loss=1.462251 lr=0.010000 Epoch[029] Batch [0299]/[3759] Speed: 67.635025 samples/sec accuracy=64.015625 loss=1.460996 lr=0.010000 Epoch[029] Batch [0349]/[3759] Speed: 67.111221 samples/sec accuracy=64.151786 loss=1.456453 lr=0.010000 Epoch[029] Batch [0399]/[3759] Speed: 68.186055 samples/sec accuracy=63.933594 loss=1.462106 lr=0.010000 Epoch[029] Batch [0449]/[3759] Speed: 67.826143 samples/sec accuracy=63.840278 loss=1.468434 lr=0.010000 Epoch[029] Batch 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accuracy=63.314145 loss=1.492897 lr=0.010000 Epoch[029] Batch [1949]/[3759] Speed: 68.147068 samples/sec accuracy=63.298077 loss=1.493704 lr=0.010000 Epoch[029] Batch [1999]/[3759] Speed: 67.308193 samples/sec accuracy=63.259375 loss=1.495614 lr=0.010000 Epoch[029] Batch [2049]/[3759] Speed: 68.243708 samples/sec accuracy=63.228659 loss=1.496136 lr=0.010000 Epoch[029] Batch [2099]/[3759] Speed: 67.351209 samples/sec accuracy=63.220982 loss=1.495533 lr=0.010000 Epoch[029] Batch [2149]/[3759] Speed: 67.788437 samples/sec accuracy=63.220203 loss=1.495793 lr=0.010000 Epoch[029] Batch [2199]/[3759] Speed: 67.825339 samples/sec accuracy=63.204545 loss=1.497139 lr=0.010000 Epoch[029] Batch [2249]/[3759] Speed: 68.224215 samples/sec accuracy=63.202778 loss=1.497410 lr=0.010000 Epoch[029] Batch [2299]/[3759] Speed: 67.636706 samples/sec accuracy=63.173913 loss=1.498129 lr=0.010000 Epoch[029] Batch [2349]/[3759] Speed: 68.607798 samples/sec accuracy=63.160239 loss=1.498734 lr=0.010000 Epoch[029] 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accuracy=63.099232 loss=1.503929 lr=0.010000 Epoch[029] Batch [2899]/[3759] Speed: 68.779417 samples/sec accuracy=63.088901 loss=1.504541 lr=0.010000 Epoch[029] Batch [2949]/[3759] Speed: 67.049721 samples/sec accuracy=63.059852 loss=1.505044 lr=0.010000 Epoch[029] Batch [2999]/[3759] Speed: 67.816852 samples/sec accuracy=63.050521 loss=1.505115 lr=0.010000 Epoch[029] Batch [3049]/[3759] Speed: 67.703158 samples/sec accuracy=63.026127 loss=1.505954 lr=0.010000 Epoch[029] Batch [3099]/[3759] Speed: 67.562578 samples/sec accuracy=62.990423 loss=1.507388 lr=0.010000 Epoch[029] Batch [3149]/[3759] Speed: 68.042171 samples/sec accuracy=62.982639 loss=1.507772 lr=0.010000 Epoch[029] Batch [3199]/[3759] Speed: 68.601618 samples/sec accuracy=62.958984 loss=1.508345 lr=0.010000 Epoch[029] Batch [3249]/[3759] Speed: 67.940470 samples/sec accuracy=62.950000 loss=1.509151 lr=0.010000 Epoch[029] Batch [3299]/[3759] Speed: 68.069192 samples/sec accuracy=62.938920 loss=1.509972 lr=0.010000 Epoch[029] 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[0099]/[0303]: acc-top1=60.328125 acc-top5=83.250000 Batch [0149]/[0303]: acc-top1=60.437500 acc-top5=83.239583 Batch [0199]/[0303]: acc-top1=60.437500 acc-top5=83.046875 Batch [0249]/[0303]: acc-top1=60.543750 acc-top5=83.175000 Batch [0299]/[0303]: acc-top1=60.223958 acc-top5=82.880208 [Epoch 029] training: accuracy=62.909850 loss=1.514042 [Epoch 029] speed: 67 samples/sec time cost: 3850.743388 [Epoch 029] validation: acc-top1=60.205239 acc-top5=82.858911 loss=1.827098 Epoch[030] Batch [0049]/[3760] Speed: 47.000606 samples/sec accuracy=63.812500 loss=1.469753 lr=0.010000 Epoch[030] Batch [0099]/[3760] Speed: 65.906351 samples/sec accuracy=63.500000 loss=1.491525 lr=0.010000 Epoch[030] Batch [0149]/[3760] Speed: 68.054788 samples/sec accuracy=63.531250 loss=1.497002 lr=0.010000 Epoch[030] Batch [0199]/[3760] Speed: 67.492501 samples/sec accuracy=63.367188 loss=1.494428 lr=0.010000 Epoch[030] Batch [0249]/[3760] Speed: 68.262514 samples/sec accuracy=63.668750 loss=1.475598 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68.069550 samples/sec accuracy=62.957899 loss=1.507393 lr=0.010000 Epoch[030] Batch [3649]/[3760] Speed: 67.565492 samples/sec accuracy=62.967466 loss=1.507128 lr=0.010000 Epoch[030] Batch [3699]/[3760] Speed: 68.405455 samples/sec accuracy=62.971706 loss=1.507561 lr=0.010000 Epoch[030] Batch [3749]/[3760] Speed: 75.940277 samples/sec accuracy=62.960000 loss=1.507792 lr=0.010000 Batch [0049]/[0303]: acc-top1=60.281250 acc-top5=83.312500 Batch [0099]/[0303]: acc-top1=60.937500 acc-top5=83.187500 Batch [0149]/[0303]: acc-top1=60.906250 acc-top5=83.177083 Batch [0199]/[0303]: acc-top1=60.828125 acc-top5=82.734375 Batch [0249]/[0303]: acc-top1=60.931250 acc-top5=82.925000 Batch [0299]/[0303]: acc-top1=60.656250 acc-top5=82.630208 [Epoch 030] training: accuracy=62.965010 loss=1.507521 [Epoch 030] speed: 67 samples/sec time cost: 3848.320610 [Epoch 030] validation: acc-top1=60.628094 acc-top5=82.621700 loss=1.802295 Epoch[031] Batch [0049]/[3760] Speed: 45.853197 samples/sec accuracy=65.718750 loss=1.416358 lr=0.010000 Epoch[031] Batch [0099]/[3760] Speed: 67.435031 samples/sec accuracy=65.203125 loss=1.423553 lr=0.010000 Epoch[031] Batch [0149]/[3760] Speed: 67.817217 samples/sec accuracy=64.791667 loss=1.435403 lr=0.010000 Epoch[031] Batch [0199]/[3760] Speed: 67.687737 samples/sec accuracy=64.492188 loss=1.450198 lr=0.010000 Epoch[031] Batch [0249]/[3760] Speed: 69.062789 samples/sec accuracy=64.706250 loss=1.447035 lr=0.010000 Epoch[031] Batch [0299]/[3760] Speed: 67.506239 samples/sec accuracy=64.401042 loss=1.454372 lr=0.010000 Epoch[031] Batch [0349]/[3760] Speed: 68.405927 samples/sec accuracy=64.187500 loss=1.465953 lr=0.010000 Epoch[031] Batch [0399]/[3760] Speed: 68.363532 samples/sec accuracy=64.253906 loss=1.462528 lr=0.010000 Epoch[031] Batch [0449]/[3760] Speed: 67.592050 samples/sec accuracy=64.326389 loss=1.459090 lr=0.010000 Epoch[031] Batch [0499]/[3760] Speed: 67.921729 samples/sec accuracy=64.262500 loss=1.461078 lr=0.010000 Epoch[031] 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accuracy=63.845312 loss=1.474197 lr=0.010000 Epoch[031] Batch [1049]/[3760] Speed: 67.788094 samples/sec accuracy=63.924107 loss=1.473082 lr=0.010000 Epoch[031] Batch [1099]/[3760] Speed: 67.682408 samples/sec accuracy=63.907670 loss=1.472897 lr=0.010000 Epoch[031] Batch [1149]/[3760] Speed: 68.124969 samples/sec accuracy=63.944293 loss=1.472803 lr=0.010000 Epoch[031] Batch [1199]/[3760] Speed: 67.864284 samples/sec accuracy=63.947917 loss=1.474274 lr=0.010000 Epoch[031] Batch [1249]/[3760] Speed: 68.066330 samples/sec accuracy=63.940000 loss=1.475766 lr=0.010000 Epoch[031] Batch [1299]/[3760] Speed: 67.462030 samples/sec accuracy=63.909856 loss=1.477376 lr=0.010000 Epoch[031] Batch [1349]/[3760] Speed: 68.117418 samples/sec accuracy=63.887731 loss=1.478091 lr=0.010000 Epoch[031] Batch [1399]/[3760] Speed: 68.303706 samples/sec accuracy=63.844866 loss=1.479531 lr=0.010000 Epoch[031] Batch [1449]/[3760] Speed: 67.680098 samples/sec accuracy=63.859914 loss=1.479674 lr=0.010000 Epoch[031] 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accuracy=63.737981 loss=1.484500 lr=0.010000 Epoch[031] Batch [1999]/[3760] Speed: 67.711381 samples/sec accuracy=63.719531 loss=1.485053 lr=0.010000 Epoch[031] Batch [2049]/[3760] Speed: 67.590823 samples/sec accuracy=63.714939 loss=1.485749 lr=0.010000 Epoch[031] Batch [2099]/[3760] Speed: 67.281201 samples/sec accuracy=63.683780 loss=1.487078 lr=0.010000 Epoch[031] Batch [2149]/[3760] Speed: 67.460000 samples/sec accuracy=63.661337 loss=1.487815 lr=0.010000 Epoch[031] Batch [2199]/[3760] Speed: 68.226653 samples/sec accuracy=63.634233 loss=1.488736 lr=0.010000 Epoch[031] Batch [2249]/[3760] Speed: 67.381123 samples/sec accuracy=63.608333 loss=1.489864 lr=0.010000 Epoch[031] Batch [2299]/[3760] Speed: 67.833751 samples/sec accuracy=63.628397 loss=1.488532 lr=0.010000 Epoch[031] Batch [2349]/[3760] Speed: 68.133648 samples/sec accuracy=63.621676 loss=1.488888 lr=0.010000 Epoch[031] Batch [2399]/[3760] Speed: 67.948711 samples/sec accuracy=63.617839 loss=1.487951 lr=0.010000 Epoch[031] 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accuracy=63.531250 loss=1.492106 lr=0.010000 Epoch[031] Batch [2949]/[3760] Speed: 67.779055 samples/sec accuracy=63.513242 loss=1.492894 lr=0.010000 Epoch[031] Batch [2999]/[3760] Speed: 68.259862 samples/sec accuracy=63.505729 loss=1.493709 lr=0.010000 Epoch[031] Batch [3049]/[3760] Speed: 67.868165 samples/sec accuracy=63.502561 loss=1.494169 lr=0.010000 Epoch[031] Batch [3099]/[3760] Speed: 68.223708 samples/sec accuracy=63.489415 loss=1.495111 lr=0.010000 Epoch[031] Batch [3149]/[3760] Speed: 67.174931 samples/sec accuracy=63.487599 loss=1.495308 lr=0.010000 Epoch[031] Batch [3199]/[3760] Speed: 68.808305 samples/sec accuracy=63.488770 loss=1.495252 lr=0.010000 Epoch[031] Batch [3249]/[3760] Speed: 67.873632 samples/sec accuracy=63.471635 loss=1.495706 lr=0.010000 Epoch[031] Batch [3299]/[3760] Speed: 67.521705 samples/sec accuracy=63.455492 loss=1.496597 lr=0.010000 Epoch[031] Batch [3349]/[3760] Speed: 67.977087 samples/sec accuracy=63.425373 loss=1.497843 lr=0.010000 Epoch[031] Batch [3399]/[3760] Speed: 67.607685 samples/sec accuracy=63.426471 loss=1.497927 lr=0.010000 Epoch[031] Batch [3449]/[3760] Speed: 68.127216 samples/sec accuracy=63.406250 loss=1.498920 lr=0.010000 Epoch[031] Batch [3499]/[3760] Speed: 67.667333 samples/sec accuracy=63.391964 loss=1.499247 lr=0.010000 Epoch[031] Batch [3549]/[3760] Speed: 67.830764 samples/sec accuracy=63.382923 loss=1.499277 lr=0.010000 Epoch[031] Batch [3599]/[3760] Speed: 67.880376 samples/sec accuracy=63.360677 loss=1.500498 lr=0.010000 Epoch[031] Batch [3649]/[3760] Speed: 68.074322 samples/sec accuracy=63.341182 loss=1.501256 lr=0.010000 Epoch[031] Batch [3699]/[3760] Speed: 67.006096 samples/sec accuracy=63.330236 loss=1.501339 lr=0.010000 Epoch[031] Batch [3749]/[3760] Speed: 76.609401 samples/sec accuracy=63.337500 loss=1.501240 lr=0.010000 Batch [0049]/[0303]: acc-top1=60.843750 acc-top5=83.406250 Batch [0099]/[0303]: acc-top1=60.500000 acc-top5=83.390625 Batch [0149]/[0303]: acc-top1=60.770833 acc-top5=83.333333 Batch [0199]/[0303]: acc-top1=60.671875 acc-top5=83.156250 Batch [0249]/[0303]: acc-top1=60.893750 acc-top5=83.431250 Batch [0299]/[0303]: acc-top1=60.697917 acc-top5=83.052083 [Epoch 031] training: accuracy=63.334441 loss=1.501482 [Epoch 031] speed: 67 samples/sec time cost: 3854.236788 [Epoch 031] validation: acc-top1=60.674505 acc-top5=83.023927 loss=1.768224 Epoch[032] Batch [0049]/[3759] Speed: 47.113774 samples/sec accuracy=63.812500 loss=1.462891 lr=0.010000 Epoch[032] Batch [0099]/[3759] Speed: 67.076006 samples/sec accuracy=64.062500 loss=1.471807 lr=0.010000 Epoch[032] Batch [0149]/[3759] Speed: 66.980726 samples/sec accuracy=64.000000 loss=1.472583 lr=0.010000 Epoch[032] Batch [0199]/[3759] Speed: 68.332304 samples/sec accuracy=63.750000 loss=1.483083 lr=0.010000 Epoch[032] Batch [0249]/[3759] Speed: 67.824728 samples/sec accuracy=63.943750 loss=1.473794 lr=0.010000 Epoch[032] Batch [0299]/[3759] Speed: 67.607336 samples/sec accuracy=64.041667 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Batch [3649]/[3759] Speed: 68.056866 samples/sec accuracy=63.457620 loss=1.492013 lr=0.010000 Epoch[032] Batch [3699]/[3759] Speed: 67.836059 samples/sec accuracy=63.438345 loss=1.493027 lr=0.010000 Epoch[032] Batch [3749]/[3759] Speed: 76.278890 samples/sec accuracy=63.422083 loss=1.493893 lr=0.010000 Batch [0049]/[0303]: acc-top1=59.781250 acc-top5=82.968750 Batch [0099]/[0303]: acc-top1=60.421875 acc-top5=82.859375 Batch [0149]/[0303]: acc-top1=60.354167 acc-top5=83.093750 Batch [0199]/[0303]: acc-top1=60.328125 acc-top5=82.812500 Batch [0249]/[0303]: acc-top1=60.550000 acc-top5=83.000000 Batch [0299]/[0303]: acc-top1=60.031250 acc-top5=82.614583 [Epoch 032] training: accuracy=63.425279 loss=1.493901 [Epoch 032] speed: 67 samples/sec time cost: 3851.029833 [Epoch 032] validation: acc-top1=60.014439 acc-top5=82.606229 loss=1.834578 Epoch[033] Batch [0049]/[3760] Speed: 46.078849 samples/sec accuracy=64.343750 loss=1.464986 lr=0.010000 Epoch[033] Batch [0099]/[3760] Speed: 66.417007 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lr=0.010000 Epoch[033] Batch [3449]/[3760] Speed: 67.926288 samples/sec accuracy=63.552536 loss=1.488593 lr=0.010000 Epoch[033] Batch [3499]/[3760] Speed: 67.557509 samples/sec accuracy=63.562946 loss=1.488367 lr=0.010000 Epoch[033] Batch [3549]/[3760] Speed: 68.109511 samples/sec accuracy=63.571743 loss=1.487989 lr=0.010000 Epoch[033] Batch [3599]/[3760] Speed: 68.294633 samples/sec accuracy=63.582899 loss=1.487575 lr=0.010000 Epoch[033] Batch [3649]/[3760] Speed: 67.508194 samples/sec accuracy=63.575771 loss=1.487660 lr=0.010000 Epoch[033] Batch [3699]/[3760] Speed: 67.712324 samples/sec accuracy=63.562922 loss=1.488231 lr=0.010000 Epoch[033] Batch [3749]/[3760] Speed: 76.906075 samples/sec accuracy=63.544583 loss=1.488597 lr=0.010000 Batch [0049]/[0303]: acc-top1=60.968750 acc-top5=83.750000 Batch [0099]/[0303]: acc-top1=61.015625 acc-top5=83.578125 Batch [0149]/[0303]: acc-top1=60.812500 acc-top5=83.437500 Batch [0199]/[0303]: acc-top1=60.796875 acc-top5=83.070312 Batch [0249]/[0303]: acc-top1=60.731250 acc-top5=83.193750 Batch [0299]/[0303]: acc-top1=60.421875 acc-top5=82.869792 [Epoch 033] training: accuracy=63.548870 loss=1.488551 [Epoch 033] speed: 67 samples/sec time cost: 3856.493505 [Epoch 033] validation: acc-top1=60.426980 acc-top5=82.838284 loss=1.831974 Epoch[034] Batch [0049]/[3759] Speed: 46.276371 samples/sec accuracy=66.187500 loss=1.384331 lr=0.010000 Epoch[034] Batch [0099]/[3759] Speed: 66.709119 samples/sec accuracy=65.640625 loss=1.394635 lr=0.010000 Epoch[034] Batch [0149]/[3759] Speed: 67.799265 samples/sec accuracy=65.593750 loss=1.393022 lr=0.010000 Epoch[034] Batch [0199]/[3759] Speed: 67.484512 samples/sec accuracy=65.476562 loss=1.401411 lr=0.010000 Epoch[034] Batch [0249]/[3759] Speed: 67.863601 samples/sec accuracy=65.331250 loss=1.404782 lr=0.010000 Epoch[034] Batch [0299]/[3759] Speed: 67.680730 samples/sec accuracy=65.380208 loss=1.409607 lr=0.010000 Epoch[034] Batch [0349]/[3759] Speed: 68.419914 samples/sec accuracy=65.272321 loss=1.415137 lr=0.010000 Epoch[034] Batch [0399]/[3759] Speed: 67.728462 samples/sec accuracy=65.152344 loss=1.420280 lr=0.010000 Epoch[034] Batch [0449]/[3759] Speed: 67.797370 samples/sec accuracy=65.093750 loss=1.422434 lr=0.010000 Epoch[034] Batch [0499]/[3759] Speed: 67.555517 samples/sec accuracy=65.178125 loss=1.422058 lr=0.010000 Epoch[034] Batch [0549]/[3759] Speed: 67.975606 samples/sec accuracy=65.181818 loss=1.422234 lr=0.010000 Epoch[034] Batch [0599]/[3759] Speed: 67.818611 samples/sec accuracy=65.114583 loss=1.423111 lr=0.010000 Epoch[034] Batch [0649]/[3759] Speed: 67.414286 samples/sec accuracy=65.012019 loss=1.427594 lr=0.010000 Epoch[034] Batch [0699]/[3759] Speed: 67.180177 samples/sec accuracy=64.888393 loss=1.432956 lr=0.010000 Epoch[034] Batch [0749]/[3759] Speed: 68.015733 samples/sec accuracy=64.843750 loss=1.434492 lr=0.010000 Epoch[034] Batch [0799]/[3759] Speed: 67.338163 samples/sec accuracy=64.906250 loss=1.433736 lr=0.010000 Epoch[034] 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Batch [3699]/[3759] Speed: 67.803341 samples/sec accuracy=63.535895 loss=1.487344 lr=0.010000 Epoch[034] Batch [3749]/[3759] Speed: 76.961495 samples/sec accuracy=63.505000 loss=1.488395 lr=0.010000 Batch [0049]/[0303]: acc-top1=61.468750 acc-top5=83.593750 Batch [0099]/[0303]: acc-top1=60.953125 acc-top5=83.468750 Batch [0149]/[0303]: acc-top1=60.552083 acc-top5=83.333333 Batch [0199]/[0303]: acc-top1=60.507812 acc-top5=83.070312 Batch [0249]/[0303]: acc-top1=60.693750 acc-top5=83.137500 Batch [0299]/[0303]: acc-top1=60.265625 acc-top5=82.750000 [Epoch 034] training: accuracy=63.506750 loss=1.488523 [Epoch 034] speed: 67 samples/sec time cost: 3855.734336 [Epoch 034] validation: acc-top1=60.231023 acc-top5=82.735149 loss=1.834176 Epoch[035] Batch [0049]/[3760] Speed: 46.224077 samples/sec accuracy=63.031250 loss=1.488553 lr=0.010000 Epoch[035] Batch [0099]/[3760] Speed: 67.284434 samples/sec accuracy=64.031250 loss=1.478996 lr=0.010000 Epoch[035] Batch [0149]/[3760] Speed: 67.765299 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lr=0.010000 Epoch[035] Batch [2549]/[3760] Speed: 67.580628 samples/sec accuracy=63.654412 loss=1.480165 lr=0.010000 Epoch[035] Batch [2599]/[3760] Speed: 67.664291 samples/sec accuracy=63.657452 loss=1.480975 lr=0.010000 Epoch[035] Batch [2649]/[3760] Speed: 67.666326 samples/sec accuracy=63.649764 loss=1.481883 lr=0.010000 Epoch[035] Batch [2699]/[3760] Speed: 68.438185 samples/sec accuracy=63.633681 loss=1.482362 lr=0.010000 Epoch[035] Batch [2749]/[3760] Speed: 68.285291 samples/sec accuracy=63.645455 loss=1.482176 lr=0.010000 Epoch[035] Batch [2799]/[3760] Speed: 67.537654 samples/sec accuracy=63.669085 loss=1.480743 lr=0.010000 Epoch[035] Batch [2849]/[3760] Speed: 67.507852 samples/sec accuracy=63.674890 loss=1.481139 lr=0.010000 Epoch[035] Batch [2899]/[3760] Speed: 67.503370 samples/sec accuracy=63.665409 loss=1.481595 lr=0.010000 Epoch[035] Batch [2949]/[3760] Speed: 67.934628 samples/sec accuracy=63.671081 loss=1.481621 lr=0.010000 Epoch[035] Batch [2999]/[3760] Speed: 67.955068 samples/sec accuracy=63.662500 loss=1.482633 lr=0.010000 Epoch[035] Batch [3049]/[3760] Speed: 67.313117 samples/sec accuracy=63.680840 loss=1.482091 lr=0.010000 Epoch[035] Batch [3099]/[3760] Speed: 68.128578 samples/sec accuracy=63.680948 loss=1.481689 lr=0.010000 Epoch[035] Batch [3149]/[3760] Speed: 67.304370 samples/sec accuracy=63.650794 loss=1.482730 lr=0.010000 Epoch[035] Batch [3199]/[3760] Speed: 67.983898 samples/sec accuracy=63.660156 loss=1.482325 lr=0.010000 Epoch[035] Batch [3249]/[3760] Speed: 66.768492 samples/sec accuracy=63.652885 loss=1.483262 lr=0.010000 Epoch[035] Batch [3299]/[3760] Speed: 68.745033 samples/sec accuracy=63.639205 loss=1.484360 lr=0.010000 Epoch[035] Batch [3349]/[3760] Speed: 67.813323 samples/sec accuracy=63.651119 loss=1.483694 lr=0.010000 Epoch[035] Batch [3399]/[3760] Speed: 67.701104 samples/sec accuracy=63.633272 loss=1.484428 lr=0.010000 Epoch[035] Batch [3449]/[3760] Speed: 68.172337 samples/sec accuracy=63.615489 loss=1.485387 lr=0.010000 Epoch[035] Batch [3499]/[3760] Speed: 67.318994 samples/sec accuracy=63.615625 loss=1.485808 lr=0.010000 Epoch[035] Batch [3549]/[3760] Speed: 68.346915 samples/sec accuracy=63.617958 loss=1.486048 lr=0.010000 Epoch[035] Batch [3599]/[3760] Speed: 67.778335 samples/sec accuracy=63.607205 loss=1.486574 lr=0.010000 Epoch[035] Batch [3649]/[3760] Speed: 67.381226 samples/sec accuracy=63.601027 loss=1.486663 lr=0.010000 Epoch[035] Batch [3699]/[3760] Speed: 68.153953 samples/sec accuracy=63.578125 loss=1.487135 lr=0.010000 Epoch[035] Batch [3749]/[3760] Speed: 75.327717 samples/sec accuracy=63.559167 loss=1.487725 lr=0.010000 Batch [0049]/[0303]: acc-top1=59.843750 acc-top5=83.312500 Batch [0099]/[0303]: acc-top1=60.031250 acc-top5=83.250000 Batch [0149]/[0303]: acc-top1=59.968750 acc-top5=83.093750 Batch [0199]/[0303]: acc-top1=60.078125 acc-top5=82.804688 Batch [0249]/[0303]: acc-top1=60.156250 acc-top5=82.993750 Batch [0299]/[0303]: acc-top1=59.890625 acc-top5=82.656250 [Epoch 035] training: accuracy=63.555103 loss=1.488034 [Epoch 035] speed: 67 samples/sec time cost: 3855.424728 [Epoch 035] validation: acc-top1=59.870050 acc-top5=82.642327 loss=1.851224 Epoch[036] Batch [0049]/[3760] Speed: 46.098215 samples/sec accuracy=65.218750 loss=1.414802 lr=0.010000 Epoch[036] Batch [0099]/[3760] Speed: 65.577667 samples/sec accuracy=64.703125 loss=1.420830 lr=0.010000 Epoch[036] Batch [0149]/[3760] Speed: 68.825725 samples/sec accuracy=64.906250 loss=1.419595 lr=0.010000 Epoch[036] Batch [0199]/[3760] Speed: 67.742184 samples/sec accuracy=64.429688 loss=1.433041 lr=0.010000 Epoch[036] Batch [0249]/[3760] Speed: 67.337738 samples/sec accuracy=64.481250 loss=1.439197 lr=0.010000 Epoch[036] Batch [0299]/[3760] Speed: 67.747013 samples/sec accuracy=64.479167 loss=1.434798 lr=0.010000 Epoch[036] Batch [0349]/[3760] Speed: 67.585309 samples/sec accuracy=64.500000 loss=1.440149 lr=0.010000 Epoch[036] Batch [0399]/[3760] Speed: 67.853818 samples/sec accuracy=64.476562 loss=1.439595 lr=0.010000 Epoch[036] Batch [0449]/[3760] Speed: 67.105655 samples/sec accuracy=64.482639 loss=1.443181 lr=0.010000 Epoch[036] Batch [0499]/[3760] Speed: 68.011509 samples/sec accuracy=64.440625 loss=1.442043 lr=0.010000 Epoch[036] Batch [0549]/[3760] Speed: 67.428126 samples/sec accuracy=64.565341 loss=1.441070 lr=0.010000 Epoch[036] Batch [0599]/[3760] Speed: 67.420855 samples/sec accuracy=64.617188 loss=1.437169 lr=0.010000 Epoch[036] Batch [0649]/[3760] Speed: 67.829653 samples/sec accuracy=64.572115 loss=1.439137 lr=0.010000 Epoch[036] Batch [0699]/[3760] Speed: 67.866470 samples/sec accuracy=64.625000 loss=1.437185 lr=0.010000 Epoch[036] Batch [0749]/[3760] Speed: 68.051485 samples/sec accuracy=64.587500 loss=1.440954 lr=0.010000 Epoch[036] Batch [0799]/[3760] Speed: 67.674008 samples/sec accuracy=64.537109 loss=1.441662 lr=0.010000 Epoch[036] Batch [0849]/[3760] Speed: 67.723992 samples/sec accuracy=64.487132 loss=1.443967 lr=0.010000 Epoch[036] 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accuracy=64.136574 loss=1.454056 lr=0.010000 Epoch[036] Batch [1399]/[3760] Speed: 67.680015 samples/sec accuracy=64.088170 loss=1.455957 lr=0.010000 Epoch[036] Batch [1449]/[3760] Speed: 67.743780 samples/sec accuracy=64.044181 loss=1.458447 lr=0.010000 Epoch[036] Batch [1499]/[3760] Speed: 68.374805 samples/sec accuracy=64.079167 loss=1.458370 lr=0.010000 Epoch[036] Batch [1549]/[3760] Speed: 67.936055 samples/sec accuracy=64.041331 loss=1.459069 lr=0.010000 Epoch[036] Batch [1599]/[3760] Speed: 67.571629 samples/sec accuracy=64.017578 loss=1.459825 lr=0.010000 Epoch[036] Batch [1649]/[3760] Speed: 68.198104 samples/sec accuracy=63.999053 loss=1.460993 lr=0.010000 Epoch[036] Batch [1699]/[3760] Speed: 68.012582 samples/sec accuracy=64.009191 loss=1.461223 lr=0.010000 Epoch[036] Batch [1749]/[3760] Speed: 68.165688 samples/sec accuracy=63.991071 loss=1.461425 lr=0.010000 Epoch[036] Batch [1799]/[3760] Speed: 67.833126 samples/sec accuracy=63.953993 loss=1.463170 lr=0.010000 Epoch[036] 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Batch [3749]/[3760] Speed: 77.267934 samples/sec accuracy=63.541250 loss=1.484154 lr=0.010000 Batch [0049]/[0303]: acc-top1=59.687500 acc-top5=84.093750 Batch [0099]/[0303]: acc-top1=59.828125 acc-top5=83.468750 Batch [0149]/[0303]: acc-top1=60.333333 acc-top5=83.500000 Batch [0199]/[0303]: acc-top1=60.375000 acc-top5=82.984375 Batch [0249]/[0303]: acc-top1=60.493750 acc-top5=83.087500 Batch [0299]/[0303]: acc-top1=60.140625 acc-top5=82.791667 [Epoch 036] training: accuracy=63.526430 loss=1.484781 [Epoch 036] speed: 67 samples/sec time cost: 3852.994114 [Epoch 036] validation: acc-top1=60.122731 acc-top5=82.771246 loss=1.803213 Epoch[037] Batch [0049]/[3759] Speed: 46.153857 samples/sec accuracy=63.437500 loss=1.448855 lr=0.010000 Epoch[037] Batch [0099]/[3759] Speed: 66.697347 samples/sec accuracy=64.375000 loss=1.418501 lr=0.010000 Epoch[037] Batch [0149]/[3759] Speed: 67.637483 samples/sec accuracy=64.750000 loss=1.404991 lr=0.010000 Epoch[037] Batch [0199]/[3759] Speed: 67.873574 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lr=0.010000 Epoch[037] Batch [1649]/[3759] Speed: 67.913636 samples/sec accuracy=63.934659 loss=1.462694 lr=0.010000 Epoch[037] Batch [1699]/[3759] Speed: 67.646436 samples/sec accuracy=63.970588 loss=1.461918 lr=0.010000 Epoch[037] Batch [1749]/[3759] Speed: 67.861098 samples/sec accuracy=63.940179 loss=1.462539 lr=0.010000 Epoch[037] Batch [1799]/[3759] Speed: 68.142423 samples/sec accuracy=63.919271 loss=1.463520 lr=0.010000 Epoch[037] Batch [1849]/[3759] Speed: 67.600463 samples/sec accuracy=63.923986 loss=1.463780 lr=0.010000 Epoch[037] Batch [1899]/[3759] Speed: 67.276669 samples/sec accuracy=63.910362 loss=1.465278 lr=0.010000 Epoch[037] Batch [1949]/[3759] Speed: 67.764696 samples/sec accuracy=63.921474 loss=1.465372 lr=0.010000 Epoch[037] Batch [1999]/[3759] Speed: 67.777225 samples/sec accuracy=63.911719 loss=1.466786 lr=0.010000 Epoch[037] Batch [2049]/[3759] Speed: 68.586734 samples/sec accuracy=63.856707 loss=1.469101 lr=0.010000 Epoch[037] Batch [2099]/[3759] Speed: 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lr=0.010000 Epoch[037] Batch [2599]/[3759] Speed: 67.136045 samples/sec accuracy=63.808894 loss=1.472274 lr=0.010000 Epoch[037] Batch [2649]/[3759] Speed: 67.851035 samples/sec accuracy=63.818986 loss=1.471346 lr=0.010000 Epoch[037] Batch [2699]/[3759] Speed: 68.252742 samples/sec accuracy=63.831597 loss=1.470810 lr=0.010000 Epoch[037] Batch [2749]/[3759] Speed: 67.863162 samples/sec accuracy=63.830114 loss=1.471406 lr=0.010000 Epoch[037] Batch [2799]/[3759] Speed: 67.884784 samples/sec accuracy=63.792411 loss=1.472439 lr=0.010000 Epoch[037] Batch [2849]/[3759] Speed: 67.142791 samples/sec accuracy=63.801535 loss=1.472519 lr=0.010000 Epoch[037] Batch [2899]/[3759] Speed: 67.614081 samples/sec accuracy=63.781789 loss=1.473276 lr=0.010000 Epoch[037] Batch [2949]/[3759] Speed: 68.508933 samples/sec accuracy=63.755297 loss=1.474317 lr=0.010000 Epoch[037] Batch [2999]/[3759] Speed: 67.963079 samples/sec accuracy=63.788542 loss=1.474191 lr=0.010000 Epoch[037] Batch [3049]/[3759] Speed: 67.748591 samples/sec accuracy=63.786885 loss=1.474248 lr=0.010000 Epoch[037] Batch [3099]/[3759] Speed: 67.669006 samples/sec accuracy=63.762601 loss=1.475043 lr=0.010000 Epoch[037] Batch [3149]/[3759] Speed: 67.482223 samples/sec accuracy=63.764881 loss=1.475536 lr=0.010000 Epoch[037] Batch [3199]/[3759] Speed: 68.406733 samples/sec accuracy=63.755371 loss=1.476323 lr=0.010000 Epoch[037] Batch [3249]/[3759] Speed: 68.050558 samples/sec accuracy=63.741827 loss=1.477407 lr=0.010000 Epoch[037] Batch [3299]/[3759] Speed: 67.407600 samples/sec accuracy=63.723485 loss=1.477838 lr=0.010000 Epoch[037] Batch [3349]/[3759] Speed: 68.177290 samples/sec accuracy=63.695896 loss=1.478756 lr=0.010000 Epoch[037] Batch [3399]/[3759] Speed: 66.951103 samples/sec accuracy=63.694393 loss=1.478646 lr=0.010000 Epoch[037] Batch [3449]/[3759] Speed: 68.678253 samples/sec accuracy=63.698370 loss=1.478883 lr=0.010000 Epoch[037] Batch [3499]/[3759] Speed: 67.537298 samples/sec accuracy=63.708036 loss=1.478594 lr=0.010000 Epoch[037] Batch [3549]/[3759] Speed: 67.583514 samples/sec accuracy=63.729313 loss=1.478088 lr=0.010000 Epoch[037] Batch [3599]/[3759] Speed: 67.756263 samples/sec accuracy=63.733507 loss=1.477789 lr=0.010000 Epoch[037] Batch [3649]/[3759] Speed: 67.787451 samples/sec accuracy=63.721747 loss=1.477920 lr=0.010000 Epoch[037] Batch [3699]/[3759] Speed: 67.614247 samples/sec accuracy=63.727618 loss=1.477868 lr=0.010000 Epoch[037] Batch [3749]/[3759] Speed: 77.544018 samples/sec accuracy=63.719167 loss=1.478351 lr=0.010000 Batch [0049]/[0303]: acc-top1=60.375000 acc-top5=84.031250 Batch [0099]/[0303]: acc-top1=60.656250 acc-top5=83.609375 Batch [0149]/[0303]: acc-top1=60.770833 acc-top5=83.427083 Batch [0199]/[0303]: acc-top1=60.679688 acc-top5=83.328125 Batch [0249]/[0303]: acc-top1=60.918750 acc-top5=83.387500 Batch [0299]/[0303]: acc-top1=60.619792 acc-top5=83.213542 [Epoch 037] training: accuracy=63.721651 loss=1.478218 [Epoch 037] speed: 67 samples/sec time cost: 3857.624764 [Epoch 037] validation: acc-top1=60.628094 acc-top5=83.209571 loss=1.818084 Epoch[038] Batch [0049]/[3760] Speed: 46.724640 samples/sec accuracy=65.031250 loss=1.410048 lr=0.010000 Epoch[038] Batch [0099]/[3760] Speed: 66.315625 samples/sec accuracy=65.578125 loss=1.389909 lr=0.010000 Epoch[038] Batch [0149]/[3760] Speed: 67.590424 samples/sec accuracy=64.635417 loss=1.429656 lr=0.010000 Epoch[038] Batch [0199]/[3760] Speed: 68.168377 samples/sec accuracy=64.460938 loss=1.429649 lr=0.010000 Epoch[038] Batch [0249]/[3760] Speed: 67.687989 samples/sec accuracy=64.337500 loss=1.432864 lr=0.010000 Epoch[038] Batch [0299]/[3760] Speed: 67.474262 samples/sec accuracy=64.265625 loss=1.435719 lr=0.010000 Epoch[038] Batch [0349]/[3760] Speed: 67.610410 samples/sec accuracy=64.258929 loss=1.440381 lr=0.010000 Epoch[038] Batch [0399]/[3760] Speed: 67.556033 samples/sec accuracy=64.074219 loss=1.448443 lr=0.010000 Epoch[038] Batch [0449]/[3760] Speed: 68.176945 samples/sec accuracy=64.138889 loss=1.445935 lr=0.010000 Epoch[038] Batch [0499]/[3760] Speed: 68.149082 samples/sec accuracy=64.050000 loss=1.450468 lr=0.010000 Epoch[038] Batch [0549]/[3760] Speed: 67.543832 samples/sec accuracy=63.988636 loss=1.455758 lr=0.010000 Epoch[038] Batch [0599]/[3760] Speed: 67.996612 samples/sec accuracy=63.979167 loss=1.459929 lr=0.010000 Epoch[038] Batch [0649]/[3760] Speed: 67.248272 samples/sec accuracy=64.040865 loss=1.460248 lr=0.010000 Epoch[038] Batch [0699]/[3760] Speed: 67.942758 samples/sec accuracy=63.995536 loss=1.460407 lr=0.010000 Epoch[038] Batch [0749]/[3760] Speed: 67.141368 samples/sec accuracy=64.039583 loss=1.458666 lr=0.010000 Epoch[038] Batch [0799]/[3760] Speed: 67.779128 samples/sec accuracy=64.021484 loss=1.459192 lr=0.010000 Epoch[038] Batch [0849]/[3760] Speed: 67.402456 samples/sec accuracy=64.009191 loss=1.459818 lr=0.010000 Epoch[038] Batch [0899]/[3760] Speed: 67.639915 samples/sec accuracy=64.079861 loss=1.458073 lr=0.010000 Epoch[038] 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[0049]/[0303]: acc-top1=60.812500 acc-top5=83.281250 Batch [0099]/[0303]: acc-top1=60.640625 acc-top5=83.343750 Batch [0149]/[0303]: acc-top1=60.479167 acc-top5=83.104167 Batch [0199]/[0303]: acc-top1=60.382813 acc-top5=82.945312 Batch [0249]/[0303]: acc-top1=60.618750 acc-top5=82.962500 Batch [0299]/[0303]: acc-top1=60.250000 acc-top5=82.645833 [Epoch 038] training: accuracy=63.680602 loss=1.477880 [Epoch 038] speed: 67 samples/sec time cost: 3860.761944 [Epoch 038] validation: acc-top1=60.231023 acc-top5=82.616543 loss=1.838293 Epoch[039] Batch [0049]/[3760] Speed: 46.047127 samples/sec accuracy=64.562500 loss=1.449727 lr=0.010000 Epoch[039] Batch [0099]/[3760] Speed: 66.040598 samples/sec accuracy=64.671875 loss=1.437701 lr=0.010000 Epoch[039] Batch [0149]/[3760] Speed: 67.715905 samples/sec accuracy=64.885417 loss=1.413335 lr=0.010000 Epoch[039] Batch [0199]/[3760] Speed: 67.834796 samples/sec accuracy=65.039062 loss=1.418339 lr=0.010000 Epoch[039] Batch [0249]/[3760] Speed: 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lr=0.010000 Epoch[039] Batch [2649]/[3760] Speed: 67.775681 samples/sec accuracy=64.130307 loss=1.459615 lr=0.010000 Epoch[039] Batch [2699]/[3760] Speed: 68.298575 samples/sec accuracy=64.122685 loss=1.459908 lr=0.010000 Epoch[039] Batch [2749]/[3760] Speed: 67.706736 samples/sec accuracy=64.109091 loss=1.460587 lr=0.010000 Epoch[039] Batch [2799]/[3760] Speed: 67.706383 samples/sec accuracy=64.117188 loss=1.460742 lr=0.010000 Epoch[039] Batch [2849]/[3760] Speed: 67.200576 samples/sec accuracy=64.088268 loss=1.461629 lr=0.010000 Epoch[039] Batch [2899]/[3760] Speed: 67.689994 samples/sec accuracy=64.073276 loss=1.461753 lr=0.010000 Epoch[039] Batch [2949]/[3760] Speed: 68.269017 samples/sec accuracy=64.030720 loss=1.463790 lr=0.010000 Epoch[039] Batch [2999]/[3760] Speed: 67.884951 samples/sec accuracy=63.980208 loss=1.465095 lr=0.010000 Epoch[039] Batch [3049]/[3760] Speed: 68.159866 samples/sec accuracy=63.967213 loss=1.465460 lr=0.010000 Epoch[039] Batch [3099]/[3760] Speed: 67.514719 samples/sec accuracy=63.917339 loss=1.467488 lr=0.010000 Epoch[039] Batch [3149]/[3760] Speed: 68.187076 samples/sec accuracy=63.922123 loss=1.467112 lr=0.010000 Epoch[039] Batch [3199]/[3760] Speed: 68.194890 samples/sec accuracy=63.910156 loss=1.467563 lr=0.010000 Epoch[039] Batch [3249]/[3760] Speed: 67.928638 samples/sec accuracy=63.928846 loss=1.466946 lr=0.010000 Epoch[039] Batch [3299]/[3760] Speed: 67.994388 samples/sec accuracy=63.910985 loss=1.467514 lr=0.010000 Epoch[039] Batch [3349]/[3760] Speed: 67.703361 samples/sec accuracy=63.879664 loss=1.468318 lr=0.010000 Epoch[039] Batch [3399]/[3760] Speed: 68.277054 samples/sec accuracy=63.882812 loss=1.468652 lr=0.010000 Epoch[039] Batch [3449]/[3760] Speed: 