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_nl5_res101_seg1_kinetics400_b8_g8_inflate311_f32s2_step_nosubsample_norm_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_nl5_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/nonlocal/i3d_nl5_res101_seg1_kinetics400_b8_g8_inflate311_f32s2_step_nosubsample_norm_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) (8): NonLocal( (theta): Conv3D(512 -> 256, kernel_size=(1, 1, 1), stride=(1, 1, 1)) (phi): Conv3D(512 -> 256, kernel_size=(1, 1, 1), stride=(1, 1, 1)) (g): Conv3D(512 -> 256, kernel_size=(1, 1, 1), stride=(1, 1, 1)) (W): Conv3D(256 -> 512, kernel_size=(1, 1, 1), stride=(1, 1, 1)) (bn): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=512) (W_bn): HybridSequential( (0): Conv3D(256 -> 512, kernel_size=(1, 1, 1), stride=(1, 1, 1)) (1): 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) (nonlocal_block): NonLocal( (theta): Conv3D(512 -> 256, kernel_size=(1, 1, 1), stride=(1, 1, 1)) (phi): Conv3D(512 -> 256, kernel_size=(1, 1, 1), stride=(1, 1, 1)) (g): Conv3D(512 -> 256, kernel_size=(1, 1, 1), stride=(1, 1, 1)) (W): Conv3D(256 -> 512, kernel_size=(1, 1, 1), stride=(1, 1, 1)) (bn): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=512) (W_bn): HybridSequential( (0): Conv3D(256 -> 512, kernel_size=(1, 1, 1), stride=(1, 1, 1)) (1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=512) ) ) ) (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) (8): NonLocal( (theta): Conv3D(512 -> 256, kernel_size=(1, 1, 1), stride=(1, 1, 1)) (phi): Conv3D(512 -> 256, kernel_size=(1, 1, 1), stride=(1, 1, 1)) (g): Conv3D(512 -> 256, kernel_size=(1, 1, 1), stride=(1, 1, 1)) (W): Conv3D(256 -> 512, kernel_size=(1, 1, 1), stride=(1, 1, 1)) (bn): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=512) (W_bn): HybridSequential( (0): Conv3D(256 -> 512, kernel_size=(1, 1, 1), stride=(1, 1, 1)) (1): 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) (nonlocal_block): NonLocal( (theta): Conv3D(512 -> 256, kernel_size=(1, 1, 1), stride=(1, 1, 1)) (phi): Conv3D(512 -> 256, kernel_size=(1, 1, 1), stride=(1, 1, 1)) (g): Conv3D(512 -> 256, kernel_size=(1, 1, 1), stride=(1, 1, 1)) (W): Conv3D(256 -> 512, kernel_size=(1, 1, 1), stride=(1, 1, 1)) (bn): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=512) (W_bn): HybridSequential( (0): Conv3D(256 -> 512, kernel_size=(1, 1, 1), stride=(1, 1, 1)) (1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=512) ) ) ) ) (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) (8): NonLocal( (theta): Conv3D(1024 -> 512, kernel_size=(1, 1, 1), stride=(1, 1, 1)) (phi): Conv3D(1024 -> 512, kernel_size=(1, 1, 1), stride=(1, 1, 1)) (g): Conv3D(1024 -> 512, kernel_size=(1, 1, 1), stride=(1, 1, 1)) (W): Conv3D(512 -> 1024, kernel_size=(1, 1, 1), stride=(1, 1, 1)) (bn): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=1024) (W_bn): HybridSequential( (0): Conv3D(512 -> 1024, kernel_size=(1, 1, 1), stride=(1, 1, 1)) (1): 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) (nonlocal_block): NonLocal( (theta): Conv3D(1024 -> 512, kernel_size=(1, 1, 1), stride=(1, 1, 1)) (phi): Conv3D(1024 -> 512, kernel_size=(1, 1, 1), stride=(1, 1, 1)) (g): Conv3D(1024 -> 512, kernel_size=(1, 1, 1), stride=(1, 1, 1)) (W): Conv3D(512 -> 1024, kernel_size=(1, 1, 1), stride=(1, 1, 1)) (bn): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=1024) (W_bn): HybridSequential( (0): Conv3D(512 -> 1024, kernel_size=(1, 1, 1), stride=(1, 1, 1)) (1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=1024) ) ) ) (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) (8): NonLocal( (theta): Conv3D(1024 -> 512, kernel_size=(1, 1, 1), stride=(1, 1, 1)) (phi): Conv3D(1024 -> 512, kernel_size=(1, 1, 1), stride=(1, 1, 1)) (g): Conv3D(1024 -> 512, kernel_size=(1, 1, 1), stride=(1, 1, 1)) (W): Conv3D(512 -> 1024, kernel_size=(1, 1, 1), stride=(1, 1, 1)) (bn): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=1024) (W_bn): HybridSequential( (0): Conv3D(512 -> 1024, kernel_size=(1, 1, 1), stride=(1, 1, 1)) (1): 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) (nonlocal_block): NonLocal( (theta): Conv3D(1024 -> 512, kernel_size=(1, 1, 1), stride=(1, 1, 1)) (phi): Conv3D(1024 -> 512, kernel_size=(1, 1, 1), stride=(1, 1, 1)) (g): Conv3D(1024 -> 512, kernel_size=(1, 1, 1), stride=(1, 1, 1)) (W): Conv3D(512 -> 1024, kernel_size=(1, 1, 1), stride=(1, 1, 1)) (bn): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=1024) (W_bn): HybridSequential( (0): Conv3D(512 -> 1024, kernel_size=(1, 1, 1), stride=(1, 1, 1)) (1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=1024) ) ) ) (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) (8): NonLocal( (theta): Conv3D(1024 -> 512, kernel_size=(1, 1, 1), stride=(1, 1, 1)) (phi): Conv3D(1024 -> 512, kernel_size=(1, 1, 1), stride=(1, 1, 1)) (g): Conv3D(1024 -> 512, kernel_size=(1, 1, 1), stride=(1, 1, 1)) (W): Conv3D(512 -> 1024, kernel_size=(1, 1, 1), stride=(1, 1, 1)) (bn): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=1024) (W_bn): HybridSequential( (0): Conv3D(512 -> 1024, kernel_size=(1, 1, 1), stride=(1, 1, 1)) (1): 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) (nonlocal_block): NonLocal( (theta): Conv3D(1024 -> 512, kernel_size=(1, 1, 1), stride=(1, 1, 1)) (phi): Conv3D(1024 -> 512, kernel_size=(1, 1, 1), stride=(1, 1, 1)) (g): Conv3D(1024 -> 512, kernel_size=(1, 1, 1), stride=(1, 1, 1)) (W): Conv3D(512 -> 1024, kernel_size=(1, 1, 1), stride=(1, 1, 1)) (bn): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=1024) (W_bn): HybridSequential( (0): Conv3D(512 -> 1024, kernel_size=(1, 1, 1), stride=(1, 1, 1)) (1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=1024) ) ) ) (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: 31.548185 samples/sec accuracy=0.906250 loss=5.929897 lr=0.010000 Epoch[000] Batch [0099]/[3759] Speed: 63.265226 samples/sec accuracy=1.953125 loss=5.794967 lr=0.010000 Epoch[000] Batch [0149]/[3759] Speed: 