67.842576 samples/sec accuracy=63.886775 loss=1.468633 lr=0.010000 Epoch[039] Batch [3499]/[3760] Speed: 67.530248 samples/sec accuracy=63.894196 loss=1.468324 lr=0.010000 Epoch[039] Batch [3549]/[3760] Speed: 67.671303 samples/sec accuracy=63.875880 loss=1.469208 lr=0.010000 Epoch[039] Batch [3599]/[3760] Speed: 68.482616 samples/sec accuracy=63.863281 loss=1.469315 lr=0.010000 Epoch[039] Batch [3649]/[3760] Speed: 67.741185 samples/sec accuracy=63.861301 loss=1.469267 lr=0.010000 Epoch[039] Batch [3699]/[3760] Speed: 68.010116 samples/sec accuracy=63.870355 loss=1.469158 lr=0.010000 Epoch[039] Batch [3749]/[3760] Speed: 76.318393 samples/sec accuracy=63.852500 loss=1.470149 lr=0.001000 Batch [0049]/[0303]: acc-top1=60.843750 acc-top5=82.843750 Batch [0099]/[0303]: acc-top1=60.265625 acc-top5=82.750000 Batch [0149]/[0303]: acc-top1=60.281250 acc-top5=82.510417 Batch [0199]/[0303]: acc-top1=60.117188 acc-top5=82.281250 Batch [0249]/[0303]: acc-top1=60.218750 acc-top5=82.456250 Batch [0299]/[0303]: acc-top1=59.838542 acc-top5=82.213542 [Epoch 039] training: accuracy=63.839345 loss=1.470628 [Epoch 039] speed: 67 samples/sec time cost: 3854.704111 [Epoch 039] validation: acc-top1=59.808168 acc-top5=82.193688 loss=1.910850 Epoch[040] Batch 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accuracy=67.875000 loss=1.296064 lr=0.001000 Epoch[040] Batch [0549]/[3759] Speed: 67.798977 samples/sec accuracy=68.073864 loss=1.288408 lr=0.001000 Epoch[040] Batch [0599]/[3759] Speed: 68.145800 samples/sec accuracy=68.192708 loss=1.283611 lr=0.001000 Epoch[040] Batch [0649]/[3759] Speed: 67.620625 samples/sec accuracy=68.317308 loss=1.278094 lr=0.001000 Epoch[040] Batch [0699]/[3759] Speed: 67.569905 samples/sec accuracy=68.441964 loss=1.274925 lr=0.001000 Epoch[040] Batch [0749]/[3759] Speed: 67.684916 samples/sec accuracy=68.593750 loss=1.268703 lr=0.001000 Epoch[040] Batch [0799]/[3759] Speed: 68.289916 samples/sec accuracy=68.640625 loss=1.266162 lr=0.001000 Epoch[040] Batch [0849]/[3759] Speed: 67.735647 samples/sec accuracy=68.759191 loss=1.262004 lr=0.001000 Epoch[040] Batch [0899]/[3759] Speed: 67.373504 samples/sec accuracy=68.789931 loss=1.260654 lr=0.001000 Epoch[040] Batch [0949]/[3759] Speed: 67.792239 samples/sec accuracy=68.876645 loss=1.257899 lr=0.001000 Epoch[040] 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accuracy=70.304571 loss=1.191677 lr=0.001000 Epoch[040] Batch [3399]/[3759] Speed: 67.300244 samples/sec accuracy=70.328585 loss=1.190982 lr=0.001000 Epoch[040] Batch [3449]/[3759] Speed: 68.079339 samples/sec accuracy=70.352355 loss=1.190429 lr=0.001000 Epoch[040] Batch [3499]/[3759] Speed: 67.343785 samples/sec accuracy=70.382589 loss=1.188901 lr=0.001000 Epoch[040] Batch [3549]/[3759] Speed: 68.313271 samples/sec accuracy=70.398327 loss=1.187967 lr=0.001000 Epoch[040] Batch [3599]/[3759] Speed: 67.958834 samples/sec accuracy=70.424913 loss=1.187387 lr=0.001000 Epoch[040] Batch [3649]/[3759] Speed: 66.906389 samples/sec accuracy=70.436644 loss=1.186928 lr=0.001000 Epoch[040] Batch [3699]/[3759] Speed: 68.328163 samples/sec accuracy=70.467061 loss=1.185411 lr=0.001000 Epoch[040] Batch [3749]/[3759] Speed: 76.284779 samples/sec accuracy=70.492917 loss=1.184713 lr=0.001000 Batch [0049]/[0303]: acc-top1=66.437500 acc-top5=86.625000 Batch [0099]/[0303]: acc-top1=66.015625 acc-top5=86.859375 Batch [0149]/[0303]: acc-top1=66.093750 acc-top5=86.791667 Batch [0199]/[0303]: acc-top1=66.203125 acc-top5=86.578125 Batch [0249]/[0303]: acc-top1=66.343750 acc-top5=86.631250 Batch [0299]/[0303]: acc-top1=66.062500 acc-top5=86.307292 [Epoch 040] training: accuracy=70.496641 loss=1.184650 [Epoch 040] speed: 67 samples/sec time cost: 3853.415005 [Epoch 040] validation: acc-top1=66.016914 acc-top5=86.267533 loss=1.564463 Epoch[041] Batch [0049]/[3760] Speed: 45.827227 samples/sec accuracy=72.031250 loss=1.099417 lr=0.001000 Epoch[041] Batch [0099]/[3760] Speed: 66.623362 samples/sec accuracy=72.359375 loss=1.088095 lr=0.001000 Epoch[041] Batch [0149]/[3760] Speed: 67.193257 samples/sec accuracy=72.479167 loss=1.095936 lr=0.001000 Epoch[041] Batch [0199]/[3760] Speed: 67.866224 samples/sec accuracy=72.335938 loss=1.100728 lr=0.001000 Epoch[041] Batch [0249]/[3760] Speed: 67.777665 samples/sec accuracy=72.450000 loss=1.100788 lr=0.001000 Epoch[041] Batch [0299]/[3760] 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accuracy=72.590802 loss=1.095555 lr=0.001000 Epoch[041] Batch [2699]/[3760] Speed: 67.987481 samples/sec accuracy=72.607639 loss=1.094770 lr=0.001000 Epoch[041] Batch [2749]/[3760] Speed: 67.046619 samples/sec accuracy=72.619318 loss=1.094475 lr=0.001000 Epoch[041] Batch [2799]/[3760] Speed: 68.390780 samples/sec accuracy=72.621094 loss=1.093943 lr=0.001000 Epoch[041] Batch [2849]/[3760] Speed: 67.885687 samples/sec accuracy=72.620066 loss=1.094556 lr=0.001000 Epoch[041] Batch [2899]/[3760] Speed: 68.382643 samples/sec accuracy=72.619612 loss=1.094415 lr=0.001000 Epoch[041] Batch [2949]/[3760] Speed: 68.517225 samples/sec accuracy=72.618114 loss=1.094473 lr=0.001000 Epoch[041] Batch [2999]/[3760] Speed: 67.111724 samples/sec accuracy=72.610417 loss=1.094554 lr=0.001000 Epoch[041] Batch [3049]/[3760] Speed: 67.915578 samples/sec accuracy=72.630635 loss=1.093948 lr=0.001000 Epoch[041] Batch [3099]/[3760] Speed: 67.649554 samples/sec accuracy=72.644153 loss=1.093102 lr=0.001000 Epoch[041] 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accuracy=72.659722 loss=1.091943 lr=0.001000 Epoch[041] Batch [3649]/[3760] Speed: 68.390024 samples/sec accuracy=72.672517 loss=1.091319 lr=0.001000 Epoch[041] Batch [3699]/[3760] Speed: 66.844014 samples/sec accuracy=72.681588 loss=1.091013 lr=0.001000 Epoch[041] Batch [3749]/[3760] Speed: 76.889280 samples/sec accuracy=72.671667 loss=1.091325 lr=0.001000 Batch [0049]/[0303]: acc-top1=66.875000 acc-top5=87.125000 Batch [0099]/[0303]: acc-top1=66.281250 acc-top5=86.843750 Batch [0149]/[0303]: acc-top1=66.187500 acc-top5=86.781250 Batch [0199]/[0303]: acc-top1=66.343750 acc-top5=86.718750 Batch [0249]/[0303]: acc-top1=66.456250 acc-top5=86.712500 Batch [0299]/[0303]: acc-top1=66.203125 acc-top5=86.390625 [Epoch 041] training: accuracy=72.678690 loss=1.091040 [Epoch 041] speed: 67 samples/sec time cost: 3859.402536 [Epoch 041] validation: acc-top1=66.135520 acc-top5=86.344884 loss=1.568475 Epoch[042] Batch [0049]/[3760] Speed: 46.099737 samples/sec accuracy=72.718750 loss=1.074801 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lr=0.001000 Epoch[042] Batch [2949]/[3760] Speed: 67.977333 samples/sec accuracy=73.415254 loss=1.057685 lr=0.001000 Epoch[042] Batch [2999]/[3760] Speed: 67.571504 samples/sec accuracy=73.414062 loss=1.057827 lr=0.001000 Epoch[042] Batch [3049]/[3760] Speed: 67.883564 samples/sec accuracy=73.415471 loss=1.057449 lr=0.001000 Epoch[042] Batch [3099]/[3760] Speed: 67.422737 samples/sec accuracy=73.433468 loss=1.056926 lr=0.001000 Epoch[042] Batch [3149]/[3760] Speed: 68.374534 samples/sec accuracy=73.433036 loss=1.056463 lr=0.001000 Epoch[042] Batch [3199]/[3760] Speed: 68.267748 samples/sec accuracy=73.429688 loss=1.056391 lr=0.001000 Epoch[042] Batch [3249]/[3760] Speed: 66.733346 samples/sec accuracy=73.432212 loss=1.056068 lr=0.001000 Epoch[042] Batch [3299]/[3760] Speed: 68.040073 samples/sec accuracy=73.436553 loss=1.055486 lr=0.001000 Epoch[042] Batch [3349]/[3760] Speed: 67.998926 samples/sec accuracy=73.443097 loss=1.054934 lr=0.001000 Epoch[042] Batch [3399]/[3760] Speed: 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acc-top1=66.914062 acc-top5=86.796875 Batch [0249]/[0303]: acc-top1=66.975000 acc-top5=86.868750 Batch [0299]/[0303]: acc-top1=66.781250 acc-top5=86.572917 [Epoch 042] training: accuracy=73.467836 loss=1.054187 [Epoch 042] speed: 67 samples/sec time cost: 3851.344847 [Epoch 042] validation: acc-top1=66.728548 acc-top5=86.561469 loss=1.555711 Epoch[043] Batch [0049]/[3759] Speed: 45.905970 samples/sec accuracy=76.156250 loss=1.004282 lr=0.001000 Epoch[043] Batch [0099]/[3759] Speed: 67.783361 samples/sec accuracy=74.343750 loss=1.042793 lr=0.001000 Epoch[043] Batch [0149]/[3759] Speed: 67.538558 samples/sec accuracy=74.437500 loss=1.029216 lr=0.001000 Epoch[043] Batch [0199]/[3759] Speed: 67.047045 samples/sec accuracy=74.593750 loss=1.017715 lr=0.001000 Epoch[043] Batch [0249]/[3759] Speed: 68.647971 samples/sec accuracy=74.450000 loss=1.015789 lr=0.001000 Epoch[043] Batch [0299]/[3759] Speed: 67.840913 samples/sec accuracy=74.609375 loss=1.013466 lr=0.001000 Epoch[043] Batch 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accuracy=74.071062 loss=1.030834 lr=0.001000 Epoch[043] Batch [3699]/[3759] Speed: 67.189978 samples/sec accuracy=74.084882 loss=1.030288 lr=0.001000 Epoch[043] Batch [3749]/[3759] Speed: 77.827736 samples/sec accuracy=74.090833 loss=1.029924 lr=0.001000 Batch [0049]/[0303]: acc-top1=67.656250 acc-top5=87.062500 Batch [0099]/[0303]: acc-top1=67.046875 acc-top5=87.000000 Batch [0149]/[0303]: acc-top1=67.052083 acc-top5=87.031250 Batch [0199]/[0303]: acc-top1=67.093750 acc-top5=86.914062 Batch [0249]/[0303]: acc-top1=67.225000 acc-top5=86.956250 Batch [0299]/[0303]: acc-top1=67.088542 acc-top5=86.593750 [Epoch 043] training: accuracy=74.096751 loss=1.029656 [Epoch 043] speed: 67 samples/sec time cost: 3852.882746 [Epoch 043] validation: acc-top1=67.043111 acc-top5=86.571782 loss=1.551151 Epoch[044] Batch [0049]/[3760] Speed: 46.104969 samples/sec accuracy=75.093750 loss=0.996041 lr=0.001000 Epoch[044] Batch [0099]/[3760] Speed: 66.860124 samples/sec accuracy=74.921875 loss=0.990907 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lr=0.001000 Epoch[044] Batch [2999]/[3760] Speed: 68.085742 samples/sec accuracy=74.475000 loss=1.013747 lr=0.001000 Epoch[044] Batch [3049]/[3760] Speed: 67.728265 samples/sec accuracy=74.478996 loss=1.013171 lr=0.001000 Epoch[044] Batch [3099]/[3760] Speed: 66.955177 samples/sec accuracy=74.469758 loss=1.013381 lr=0.001000 Epoch[044] Batch [3149]/[3760] Speed: 68.102991 samples/sec accuracy=74.451389 loss=1.013974 lr=0.001000 Epoch[044] Batch [3199]/[3760] Speed: 68.375347 samples/sec accuracy=74.445801 loss=1.013605 lr=0.001000 Epoch[044] Batch [3249]/[3760] Speed: 66.522605 samples/sec accuracy=74.442788 loss=1.013775 lr=0.001000 Epoch[044] Batch [3299]/[3760] Speed: 68.214974 samples/sec accuracy=74.450758 loss=1.013414 lr=0.001000 Epoch[044] Batch [3349]/[3760] Speed: 67.903592 samples/sec accuracy=74.455224 loss=1.013423 lr=0.001000 Epoch[044] Batch [3399]/[3760] Speed: 67.825342 samples/sec accuracy=74.460018 loss=1.013319 lr=0.001000 Epoch[044] Batch [3449]/[3760] Speed: 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[0299]/[0303]: acc-top1=67.083333 acc-top5=86.812500 [Epoch 044] training: accuracy=74.484707 loss=1.011712 [Epoch 044] speed: 67 samples/sec time cost: 3851.067143 [Epoch 044] validation: acc-top1=67.022483 acc-top5=86.783210 loss=1.561540 Epoch[045] Batch [0049]/[3760] Speed: 46.161447 samples/sec accuracy=75.625000 loss=0.976359 lr=0.001000 Epoch[045] Batch [0099]/[3760] Speed: 66.733245 samples/sec accuracy=75.328125 loss=0.967048 lr=0.001000 Epoch[045] Batch [0149]/[3760] Speed: 67.736884 samples/sec accuracy=75.229167 loss=0.974624 lr=0.001000 Epoch[045] Batch [0199]/[3760] Speed: 68.397687 samples/sec accuracy=74.882812 loss=0.991125 lr=0.001000 Epoch[045] Batch [0249]/[3760] Speed: 66.721625 samples/sec accuracy=74.731250 loss=0.991894 lr=0.001000 Epoch[045] Batch [0299]/[3760] Speed: 68.452648 samples/sec accuracy=74.765625 loss=0.992676 lr=0.001000 Epoch[045] Batch [0349]/[3760] Speed: 68.103788 samples/sec accuracy=74.937500 loss=0.985813 lr=0.001000 Epoch[045] Batch 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accuracy=75.102941 loss=0.984806 lr=0.001000 Epoch[045] Batch [0899]/[3760] Speed: 68.242468 samples/sec accuracy=75.111111 loss=0.984509 lr=0.001000 Epoch[045] Batch [0949]/[3760] Speed: 67.144394 samples/sec accuracy=75.105263 loss=0.984499 lr=0.001000 Epoch[045] Batch [0999]/[3760] Speed: 68.068737 samples/sec accuracy=75.126563 loss=0.983263 lr=0.001000 Epoch[045] Batch [1049]/[3760] Speed: 68.062403 samples/sec accuracy=75.126488 loss=0.983855 lr=0.001000 Epoch[045] Batch [1099]/[3760] Speed: 67.729444 samples/sec accuracy=75.133523 loss=0.983239 lr=0.001000 Epoch[045] Batch [1149]/[3760] Speed: 67.885729 samples/sec accuracy=75.107337 loss=0.983993 lr=0.001000 Epoch[045] Batch [1199]/[3760] Speed: 68.039604 samples/sec accuracy=75.080729 loss=0.984675 lr=0.001000 Epoch[045] Batch [1249]/[3760] Speed: 67.721905 samples/sec accuracy=75.091250 loss=0.984405 lr=0.001000 Epoch[045] Batch [1299]/[3760] Speed: 68.063393 samples/sec accuracy=75.099760 loss=0.983549 lr=0.001000 Epoch[045] 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accuracy=75.128472 loss=0.982471 lr=0.001000 Epoch[045] Batch [1849]/[3760] Speed: 68.088201 samples/sec accuracy=75.124155 loss=0.983036 lr=0.001000 Epoch[045] Batch [1899]/[3760] Speed: 68.091237 samples/sec accuracy=75.160362 loss=0.981667 lr=0.001000 Epoch[045] Batch [1949]/[3760] Speed: 67.874611 samples/sec accuracy=75.141026 loss=0.982400 lr=0.001000 Epoch[045] Batch [1999]/[3760] Speed: 67.632453 samples/sec accuracy=75.135938 loss=0.982914 lr=0.001000 Epoch[045] Batch [2049]/[3760] Speed: 67.973564 samples/sec accuracy=75.124238 loss=0.984145 lr=0.001000 Epoch[045] Batch [2099]/[3760] Speed: 68.076345 samples/sec accuracy=75.127232 loss=0.984518 lr=0.001000 Epoch[045] Batch [2149]/[3760] Speed: 67.532295 samples/sec accuracy=75.148256 loss=0.983981 lr=0.001000 Epoch[045] Batch [2199]/[3760] Speed: 68.131629 samples/sec accuracy=75.110795 loss=0.985309 lr=0.001000 Epoch[045] Batch [2249]/[3760] Speed: 67.852928 samples/sec accuracy=75.115278 loss=0.985054 lr=0.001000 Epoch[045] 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accuracy=75.015341 loss=0.988068 lr=0.001000 Epoch[045] Batch [2799]/[3760] Speed: 67.478730 samples/sec accuracy=75.010045 loss=0.988610 lr=0.001000 Epoch[045] Batch [2849]/[3760] Speed: 67.000633 samples/sec accuracy=75.024123 loss=0.987961 lr=0.001000 Epoch[045] Batch [2899]/[3760] Speed: 68.958947 samples/sec accuracy=75.049569 loss=0.987605 lr=0.001000 Epoch[045] Batch [2949]/[3760] Speed: 67.965574 samples/sec accuracy=75.057203 loss=0.987529 lr=0.001000 Epoch[045] Batch [2999]/[3760] Speed: 67.251550 samples/sec accuracy=75.041146 loss=0.987951 lr=0.001000 Epoch[045] Batch [3049]/[3760] Speed: 68.387895 samples/sec accuracy=75.048156 loss=0.987434 lr=0.001000 Epoch[045] Batch [3099]/[3760] Speed: 68.342308 samples/sec accuracy=75.033266 loss=0.987739 lr=0.001000 Epoch[045] Batch [3149]/[3760] Speed: 67.169688 samples/sec accuracy=75.024306 loss=0.988421 lr=0.001000 Epoch[045] Batch [3199]/[3760] Speed: 67.730009 samples/sec accuracy=75.036133 loss=0.988070 lr=0.001000 Epoch[045] Batch [3249]/[3760] Speed: 67.520352 samples/sec accuracy=75.037981 loss=0.987527 lr=0.001000 Epoch[045] Batch [3299]/[3760] Speed: 67.678184 samples/sec accuracy=75.039299 loss=0.987538 lr=0.001000 Epoch[045] Batch [3349]/[3760] Speed: 68.274899 samples/sec accuracy=75.034515 loss=0.987452 lr=0.001000 Epoch[045] Batch [3399]/[3760] Speed: 67.979986 samples/sec accuracy=75.044577 loss=0.987508 lr=0.001000 Epoch[045] Batch [3449]/[3760] Speed: 67.092276 samples/sec accuracy=75.021286 loss=0.988462 lr=0.001000 Epoch[045] Batch [3499]/[3760] Speed: 68.013230 samples/sec accuracy=74.996875 loss=0.989390 lr=0.001000 Epoch[045] Batch [3549]/[3760] Speed: 68.155044 samples/sec accuracy=74.978873 loss=0.990104 lr=0.001000 Epoch[045] Batch [3599]/[3760] Speed: 67.867407 samples/sec accuracy=74.989149 loss=0.989917 lr=0.001000 Epoch[045] Batch [3649]/[3760] Speed: 68.114390 samples/sec accuracy=74.979880 loss=0.990286 lr=0.001000 Epoch[045] Batch [3699]/[3760] Speed: 67.773470 samples/sec accuracy=74.980574 loss=0.990063 lr=0.001000 Epoch[045] Batch [3749]/[3760] Speed: 76.075214 samples/sec accuracy=74.955833 loss=0.991024 lr=0.001000 Batch [0049]/[0303]: acc-top1=67.593750 acc-top5=87.375000 Batch [0099]/[0303]: acc-top1=67.109375 acc-top5=87.343750 Batch [0149]/[0303]: acc-top1=66.937500 acc-top5=87.218750 Batch [0199]/[0303]: acc-top1=67.109375 acc-top5=87.039062 Batch [0249]/[0303]: acc-top1=67.306250 acc-top5=87.106250 Batch [0299]/[0303]: acc-top1=67.177083 acc-top5=86.838542 [Epoch 045] training: accuracy=74.957197 loss=0.990905 [Epoch 045] speed: 67 samples/sec time cost: 3850.300934 [Epoch 045] validation: acc-top1=67.130776 acc-top5=86.829620 loss=1.554249 Epoch[046] Batch [0049]/[3759] Speed: 46.331943 samples/sec accuracy=77.187500 loss=0.906547 lr=0.001000 Epoch[046] Batch [0099]/[3759] Speed: 66.712508 samples/sec accuracy=76.406250 loss=0.925978 lr=0.001000 Epoch[046] Batch [0149]/[3759] Speed: 68.141944 samples/sec accuracy=75.531250 loss=0.958177 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67.942301 samples/sec accuracy=75.471154 loss=0.969473 lr=0.001000 Epoch[046] Batch [0699]/[3759] Speed: 66.832377 samples/sec accuracy=75.560268 loss=0.966297 lr=0.001000 Epoch[046] Batch [0749]/[3759] Speed: 68.220640 samples/sec accuracy=75.508333 loss=0.967807 lr=0.001000 Epoch[046] Batch [0799]/[3759] Speed: 68.087120 samples/sec accuracy=75.474609 loss=0.970096 lr=0.001000 Epoch[046] Batch [0849]/[3759] Speed: 66.907133 samples/sec accuracy=75.446691 loss=0.971262 lr=0.001000 Epoch[046] Batch [0899]/[3759] Speed: 69.181982 samples/sec accuracy=75.416667 loss=0.972528 lr=0.001000 Epoch[046] Batch [0949]/[3759] Speed: 68.216560 samples/sec accuracy=75.468750 loss=0.969657 lr=0.001000 Epoch[046] Batch [0999]/[3759] Speed: 67.267101 samples/sec accuracy=75.495312 loss=0.969364 lr=0.001000 Epoch[046] Batch [1049]/[3759] Speed: 68.814650 samples/sec accuracy=75.464286 loss=0.969168 lr=0.001000 Epoch[046] Batch [1099]/[3759] Speed: 67.571071 samples/sec accuracy=75.458807 loss=0.969916 lr=0.001000 Epoch[046] Batch [1149]/[3759] Speed: 67.094396 samples/sec accuracy=75.527174 loss=0.968197 lr=0.001000 Epoch[046] Batch [1199]/[3759] Speed: 68.561821 samples/sec accuracy=75.490885 loss=0.969868 lr=0.001000 Epoch[046] Batch [1249]/[3759] Speed: 68.240596 samples/sec accuracy=75.515000 loss=0.968245 lr=0.001000 Epoch[046] Batch [1299]/[3759] Speed: 67.323067 samples/sec accuracy=75.512019 loss=0.968499 lr=0.001000 Epoch[046] Batch [1349]/[3759] Speed: 68.865230 samples/sec accuracy=75.523148 loss=0.968616 lr=0.001000 Epoch[046] Batch [1399]/[3759] Speed: 68.204397 samples/sec accuracy=75.508929 loss=0.968782 lr=0.001000 Epoch[046] Batch [1449]/[3759] Speed: 67.957183 samples/sec accuracy=75.480603 loss=0.969684 lr=0.001000 Epoch[046] Batch [1499]/[3759] Speed: 68.159485 samples/sec accuracy=75.435417 loss=0.970530 lr=0.001000 Epoch[046] Batch [1549]/[3759] Speed: 68.372553 samples/sec accuracy=75.438508 loss=0.970511 lr=0.001000 Epoch[046] Batch [1599]/[3759] Speed: 67.347347 samples/sec accuracy=75.430664 loss=0.970587 lr=0.001000 Epoch[046] Batch [1649]/[3759] Speed: 68.453817 samples/sec accuracy=75.412879 loss=0.971859 lr=0.001000 Epoch[046] Batch [1699]/[3759] Speed: 67.483685 samples/sec accuracy=75.398897 loss=0.971944 lr=0.001000 Epoch[046] Batch [1749]/[3759] Speed: 67.281155 samples/sec accuracy=75.374107 loss=0.972235 lr=0.001000 Epoch[046] Batch [1799]/[3759] Speed: 68.530293 samples/sec accuracy=75.357639 loss=0.973027 lr=0.001000 Epoch[046] Batch [1849]/[3759] Speed: 67.906451 samples/sec accuracy=75.377534 loss=0.973010 lr=0.001000 Epoch[046] Batch [1899]/[3759] Speed: 67.172189 samples/sec accuracy=75.401316 loss=0.972246 lr=0.001000 Epoch[046] Batch [1949]/[3759] Speed: 68.708231 samples/sec accuracy=75.403846 loss=0.971245 lr=0.001000 Epoch[046] Batch [1999]/[3759] Speed: 68.072275 samples/sec accuracy=75.373437 loss=0.972560 lr=0.001000 Epoch[046] Batch [2049]/[3759] Speed: 67.255473 samples/sec accuracy=75.340701 loss=0.974099 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68.197672 samples/sec accuracy=75.330882 loss=0.976466 lr=0.001000 Epoch[046] Batch [2599]/[3759] Speed: 67.977380 samples/sec accuracy=75.325721 loss=0.976280 lr=0.001000 Epoch[046] Batch [2649]/[3759] Speed: 67.879853 samples/sec accuracy=75.349646 loss=0.975561 lr=0.001000 Epoch[046] Batch [2699]/[3759] Speed: 68.357050 samples/sec accuracy=75.344907 loss=0.976019 lr=0.001000 Epoch[046] Batch [2749]/[3759] Speed: 67.744689 samples/sec accuracy=75.348295 loss=0.975572 lr=0.001000 Epoch[046] Batch [2799]/[3759] Speed: 66.727358 samples/sec accuracy=75.324777 loss=0.976525 lr=0.001000 Epoch[046] Batch [2849]/[3759] Speed: 68.471443 samples/sec accuracy=75.340461 loss=0.976390 lr=0.001000 Epoch[046] Batch [2899]/[3759] Speed: 67.470775 samples/sec accuracy=75.344289 loss=0.976454 lr=0.001000 Epoch[046] Batch [2949]/[3759] Speed: 67.388081 samples/sec accuracy=75.344280 loss=0.976708 lr=0.001000 Epoch[046] Batch [2999]/[3759] Speed: 68.163316 samples/sec accuracy=75.355729 loss=0.976170 lr=0.001000 Epoch[046] Batch [3049]/[3759] Speed: 68.170751 samples/sec accuracy=75.356045 loss=0.976071 lr=0.001000 Epoch[046] Batch [3099]/[3759] Speed: 67.362853 samples/sec accuracy=75.345262 loss=0.976733 lr=0.001000 Epoch[046] Batch [3149]/[3759] Speed: 68.550182 samples/sec accuracy=75.353671 loss=0.976770 lr=0.001000 Epoch[046] Batch [3199]/[3759] Speed: 68.114004 samples/sec accuracy=75.336914 loss=0.977541 lr=0.001000 Epoch[046] Batch [3249]/[3759] Speed: 67.175963 samples/sec accuracy=75.330769 loss=0.977580 lr=0.001000 Epoch[046] Batch [3299]/[3759] Speed: 68.357133 samples/sec accuracy=75.344697 loss=0.976888 lr=0.001000 Epoch[046] Batch [3349]/[3759] Speed: 67.728390 samples/sec accuracy=75.359142 loss=0.976286 lr=0.001000 Epoch[046] Batch [3399]/[3759] Speed: 67.268664 samples/sec accuracy=75.354779 loss=0.976652 lr=0.001000 Epoch[046] Batch [3449]/[3759] Speed: 68.591182 samples/sec accuracy=75.360054 loss=0.976606 lr=0.001000 Epoch[046] Batch [3499]/[3759] Speed: 67.574619 samples/sec accuracy=75.366964 loss=0.976302 lr=0.001000 Epoch[046] Batch [3549]/[3759] Speed: 67.152625 samples/sec accuracy=75.372799 loss=0.976190 lr=0.001000 Epoch[046] Batch [3599]/[3759] Speed: 68.687824 samples/sec accuracy=75.375000 loss=0.975995 lr=0.001000 Epoch[046] Batch [3649]/[3759] Speed: 67.850324 samples/sec accuracy=75.392551 loss=0.975524 lr=0.001000 Epoch[046] Batch [3699]/[3759] Speed: 67.282730 samples/sec accuracy=75.394003 loss=0.975013 lr=0.001000 Epoch[046] Batch [3749]/[3759] Speed: 77.984109 samples/sec accuracy=75.409583 loss=0.974976 lr=0.001000 Batch [0049]/[0303]: acc-top1=68.562500 acc-top5=87.343750 Batch [0099]/[0303]: acc-top1=67.562500 acc-top5=87.062500 Batch [0149]/[0303]: acc-top1=67.343750 acc-top5=87.093750 Batch [0199]/[0303]: acc-top1=67.359375 acc-top5=86.984375 Batch [0249]/[0303]: acc-top1=67.550000 acc-top5=87.056250 Batch [0299]/[0303]: acc-top1=67.286458 acc-top5=86.812500 [Epoch 046] training: accuracy=75.414006 loss=0.974967 [Epoch 046] speed: 67 samples/sec time cost: 3851.335591 [Epoch 046] validation: acc-top1=67.239068 acc-top5=86.778053 loss=1.555299 Epoch[047] Batch [0049]/[3760] Speed: 45.812019 samples/sec accuracy=76.031250 loss=0.963455 lr=0.001000 Epoch[047] Batch [0099]/[3760] Speed: 66.730188 samples/sec accuracy=75.578125 loss=0.963074 lr=0.001000 Epoch[047] Batch [0149]/[3760] Speed: 68.054590 samples/sec accuracy=76.020833 loss=0.954464 lr=0.001000 Epoch[047] Batch [0199]/[3760] Speed: 67.940358 samples/sec accuracy=75.898438 loss=0.951189 lr=0.001000 Epoch[047] Batch [0249]/[3760] Speed: 67.600255 samples/sec accuracy=75.937500 loss=0.948953 lr=0.001000 Epoch[047] Batch [0299]/[3760] Speed: 67.499985 samples/sec accuracy=75.588542 loss=0.963645 lr=0.001000 Epoch[047] Batch [0349]/[3760] Speed: 68.481592 samples/sec accuracy=75.790179 loss=0.962305 lr=0.001000 Epoch[047] Batch [0399]/[3760] Speed: 67.946207 samples/sec accuracy=75.605469 loss=0.967241 lr=0.001000 Epoch[047] Batch 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accuracy=75.715278 loss=0.959880 lr=0.001000 Epoch[047] Batch [0949]/[3760] Speed: 68.201114 samples/sec accuracy=75.656250 loss=0.961070 lr=0.001000 Epoch[047] Batch [0999]/[3760] Speed: 68.156003 samples/sec accuracy=75.656250 loss=0.961509 lr=0.001000 Epoch[047] Batch [1049]/[3760] Speed: 67.737565 samples/sec accuracy=75.635417 loss=0.963518 lr=0.001000 Epoch[047] Batch [1099]/[3760] Speed: 67.438184 samples/sec accuracy=75.623580 loss=0.964202 lr=0.001000 Epoch[047] Batch [1149]/[3760] Speed: 67.739092 samples/sec accuracy=75.652174 loss=0.962738 lr=0.001000 Epoch[047] Batch [1199]/[3760] Speed: 68.144095 samples/sec accuracy=75.611979 loss=0.963949 lr=0.001000 Epoch[047] Batch [1249]/[3760] Speed: 68.071214 samples/sec accuracy=75.633750 loss=0.962485 lr=0.001000 Epoch[047] Batch [1299]/[3760] Speed: 67.369090 samples/sec accuracy=75.640625 loss=0.963213 lr=0.001000 Epoch[047] Batch [1349]/[3760] Speed: 67.490141 samples/sec accuracy=75.671296 loss=0.962001 lr=0.001000 Epoch[047] 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accuracy=75.801520 loss=0.958250 lr=0.001000 Epoch[047] Batch [1899]/[3760] Speed: 67.706157 samples/sec accuracy=75.804276 loss=0.958260 lr=0.001000 Epoch[047] Batch [1949]/[3760] Speed: 68.025983 samples/sec accuracy=75.770833 loss=0.959442 lr=0.001000 Epoch[047] Batch [1999]/[3760] Speed: 67.411529 samples/sec accuracy=75.760156 loss=0.960431 lr=0.001000 Epoch[047] Batch [2049]/[3760] Speed: 67.891500 samples/sec accuracy=75.732470 loss=0.961421 lr=0.001000 Epoch[047] Batch [2099]/[3760] Speed: 68.412047 samples/sec accuracy=75.715774 loss=0.962939 lr=0.001000 Epoch[047] Batch [2149]/[3760] Speed: 68.317555 samples/sec accuracy=75.715116 loss=0.963794 lr=0.001000 Epoch[047] Batch [2199]/[3760] Speed: 67.724125 samples/sec accuracy=75.687500 loss=0.964673 lr=0.001000 Epoch[047] Batch [2249]/[3760] Speed: 68.553748 samples/sec accuracy=75.669444 loss=0.965150 lr=0.001000 Epoch[047] Batch [2299]/[3760] Speed: 67.786241 samples/sec accuracy=75.659647 loss=0.965123 lr=0.001000 Epoch[047] 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accuracy=75.645647 loss=0.964821 lr=0.001000 Epoch[047] Batch [2849]/[3760] Speed: 68.965664 samples/sec accuracy=75.646930 loss=0.964139 lr=0.001000 Epoch[047] Batch [2899]/[3760] Speed: 67.662708 samples/sec accuracy=75.682651 loss=0.963073 lr=0.001000 Epoch[047] Batch [2949]/[3760] Speed: 67.959393 samples/sec accuracy=75.694915 loss=0.962755 lr=0.001000 Epoch[047] Batch [2999]/[3760] Speed: 67.865314 samples/sec accuracy=75.689063 loss=0.962989 lr=0.001000 Epoch[047] Batch [3049]/[3760] Speed: 67.614872 samples/sec accuracy=75.687500 loss=0.963083 lr=0.001000 Epoch[047] Batch [3099]/[3760] Speed: 68.356730 samples/sec accuracy=75.701109 loss=0.962630 lr=0.001000 Epoch[047] Batch [3149]/[3760] Speed: 67.082609 samples/sec accuracy=75.686012 loss=0.963233 lr=0.001000 Epoch[047] Batch [3199]/[3760] Speed: 68.323336 samples/sec accuracy=75.697754 loss=0.962793 lr=0.001000 Epoch[047] Batch [3249]/[3760] Speed: 67.600329 samples/sec accuracy=75.699038 loss=0.962588 lr=0.001000 Epoch[047] Batch [3299]/[3760] Speed: 67.789515 samples/sec accuracy=75.667140 loss=0.963447 lr=0.001000 Epoch[047] Batch [3349]/[3760] Speed: 68.030095 samples/sec accuracy=75.646922 loss=0.964807 lr=0.001000 Epoch[047] Batch [3399]/[3760] Speed: 68.051010 samples/sec accuracy=75.645680 loss=0.965417 lr=0.001000 Epoch[047] Batch [3449]/[3760] Speed: 67.817921 samples/sec accuracy=75.620471 loss=0.965609 lr=0.001000 Epoch[047] Batch [3499]/[3760] Speed: 67.270833 samples/sec accuracy=75.610714 loss=0.966160 lr=0.001000 Epoch[047] Batch [3549]/[3760] Speed: 67.947832 samples/sec accuracy=75.601232 loss=0.966789 lr=0.001000 Epoch[047] Batch [3599]/[3760] Speed: 67.557993 samples/sec accuracy=75.593316 loss=0.966878 lr=0.001000 Epoch[047] Batch [3649]/[3760] Speed: 67.377929 samples/sec accuracy=75.597175 loss=0.966533 lr=0.001000 Epoch[047] Batch [3699]/[3760] Speed: 68.085965 samples/sec accuracy=75.599240 loss=0.966211 lr=0.001000 Epoch[047] Batch [3749]/[3760] Speed: 76.353516 samples/sec accuracy=75.596667 loss=0.966145 lr=0.001000 Batch [0049]/[0303]: acc-top1=67.812500 acc-top5=86.968750 Batch [0099]/[0303]: acc-top1=67.343750 acc-top5=86.906250 Batch [0149]/[0303]: acc-top1=67.104167 acc-top5=86.875000 Batch [0199]/[0303]: acc-top1=67.140625 acc-top5=86.843750 Batch [0249]/[0303]: acc-top1=67.393750 acc-top5=86.981250 Batch [0299]/[0303]: acc-top1=67.151042 acc-top5=86.671875 [Epoch 047] training: accuracy=75.589262 loss=0.966376 [Epoch 047] speed: 67 samples/sec time cost: 3854.306807 [Epoch 047] validation: acc-top1=67.125619 acc-top5=86.638820 loss=1.542516 Epoch[048] Batch [0049]/[3760] Speed: 46.138186 samples/sec accuracy=76.437500 loss=0.940513 lr=0.001000 Epoch[048] Batch [0099]/[3760] Speed: 67.116978 samples/sec accuracy=75.953125 loss=0.961263 lr=0.001000 Epoch[048] Batch [0149]/[3760] Speed: 67.285146 samples/sec accuracy=75.791667 loss=0.961632 lr=0.001000 Epoch[048] Batch [0199]/[3760] Speed: 67.650625 samples/sec accuracy=75.960938 loss=0.957639 lr=0.001000 Epoch[048] Batch [0249]/[3760] Speed: 67.372926 samples/sec accuracy=76.062500 loss=0.953742 lr=0.001000 Epoch[048] Batch [0299]/[3760] Speed: 67.557633 samples/sec accuracy=76.166667 loss=0.949017 lr=0.001000 Epoch[048] Batch [0349]/[3760] Speed: 68.475816 samples/sec accuracy=76.205357 loss=0.949927 lr=0.001000 Epoch[048] Batch [0399]/[3760] Speed: 67.879312 samples/sec accuracy=76.246094 loss=0.949446 lr=0.001000 Epoch[048] Batch [0449]/[3760] Speed: 67.462286 samples/sec accuracy=76.218750 loss=0.947785 lr=0.001000 Epoch[048] Batch [0499]/[3760] Speed: 68.405550 samples/sec accuracy=76.203125 loss=0.946106 lr=0.001000 Epoch[048] Batch [0549]/[3760] Speed: 68.001590 samples/sec accuracy=76.181818 loss=0.944181 lr=0.001000 Epoch[048] Batch [0599]/[3760] Speed: 67.301906 samples/sec accuracy=76.190104 loss=0.943522 lr=0.001000 Epoch[048] Batch [0649]/[3760] Speed: 68.410609 samples/sec accuracy=76.250000 loss=0.942952 lr=0.001000 Epoch[048] Batch [0699]/[3760] Speed: 67.914972 samples/sec accuracy=76.245536 loss=0.942339 lr=0.001000 Epoch[048] Batch [0749]/[3760] Speed: 67.703290 samples/sec accuracy=76.233333 loss=0.942462 lr=0.001000 Epoch[048] Batch [0799]/[3760] Speed: 67.866890 samples/sec accuracy=76.101562 loss=0.945142 lr=0.001000 Epoch[048] Batch [0849]/[3760] Speed: 68.160661 samples/sec accuracy=76.007353 loss=0.946893 lr=0.001000 Epoch[048] Batch [0899]/[3760] Speed: 67.295952 samples/sec accuracy=75.970486 loss=0.946691 lr=0.001000 Epoch[048] Batch [0949]/[3760] Speed: 68.357408 samples/sec accuracy=75.986842 loss=0.945443 lr=0.001000 Epoch[048] Batch [0999]/[3760] Speed: 67.850226 samples/sec accuracy=76.046875 loss=0.944439 lr=0.001000 Epoch[048] Batch [1049]/[3760] Speed: 67.391461 samples/sec accuracy=75.995536 loss=0.946521 lr=0.001000 Epoch[048] Batch [1099]/[3760] Speed: 68.123928 samples/sec accuracy=76.025568 loss=0.946015 lr=0.001000 Epoch[048] Batch [1149]/[3760] Speed: 68.308175 samples/sec accuracy=76.096467 loss=0.944632 lr=0.001000 Epoch[048] Batch [1199]/[3760] Speed: 67.273353 samples/sec accuracy=76.074219 loss=0.945721 lr=0.001000 Epoch[048] Batch [1249]/[3760] Speed: 67.863902 samples/sec accuracy=76.032500 loss=0.947443 lr=0.001000 Epoch[048] Batch [1299]/[3760] Speed: 68.246036 samples/sec accuracy=76.006010 loss=0.949611 lr=0.001000 Epoch[048] Batch [1349]/[3760] Speed: 67.370291 samples/sec accuracy=75.962963 loss=0.950701 lr=0.001000 Epoch[048] Batch [1399]/[3760] Speed: 67.757311 samples/sec accuracy=75.926339 loss=0.950931 lr=0.001000 Epoch[048] Batch [1449]/[3760] Speed: 68.461304 samples/sec accuracy=75.924569 loss=0.950756 lr=0.001000 Epoch[048] Batch [1499]/[3760] Speed: 67.700851 samples/sec accuracy=75.981250 loss=0.948521 lr=0.001000 Epoch[048] Batch [1549]/[3760] Speed: 68.214965 samples/sec accuracy=75.937500 loss=0.950032 lr=0.001000 Epoch[048] Batch [1599]/[3760] Speed: 67.791892 samples/sec accuracy=75.934570 loss=0.950814 lr=0.001000 Epoch[048] Batch [1649]/[3760] Speed: 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67.575879 samples/sec accuracy=75.816901 loss=0.956340 lr=0.001000 Epoch[048] Batch [3599]/[3760] Speed: 67.601716 samples/sec accuracy=75.825521 loss=0.956175 lr=0.001000 Epoch[048] Batch [3649]/[3760] Speed: 67.649229 samples/sec accuracy=75.839469 loss=0.955421 lr=0.001000 Epoch[048] Batch [3699]/[3760] Speed: 67.716215 samples/sec accuracy=75.840372 loss=0.955429 lr=0.001000 Epoch[048] Batch [3749]/[3760] Speed: 76.629967 samples/sec accuracy=75.845833 loss=0.954887 lr=0.001000 Batch [0049]/[0303]: acc-top1=68.187500 acc-top5=87.718750 Batch [0099]/[0303]: acc-top1=67.609375 acc-top5=87.578125 Batch [0149]/[0303]: acc-top1=67.427083 acc-top5=87.427083 Batch [0199]/[0303]: acc-top1=67.546875 acc-top5=87.218750 Batch [0249]/[0303]: acc-top1=67.793750 acc-top5=87.281250 Batch [0299]/[0303]: acc-top1=67.489583 acc-top5=86.984375 [Epoch 048] training: accuracy=75.844415 loss=0.954920 [Epoch 048] speed: 67 samples/sec time cost: 3852.504272 [Epoch 048] validation: acc-top1=67.445338 acc-top5=86.943069 loss=1.544123 Epoch[049] Batch [0049]/[3759] Speed: 46.143041 samples/sec accuracy=76.156250 loss=0.950649 lr=0.001000 Epoch[049] Batch [0099]/[3759] Speed: 66.501439 samples/sec accuracy=76.390625 loss=0.935319 lr=0.001000 Epoch[049] Batch [0149]/[3759] Speed: 67.751389 samples/sec accuracy=76.395833 loss=0.928132 lr=0.001000 Epoch[049] Batch [0199]/[3759] Speed: 67.515702 samples/sec accuracy=76.281250 loss=0.931690 lr=0.001000 Epoch[049] Batch [0249]/[3759] Speed: 68.469365 samples/sec accuracy=76.318750 loss=0.932500 lr=0.001000 Epoch[049] Batch [0299]/[3759] Speed: 67.109232 samples/sec accuracy=76.473958 loss=0.928392 lr=0.001000 Epoch[049] Batch [0349]/[3759] Speed: 68.013152 samples/sec accuracy=76.718750 loss=0.922684 lr=0.001000 Epoch[049] Batch [0399]/[3759] Speed: 67.552292 samples/sec accuracy=76.718750 loss=0.921982 lr=0.001000 Epoch[049] Batch [0449]/[3759] Speed: 68.176466 samples/sec accuracy=76.750000 loss=0.922927 lr=0.001000 Epoch[049] Batch 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accuracy=76.329770 loss=0.934612 lr=0.001000 Epoch[049] Batch [1949]/[3759] Speed: 68.012535 samples/sec accuracy=76.314103 loss=0.934835 lr=0.001000 Epoch[049] Batch [1999]/[3759] Speed: 67.976047 samples/sec accuracy=76.305469 loss=0.934896 lr=0.001000 Epoch[049] Batch [2049]/[3759] Speed: 67.906340 samples/sec accuracy=76.285061 loss=0.935332 lr=0.001000 Epoch[049] Batch [2099]/[3759] Speed: 67.241968 samples/sec accuracy=76.264881 loss=0.935709 lr=0.001000 Epoch[049] Batch [2149]/[3759] Speed: 68.585339 samples/sec accuracy=76.284157 loss=0.935421 lr=0.001000 Epoch[049] Batch [2199]/[3759] Speed: 68.093694 samples/sec accuracy=76.264205 loss=0.936407 lr=0.001000 Epoch[049] Batch [2249]/[3759] Speed: 68.402836 samples/sec accuracy=76.248611 loss=0.937267 lr=0.001000 Epoch[049] Batch [2299]/[3759] Speed: 67.116659 samples/sec accuracy=76.218071 loss=0.938407 lr=0.001000 Epoch[049] Batch [2349]/[3759] Speed: 68.146291 samples/sec accuracy=76.195479 loss=0.939666 lr=0.001000 Epoch[049] 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accuracy=76.200110 loss=0.939666 lr=0.001000 Epoch[049] Batch [2899]/[3759] Speed: 67.475080 samples/sec accuracy=76.180496 loss=0.940590 lr=0.001000 Epoch[049] Batch [2949]/[3759] Speed: 68.082299 samples/sec accuracy=76.165254 loss=0.940916 lr=0.001000 Epoch[049] Batch [2999]/[3759] Speed: 68.038585 samples/sec accuracy=76.181771 loss=0.940260 lr=0.001000 Epoch[049] Batch [3049]/[3759] Speed: 67.388755 samples/sec accuracy=76.176742 loss=0.940295 lr=0.001000 Epoch[049] Batch [3099]/[3759] Speed: 68.697155 samples/sec accuracy=76.181452 loss=0.940042 lr=0.001000 Epoch[049] Batch [3149]/[3759] Speed: 67.740239 samples/sec accuracy=76.200893 loss=0.939491 lr=0.001000 Epoch[049] Batch [3199]/[3759] Speed: 68.154789 samples/sec accuracy=76.201660 loss=0.939540 lr=0.001000 Epoch[049] Batch [3249]/[3759] Speed: 67.727763 samples/sec accuracy=76.203846 loss=0.939724 lr=0.001000 Epoch[049] Batch [3299]/[3759] Speed: 68.074212 samples/sec accuracy=76.201231 loss=0.939649 lr=0.001000 Epoch[049] 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[0099]/[0303]: acc-top1=67.656250 acc-top5=87.296875 Batch [0149]/[0303]: acc-top1=67.343750 acc-top5=87.197917 Batch [0199]/[0303]: acc-top1=67.414062 acc-top5=86.984375 Batch [0249]/[0303]: acc-top1=67.568750 acc-top5=87.081250 Batch [0299]/[0303]: acc-top1=67.395833 acc-top5=86.838542 [Epoch 049] training: accuracy=76.241188 loss=0.938822 [Epoch 049] speed: 67 samples/sec time cost: 3851.932483 [Epoch 049] validation: acc-top1=67.357673 acc-top5=86.814150 loss=1.569166 Epoch[050] Batch [0049]/[3760] Speed: 45.901431 samples/sec accuracy=76.593750 loss=0.924222 lr=0.001000 Epoch[050] Batch [0099]/[3760] Speed: 65.884020 samples/sec accuracy=76.687500 loss=0.928357 lr=0.001000 Epoch[050] Batch [0149]/[3760] Speed: 68.491418 samples/sec accuracy=76.729167 loss=0.921165 lr=0.001000 Epoch[050] Batch [0199]/[3760] Speed: 68.006726 samples/sec accuracy=76.976562 loss=0.910357 lr=0.001000 Epoch[050] Batch [0249]/[3760] Speed: 67.797353 samples/sec accuracy=77.050000 loss=0.913231 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accuracy=77.000000 loss=0.925410 lr=0.001000 Epoch[051] Batch [0099]/[3760] Speed: 67.235588 samples/sec accuracy=76.453125 loss=0.936109 lr=0.001000 Epoch[051] Batch [0149]/[3760] Speed: 68.345713 samples/sec accuracy=76.572917 loss=0.933487 lr=0.001000 Epoch[051] Batch [0199]/[3760] Speed: 67.435201 samples/sec accuracy=76.507812 loss=0.930491 lr=0.001000 Epoch[051] Batch [0249]/[3760] Speed: 67.629236 samples/sec accuracy=76.668750 loss=0.929438 lr=0.001000 Epoch[051] Batch [0299]/[3760] Speed: 68.174912 samples/sec accuracy=76.750000 loss=0.930969 lr=0.001000 Epoch[051] Batch [0349]/[3760] Speed: 67.013659 samples/sec accuracy=76.808036 loss=0.920736 lr=0.001000 Epoch[051] Batch [0399]/[3760] Speed: 68.487801 samples/sec accuracy=76.949219 loss=0.913008 lr=0.001000 Epoch[051] Batch [0449]/[3760] Speed: 68.207630 samples/sec accuracy=76.784722 loss=0.917563 lr=0.001000 Epoch[051] Batch [0499]/[3760] Speed: 68.166875 samples/sec accuracy=76.756250 loss=0.916424 lr=0.001000 Epoch[051] 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accuracy=76.744391 loss=0.918607 lr=0.001000 Epoch[051] Batch [1999]/[3760] Speed: 68.285574 samples/sec accuracy=76.704688 loss=0.919711 lr=0.001000 Epoch[051] Batch [2049]/[3760] Speed: 67.864946 samples/sec accuracy=76.689787 loss=0.919938 lr=0.001000 Epoch[051] Batch [2099]/[3760] Speed: 66.851586 samples/sec accuracy=76.694940 loss=0.919777 lr=0.001000 Epoch[051] Batch [2149]/[3760] Speed: 68.538214 samples/sec accuracy=76.729651 loss=0.918593 lr=0.001000 Epoch[051] Batch [2199]/[3760] Speed: 68.205433 samples/sec accuracy=76.733665 loss=0.918678 lr=0.001000 Epoch[051] Batch [2249]/[3760] Speed: 67.606988 samples/sec accuracy=76.728472 loss=0.918409 lr=0.001000 Epoch[051] Batch [2299]/[3760] Speed: 67.652729 samples/sec accuracy=76.708560 loss=0.919262 lr=0.001000 Epoch[051] Batch [2349]/[3760] Speed: 67.854210 samples/sec accuracy=76.700798 loss=0.919365 lr=0.001000 Epoch[051] Batch [2399]/[3760] Speed: 68.001805 samples/sec accuracy=76.694010 loss=0.919674 lr=0.001000 Epoch[051] 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accuracy=76.676185 loss=0.920428 lr=0.001000 Epoch[051] Batch [2949]/[3760] Speed: 67.993406 samples/sec accuracy=76.665254 loss=0.920754 lr=0.001000 Epoch[051] Batch [2999]/[3760] Speed: 67.510919 samples/sec accuracy=76.660938 loss=0.920853 lr=0.001000 Epoch[051] Batch [3049]/[3760] Speed: 68.232160 samples/sec accuracy=76.669570 loss=0.920498 lr=0.001000 Epoch[051] Batch [3099]/[3760] Speed: 67.949480 samples/sec accuracy=76.674395 loss=0.920597 lr=0.001000 Epoch[051] Batch [3149]/[3760] Speed: 67.759444 samples/sec accuracy=76.682044 loss=0.920339 lr=0.001000 Epoch[051] Batch [3199]/[3760] Speed: 68.503802 samples/sec accuracy=76.686523 loss=0.920227 lr=0.001000 Epoch[051] Batch [3249]/[3760] Speed: 67.959152 samples/sec accuracy=76.697115 loss=0.919893 lr=0.001000 Epoch[051] Batch [3299]/[3760] Speed: 67.663947 samples/sec accuracy=76.706439 loss=0.919644 lr=0.001000 Epoch[051] Batch [3349]/[3760] Speed: 67.595192 samples/sec accuracy=76.713153 loss=0.919974 lr=0.001000 Epoch[051] Batch [3399]/[3760] Speed: 67.245671 samples/sec accuracy=76.730239 loss=0.918868 lr=0.001000 Epoch[051] Batch [3449]/[3760] Speed: 67.999231 samples/sec accuracy=76.734149 loss=0.918795 lr=0.001000 Epoch[051] Batch [3499]/[3760] Speed: 68.017144 samples/sec accuracy=76.736161 loss=0.918686 lr=0.001000 Epoch[051] Batch [3549]/[3760] Speed: 67.590757 samples/sec accuracy=76.741637 loss=0.918319 lr=0.001000 Epoch[051] Batch [3599]/[3760] Speed: 67.792441 samples/sec accuracy=76.736979 loss=0.918523 lr=0.001000 Epoch[051] Batch [3649]/[3760] Speed: 67.724620 samples/sec accuracy=76.738014 loss=0.918931 lr=0.001000 Epoch[051] Batch [3699]/[3760] Speed: 67.794775 samples/sec accuracy=76.738598 loss=0.918674 lr=0.001000 Epoch[051] Batch [3749]/[3760] Speed: 75.978530 samples/sec accuracy=76.736667 loss=0.918898 lr=0.001000 Batch [0049]/[0303]: acc-top1=68.937500 acc-top5=87.781250 Batch [0099]/[0303]: acc-top1=67.859375 acc-top5=87.531250 Batch [0149]/[0303]: acc-top1=67.447917 acc-top5=87.375000 Batch [0199]/[0303]: acc-top1=67.507812 acc-top5=87.289062 Batch [0249]/[0303]: acc-top1=67.725000 acc-top5=87.262500 Batch [0299]/[0303]: acc-top1=67.510417 acc-top5=87.005208 [Epoch 051] training: accuracy=76.732879 loss=0.918975 [Epoch 051] speed: 67 samples/sec time cost: 3850.506062 [Epoch 051] validation: acc-top1=67.455652 acc-top5=86.958540 loss=1.558212 Epoch[052] Batch [0049]/[3759] Speed: 46.013931 samples/sec accuracy=77.593750 loss=0.894903 lr=0.001000 Epoch[052] Batch [0099]/[3759] Speed: 67.413165 samples/sec accuracy=77.734375 loss=0.877011 lr=0.001000 Epoch[052] Batch [0149]/[3759] Speed: 67.505329 samples/sec accuracy=77.364583 loss=0.889227 lr=0.001000 Epoch[052] Batch [0199]/[3759] Speed: 68.029597 samples/sec accuracy=77.429688 loss=0.885522 lr=0.001000 Epoch[052] Batch [0249]/[3759] Speed: 68.003368 samples/sec accuracy=77.587500 loss=0.881673 lr=0.001000 Epoch[052] Batch [0299]/[3759] Speed: 68.494292 samples/sec accuracy=77.682292 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lr=0.001000 Epoch[053] Batch [3449]/[3760] Speed: 68.382700 samples/sec accuracy=77.258605 loss=0.894785 lr=0.001000 Epoch[053] Batch [3499]/[3760] Speed: 68.088913 samples/sec accuracy=77.271429 loss=0.894799 lr=0.001000 Epoch[053] Batch [3549]/[3760] Speed: 67.092104 samples/sec accuracy=77.266285 loss=0.894924 lr=0.001000 Epoch[053] Batch [3599]/[3760] Speed: 68.089321 samples/sec accuracy=77.264757 loss=0.895391 lr=0.001000 Epoch[053] Batch [3649]/[3760] Speed: 67.813780 samples/sec accuracy=77.278682 loss=0.895336 lr=0.001000 Epoch[053] Batch [3699]/[3760] Speed: 67.832484 samples/sec accuracy=77.268159 loss=0.895692 lr=0.001000 Epoch[053] Batch [3749]/[3760] Speed: 76.341176 samples/sec accuracy=77.252500 loss=0.896430 lr=0.001000 Batch [0049]/[0303]: acc-top1=69.625000 acc-top5=87.500000 Batch [0099]/[0303]: acc-top1=68.218750 acc-top5=87.234375 Batch [0149]/[0303]: acc-top1=67.854167 acc-top5=87.093750 Batch [0199]/[0303]: acc-top1=67.812500 acc-top5=87.000000 Batch [0249]/[0303]: acc-top1=67.862500 acc-top5=87.012500 Batch [0299]/[0303]: acc-top1=67.645833 acc-top5=86.760417 [Epoch 053] training: accuracy=77.252743 loss=0.896344 [Epoch 053] speed: 67 samples/sec time cost: 3852.664666 [Epoch 053] validation: acc-top1=67.620668 acc-top5=86.736799 loss=1.567121 Epoch[054] Batch [0049]/[3760] Speed: 46.401788 samples/sec accuracy=77.781250 loss=0.882133 lr=0.001000 Epoch[054] Batch [0099]/[3760] Speed: 66.481423 samples/sec accuracy=77.921875 loss=0.863727 lr=0.001000 Epoch[054] Batch [0149]/[3760] Speed: 68.352185 samples/sec accuracy=77.552083 loss=0.880728 lr=0.001000 Epoch[054] Batch [0199]/[3760] Speed: 67.327543 samples/sec accuracy=77.718750 loss=0.876607 lr=0.001000 Epoch[054] Batch [0249]/[3760] Speed: 68.154121 samples/sec accuracy=77.887500 loss=0.867998 lr=0.001000 Epoch[054] Batch [0299]/[3760] Speed: 67.442105 samples/sec accuracy=77.968750 loss=0.865272 lr=0.001000 Epoch[054] Batch [0349]/[3760] Speed: 67.763021 samples/sec 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Batch [3699]/[3760] Speed: 67.626783 samples/sec accuracy=77.467483 loss=0.887098 lr=0.001000 Epoch[054] Batch [3749]/[3760] Speed: 77.022832 samples/sec accuracy=77.472917 loss=0.886942 lr=0.001000 Batch [0049]/[0303]: acc-top1=68.968750 acc-top5=87.468750 Batch [0099]/[0303]: acc-top1=67.765625 acc-top5=86.953125 Batch [0149]/[0303]: acc-top1=67.520833 acc-top5=87.104167 Batch [0199]/[0303]: acc-top1=67.585938 acc-top5=86.906250 Batch [0249]/[0303]: acc-top1=67.631250 acc-top5=86.968750 Batch [0299]/[0303]: acc-top1=67.333333 acc-top5=86.713542 [Epoch 054] training: accuracy=77.473820 loss=0.886921 [Epoch 054] speed: 67 samples/sec time cost: 3852.233380 [Epoch 054] validation: acc-top1=67.285479 acc-top5=86.700701 loss=1.587380 Epoch[055] Batch [0049]/[3759] Speed: 45.915928 samples/sec accuracy=78.531250 loss=0.863171 lr=0.001000 Epoch[055] Batch [0099]/[3759] Speed: 66.766753 samples/sec accuracy=78.109375 loss=0.857554 lr=0.001000 Epoch[055] Batch [0149]/[3759] Speed: 67.280706 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68.033283 samples/sec accuracy=77.803125 loss=0.874070 lr=0.001000 Epoch[055] Batch [3049]/[3759] Speed: 68.117449 samples/sec accuracy=77.788934 loss=0.874605 lr=0.001000 Epoch[055] Batch [3099]/[3759] Speed: 67.740458 samples/sec accuracy=77.784778 loss=0.874739 lr=0.001000 Epoch[055] Batch [3149]/[3759] Speed: 68.073627 samples/sec accuracy=77.780754 loss=0.874595 lr=0.001000 Epoch[055] Batch [3199]/[3759] Speed: 67.706425 samples/sec accuracy=77.776367 loss=0.874679 lr=0.001000 Epoch[055] Batch [3249]/[3759] Speed: 68.411793 samples/sec accuracy=77.770673 loss=0.874815 lr=0.001000 Epoch[055] Batch [3299]/[3759] Speed: 67.720787 samples/sec accuracy=77.787405 loss=0.874497 lr=0.001000 Epoch[055] Batch [3349]/[3759] Speed: 67.712213 samples/sec accuracy=77.788713 loss=0.874604 lr=0.001000 Epoch[055] Batch [3399]/[3759] Speed: 68.417139 samples/sec accuracy=77.787684 loss=0.874677 lr=0.001000 Epoch[055] Batch [3449]/[3759] Speed: 67.430366 samples/sec accuracy=77.789855 loss=0.874783 lr=0.001000 Epoch[055] Batch [3499]/[3759] Speed: 68.158088 samples/sec accuracy=77.784821 loss=0.874987 lr=0.001000 Epoch[055] Batch [3549]/[3759] Speed: 67.696442 samples/sec accuracy=77.775088 loss=0.875302 lr=0.001000 Epoch[055] Batch [3599]/[3759] Speed: 67.510784 samples/sec accuracy=77.769965 loss=0.875707 lr=0.001000 Epoch[055] Batch [3649]/[3759] Speed: 67.675554 samples/sec accuracy=77.764555 loss=0.875442 lr=0.001000 Epoch[055] Batch [3699]/[3759] Speed: 68.069189 samples/sec accuracy=77.748733 loss=0.875630 lr=0.001000 Epoch[055] Batch [3749]/[3759] Speed: 77.050761 samples/sec accuracy=77.739167 loss=0.876079 lr=0.001000 Batch [0049]/[0303]: acc-top1=67.781250 acc-top5=87.718750 Batch [0099]/[0303]: acc-top1=67.140625 acc-top5=87.375000 Batch [0149]/[0303]: acc-top1=67.000000 acc-top5=87.395833 Batch [0199]/[0303]: acc-top1=67.078125 acc-top5=87.226562 Batch [0249]/[0303]: acc-top1=67.293750 acc-top5=87.281250 Batch [0299]/[0303]: acc-top1=67.109375 acc-top5=86.916667 [Epoch 055] training: accuracy=77.734271 loss=0.876198 [Epoch 055] speed: 67 samples/sec time cost: 3854.667700 [Epoch 055] validation: acc-top1=67.084365 acc-top5=86.906972 loss=1.580577 Epoch[056] Batch [0049]/[3760] Speed: 46.023564 samples/sec accuracy=78.125000 loss=0.869403 lr=0.001000 Epoch[056] Batch [0099]/[3760] Speed: 67.025568 samples/sec accuracy=78.250000 loss=0.859252 lr=0.001000 Epoch[056] Batch [0149]/[3760] Speed: 68.061163 samples/sec accuracy=77.968750 loss=0.875697 lr=0.001000 Epoch[056] Batch [0199]/[3760] Speed: 67.097964 samples/sec accuracy=77.859375 loss=0.874158 lr=0.001000 Epoch[056] Batch [0249]/[3760] Speed: 68.329136 samples/sec accuracy=77.887500 loss=0.872845 lr=0.001000 Epoch[056] Batch [0299]/[3760] Speed: 67.860453 samples/sec accuracy=78.015625 loss=0.869071 lr=0.001000 Epoch[056] Batch [0349]/[3760] Speed: 67.465186 samples/sec accuracy=78.062500 loss=0.865494 lr=0.001000 Epoch[056] Batch [0399]/[3760] Speed: 67.649113 samples/sec accuracy=78.117188 loss=0.863376 lr=0.001000 Epoch[056] Batch [0449]/[3760] Speed: 68.482716 samples/sec accuracy=78.211806 loss=0.861290 lr=0.001000 Epoch[056] Batch [0499]/[3760] Speed: 67.756696 samples/sec accuracy=78.246875 loss=0.863320 lr=0.001000 Epoch[056] Batch [0549]/[3760] Speed: 67.727032 samples/sec accuracy=78.360795 loss=0.859922 lr=0.001000 Epoch[056] Batch [0599]/[3760] Speed: 67.858749 samples/sec accuracy=78.315104 loss=0.859520 lr=0.001000 Epoch[056] Batch [0649]/[3760] Speed: 68.278652 samples/sec accuracy=78.317308 loss=0.858718 lr=0.001000 Epoch[056] Batch [0699]/[3760] Speed: 67.659559 samples/sec accuracy=78.372768 loss=0.855982 lr=0.001000 Epoch[056] Batch [0749]/[3760] Speed: 67.667319 samples/sec accuracy=78.287500 loss=0.858278 lr=0.001000 Epoch[056] Batch [0799]/[3760] Speed: 68.341572 samples/sec accuracy=78.267578 loss=0.856672 lr=0.001000 Epoch[056] Batch [0849]/[3760] Speed: 67.227811 samples/sec accuracy=78.235294 loss=0.858883 lr=0.001000 Epoch[056] 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accuracy=78.135417 loss=0.861022 lr=0.001000 Epoch[056] Batch [1399]/[3760] Speed: 67.858940 samples/sec accuracy=78.139509 loss=0.861277 lr=0.001000 Epoch[056] Batch [1449]/[3760] Speed: 67.934143 samples/sec accuracy=78.120690 loss=0.861129 lr=0.001000 Epoch[056] Batch [1499]/[3760] Speed: 68.204426 samples/sec accuracy=78.093750 loss=0.860881 lr=0.001000 Epoch[056] Batch [1549]/[3760] Speed: 67.876224 samples/sec accuracy=78.064516 loss=0.861541 lr=0.001000 Epoch[056] Batch [1599]/[3760] Speed: 67.812916 samples/sec accuracy=78.066406 loss=0.861929 lr=0.001000 Epoch[056] Batch [1649]/[3760] Speed: 67.901625 samples/sec accuracy=78.023674 loss=0.863101 lr=0.001000 Epoch[056] Batch [1699]/[3760] Speed: 68.194352 samples/sec accuracy=78.030331 loss=0.863112 lr=0.001000 Epoch[056] Batch [1749]/[3760] Speed: 67.815355 samples/sec accuracy=78.017857 loss=0.863262 lr=0.001000 Epoch[056] Batch [1799]/[3760] Speed: 67.004879 samples/sec accuracy=78.032118 loss=0.862955 lr=0.001000 Epoch[056] 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accuracy=77.971467 loss=0.864572 lr=0.001000 Epoch[056] Batch [2349]/[3760] Speed: 67.760358 samples/sec accuracy=77.960106 loss=0.865650 lr=0.001000 Epoch[056] Batch [2399]/[3760] Speed: 67.746073 samples/sec accuracy=77.962240 loss=0.865660 lr=0.001000 Epoch[056] Batch [2449]/[3760] Speed: 67.670796 samples/sec accuracy=77.959184 loss=0.865916 lr=0.001000 Epoch[056] Batch [2499]/[3760] Speed: 67.574298 samples/sec accuracy=77.963125 loss=0.865998 lr=0.001000 Epoch[056] Batch [2549]/[3760] Speed: 68.592101 samples/sec accuracy=77.993260 loss=0.865153 lr=0.001000 Epoch[056] Batch [2599]/[3760] Speed: 68.377986 samples/sec accuracy=77.981971 loss=0.866086 lr=0.001000 Epoch[056] Batch [2649]/[3760] Speed: 67.378054 samples/sec accuracy=78.002948 loss=0.865421 lr=0.001000 Epoch[056] Batch [2699]/[3760] Speed: 67.733496 samples/sec accuracy=78.010417 loss=0.865136 lr=0.001000 Epoch[056] Batch [2749]/[3760] Speed: 68.137603 samples/sec accuracy=78.001705 loss=0.865681 lr=0.001000 Epoch[056] 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Batch [3749]/[3760] Speed: 75.331558 samples/sec accuracy=77.940417 loss=0.867530 lr=0.001000 Batch [0049]/[0303]: acc-top1=68.843750 acc-top5=87.625000 Batch [0099]/[0303]: acc-top1=68.062500 acc-top5=87.281250 Batch [0149]/[0303]: acc-top1=67.635417 acc-top5=87.291667 Batch [0199]/[0303]: acc-top1=67.625000 acc-top5=87.242188 Batch [0249]/[0303]: acc-top1=67.825000 acc-top5=87.287500 Batch [0299]/[0303]: acc-top1=67.515625 acc-top5=87.041667 [Epoch 056] training: accuracy=77.935505 loss=0.867762 [Epoch 056] speed: 67 samples/sec time cost: 3854.539011 [Epoch 056] validation: acc-top1=67.507219 acc-top5=87.020421 loss=1.576898 Epoch[057] Batch [0049]/[3760] Speed: 47.104624 samples/sec accuracy=79.000000 loss=0.842653 lr=0.001000 Epoch[057] Batch [0099]/[3760] Speed: 66.956485 samples/sec accuracy=78.796875 loss=0.840823 lr=0.001000 Epoch[057] Batch [0149]/[3760] Speed: 67.693152 samples/sec accuracy=78.583333 loss=0.844290 lr=0.001000 Epoch[057] Batch [0199]/[3760] Speed: 67.926001 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67.399927 samples/sec accuracy=78.041496 loss=0.862323 lr=0.001000 Epoch[057] Batch [3099]/[3760] Speed: 67.647752 samples/sec accuracy=78.046371 loss=0.861729 lr=0.001000 Epoch[057] Batch [3149]/[3760] Speed: 68.094248 samples/sec accuracy=78.037698 loss=0.861966 lr=0.001000 Epoch[057] Batch [3199]/[3760] Speed: 67.614283 samples/sec accuracy=78.045898 loss=0.861803 lr=0.001000 Epoch[057] Batch [3249]/[3760] Speed: 68.095605 samples/sec accuracy=78.063942 loss=0.861294 lr=0.001000 Epoch[057] Batch [3299]/[3760] Speed: 67.775897 samples/sec accuracy=78.063920 loss=0.861046 lr=0.001000 Epoch[057] Batch [3349]/[3760] Speed: 67.820378 samples/sec accuracy=78.031250 loss=0.861882 lr=0.001000 Epoch[057] Batch [3399]/[3760] Speed: 67.939344 samples/sec accuracy=78.057445 loss=0.860948 lr=0.001000 Epoch[057] Batch [3449]/[3760] Speed: 67.678208 samples/sec accuracy=78.067029 loss=0.861092 lr=0.001000 Epoch[057] Batch [3499]/[3760] Speed: 68.412549 samples/sec accuracy=78.087946 loss=0.860754 lr=0.001000 Epoch[057] Batch [3549]/[3760] Speed: 67.919226 samples/sec accuracy=78.077905 loss=0.861236 lr=0.001000 Epoch[057] Batch [3599]/[3760] Speed: 68.152321 samples/sec accuracy=78.068576 loss=0.861545 lr=0.001000 Epoch[057] Batch [3649]/[3760] Speed: 67.687700 samples/sec accuracy=78.063356 loss=0.862344 lr=0.001000 Epoch[057] Batch [3699]/[3760] Speed: 68.354264 samples/sec accuracy=78.063767 loss=0.862147 lr=0.001000 Epoch[057] Batch [3749]/[3760] Speed: 75.710044 samples/sec accuracy=78.047083 loss=0.862598 lr=0.001000 Batch [0049]/[0303]: acc-top1=68.250000 acc-top5=87.218750 Batch [0099]/[0303]: acc-top1=67.593750 acc-top5=87.171875 Batch [0149]/[0303]: acc-top1=67.239583 acc-top5=87.239583 Batch [0199]/[0303]: acc-top1=67.398438 acc-top5=87.031250 Batch [0249]/[0303]: acc-top1=67.600000 acc-top5=87.112500 Batch [0299]/[0303]: acc-top1=67.296875 acc-top5=86.817708 [Epoch 057] training: accuracy=78.056017 loss=0.862279 [Epoch 057] speed: 67 samples/sec time cost: 3847.533442 [Epoch 057] validation: acc-top1=67.254538 acc-top5=86.793523 loss=1.579684 Epoch[058] Batch [0049]/[3759] Speed: 46.335392 samples/sec accuracy=77.125000 loss=0.874072 lr=0.001000 Epoch[058] Batch [0099]/[3759] Speed: 67.423404 samples/sec accuracy=77.953125 loss=0.857456 lr=0.001000 Epoch[058] Batch [0149]/[3759] Speed: 68.092183 samples/sec accuracy=78.104167 loss=0.856246 lr=0.001000 Epoch[058] Batch [0199]/[3759] Speed: 67.654778 samples/sec accuracy=78.281250 loss=0.844633 lr=0.001000 Epoch[058] Batch [0249]/[3759] Speed: 67.520039 samples/sec accuracy=78.225000 loss=0.844781 lr=0.001000 Epoch[058] Batch [0299]/[3759] Speed: 67.968055 samples/sec accuracy=78.229167 loss=0.844459 lr=0.001000 Epoch[058] Batch [0349]/[3759] Speed: 68.004345 samples/sec accuracy=78.187500 loss=0.845851 lr=0.001000 Epoch[058] Batch [0399]/[3759] Speed: 67.582265 samples/sec accuracy=78.128906 loss=0.846523 lr=0.001000 Epoch[058] Batch [0449]/[3759] Speed: 68.666419 samples/sec 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[0049]/[0303]: acc-top1=68.437500 acc-top5=87.718750 Batch [0099]/[0303]: acc-top1=67.578125 acc-top5=87.453125 Batch [0149]/[0303]: acc-top1=67.218750 acc-top5=87.208333 Batch [0199]/[0303]: acc-top1=67.289062 acc-top5=87.070312 Batch [0249]/[0303]: acc-top1=67.512500 acc-top5=87.231250 Batch [0299]/[0303]: acc-top1=67.255208 acc-top5=86.958333 [Epoch 058] training: accuracy=78.190260 loss=0.855527 [Epoch 058] speed: 67 samples/sec time cost: 3848.456859 [Epoch 058] validation: acc-top1=67.233911 acc-top5=86.932756 loss=1.567430 Epoch[059] Batch [0049]/[3760] Speed: 45.911259 samples/sec accuracy=78.843750 loss=0.835382 lr=0.001000 Epoch[059] Batch [0099]/[3760] Speed: 65.887172 samples/sec accuracy=78.890625 loss=0.831424 lr=0.001000 Epoch[059] Batch [0149]/[3760] Speed: 68.165257 samples/sec accuracy=79.135417 loss=0.819371 lr=0.001000 Epoch[059] Batch [0199]/[3760] Speed: 67.723991 samples/sec accuracy=78.984375 loss=0.820138 lr=0.001000 Epoch[059] Batch [0249]/[3760] Speed: 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lr=0.001000 Epoch[059] Batch [3599]/[3760] Speed: 67.778337 samples/sec accuracy=78.638455 loss=0.841052 lr=0.001000 Epoch[059] Batch [3649]/[3760] Speed: 67.874705 samples/sec accuracy=78.634418 loss=0.841217 lr=0.001000 Epoch[059] Batch [3699]/[3760] Speed: 68.007355 samples/sec accuracy=78.638514 loss=0.841330 lr=0.001000 Epoch[059] Batch [3749]/[3760] Speed: 76.905283 samples/sec accuracy=78.622917 loss=0.842081 lr=0.001000 Batch [0049]/[0303]: acc-top1=68.125000 acc-top5=87.500000 Batch [0099]/[0303]: acc-top1=67.718750 acc-top5=87.187500 Batch [0149]/[0303]: acc-top1=67.322917 acc-top5=87.041667 Batch [0199]/[0303]: acc-top1=67.312500 acc-top5=86.976562 Batch [0249]/[0303]: acc-top1=67.537500 acc-top5=87.050000 Batch [0299]/[0303]: acc-top1=67.270833 acc-top5=86.692708 [Epoch 059] training: accuracy=78.614943 loss=0.842343 [Epoch 059] speed: 67 samples/sec time cost: 3852.472449 [Epoch 059] validation: acc-top1=67.213284 acc-top5=86.695545 loss=1.595994 Epoch[060] Batch 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accuracy=78.687500 loss=0.837982 lr=0.001000 Epoch[060] Batch [3399]/[3760] Speed: 68.170887 samples/sec accuracy=78.684283 loss=0.838353 lr=0.001000 Epoch[060] Batch [3449]/[3760] Speed: 68.036932 samples/sec accuracy=78.659873 loss=0.838975 lr=0.001000 Epoch[060] Batch [3499]/[3760] Speed: 67.585840 samples/sec accuracy=78.657143 loss=0.839009 lr=0.001000 Epoch[060] Batch [3549]/[3760] Speed: 67.725470 samples/sec accuracy=78.656250 loss=0.839089 lr=0.001000 Epoch[060] Batch [3599]/[3760] Speed: 68.055893 samples/sec accuracy=78.658854 loss=0.838875 lr=0.001000 Epoch[060] Batch [3649]/[3760] Speed: 67.932698 samples/sec accuracy=78.637842 loss=0.839447 lr=0.001000 Epoch[060] Batch [3699]/[3760] Speed: 68.089167 samples/sec accuracy=78.639358 loss=0.839642 lr=0.001000 Epoch[060] Batch [3749]/[3760] Speed: 75.637641 samples/sec accuracy=78.645000 loss=0.839524 lr=0.001000 Batch [0049]/[0303]: acc-top1=67.750000 acc-top5=87.468750 Batch [0099]/[0303]: acc-top1=67.593750 acc-top5=87.250000 Batch [0149]/[0303]: acc-top1=67.302083 acc-top5=87.104167 Batch [0199]/[0303]: acc-top1=67.320312 acc-top5=87.023438 Batch [0249]/[0303]: acc-top1=67.518750 acc-top5=87.118750 Batch [0299]/[0303]: acc-top1=67.255208 acc-top5=86.723958 [Epoch 060] training: accuracy=78.652759 loss=0.839165 [Epoch 060] speed: 67 samples/sec time cost: 3852.931517 [Epoch 060] validation: acc-top1=67.197814 acc-top5=86.695545 loss=1.586621 Epoch[061] Batch [0049]/[3759] Speed: 46.572163 samples/sec accuracy=78.875000 loss=0.834314 lr=0.001000 Epoch[061] Batch [0099]/[3759] Speed: 67.161721 samples/sec accuracy=78.812500 loss=0.834706 lr=0.001000 Epoch[061] Batch [0149]/[3759] Speed: 67.932048 samples/sec accuracy=79.500000 loss=0.812781 lr=0.001000 Epoch[061] Batch [0199]/[3759] Speed: 68.026415 samples/sec accuracy=79.195312 loss=0.812326 lr=0.001000 Epoch[061] Batch [0249]/[3759] Speed: 68.410926 samples/sec accuracy=78.993750 loss=0.823353 lr=0.001000 Epoch[061] Batch [0299]/[3759] 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accuracy=78.847656 loss=0.828847 lr=0.001000 Epoch[061] Batch [3649]/[3759] Speed: 67.626959 samples/sec accuracy=78.838613 loss=0.828976 lr=0.001000 Epoch[061] Batch [3699]/[3759] Speed: 68.518000 samples/sec accuracy=78.820524 loss=0.829474 lr=0.001000 Epoch[061] Batch [3749]/[3759] Speed: 76.787515 samples/sec accuracy=78.800000 loss=0.830158 lr=0.001000 Batch [0049]/[0303]: acc-top1=66.968750 acc-top5=87.531250 Batch [0099]/[0303]: acc-top1=66.859375 acc-top5=87.281250 Batch [0149]/[0303]: acc-top1=66.677083 acc-top5=87.135417 Batch [0199]/[0303]: acc-top1=66.835938 acc-top5=86.914062 Batch [0249]/[0303]: acc-top1=67.037500 acc-top5=87.050000 Batch [0299]/[0303]: acc-top1=66.812500 acc-top5=86.671875 [Epoch 061] training: accuracy=78.801294 loss=0.830047 [Epoch 061] speed: 67 samples/sec time cost: 3849.623662 [Epoch 061] validation: acc-top1=66.805899 acc-top5=86.649134 loss=1.614434 Epoch[062] Batch [0049]/[3760] Speed: 46.849079 samples/sec accuracy=80.156250 loss=0.806745 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acc-top1=67.101562 acc-top5=87.023438 Batch [0249]/[0303]: acc-top1=67.206250 acc-top5=87.056250 Batch [0299]/[0303]: acc-top1=66.916667 acc-top5=86.666667 [Epoch 062] training: accuracy=79.143949 loss=0.822801 [Epoch 062] speed: 67 samples/sec time cost: 3852.876851 [Epoch 062] validation: acc-top1=66.893564 acc-top5=86.643977 loss=1.609123 Epoch[063] Batch [0049]/[3760] Speed: 46.331271 samples/sec accuracy=80.218750 loss=0.768001 lr=0.001000 Epoch[063] Batch [0099]/[3760] Speed: 66.999022 samples/sec accuracy=79.921875 loss=0.769827 lr=0.001000 Epoch[063] Batch [0149]/[3760] Speed: 66.754577 samples/sec accuracy=79.927083 loss=0.770925 lr=0.001000 Epoch[063] Batch [0199]/[3760] Speed: 68.147291 samples/sec accuracy=79.703125 loss=0.775382 lr=0.001000 Epoch[063] Batch [0249]/[3760] Speed: 68.194910 samples/sec accuracy=79.525000 loss=0.789936 lr=0.001000 Epoch[063] Batch [0299]/[3760] Speed: 67.742997 samples/sec accuracy=79.500000 loss=0.795821 lr=0.001000 Epoch[063] Batch 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accuracy=79.137731 loss=0.815831 lr=0.001000 Epoch[063] Batch [2749]/[3760] Speed: 67.833827 samples/sec accuracy=79.126136 loss=0.815944 lr=0.001000 Epoch[063] Batch [2799]/[3760] Speed: 67.812258 samples/sec accuracy=79.145089 loss=0.815425 lr=0.001000 Epoch[063] Batch [2849]/[3760] Speed: 67.958489 samples/sec accuracy=79.140351 loss=0.815615 lr=0.001000 Epoch[063] Batch [2899]/[3760] Speed: 67.538313 samples/sec accuracy=79.146552 loss=0.815271 lr=0.001000 Epoch[063] Batch [2949]/[3760] Speed: 68.707991 samples/sec accuracy=79.127648 loss=0.816103 lr=0.001000 Epoch[063] Batch [2999]/[3760] Speed: 67.658878 samples/sec accuracy=79.122396 loss=0.816226 lr=0.001000 Epoch[063] Batch [3049]/[3760] Speed: 67.146808 samples/sec accuracy=79.126025 loss=0.816210 lr=0.001000 Epoch[063] Batch [3099]/[3760] Speed: 68.203603 samples/sec accuracy=79.123992 loss=0.816169 lr=0.001000 Epoch[063] Batch [3149]/[3760] Speed: 67.458171 samples/sec accuracy=79.125496 loss=0.816154 lr=0.001000 Epoch[063] 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accuracy=79.154538 loss=0.816410 lr=0.001000 Epoch[063] Batch [3699]/[3760] Speed: 68.244401 samples/sec accuracy=79.159628 loss=0.815997 lr=0.001000 Epoch[063] Batch [3749]/[3760] Speed: 76.820176 samples/sec accuracy=79.176667 loss=0.815699 lr=0.001000 Batch [0049]/[0303]: acc-top1=68.281250 acc-top5=87.468750 Batch [0099]/[0303]: acc-top1=67.859375 acc-top5=87.203125 Batch [0149]/[0303]: acc-top1=67.406250 acc-top5=87.052083 Batch [0199]/[0303]: acc-top1=67.398438 acc-top5=86.906250 Batch [0249]/[0303]: acc-top1=67.456250 acc-top5=86.968750 Batch [0299]/[0303]: acc-top1=67.208333 acc-top5=86.562500 [Epoch 063] training: accuracy=79.173870 loss=0.815825 [Epoch 063] speed: 67 samples/sec time cost: 3851.382221 [Epoch 063] validation: acc-top1=67.125619 acc-top5=86.525371 loss=1.626264 Epoch[064] Batch [0049]/[3759] Speed: 46.109219 samples/sec accuracy=79.250000 