63.772796 samples/sec accuracy=2.812500 loss=5.630750 lr=0.010000 Epoch[000] Batch [0199]/[3759] Speed: 63.652773 samples/sec accuracy=3.875000 loss=5.489071 lr=0.010000 Epoch[000] Batch [0249]/[3759] Speed: 63.664334 samples/sec accuracy=4.768750 loss=5.350647 lr=0.010000 Epoch[000] Batch [0299]/[3759] Speed: 63.363502 samples/sec accuracy=5.593750 loss=5.242867 lr=0.010000 Epoch[000] Batch [0349]/[3759] Speed: 64.083929 samples/sec accuracy=6.388393 loss=5.148127 lr=0.010000 Epoch[000] Batch [0399]/[3759] Speed: 63.395808 samples/sec accuracy=7.128906 loss=5.070460 lr=0.010000 Epoch[000] Batch [0449]/[3759] Speed: 63.700789 samples/sec accuracy=7.819444 loss=4.998230 lr=0.010000 Epoch[000] Batch [0499]/[3759] Speed: 63.358540 samples/sec 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accuracy=20.097005 loss=3.891947 lr=0.010000 Epoch[000] Batch [2449]/[3759] Speed: 63.900645 samples/sec accuracy=20.298469 loss=3.878183 lr=0.010000 Epoch[000] Batch [2499]/[3759] Speed: 63.247454 samples/sec accuracy=20.469375 loss=3.864467 lr=0.010000 Epoch[000] Batch [2549]/[3759] Speed: 63.401790 samples/sec accuracy=20.645221 loss=3.852094 lr=0.010000 Epoch[000] Batch [2599]/[3759] Speed: 63.524939 samples/sec accuracy=20.810096 loss=3.840253 lr=0.010000 Epoch[000] Batch [2649]/[3759] Speed: 63.527239 samples/sec accuracy=20.995873 loss=3.827722 lr=0.010000 Epoch[000] Batch [2699]/[3759] Speed: 63.177464 samples/sec accuracy=21.196759 loss=3.814946 lr=0.010000 Epoch[000] Batch [2749]/[3759] Speed: 63.143974 samples/sec accuracy=21.350568 loss=3.803196 lr=0.010000 Epoch[000] Batch [2799]/[3759] Speed: 63.692240 samples/sec accuracy=21.506696 loss=3.791829 lr=0.010000 Epoch[000] Batch [2849]/[3759] Speed: 63.017251 samples/sec accuracy=21.663377 loss=3.780486 lr=0.010000 Epoch[000] 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accuracy=23.179571 loss=3.675970 lr=0.010000 Epoch[000] Batch [3399]/[3759] Speed: 63.489774 samples/sec accuracy=23.308824 loss=3.667601 lr=0.010000 Epoch[000] Batch [3449]/[3759] Speed: 63.827497 samples/sec accuracy=23.454257 loss=3.658391 lr=0.010000 Epoch[000] Batch [3499]/[3759] Speed: 63.141810 samples/sec accuracy=23.612946 loss=3.648923 lr=0.010000 Epoch[000] Batch [3549]/[3759] Speed: 63.727742 samples/sec accuracy=23.749120 loss=3.639283 lr=0.010000 Epoch[000] Batch [3599]/[3759] Speed: 63.716261 samples/sec accuracy=23.901042 loss=3.629372 lr=0.010000 Epoch[000] Batch [3649]/[3759] Speed: 63.724123 samples/sec accuracy=24.056079 loss=3.619750 lr=0.010000 Epoch[000] Batch [3699]/[3759] Speed: 63.733070 samples/sec accuracy=24.211571 loss=3.610388 lr=0.010000 Epoch[000] Batch [3749]/[3759] Speed: 71.466030 samples/sec accuracy=24.323750 loss=3.602154 lr=0.010000 Batch [0049]/[0303]: acc-top1=37.656250 acc-top5=66.218750 Batch [0099]/[0303]: acc-top1=37.359375 acc-top5=65.359375 Batch [0149]/[0303]: acc-top1=37.343750 acc-top5=64.979167 Batch [0199]/[0303]: acc-top1=37.117188 acc-top5=64.921875 Batch [0249]/[0303]: acc-top1=37.050000 acc-top5=65.112500 Batch [0299]/[0303]: acc-top1=37.041667 acc-top5=65.083333 [Epoch 000] training: accuracy=24.341580 loss=3.600598 [Epoch 000] speed: 62 samples/sec time cost: 4114.121085 [Epoch 000] validation: acc-top1=37.092616 acc-top5=65.135107 loss=2.823873 Epoch[001] Batch [0049]/[3760] Speed: 43.042559 samples/sec accuracy=36.093750 loss=2.858593 lr=0.010000 Epoch[001] Batch [0099]/[3760] Speed: 63.829345 samples/sec accuracy=36.734375 loss=2.808067 lr=0.010000 Epoch[001] Batch [0149]/[3760] Speed: 64.932475 samples/sec accuracy=36.270833 loss=2.831718 lr=0.010000 Epoch[001] Batch [0199]/[3760] Speed: 63.415855 samples/sec accuracy=36.093750 loss=2.844517 lr=0.010000 Epoch[001] Batch [0249]/[3760] Speed: 64.253540 samples/sec accuracy=36.156250 loss=2.851963 lr=0.010000 Epoch[001] Batch [0299]/[3760] 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accuracy=38.895399 loss=2.693399 lr=0.010000 Epoch[001] Batch [3649]/[3760] Speed: 64.494768 samples/sec accuracy=38.927226 loss=2.691341 lr=0.010000 Epoch[001] Batch [3699]/[3760] Speed: 64.162098 samples/sec accuracy=38.976774 loss=2.688771 lr=0.010000 Epoch[001] Batch [3749]/[3760] Speed: 72.043747 samples/sec accuracy=38.997500 loss=2.687711 lr=0.010000 Batch [0049]/[0303]: acc-top1=45.656250 acc-top5=72.593750 Batch [0099]/[0303]: acc-top1=44.953125 acc-top5=71.703125 Batch [0149]/[0303]: acc-top1=44.687500 acc-top5=71.260417 Batch [0199]/[0303]: acc-top1=44.437500 acc-top5=71.210938 Batch [0249]/[0303]: acc-top1=44.425000 acc-top5=71.312500 Batch [0299]/[0303]: acc-top1=44.536458 acc-top5=71.437500 [Epoch 001] training: accuracy=39.004737 loss=2.687072 [Epoch 001] speed: 64 samples/sec time cost: 4033.523829 [Epoch 001] validation: acc-top1=44.580239 acc-top5=71.457302 loss=2.477000 Epoch[002] Batch [0049]/[3759] Speed: 43.493288 samples/sec accuracy=43.062500 loss=2.417232 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lr=0.010000 Epoch[002] Batch [2949]/[3759] Speed: 64.467721 samples/sec accuracy=44.434852 loss=2.399872 lr=0.010000 Epoch[002] Batch [2999]/[3759] Speed: 64.734121 samples/sec accuracy=44.435938 loss=2.399447 lr=0.010000 Epoch[002] Batch [3049]/[3759] Speed: 64.748778 samples/sec accuracy=44.445697 loss=2.399305 lr=0.010000 Epoch[002] Batch [3099]/[3759] Speed: 64.283335 samples/sec accuracy=44.481855 loss=2.397484 lr=0.010000 Epoch[002] Batch [3149]/[3759] Speed: 64.371459 samples/sec accuracy=44.495536 loss=2.395976 lr=0.010000 Epoch[002] Batch [3199]/[3759] Speed: 64.357785 samples/sec accuracy=44.479492 loss=2.396385 lr=0.010000 Epoch[002] Batch [3249]/[3759] Speed: 64.045995 samples/sec accuracy=44.480288 loss=2.395795 lr=0.010000 Epoch[002] Batch [3299]/[3759] Speed: 64.482270 samples/sec accuracy=44.535511 loss=2.393788 lr=0.010000 Epoch[002] Batch [3349]/[3759] Speed: 64.500411 samples/sec accuracy=44.543843 loss=2.392630 lr=0.010000 Epoch[002] Batch [3399]/[3759] Speed: 64.750100 samples/sec