loss=0.803581 lr=0.001000 Epoch[064] Batch [0099]/[3759] Speed: 66.104254 samples/sec accuracy=79.343750 loss=0.805489 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[0299]/[0303]: acc-top1=67.208333 acc-top5=86.645833 [Epoch 064] training: accuracy=79.497539 loss=0.807600 [Epoch 064] speed: 67 samples/sec time cost: 3850.745680 [Epoch 064] validation: acc-top1=67.146246 acc-top5=86.623350 loss=1.587002 Epoch[065] Batch [0049]/[3760] Speed: 46.698775 samples/sec accuracy=80.500000 loss=0.781836 lr=0.001000 Epoch[065] Batch [0099]/[3760] Speed: 66.091456 samples/sec accuracy=80.437500 loss=0.782525 lr=0.001000 Epoch[065] Batch [0149]/[3760] Speed: 69.263195 samples/sec accuracy=80.427083 loss=0.789292 lr=0.001000 Epoch[065] Batch [0199]/[3760] Speed: 67.635594 samples/sec accuracy=80.125000 loss=0.791700 lr=0.001000 Epoch[065] Batch [0249]/[3760] Speed: 68.172331 samples/sec accuracy=80.200000 loss=0.791911 lr=0.001000 Epoch[065] Batch [0299]/[3760] Speed: 66.861604 samples/sec accuracy=80.062500 loss=0.799175 lr=0.001000 Epoch[065] Batch [0349]/[3760] Speed: 68.751195 samples/sec accuracy=80.098214 loss=0.793300 lr=0.001000 Epoch[065] Batch 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accuracy=79.621023 loss=0.801880 lr=0.001000 Epoch[065] Batch [2799]/[3760] Speed: 67.534015 samples/sec accuracy=79.613281 loss=0.801710 lr=0.001000 Epoch[065] Batch [2849]/[3760] Speed: 68.026945 samples/sec accuracy=79.591557 loss=0.801756 lr=0.001000 Epoch[065] Batch [2899]/[3760] Speed: 67.312847 samples/sec accuracy=79.598599 loss=0.801272 lr=0.001000 Epoch[065] Batch [2949]/[3760] Speed: 67.737733 samples/sec accuracy=79.622881 loss=0.800504 lr=0.001000 Epoch[065] Batch [2999]/[3760] Speed: 67.430666 samples/sec accuracy=79.600521 loss=0.800785 lr=0.001000 Epoch[065] Batch [3049]/[3760] Speed: 68.621924 samples/sec accuracy=79.592213 loss=0.801372 lr=0.001000 Epoch[065] Batch [3099]/[3760] Speed: 67.818809 samples/sec accuracy=79.595766 loss=0.801156 lr=0.001000 Epoch[065] Batch [3149]/[3760] Speed: 68.237827 samples/sec accuracy=79.583333 loss=0.801232 lr=0.001000 Epoch[065] Batch [3199]/[3760] Speed: 67.621701 samples/sec accuracy=79.575195 loss=0.801409 lr=0.001000 Epoch[065] 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accuracy=79.593750 loss=0.800909 lr=0.001000 Epoch[065] Batch [3749]/[3760] Speed: 77.303796 samples/sec accuracy=79.582917 loss=0.801246 lr=0.001000 Batch [0049]/[0303]: acc-top1=67.812500 acc-top5=87.062500 Batch [0099]/[0303]: acc-top1=67.406250 acc-top5=86.968750 Batch [0149]/[0303]: acc-top1=67.145833 acc-top5=86.885417 Batch [0199]/[0303]: acc-top1=67.187500 acc-top5=86.695312 Batch [0249]/[0303]: acc-top1=67.437500 acc-top5=86.793750 Batch [0299]/[0303]: acc-top1=67.177083 acc-top5=86.515625 [Epoch 065] training: accuracy=79.581117 loss=0.801276 [Epoch 065] speed: 67 samples/sec time cost: 3851.983606 [Epoch 065] validation: acc-top1=67.151403 acc-top5=86.468647 loss=1.610772 Epoch[066] Batch [0049]/[3759] Speed: 46.766245 samples/sec accuracy=79.250000 loss=0.805427 lr=0.001000 Epoch[066] Batch [0099]/[3759] Speed: 66.876164 samples/sec accuracy=79.375000 loss=0.805353 lr=0.001000 Epoch[066] Batch [0149]/[3759] Speed: 67.188257 samples/sec accuracy=79.635417 loss=0.808169 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68.471397 samples/sec accuracy=79.636607 loss=0.795664 lr=0.001000 Epoch[066] Batch [3549]/[3759] Speed: 67.165832 samples/sec accuracy=79.625440 loss=0.796063 lr=0.001000 Epoch[066] Batch [3599]/[3759] Speed: 67.547511 samples/sec accuracy=79.627604 loss=0.795887 lr=0.001000 Epoch[066] Batch [3649]/[3759] Speed: 68.247795 samples/sec accuracy=79.627997 loss=0.795890 lr=0.001000 Epoch[066] Batch [3699]/[3759] Speed: 68.210433 samples/sec accuracy=79.619510 loss=0.795704 lr=0.001000 Epoch[066] Batch [3749]/[3759] Speed: 77.021524 samples/sec accuracy=79.620833 loss=0.795956 lr=0.001000 Batch [0049]/[0303]: acc-top1=67.343750 acc-top5=87.593750 Batch [0099]/[0303]: acc-top1=67.156250 acc-top5=87.218750 Batch [0149]/[0303]: acc-top1=66.729167 acc-top5=87.187500 Batch [0199]/[0303]: acc-top1=66.773438 acc-top5=87.046875 Batch [0249]/[0303]: acc-top1=66.943750 acc-top5=87.106250 Batch [0299]/[0303]: acc-top1=66.734375 acc-top5=86.671875 [Epoch 066] training: accuracy=79.615174 loss=0.796055 [Epoch 066] speed: 67 samples/sec time cost: 3851.531679 [Epoch 066] validation: acc-top1=66.707921 acc-top5=86.649134 loss=1.655775 Epoch[067] Batch [0049]/[3760] Speed: 45.999570 samples/sec accuracy=79.593750 loss=0.805852 lr=0.001000 Epoch[067] Batch [0099]/[3760] Speed: 66.759114 samples/sec accuracy=80.140625 loss=0.788976 lr=0.001000 Epoch[067] Batch [0149]/[3760] Speed: 67.882916 samples/sec accuracy=80.000000 loss=0.787756 lr=0.001000 Epoch[067] Batch [0199]/[3760] Speed: 67.962019 samples/sec accuracy=80.218750 loss=0.772985 lr=0.001000 Epoch[067] Batch [0249]/[3760] Speed: 67.744226 samples/sec accuracy=80.481250 loss=0.766255 lr=0.001000 Epoch[067] Batch [0299]/[3760] Speed: 68.109546 samples/sec accuracy=80.546875 loss=0.765065 lr=0.001000 Epoch[067] Batch [0349]/[3760] Speed: 68.635486 samples/sec accuracy=80.468750 loss=0.765808 lr=0.001000 Epoch[067] Batch [0399]/[3760] Speed: 68.203732 samples/sec accuracy=80.640625 loss=0.761098 lr=0.001000 Epoch[067] Batch 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accuracy=80.196181 loss=0.777371 lr=0.001000 Epoch[067] Batch [0949]/[3760] Speed: 67.280696 samples/sec accuracy=80.161184 loss=0.779049 lr=0.001000 Epoch[067] Batch [0999]/[3760] Speed: 68.144899 samples/sec accuracy=80.140625 loss=0.778457 lr=0.001000 Epoch[067] Batch [1049]/[3760] Speed: 66.928029 samples/sec accuracy=80.132440 loss=0.778369 lr=0.001000 Epoch[067] Batch [1099]/[3760] Speed: 68.586782 samples/sec accuracy=80.129261 loss=0.778258 lr=0.001000 Epoch[067] Batch [1149]/[3760] Speed: 68.287860 samples/sec accuracy=80.129076 loss=0.777965 lr=0.001000 Epoch[067] Batch [1199]/[3760] Speed: 67.884536 samples/sec accuracy=80.122396 loss=0.778592 lr=0.001000 Epoch[067] Batch [1249]/[3760] Speed: 67.952088 samples/sec accuracy=80.105000 loss=0.779254 lr=0.001000 Epoch[067] Batch [1299]/[3760] Speed: 67.351608 samples/sec accuracy=80.084135 loss=0.780582 lr=0.001000 Epoch[067] Batch [1349]/[3760] Speed: 68.172314 samples/sec accuracy=80.070602 loss=0.781262 lr=0.001000 Epoch[067] 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accuracy=80.026182 loss=0.783157 lr=0.001000 Epoch[067] Batch [1899]/[3760] Speed: 67.335399 samples/sec accuracy=80.018914 loss=0.783334 lr=0.001000 Epoch[067] Batch [1949]/[3760] Speed: 67.914196 samples/sec accuracy=80.035256 loss=0.782488 lr=0.001000 Epoch[067] Batch [1999]/[3760] Speed: 67.700663 samples/sec accuracy=80.026562 loss=0.782671 lr=0.001000 Epoch[067] Batch [2049]/[3760] Speed: 67.662243 samples/sec accuracy=80.015244 loss=0.783145 lr=0.001000 Epoch[067] Batch [2099]/[3760] Speed: 68.050785 samples/sec accuracy=80.008929 loss=0.783502 lr=0.001000 Epoch[067] Batch [2149]/[3760] Speed: 68.123738 samples/sec accuracy=79.998547 loss=0.784050 lr=0.001000 Epoch[067] Batch [2199]/[3760] Speed: 67.804731 samples/sec accuracy=79.993608 loss=0.784306 lr=0.001000 Epoch[067] Batch [2249]/[3760] Speed: 67.670874 samples/sec accuracy=80.028472 loss=0.784053 lr=0.001000 Epoch[067] Batch [2299]/[3760] Speed: 67.371325 samples/sec accuracy=79.999321 loss=0.784533 lr=0.001000 Epoch[067] 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accuracy=79.991629 loss=0.784659 lr=0.001000 Epoch[067] Batch [2849]/[3760] Speed: 68.442230 samples/sec accuracy=79.995066 loss=0.784832 lr=0.001000 Epoch[067] Batch [2899]/[3760] Speed: 66.950233 samples/sec accuracy=79.989763 loss=0.785158 lr=0.001000 Epoch[067] Batch [2949]/[3760] Speed: 67.826051 samples/sec accuracy=79.962394 loss=0.786092 lr=0.001000 Epoch[067] Batch [2999]/[3760] Speed: 67.891977 samples/sec accuracy=79.961458 loss=0.786265 lr=0.001000 Epoch[067] Batch [3049]/[3760] Speed: 67.887903 samples/sec accuracy=79.944672 loss=0.786908 lr=0.001000 Epoch[067] Batch [3099]/[3760] Speed: 66.930932 samples/sec accuracy=79.940524 loss=0.786842 lr=0.001000 Epoch[067] Batch [3149]/[3760] Speed: 68.445298 samples/sec accuracy=79.940476 loss=0.787120 lr=0.001000 Epoch[067] Batch [3199]/[3760] Speed: 67.528874 samples/sec accuracy=79.922852 loss=0.787815 lr=0.001000 Epoch[067] Batch [3249]/[3760] Speed: 68.518433 samples/sec accuracy=79.909615 loss=0.788449 lr=0.001000 Epoch[067] Batch [3299]/[3760] Speed: 66.563385 samples/sec accuracy=79.901989 loss=0.788450 lr=0.001000 Epoch[067] Batch [3349]/[3760] Speed: 68.950712 samples/sec accuracy=79.881063 loss=0.788746 lr=0.001000 Epoch[067] Batch [3399]/[3760] Speed: 67.931490 samples/sec accuracy=79.895680 loss=0.787804 lr=0.001000 Epoch[067] Batch [3449]/[3760] Speed: 67.894958 samples/sec accuracy=79.891304 loss=0.787900 lr=0.001000 Epoch[067] Batch [3499]/[3760] Speed: 67.399564 samples/sec accuracy=79.879018 loss=0.788425 lr=0.001000 Epoch[067] Batch [3549]/[3760] Speed: 68.542289 samples/sec accuracy=79.873680 loss=0.788286 lr=0.001000 Epoch[067] Batch [3599]/[3760] Speed: 67.194928 samples/sec accuracy=79.882812 loss=0.788335 lr=0.001000 Epoch[067] Batch [3649]/[3760] Speed: 67.909568 samples/sec accuracy=79.884418 loss=0.788500 lr=0.001000 Epoch[067] Batch [3699]/[3760] Speed: 67.528991 samples/sec accuracy=79.857686 loss=0.789272 lr=0.001000 Epoch[067] Batch [3749]/[3760] Speed: 76.269871 samples/sec accuracy=79.857500 loss=0.789363 lr=0.001000 Batch [0049]/[0303]: acc-top1=68.218750 acc-top5=87.437500 Batch [0099]/[0303]: acc-top1=67.453125 acc-top5=87.125000 Batch [0149]/[0303]: acc-top1=67.208333 acc-top5=87.135417 Batch [0199]/[0303]: acc-top1=67.273438 acc-top5=87.039062 Batch [0249]/[0303]: acc-top1=67.443750 acc-top5=87.062500 Batch [0299]/[0303]: acc-top1=67.171875 acc-top5=86.645833 [Epoch 067] training: accuracy=79.854139 loss=0.789537 [Epoch 067] speed: 67 samples/sec time cost: 3853.969000 [Epoch 067] validation: acc-top1=67.135932 acc-top5=86.628507 loss=1.624829 Epoch[068] Batch [0049]/[3760] Speed: 46.350635 samples/sec accuracy=78.437500 loss=0.830753 lr=0.001000 Epoch[068] Batch [0099]/[3760] Speed: 67.239259 samples/sec accuracy=78.984375 loss=0.824111 lr=0.001000 Epoch[068] Batch [0149]/[3760] Speed: 67.633852 samples/sec accuracy=79.583333 loss=0.803363 lr=0.001000 Epoch[068] Batch [0199]/[3760] Speed: 67.670743 samples/sec accuracy=79.703125 loss=0.799637 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acc-top5=86.422236 loss=1.654462 Epoch[069] Batch [0049]/[3759] Speed: 46.483916 samples/sec accuracy=81.687500 loss=0.708442 lr=0.001000 Epoch[069] Batch [0099]/[3759] Speed: 66.772421 samples/sec accuracy=81.296875 loss=0.735804 lr=0.001000 Epoch[069] Batch [0149]/[3759] Speed: 66.545488 samples/sec accuracy=81.031250 loss=0.749982 lr=0.001000 Epoch[069] Batch [0199]/[3759] Speed: 68.935100 samples/sec accuracy=80.546875 loss=0.762382 lr=0.001000 Epoch[069] Batch [0249]/[3759] Speed: 67.495896 samples/sec accuracy=80.381250 loss=0.766644 lr=0.001000 Epoch[069] Batch [0299]/[3759] Speed: 68.182163 samples/sec accuracy=80.385417 loss=0.763688 lr=0.001000 Epoch[069] Batch [0349]/[3759] Speed: 68.269569 samples/sec accuracy=80.321429 loss=0.765454 lr=0.001000 Epoch[069] Batch [0399]/[3759] Speed: 68.055476 samples/sec accuracy=80.324219 loss=0.766320 lr=0.001000 Epoch[069] Batch [0449]/[3759] Speed: 67.343260 samples/sec accuracy=80.378472 loss=0.764356 lr=0.001000 Epoch[069] Batch 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accuracy=80.379112 loss=0.768602 lr=0.001000 Epoch[069] Batch [1949]/[3759] Speed: 67.479025 samples/sec accuracy=80.354167 loss=0.769727 lr=0.001000 Epoch[069] Batch [1999]/[3759] Speed: 68.153594 samples/sec accuracy=80.367969 loss=0.769086 lr=0.001000 Epoch[069] Batch [2049]/[3759] Speed: 67.226494 samples/sec accuracy=80.345274 loss=0.769451 lr=0.001000 Epoch[069] Batch [2099]/[3759] Speed: 68.328032 samples/sec accuracy=80.336310 loss=0.769841 lr=0.001000 Epoch[069] Batch [2149]/[3759] Speed: 68.005657 samples/sec accuracy=80.345203 loss=0.769745 lr=0.001000 Epoch[069] Batch [2199]/[3759] Speed: 68.158526 samples/sec accuracy=80.323153 loss=0.770624 lr=0.001000 Epoch[069] Batch [2249]/[3759] Speed: 66.687896 samples/sec accuracy=80.291667 loss=0.771361 lr=0.001000 Epoch[069] Batch [2299]/[3759] Speed: 67.839207 samples/sec accuracy=80.296196 loss=0.770824 lr=0.001000 Epoch[069] Batch [2349]/[3759] Speed: 68.274571 samples/sec accuracy=80.289894 loss=0.771831 lr=0.001000 Epoch[069] 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accuracy=80.282346 loss=0.771363 lr=0.001000 Epoch[069] Batch [2899]/[3759] Speed: 67.618377 samples/sec accuracy=80.289871 loss=0.771120 lr=0.001000 Epoch[069] Batch [2949]/[3759] Speed: 68.588758 samples/sec accuracy=80.290784 loss=0.771375 lr=0.001000 Epoch[069] Batch [2999]/[3759] Speed: 67.632132 samples/sec accuracy=80.291146 loss=0.771329 lr=0.001000 Epoch[069] Batch [3049]/[3759] Speed: 67.278317 samples/sec accuracy=80.285861 loss=0.771768 lr=0.001000 Epoch[069] Batch [3099]/[3759] Speed: 67.245157 samples/sec accuracy=80.269153 loss=0.772191 lr=0.001000 Epoch[069] Batch [3149]/[3759] Speed: 67.754886 samples/sec accuracy=80.250000 loss=0.772725 lr=0.001000 Epoch[069] Batch [3199]/[3759] Speed: 67.697818 samples/sec accuracy=80.246582 loss=0.772500 lr=0.001000 Epoch[069] Batch [3249]/[3759] Speed: 67.833581 samples/sec accuracy=80.235096 loss=0.772859 lr=0.001000 Epoch[069] Batch [3299]/[3759] Speed: 66.607137 samples/sec accuracy=80.208807 loss=0.773774 lr=0.001000 Epoch[069] 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[0099]/[0303]: acc-top1=67.359375 acc-top5=86.765625 Batch [0149]/[0303]: acc-top1=66.802083 acc-top5=86.677083 Batch [0199]/[0303]: acc-top1=66.804688 acc-top5=86.562500 Batch [0249]/[0303]: acc-top1=66.937500 acc-top5=86.668750 Batch [0299]/[0303]: acc-top1=66.687500 acc-top5=86.348958 [Epoch 069] training: accuracy=80.177158 loss=0.776091 [Epoch 069] speed: 67 samples/sec time cost: 3853.832635 [Epoch 069] validation: acc-top1=66.666667 acc-top5=86.319101 loss=1.651571 Epoch[070] Batch [0049]/[3760] Speed: 46.360393 samples/sec accuracy=80.812500 loss=0.755473 lr=0.001000 Epoch[070] Batch [0099]/[3760] Speed: 67.310283 samples/sec accuracy=80.437500 loss=0.765938 lr=0.001000 Epoch[070] Batch [0149]/[3760] Speed: 67.447557 samples/sec accuracy=80.614583 loss=0.757711 lr=0.001000 Epoch[070] Batch [0199]/[3760] Speed: 68.023620 samples/sec accuracy=80.250000 loss=0.765863 lr=0.001000 Epoch[070] Batch [0249]/[3760] Speed: 67.472253 samples/sec accuracy=80.368750 loss=0.766690 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67.842559 samples/sec accuracy=80.516927 loss=0.766722 lr=0.001000 Epoch[070] Batch [3649]/[3760] Speed: 67.800145 samples/sec accuracy=80.508990 loss=0.766914 lr=0.001000 Epoch[070] Batch [3699]/[3760] Speed: 67.679092 samples/sec accuracy=80.503801 loss=0.767119 lr=0.001000 Epoch[070] Batch [3749]/[3760] Speed: 77.138796 samples/sec accuracy=80.503333 loss=0.767101 lr=0.001000 Batch [0049]/[0303]: acc-top1=67.406250 acc-top5=87.250000 Batch [0099]/[0303]: acc-top1=67.109375 acc-top5=86.765625 Batch [0149]/[0303]: acc-top1=66.791667 acc-top5=86.614583 Batch [0199]/[0303]: acc-top1=67.031250 acc-top5=86.500000 Batch [0249]/[0303]: acc-top1=67.212500 acc-top5=86.650000 Batch [0299]/[0303]: acc-top1=66.755208 acc-top5=86.328125 [Epoch 070] training: accuracy=80.502410 loss=0.767152 [Epoch 070] speed: 67 samples/sec time cost: 3854.000468 [Epoch 070] validation: acc-top1=66.723391 acc-top5=86.288160 loss=1.669147 Epoch[071] Batch [0049]/[3760] Speed: 46.195938 samples/sec accuracy=80.687500 loss=0.768149 lr=0.001000 Epoch[071] Batch [0099]/[3760] Speed: 66.447559 samples/sec accuracy=80.765625 loss=0.760903 lr=0.001000 Epoch[071] Batch [0149]/[3760] Speed: 67.835797 samples/sec accuracy=80.489583 loss=0.773121 lr=0.001000 Epoch[071] Batch [0199]/[3760] Speed: 67.864954 samples/sec accuracy=80.992188 loss=0.755513 lr=0.001000 Epoch[071] Batch [0249]/[3760] Speed: 67.668814 samples/sec accuracy=80.918750 loss=0.751778 lr=0.001000 Epoch[071] Batch [0299]/[3760] Speed: 67.713294 samples/sec accuracy=81.020833 loss=0.748485 lr=0.001000 Epoch[071] Batch [0349]/[3760] Speed: 68.643449 samples/sec accuracy=80.919643 loss=0.752006 lr=0.001000 Epoch[071] Batch [0399]/[3760] Speed: 67.838043 samples/sec accuracy=80.964844 loss=0.750181 lr=0.001000 Epoch[071] Batch [0449]/[3760] Speed: 68.283306 samples/sec accuracy=80.878472 loss=0.750258 lr=0.001000 Epoch[071] Batch [0499]/[3760] Speed: 66.757094 samples/sec accuracy=80.940625 loss=0.749574 lr=0.001000 Epoch[071] 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accuracy=80.745994 loss=0.761551 lr=0.001000 Epoch[071] Batch [1999]/[3760] Speed: 68.031213 samples/sec accuracy=80.764844 loss=0.760850 lr=0.001000 Epoch[071] Batch [2049]/[3760] Speed: 67.650433 samples/sec accuracy=80.767530 loss=0.760076 lr=0.001000 Epoch[071] Batch [2099]/[3760] Speed: 67.322282 samples/sec accuracy=80.747024 loss=0.759802 lr=0.001000 Epoch[071] Batch [2149]/[3760] Speed: 67.782334 samples/sec accuracy=80.750727 loss=0.759907 lr=0.001000 Epoch[071] Batch [2199]/[3760] Speed: 68.027043 samples/sec accuracy=80.720170 loss=0.761180 lr=0.001000 Epoch[071] Batch [2249]/[3760] Speed: 67.476393 samples/sec accuracy=80.710417 loss=0.762226 lr=0.001000 Epoch[071] Batch [2299]/[3760] Speed: 67.994621 samples/sec accuracy=80.739810 loss=0.761825 lr=0.001000 Epoch[071] Batch [2349]/[3760] Speed: 67.674334 samples/sec accuracy=80.756649 loss=0.761296 lr=0.001000 Epoch[071] Batch [2399]/[3760] Speed: 68.438593 samples/sec accuracy=80.718099 loss=0.761909 lr=0.001000 Epoch[071] 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accuracy=80.688578 loss=0.762401 lr=0.001000 Epoch[071] Batch [2949]/[3760] Speed: 68.017823 samples/sec accuracy=80.704449 loss=0.761818 lr=0.001000 Epoch[071] Batch [2999]/[3760] Speed: 67.583799 samples/sec accuracy=80.717708 loss=0.761201 lr=0.001000 Epoch[071] Batch [3049]/[3760] Speed: 67.941491 samples/sec accuracy=80.724898 loss=0.760812 lr=0.001000 Epoch[071] Batch [3099]/[3760] Speed: 67.372026 samples/sec accuracy=80.709173 loss=0.760797 lr=0.001000 Epoch[071] Batch [3149]/[3760] Speed: 67.778684 samples/sec accuracy=80.686508 loss=0.760861 lr=0.001000 Epoch[071] Batch [3199]/[3760] Speed: 68.041810 samples/sec accuracy=80.675293 loss=0.760932 lr=0.001000 Epoch[071] Batch [3249]/[3760] Speed: 68.107147 samples/sec accuracy=80.671635 loss=0.761188 lr=0.001000 Epoch[071] Batch [3299]/[3760] Speed: 67.775999 samples/sec accuracy=80.671402 loss=0.761485 lr=0.001000 Epoch[071] Batch [3349]/[3760] Speed: 67.599928 samples/sec accuracy=80.672575 loss=0.761234 lr=0.001000 Epoch[071] Batch [3399]/[3760] Speed: 68.348433 samples/sec accuracy=80.680607 loss=0.760818 lr=0.001000 Epoch[071] Batch [3449]/[3760] Speed: 67.836090 samples/sec accuracy=80.681159 loss=0.760681 lr=0.001000 Epoch[071] Batch [3499]/[3760] Speed: 67.700143 samples/sec accuracy=80.684821 loss=0.760459 lr=0.001000 Epoch[071] Batch [3549]/[3760] Speed: 67.703728 samples/sec accuracy=80.686180 loss=0.760412 lr=0.001000 Epoch[071] Batch [3599]/[3760] Speed: 68.045793 samples/sec accuracy=80.696181 loss=0.760146 lr=0.001000 Epoch[071] Batch [3649]/[3760] Speed: 68.027344 samples/sec accuracy=80.677226 loss=0.760613 lr=0.001000 Epoch[071] Batch [3699]/[3760] Speed: 68.298343 samples/sec accuracy=80.666385 loss=0.760775 lr=0.001000 Epoch[071] Batch [3749]/[3760] Speed: 74.942701 samples/sec accuracy=80.676250 loss=0.760777 lr=0.001000 Batch [0049]/[0303]: acc-top1=68.281250 acc-top5=87.218750 Batch [0099]/[0303]: acc-top1=67.500000 acc-top5=86.750000 Batch [0149]/[0303]: acc-top1=66.958333 acc-top5=86.510417 Batch [0199]/[0303]: acc-top1=66.953125 acc-top5=86.453125 Batch [0249]/[0303]: acc-top1=67.118750 acc-top5=86.637500 Batch [0299]/[0303]: acc-top1=66.713542 acc-top5=86.270833 [Epoch 071] training: accuracy=80.672374 loss=0.760992 [Epoch 071] speed: 67 samples/sec time cost: 3852.416779 [Epoch 071] validation: acc-top1=66.697607 acc-top5=86.246906 loss=1.662565 Epoch[072] Batch [0049]/[3759] Speed: 46.590005 samples/sec accuracy=80.593750 loss=0.745668 lr=0.001000 Epoch[072] Batch [0099]/[3759] Speed: 66.843690 samples/sec accuracy=80.375000 loss=0.751227 lr=0.001000 Epoch[072] Batch [0149]/[3759] Speed: 67.609278 samples/sec accuracy=80.822917 loss=0.738615 lr=0.001000 Epoch[072] Batch [0199]/[3759] Speed: 66.870589 samples/sec accuracy=80.796875 loss=0.744810 lr=0.001000 Epoch[072] Batch [0249]/[3759] Speed: 68.381798 samples/sec accuracy=80.556250 loss=0.754257 lr=0.001000 Epoch[072] Batch [0299]/[3759] Speed: 67.600998 samples/sec accuracy=80.697917 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accuracy=81.037500 loss=0.741606 lr=0.001000 Epoch[072] Batch [1299]/[3759] Speed: 67.881510 samples/sec accuracy=81.063702 loss=0.741217 lr=0.001000 Epoch[072] Batch [1349]/[3759] Speed: 67.128715 samples/sec accuracy=81.006944 loss=0.741310 lr=0.001000 Epoch[072] Batch [1399]/[3759] Speed: 68.344736 samples/sec accuracy=81.005580 loss=0.742050 lr=0.001000 Epoch[072] Batch [1449]/[3759] Speed: 67.975077 samples/sec accuracy=81.014009 loss=0.740975 lr=0.001000 Epoch[072] Batch [1499]/[3759] Speed: 67.925047 samples/sec accuracy=81.001042 loss=0.742586 lr=0.001000 Epoch[072] Batch [1549]/[3759] Speed: 67.377007 samples/sec accuracy=81.003024 loss=0.742765 lr=0.001000 Epoch[072] Batch [1599]/[3759] Speed: 67.331305 samples/sec accuracy=81.014648 loss=0.742064 lr=0.001000 Epoch[072] Batch [1649]/[3759] Speed: 68.874091 samples/sec accuracy=80.979167 loss=0.742806 lr=0.001000 Epoch[072] Batch [1699]/[3759] Speed: 67.553369 samples/sec accuracy=81.005515 loss=0.742765 lr=0.001000 Epoch[072] 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Batch [3649]/[3759] Speed: 68.230373 samples/sec accuracy=80.684503 loss=0.754903 lr=0.001000 Epoch[072] Batch [3699]/[3759] Speed: 67.990862 samples/sec accuracy=80.665541 loss=0.755378 lr=0.001000 Epoch[072] Batch [3749]/[3759] Speed: 77.259628 samples/sec accuracy=80.662917 loss=0.755428 lr=0.001000 Batch [0049]/[0303]: acc-top1=68.062500 acc-top5=87.281250 Batch [0099]/[0303]: acc-top1=67.750000 acc-top5=86.671875 Batch [0149]/[0303]: acc-top1=67.239583 acc-top5=86.604167 Batch [0199]/[0303]: acc-top1=67.335938 acc-top5=86.484375 Batch [0249]/[0303]: acc-top1=67.625000 acc-top5=86.650000 Batch [0299]/[0303]: acc-top1=67.276042 acc-top5=86.296875 [Epoch 072] training: accuracy=80.665569 loss=0.755318 [Epoch 072] speed: 67 samples/sec time cost: 3852.614563 [Epoch 072] validation: acc-top1=67.233911 acc-top5=86.288160 loss=1.673988 Epoch[073] Batch [0049]/[3760] Speed: 45.928749 samples/sec accuracy=81.500000 loss=0.731882 lr=0.001000 Epoch[073] Batch [0099]/[3760] Speed: 66.337357 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lr=0.001000 Epoch[073] Batch [3449]/[3760] Speed: 68.297331 samples/sec accuracy=80.835598 loss=0.750901 lr=0.001000 Epoch[073] Batch [3499]/[3760] Speed: 68.097116 samples/sec accuracy=80.824554 loss=0.751593 lr=0.001000 Epoch[073] Batch [3549]/[3760] Speed: 67.379244 samples/sec accuracy=80.813820 loss=0.751773 lr=0.001000 Epoch[073] Batch [3599]/[3760] Speed: 67.286666 samples/sec accuracy=80.802083 loss=0.751984 lr=0.001000 Epoch[073] Batch [3649]/[3760] Speed: 68.760000 samples/sec accuracy=80.795377 loss=0.752056 lr=0.001000 Epoch[073] Batch [3699]/[3760] Speed: 67.871109 samples/sec accuracy=80.802365 loss=0.751740 lr=0.001000 Epoch[073] Batch [3749]/[3760] Speed: 76.579363 samples/sec accuracy=80.792500 loss=0.751852 lr=0.001000 Batch [0049]/[0303]: acc-top1=68.531250 acc-top5=87.281250 Batch [0099]/[0303]: acc-top1=67.640625 acc-top5=86.906250 Batch [0149]/[0303]: acc-top1=67.000000 acc-top5=86.593750 Batch [0199]/[0303]: acc-top1=67.039062 acc-top5=86.507812 Batch [0249]/[0303]: acc-top1=67.237500 acc-top5=86.643750 Batch [0299]/[0303]: acc-top1=66.911458 acc-top5=86.333333 [Epoch 073] training: accuracy=80.791639 loss=0.751994 [Epoch 073] speed: 67 samples/sec time cost: 3851.061266 [Epoch 073] validation: acc-top1=66.893564 acc-top5=86.298474 loss=1.679862 Epoch[074] Batch [0049]/[3760] Speed: 46.276297 samples/sec accuracy=82.156250 loss=0.730154 lr=0.001000 Epoch[074] Batch [0099]/[3760] Speed: 67.172356 samples/sec accuracy=82.125000 loss=0.714922 lr=0.001000 Epoch[074] Batch [0149]/[3760] Speed: 68.216749 samples/sec accuracy=82.125000 loss=0.710787 lr=0.001000 Epoch[074] Batch [0199]/[3760] Speed: 67.505850 samples/sec accuracy=81.796875 loss=0.724382 lr=0.001000 Epoch[074] Batch [0249]/[3760] Speed: 68.209084 samples/sec accuracy=81.806250 loss=0.723917 lr=0.001000 Epoch[074] Batch [0299]/[3760] Speed: 67.598950 samples/sec accuracy=81.750000 loss=0.724335 lr=0.001000 Epoch[074] Batch [0349]/[3760] Speed: 68.492030 samples/sec 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Batch [3699]/[3760] Speed: 67.997084 samples/sec accuracy=81.074747 loss=0.742370 lr=0.001000 Epoch[074] Batch [3749]/[3760] Speed: 76.812241 samples/sec accuracy=81.072083 loss=0.742627 lr=0.001000 Batch [0049]/[0303]: acc-top1=67.531250 acc-top5=87.125000 Batch [0099]/[0303]: acc-top1=67.515625 acc-top5=86.625000 Batch [0149]/[0303]: acc-top1=66.854167 acc-top5=86.479167 Batch [0199]/[0303]: acc-top1=66.953125 acc-top5=86.523438 Batch [0249]/[0303]: acc-top1=67.200000 acc-top5=86.612500 Batch [0299]/[0303]: acc-top1=66.953125 acc-top5=86.234375 [Epoch 074] training: accuracy=81.065908 loss=0.742829 [Epoch 074] speed: 67 samples/sec time cost: 3849.582687 [Epoch 074] validation: acc-top1=66.929662 acc-top5=86.205652 loss=1.686606 Epoch[075] Batch [0049]/[3759] Speed: 46.126729 samples/sec accuracy=81.093750 loss=0.718942 lr=0.001000 Epoch[075] Batch [0099]/[3759] Speed: 66.690222 samples/sec accuracy=82.015625 loss=0.689801 lr=0.001000 Epoch[075] Batch [0149]/[3759] Speed: 67.768599 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lr=0.001000 Epoch[075] Batch [1599]/[3759] Speed: 67.900608 samples/sec accuracy=81.476562 loss=0.724483 lr=0.001000 Epoch[075] Batch [1649]/[3759] Speed: 68.070473 samples/sec accuracy=81.492424 loss=0.724033 lr=0.001000 Epoch[075] Batch [1699]/[3759] Speed: 66.696759 samples/sec accuracy=81.462316 loss=0.724836 lr=0.001000 Epoch[075] Batch [1749]/[3759] Speed: 67.970375 samples/sec accuracy=81.419643 loss=0.725922 lr=0.001000 Epoch[075] Batch [1799]/[3759] Speed: 67.888745 samples/sec accuracy=81.424479 loss=0.725354 lr=0.001000 Epoch[075] Batch [1849]/[3759] Speed: 67.779761 samples/sec accuracy=81.392736 loss=0.726347 lr=0.001000 Epoch[075] Batch [1899]/[3759] Speed: 67.707259 samples/sec accuracy=81.391447 loss=0.726713 lr=0.001000 Epoch[075] Batch [1949]/[3759] Speed: 67.176137 samples/sec accuracy=81.404647 loss=0.726428 lr=0.001000 Epoch[075] Batch [1999]/[3759] Speed: 68.278011 samples/sec accuracy=81.395313 loss=0.726603 lr=0.001000 Epoch[075] Batch [2049]/[3759] Speed: 67.566441 samples/sec accuracy=81.397866 loss=0.726434 lr=0.001000 Epoch[075] Batch [2099]/[3759] Speed: 68.295109 samples/sec accuracy=81.400298 loss=0.726780 lr=0.001000 Epoch[075] Batch [2149]/[3759] Speed: 67.171533 samples/sec accuracy=81.395349 loss=0.727095 lr=0.001000 Epoch[075] Batch [2199]/[3759] Speed: 68.077044 samples/sec accuracy=81.365767 loss=0.728064 lr=0.001000 Epoch[075] Batch [2249]/[3759] Speed: 67.784929 samples/sec accuracy=81.361806 loss=0.728393 lr=0.001000 Epoch[075] Batch [2299]/[3759] Speed: 68.323789 samples/sec accuracy=81.353261 loss=0.729030 lr=0.001000 Epoch[075] Batch [2349]/[3759] Speed: 67.454418 samples/sec accuracy=81.360372 loss=0.728949 lr=0.001000 Epoch[075] Batch [2399]/[3759] Speed: 67.611037 samples/sec accuracy=81.342448 loss=0.729718 lr=0.001000 Epoch[075] Batch [2449]/[3759] Speed: 68.070985 samples/sec accuracy=81.317602 loss=0.730882 lr=0.001000 Epoch[075] Batch [2499]/[3759] Speed: 68.014257 samples/sec accuracy=81.316250 loss=0.730957 lr=0.001000 Epoch[075] Batch [2549]/[3759] Speed: 67.522788 samples/sec accuracy=81.324755 loss=0.730937 lr=0.001000 Epoch[075] Batch [2599]/[3759] Speed: 66.819650 samples/sec accuracy=81.322115 loss=0.730947 lr=0.001000 Epoch[075] Batch [2649]/[3759] Speed: 68.836362 samples/sec accuracy=81.324292 loss=0.731287 lr=0.001000 Epoch[075] Batch [2699]/[3759] Speed: 68.487606 samples/sec accuracy=81.315394 loss=0.732049 lr=0.001000 Epoch[075] Batch [2749]/[3759] Speed: 67.559864 samples/sec accuracy=81.319886 loss=0.732308 lr=0.001000 Epoch[075] Batch [2799]/[3759] Speed: 67.312000 samples/sec accuracy=81.311942 loss=0.733005 lr=0.001000 Epoch[075] Batch [2849]/[3759] Speed: 67.748967 samples/sec accuracy=81.313596 loss=0.732914 lr=0.001000 Epoch[075] Batch [2899]/[3759] Speed: 68.300826 samples/sec accuracy=81.321121 loss=0.733021 lr=0.001000 Epoch[075] Batch [2949]/[3759] Speed: 67.404997 samples/sec accuracy=81.314089 loss=0.733606 lr=0.001000 Epoch[075] Batch [2999]/[3759] Speed: 67.528520 samples/sec accuracy=81.321875 loss=0.733732 lr=0.001000 Epoch[075] Batch [3049]/[3759] Speed: 67.039090 samples/sec accuracy=81.305840 loss=0.734017 lr=0.001000 Epoch[075] Batch [3099]/[3759] Speed: 69.235090 samples/sec accuracy=81.289315 loss=0.734887 lr=0.001000 Epoch[075] Batch [3149]/[3759] Speed: 67.822213 samples/sec accuracy=81.281250 loss=0.735644 lr=0.001000 Epoch[075] Batch [3199]/[3759] Speed: 67.855296 samples/sec accuracy=81.286621 loss=0.735210 lr=0.001000 Epoch[075] Batch [3249]/[3759] Speed: 68.085625 samples/sec accuracy=81.285577 loss=0.734893 lr=0.001000 Epoch[075] Batch [3299]/[3759] Speed: 67.836235 samples/sec accuracy=81.258049 loss=0.735594 lr=0.001000 Epoch[075] Batch [3349]/[3759] Speed: 67.333911 samples/sec accuracy=81.255597 loss=0.735991 lr=0.001000 Epoch[075] Batch [3399]/[3759] Speed: 67.934832 samples/sec accuracy=81.250000 loss=0.736422 lr=0.001000 Epoch[075] Batch [3449]/[3759] Speed: 68.168372 samples/sec accuracy=81.234601 loss=0.736775 lr=0.001000 Epoch[075] Batch [3499]/[3759] Speed: 67.712428 samples/sec accuracy=81.239732 loss=0.736439 lr=0.001000 Epoch[075] Batch [3549]/[3759] Speed: 67.445529 samples/sec accuracy=81.223592 loss=0.736880 lr=0.001000 Epoch[075] Batch [3599]/[3759] Speed: 67.472513 samples/sec accuracy=81.207031 loss=0.737650 lr=0.001000 Epoch[075] Batch [3649]/[3759] Speed: 68.076721 samples/sec accuracy=81.175086 loss=0.738747 lr=0.001000 Epoch[075] Batch [3699]/[3759] Speed: 67.727684 samples/sec accuracy=81.199747 loss=0.737986 lr=0.001000 Epoch[075] Batch [3749]/[3759] Speed: 75.414910 samples/sec accuracy=81.194167 loss=0.738265 lr=0.001000 Batch [0049]/[0303]: acc-top1=67.656250 acc-top5=87.375000 Batch [0099]/[0303]: acc-top1=67.062500 acc-top5=86.859375 Batch [0149]/[0303]: acc-top1=66.677083 acc-top5=86.833333 Batch [0199]/[0303]: acc-top1=66.765625 acc-top5=86.695312 Batch [0249]/[0303]: acc-top1=66.925000 acc-top5=86.925000 Batch [0299]/[0303]: acc-top1=66.614583 acc-top5=86.520833 [Epoch 075] training: accuracy=81.195963 loss=0.738171 [Epoch 075] speed: 67 samples/sec time cost: 3855.450410 [Epoch 075] validation: acc-top1=66.568688 acc-top5=86.494431 loss=1.669108 Epoch[076] Batch [0049]/[3760] Speed: 46.925772 samples/sec accuracy=81.750000 loss=0.769437 lr=0.001000 Epoch[076] Batch [0099]/[3760] Speed: 66.603728 samples/sec accuracy=81.328125 loss=0.750368 lr=0.001000 Epoch[076] Batch [0149]/[3760] Speed: 68.036035 samples/sec accuracy=81.312500 loss=0.749858 lr=0.001000 Epoch[076] Batch [0199]/[3760] Speed: 67.123009 samples/sec accuracy=81.367188 loss=0.743683 lr=0.001000 Epoch[076] Batch [0249]/[3760] Speed: 67.527127 samples/sec accuracy=81.600000 loss=0.739503 lr=0.001000 Epoch[076] Batch [0299]/[3760] Speed: 68.160759 samples/sec accuracy=81.651042 loss=0.731029 lr=0.001000 Epoch[076] Batch [0349]/[3760] Speed: 68.228586 samples/sec accuracy=81.647321 loss=0.727772 lr=0.001000 Epoch[076] Batch [0399]/[3760] Speed: 66.899308 samples/sec accuracy=81.714844 loss=0.725623 lr=0.001000 Epoch[076] Batch [0449]/[3760] Speed: 67.903845 samples/sec accuracy=81.663194 loss=0.727477 lr=0.001000 Epoch[076] Batch [0499]/[3760] Speed: 67.042319 samples/sec accuracy=81.671875 loss=0.724310 lr=0.001000 Epoch[076] Batch [0549]/[3760] Speed: 68.485865 samples/sec accuracy=81.667614 loss=0.723691 lr=0.001000 Epoch[076] Batch [0599]/[3760] Speed: 68.191177 samples/sec accuracy=81.565104 loss=0.725381 lr=0.001000 Epoch[076] Batch [0649]/[3760] Speed: 67.378215 samples/sec accuracy=81.574519 loss=0.724888 lr=0.001000 Epoch[076] Batch [0699]/[3760] Speed: 68.061648 samples/sec accuracy=81.609375 loss=0.722911 lr=0.001000 Epoch[076] Batch [0749]/[3760] Speed: 67.303247 samples/sec accuracy=81.502083 loss=0.724250 lr=0.001000 Epoch[076] Batch [0799]/[3760] Speed: 68.433805 samples/sec accuracy=81.501953 loss=0.725256 lr=0.001000 Epoch[076] Batch [0849]/[3760] Speed: 67.856374 samples/sec accuracy=81.371324 loss=0.729365 lr=0.001000 Epoch[076] 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accuracy=81.365741 loss=0.729111 lr=0.001000 Epoch[076] Batch [1399]/[3760] Speed: 68.028979 samples/sec accuracy=81.359375 loss=0.730055 lr=0.001000 Epoch[076] Batch [1449]/[3760] Speed: 67.895744 samples/sec accuracy=81.385776 loss=0.729922 lr=0.001000 Epoch[076] Batch [1499]/[3760] Speed: 67.734378 samples/sec accuracy=81.383333 loss=0.730502 lr=0.001000 Epoch[076] Batch [1549]/[3760] Speed: 67.756897 samples/sec accuracy=81.385081 loss=0.730297 lr=0.001000 Epoch[076] Batch [1599]/[3760] Speed: 68.269342 samples/sec accuracy=81.429688 loss=0.729000 lr=0.001000 Epoch[076] Batch [1649]/[3760] Speed: 67.792485 samples/sec accuracy=81.460227 loss=0.728173 lr=0.001000 Epoch[076] Batch [1699]/[3760] Speed: 67.760113 samples/sec accuracy=81.448529 loss=0.728051 lr=0.001000 Epoch[076] Batch [1749]/[3760] Speed: 67.791798 samples/sec accuracy=81.445536 loss=0.728228 lr=0.001000 Epoch[076] Batch [1799]/[3760] Speed: 68.251582 samples/sec accuracy=81.428819 loss=0.728514 lr=0.001000 Epoch[076] 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accuracy=81.394701 loss=0.729691 lr=0.001000 Epoch[076] Batch [2349]/[3760] Speed: 67.428025 samples/sec accuracy=81.377660 loss=0.730275 lr=0.001000 Epoch[076] Batch [2399]/[3760] Speed: 67.570082 samples/sec accuracy=81.355469 loss=0.731052 lr=0.001000 Epoch[076] Batch [2449]/[3760] Speed: 68.169850 samples/sec accuracy=81.338648 loss=0.730874 lr=0.001000 Epoch[076] Batch [2499]/[3760] Speed: 68.011152 samples/sec accuracy=81.324375 loss=0.731180 lr=0.001000 Epoch[076] Batch [2549]/[3760] Speed: 66.716270 samples/sec accuracy=81.333333 loss=0.730798 lr=0.001000 Epoch[076] Batch [2599]/[3760] Speed: 69.096141 samples/sec accuracy=81.345553 loss=0.730348 lr=0.001000 Epoch[076] Batch [2649]/[3760] Speed: 67.290849 samples/sec accuracy=81.323113 loss=0.731241 lr=0.001000 Epoch[076] Batch [2699]/[3760] Speed: 67.869507 samples/sec accuracy=81.325810 loss=0.731665 lr=0.001000 Epoch[076] Batch [2749]/[3760] Speed: 67.965576 samples/sec accuracy=81.321023 loss=0.732217 lr=0.001000 Epoch[076] 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Batch [3749]/[3760] Speed: 77.465780 samples/sec accuracy=81.150417 loss=0.736947 lr=0.001000 Batch [0049]/[0303]: acc-top1=67.500000 acc-top5=86.718750 Batch [0099]/[0303]: acc-top1=67.062500 acc-top5=86.437500 Batch [0149]/[0303]: acc-top1=66.500000 acc-top5=86.208333 Batch [0199]/[0303]: acc-top1=66.601562 acc-top5=86.195312 Batch [0249]/[0303]: acc-top1=66.818750 acc-top5=86.412500 Batch [0299]/[0303]: acc-top1=66.593750 acc-top5=86.036458 [Epoch 076] training: accuracy=81.151513 loss=0.737058 [Epoch 076] speed: 67 samples/sec time cost: 3849.813944 [Epoch 076] validation: acc-top1=66.563531 acc-top5=86.014851 loss=1.703442 Epoch[077] Batch [0049]/[3760] Speed: 45.741229 samples/sec accuracy=82.093750 loss=0.707903 lr=0.001000 Epoch[077] Batch [0099]/[3760] Speed: 66.804043 samples/sec accuracy=82.343750 loss=0.687044 lr=0.001000 Epoch[077] Batch [0149]/[3760] Speed: 67.632830 samples/sec accuracy=82.552083 loss=0.685406 lr=0.001000 Epoch[077] Batch [0199]/[3760] Speed: 66.817941 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lr=0.001000 Epoch[077] Batch [3549]/[3760] Speed: 68.141695 samples/sec accuracy=81.510123 loss=0.725736 lr=0.001000 Epoch[077] Batch [3599]/[3760] Speed: 68.375759 samples/sec accuracy=81.510851 loss=0.725693 lr=0.001000 Epoch[077] Batch [3649]/[3760] Speed: 68.085226 samples/sec accuracy=81.504281 loss=0.725754 lr=0.001000 Epoch[077] Batch [3699]/[3760] Speed: 67.692488 samples/sec accuracy=81.499155 loss=0.725736 lr=0.001000 Epoch[077] Batch [3749]/[3760] Speed: 75.658410 samples/sec accuracy=81.487083 loss=0.726249 lr=0.001000 Batch [0049]/[0303]: acc-top1=66.656250 acc-top5=86.937500 Batch [0099]/[0303]: acc-top1=66.812500 acc-top5=86.609375 Batch [0149]/[0303]: acc-top1=66.187500 acc-top5=86.437500 Batch [0199]/[0303]: acc-top1=66.421875 acc-top5=86.367188 Batch [0249]/[0303]: acc-top1=66.618750 acc-top5=86.500000 Batch [0299]/[0303]: acc-top1=66.359375 acc-top5=86.067708 [Epoch 077] training: accuracy=81.486453 loss=0.726275 [Epoch 077] speed: 67 samples/sec time cost: 3852.958701 [Epoch 077] validation: acc-top1=66.336634 acc-top5=86.040635 loss=1.720845 Epoch[078] Batch [0049]/[3759] Speed: 46.808433 samples/sec accuracy=81.906250 loss=0.706434 lr=0.001000 Epoch[078] Batch [0099]/[3759] Speed: 67.028257 samples/sec accuracy=81.906250 loss=0.706875 lr=0.001000 Epoch[078] Batch [0149]/[3759] Speed: 66.515800 samples/sec accuracy=82.135417 loss=0.697691 lr=0.001000 Epoch[078] Batch [0199]/[3759] Speed: 67.936674 samples/sec accuracy=81.875000 loss=0.701474 lr=0.001000 Epoch[078] Batch [0249]/[3759] Speed: 68.784844 samples/sec accuracy=82.156250 loss=0.696947 lr=0.001000 Epoch[078] Batch [0299]/[3759] Speed: 67.814095 samples/sec accuracy=82.218750 loss=0.698029 lr=0.001000 Epoch[078] Batch [0349]/[3759] Speed: 67.714444 samples/sec accuracy=82.236607 loss=0.693711 lr=0.001000 Epoch[078] Batch [0399]/[3759] Speed: 67.636358 samples/sec accuracy=82.238281 loss=0.692690 lr=0.001000 Epoch[078] Batch [0449]/[3759] Speed: 68.722913 samples/sec 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[0049]/[0303]: acc-top1=67.156250 acc-top5=86.968750 Batch [0099]/[0303]: acc-top1=66.937500 acc-top5=86.656250 Batch [0149]/[0303]: acc-top1=66.479167 acc-top5=86.583333 Batch [0199]/[0303]: acc-top1=66.546875 acc-top5=86.437500 Batch [0249]/[0303]: acc-top1=66.718750 acc-top5=86.393750 Batch [0299]/[0303]: acc-top1=66.468750 acc-top5=86.088542 [Epoch 078] training: accuracy=81.463238 loss=0.724885 [Epoch 078] speed: 67 samples/sec time cost: 3849.556936 [Epoch 078] validation: acc-top1=66.444926 acc-top5=86.061262 loss=1.743470 Epoch[079] Batch [0049]/[3760] Speed: 46.669038 samples/sec accuracy=80.406250 loss=0.769048 lr=0.001000 Epoch[079] Batch [0099]/[3760] Speed: 66.838572 samples/sec accuracy=81.000000 loss=0.745278 lr=0.001000 Epoch[079] Batch [0149]/[3760] Speed: 67.776022 samples/sec accuracy=81.364583 loss=0.737804 lr=0.001000 Epoch[079] Batch [0199]/[3760] Speed: 67.621776 samples/sec accuracy=81.109375 loss=0.739782 lr=0.001000 Epoch[079] Batch [0249]/[3760] Speed: 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lr=0.001000 Epoch[079] Batch [2649]/[3760] Speed: 67.652064 samples/sec accuracy=81.945165 loss=0.707994 lr=0.001000 Epoch[079] Batch [2699]/[3760] Speed: 68.250076 samples/sec accuracy=81.946181 loss=0.707932 lr=0.001000 Epoch[079] Batch [2749]/[3760] Speed: 67.881356 samples/sec accuracy=81.921591 loss=0.708867 lr=0.001000 Epoch[079] Batch [2799]/[3760] Speed: 66.812509 samples/sec accuracy=81.926339 loss=0.709013 lr=0.001000 Epoch[079] Batch [2849]/[3760] Speed: 68.173422 samples/sec accuracy=81.924890 loss=0.709538 lr=0.001000 Epoch[079] Batch [2899]/[3760] Speed: 67.539297 samples/sec accuracy=81.922953 loss=0.709612 lr=0.001000 Epoch[079] Batch [2949]/[3760] Speed: 68.373444 samples/sec accuracy=81.932733 loss=0.709525 lr=0.001000 Epoch[079] Batch [2999]/[3760] Speed: 68.055686 samples/sec accuracy=81.938021 loss=0.708833 lr=0.001000 Epoch[079] Batch [3049]/[3760] Speed: 67.534231 samples/sec accuracy=81.916496 loss=0.709717 lr=0.001000 Epoch[079] Batch [3099]/[3760] Speed: 68.387051 samples/sec accuracy=81.906250 loss=0.709571 lr=0.001000 Epoch[079] Batch [3149]/[3760] Speed: 67.588476 samples/sec accuracy=81.905258 loss=0.709867 lr=0.001000 Epoch[079] Batch [3199]/[3760] Speed: 67.953170 samples/sec accuracy=81.894531 loss=0.710176 lr=0.001000 Epoch[079] Batch [3249]/[3760] Speed: 67.477810 samples/sec accuracy=81.907212 loss=0.709933 lr=0.001000 Epoch[079] Batch [3299]/[3760] Speed: 68.334597 samples/sec accuracy=81.895833 loss=0.710220 lr=0.001000 Epoch[079] Batch [3349]/[3760] Speed: 68.302604 samples/sec accuracy=81.865205 loss=0.711038 lr=0.001000 Epoch[079] Batch [3399]/[3760] Speed: 67.476966 samples/sec accuracy=81.857077 loss=0.710790 lr=0.001000 Epoch[079] Batch [3449]/[3760] Speed: 67.397636 samples/sec accuracy=81.843750 loss=0.711017 lr=0.001000 Epoch[079] Batch [3499]/[3760] Speed: 67.763355 samples/sec accuracy=81.850000 loss=0.711085 lr=0.001000 Epoch[079] Batch [3549]/[3760] Speed: 68.192485 samples/sec accuracy=81.862676 loss=0.710921 lr=0.001000 Epoch[079] Batch [3599]/[3760] Speed: 67.406972 samples/sec accuracy=81.851128 loss=0.711419 lr=0.001000 Epoch[079] Batch [3649]/[3760] Speed: 67.604102 samples/sec accuracy=81.833904 loss=0.712301 lr=0.001000 Epoch[079] Batch [3699]/[3760] Speed: 68.200502 samples/sec accuracy=81.834459 loss=0.712577 lr=0.001000 Epoch[079] Batch [3749]/[3760] Speed: 76.012620 samples/sec accuracy=81.813750 loss=0.712897 lr=0.000100 Batch [0049]/[0303]: acc-top1=66.718750 acc-top5=87.031250 Batch [0099]/[0303]: acc-top1=66.531250 acc-top5=86.593750 Batch [0149]/[0303]: acc-top1=66.083333 acc-top5=86.593750 Batch [0199]/[0303]: acc-top1=66.351562 acc-top5=86.445312 Batch [0249]/[0303]: acc-top1=66.431250 acc-top5=86.543750 Batch [0299]/[0303]: acc-top1=66.260417 acc-top5=86.255208 [Epoch 079] training: accuracy=81.809757 loss=0.713120 [Epoch 079] speed: 67 samples/sec time cost: 3852.078863 [Epoch 079] validation: acc-top1=66.259282 acc-top5=86.241749 loss=1.721585 Epoch[080] Batch 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accuracy=82.743750 loss=0.685041 lr=0.000100 Epoch[080] Batch [0549]/[3760] Speed: 67.850655 samples/sec accuracy=82.710227 loss=0.687369 lr=0.000100 Epoch[080] Batch [0599]/[3760] Speed: 68.043869 samples/sec accuracy=82.684896 loss=0.685557 lr=0.000100 Epoch[080] Batch [0649]/[3760] Speed: 67.921307 samples/sec accuracy=82.697115 loss=0.683370 lr=0.000100 Epoch[080] Batch [0699]/[3760] Speed: 66.978800 samples/sec accuracy=82.651786 loss=0.681222 lr=0.000100 Epoch[080] Batch [0749]/[3760] Speed: 67.970694 samples/sec accuracy=82.752083 loss=0.679332 lr=0.000100 Epoch[080] Batch [0799]/[3760] Speed: 67.617137 samples/sec accuracy=82.789062 loss=0.677275 lr=0.000100 Epoch[080] Batch [0849]/[3760] Speed: 67.905054 samples/sec accuracy=82.709559 loss=0.678804 lr=0.000100 Epoch[080] Batch [0899]/[3760] Speed: 66.990022 samples/sec accuracy=82.835069 loss=0.674703 lr=0.000100 Epoch[080] Batch [0949]/[3760] Speed: 68.532664 samples/sec accuracy=82.899671 loss=0.673558 lr=0.000100 Epoch[080] 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accuracy=83.090517 loss=0.664869 lr=0.000100 Epoch[080] Batch [1499]/[3760] Speed: 67.747189 samples/sec accuracy=83.123958 loss=0.664482 lr=0.000100 Epoch[080] Batch [1549]/[3760] Speed: 67.861100 samples/sec accuracy=83.144153 loss=0.663102 lr=0.000100 Epoch[080] Batch [1599]/[3760] Speed: 68.136359 samples/sec accuracy=83.132812 loss=0.662935 lr=0.000100 Epoch[080] Batch [1649]/[3760] Speed: 68.149764 samples/sec accuracy=83.158144 loss=0.662690 lr=0.000100 Epoch[080] Batch [1699]/[3760] Speed: 68.092153 samples/sec accuracy=83.159926 loss=0.662167 lr=0.000100 Epoch[080] Batch [1749]/[3760] Speed: 67.814857 samples/sec accuracy=83.165179 loss=0.661235 lr=0.000100 Epoch[080] Batch [1799]/[3760] Speed: 68.039868 samples/sec accuracy=83.180556 loss=0.659488 lr=0.000100 Epoch[080] Batch [1849]/[3760] Speed: 68.115473 samples/sec accuracy=83.172297 loss=0.659353 lr=0.000100 Epoch[080] Batch [1899]/[3760] Speed: 67.510479 samples/sec accuracy=83.179276 loss=0.659035 lr=0.000100 Epoch[080] 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accuracy=83.233073 loss=0.658966 lr=0.000100 Epoch[080] Batch [2449]/[3760] Speed: 68.089206 samples/sec accuracy=83.218750 loss=0.659321 lr=0.000100 Epoch[080] Batch [2499]/[3760] Speed: 67.622199 samples/sec accuracy=83.232500 loss=0.658667 lr=0.000100 Epoch[080] Batch [2549]/[3760] Speed: 67.174281 samples/sec accuracy=83.219363 loss=0.658928 lr=0.000100 Epoch[080] Batch [2599]/[3760] Speed: 67.304527 samples/sec accuracy=83.225962 loss=0.658881 lr=0.000100 Epoch[080] Batch [2649]/[3760] Speed: 67.849722 samples/sec accuracy=83.232901 loss=0.658994 lr=0.000100 Epoch[080] Batch [2699]/[3760] Speed: 67.868776 samples/sec accuracy=83.239583 loss=0.659166 lr=0.000100 Epoch[080] Batch [2749]/[3760] Speed: 68.276523 samples/sec accuracy=83.247727 loss=0.658839 lr=0.000100 Epoch[080] Batch [2799]/[3760] Speed: 67.915265 samples/sec accuracy=83.263393 loss=0.658766 lr=0.000100 Epoch[080] Batch [2849]/[3760] Speed: 67.153712 samples/sec accuracy=83.264803 loss=0.658830 lr=0.000100 Epoch[080] Batch [2899]/[3760] Speed: 68.195810 samples/sec accuracy=83.259698 loss=0.658908 lr=0.000100 Epoch[080] Batch [2949]/[3760] Speed: 67.645898 samples/sec accuracy=83.263771 loss=0.658638 lr=0.000100 Epoch[080] Batch [2999]/[3760] Speed: 67.526089 samples/sec accuracy=83.263542 loss=0.658737 lr=0.000100 Epoch[080] Batch [3049]/[3760] Speed: 68.020940 samples/sec accuracy=83.260758 loss=0.658563 lr=0.000100 Epoch[080] Batch [3099]/[3760] Speed: 67.946605 samples/sec accuracy=83.274194 loss=0.657749 lr=0.000100 Epoch[080] Batch [3149]/[3760] Speed: 67.861034 samples/sec accuracy=83.280258 loss=0.657625 lr=0.000100 Epoch[080] Batch [3199]/[3760] Speed: 68.140879 samples/sec accuracy=83.280273 loss=0.657323 lr=0.000100 Epoch[080] Batch [3249]/[3760] Speed: 67.819095 samples/sec accuracy=83.266346 loss=0.658044 lr=0.000100 Epoch[080] Batch [3299]/[3760] Speed: 66.845692 samples/sec accuracy=83.268466 loss=0.657335 lr=0.000100 Epoch[080] Batch [3349]/[3760] Speed: 69.104849 samples/sec accuracy=83.273321 loss=0.657370 lr=0.000100 Epoch[080] Batch [3399]/[3760] Speed: 67.542493 samples/sec accuracy=83.268842 loss=0.657355 lr=0.000100 Epoch[080] Batch [3449]/[3760] Speed: 68.211271 samples/sec accuracy=83.287591 loss=0.656384 lr=0.000100 Epoch[080] Batch [3499]/[3760] Speed: 67.777673 samples/sec accuracy=83.270982 loss=0.656911 lr=0.000100 Epoch[080] Batch [3549]/[3760] Speed: 67.050869 samples/sec accuracy=83.267165 loss=0.656687 lr=0.000100 Epoch[080] Batch [3599]/[3760] Speed: 68.178055 samples/sec accuracy=83.290799 loss=0.656052 lr=0.000100 Epoch[080] Batch [3649]/[3760] Speed: 67.864376 samples/sec accuracy=83.293236 loss=0.656186 lr=0.000100 Epoch[080] Batch [3699]/[3760] Speed: 68.759536 samples/sec accuracy=83.291385 loss=0.656060 lr=0.000100 Epoch[080] Batch [3749]/[3760] Speed: 75.953421 samples/sec accuracy=83.273333 loss=0.656469 lr=0.000100 Batch [0049]/[0303]: acc-top1=67.687500 acc-top5=87.656250 Batch [0099]/[0303]: acc-top1=67.312500 acc-top5=87.187500 Batch [0149]/[0303]: acc-top1=66.906250 acc-top5=87.072917 Batch [0199]/[0303]: acc-top1=66.968750 acc-top5=86.851562 Batch [0249]/[0303]: acc-top1=67.225000 acc-top5=86.975000 Batch [0299]/[0303]: acc-top1=67.093750 acc-top5=86.583333 [Epoch 080] training: accuracy=83.270445 loss=0.656566 [Epoch 080] speed: 67 samples/sec time cost: 3854.176708 [Epoch 080] validation: acc-top1=67.074051 acc-top5=86.566625 loss=1.674215 Epoch[081] Batch [0049]/[3759] Speed: 46.142566 samples/sec accuracy=83.125000 loss=0.660390 lr=0.000100 Epoch[081] Batch [0099]/[3759] Speed: 66.813062 samples/sec accuracy=83.453125 loss=0.655736 lr=0.000100 Epoch[081] Batch [0149]/[3759] Speed: 67.379518 samples/sec accuracy=83.458333 loss=0.653303 lr=0.000100 Epoch[081] Batch [0199]/[3759] Speed: 67.429025 samples/sec accuracy=83.468750 loss=0.648781 lr=0.000100 Epoch[081] Batch [0249]/[3759] Speed: 67.784623 samples/sec accuracy=83.543750 loss=0.647691 lr=0.000100 Epoch[081] Batch [0299]/[3759] 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accuracy=83.913603 loss=0.632114 lr=0.000100 Epoch[081] Batch [1749]/[3759] Speed: 69.280581 samples/sec accuracy=83.914286 loss=0.631892 lr=0.000100 Epoch[081] Batch [1799]/[3759] Speed: 67.630247 samples/sec accuracy=83.878472 loss=0.632986 lr=0.000100 Epoch[081] Batch [1849]/[3759] Speed: 68.197959 samples/sec accuracy=83.906250 loss=0.632680 lr=0.000100 Epoch[081] Batch [1899]/[3759] Speed: 68.025618 samples/sec accuracy=83.894737 loss=0.632947 lr=0.000100 Epoch[081] Batch [1949]/[3759] Speed: 67.156885 samples/sec accuracy=83.883814 loss=0.633643 lr=0.000100 Epoch[081] Batch [1999]/[3759] Speed: 68.453985 samples/sec accuracy=83.878125 loss=0.633885 lr=0.000100 Epoch[081] Batch [2049]/[3759] Speed: 68.217787 samples/sec accuracy=83.878049 loss=0.634217 lr=0.000100 Epoch[081] Batch [2099]/[3759] Speed: 67.972473 samples/sec accuracy=83.866815 loss=0.634143 lr=0.000100 Epoch[081] Batch [2149]/[3759] Speed: 67.872133 samples/sec accuracy=83.854651 loss=0.633878 lr=0.000100 Epoch[081] 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accuracy=83.840212 loss=0.634349 lr=0.000100 Epoch[081] Batch [2699]/[3759] Speed: 67.328340 samples/sec accuracy=83.845486 loss=0.634479 lr=0.000100 Epoch[081] Batch [2749]/[3759] Speed: 67.990714 samples/sec accuracy=83.839773 loss=0.634327 lr=0.000100 Epoch[081] Batch [2799]/[3759] Speed: 68.424636 samples/sec accuracy=83.831473 loss=0.634372 lr=0.000100 Epoch[081] Batch [2849]/[3759] Speed: 67.683346 samples/sec accuracy=83.804276 loss=0.634904 lr=0.000100 Epoch[081] Batch [2899]/[3759] Speed: 68.399968 samples/sec accuracy=83.767780 loss=0.636105 lr=0.000100 Epoch[081] Batch [2949]/[3759] Speed: 67.550102 samples/sec accuracy=83.781250 loss=0.635633 lr=0.000100 Epoch[081] Batch [2999]/[3759] Speed: 67.831057 samples/sec accuracy=83.785417 loss=0.635751 lr=0.000100 Epoch[081] Batch [3049]/[3759] Speed: 68.060225 samples/sec accuracy=83.781250 loss=0.635671 lr=0.000100 Epoch[081] Batch [3099]/[3759] Speed: 68.211602 samples/sec accuracy=83.798891 loss=0.635358 lr=0.000100 Epoch[081] Batch [3149]/[3759] Speed: 68.325354 samples/sec accuracy=83.798115 loss=0.635449 lr=0.000100 Epoch[081] Batch [3199]/[3759] Speed: 67.205544 samples/sec accuracy=83.794922 loss=0.635380 lr=0.000100 Epoch[081] Batch [3249]/[3759] Speed: 68.403719 samples/sec accuracy=83.797115 loss=0.635546 lr=0.000100 Epoch[081] Batch [3299]/[3759] Speed: 67.928840 samples/sec accuracy=83.784091 loss=0.636108 lr=0.000100 Epoch[081] Batch [3349]/[3759] Speed: 67.853681 samples/sec accuracy=83.791511 loss=0.635904 lr=0.000100 Epoch[081] Batch [3399]/[3759] Speed: 68.438626 samples/sec accuracy=83.778493 loss=0.636383 lr=0.000100 Epoch[081] Batch [3449]/[3759] Speed: 66.775514 samples/sec accuracy=83.775362 loss=0.636844 lr=0.000100 Epoch[081] Batch [3499]/[3759] Speed: 68.137484 samples/sec accuracy=83.787500 loss=0.636332 lr=0.000100 Epoch[081] Batch [3549]/[3759] Speed: 67.801676 samples/sec accuracy=83.794894 loss=0.636527 lr=0.000100 Epoch[081] Batch [3599]/[3759] Speed: 68.245160 samples/sec accuracy=83.790365 loss=0.636674 lr=0.000100 Epoch[081] Batch [3649]/[3759] Speed: 66.926117 samples/sec accuracy=83.797945 loss=0.636306 lr=0.000100 Epoch[081] Batch [3699]/[3759] Speed: 68.698667 samples/sec accuracy=83.784628 loss=0.636735 lr=0.000100 Epoch[081] Batch [3749]/[3759] Speed: 77.394072 samples/sec accuracy=83.785000 loss=0.636658 lr=0.000100 Batch [0049]/[0303]: acc-top1=67.718750 acc-top5=87.937500 Batch [0099]/[0303]: acc-top1=67.437500 acc-top5=87.312500 Batch [0149]/[0303]: acc-top1=67.114583 acc-top5=86.989583 Batch [0199]/[0303]: acc-top1=67.125000 acc-top5=86.789062 Batch [0249]/[0303]: acc-top1=67.306250 acc-top5=86.925000 Batch [0299]/[0303]: acc-top1=67.046875 acc-top5=86.598958 [Epoch 081] training: accuracy=83.783503 loss=0.636684 [Epoch 081] speed: 67 samples/sec time cost: 3850.390214 [Epoch 081] validation: acc-top1=67.017327 acc-top5=86.571782 loss=1.665969 Epoch[082] Batch [0049]/[3760] Speed: 46.228889 samples/sec accuracy=84.562500 loss=0.622366 lr=0.000100 Epoch[082] Batch [0099]/[3760] Speed: 67.729494 samples/sec accuracy=84.421875 loss=0.617916 lr=0.000100 Epoch[082] Batch [0149]/[3760] Speed: 66.840369 samples/sec accuracy=84.437500 loss=0.613758 lr=0.000100 Epoch[082] Batch [0199]/[3760] Speed: 68.983271 samples/sec accuracy=84.265625 loss=0.621792 lr=0.000100 Epoch[082] Batch [0249]/[3760] Speed: 68.195970 samples/sec accuracy=84.025000 loss=0.631437 lr=0.000100 Epoch[082] Batch [0299]/[3760] Speed: 67.318582 samples/sec accuracy=84.135417 loss=0.631017 lr=0.000100 Epoch[082] Batch [0349]/[3760] Speed: 68.233651 samples/sec accuracy=84.241071 loss=0.628795 lr=0.000100 Epoch[082] Batch [0399]/[3760] Speed: 67.191943 samples/sec accuracy=84.351562 loss=0.626565 lr=0.000100 Epoch[082] Batch [0449]/[3760] Speed: 68.474872 samples/sec accuracy=84.336806 loss=0.624318 lr=0.000100 Epoch[082] Batch [0499]/[3760] Speed: 67.970947 samples/sec accuracy=84.353125 loss=0.624599 lr=0.000100 Epoch[082] Batch [0549]/[3760] Speed: 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lr=0.000100 Epoch[082] Batch [2949]/[3760] Speed: 67.754403 samples/sec accuracy=83.941208 loss=0.632157 lr=0.000100 Epoch[082] Batch [2999]/[3760] Speed: 68.245071 samples/sec accuracy=83.958333 loss=0.631814 lr=0.000100 Epoch[082] Batch [3049]/[3760] Speed: 67.623757 samples/sec accuracy=83.970799 loss=0.631367 lr=0.000100 Epoch[082] Batch [3099]/[3760] Speed: 67.161767 samples/sec accuracy=83.987399 loss=0.630889 lr=0.000100 Epoch[082] Batch [3149]/[3760] Speed: 68.320959 samples/sec accuracy=83.986111 loss=0.630828 lr=0.000100 Epoch[082] Batch [3199]/[3760] Speed: 68.095529 samples/sec accuracy=83.994141 loss=0.630493 lr=0.000100 Epoch[082] Batch [3249]/[3760] Speed: 67.720979 samples/sec accuracy=83.990865 loss=0.630294 lr=0.000100 Epoch[082] Batch [3299]/[3760] Speed: 67.426299 samples/sec accuracy=83.989583 loss=0.630148 lr=0.000100 Epoch[082] Batch [3349]/[3760] Speed: 67.071969 samples/sec accuracy=83.996269 loss=0.629906 lr=0.000100 Epoch[082] Batch [3399]/[3760] Speed: 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acc-top1=67.304688 acc-top5=86.687500 Batch [0249]/[0303]: acc-top1=67.487500 acc-top5=86.750000 Batch [0299]/[0303]: acc-top1=67.276042 acc-top5=86.437500 [Epoch 082] training: accuracy=84.033826 loss=0.628021 [Epoch 082] speed: 67 samples/sec time cost: 3852.360838 [Epoch 082] validation: acc-top1=67.249381 acc-top5=86.417079 loss=1.686562 Epoch[083] Batch [0049]/[3760] Speed: 47.214459 samples/sec accuracy=83.281250 loss=0.647194 lr=0.000100 Epoch[083] Batch [0099]/[3760] Speed: 66.055484 samples/sec accuracy=84.046875 loss=0.642322 lr=0.000100 Epoch[083] Batch [0149]/[3760] Speed: 68.226734 samples/sec accuracy=84.166667 loss=0.639709 lr=0.000100 Epoch[083] Batch [0199]/[3760] Speed: 67.478060 samples/sec accuracy=84.164062 loss=0.641432 lr=0.000100 Epoch[083] Batch [0249]/[3760] Speed: 67.624511 samples/sec accuracy=83.956250 loss=0.645070 lr=0.000100 Epoch[083] Batch [0299]/[3760] Speed: 68.202955 samples/sec accuracy=84.015625 loss=0.642296 lr=0.000100 Epoch[083] Batch 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accuracy=84.201172 loss=0.627320 lr=0.000100 Epoch[083] Batch [0849]/[3760] Speed: 68.123844 samples/sec accuracy=84.216912 loss=0.625677 lr=0.000100 Epoch[083] Batch [0899]/[3760] Speed: 67.764773 samples/sec accuracy=84.244792 loss=0.624875 lr=0.000100 Epoch[083] Batch [0949]/[3760] Speed: 67.818707 samples/sec accuracy=84.238487 loss=0.624953 lr=0.000100 Epoch[083] Batch [0999]/[3760] Speed: 68.052903 samples/sec accuracy=84.246875 loss=0.624955 lr=0.000100 Epoch[083] Batch [1049]/[3760] Speed: 66.581159 samples/sec accuracy=84.217262 loss=0.625673 lr=0.000100 Epoch[083] Batch [1099]/[3760] Speed: 69.026025 samples/sec accuracy=84.248580 loss=0.623294 lr=0.000100 Epoch[083] Batch [1149]/[3760] Speed: 68.086153 samples/sec accuracy=84.267663 loss=0.621705 lr=0.000100 Epoch[083] Batch [1199]/[3760] Speed: 67.601965 samples/sec accuracy=84.260417 loss=0.623005 lr=0.000100 Epoch[083] Batch [1249]/[3760] Speed: 67.986263 samples/sec accuracy=84.260000 loss=0.622640 lr=0.000100 Epoch[083] 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accuracy=84.232143 loss=0.620825 lr=0.000100 Epoch[083] Batch [1799]/[3760] Speed: 67.538599 samples/sec accuracy=84.233507 loss=0.620827 lr=0.000100 Epoch[083] Batch [1849]/[3760] Speed: 67.900241 samples/sec accuracy=84.248311 loss=0.620150 lr=0.000100 Epoch[083] Batch [1899]/[3760] Speed: 68.071577 samples/sec accuracy=84.259046 loss=0.619649 lr=0.000100 Epoch[083] Batch [1949]/[3760] Speed: 67.675930 samples/sec accuracy=84.241186 loss=0.620026 lr=0.000100 Epoch[083] Batch [1999]/[3760] Speed: 67.940917 samples/sec accuracy=84.230469 loss=0.620442 lr=0.000100 Epoch[083] Batch [2049]/[3760] Speed: 67.444710 samples/sec accuracy=84.252287 loss=0.619591 lr=0.000100 Epoch[083] Batch [2099]/[3760] Speed: 67.981386 samples/sec accuracy=84.240327 loss=0.620751 lr=0.000100 Epoch[083] Batch [2149]/[3760] Speed: 67.982214 samples/sec accuracy=84.243459 loss=0.621012 lr=0.000100 Epoch[083] Batch [2199]/[3760] Speed: 68.197555 samples/sec accuracy=84.230824 loss=0.621481 lr=0.000100 Epoch[083] 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accuracy=84.200231 loss=0.622041 lr=0.000100 Epoch[083] Batch [2749]/[3760] Speed: 67.477732 samples/sec accuracy=84.204545 loss=0.621208 lr=0.000100 Epoch[083] Batch [2799]/[3760] Speed: 67.555021 samples/sec accuracy=84.195312 loss=0.621845 lr=0.000100 Epoch[083] Batch [2849]/[3760] Speed: 68.257969 samples/sec accuracy=84.201754 loss=0.621424 lr=0.000100 Epoch[083] Batch [2899]/[3760] Speed: 67.742847 samples/sec accuracy=84.184267 loss=0.622019 lr=0.000100 Epoch[083] Batch [2949]/[3760] Speed: 67.904663 samples/sec accuracy=84.180085 loss=0.622261 lr=0.000100 Epoch[083] Batch [2999]/[3760] Speed: 68.065387 samples/sec accuracy=84.170833 loss=0.622283 lr=0.000100 Epoch[083] Batch [3049]/[3760] Speed: 67.720359 samples/sec accuracy=84.171619 loss=0.622412 lr=0.000100 Epoch[083] Batch [3099]/[3760] Speed: 67.950591 samples/sec accuracy=84.168347 loss=0.622421 lr=0.000100 Epoch[083] Batch [3149]/[3760] Speed: 67.854116 samples/sec accuracy=84.169147 loss=0.622251 lr=0.000100 Epoch[083] 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accuracy=84.136986 loss=0.622424 lr=0.000100 Epoch[083] Batch [3699]/[3760] Speed: 68.145828 samples/sec accuracy=84.147804 loss=0.622288 lr=0.000100 Epoch[083] Batch [3749]/[3760] Speed: 76.649343 samples/sec accuracy=84.158750 loss=0.622218 lr=0.000100 Batch [0049]/[0303]: acc-top1=68.281250 acc-top5=87.656250 Batch [0099]/[0303]: acc-top1=67.875000 acc-top5=87.296875 Batch [0149]/[0303]: acc-top1=67.447917 acc-top5=87.000000 Batch [0199]/[0303]: acc-top1=67.273438 acc-top5=86.835938 Batch [0249]/[0303]: acc-top1=67.418750 acc-top5=86.906250 Batch [0299]/[0303]: acc-top1=67.218750 acc-top5=86.567708 [Epoch 083] training: accuracy=84.162650 loss=0.622190 [Epoch 083] speed: 67 samples/sec time cost: 3848.596655 [Epoch 083] validation: acc-top1=67.197814 acc-top5=86.540842 loss=1.679139 Epoch[084] Batch [0049]/[3759] Speed: 45.554642 samples/sec accuracy=84.187500 loss=0.615254 lr=0.000100 Epoch[084] Batch [0099]/[3759] Speed: 67.102553 samples/sec accuracy=84.265625 loss=0.631202 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lr=0.000100 Epoch[084] Batch [2999]/[3759] Speed: 68.512266 samples/sec accuracy=84.290625 loss=0.620082 lr=0.000100 Epoch[084] Batch [3049]/[3759] Speed: 67.440959 samples/sec accuracy=84.277664 loss=0.620162 lr=0.000100 Epoch[084] Batch [3099]/[3759] Speed: 68.269497 samples/sec accuracy=84.275202 loss=0.619968 lr=0.000100 Epoch[084] Batch [3149]/[3759] Speed: 67.986823 samples/sec accuracy=84.279762 loss=0.619691 lr=0.000100 Epoch[084] Batch [3199]/[3759] Speed: 68.519409 samples/sec accuracy=84.286133 loss=0.619630 lr=0.000100 Epoch[084] Batch [3249]/[3759] Speed: 67.875983 samples/sec accuracy=84.287981 loss=0.619577 lr=0.000100 Epoch[084] Batch [3299]/[3759] Speed: 67.568816 samples/sec accuracy=84.301610 loss=0.619067 lr=0.000100 Epoch[084] Batch [3349]/[3759] Speed: 68.283999 samples/sec accuracy=84.313433 loss=0.618520 lr=0.000100 Epoch[084] Batch [3399]/[3759] Speed: 67.824600 samples/sec accuracy=84.318474 loss=0.618326 lr=0.000100 Epoch[084] Batch [3449]/[3759] Speed: 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[0299]/[0303]: acc-top1=67.234375 acc-top5=86.656250 [Epoch 084] training: accuracy=84.308077 loss=0.618577 [Epoch 084] speed: 67 samples/sec time cost: 3843.550017 [Epoch 084] validation: acc-top1=67.213284 acc-top5=86.613036 loss=1.686328 Epoch[085] Batch [0049]/[3760] Speed: 46.438439 samples/sec accuracy=85.000000 loss=0.597798 lr=0.000100 Epoch[085] Batch [0099]/[3760] Speed: 68.208178 samples/sec accuracy=83.625000 loss=0.632592 lr=0.000100 Epoch[085] Batch [0149]/[3760] Speed: 68.439952 samples/sec accuracy=84.000000 loss=0.630197 lr=0.000100 Epoch[085] Batch [0199]/[3760] Speed: 69.031250 samples/sec accuracy=84.054688 loss=0.630508 lr=0.000100 Epoch[085] Batch [0249]/[3760] Speed: 67.254390 samples/sec accuracy=84.137500 loss=0.623782 lr=0.000100 Epoch[085] Batch [0299]/[3760] Speed: 68.949624 samples/sec accuracy=84.187500 loss=0.622919 lr=0.000100 Epoch[085] Batch [0349]/[3760] Speed: 69.423376 samples/sec accuracy=84.290179 loss=0.620793 lr=0.000100 Epoch[085] Batch 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accuracy=84.411765 loss=0.612147 lr=0.000100 Epoch[085] Batch [0899]/[3760] Speed: 67.932358 samples/sec accuracy=84.421875 loss=0.611113 lr=0.000100 Epoch[085] Batch [0949]/[3760] Speed: 69.097274 samples/sec accuracy=84.378289 loss=0.612569 lr=0.000100 Epoch[085] Batch [0999]/[3760] Speed: 67.590274 samples/sec accuracy=84.376562 loss=0.612280 lr=0.000100 Epoch[085] Batch [1049]/[3760] Speed: 69.610412 samples/sec accuracy=84.386905 loss=0.610884 lr=0.000100 Epoch[085] Batch [1099]/[3760] Speed: 69.221531 samples/sec accuracy=84.401989 loss=0.611161 lr=0.000100 Epoch[085] Batch [1149]/[3760] Speed: 68.751013 samples/sec accuracy=84.406250 loss=0.611187 lr=0.000100 Epoch[085] Batch [1199]/[3760] Speed: 68.819425 samples/sec accuracy=84.411458 loss=0.609767 lr=0.000100 Epoch[085] Batch [1249]/[3760] Speed: 68.635258 samples/sec accuracy=84.443750 loss=0.609619 lr=0.000100 Epoch[085] Batch [1299]/[3760] Speed: 68.166377 samples/sec accuracy=84.449519 loss=0.609068 lr=0.000100 Epoch[085] Batch [1349]/[3760] Speed: 68.691736 samples/sec accuracy=84.447917 loss=0.610184 lr=0.000100 Epoch[085] Batch [1399]/[3760] Speed: 68.517668 samples/sec accuracy=84.429688 loss=0.610639 lr=0.000100 Epoch[085] Batch [1449]/[3760] Speed: 68.999572 samples/sec accuracy=84.439655 loss=0.610608 lr=0.000100 Epoch[085] Batch [1499]/[3760] Speed: 68.793315 samples/sec accuracy=84.444792 loss=0.610534 lr=0.000100 Epoch[085] Batch [1549]/[3760] Speed: 68.027058 samples/sec accuracy=84.417339 loss=0.611353 lr=0.000100 Epoch[085] Batch [1599]/[3760] Speed: 68.858893 samples/sec accuracy=84.396484 loss=0.611361 lr=0.000100 Epoch[085] Batch [1649]/[3760] Speed: 68.765245 samples/sec accuracy=84.407197 loss=0.610865 lr=0.000100 Epoch[085] Batch [1699]/[3760] Speed: 68.843298 samples/sec accuracy=84.405331 loss=0.610429 lr=0.000100 Epoch[085] Batch [1749]/[3760] Speed: 68.952174 samples/sec accuracy=84.394643 loss=0.610745 lr=0.000100 Epoch[085] Batch [1799]/[3760] Speed: 67.505679 samples/sec accuracy=84.401910 loss=0.610542 lr=0.000100 Epoch[085] Batch [1849]/[3760] Speed: 69.385493 samples/sec accuracy=84.417230 loss=0.610283 lr=0.000100 Epoch[085] Batch [1899]/[3760] Speed: 68.737820 samples/sec accuracy=84.422697 loss=0.610355 lr=0.000100 Epoch[085] Batch [1949]/[3760] Speed: 68.359540 samples/sec accuracy=84.411058 loss=0.610075 lr=0.000100 Epoch[085] Batch [1999]/[3760] Speed: 68.380506 samples/sec accuracy=84.386719 loss=0.610355 lr=0.000100 Epoch[085] Batch [2049]/[3760] Speed: 68.515139 samples/sec accuracy=84.367378 loss=0.610991 lr=0.000100 Epoch[085] Batch [2099]/[3760] Speed: 69.179181 samples/sec accuracy=84.381696 loss=0.610891 lr=0.000100 Epoch[085] Batch [2149]/[3760] Speed: 68.215395 samples/sec accuracy=84.381541 loss=0.611158 lr=0.000100 Epoch[085] Batch [2199]/[3760] Speed: 69.134076 samples/sec accuracy=84.394176 loss=0.610865 lr=0.000100 Epoch[085] Batch [2249]/[3760] Speed: 67.956254 samples/sec accuracy=84.414583 loss=0.610210 lr=0.000100 Epoch[085] Batch [2299]/[3760] Speed: 68.410984 samples/sec accuracy=84.410326 loss=0.610331 lr=0.000100 Epoch[085] Batch [2349]/[3760] Speed: 68.908733 samples/sec accuracy=84.403590 loss=0.611087 lr=0.000100 Epoch[085] Batch [2399]/[3760] Speed: 68.905175 samples/sec accuracy=84.435547 loss=0.609925 lr=0.000100 Epoch[085] Batch [2449]/[3760] Speed: 68.324675 samples/sec accuracy=84.452168 loss=0.609349 lr=0.000100 Epoch[085] Batch [2499]/[3760] Speed: 68.810443 samples/sec accuracy=84.441250 loss=0.609801 lr=0.000100 Epoch[085] Batch [2549]/[3760] Speed: 68.636822 samples/sec accuracy=84.448529 loss=0.609705 lr=0.000100 Epoch[085] Batch [2599]/[3760] Speed: 68.655118 samples/sec accuracy=84.456130 loss=0.609604 lr=0.000100 Epoch[085] Batch [2649]/[3760] Speed: 68.883292 samples/sec accuracy=84.439858 loss=0.610211 lr=0.000100 Epoch[085] Batch [2699]/[3760] Speed: 68.289971 samples/sec accuracy=84.429398 loss=0.610708 lr=0.000100 Epoch[085] Batch [2749]/[3760] Speed: 68.834505 samples/sec accuracy=84.435227 loss=0.610365 lr=0.000100 Epoch[085] Batch [2799]/[3760] Speed: 68.399694 samples/sec accuracy=84.411272 loss=0.610744 lr=0.000100 Epoch[085] Batch [2849]/[3760] Speed: 68.853032 samples/sec accuracy=84.398575 loss=0.610917 lr=0.000100 Epoch[085] Batch [2899]/[3760] Speed: 69.153152 samples/sec accuracy=84.394935 loss=0.611587 lr=0.000100 Epoch[085] Batch [2949]/[3760] Speed: 68.918757 samples/sec accuracy=84.405720 loss=0.611069 lr=0.000100 Epoch[085] Batch [2999]/[3760] Speed: 68.340299 samples/sec accuracy=84.398958 loss=0.611043 lr=0.000100 Epoch[085] Batch [3049]/[3760] Speed: 68.473333 samples/sec accuracy=84.389344 loss=0.611576 lr=0.000100 Epoch[085] Batch [3099]/[3760] Speed: 68.819695 samples/sec accuracy=84.395665 loss=0.611791 lr=0.000100 Epoch[085] Batch [3149]/[3760] Speed: 68.698288 samples/sec accuracy=84.380952 loss=0.612328 lr=0.000100 Epoch[085] Batch [3199]/[3760] Speed: 68.887535 samples/sec accuracy=84.370605 loss=0.612782 lr=0.000100 Epoch[085] Batch [3249]/[3760] Speed: 68.645912 samples/sec accuracy=84.363942 loss=0.613387 lr=0.000100 Epoch[085] Batch [3299]/[3760] Speed: 68.209057 samples/sec accuracy=84.361742 loss=0.613093 lr=0.000100 Epoch[085] Batch [3349]/[3760] Speed: 68.824135 samples/sec accuracy=84.370336 loss=0.612604 lr=0.000100 Epoch[085] Batch [3399]/[3760] Speed: 69.200372 samples/sec accuracy=84.359835 loss=0.612862 lr=0.000100 Epoch[085] Batch [3449]/[3760] Speed: 69.440273 samples/sec accuracy=84.364583 loss=0.612920 lr=0.000100 Epoch[085] Batch [3499]/[3760] Speed: 68.269637 samples/sec accuracy=84.363393 loss=0.613035 lr=0.000100 Epoch[085] Batch [3549]/[3760] Speed: 68.487209 samples/sec accuracy=84.359155 loss=0.613161 lr=0.000100 Epoch[085] Batch [3599]/[3760] Speed: 68.701214 samples/sec accuracy=84.351562 loss=0.613352 lr=0.000100 Epoch[085] Batch [3649]/[3760] Speed: 69.306805 samples/sec accuracy=84.350599 loss=0.613287 lr=0.000100 Epoch[085] Batch [3699]/[3760] Speed: 68.691732 samples/sec accuracy=84.350929 loss=0.613168 lr=0.000100 Epoch[085] Batch [3749]/[3760] Speed: 76.802631 samples/sec accuracy=84.336250 loss=0.613473 lr=0.000100 Batch [0049]/[0303]: acc-top1=68.218750 acc-top5=87.406250 Batch [0099]/[0303]: acc-top1=67.734375 acc-top5=87.015625 Batch [0149]/[0303]: acc-top1=67.208333 acc-top5=86.802083 Batch [0199]/[0303]: acc-top1=67.226562 acc-top5=86.718750 Batch [0249]/[0303]: acc-top1=67.450000 acc-top5=86.931250 Batch [0299]/[0303]: acc-top1=67.250000 acc-top5=86.546875 [Epoch 085] training: accuracy=84.338846 loss=0.613448 [Epoch 085] speed: 68 samples/sec time cost: 3805.124232 [Epoch 085] validation: acc-top1=67.223597 acc-top5=86.515058 loss=1.688022 Epoch[086] Batch [0049]/[3760] Speed: 46.470584 samples/sec accuracy=84.062500 loss=0.600598 lr=0.000100 Epoch[086] Batch [0099]/[3760] Speed: 67.789088 samples/sec accuracy=83.906250 loss=0.625520 lr=0.000100 Epoch[086] Batch [0149]/[3760] Speed: 68.714840 samples/sec accuracy=84.270833 loss=0.609130 lr=0.000100 Epoch[086] Batch [0199]/[3760] Speed: 68.577286 samples/sec accuracy=84.117188 loss=0.613801 lr=0.000100 Epoch[086] Batch [0249]/[3760] Speed: 68.216946 samples/sec accuracy=84.331250 loss=0.611183 lr=0.000100 Epoch[086] Batch [0299]/[3760] Speed: 68.221978 samples/sec accuracy=84.265625 loss=0.616148 lr=0.000100 Epoch[086] Batch [0349]/[3760] Speed: 68.710942 samples/sec accuracy=84.339286 loss=0.614628 lr=0.000100 Epoch[086] Batch [0399]/[3760] Speed: 68.779012 samples/sec accuracy=84.343750 loss=0.615098 lr=0.000100 Epoch[086] Batch [0449]/[3760] Speed: 68.406996 samples/sec accuracy=84.256944 loss=0.616501 lr=0.000100 Epoch[086] Batch [0499]/[3760] Speed: 69.226407 samples/sec accuracy=84.312500 loss=0.614523 lr=0.000100 Epoch[086] Batch [0549]/[3760] Speed: 68.003132 samples/sec accuracy=84.406250 loss=0.613028 lr=0.000100 Epoch[086] Batch [0599]/[3760] Speed: 69.708039 samples/sec accuracy=84.385417 loss=0.614122 lr=0.000100 Epoch[086] Batch [0649]/[3760] Speed: 68.306520 samples/sec accuracy=84.430288 loss=0.613240 lr=0.000100 Epoch[086] Batch [0699]/[3760] Speed: 69.008421 samples/sec accuracy=84.430804 loss=0.614018 lr=0.000100 Epoch[086] Batch [0749]/[3760] Speed: 69.055778 samples/sec accuracy=84.464583 loss=0.613400 lr=0.000100 Epoch[086] Batch [0799]/[3760] Speed: 68.165307 samples/sec accuracy=84.437500 loss=0.613212 lr=0.000100 Epoch[086] Batch [0849]/[3760] Speed: 68.875909 samples/sec accuracy=84.459559 loss=0.612637 lr=0.000100 Epoch[086] Batch [0899]/[3760] Speed: 69.355780 samples/sec accuracy=84.451389 loss=0.613303 lr=0.000100 Epoch[086] Batch [0949]/[3760] Speed: 68.562765 samples/sec accuracy=84.483553 loss=0.612213 lr=0.000100 Epoch[086] Batch [0999]/[3760] Speed: 68.802946 samples/sec accuracy=84.464062 loss=0.612465 lr=0.000100 Epoch[086] Batch [1049]/[3760] Speed: 67.563926 samples/sec accuracy=84.443452 loss=0.612947 lr=0.000100 Epoch[086] Batch [1099]/[3760] Speed: 69.072562 samples/sec accuracy=84.394886 loss=0.615133 lr=0.000100 Epoch[086] Batch [1149]/[3760] Speed: 68.780653 samples/sec accuracy=84.399457 loss=0.614165 lr=0.000100 Epoch[086] Batch [1199]/[3760] Speed: 68.099878 samples/sec accuracy=84.412760 loss=0.614652 lr=0.000100 Epoch[086] Batch [1249]/[3760] Speed: 68.805723 samples/sec accuracy=84.425000 loss=0.613649 lr=0.000100 Epoch[086] Batch [1299]/[3760] Speed: 67.385596 samples/sec accuracy=84.435096 loss=0.614218 lr=0.000100 Epoch[086] Batch [1349]/[3760] Speed: 69.513379 samples/sec accuracy=84.418981 loss=0.614968 lr=0.000100 Epoch[086] Batch [1399]/[3760] Speed: 68.819205 samples/sec accuracy=84.421875 loss=0.614676 lr=0.000100 Epoch[086] Batch [1449]/[3760] Speed: 69.081355 samples/sec accuracy=84.447198 loss=0.613472 lr=0.000100 Epoch[086] Batch [1499]/[3760] Speed: 68.738269 samples/sec accuracy=84.445833 loss=0.613085 lr=0.000100 Epoch[086] Batch [1549]/[3760] Speed: 67.644425 samples/sec accuracy=84.440524 loss=0.614159 lr=0.000100 Epoch[086] Batch [1599]/[3760] Speed: 69.951140 samples/sec accuracy=84.460938 loss=0.614020 lr=0.000100 Epoch[086] Batch [1649]/[3760] Speed: 68.148989 samples/sec accuracy=84.469697 loss=0.613899 lr=0.000100 Epoch[086] Batch [1699]/[3760] Speed: 68.734283 samples/sec accuracy=84.465993 loss=0.613988 lr=0.000100 Epoch[086] Batch [1749]/[3760] Speed: 69.215526 samples/sec accuracy=84.495536 loss=0.612740 lr=0.000100 Epoch[086] Batch [1799]/[3760] Speed: 66.912838 samples/sec accuracy=84.506076 loss=0.612005 lr=0.000100 Epoch[086] Batch [1849]/[3760] Speed: 68.958995 samples/sec accuracy=84.497466 loss=0.612055 lr=0.000100 Epoch[086] Batch [1899]/[3760] Speed: 68.984921 samples/sec accuracy=84.511513 loss=0.611278 lr=0.000100 Epoch[086] Batch [1949]/[3760] Speed: 68.596128 samples/sec accuracy=84.515224 loss=0.610516 lr=0.000100 Epoch[086] Batch [1999]/[3760] Speed: 69.174218 samples/sec accuracy=84.485156 loss=0.612133 lr=0.000100 Epoch[086] Batch [2049]/[3760] Speed: 68.528463 samples/sec accuracy=84.486280 loss=0.612101 lr=0.000100 Epoch[086] Batch [2099]/[3760] Speed: 68.446374 samples/sec accuracy=84.491815 loss=0.611584 lr=0.000100 Epoch[086] Batch [2149]/[3760] Speed: 68.050781 samples/sec accuracy=84.518169 loss=0.610531 lr=0.000100 Epoch[086] Batch [2199]/[3760] Speed: 68.863446 samples/sec accuracy=84.525568 loss=0.610514 lr=0.000100 Epoch[086] Batch [2249]/[3760] Speed: 68.310952 samples/sec accuracy=84.510417 loss=0.611472 lr=0.000100 Epoch[086] Batch [2299]/[3760] Speed: 68.627323 samples/sec accuracy=84.477582 loss=0.612185 lr=0.000100 Epoch[086] Batch [2349]/[3760] Speed: 68.588358 samples/sec accuracy=84.465426 loss=0.612687 lr=0.000100 Epoch[086] Batch [2399]/[3760] Speed: 68.609659 samples/sec accuracy=84.487630 loss=0.611325 lr=0.000100 Epoch[086] Batch [2449]/[3760] Speed: 68.047875 samples/sec accuracy=84.484056 loss=0.611459 lr=0.000100 Epoch[086] Batch [2499]/[3760] Speed: 69.211075 samples/sec accuracy=84.483125 loss=0.611898 lr=0.000100 Epoch[086] Batch [2549]/[3760] Speed: 68.719788 samples/sec accuracy=84.496324 loss=0.611440 lr=0.000100 Epoch[086] Batch [2599]/[3760] Speed: 67.782729 samples/sec accuracy=84.493990 loss=0.611249 lr=0.000100 Epoch[086] Batch [2649]/[3760] Speed: 69.084402 samples/sec accuracy=84.482901 loss=0.611002 lr=0.000100 Epoch[086] Batch [2699]/[3760] Speed: 68.713487 samples/sec accuracy=84.491319 loss=0.610958 lr=0.000100 Epoch[086] Batch [2749]/[3760] Speed: 68.687173 samples/sec accuracy=84.505114 loss=0.610612 lr=0.000100 Epoch[086] Batch [2799]/[3760] Speed: 69.054534 samples/sec accuracy=84.512835 loss=0.610229 lr=0.000100 Epoch[086] Batch [2849]/[3760] Speed: 68.676797 samples/sec accuracy=84.496711 loss=0.610696 lr=0.000100 Epoch[086] Batch [2899]/[3760] Speed: 68.408141 samples/sec accuracy=84.492996 loss=0.610635 lr=0.000100 Epoch[086] Batch [2949]/[3760] Speed: 68.652382 samples/sec accuracy=84.489407 loss=0.610931 lr=0.000100 Epoch[086] Batch [2999]/[3760] Speed: 68.962640 samples/sec accuracy=84.478646 loss=0.611272 lr=0.000100 Epoch[086] Batch [3049]/[3760] Speed: 68.566327 samples/sec accuracy=84.477971 loss=0.611414 lr=0.000100 Epoch[086] Batch [3099]/[3760] Speed: 67.860725 samples/sec accuracy=84.494960 loss=0.610943 lr=0.000100 Epoch[086] Batch [3149]/[3760] Speed: 69.014730 samples/sec accuracy=84.512897 loss=0.610667 lr=0.000100 Epoch[086] Batch [3199]/[3760] Speed: 68.381338 samples/sec accuracy=84.500977 loss=0.610843 lr=0.000100 Epoch[086] Batch [3249]/[3760] Speed: 68.511571 samples/sec accuracy=84.506731 loss=0.610904 lr=0.000100 Epoch[086] Batch [3299]/[3760] Speed: 68.349079 samples/sec accuracy=84.507576 loss=0.610503 lr=0.000100 Epoch[086] Batch [3349]/[3760] Speed: 67.919236 samples/sec accuracy=84.513993 loss=0.610144 lr=0.000100 Epoch[086] Batch [3399]/[3760] Speed: 68.813307 samples/sec accuracy=84.504136 loss=0.610912 lr=0.000100 Epoch[086] Batch [3449]/[3760] Speed: 68.966843 samples/sec accuracy=84.511775 loss=0.610489 lr=0.000100 Epoch[086] Batch [3499]/[3760] Speed: 68.435273 samples/sec accuracy=84.516518 loss=0.610244 lr=0.000100 Epoch[086] Batch [3549]/[3760] Speed: 68.688163 samples/sec accuracy=84.514965 loss=0.610504 lr=0.000100 Epoch[086] Batch [3599]/[3760] Speed: 68.343925 samples/sec accuracy=84.511285 loss=0.610570 lr=0.000100 Epoch[086] Batch [3649]/[3760] Speed: 68.594668 samples/sec accuracy=84.526969 loss=0.610179 lr=0.000100 Epoch[086] Batch [3699]/[3760] Speed: 68.462418 samples/sec accuracy=84.524071 loss=0.610296 lr=0.000100 Epoch[086] Batch [3749]/[3760] Speed: 77.286237 samples/sec accuracy=84.521250 loss=0.610206 lr=0.000100 Batch [0049]/[0303]: acc-top1=68.031250 acc-top5=87.468750 Batch [0099]/[0303]: acc-top1=67.578125 acc-top5=86.953125 Batch [0149]/[0303]: acc-top1=67.104167 acc-top5=86.885417 Batch [0199]/[0303]: acc-top1=67.281250 acc-top5=86.820312 Batch [0249]/[0303]: acc-top1=67.362500 acc-top5=86.918750 Batch [0299]/[0303]: acc-top1=67.125000 acc-top5=86.583333 [Epoch 086] training: accuracy=84.519614 loss=0.610265 [Epoch 086] speed: 68 samples/sec time cost: 3808.945774 [Epoch 086] validation: acc-top1=67.089521 acc-top5=86.551155 loss=1.689845 Epoch[087] Batch [0049]/[3759] Speed: 46.876195 samples/sec accuracy=85.000000 loss=0.588470 lr=0.000100 Epoch[087] Batch [0099]/[3759] Speed: 66.047310 samples/sec accuracy=84.671875 loss=0.599872 lr=0.000100 Epoch[087] Batch [0149]/[3759] Speed: 69.678605 samples/sec accuracy=85.010417 loss=0.595635 lr=0.000100 Epoch[087] Batch [0199]/[3759] Speed: 68.384368 samples/sec accuracy=84.929688 loss=0.604465 lr=0.000100 Epoch[087] Batch [0249]/[3759] Speed: 69.096582 samples/sec accuracy=84.893750 loss=0.604465 lr=0.000100 Epoch[087] Batch [0299]/[3759] Speed: 68.530232 samples/sec accuracy=84.921875 loss=0.602029 lr=0.000100 Epoch[087] Batch [0349]/[3759] Speed: 68.032367 samples/sec accuracy=84.870536 loss=0.602085 lr=0.000100 Epoch[087] Batch [0399]/[3759] Speed: 68.590249 samples/sec accuracy=84.917969 loss=0.600970 lr=0.000100 Epoch[087] Batch 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accuracy=84.755208 loss=0.604697 lr=0.000100 Epoch[087] Batch [0949]/[3759] Speed: 68.772115 samples/sec accuracy=84.715461 loss=0.606007 lr=0.000100 Epoch[087] Batch [0999]/[3759] Speed: 68.528696 samples/sec accuracy=84.621875 loss=0.608709 lr=0.000100 Epoch[087] Batch [1049]/[3759] Speed: 68.544688 samples/sec accuracy=84.578869 loss=0.609945 lr=0.000100 Epoch[087] Batch [1099]/[3759] Speed: 68.800953 samples/sec accuracy=84.552557 loss=0.611273 lr=0.000100 Epoch[087] Batch [1149]/[3759] Speed: 68.375561 samples/sec accuracy=84.586957 loss=0.610781 lr=0.000100 Epoch[087] Batch [1199]/[3759] Speed: 68.655565 samples/sec accuracy=84.585938 loss=0.610549 lr=0.000100 Epoch[087] Batch [1249]/[3759] Speed: 68.745225 samples/sec accuracy=84.612500 loss=0.609924 lr=0.000100 Epoch[087] Batch [1299]/[3759] Speed: 68.924098 samples/sec accuracy=84.612981 loss=0.609601 lr=0.000100 Epoch[087] Batch [1349]/[3759] Speed: 68.399765 samples/sec accuracy=84.622685 loss=0.609255 lr=0.000100 Epoch[087] Batch [1399]/[3759] Speed: 67.922681 samples/sec accuracy=84.610491 loss=0.609932 lr=0.000100 Epoch[087] Batch [1449]/[3759] Speed: 69.368665 samples/sec accuracy=84.627155 loss=0.608897 lr=0.000100 Epoch[087] Batch [1499]/[3759] Speed: 68.700268 samples/sec accuracy=84.623958 loss=0.609334 lr=0.000100 Epoch[087] Batch [1549]/[3759] Speed: 69.126337 samples/sec accuracy=84.628024 loss=0.609610 lr=0.000100 Epoch[087] Batch [1599]/[3759] Speed: 68.404380 samples/sec accuracy=84.620117 loss=0.609712 lr=0.000100 Epoch[087] Batch [1649]/[3759] Speed: 68.139512 samples/sec accuracy=84.613636 loss=0.609686 lr=0.000100 Epoch[087] Batch [1699]/[3759] Speed: 69.010877 samples/sec accuracy=84.625000 loss=0.609630 lr=0.000100 Epoch[087] Batch [1749]/[3759] Speed: 68.609758 samples/sec accuracy=84.640179 loss=0.609103 lr=0.000100 Epoch[087] Batch [1799]/[3759] Speed: 68.663809 samples/sec accuracy=84.630208 loss=0.609360 lr=0.000100 Epoch[087] Batch [1849]/[3759] Speed: 68.822799 samples/sec accuracy=84.630912 loss=0.608856 lr=0.000100 Epoch[087] Batch [1899]/[3759] Speed: 66.918082 samples/sec accuracy=84.623355 loss=0.609466 lr=0.000100 Epoch[087] Batch [1949]/[3759] Speed: 70.185296 samples/sec accuracy=84.620994 loss=0.609534 lr=0.000100 Epoch[087] Batch [1999]/[3759] Speed: 68.988506 samples/sec accuracy=84.639844 loss=0.608587 lr=0.000100 Epoch[087] Batch [2049]/[3759] Speed: 69.007478 samples/sec accuracy=84.657774 loss=0.607418 lr=0.000100 Epoch[087] Batch [2099]/[3759] Speed: 68.157991 samples/sec accuracy=84.665179 loss=0.607158 lr=0.000100 Epoch[087] Batch [2149]/[3759] Speed: 68.579129 samples/sec accuracy=84.660610 loss=0.607078 lr=0.000100 Epoch[087] Batch [2199]/[3759] Speed: 68.490011 samples/sec accuracy=84.647727 loss=0.607069 lr=0.000100 Epoch[087] Batch [2249]/[3759] Speed: 69.081121 samples/sec accuracy=84.622917 loss=0.608268 lr=0.000100 Epoch[087] Batch [2299]/[3759] Speed: 68.240780 samples/sec accuracy=84.617527 loss=0.608252 lr=0.000100 Epoch[087] Batch [2349]/[3759] Speed: 68.879214 samples/sec accuracy=84.624335 loss=0.608121 lr=0.000100 Epoch[087] Batch [2399]/[3759] Speed: 68.956702 samples/sec accuracy=84.643229 loss=0.607502 lr=0.000100 Epoch[087] Batch [2449]/[3759] Speed: 68.267065 samples/sec accuracy=84.637117 loss=0.607474 lr=0.000100 Epoch[087] Batch [2499]/[3759] Speed: 69.194717 samples/sec accuracy=84.638750 loss=0.607452 lr=0.000100 Epoch[087] Batch [2549]/[3759] Speed: 68.215368 samples/sec accuracy=84.625613 loss=0.607826 lr=0.000100 Epoch[087] Batch [2599]/[3759] Speed: 68.670723 samples/sec accuracy=84.629207 loss=0.607814 lr=0.000100 Epoch[087] Batch [2649]/[3759] Speed: 68.070231 samples/sec accuracy=84.638561 loss=0.607327 lr=0.000100 Epoch[087] Batch [2699]/[3759] Speed: 68.791426 samples/sec accuracy=84.625579 loss=0.607214 lr=0.000100 Epoch[087] Batch [2749]/[3759] Speed: 68.515766 samples/sec accuracy=84.642045 loss=0.606566 lr=0.000100 Epoch[087] Batch [2799]/[3759] Speed: 68.432132 samples/sec accuracy=84.642857 loss=0.606534 lr=0.000100 Epoch[087] Batch [2849]/[3759] Speed: 68.720542 samples/sec accuracy=84.655154 loss=0.605838 lr=0.000100 Epoch[087] Batch [2899]/[3759] Speed: 68.196640 samples/sec accuracy=84.646013 loss=0.606001 lr=0.000100 Epoch[087] Batch [2949]/[3759] Speed: 68.002344 samples/sec accuracy=84.645657 loss=0.606263 lr=0.000100 Epoch[087] Batch [2999]/[3759] Speed: 68.925073 samples/sec accuracy=84.656250 loss=0.605716 lr=0.000100 Epoch[087] Batch [3049]/[3759] Speed: 68.860599 samples/sec accuracy=84.647029 loss=0.605807 lr=0.000100 Epoch[087] Batch [3099]/[3759] Speed: 68.722174 samples/sec accuracy=84.635081 loss=0.606118 lr=0.000100 Epoch[087] Batch [3149]/[3759] Speed: 68.416199 samples/sec accuracy=84.632937 loss=0.605934 lr=0.000100 Epoch[087] Batch [3199]/[3759] Speed: 68.324101 samples/sec accuracy=84.623535 loss=0.606503 lr=0.000100 Epoch[087] Batch [3249]/[3759] Speed: 68.906568 samples/sec accuracy=84.608654 loss=0.607181 lr=0.000100 Epoch[087] Batch [3299]/[3759] Speed: 68.719423 samples/sec accuracy=84.588068 loss=0.608155 lr=0.000100 Epoch[087] Batch [3349]/[3759] Speed: 68.614064 samples/sec accuracy=84.596082 loss=0.607964 lr=0.000100 Epoch[087] Batch [3399]/[3759] Speed: 68.758812 samples/sec accuracy=84.593750 loss=0.608342 lr=0.000100 Epoch[087] Batch [3449]/[3759] Speed: 67.596866 samples/sec accuracy=84.604620 loss=0.608052 lr=0.000100 Epoch[087] Batch [3499]/[3759] Speed: 69.633733 samples/sec accuracy=84.603571 loss=0.608410 lr=0.000100 Epoch[087] Batch [3549]/[3759] Speed: 68.651076 samples/sec accuracy=84.596391 loss=0.608645 lr=0.000100 Epoch[087] Batch [3599]/[3759] Speed: 68.655611 samples/sec accuracy=84.591580 loss=0.608761 lr=0.000100 Epoch[087] Batch [3649]/[3759] Speed: 68.861309 samples/sec accuracy=84.575342 loss=0.609370 lr=0.000100 Epoch[087] Batch [3699]/[3759] Speed: 68.622485 samples/sec accuracy=84.584459 loss=0.609066 lr=0.000100 Epoch[087] Batch [3749]/[3759] Speed: 77.279225 samples/sec accuracy=84.567917 loss=0.609349 lr=0.000100 Batch [0049]/[0303]: acc-top1=68.562500 acc-top5=87.562500 Batch [0099]/[0303]: acc-top1=67.984375 acc-top5=87.140625 Batch [0149]/[0303]: acc-top1=67.375000 acc-top5=87.020833 Batch [0199]/[0303]: acc-top1=67.515625 acc-top5=86.945312 Batch [0249]/[0303]: acc-top1=67.662500 acc-top5=87.018750 Batch [0299]/[0303]: acc-top1=67.375000 acc-top5=86.656250 [Epoch 087] training: accuracy=84.570364 loss=0.609350 [Epoch 087] speed: 68 samples/sec time cost: 3807.074861 [Epoch 087] validation: acc-top1=67.337046 acc-top5=86.628507 loss=1.689023 Epoch[088] Batch [0049]/[3760] Speed: 47.074819 samples/sec accuracy=84.812500 loss=0.630129 lr=0.000100 Epoch[088] Batch [0099]/[3760] Speed: 68.524466 samples/sec accuracy=84.796875 loss=0.614957 lr=0.000100 Epoch[088] Batch [0149]/[3760] Speed: 68.411383 samples/sec accuracy=85.114583 loss=0.598473 lr=0.000100 Epoch[088] Batch [0199]/[3760] Speed: 68.714856 samples/sec accuracy=84.796875 loss=0.611347 lr=0.000100 Epoch[088] Batch [0249]/[3760] Speed: 67.741238 samples/sec accuracy=84.756250 loss=0.609006 lr=0.000100 Epoch[088] Batch [0299]/[3760] Speed: 68.992994 samples/sec accuracy=84.661458 loss=0.610609 lr=0.000100 Epoch[088] Batch [0349]/[3760] Speed: 69.138958 samples/sec accuracy=84.763393 loss=0.606616 lr=0.000100 Epoch[088] Batch [0399]/[3760] Speed: 68.007196 samples/sec accuracy=84.730469 loss=0.608627 lr=0.000100 Epoch[088] Batch [0449]/[3760] Speed: 69.217927 samples/sec accuracy=84.656250 loss=0.609003 lr=0.000100 Epoch[088] Batch [0499]/[3760] Speed: 68.032360 samples/sec accuracy=84.671875 loss=0.610328 lr=0.000100 Epoch[088] Batch [0549]/[3760] Speed: 68.688529 samples/sec accuracy=84.690341 loss=0.608550 lr=0.000100 Epoch[088] Batch [0599]/[3760] Speed: 68.882425 samples/sec accuracy=84.703125 loss=0.607903 lr=0.000100 Epoch[088] Batch [0649]/[3760] Speed: 68.627403 samples/sec accuracy=84.704327 loss=0.607355 lr=0.000100 Epoch[088] Batch [0699]/[3760] Speed: 68.683104 samples/sec accuracy=84.642857 loss=0.609948 lr=0.000100 Epoch[088] Batch [0749]/[3760] Speed: 68.029901 samples/sec accuracy=84.712500 loss=0.606418 lr=0.000100 Epoch[088] Batch [0799]/[3760] Speed: 68.377919 samples/sec accuracy=84.742188 loss=0.604424 lr=0.000100 Epoch[088] Batch [0849]/[3760] Speed: 68.625637 samples/sec accuracy=84.753676 loss=0.604818 lr=0.000100 Epoch[088] Batch [0899]/[3760] Speed: 68.965589 samples/sec accuracy=84.743056 loss=0.604996 lr=0.000100 Epoch[088] Batch [0949]/[3760] Speed: 68.369707 samples/sec accuracy=84.771382 loss=0.603837 lr=0.000100 Epoch[088] Batch [0999]/[3760] Speed: 69.064911 samples/sec accuracy=84.700000 loss=0.605199 lr=0.000100 Epoch[088] Batch [1049]/[3760] Speed: 68.615069 samples/sec accuracy=84.741071 loss=0.603527 lr=0.000100 Epoch[088] Batch [1099]/[3760] Speed: 68.890502 samples/sec accuracy=84.757102 loss=0.603464 lr=0.000100 Epoch[088] Batch [1149]/[3760] Speed: 68.674833 samples/sec accuracy=84.726902 loss=0.604376 lr=0.000100 Epoch[088] Batch [1199]/[3760] Speed: 68.588988 samples/sec accuracy=84.712240 loss=0.604250 lr=0.000100 Epoch[088] Batch [1249]/[3760] Speed: 68.620423 samples/sec accuracy=84.721250 loss=0.604447 lr=0.000100 Epoch[088] Batch [1299]/[3760] Speed: 67.423342 samples/sec accuracy=84.697115 loss=0.604924 lr=0.000100 Epoch[088] Batch [1349]/[3760] Speed: 69.569297 samples/sec accuracy=84.692130 loss=0.604889 lr=0.000100 Epoch[088] Batch [1399]/[3760] Speed: 69.091762 samples/sec accuracy=84.680804 loss=0.605551 lr=0.000100 Epoch[088] Batch [1449]/[3760] Speed: 68.777707 samples/sec accuracy=84.687500 loss=0.605282 lr=0.000100 Epoch[088] Batch [1499]/[3760] Speed: 69.088883 samples/sec accuracy=84.671875 loss=0.605766 lr=0.000100 Epoch[088] Batch [1549]/[3760] Speed: 67.262306 samples/sec accuracy=84.678427 loss=0.604845 lr=0.000100 Epoch[088] Batch [1599]/[3760] Speed: 69.416198 samples/sec accuracy=84.664062 loss=0.605222 lr=0.000100 Epoch[088] Batch [1649]/[3760] Speed: 68.604314 samples/sec accuracy=84.653409 loss=0.605968 lr=0.000100 Epoch[088] Batch [1699]/[3760] Speed: 68.661007 samples/sec accuracy=84.647978 loss=0.606487 lr=0.000100 Epoch[088] Batch [1749]/[3760] Speed: 68.193596 samples/sec accuracy=84.662500 loss=0.606526 lr=0.000100 Epoch[088] Batch [1799]/[3760] Speed: 68.957914 samples/sec accuracy=84.697049 loss=0.604870 lr=0.000100 Epoch[088] Batch [1849]/[3760] Speed: 68.152321 samples/sec accuracy=84.696791 loss=0.604212 lr=0.000100 Epoch[088] Batch [1899]/[3760] Speed: 68.628437 samples/sec accuracy=84.675164 loss=0.604599 lr=0.000100 Epoch[088] Batch [1949]/[3760] Speed: 68.555657 samples/sec accuracy=84.665064 loss=0.604658 lr=0.000100 Epoch[088] Batch [1999]/[3760] Speed: 68.406295 samples/sec accuracy=84.705469 loss=0.603208 lr=0.000100 Epoch[088] Batch [2049]/[3760] Speed: 68.581715 samples/sec accuracy=84.701220 loss=0.603178 lr=0.000100 Epoch[088] Batch [2099]/[3760] Speed: 69.078534 samples/sec accuracy=84.706101 loss=0.603074 lr=0.000100 Epoch[088] Batch [2149]/[3760] Speed: 68.350060 samples/sec accuracy=84.702035 loss=0.603229 lr=0.000100 Epoch[088] Batch [2199]/[3760] Speed: 68.699931 samples/sec accuracy=84.720881 loss=0.602823 lr=0.000100 Epoch[088] Batch [2249]/[3760] Speed: 69.047674 samples/sec accuracy=84.737500 loss=0.602194 lr=0.000100 Epoch[088] Batch [2299]/[3760] Speed: 68.441252 samples/sec accuracy=84.721467 loss=0.602917 lr=0.000100 Epoch[088] Batch [2349]/[3760] Speed: 67.996682 samples/sec accuracy=84.716755 loss=0.602881 lr=0.000100 Epoch[088] Batch [2399]/[3760] Speed: 68.964138 samples/sec accuracy=84.710938 loss=0.602922 lr=0.000100 Epoch[088] Batch [2449]/[3760] Speed: 68.729454 samples/sec accuracy=84.712372 loss=0.602576 lr=0.000100 Epoch[088] Batch [2499]/[3760] Speed: 69.052406 samples/sec accuracy=84.714375 loss=0.602625 lr=0.000100 Epoch[088] Batch [2549]/[3760] Speed: 67.980537 samples/sec accuracy=84.719975 loss=0.602301 lr=0.000100 Epoch[088] Batch [2599]/[3760] Speed: 68.302127 samples/sec accuracy=84.703125 loss=0.602715 lr=0.000100 Epoch[088] Batch [2649]/[3760] Speed: 68.948666 samples/sec accuracy=84.703420 loss=0.603168 lr=0.000100 Epoch[088] Batch [2699]/[3760] Speed: 68.408754 samples/sec accuracy=84.686343 loss=0.603721 lr=0.000100 Epoch[088] Batch [2749]/[3760] Speed: 69.284762 samples/sec accuracy=84.702273 loss=0.603174 lr=0.000100 Epoch[088] Batch [2799]/[3760] Speed: 68.465353 samples/sec accuracy=84.710379 loss=0.602654 lr=0.000100 Epoch[088] Batch [2849]/[3760] Speed: 68.911318 samples/sec accuracy=84.691886 loss=0.602906 lr=0.000100 Epoch[088] Batch [2899]/[3760] Speed: 68.417898 samples/sec accuracy=84.686422 loss=0.602972 lr=0.000100 Epoch[088] Batch [2949]/[3760] Speed: 69.129266 samples/sec accuracy=84.712924 loss=0.601840 lr=0.000100 Epoch[088] Batch [2999]/[3760] Speed: 68.267057 samples/sec accuracy=84.706771 loss=0.602000 lr=0.000100 Epoch[088] Batch [3049]/[3760] Speed: 68.691633 samples/sec accuracy=84.701844 loss=0.602519 lr=0.000100 Epoch[088] Batch [3099]/[3760] Speed: 68.570168 samples/sec accuracy=84.701109 loss=0.602689 lr=0.000100 Epoch[088] Batch [3149]/[3760] Speed: 68.329702 samples/sec accuracy=84.684524 loss=0.602880 lr=0.000100 Epoch[088] Batch [3199]/[3760] Speed: 68.381124 samples/sec accuracy=84.705566 loss=0.602456 lr=0.000100 Epoch[088] Batch [3249]/[3760] Speed: 69.343785 samples/sec accuracy=84.702404 loss=0.602784 lr=0.000100 Epoch[088] Batch [3299]/[3760] Speed: 68.869443 samples/sec accuracy=84.710701 loss=0.602663 lr=0.000100 Epoch[088] Batch [3349]/[3760] Speed: 68.772544 samples/sec accuracy=84.707556 loss=0.602673 lr=0.000100 Epoch[088] Batch [3399]/[3760] Speed: 68.450122 samples/sec accuracy=84.695772 loss=0.603134 lr=0.000100 Epoch[088] Batch [3449]/[3760] Speed: 68.153115 samples/sec accuracy=84.708333 loss=0.602672 lr=0.000100 Epoch[088] Batch [3499]/[3760] Speed: 69.277225 samples/sec accuracy=84.701339 loss=0.602486 lr=0.000100 Epoch[088] Batch [3549]/[3760] Speed: 68.512831 samples/sec accuracy=84.705986 loss=0.602644 lr=0.000100 Epoch[088] Batch [3599]/[3760] Speed: 68.408284 samples/sec accuracy=84.709201 loss=0.602396 lr=0.000100 Epoch[088] Batch [3649]/[3760] Speed: 68.169859 samples/sec accuracy=84.694777 loss=0.602827 lr=0.000100 Epoch[088] Batch [3699]/[3760] Speed: 69.550598 samples/sec accuracy=84.700591 loss=0.603036 lr=0.000100 Epoch[088] Batch [3749]/[3760] Speed: 76.827412 samples/sec accuracy=84.707917 loss=0.602379 lr=0.000100 Batch [0049]/[0303]: acc-top1=68.031250 acc-top5=87.750000 Batch [0099]/[0303]: acc-top1=67.609375 acc-top5=87.125000 Batch [0149]/[0303]: acc-top1=66.979167 acc-top5=86.864583 Batch [0199]/[0303]: acc-top1=67.117188 acc-top5=86.843750 Batch [0249]/[0303]: acc-top1=67.356250 acc-top5=86.868750 Batch [0299]/[0303]: acc-top1=67.125000 acc-top5=86.479167 [Epoch 088] training: accuracy=84.702045 loss=0.602414 [Epoch 088] speed: 68 samples/sec time cost: 3805.852372 [Epoch 088] validation: acc-top1=67.099835 acc-top5=86.453177 loss=1.714139 Epoch[089] Batch [0049]/[3760] Speed: 47.321549 samples/sec accuracy=84.500000 loss=0.609689 lr=0.000100 Epoch[089] Batch [0099]/[3760] Speed: 67.122114 samples/sec accuracy=84.609375 loss=0.600526 lr=0.000100 Epoch[089] Batch [0149]/[3760] Speed: 68.324878 samples/sec accuracy=84.541667 loss=0.600407 lr=0.000100 Epoch[089] Batch [0199]/[3760] Speed: 67.749655 samples/sec accuracy=84.445312 loss=0.602109 lr=0.000100 Epoch[089] Batch [0249]/[3760] Speed: 69.334947 samples/sec accuracy=84.475000 loss=0.606574 lr=0.000100 Epoch[089] Batch [0299]/[3760] Speed: 68.994437 samples/sec accuracy=84.265625 loss=0.612558 lr=0.000100 Epoch[089] Batch [0349]/[3760] Speed: 68.719465 samples/sec accuracy=84.290179 loss=0.610876 lr=0.000100 Epoch[089] Batch [0399]/[3760] Speed: 68.742125 samples/sec accuracy=84.359375 loss=0.608088 lr=0.000100 Epoch[089] Batch [0449]/[3760] Speed: 67.582500 samples/sec accuracy=84.423611 loss=0.606145 lr=0.000100 Epoch[089] Batch 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accuracy=84.491776 loss=0.603885 lr=0.000100 Epoch[089] Batch [0999]/[3760] Speed: 69.845742 samples/sec accuracy=84.465625 loss=0.604376 lr=0.000100 Epoch[089] Batch [1049]/[3760] Speed: 68.821998 samples/sec accuracy=84.519345 loss=0.603405 lr=0.000100 Epoch[089] Batch [1099]/[3760] Speed: 68.484910 samples/sec accuracy=84.529830 loss=0.604441 lr=0.000100 Epoch[089] Batch [1149]/[3760] Speed: 69.355927 samples/sec accuracy=84.547554 loss=0.603819 lr=0.000100 Epoch[089] Batch [1199]/[3760] Speed: 68.403017 samples/sec accuracy=84.579427 loss=0.603180 lr=0.000100 Epoch[089] Batch [1249]/[3760] Speed: 68.849360 samples/sec accuracy=84.587500 loss=0.602833 lr=0.000100 Epoch[089] Batch [1299]/[3760] Speed: 68.502473 samples/sec accuracy=84.575721 loss=0.604651 lr=0.000100 Epoch[089] Batch [1349]/[3760] Speed: 68.613056 samples/sec accuracy=84.619213 loss=0.603011 lr=0.000100 Epoch[089] Batch [1399]/[3760] Speed: 68.456504 samples/sec accuracy=84.601562 loss=0.604146 lr=0.000100 Epoch[089] 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accuracy=84.610197 loss=0.605459 lr=0.000100 Epoch[089] Batch [1949]/[3760] Speed: 68.725204 samples/sec accuracy=84.615385 loss=0.605212 lr=0.000100 Epoch[089] Batch [1999]/[3760] Speed: 68.635211 samples/sec accuracy=84.615625 loss=0.605495 lr=0.000100 Epoch[089] Batch [2049]/[3760] Speed: 68.571833 samples/sec accuracy=84.612805 loss=0.605574 lr=0.000100 Epoch[089] Batch [2099]/[3760] Speed: 68.596193 samples/sec accuracy=84.635417 loss=0.604637 lr=0.000100 Epoch[089] Batch [2149]/[3760] Speed: 68.344735 samples/sec accuracy=84.654797 loss=0.603918 lr=0.000100 Epoch[089] Batch [2199]/[3760] Speed: 68.648924 samples/sec accuracy=84.666903 loss=0.603470 lr=0.000100 Epoch[089] Batch [2249]/[3760] Speed: 68.784319 samples/sec accuracy=84.656944 loss=0.603347 lr=0.000100 Epoch[089] Batch [2299]/[3760] Speed: 68.319505 samples/sec