accuracy=44.552390 loss=2.392463 lr=0.010000 Epoch[002] Batch [3449]/[3759] Speed: 64.175507 samples/sec accuracy=44.567935 loss=2.391878 lr=0.010000 Epoch[002] Batch [3499]/[3759] Speed: 64.729527 samples/sec accuracy=44.571875 loss=2.391291 lr=0.010000 Epoch[002] Batch [3549]/[3759] Speed: 64.613161 samples/sec accuracy=44.590669 loss=2.389753 lr=0.010000 Epoch[002] Batch [3599]/[3759] Speed: 64.721109 samples/sec accuracy=44.603733 loss=2.388865 lr=0.010000 Epoch[002] Batch [3649]/[3759] Speed: 64.342161 samples/sec accuracy=44.633990 loss=2.387249 lr=0.010000 Epoch[002] Batch [3699]/[3759] Speed: 64.246715 samples/sec accuracy=44.647804 loss=2.386787 lr=0.010000 Epoch[002] Batch [3749]/[3759] Speed: 71.769199 samples/sec accuracy=44.673333 loss=2.385559 lr=0.010000 Batch [0049]/[0303]: acc-top1=49.687500 acc-top5=74.406250 Batch [0099]/[0303]: acc-top1=48.265625 acc-top5=74.265625 Batch [0149]/[0303]: acc-top1=48.020833 acc-top5=74.260417 Batch [0199]/[0303]: acc-top1=47.828125 acc-top5=74.281250 Batch [0249]/[0303]: acc-top1=47.875000 acc-top5=74.400000 Batch [0299]/[0303]: acc-top1=48.067708 acc-top5=74.489583 [Epoch 002] training: accuracy=44.681099 loss=2.385289 [Epoch 002] speed: 64 samples/sec time cost: 4029.694296 [Epoch 002] validation: acc-top1=48.086840 acc-top5=74.515264 loss=2.296156 Epoch[003] Batch [0049]/[3760] Speed: 43.962737 samples/sec accuracy=47.218750 loss=2.249978 lr=0.010000 Epoch[003] Batch [0099]/[3760] Speed: 63.225831 samples/sec accuracy=48.078125 loss=2.212242 lr=0.010000 Epoch[003] Batch [0149]/[3760] Speed: 64.383895 samples/sec accuracy=47.541667 loss=2.221416 lr=0.010000 Epoch[003] Batch [0199]/[3760] Speed: 64.262959 samples/sec accuracy=47.335938 loss=2.240499 lr=0.010000 Epoch[003] Batch [0249]/[3760] Speed: 64.462819 samples/sec accuracy=47.350000 loss=2.244757 lr=0.010000 Epoch[003] Batch [0299]/[3760] Speed: 64.191297 samples/sec accuracy=47.447917 loss=2.242041 lr=0.010000 Epoch[003] Batch 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accuracy=47.488281 loss=2.232849 lr=0.010000 Epoch[003] Batch [0849]/[3760] Speed: 64.183115 samples/sec accuracy=47.564338 loss=2.232039 lr=0.010000 Epoch[003] Batch [0899]/[3760] Speed: 64.729809 samples/sec accuracy=47.612847 loss=2.229865 lr=0.010000 Epoch[003] Batch [0949]/[3760] Speed: 63.875654 samples/sec accuracy=47.583882 loss=2.229312 lr=0.010000 Epoch[003] Batch [0999]/[3760] Speed: 64.542157 samples/sec accuracy=47.620312 loss=2.227404 lr=0.010000 Epoch[003] Batch [1049]/[3760] Speed: 63.799455 samples/sec accuracy=47.629464 loss=2.228192 lr=0.010000 Epoch[003] Batch [1099]/[3760] Speed: 64.753018 samples/sec accuracy=47.657670 loss=2.227642 lr=0.010000 Epoch[003] Batch [1149]/[3760] Speed: 64.104873 samples/sec accuracy=47.687500 loss=2.227102 lr=0.010000 Epoch[003] Batch [1199]/[3760] Speed: 64.481495 samples/sec accuracy=47.638021 loss=2.229304 lr=0.010000 Epoch[003] Batch [1249]/[3760] Speed: 64.544312 samples/sec accuracy=47.668750 