accuracy=84.649457 loss=0.603696 lr=0.000100 Epoch[089] Batch [2349]/[3760] Speed: 69.130524 samples/sec accuracy=84.676862 loss=0.602811 lr=0.000100 Epoch[089] 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accuracy=84.675439 loss=0.602266 lr=0.000100 Epoch[089] Batch [2899]/[3760] Speed: 68.652095 samples/sec accuracy=84.673491 loss=0.602393 lr=0.000100 Epoch[089] Batch [2949]/[3760] Speed: 68.869592 samples/sec accuracy=84.673199 loss=0.602354 lr=0.000100 Epoch[089] Batch [2999]/[3760] Speed: 68.126700 samples/sec accuracy=84.681771 loss=0.602020 lr=0.000100 Epoch[089] Batch [3049]/[3760] Speed: 68.636668 samples/sec accuracy=84.685963 loss=0.601991 lr=0.000100 Epoch[089] Batch [3099]/[3760] Speed: 68.350186 samples/sec accuracy=84.695565 loss=0.601829 lr=0.000100 Epoch[089] Batch [3149]/[3760] Speed: 68.532957 samples/sec accuracy=84.690972 loss=0.602246 lr=0.000100 Epoch[089] Batch [3199]/[3760] Speed: 68.813881 samples/sec accuracy=84.701660 loss=0.601993 lr=0.000100 Epoch[089] Batch [3249]/[3760] Speed: 68.536196 samples/sec accuracy=84.709135 loss=0.601650 lr=0.000100 Epoch[089] Batch [3299]/[3760] Speed: 68.308693 samples/sec accuracy=84.705019 loss=0.601776 lr=0.000100 Epoch[089] Batch [3349]/[3760] Speed: 68.220139 samples/sec accuracy=84.720149 loss=0.601002 lr=0.000100 Epoch[089] Batch [3399]/[3760] Speed: 69.231259 samples/sec accuracy=84.724265 loss=0.601080 lr=0.000100 Epoch[089] Batch [3449]/[3760] Speed: 68.814165 samples/sec accuracy=84.727808 loss=0.600851 lr=0.000100 Epoch[089] Batch [3499]/[3760] Speed: 68.762994 samples/sec accuracy=84.724554 loss=0.601002 lr=0.000100 Epoch[089] Batch [3549]/[3760] Speed: 69.160266 samples/sec accuracy=84.720951 loss=0.600761 lr=0.000100 Epoch[089] Batch [3599]/[3760] Speed: 67.693603 samples/sec accuracy=84.728733 loss=0.600770 lr=0.000100 Epoch[089] Batch [3649]/[3760] Speed: 69.704540 samples/sec accuracy=84.741866 loss=0.600223 lr=0.000100 Epoch[089] Batch [3699]/[3760] Speed: 68.535406 samples/sec accuracy=84.737331 loss=0.600246 lr=0.000100 Epoch[089] Batch [3749]/[3760] Speed: 76.590694 samples/sec accuracy=84.715833 loss=0.601146 lr=0.000100 Batch [0049]/[0303]: acc-top1=68.562500 acc-top5=87.718750 Batch [0099]/[0303]: acc-top1=68.000000 acc-top5=87.218750 Batch [0149]/[0303]: acc-top1=67.510417 acc-top5=87.031250 Batch [0199]/[0303]: acc-top1=67.570312 acc-top5=86.882812 Batch [0249]/[0303]: acc-top1=67.800000 acc-top5=86.925000 Batch [0299]/[0303]: acc-top1=67.536458 acc-top5=86.546875 [Epoch 089] training: accuracy=84.715342 loss=0.601128 [Epoch 089] speed: 68 samples/sec time cost: 3807.783134 [Epoch 089] validation: acc-top1=67.507219 acc-top5=86.520215 loss=1.686985 Epoch[090] Batch [0049]/[3759] Speed: 47.192484 samples/sec accuracy=84.531250 loss=0.628976 lr=0.000100 Epoch[090] Batch [0099]/[3759] Speed: 67.490227 samples/sec accuracy=85.312500 loss=0.595480 lr=0.000100 Epoch[090] Batch [0149]/[3759] Speed: 67.742183 samples/sec accuracy=85.302083 loss=0.591220 lr=0.000100 Epoch[090] Batch [0199]/[3759] Speed: 67.925876 samples/sec accuracy=85.007812 loss=0.593803 lr=0.000100 Epoch[090] Batch [0249]/[3759] Speed: 68.428973 samples/sec accuracy=85.043750 loss=0.590917 lr=0.000100 Epoch[090] Batch [0299]/[3759] Speed: 68.399933 samples/sec accuracy=85.218750 loss=0.586241 lr=0.000100 Epoch[090] Batch [0349]/[3759] Speed: 67.895052 samples/sec accuracy=85.236607 loss=0.586751 lr=0.000100 Epoch[090] Batch [0399]/[3759] Speed: 68.383433 samples/sec accuracy=85.289062 loss=0.581972 lr=0.000100 Epoch[090] Batch [0449]/[3759] Speed: 68.639623 samples/sec accuracy=85.201389 loss=0.584051 lr=0.000100 Epoch[090] Batch [0499]/[3759] Speed: 68.440664 samples/sec accuracy=85.175000 loss=0.583945 lr=0.000100 Epoch[090] Batch [0549]/[3759] Speed: 68.847339 samples/sec accuracy=85.232955 loss=0.583032 lr=0.000100 Epoch[090] Batch [0599]/[3759] Speed: 68.182817 samples/sec accuracy=85.184896 loss=0.585846 lr=0.000100 Epoch[090] Batch [0649]/[3759] Speed: 67.899566 samples/sec accuracy=85.144231 loss=0.589113 lr=0.000100 Epoch[090] Batch [0699]/[3759] Speed: 69.013325 samples/sec accuracy=85.129464 loss=0.590404 lr=0.000100 Epoch[090] Batch [0749]/[3759] Speed: 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lr=0.000100 Epoch[090] Batch [3149]/[3759] Speed: 68.671591 samples/sec accuracy=84.902778 loss=0.596285 lr=0.000100 Epoch[090] Batch [3199]/[3759] Speed: 68.752081 samples/sec accuracy=84.907715 loss=0.596403 lr=0.000100 Epoch[090] Batch [3249]/[3759] Speed: 68.408029 samples/sec accuracy=84.895192 loss=0.596841 lr=0.000100 Epoch[090] Batch [3299]/[3759] Speed: 67.336602 samples/sec accuracy=84.883049 loss=0.596925 lr=0.000100 Epoch[090] Batch [3349]/[3759] Speed: 69.876694 samples/sec accuracy=84.880597 loss=0.596865 lr=0.000100 Epoch[090] Batch [3399]/[3759] Speed: 68.649713 samples/sec accuracy=84.865809 loss=0.597399 lr=0.000100 Epoch[090] Batch [3449]/[3759] Speed: 68.787052 samples/sec accuracy=84.859149 loss=0.597419 lr=0.000100 Epoch[090] Batch [3499]/[3759] Speed: 68.815829 samples/sec accuracy=84.864732 loss=0.597206 lr=0.000100 Epoch[090] Batch [3549]/[3759] Speed: 69.013771 samples/sec accuracy=84.854313 loss=0.597499 lr=0.000100 Epoch[090] Batch [3599]/[3759] Speed: 67.667950 samples/sec accuracy=84.864149 loss=0.597293 lr=0.000100 Epoch[090] Batch [3649]/[3759] Speed: 68.540924 samples/sec accuracy=84.863014 loss=0.597606 lr=0.000100 Epoch[090] Batch [3699]/[3759] Speed: 68.773072 samples/sec accuracy=84.880490 loss=0.596985 lr=0.000100 Epoch[090] Batch [3749]/[3759] Speed: 77.888843 samples/sec accuracy=84.875833 loss=0.597293 lr=0.000100 Batch [0049]/[0303]: acc-top1=68.218750 acc-top5=87.718750 Batch [0099]/[0303]: acc-top1=67.734375 acc-top5=87.218750 Batch [0149]/[0303]: acc-top1=67.281250 acc-top5=87.020833 Batch [0199]/[0303]: acc-top1=67.382812 acc-top5=87.015625 Batch [0249]/[0303]: acc-top1=67.531250 acc-top5=87.081250 Batch [0299]/[0303]: acc-top1=67.281250 acc-top5=86.713542 [Epoch 090] training: accuracy=84.875881 loss=0.597267 [Epoch 090] speed: 68 samples/sec time cost: 3807.745894 [Epoch 090] validation: acc-top1=67.249381 acc-top5=86.680074 loss=1.696875 Epoch[091] Batch [0049]/[3760] Speed: 47.184804 samples/sec accuracy=85.562500 loss=0.602409 lr=0.000100 Epoch[091] Batch [0099]/[3760] Speed: 66.959075 samples/sec accuracy=84.859375 loss=0.611371 lr=0.000100 Epoch[091] Batch [0149]/[3760] Speed: 68.956072 samples/sec accuracy=84.895833 loss=0.607541 lr=0.000100 Epoch[091] Batch [0199]/[3760] Speed: 68.703975 samples/sec accuracy=85.054688 loss=0.597927 lr=0.000100 Epoch[091] Batch [0249]/[3760] Speed: 68.439281 samples/sec accuracy=85.312500 loss=0.589597 lr=0.000100 Epoch[091] Batch [0299]/[3760] Speed: 68.277883 samples/sec accuracy=85.234375 loss=0.588936 lr=0.000100 Epoch[091] Batch [0349]/[3760] Speed: 69.317932 samples/sec accuracy=85.053571 loss=0.591533 lr=0.000100 Epoch[091] Batch [0399]/[3760] Speed: 68.672007 samples/sec accuracy=85.109375 loss=0.590268 lr=0.000100 Epoch[091] Batch [0449]/[3760] Speed: 68.688369 samples/sec accuracy=85.052083 loss=0.589583 lr=0.000100 Epoch[091] Batch [0499]/[3760] Speed: 69.123616 samples/sec accuracy=85.071875 loss=0.588675 lr=0.000100 Epoch[091] 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accuracy=85.112500 loss=0.584946 lr=0.000100 Epoch[091] Batch [1049]/[3760] Speed: 68.706691 samples/sec accuracy=85.058036 loss=0.587397 lr=0.000100 Epoch[091] Batch [1099]/[3760] Speed: 68.537825 samples/sec accuracy=85.035511 loss=0.587462 lr=0.000100 Epoch[091] Batch [1149]/[3760] Speed: 69.337011 samples/sec accuracy=85.091033 loss=0.585878 lr=0.000100 Epoch[091] Batch [1199]/[3760] Speed: 67.918672 samples/sec accuracy=85.104167 loss=0.585645 lr=0.000100 Epoch[091] Batch [1249]/[3760] Speed: 68.769445 samples/sec accuracy=85.095000 loss=0.585881 lr=0.000100 Epoch[091] Batch [1299]/[3760] Speed: 68.938975 samples/sec accuracy=85.084135 loss=0.585935 lr=0.000100 Epoch[091] Batch [1349]/[3760] Speed: 68.722313 samples/sec accuracy=85.096065 loss=0.585317 lr=0.000100 Epoch[091] Batch [1399]/[3760] Speed: 68.919956 samples/sec accuracy=85.112723 loss=0.583997 lr=0.000100 Epoch[091] Batch [1449]/[3760] Speed: 67.922434 samples/sec accuracy=85.090517 loss=0.584389 lr=0.000100 Epoch[091] 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accuracy=85.012821 loss=0.588154 lr=0.000100 Epoch[091] Batch [1999]/[3760] Speed: 67.830519 samples/sec accuracy=85.036719 loss=0.588433 lr=0.000100 Epoch[091] Batch [2049]/[3760] Speed: 69.179748 samples/sec accuracy=85.028963 loss=0.588788 lr=0.000100 Epoch[091] Batch [2099]/[3760] Speed: 68.320839 samples/sec accuracy=85.031994 loss=0.588595 lr=0.000100 Epoch[091] Batch [2149]/[3760] Speed: 68.329096 samples/sec accuracy=85.023256 loss=0.588588 lr=0.000100 Epoch[091] Batch [2199]/[3760] Speed: 68.907084 samples/sec accuracy=85.005682 loss=0.588952 lr=0.000100 Epoch[091] Batch [2249]/[3760] Speed: 68.021328 samples/sec accuracy=84.980556 loss=0.589727 lr=0.000100 Epoch[091] Batch [2299]/[3760] Speed: 68.864947 samples/sec accuracy=84.989130 loss=0.589595 lr=0.000100 Epoch[091] Batch [2349]/[3760] Speed: 68.708740 samples/sec accuracy=84.962766 loss=0.590822 lr=0.000100 Epoch[091] Batch [2399]/[3760] Speed: 68.694848 samples/sec accuracy=84.958984 loss=0.590814 lr=0.000100 Epoch[091] 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accuracy=84.897091 loss=0.592077 lr=0.000100 Epoch[091] Batch [2949]/[3760] Speed: 69.146931 samples/sec accuracy=84.889831 loss=0.592192 lr=0.000100 Epoch[091] Batch [2999]/[3760] Speed: 69.447226 samples/sec accuracy=84.875521 loss=0.592618 lr=0.000100 Epoch[091] Batch [3049]/[3760] Speed: 67.354395 samples/sec accuracy=84.870389 loss=0.592873 lr=0.000100 Epoch[091] Batch [3099]/[3760] Speed: 69.208128 samples/sec accuracy=84.877016 loss=0.592692 lr=0.000100 Epoch[091] Batch [3149]/[3760] Speed: 68.524878 samples/sec accuracy=84.888393 loss=0.592125 lr=0.000100 Epoch[091] Batch [3199]/[3760] Speed: 68.709473 samples/sec accuracy=84.894043 loss=0.591890 lr=0.000100 Epoch[091] Batch [3249]/[3760] Speed: 68.125758 samples/sec accuracy=84.902885 loss=0.591773 lr=0.000100 Epoch[091] Batch [3299]/[3760] Speed: 69.023866 samples/sec accuracy=84.903409 loss=0.591826 lr=0.000100 Epoch[091] Batch [3349]/[3760] Speed: 67.959984 samples/sec accuracy=84.903451 loss=0.591594 lr=0.000100 Epoch[091] Batch [3399]/[3760] Speed: 69.318407 samples/sec accuracy=84.913603 loss=0.591525 lr=0.000100 Epoch[091] Batch [3449]/[3760] Speed: 68.684242 samples/sec accuracy=84.923007 loss=0.591565 lr=0.000100 Epoch[091] Batch [3499]/[3760] Speed: 69.143706 samples/sec accuracy=84.913839 loss=0.591945 lr=0.000100 Epoch[091] Batch [3549]/[3760] Speed: 68.173744 samples/sec accuracy=84.908451 loss=0.592072 lr=0.000100 Epoch[091] Batch [3599]/[3760] Speed: 68.674279 samples/sec accuracy=84.885417 loss=0.592602 lr=0.000100 Epoch[091] Batch [3649]/[3760] Speed: 68.792437 samples/sec accuracy=84.898973 loss=0.592095 lr=0.000100 Epoch[091] Batch [3699]/[3760] Speed: 68.857247 samples/sec accuracy=84.893159 loss=0.592173 lr=0.000100 Epoch[091] Batch [3749]/[3760] Speed: 77.208303 samples/sec accuracy=84.885417 loss=0.592543 lr=0.000100 Batch [0049]/[0303]: acc-top1=68.406250 acc-top5=87.500000 Batch [0099]/[0303]: acc-top1=68.078125 acc-top5=86.984375 Batch [0149]/[0303]: acc-top1=67.656250 acc-top5=86.781250 Batch [0199]/[0303]: acc-top1=67.554688 acc-top5=86.765625 Batch [0249]/[0303]: acc-top1=67.718750 acc-top5=86.856250 Batch [0299]/[0303]: acc-top1=67.473958 acc-top5=86.526042 [Epoch 091] training: accuracy=84.887799 loss=0.592386 [Epoch 091] speed: 68 samples/sec time cost: 3805.572682 [Epoch 091] validation: acc-top1=67.435025 acc-top5=86.494431 loss=1.695815 Epoch[092] Batch [0049]/[3760] Speed: 46.678044 samples/sec accuracy=86.031250 loss=0.570971 lr=0.000100 Epoch[092] Batch [0099]/[3760] Speed: 66.411734 samples/sec accuracy=85.968750 loss=0.568316 lr=0.000100 Epoch[092] Batch [0149]/[3760] Speed: 69.392840 samples/sec accuracy=85.916667 loss=0.562271 lr=0.000100 Epoch[092] Batch [0199]/[3760] Speed: 68.529782 samples/sec accuracy=85.531250 loss=0.578510 lr=0.000100 Epoch[092] Batch [0249]/[3760] Speed: 68.613849 samples/sec accuracy=85.518750 loss=0.576051 lr=0.000100 Epoch[092] Batch [0299]/[3760] Speed: 68.188016 samples/sec accuracy=85.520833 loss=0.575409 lr=0.000100 Epoch[092] Batch [0349]/[3760] Speed: 69.042040 samples/sec accuracy=85.598214 loss=0.571835 lr=0.000100 Epoch[092] Batch [0399]/[3760] Speed: 68.434026 samples/sec accuracy=85.562500 loss=0.572217 lr=0.000100 Epoch[092] Batch [0449]/[3760] Speed: 68.630503 samples/sec accuracy=85.416667 loss=0.578179 lr=0.000100 Epoch[092] Batch [0499]/[3760] Speed: 68.831628 samples/sec accuracy=85.362500 loss=0.579191 lr=0.000100 Epoch[092] Batch [0549]/[3760] Speed: 68.931895 samples/sec accuracy=85.332386 loss=0.580257 lr=0.000100 Epoch[092] Batch [0599]/[3760] Speed: 68.996824 samples/sec accuracy=85.156250 loss=0.585367 lr=0.000100 Epoch[092] Batch [0649]/[3760] Speed: 67.839021 samples/sec accuracy=85.134615 loss=0.585497 lr=0.000100 Epoch[092] Batch [0699]/[3760] Speed: 69.289893 samples/sec accuracy=85.107143 loss=0.586769 lr=0.000100 Epoch[092] Batch [0749]/[3760] Speed: 68.680157 samples/sec accuracy=85.058333 loss=0.588193 lr=0.000100 Epoch[092] Batch 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accuracy=84.880000 loss=0.596223 lr=0.000100 Epoch[092] Batch [1299]/[3760] Speed: 69.144727 samples/sec accuracy=84.871394 loss=0.596020 lr=0.000100 Epoch[092] Batch [1349]/[3760] Speed: 68.705932 samples/sec accuracy=84.869213 loss=0.596367 lr=0.000100 Epoch[092] Batch [1399]/[3760] Speed: 68.214254 samples/sec accuracy=84.901786 loss=0.594940 lr=0.000100 Epoch[092] Batch [1449]/[3760] Speed: 67.599808 samples/sec accuracy=84.890086 loss=0.595903 lr=0.000100 Epoch[092] Batch [1499]/[3760] Speed: 69.311704 samples/sec accuracy=84.919792 loss=0.595067 lr=0.000100 Epoch[092] Batch [1549]/[3760] Speed: 68.765399 samples/sec accuracy=84.923387 loss=0.595269 lr=0.000100 Epoch[092] Batch [1599]/[3760] Speed: 68.608329 samples/sec accuracy=84.932617 loss=0.594834 lr=0.000100 Epoch[092] Batch [1649]/[3760] Speed: 68.718607 samples/sec accuracy=84.921402 loss=0.595167 lr=0.000100 Epoch[092] Batch [1699]/[3760] Speed: 68.797440 samples/sec accuracy=84.926471 loss=0.594439 lr=0.000100 Epoch[092] 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accuracy=84.997869 loss=0.592190 lr=0.000100 Epoch[092] Batch [2249]/[3760] Speed: 68.410057 samples/sec accuracy=85.003472 loss=0.591972 lr=0.000100 Epoch[092] Batch [2299]/[3760] Speed: 68.618889 samples/sec accuracy=85.011549 loss=0.591336 lr=0.000100 Epoch[092] Batch [2349]/[3760] Speed: 68.273376 samples/sec accuracy=85.027261 loss=0.590642 lr=0.000100 Epoch[092] Batch [2399]/[3760] Speed: 68.991032 samples/sec accuracy=85.028646 loss=0.590932 lr=0.000100 Epoch[092] Batch [2449]/[3760] Speed: 68.346801 samples/sec accuracy=85.029337 loss=0.590834 lr=0.000100 Epoch[092] Batch [2499]/[3760] Speed: 68.783596 samples/sec accuracy=85.044375 loss=0.589983 lr=0.000100 Epoch[092] Batch [2549]/[3760] Speed: 68.318814 samples/sec accuracy=85.062500 loss=0.589409 lr=0.000100 Epoch[092] Batch [2599]/[3760] Speed: 68.529005 samples/sec accuracy=85.047476 loss=0.589588 lr=0.000100 Epoch[092] Batch [2649]/[3760] Speed: 68.613953 samples/sec accuracy=85.088443 loss=0.588229 lr=0.000100 Epoch[092] Batch [2699]/[3760] Speed: 68.419026 samples/sec accuracy=85.087384 loss=0.588104 lr=0.000100 Epoch[092] Batch [2749]/[3760] Speed: 69.065523 samples/sec accuracy=85.081250 loss=0.588240 lr=0.000100 Epoch[092] Batch [2799]/[3760] Speed: 68.661931 samples/sec accuracy=85.089286 loss=0.588054 lr=0.000100 Epoch[092] Batch [2849]/[3760] Speed: 68.046536 samples/sec accuracy=85.083882 loss=0.588415 lr=0.000100 Epoch[092] Batch [2899]/[3760] Speed: 68.609389 samples/sec accuracy=85.063039 loss=0.589143 lr=0.000100 Epoch[092] Batch [2949]/[3760] Speed: 68.708262 samples/sec accuracy=85.054555 loss=0.589325 lr=0.000100 Epoch[092] Batch [2999]/[3760] Speed: 68.506593 samples/sec accuracy=85.047917 loss=0.589738 lr=0.000100 Epoch[092] Batch [3049]/[3760] Speed: 68.839809 samples/sec accuracy=85.047643 loss=0.589852 lr=0.000100 Epoch[092] Batch [3099]/[3760] Speed: 68.196946 samples/sec accuracy=85.046875 loss=0.589637 lr=0.000100 Epoch[092] Batch [3149]/[3760] Speed: 68.922743 samples/sec accuracy=85.057540 loss=0.589497 lr=0.000100 Epoch[092] Batch [3199]/[3760] Speed: 68.157052 samples/sec accuracy=85.057129 loss=0.589294 lr=0.000100 Epoch[092] Batch [3249]/[3760] Speed: 68.828506 samples/sec accuracy=85.031731 loss=0.590054 lr=0.000100 Epoch[092] Batch [3299]/[3760] Speed: 68.846537 samples/sec accuracy=85.023201 loss=0.590378 lr=0.000100 Epoch[092] Batch [3349]/[3760] Speed: 67.418223 samples/sec accuracy=85.028918 loss=0.590352 lr=0.000100 Epoch[092] Batch [3399]/[3760] Speed: 69.708388 samples/sec accuracy=85.028493 loss=0.590427 lr=0.000100 Epoch[092] Batch [3449]/[3760] Speed: 68.802102 samples/sec accuracy=85.034420 loss=0.590135 lr=0.000100 Epoch[092] Batch [3499]/[3760] Speed: 68.462649 samples/sec accuracy=85.033482 loss=0.590374 lr=0.000100 Epoch[092] Batch [3549]/[3760] Speed: 68.674163 samples/sec accuracy=85.018926 loss=0.590583 lr=0.000100 Epoch[092] Batch [3599]/[3760] Speed: 68.778720 samples/sec accuracy=85.016059 loss=0.590481 lr=0.000100 Epoch[092] Batch [3649]/[3760] Speed: 68.189938 samples/sec accuracy=85.032106 loss=0.590154 lr=0.000100 Epoch[092] Batch [3699]/[3760] Speed: 68.810730 samples/sec accuracy=85.037162 loss=0.589996 lr=0.000100 Epoch[092] Batch [3749]/[3760] Speed: 77.435369 samples/sec accuracy=85.022500 loss=0.590398 lr=0.000100 Batch [0049]/[0303]: acc-top1=67.937500 acc-top5=87.468750 Batch [0099]/[0303]: acc-top1=67.687500 acc-top5=87.046875 Batch [0149]/[0303]: acc-top1=67.333333 acc-top5=86.927083 Batch [0199]/[0303]: acc-top1=67.367188 acc-top5=86.773438 Batch [0249]/[0303]: acc-top1=67.481250 acc-top5=86.868750 Batch [0299]/[0303]: acc-top1=67.270833 acc-top5=86.531250 [Epoch 092] training: accuracy=85.027011 loss=0.590279 [Epoch 092] speed: 68 samples/sec time cost: 3808.436493 [Epoch 092] validation: acc-top1=67.244224 acc-top5=86.499587 loss=1.705781 Epoch[093] Batch [0049]/[3759] Speed: 46.084023 samples/sec accuracy=84.718750 loss=0.592120 lr=0.000100 Epoch[093] Batch [0099]/[3759] Speed: 67.430812 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lr=0.000100 Epoch[093] Batch [1549]/[3759] Speed: 68.704905 samples/sec accuracy=85.045363 loss=0.587409 lr=0.000100 Epoch[093] Batch [1599]/[3759] Speed: 68.334491 samples/sec accuracy=85.033203 loss=0.587083 lr=0.000100 Epoch[093] Batch [1649]/[3759] Speed: 68.629760 samples/sec accuracy=85.022727 loss=0.587410 lr=0.000100 Epoch[093] Batch [1699]/[3759] Speed: 69.007320 samples/sec accuracy=85.004596 loss=0.588184 lr=0.000100 Epoch[093] Batch [1749]/[3759] Speed: 68.561686 samples/sec accuracy=85.004464 loss=0.587908 lr=0.000100 Epoch[093] Batch [1799]/[3759] Speed: 67.283592 samples/sec accuracy=84.947917 loss=0.589528 lr=0.000100 Epoch[093] Batch [1849]/[3759] Speed: 69.796834 samples/sec accuracy=84.967905 loss=0.589396 lr=0.000100 Epoch[093] Batch [1899]/[3759] Speed: 68.986322 samples/sec accuracy=84.972862 loss=0.589289 lr=0.000100 Epoch[093] Batch [1949]/[3759] Speed: 68.538783 samples/sec accuracy=84.988782 loss=0.588453 lr=0.000100 Epoch[093] Batch [1999]/[3759] Speed: 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lr=0.000100 Epoch[093] Batch [2499]/[3759] Speed: 68.128930 samples/sec accuracy=85.047500 loss=0.588949 lr=0.000100 Epoch[093] Batch [2549]/[3759] Speed: 68.842713 samples/sec accuracy=85.043505 loss=0.589081 lr=0.000100 Epoch[093] Batch [2599]/[3759] Speed: 68.551671 samples/sec accuracy=85.021635 loss=0.589744 lr=0.000100 Epoch[093] Batch [2649]/[3759] Speed: 68.983795 samples/sec accuracy=85.016509 loss=0.590107 lr=0.000100 Epoch[093] Batch [2699]/[3759] Speed: 68.128574 samples/sec accuracy=85.000579 loss=0.590758 lr=0.000100 Epoch[093] Batch [2749]/[3759] Speed: 69.170690 samples/sec accuracy=85.022727 loss=0.590079 lr=0.000100 Epoch[093] Batch [2799]/[3759] Speed: 68.550900 samples/sec accuracy=85.026228 loss=0.589817 lr=0.000100 Epoch[093] Batch [2849]/[3759] Speed: 68.354031 samples/sec accuracy=85.030702 loss=0.589652 lr=0.000100 Epoch[093] Batch [2899]/[3759] Speed: 67.654758 samples/sec accuracy=85.036638 loss=0.589381 lr=0.000100 Epoch[093] Batch [2949]/[3759] Speed: 68.926949 samples/sec accuracy=85.038136 loss=0.589433 lr=0.000100 Epoch[093] Batch [2999]/[3759] Speed: 68.296309 samples/sec accuracy=85.028125 loss=0.589547 lr=0.000100 Epoch[093] Batch [3049]/[3759] Speed: 69.234041 samples/sec accuracy=85.040471 loss=0.589228 lr=0.000100 Epoch[093] Batch [3099]/[3759] Speed: 69.051442 samples/sec accuracy=85.055948 loss=0.589051 lr=0.000100 Epoch[093] Batch [3149]/[3759] Speed: 68.525440 samples/sec accuracy=85.057044 loss=0.588605 lr=0.000100 Epoch[093] Batch [3199]/[3759] Speed: 68.071031 samples/sec accuracy=85.062012 loss=0.588416 lr=0.000100 Epoch[093] Batch [3249]/[3759] Speed: 68.319374 samples/sec accuracy=85.058173 loss=0.588347 lr=0.000100 Epoch[093] Batch [3299]/[3759] Speed: 69.018755 samples/sec accuracy=85.059186 loss=0.588183 lr=0.000100 Epoch[093] Batch [3349]/[3759] Speed: 68.724097 samples/sec accuracy=85.039179 loss=0.589047 lr=0.000100 Epoch[093] Batch [3399]/[3759] Speed: 68.752093 samples/sec accuracy=85.028033 loss=0.589244 lr=0.000100 Epoch[093] Batch [3449]/[3759] Speed: 67.587223 samples/sec accuracy=85.020380 loss=0.589498 lr=0.000100 Epoch[093] Batch [3499]/[3759] Speed: 69.480223 samples/sec accuracy=85.022768 loss=0.589515 lr=0.000100 Epoch[093] Batch [3549]/[3759] Speed: 68.472009 samples/sec accuracy=85.022007 loss=0.589464 lr=0.000100 Epoch[093] Batch [3599]/[3759] Speed: 68.784257 samples/sec accuracy=85.043403 loss=0.588869 lr=0.000100 Epoch[093] Batch [3649]/[3759] Speed: 68.270807 samples/sec accuracy=85.040240 loss=0.588882 lr=0.000100 Epoch[093] Batch [3699]/[3759] Speed: 68.675048 samples/sec accuracy=85.047297 loss=0.588760 lr=0.000100 Epoch[093] Batch [3749]/[3759] Speed: 76.802347 samples/sec accuracy=85.056250 loss=0.588630 lr=0.000100 Batch [0049]/[0303]: acc-top1=68.000000 acc-top5=87.625000 Batch [0099]/[0303]: acc-top1=67.781250 acc-top5=87.250000 Batch [0149]/[0303]: acc-top1=67.270833 acc-top5=86.958333 Batch [0199]/[0303]: acc-top1=67.335938 acc-top5=86.835938 Batch [0249]/[0303]: acc-top1=67.550000 acc-top5=86.893750 Batch [0299]/[0303]: acc-top1=67.307292 acc-top5=86.541667 [Epoch 093] training: accuracy=85.056697 loss=0.588650 [Epoch 093] speed: 68 samples/sec time cost: 3807.363351 [Epoch 093] validation: acc-top1=67.275165 acc-top5=86.525371 loss=1.710351 Epoch[094] Batch [0049]/[3760] Speed: 47.421035 samples/sec accuracy=84.781250 loss=0.604445 lr=0.000100 Epoch[094] Batch [0099]/[3760] Speed: 67.862553 samples/sec accuracy=84.406250 loss=0.618676 lr=0.000100 Epoch[094] Batch [0149]/[3760] Speed: 68.375875 samples/sec accuracy=84.791667 loss=0.604708 lr=0.000100 Epoch[094] Batch [0199]/[3760] Speed: 68.436613 samples/sec accuracy=84.914062 loss=0.600226 lr=0.000100 Epoch[094] Batch [0249]/[3760] Speed: 68.667739 samples/sec accuracy=85.156250 loss=0.596061 lr=0.000100 Epoch[094] Batch [0299]/[3760] Speed: 67.856143 samples/sec accuracy=85.104167 loss=0.596535 lr=0.000100 Epoch[094] Batch [0349]/[3760] Speed: 68.412460 samples/sec accuracy=85.169643 loss=0.595660 lr=0.000100 Epoch[094] Batch [0399]/[3760] Speed: 68.992671 samples/sec accuracy=85.246094 loss=0.589343 lr=0.000100 Epoch[094] Batch [0449]/[3760] Speed: 68.156789 samples/sec accuracy=85.222222 loss=0.587182 lr=0.000100 Epoch[094] Batch [0499]/[3760] Speed: 68.378732 samples/sec accuracy=85.165625 loss=0.589352 lr=0.000100 Epoch[094] Batch [0549]/[3760] Speed: 68.472783 samples/sec accuracy=85.116477 loss=0.591294 lr=0.000100 Epoch[094] Batch [0599]/[3760] Speed: 68.739238 samples/sec accuracy=85.088542 loss=0.592106 lr=0.000100 Epoch[094] Batch [0649]/[3760] Speed: 68.664172 samples/sec accuracy=85.055288 loss=0.592920 lr=0.000100 Epoch[094] Batch [0699]/[3760] Speed: 68.661424 samples/sec accuracy=85.073661 loss=0.593189 lr=0.000100 Epoch[094] Batch [0749]/[3760] Speed: 68.829362 samples/sec accuracy=85.127083 loss=0.592088 lr=0.000100 Epoch[094] Batch [0799]/[3760] Speed: 68.653352 samples/sec accuracy=85.113281 loss=0.592048 lr=0.000100 Epoch[094] 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accuracy=85.207933 loss=0.586240 lr=0.000100 Epoch[094] Batch [1349]/[3760] Speed: 68.663990 samples/sec accuracy=85.203704 loss=0.586353 lr=0.000100 Epoch[094] Batch [1399]/[3760] Speed: 68.063471 samples/sec accuracy=85.223214 loss=0.585727 lr=0.000100 Epoch[094] Batch [1449]/[3760] Speed: 68.539693 samples/sec accuracy=85.221983 loss=0.585991 lr=0.000100 Epoch[094] Batch [1499]/[3760] Speed: 68.373976 samples/sec accuracy=85.169792 loss=0.587899 lr=0.000100 Epoch[094] Batch [1549]/[3760] Speed: 68.670257 samples/sec accuracy=85.202621 loss=0.586890 lr=0.000100 Epoch[094] Batch [1599]/[3760] Speed: 68.060857 samples/sec accuracy=85.166016 loss=0.587710 lr=0.000100 Epoch[094] Batch [1649]/[3760] Speed: 69.135513 samples/sec accuracy=85.168561 loss=0.587132 lr=0.000100 Epoch[094] Batch [1699]/[3760] Speed: 68.209663 samples/sec accuracy=85.175551 loss=0.586781 lr=0.000100 Epoch[094] Batch [1749]/[3760] Speed: 68.578613 samples/sec accuracy=85.156250 loss=0.587234 lr=0.000100 Epoch[094] 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accuracy=85.075000 loss=0.591890 lr=0.000100 Epoch[094] Batch [2299]/[3760] Speed: 68.581002 samples/sec accuracy=85.067935 loss=0.592270 lr=0.000100 Epoch[094] Batch [2349]/[3760] Speed: 68.811226 samples/sec accuracy=85.066489 loss=0.592407 lr=0.000100 Epoch[094] Batch [2399]/[3760] Speed: 68.808285 samples/sec accuracy=85.080729 loss=0.591817 lr=0.000100 Epoch[094] Batch [2449]/[3760] Speed: 67.911854 samples/sec accuracy=85.078444 loss=0.591883 lr=0.000100 Epoch[094] Batch [2499]/[3760] Speed: 68.440737 samples/sec accuracy=85.085000 loss=0.591523 lr=0.000100 Epoch[094] Batch [2549]/[3760] Speed: 68.208093 samples/sec accuracy=85.090074 loss=0.591382 lr=0.000100 Epoch[094] Batch [2599]/[3760] Speed: 68.447643 samples/sec accuracy=85.083534 loss=0.591409 lr=0.000100 Epoch[094] Batch [2649]/[3760] Speed: 68.785593 samples/sec accuracy=85.076651 loss=0.591679 lr=0.000100 Epoch[094] Batch [2699]/[3760] Speed: 68.430624 samples/sec accuracy=85.100116 loss=0.590664 lr=0.000100 Epoch[094] Batch [2749]/[3760] Speed: 68.201010 samples/sec accuracy=85.106818 loss=0.590415 lr=0.000100 Epoch[094] Batch [2799]/[3760] Speed: 68.484875 samples/sec accuracy=85.124442 loss=0.589615 lr=0.000100 Epoch[094] Batch [2849]/[3760] Speed: 67.925239 samples/sec accuracy=85.118969 loss=0.589185 lr=0.000100 Epoch[094] Batch [2899]/[3760] Speed: 68.883870 samples/sec accuracy=85.105065 loss=0.589340 lr=0.000100 Epoch[094] Batch [2949]/[3760] Speed: 68.246689 samples/sec accuracy=85.122352 loss=0.588782 lr=0.000100 Epoch[094] Batch [2999]/[3760] Speed: 67.892035 samples/sec accuracy=85.115104 loss=0.588896 lr=0.000100 Epoch[094] Batch [3049]/[3760] Speed: 68.100453 samples/sec accuracy=85.115266 loss=0.589028 lr=0.000100 Epoch[094] Batch [3099]/[3760] Speed: 68.789869 samples/sec accuracy=85.099798 loss=0.589606 lr=0.000100 Epoch[094] Batch [3149]/[3760] Speed: 68.303012 samples/sec accuracy=85.094246 loss=0.589475 lr=0.000100 Epoch[094] Batch [3199]/[3760] Speed: 68.636906 samples/sec accuracy=85.075684 loss=0.590022 lr=0.000100 Epoch[094] Batch [3249]/[3760] Speed: 69.202390 samples/sec accuracy=85.071635 loss=0.589951 lr=0.000100 Epoch[094] Batch [3299]/[3760] Speed: 68.061776 samples/sec accuracy=85.057765 loss=0.590387 lr=0.000100 Epoch[094] Batch [3349]/[3760] Speed: 68.646094 samples/sec accuracy=85.055970 loss=0.590449 lr=0.000100 Epoch[094] Batch [3399]/[3760] Speed: 68.147495 samples/sec accuracy=85.043199 loss=0.590688 lr=0.000100 Epoch[094] Batch [3449]/[3760] Speed: 68.320802 samples/sec accuracy=85.028986 loss=0.591123 lr=0.000100 Epoch[094] Batch [3499]/[3760] Speed: 68.605343 samples/sec accuracy=85.028571 loss=0.590902 lr=0.000100 Epoch[094] Batch [3549]/[3760] Speed: 68.646661 samples/sec accuracy=85.015845 loss=0.591458 lr=0.000100 Epoch[094] Batch [3599]/[3760] Speed: 67.920310 samples/sec accuracy=85.018229 loss=0.590999 lr=0.000100 Epoch[094] Batch [3649]/[3760] Speed: 69.539196 samples/sec accuracy=85.024401 loss=0.590987 lr=0.000100 Epoch[094] Batch [3699]/[3760] Speed: 68.663858 samples/sec accuracy=85.012669 loss=0.591490 lr=0.000100 Epoch[094] Batch [3749]/[3760] Speed: 76.903417 samples/sec accuracy=85.027917 loss=0.590897 lr=0.000100 Batch [0049]/[0303]: acc-top1=68.562500 acc-top5=87.781250 Batch [0099]/[0303]: acc-top1=67.921875 acc-top5=87.218750 Batch [0149]/[0303]: acc-top1=67.322917 acc-top5=86.916667 Batch [0199]/[0303]: acc-top1=67.296875 acc-top5=86.812500 Batch [0249]/[0303]: acc-top1=67.487500 acc-top5=86.875000 Batch [0299]/[0303]: acc-top1=67.203125 acc-top5=86.572917 [Epoch 094] training: accuracy=85.035322 loss=0.590733 [Epoch 094] speed: 68 samples/sec time cost: 3810.485866 [Epoch 094] validation: acc-top1=67.166873 acc-top5=86.540842 loss=1.711044 Epoch[095] Batch [0049]/[3760] Speed: 46.979475 samples/sec accuracy=85.250000 loss=0.579286 lr=0.000100 Epoch[095] Batch [0099]/[3760] Speed: 67.671495 samples/sec accuracy=84.796875 loss=0.594276 lr=0.000100 Epoch[095] Batch [0149]/[3760] Speed: 67.934760 samples/sec accuracy=85.114583 loss=0.588524 lr=0.000100 Epoch[095] Batch [0199]/[3760] Speed: 68.790218 samples/sec accuracy=85.054688 loss=0.586551 lr=0.000100 Epoch[095] Batch [0249]/[3760] Speed: 68.830703 samples/sec accuracy=85.068750 loss=0.587125 lr=0.000100 Epoch[095] Batch [0299]/[3760] Speed: 68.205085 samples/sec accuracy=85.005208 loss=0.588352 lr=0.000100 Epoch[095] Batch [0349]/[3760] Speed: 68.973321 samples/sec accuracy=84.973214 loss=0.589963 lr=0.000100 Epoch[095] Batch [0399]/[3760] Speed: 68.176378 samples/sec accuracy=85.261719 loss=0.579292 lr=0.000100 Epoch[095] Batch [0449]/[3760] Speed: 68.498881 samples/sec accuracy=85.236111 loss=0.582080 lr=0.000100 Epoch[095] Batch [0499]/[3760] Speed: 68.475905 samples/sec accuracy=85.281250 loss=0.580322 lr=0.000100 Epoch[095] Batch [0549]/[3760] Speed: 68.388592 samples/sec accuracy=85.318182 loss=0.579444 lr=0.000100 Epoch[095] Batch [0599]/[3760] Speed: 69.084129 samples/sec accuracy=85.320312 loss=0.579604 lr=0.000100 Epoch[095] Batch [0649]/[3760] Speed: 68.547290 samples/sec accuracy=85.257212 loss=0.581859 lr=0.000100 Epoch[095] Batch [0699]/[3760] Speed: 67.837771 samples/sec accuracy=85.267857 loss=0.583653 lr=0.000100 Epoch[095] Batch [0749]/[3760] Speed: 69.346933 samples/sec accuracy=85.204167 loss=0.585813 lr=0.000100 Epoch[095] Batch [0799]/[3760] Speed: 69.076207 samples/sec accuracy=85.226562 loss=0.584816 lr=0.000100 Epoch[095] Batch [0849]/[3760] Speed: 68.025659 samples/sec accuracy=85.170956 loss=0.586067 lr=0.000100 Epoch[095] Batch [0899]/[3760] Speed: 68.942777 samples/sec accuracy=85.175347 loss=0.586276 lr=0.000100 Epoch[095] Batch [0949]/[3760] Speed: 68.935329 samples/sec accuracy=85.208882 loss=0.584858 lr=0.000100 Epoch[095] Batch [0999]/[3760] Speed: 68.071639 samples/sec accuracy=85.204687 loss=0.585364 lr=0.000100 Epoch[095] Batch [1049]/[3760] Speed: 68.434938 samples/sec accuracy=85.227679 loss=0.584754 lr=0.000100 Epoch[095] Batch [1099]/[3760] Speed: 68.973867 samples/sec accuracy=85.184659 loss=0.586091 lr=0.000100 Epoch[095] Batch [1149]/[3760] Speed: 68.349222 samples/sec accuracy=85.183424 loss=0.585855 lr=0.000100 Epoch[095] Batch [1199]/[3760] Speed: 68.161807 samples/sec accuracy=85.171875 loss=0.587429 lr=0.000100 Epoch[095] Batch [1249]/[3760] Speed: 67.438760 samples/sec