loss=2.226609 lr=0.010000 Epoch[003] 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accuracy=47.741071 loss=2.228647 lr=0.010000 Epoch[003] Batch [1799]/[3760] Speed: 64.273653 samples/sec accuracy=47.764757 loss=2.228450 lr=0.010000 Epoch[003] Batch [1849]/[3760] Speed: 64.041406 samples/sec accuracy=47.786318 loss=2.227188 lr=0.010000 Epoch[003] Batch [1899]/[3760] Speed: 63.871829 samples/sec accuracy=47.807566 loss=2.225518 lr=0.010000 Epoch[003] Batch [1949]/[3760] Speed: 64.573117 samples/sec accuracy=47.804487 loss=2.226135 lr=0.010000 Epoch[003] Batch [1999]/[3760] Speed: 64.166741 samples/sec accuracy=47.820312 loss=2.226098 lr=0.010000 Epoch[003] Batch [2049]/[3760] Speed: 64.556999 samples/sec accuracy=47.843750 loss=2.225387 lr=0.010000 Epoch[003] Batch [2099]/[3760] Speed: 64.277436 samples/sec accuracy=47.883929 loss=2.224715 lr=0.010000 Epoch[003] Batch [2149]/[3760] Speed: 64.186187 samples/sec accuracy=47.866279 loss=2.224182 lr=0.010000 Epoch[003] Batch [2199]/[3760] Speed: 64.181309 samples/sec accuracy=47.840199 loss=2.225350 lr=0.010000 Epoch[003] 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accuracy=47.905093 loss=2.222637 lr=0.010000 Epoch[003] Batch [2749]/[3760] Speed: 64.894896 samples/sec accuracy=47.900000 loss=2.223719 lr=0.010000 Epoch[003] Batch [2799]/[3760] Speed: 64.783590 samples/sec accuracy=47.896763 loss=2.223392 lr=0.010000 Epoch[003] Batch [2849]/[3760] Speed: 64.435712 samples/sec accuracy=47.930373 loss=2.222498 lr=0.010000 Epoch[003] Batch [2899]/[3760] Speed: 63.671958 samples/sec accuracy=47.983836 loss=2.220891 lr=0.010000 Epoch[003] Batch [2949]/[3760] Speed: 64.792807 samples/sec accuracy=48.013242 loss=2.219653 lr=0.010000 Epoch[003] Batch [2999]/[3760] Speed: 63.770100 samples/sec accuracy=48.043750 loss=2.218099 lr=0.010000 Epoch[003] Batch [3049]/[3760] Speed: 64.508069 samples/sec accuracy=48.045082 loss=2.218958 lr=0.010000 Epoch[003] Batch [3099]/[3760] Speed: 64.541834 samples/sec accuracy=48.030746 loss=2.218887 lr=0.010000 Epoch[003] Batch [3149]/[3760] Speed: 63.928794 samples/sec accuracy=48.029762 loss=2.218445 lr=0.010000 Epoch[003] 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accuracy=48.134846 loss=2.214257 lr=0.010000 Epoch[003] Batch [3699]/[3760] Speed: 64.553322 samples/sec accuracy=48.158361 loss=2.212709 lr=0.010000 Epoch[003] Batch [3749]/[3760] Speed: 71.702321 samples/sec accuracy=48.180417 loss=2.211780 lr=0.010000 Batch [0049]/[0303]: acc-top1=51.343750 acc-top5=76.281250 Batch [0099]/[0303]: acc-top1=50.921875 acc-top5=76.703125 Batch [0149]/[0303]: acc-top1=50.937500 acc-top5=76.572917 Batch [0199]/[0303]: acc-top1=50.750000 acc-top5=76.578125 Batch [0249]/[0303]: acc-top1=50.918750 acc-top5=76.625000 Batch [0299]/[0303]: acc-top1=51.130208 acc-top5=76.755208 [Epoch 003] training: accuracy=48.167387 loss=2.212291 [Epoch 003] speed: 64 samples/sec time cost: 4034.683483 [Epoch 003] validation: acc-top1=51.139645 acc-top5=76.799711 