accuracy=85.170000 loss=0.587718 lr=0.000100 Epoch[095] Batch [1299]/[3760] Speed: 69.896402 samples/sec accuracy=85.213942 loss=0.585780 lr=0.000100 Epoch[095] Batch [1349]/[3760] Speed: 68.504062 samples/sec accuracy=85.196759 loss=0.586805 lr=0.000100 Epoch[095] Batch [1399]/[3760] Speed: 68.692455 samples/sec accuracy=85.143973 loss=0.588495 lr=0.000100 Epoch[095] Batch [1449]/[3760] Speed: 69.183313 samples/sec accuracy=85.162716 loss=0.587456 lr=0.000100 Epoch[095] Batch [1499]/[3760] Speed: 68.842521 samples/sec accuracy=85.164583 loss=0.587603 lr=0.000100 Epoch[095] Batch [1549]/[3760] Speed: 68.601082 samples/sec accuracy=85.168347 loss=0.586809 lr=0.000100 Epoch[095] Batch [1599]/[3760] Speed: 68.423133 samples/sec accuracy=85.143555 loss=0.586938 lr=0.000100 Epoch[095] Batch [1649]/[3760] Speed: 68.574934 samples/sec accuracy=85.129735 loss=0.587773 lr=0.000100 Epoch[095] Batch [1699]/[3760] Speed: 68.505741 samples/sec accuracy=85.118566 loss=0.587960 lr=0.000100 Epoch[095] Batch [1749]/[3760] Speed: 68.701574 samples/sec accuracy=85.125893 loss=0.587926 lr=0.000100 Epoch[095] Batch [1799]/[3760] Speed: 68.411839 samples/sec accuracy=85.132812 loss=0.587670 lr=0.000100 Epoch[095] Batch [1849]/[3760] Speed: 68.120150 samples/sec accuracy=85.123311 loss=0.588092 lr=0.000100 Epoch[095] Batch [1899]/[3760] Speed: 69.052558 samples/sec accuracy=85.105263 loss=0.588779 lr=0.000100 Epoch[095] Batch [1949]/[3760] Speed: 67.947902 samples/sec accuracy=85.102564 loss=0.588647 lr=0.000100 Epoch[095] Batch [1999]/[3760] Speed: 69.201338 samples/sec accuracy=85.080469 loss=0.589750 lr=0.000100 Epoch[095] Batch [2049]/[3760] Speed: 68.647988 samples/sec accuracy=85.071646 loss=0.589433 lr=0.000100 Epoch[095] Batch [2099]/[3760] Speed: 68.186630 samples/sec accuracy=85.038690 loss=0.590966 lr=0.000100 Epoch[095] Batch [2149]/[3760] Speed: 68.841335 samples/sec accuracy=85.069041 loss=0.589892 lr=0.000100 Epoch[095] Batch [2199]/[3760] Speed: 68.649974 samples/sec accuracy=85.078835 loss=0.589149 lr=0.000100 Epoch[095] Batch [2249]/[3760] Speed: 68.472866 samples/sec accuracy=85.070833 loss=0.589369 lr=0.000100 Epoch[095] Batch [2299]/[3760] Speed: 68.608995 samples/sec accuracy=85.050272 loss=0.590253 lr=0.000100 Epoch[095] Batch [2349]/[3760] Speed: 69.065596 samples/sec accuracy=85.058511 loss=0.589981 lr=0.000100 Epoch[095] Batch [2399]/[3760] Speed: 67.811435 samples/sec accuracy=85.080078 loss=0.589354 lr=0.000100 Epoch[095] Batch [2449]/[3760] Speed: 68.724071 samples/sec accuracy=85.089923 loss=0.589216 lr=0.000100 Epoch[095] Batch [2499]/[3760] Speed: 68.977991 samples/sec accuracy=85.071250 loss=0.589570 lr=0.000100 Epoch[095] Batch [2549]/[3760] Speed: 68.670053 samples/sec accuracy=85.065564 loss=0.589609 lr=0.000100 Epoch[095] Batch [2599]/[3760] Speed: 68.825195 samples/sec accuracy=85.056490 loss=0.589556 lr=0.000100 Epoch[095] Batch [2649]/[3760] Speed: 66.994658 samples/sec accuracy=85.054245 loss=0.589619 lr=0.000100 Epoch[095] Batch [2699]/[3760] Speed: 70.072842 samples/sec accuracy=85.049769 loss=0.590101 lr=0.000100 Epoch[095] Batch [2749]/[3760] Speed: 68.651251 samples/sec accuracy=85.032386 loss=0.590662 lr=0.000100 Epoch[095] Batch [2799]/[3760] Speed: 68.815274 samples/sec accuracy=85.048549 loss=0.590163 lr=0.000100 Epoch[095] Batch [2849]/[3760] Speed: 68.914581 samples/sec accuracy=85.061404 loss=0.589733 lr=0.000100 Epoch[095] Batch [2899]/[3760] Speed: 68.692405 samples/sec accuracy=85.044720 loss=0.590287 lr=0.000100 Epoch[095] Batch [2949]/[3760] Speed: 68.603826 samples/sec accuracy=85.026483 loss=0.590684 lr=0.000100 Epoch[095] Batch [2999]/[3760] Speed: 68.038888 samples/sec accuracy=85.031250 loss=0.590566 lr=0.000100 Epoch[095] Batch [3049]/[3760] Speed: 68.548106 samples/sec accuracy=85.027664 loss=0.590721 lr=0.000100 Epoch[095] Batch [3099]/[3760] Speed: 68.624760 samples/sec accuracy=85.042339 loss=0.590405 lr=0.000100 Epoch[095] Batch [3149]/[3760] Speed: 68.437215 samples/sec accuracy=85.030754 loss=0.590608 lr=0.000100 Epoch[095] Batch [3199]/[3760] Speed: 66.932404 samples/sec accuracy=85.024414 loss=0.590895 lr=0.000100 Epoch[095] Batch [3249]/[3760] Speed: 70.167259 samples/sec accuracy=85.037981 loss=0.590335 lr=0.000100 Epoch[095] Batch [3299]/[3760] Speed: 68.620670 samples/sec accuracy=85.036932 loss=0.590723 lr=0.000100 Epoch[095] Batch [3349]/[3760] Speed: 68.684440 samples/sec accuracy=85.030784 loss=0.590824 lr=0.000100 Epoch[095] Batch [3399]/[3760] Speed: 68.700416 samples/sec accuracy=85.047335 loss=0.590530 lr=0.000100 Epoch[095] Batch [3449]/[3760] Speed: 68.750438 samples/sec accuracy=85.029438 loss=0.591134 lr=0.000100 Epoch[095] Batch [3499]/[3760] Speed: 67.972443 samples/sec accuracy=85.035714 loss=0.591283 lr=0.000100 Epoch[095] Batch [3549]/[3760] Speed: 68.678327 samples/sec accuracy=85.025088 loss=0.591423 lr=0.000100 Epoch[095] Batch [3599]/[3760] Speed: 68.957450 samples/sec accuracy=85.028646 loss=0.591268 lr=0.000100 Epoch[095] Batch [3649]/[3760] Speed: 68.705142 samples/sec accuracy=85.036387 loss=0.591008 lr=0.000100 Epoch[095] Batch [3699]/[3760] Speed: 68.497180 samples/sec accuracy=85.043074 loss=0.590694 lr=0.000100 Epoch[095] Batch [3749]/[3760] Speed: 76.948610 samples/sec accuracy=85.044167 loss=0.590471 lr=0.000100 Batch [0049]/[0303]: acc-top1=68.468750 acc-top5=87.312500 Batch [0099]/[0303]: acc-top1=67.953125 acc-top5=86.953125 Batch [0149]/[0303]: acc-top1=67.479167 acc-top5=86.875000 Batch [0199]/[0303]: acc-top1=67.617188 acc-top5=86.898438 Batch [0249]/[0303]: acc-top1=67.818750 acc-top5=86.931250 Batch [0299]/[0303]: acc-top1=67.505208 acc-top5=86.557292 [Epoch 095] training: accuracy=85.045296 loss=0.590410 [Epoch 095] speed: 68 samples/sec time cost: 3808.508671 [Epoch 095] validation: acc-top1=67.460809 acc-top5=86.545998 loss=1.702189 Epoch[096] Batch [0049]/[3759] Speed: 47.149529 samples/sec accuracy=84.156250 loss=0.616160 lr=0.000100 Epoch[096] Batch [0099]/[3759] Speed: 67.346814 samples/sec accuracy=84.546875 loss=0.598827 lr=0.000100 Epoch[096] Batch [0149]/[3759] Speed: 68.853593 samples/sec accuracy=84.895833 loss=0.587053 lr=0.000100 Epoch[096] Batch [0199]/[3759] Speed: 68.796427 samples/sec accuracy=84.812500 loss=0.592396 lr=0.000100 Epoch[096] Batch [0249]/[3759] Speed: 68.781769 samples/sec accuracy=85.012500 loss=0.585527 lr=0.000100 Epoch[096] Batch [0299]/[3759] Speed: 68.277972 samples/sec accuracy=84.843750 loss=0.594256 lr=0.000100 Epoch[096] Batch [0349]/[3759] Speed: 68.607322 samples/sec accuracy=84.875000 loss=0.593501 lr=0.000100 Epoch[096] Batch [0399]/[3759] Speed: 68.546770 samples/sec accuracy=84.773438 loss=0.598539 lr=0.000100 Epoch[096] Batch [0449]/[3759] Speed: 69.091380 samples/sec accuracy=84.739583 loss=0.598215 lr=0.000100 Epoch[096] Batch [0499]/[3759] Speed: 68.227689 samples/sec accuracy=84.668750 loss=0.601862 lr=0.000100 Epoch[096] Batch [0549]/[3759] Speed: 68.529525 samples/sec accuracy=84.698864 loss=0.600465 lr=0.000100 Epoch[096] Batch [0599]/[3759] Speed: 68.405458 samples/sec accuracy=84.822917 loss=0.598356 lr=0.000100 Epoch[096] Batch [0649]/[3759] Speed: 69.196268 samples/sec accuracy=84.867788 loss=0.597495 lr=0.000100 Epoch[096] Batch [0699]/[3759] Speed: 68.193076 samples/sec accuracy=84.879464 loss=0.597670 lr=0.000100 Epoch[096] Batch [0749]/[3759] Speed: 69.034047 samples/sec accuracy=84.950000 loss=0.595897 lr=0.000100 Epoch[096] Batch [0799]/[3759] Speed: 68.766538 samples/sec accuracy=85.000000 loss=0.593920 lr=0.000100 Epoch[096] Batch [0849]/[3759] Speed: 67.731302 samples/sec accuracy=85.003676 loss=0.594486 lr=0.000100 Epoch[096] 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accuracy=85.039352 loss=0.591483 lr=0.000100 Epoch[096] Batch [1399]/[3759] Speed: 66.605032 samples/sec accuracy=85.047991 loss=0.590419 lr=0.000100 Epoch[096] Batch [1449]/[3759] Speed: 70.790377 samples/sec accuracy=85.045259 loss=0.590075 lr=0.000100 Epoch[096] Batch [1499]/[3759] Speed: 68.421464 samples/sec accuracy=85.041667 loss=0.590664 lr=0.000100 Epoch[096] Batch [1549]/[3759] Speed: 68.093903 samples/sec accuracy=85.002016 loss=0.591849 lr=0.000100 Epoch[096] Batch [1599]/[3759] Speed: 69.002984 samples/sec accuracy=85.020508 loss=0.591237 lr=0.000100 Epoch[096] Batch [1649]/[3759] Speed: 69.246709 samples/sec accuracy=85.031250 loss=0.590852 lr=0.000100 Epoch[096] Batch [1699]/[3759] Speed: 67.427428 samples/sec accuracy=85.063419 loss=0.589661 lr=0.000100 Epoch[096] Batch [1749]/[3759] Speed: 69.012948 samples/sec accuracy=85.053571 loss=0.589641 lr=0.000100 Epoch[096] Batch [1799]/[3759] Speed: 68.867101 samples/sec accuracy=85.065972 loss=0.590216 lr=0.000100 Epoch[096] Batch [1849]/[3759] Speed: 68.285488 samples/sec accuracy=85.043919 loss=0.590695 lr=0.000100 Epoch[096] Batch [1899]/[3759] Speed: 69.420349 samples/sec accuracy=85.052632 loss=0.590172 lr=0.000100 Epoch[096] Batch [1949]/[3759] Speed: 68.405406 samples/sec accuracy=85.044872 loss=0.590712 lr=0.000100 Epoch[096] Batch [1999]/[3759] Speed: 68.189226 samples/sec accuracy=85.055469 loss=0.590360 lr=0.000100 Epoch[096] Batch [2049]/[3759] Speed: 68.690464 samples/sec accuracy=85.053354 loss=0.590888 lr=0.000100 Epoch[096] Batch [2099]/[3759] Speed: 68.399678 samples/sec accuracy=85.045387 loss=0.591388 lr=0.000100 Epoch[096] Batch [2149]/[3759] Speed: 68.540954 samples/sec accuracy=85.052326 loss=0.591050 lr=0.000100 Epoch[096] Batch [2199]/[3759] Speed: 68.497934 samples/sec accuracy=85.052557 loss=0.590862 lr=0.000100 Epoch[096] Batch [2249]/[3759] Speed: 66.911814 samples/sec accuracy=85.049306 loss=0.591363 lr=0.000100 Epoch[096] Batch [2299]/[3759] Speed: 69.912613 samples/sec accuracy=85.052989 loss=0.591271 lr=0.000100 Epoch[096] Batch [2349]/[3759] Speed: 68.421227 samples/sec accuracy=85.061835 loss=0.591148 lr=0.000100 Epoch[096] Batch [2399]/[3759] Speed: 68.529085 samples/sec accuracy=85.054036 loss=0.591061 lr=0.000100 Epoch[096] Batch [2449]/[3759] Speed: 68.540109 samples/sec accuracy=85.072704 loss=0.590535 lr=0.000100 Epoch[096] Batch [2499]/[3759] Speed: 68.833648 samples/sec accuracy=85.056875 loss=0.591071 lr=0.000100 Epoch[096] Batch [2549]/[3759] Speed: 68.652712 samples/sec accuracy=85.041667 loss=0.591302 lr=0.000100 Epoch[096] Batch [2599]/[3759] Speed: 68.677944 samples/sec accuracy=85.038462 loss=0.591348 lr=0.000100 Epoch[096] Batch [2649]/[3759] Speed: 68.500602 samples/sec accuracy=85.054835 loss=0.590456 lr=0.000100 Epoch[096] Batch [2699]/[3759] Speed: 68.592019 samples/sec accuracy=85.074653 loss=0.589902 lr=0.000100 Epoch[096] Batch [2749]/[3759] Speed: 68.908729 samples/sec accuracy=85.075568 loss=0.590129 lr=0.000100 Epoch[096] Batch [2799]/[3759] Speed: 68.571698 samples/sec accuracy=85.073661 loss=0.590366 lr=0.000100 Epoch[096] Batch [2849]/[3759] Speed: 68.087905 samples/sec accuracy=85.082237 loss=0.589953 lr=0.000100 Epoch[096] Batch [2899]/[3759] Speed: 68.798480 samples/sec accuracy=85.067349 loss=0.590176 lr=0.000100 Epoch[096] Batch [2949]/[3759] Speed: 68.787645 samples/sec accuracy=85.058263 loss=0.590175 lr=0.000100 Epoch[096] Batch [2999]/[3759] Speed: 68.445841 samples/sec accuracy=85.066146 loss=0.589574 lr=0.000100 Epoch[096] Batch [3049]/[3759] Speed: 68.662830 samples/sec accuracy=85.074283 loss=0.589185 lr=0.000100 Epoch[096] Batch [3099]/[3759] Speed: 67.785935 samples/sec accuracy=85.072077 loss=0.589226 lr=0.000100 Epoch[096] Batch [3149]/[3759] Speed: 69.412994 samples/sec accuracy=85.075397 loss=0.588793 lr=0.000100 Epoch[096] Batch [3199]/[3759] Speed: 68.783564 samples/sec accuracy=85.089355 loss=0.588582 lr=0.000100 Epoch[096] Batch [3249]/[3759] Speed: 68.617624 samples/sec accuracy=85.072596 loss=0.588646 lr=0.000100 Epoch[096] Batch [3299]/[3759] Speed: 68.686314 samples/sec accuracy=85.076705 loss=0.588577 lr=0.000100 Epoch[096] Batch [3349]/[3759] Speed: 68.179408 samples/sec accuracy=85.085354 loss=0.588242 lr=0.000100 Epoch[096] Batch [3399]/[3759] Speed: 68.167716 samples/sec accuracy=85.078125 loss=0.588404 lr=0.000100 Epoch[096] Batch [3449]/[3759] Speed: 68.677374 samples/sec accuracy=85.071105 loss=0.588519 lr=0.000100 Epoch[096] Batch [3499]/[3759] Speed: 68.628750 samples/sec accuracy=85.086161 loss=0.588115 lr=0.000100 Epoch[096] Batch [3549]/[3759] Speed: 68.556792 samples/sec accuracy=85.091109 loss=0.587975 lr=0.000100 Epoch[096] Batch [3599]/[3759] Speed: 68.643052 samples/sec accuracy=85.092014 loss=0.587714 lr=0.000100 Epoch[096] Batch [3649]/[3759] Speed: 68.597771 samples/sec accuracy=85.085616 loss=0.587701 lr=0.000100 Epoch[096] Batch [3699]/[3759] Speed: 68.036235 samples/sec accuracy=85.087416 loss=0.587823 lr=0.000100 Epoch[096] Batch [3749]/[3759] Speed: 77.771028 samples/sec accuracy=85.087083 loss=0.587767 lr=0.000100 Batch [0049]/[0303]: acc-top1=68.312500 acc-top5=87.468750 Batch [0099]/[0303]: acc-top1=67.906250 acc-top5=86.921875 Batch [0149]/[0303]: acc-top1=67.364583 acc-top5=86.854167 Batch [0199]/[0303]: acc-top1=67.507812 acc-top5=86.828125 Batch [0249]/[0303]: acc-top1=67.706250 acc-top5=86.893750 Batch [0299]/[0303]: acc-top1=67.463542 acc-top5=86.557292 [Epoch 096] training: accuracy=85.079559 loss=0.587831 [Epoch 096] speed: 68 samples/sec time cost: 3805.233222 [Epoch 096] validation: acc-top1=67.404084 acc-top5=86.530528 loss=1.713045 Epoch[097] Batch [0049]/[3760] Speed: 46.724221 samples/sec accuracy=84.843750 loss=0.630120 lr=0.000100 Epoch[097] Batch [0099]/[3760] Speed: 67.704626 samples/sec accuracy=84.968750 loss=0.610849 lr=0.000100 Epoch[097] Batch [0149]/[3760] Speed: 68.184654 samples/sec accuracy=85.187500 loss=0.595996 lr=0.000100 Epoch[097] Batch [0199]/[3760] Speed: 68.206946 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lr=0.000100 Epoch[097] Batch [0699]/[3760] Speed: 68.392303 samples/sec accuracy=85.207589 loss=0.584092 lr=0.000100 Epoch[097] Batch [0749]/[3760] Speed: 68.607032 samples/sec accuracy=85.129167 loss=0.585690 lr=0.000100 Epoch[097] Batch [0799]/[3760] Speed: 68.024234 samples/sec accuracy=85.164062 loss=0.584962 lr=0.000100 Epoch[097] Batch [0849]/[3760] Speed: 68.714247 samples/sec accuracy=85.185662 loss=0.583506 lr=0.000100 Epoch[097] Batch [0899]/[3760] Speed: 68.238924 samples/sec accuracy=85.100694 loss=0.585962 lr=0.000100 Epoch[097] Batch [0949]/[3760] Speed: 68.503366 samples/sec accuracy=85.138158 loss=0.585590 lr=0.000100 Epoch[097] Batch [0999]/[3760] Speed: 68.979516 samples/sec accuracy=85.162500 loss=0.585429 lr=0.000100 Epoch[097] Batch [1049]/[3760] Speed: 68.650937 samples/sec accuracy=85.194940 loss=0.585368 lr=0.000100 Epoch[097] Batch [1099]/[3760] Speed: 67.908840 samples/sec accuracy=85.197443 loss=0.585637 lr=0.000100 Epoch[097] Batch [1149]/[3760] Speed: 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lr=0.000100 Epoch[097] Batch [1649]/[3760] Speed: 68.014166 samples/sec accuracy=85.162879 loss=0.586504 lr=0.000100 Epoch[097] Batch [1699]/[3760] Speed: 68.990074 samples/sec accuracy=85.171875 loss=0.586485 lr=0.000100 Epoch[097] Batch [1749]/[3760] Speed: 69.071261 samples/sec accuracy=85.137500 loss=0.586883 lr=0.000100 Epoch[097] Batch [1799]/[3760] Speed: 68.721124 samples/sec accuracy=85.157118 loss=0.585895 lr=0.000100 Epoch[097] Batch [1849]/[3760] Speed: 68.813732 samples/sec accuracy=85.172297 loss=0.585359 lr=0.000100 Epoch[097] Batch [1899]/[3760] Speed: 68.477088 samples/sec accuracy=85.159539 loss=0.586331 lr=0.000100 Epoch[097] Batch [1949]/[3760] Speed: 67.864238 samples/sec accuracy=85.143429 loss=0.586255 lr=0.000100 Epoch[097] Batch [1999]/[3760] Speed: 69.524982 samples/sec accuracy=85.159375 loss=0.585308 lr=0.000100 Epoch[097] Batch [2049]/[3760] Speed: 69.131050 samples/sec accuracy=85.180640 loss=0.584606 lr=0.000100 Epoch[097] Batch [2099]/[3760] Speed: 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lr=0.000100 Epoch[097] Batch [2599]/[3760] Speed: 69.053856 samples/sec accuracy=85.221755 loss=0.582147 lr=0.000100 Epoch[097] Batch [2649]/[3760] Speed: 68.509724 samples/sec accuracy=85.211085 loss=0.582215 lr=0.000100 Epoch[097] Batch [2699]/[3760] Speed: 69.022730 samples/sec accuracy=85.203125 loss=0.582706 lr=0.000100 Epoch[097] Batch [2749]/[3760] Speed: 68.502630 samples/sec accuracy=85.193750 loss=0.582809 lr=0.000100 Epoch[097] Batch [2799]/[3760] Speed: 67.782912 samples/sec accuracy=85.210379 loss=0.581871 lr=0.000100 Epoch[097] Batch [2849]/[3760] Speed: 68.760811 samples/sec accuracy=85.208333 loss=0.582038 lr=0.000100 Epoch[097] Batch [2899]/[3760] Speed: 68.569434 samples/sec accuracy=85.196659 loss=0.582472 lr=0.000100 Epoch[097] Batch [2949]/[3760] Speed: 68.425324 samples/sec accuracy=85.170021 loss=0.583322 lr=0.000100 Epoch[097] Batch [2999]/[3760] Speed: 68.933942 samples/sec accuracy=85.173438 loss=0.583257 lr=0.000100 Epoch[097] Batch [3049]/[3760] Speed: 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lr=0.000100 Epoch[097] Batch [3549]/[3760] Speed: 68.989519 samples/sec accuracy=85.171655 loss=0.582771 lr=0.000100 Epoch[097] Batch [3599]/[3760] Speed: 68.797502 samples/sec accuracy=85.168403 loss=0.582776 lr=0.000100 Epoch[097] Batch [3649]/[3760] Speed: 67.730723 samples/sec accuracy=85.158390 loss=0.582693 lr=0.000100 Epoch[097] Batch [3699]/[3760] Speed: 68.687262 samples/sec accuracy=85.150338 loss=0.582878 lr=0.000100 Epoch[097] Batch [3749]/[3760] Speed: 77.200075 samples/sec accuracy=85.153333 loss=0.582747 lr=0.000100 Batch [0049]/[0303]: acc-top1=68.375000 acc-top5=87.531250 Batch [0099]/[0303]: acc-top1=68.031250 acc-top5=87.093750 Batch [0149]/[0303]: acc-top1=67.489583 acc-top5=86.958333 Batch [0199]/[0303]: acc-top1=67.507812 acc-top5=86.906250 Batch [0249]/[0303]: acc-top1=67.768750 acc-top5=86.937500 Batch [0299]/[0303]: acc-top1=67.526042 acc-top5=86.536458 [Epoch 097] training: accuracy=85.155419 loss=0.582740 [Epoch 097] speed: 68 samples/sec time cost: 3806.444474 [Epoch 097] validation: acc-top1=67.476279 acc-top5=86.499587 loss=1.712864 Epoch[098] Batch [0049]/[3759] Speed: 46.741047 samples/sec accuracy=85.250000 loss=0.583086 lr=0.000100 Epoch[098] Batch [0099]/[3759] Speed: 67.445014 samples/sec accuracy=85.265625 loss=0.579910 lr=0.000100 Epoch[098] Batch [0149]/[3759] Speed: 68.975675 samples/sec accuracy=85.427083 loss=0.576492 lr=0.000100 Epoch[098] Batch [0199]/[3759] Speed: 67.256245 samples/sec accuracy=85.671875 loss=0.573540 lr=0.000100 Epoch[098] Batch [0249]/[3759] Speed: 68.795466 samples/sec accuracy=85.668750 loss=0.573515 lr=0.000100 Epoch[098] Batch [0299]/[3759] Speed: 69.153057 samples/sec accuracy=85.661458 loss=0.568956 lr=0.000100 Epoch[098] Batch [0349]/[3759] Speed: 68.517208 samples/sec accuracy=85.665179 loss=0.567678 lr=0.000100 Epoch[098] Batch [0399]/[3759] Speed: 68.518052 samples/sec accuracy=85.640625 loss=0.570190 lr=0.000100 Epoch[098] Batch [0449]/[3759] Speed: 68.317970 samples/sec accuracy=85.590278 loss=0.572199 lr=0.000100 Epoch[098] Batch [0499]/[3759] Speed: 68.600616 samples/sec accuracy=85.612500 loss=0.569859 lr=0.000100 Epoch[098] Batch [0549]/[3759] Speed: 68.704857 samples/sec accuracy=85.517045 loss=0.572888 lr=0.000100 Epoch[098] Batch [0599]/[3759] Speed: 68.025377 samples/sec accuracy=85.500000 loss=0.573196 lr=0.000100 Epoch[098] Batch [0649]/[3759] Speed: 68.971040 samples/sec accuracy=85.526442 loss=0.572678 lr=0.000100 Epoch[098] Batch [0699]/[3759] Speed: 68.763861 samples/sec accuracy=85.535714 loss=0.573430 lr=0.000100 Epoch[098] Batch [0749]/[3759] Speed: 68.461995 samples/sec accuracy=85.514583 loss=0.572811 lr=0.000100 Epoch[098] Batch [0799]/[3759] Speed: 68.222553 samples/sec accuracy=85.468750 loss=0.573493 lr=0.000100 Epoch[098] Batch [0849]/[3759] Speed: 68.700233 samples/sec accuracy=85.439338 loss=0.573365 lr=0.000100 Epoch[098] Batch [0899]/[3759] Speed: 68.217503 samples/sec accuracy=85.359375 loss=0.575957 lr=0.000100 Epoch[098] 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accuracy=85.376116 loss=0.576907 lr=0.000100 Epoch[098] Batch [1449]/[3759] Speed: 68.960214 samples/sec accuracy=85.356681 loss=0.577399 lr=0.000100 Epoch[098] Batch [1499]/[3759] Speed: 68.209228 samples/sec accuracy=85.357292 loss=0.577625 lr=0.000100 Epoch[098] Batch [1549]/[3759] Speed: 69.056088 samples/sec accuracy=85.389113 loss=0.576113 lr=0.000100 Epoch[098] Batch [1599]/[3759] Speed: 68.533379 samples/sec accuracy=85.393555 loss=0.575925 lr=0.000100 Epoch[098] Batch [1649]/[3759] Speed: 67.811026 samples/sec accuracy=85.386364 loss=0.575501 lr=0.000100 Epoch[098] Batch [1699]/[3759] Speed: 68.959190 samples/sec accuracy=85.420956 loss=0.573728 lr=0.000100 Epoch[098] Batch [1749]/[3759] Speed: 68.531209 samples/sec accuracy=85.376786 loss=0.575044 lr=0.000100 Epoch[098] Batch [1799]/[3759] Speed: 68.920627 samples/sec accuracy=85.391493 loss=0.573766 lr=0.000100 Epoch[098] Batch [1849]/[3759] Speed: 68.119343 samples/sec accuracy=85.403716 loss=0.573716 lr=0.000100 Epoch[098] 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accuracy=85.404255 loss=0.574611 lr=0.000100 Epoch[098] Batch [2399]/[3759] Speed: 68.835748 samples/sec accuracy=85.403646 loss=0.574643 lr=0.000100 Epoch[098] Batch [2449]/[3759] Speed: 68.652144 samples/sec accuracy=85.387755 loss=0.575102 lr=0.000100 Epoch[098] Batch [2499]/[3759] Speed: 67.584797 samples/sec accuracy=85.383750 loss=0.575371 lr=0.000100 Epoch[098] Batch [2549]/[3759] Speed: 69.192767 samples/sec accuracy=85.389093 loss=0.575512 lr=0.000100 Epoch[098] Batch [2599]/[3759] Speed: 68.275720 samples/sec accuracy=85.390625 loss=0.575184 lr=0.000100 Epoch[098] Batch [2649]/[3759] Speed: 68.621215 samples/sec accuracy=85.375590 loss=0.575835 lr=0.000100 Epoch[098] Batch [2699]/[3759] Speed: 68.186641 samples/sec accuracy=85.390625 loss=0.575376 lr=0.000100 Epoch[098] Batch [2749]/[3759] Speed: 68.304963 samples/sec accuracy=85.381818 loss=0.575355 lr=0.000100 Epoch[098] Batch [2799]/[3759] Speed: 68.165911 samples/sec accuracy=85.385045 loss=0.575240 lr=0.000100 Epoch[098] 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accuracy=85.320076 loss=0.577068 lr=0.000100 Epoch[098] Batch [3349]/[3759] Speed: 67.532284 samples/sec accuracy=85.313433 loss=0.577222 lr=0.000100 Epoch[098] Batch [3399]/[3759] Speed: 69.277305 samples/sec accuracy=85.302390 loss=0.577604 lr=0.000100 Epoch[098] Batch [3449]/[3759] Speed: 68.738799 samples/sec accuracy=85.291214 loss=0.578373 lr=0.000100 Epoch[098] Batch [3499]/[3759] Speed: 68.397960 samples/sec accuracy=85.294643 loss=0.578368 lr=0.000100 Epoch[098] Batch [3549]/[3759] Speed: 69.222078 samples/sec accuracy=85.307658 loss=0.578076 lr=0.000100 Epoch[098] Batch [3599]/[3759] Speed: 68.551279 samples/sec accuracy=85.313368 loss=0.577800 lr=0.000100 Epoch[098] Batch [3649]/[3759] Speed: 67.591233 samples/sec accuracy=85.308219 loss=0.578016 lr=0.000100 Epoch[098] Batch [3699]/[3759] Speed: 68.966705 samples/sec accuracy=85.306588 loss=0.577967 lr=0.000100 Epoch[098] Batch [3749]/[3759] Speed: 77.882421 samples/sec accuracy=85.307917 loss=0.578000 lr=0.000100 Batch [0049]/[0303]: acc-top1=68.062500 acc-top5=87.437500 Batch [0099]/[0303]: acc-top1=67.703125 acc-top5=87.078125 Batch [0149]/[0303]: acc-top1=67.114583 acc-top5=86.937500 Batch [0199]/[0303]: acc-top1=67.250000 acc-top5=86.859375 Batch [0249]/[0303]: acc-top1=67.437500 acc-top5=86.931250 Batch [0299]/[0303]: acc-top1=67.229167 acc-top5=86.520833 [Epoch 098] training: accuracy=85.305267 loss=0.578206 [Epoch 098] speed: 68 samples/sec time cost: 3809.926729 [Epoch 098] validation: acc-top1=67.197814 acc-top5=86.489274 loss=1.726586 Epoch[099] Batch [0049]/[3760] Speed: 47.403575 samples/sec accuracy=85.562500 loss=0.558905 lr=0.000100 Epoch[099] Batch [0099]/[3760] Speed: 67.845962 samples/sec accuracy=85.234375 loss=0.580003 lr=0.000100 Epoch[099] Batch [0149]/[3760] Speed: 68.428475 samples/sec accuracy=85.197917 loss=0.581770 lr=0.000100 Epoch[099] Batch [0199]/[3760] Speed: 66.958507 samples/sec accuracy=85.367188 loss=0.582042 lr=0.000100 Epoch[099] Batch [0249]/[3760] Speed: 69.163852 samples/sec accuracy=85.381250 loss=0.581224 lr=0.000100 Epoch[099] Batch [0299]/[3760] Speed: 68.431240 samples/sec accuracy=85.442708 loss=0.580955 lr=0.000100 Epoch[099] Batch [0349]/[3760] Speed: 68.865863 samples/sec accuracy=85.441964 loss=0.578022 lr=0.000100 Epoch[099] Batch [0399]/[3760] Speed: 68.832025 samples/sec accuracy=85.500000 loss=0.573458 lr=0.000100 Epoch[099] Batch [0449]/[3760] Speed: 68.680844 samples/sec accuracy=85.569444 loss=0.573004 lr=0.000100 Epoch[099] Batch [0499]/[3760] Speed: 67.678827 samples/sec accuracy=85.600000 loss=0.572637 lr=0.000100 Epoch[099] Batch [0549]/[3760] Speed: 69.427387 samples/sec accuracy=85.633523 loss=0.570643 lr=0.000100 Epoch[099] Batch [0599]/[3760] Speed: 68.258860 samples/sec accuracy=85.598958 loss=0.572198 lr=0.000100 Epoch[099] Batch [0649]/[3760] Speed: 68.999898 samples/sec accuracy=85.596154 loss=0.573102 lr=0.000100 Epoch[099] Batch [0699]/[3760] Speed: 68.155883 samples/sec accuracy=85.566964 loss=0.573729 lr=0.000100 Epoch[099] Batch [0749]/[3760] Speed: 68.867948 samples/sec accuracy=85.545833 loss=0.574509 lr=0.000100 Epoch[099] Batch [0799]/[3760] Speed: 68.458141 samples/sec accuracy=85.562500 loss=0.574419 lr=0.000100 Epoch[099] Batch [0849]/[3760] Speed: 68.298531 samples/sec accuracy=85.582721 loss=0.573870 lr=0.000100 Epoch[099] Batch [0899]/[3760] Speed: 68.752211 samples/sec accuracy=85.572917 loss=0.575179 lr=0.000100 Epoch[099] Batch [0949]/[3760] Speed: 69.070720 samples/sec accuracy=85.567434 loss=0.575400 lr=0.000100 Epoch[099] Batch [0999]/[3760] Speed: 68.144692 samples/sec accuracy=85.532813 loss=0.576566 lr=0.000100 Epoch[099] Batch [1049]/[3760] Speed: 68.799940 samples/sec accuracy=85.523810 loss=0.577302 lr=0.000100 Epoch[099] Batch [1099]/[3760] Speed: 68.012804 samples/sec accuracy=85.460227 loss=0.579051 lr=0.000100 Epoch[099] Batch [1149]/[3760] Speed: 68.423531 samples/sec accuracy=85.483696 loss=0.578766 lr=0.000100 Epoch[099] Batch [1199]/[3760] Speed: 68.155558 samples/sec accuracy=85.479167 loss=0.579415 lr=0.000100 Epoch[099] Batch [1249]/[3760] Speed: 68.882778 samples/sec accuracy=85.455000 loss=0.580702 lr=0.000100 Epoch[099] Batch [1299]/[3760] Speed: 68.385334 samples/sec accuracy=85.447115 loss=0.579412 lr=0.000100 Epoch[099] Batch [1349]/[3760] Speed: 67.502362 samples/sec accuracy=85.407407 loss=0.580358 lr=0.000100 Epoch[099] Batch [1399]/[3760] Speed: 69.806841 samples/sec accuracy=85.378348 loss=0.580331 lr=0.000100 Epoch[099] Batch [1449]/[3760] Speed: 68.594161 samples/sec accuracy=85.379310 loss=0.580313 lr=0.000100 Epoch[099] Batch [1499]/[3760] Speed: 68.639015 samples/sec accuracy=85.392708 loss=0.579981 lr=0.000100 Epoch[099] Batch [1549]/[3760] Speed: 68.797269 samples/sec accuracy=85.417339 loss=0.578524 lr=0.000100 Epoch[099] Batch [1599]/[3760] Speed: 68.737777 samples/sec accuracy=85.418945 loss=0.577799 lr=0.000100 Epoch[099] Batch [1649]/[3760] Speed: 67.497979 samples/sec accuracy=85.416667 loss=0.577755 lr=0.000100 Epoch[099] Batch [1699]/[3760] Speed: 69.424248 samples/sec accuracy=85.399816 loss=0.578417 lr=0.000100 Epoch[099] Batch [1749]/[3760] Speed: 68.445766 samples/sec accuracy=85.390179 loss=0.578410 lr=0.000100 Epoch[099] Batch [1799]/[3760] Speed: 68.800358 samples/sec accuracy=85.409722 loss=0.577215 lr=0.000100 Epoch[099] Batch [1849]/[3760] Speed: 69.111284 samples/sec accuracy=85.402027 loss=0.577293 lr=0.000100 Epoch[099] Batch [1899]/[3760] Speed: 68.606118 samples/sec accuracy=85.391447 loss=0.577616 lr=0.000100 Epoch[099] Batch [1949]/[3760] Speed: 67.790771 samples/sec accuracy=85.395032 loss=0.577803 lr=0.000100 Epoch[099] Batch [1999]/[3760] Speed: 69.220285 samples/sec accuracy=85.364844 loss=0.579290 lr=0.000100 Epoch[099] Batch [2049]/[3760] Speed: 68.760865 samples/sec accuracy=85.356707 loss=0.579478 lr=0.000100 Epoch[099] Batch [2099]/[3760] Speed: 68.562975 samples/sec accuracy=85.337798 loss=0.579659 lr=0.000100 Epoch[099] Batch [2149]/[3760] Speed: 68.889846 samples/sec accuracy=85.332849 loss=0.579887 lr=0.000100 Epoch[099] Batch [2199]/[3760] Speed: 68.479041 samples/sec accuracy=85.382102 loss=0.578369 lr=0.000100 Epoch[099] Batch [2249]/[3760] Speed: 68.485828 samples/sec accuracy=85.369444 loss=0.578927 lr=0.000100 Epoch[099] Batch [2299]/[3760] Speed: 69.102353 samples/sec accuracy=85.392663 loss=0.578075 lr=0.000100 Epoch[099] Batch [2349]/[3760] Speed: 68.481343 samples/sec accuracy=85.413564 loss=0.577465 lr=0.000100 Epoch[099] Batch [2399]/[3760] Speed: 68.818911 samples/sec accuracy=85.402344 loss=0.577328 lr=0.000100 Epoch[099] Batch [2449]/[3760] Speed: 69.066519 samples/sec accuracy=85.387755 loss=0.577779 lr=0.000100 Epoch[099] Batch [2499]/[3760] Speed: 68.114499 samples/sec accuracy=85.386250 loss=0.577770 lr=0.000100 Epoch[099] Batch [2549]/[3760] Speed: 68.146777 samples/sec accuracy=85.357843 loss=0.578798 lr=0.000100 Epoch[099] Batch [2599]/[3760] Speed: 68.827279 samples/sec accuracy=85.361178 loss=0.578569 lr=0.000100 Epoch[099] Batch [2649]/[3760] Speed: 68.891209 samples/sec accuracy=85.372052 loss=0.578199 lr=0.000100 Epoch[099] Batch [2699]/[3760] Speed: 68.583240 samples/sec accuracy=85.379630 loss=0.577826 lr=0.000100 Epoch[099] Batch [2749]/[3760] Speed: 68.366660 samples/sec accuracy=85.381818 loss=0.577867 lr=0.000100 Epoch[099] Batch [2799]/[3760] Speed: 69.611045 samples/sec accuracy=85.390625 loss=0.577325 lr=0.000100 Epoch[099] Batch [2849]/[3760] Speed: 68.693325 samples/sec accuracy=85.394189 loss=0.577444 lr=0.000100 Epoch[099] Batch [2899]/[3760] Speed: 68.235569 samples/sec accuracy=85.396552 loss=0.577438 lr=0.000100 Epoch[099] Batch [2949]/[3760] Speed: 68.397394 samples/sec accuracy=85.377648 loss=0.578314 lr=0.000100 Epoch[099] Batch [2999]/[3760] Speed: 68.833498 samples/sec accuracy=85.385937 loss=0.578242 lr=0.000100 Epoch[099] Batch [3049]/[3760] Speed: 68.736848 samples/sec accuracy=85.384734 loss=0.578687 lr=0.000100 Epoch[099] Batch [3099]/[3760] Speed: 67.466072 samples/sec accuracy=85.381552 loss=0.578790 lr=0.000100 Epoch[099] Batch [3149]/[3760] Speed: 69.848680 samples/sec accuracy=85.370040 loss=0.578929 lr=0.000100 Epoch[099] Batch [3199]/[3760] Speed: 68.432239 samples/sec accuracy=85.355957 loss=0.578988 lr=0.000100 Epoch[099] Batch [3249]/[3760] Speed: 69.039795 samples/sec accuracy=85.387981 loss=0.578219 lr=0.000100 Epoch[099] Batch [3299]/[3760] Speed: 68.649862 samples/sec accuracy=85.382576 loss=0.578547 lr=0.000100 Epoch[099] Batch [3349]/[3760] Speed: 68.968912 samples/sec accuracy=85.373601 loss=0.578872 lr=0.000100 Epoch[099] Batch [3399]/[3760] Speed: 68.066778 samples/sec accuracy=85.359835 loss=0.579109 lr=0.000100 Epoch[099] Batch [3449]/[3760] Speed: 68.898769 samples/sec accuracy=85.346920 loss=0.579386 lr=0.000100 Epoch[099] Batch [3499]/[3760] Speed: 68.539711 samples/sec accuracy=85.347321 loss=0.579435 lr=0.000100 Epoch[099] Batch [3549]/[3760] Speed: 69.245641 samples/sec accuracy=85.345951 loss=0.579389 lr=0.000100 Epoch[099] Batch [3599]/[3760] Speed: 68.837231 samples/sec accuracy=85.342448 loss=0.579224 lr=0.000100 Epoch[099] Batch [3649]/[3760] Speed: 68.909385 samples/sec accuracy=85.351884 loss=0.578749 lr=0.000100 Epoch[099] Batch [3699]/[3760] Speed: 67.527050 samples/sec accuracy=85.341639 loss=0.579023 lr=0.000100 Epoch[099] Batch [3749]/[3760] Speed: 76.483578 samples/sec accuracy=85.333333 loss=0.579539 lr=0.000100 Batch [0049]/[0303]: acc-top1=68.625000 acc-top5=87.375000 Batch [0099]/[0303]: acc-top1=68.156250 acc-top5=86.953125 Batch [0149]/[0303]: acc-top1=67.520833 acc-top5=86.843750 Batch [0199]/[0303]: acc-top1=67.523438 acc-top5=86.796875 Batch [0249]/[0303]: acc-top1=67.637500 acc-top5=86.837500 Batch [0299]/[0303]: acc-top1=67.385417 acc-top5=86.510417 [Epoch 099] training: accuracy=85.337849 loss=0.579320 [Epoch 099] speed: 68 samples/sec time cost: 3807.484772 [Epoch 099] validation: acc-top1=67.367987 acc-top5=86.473804 loss=1.718973