loss=2.114047 Epoch[004] Batch [0049]/[3760] Speed: 43.367540 samples/sec accuracy=49.906250 loss=2.152397 lr=0.010000 Epoch[004] Batch [0099]/[3760] Speed: 63.700583 samples/sec accuracy=50.953125 loss=2.077860 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lr=0.010000 Epoch[004] Batch [2999]/[3760] Speed: 65.302377 samples/sec accuracy=50.690625 loss=2.085229 lr=0.010000 Epoch[004] Batch [3049]/[3760] Speed: 63.996958 samples/sec accuracy=50.689037 loss=2.085299 lr=0.010000 Epoch[004] Batch [3099]/[3760] Speed: 64.512361 samples/sec accuracy=50.689516 loss=2.084564 lr=0.010000 Epoch[004] Batch [3149]/[3760] Speed: 64.630632 samples/sec accuracy=50.699405 loss=2.084823 lr=0.010000 Epoch[004] Batch [3199]/[3760] Speed: 64.041588 samples/sec accuracy=50.707520 loss=2.085048 lr=0.010000 Epoch[004] Batch [3249]/[3760] Speed: 64.935616 samples/sec accuracy=50.712500 loss=2.084832 lr=0.010000 Epoch[004] Batch [3299]/[3760] Speed: 64.526383 samples/sec accuracy=50.715909 loss=2.084414 lr=0.010000 Epoch[004] Batch [3349]/[3760] Speed: 63.978634 samples/sec accuracy=50.732743 loss=2.083488 lr=0.010000 Epoch[004] Batch [3399]/[3760] Speed: 64.712208 samples/sec accuracy=50.730239 loss=2.083644 lr=0.010000 Epoch[004] Batch [3449]/[3760] Speed: 64.555357 samples/sec accuracy=50.727808 loss=2.083368 lr=0.010000 Epoch[004] Batch [3499]/[3760] Speed: 64.144488 samples/sec accuracy=50.743304 loss=2.082952 lr=0.010000 Epoch[004] Batch [3549]/[3760] Speed: 65.135293 samples/sec accuracy=50.769366 loss=2.081834 lr=0.010000 Epoch[004] Batch [3599]/[3760] Speed: 64.416471 samples/sec accuracy=50.772135 loss=2.082126 lr=0.010000 Epoch[004] Batch [3649]/[3760] Speed: 65.189240 samples/sec accuracy=50.770548 loss=2.081999 lr=0.010000 Epoch[004] Batch [3699]/[3760] Speed: 64.607080 samples/sec accuracy=50.766047 loss=2.081861 lr=0.010000 Epoch[004] Batch [3749]/[3760] Speed: 71.628565 samples/sec accuracy=50.779583 loss=2.081904 lr=0.010000 Batch [0049]/[0303]: acc-top1=53.375000 acc-top5=78.468750 Batch [0099]/[0303]: acc-top1=52.625000 acc-top5=78.265625 Batch [0149]/[0303]: acc-top1=53.177083 acc-top5=78.041667 Batch [0199]/[0303]: acc-top1=52.992188 acc-top5=77.976562 Batch [0249]/[0303]: acc-top1=52.918750 acc-top5=77.893750 Batch [0299]/[0303]: acc-top1=53.281250 acc-top5=78.130208 [Epoch 004] training: accuracy=50.784990 loss=2.081723 [Epoch 004] speed: 64 samples/sec time cost: 4025.847953 [Epoch 004] validation: acc-top1=53.295173 acc-top5=78.171411 loss=2.031244 Epoch[005] Batch [0049]/[3759] Speed: 43.395075 samples/sec accuracy=52.125000 loss=2.013145 lr=0.010000 Epoch[005] Batch [0099]/[3759] Speed: 63.439273 samples/sec accuracy=51.906250 loss=2.008311 lr=0.010000 Epoch[005] Batch [0149]/[3759] Speed: 64.891560 samples/sec accuracy=51.979167 loss=1.998046 lr=0.010000 Epoch[005] Batch [0199]/[3759] Speed: 64.301572 samples/sec accuracy=51.851562 loss=1.999917 lr=0.010000 Epoch[005] Batch [0249]/[3759] Speed: 64.600653 samples/sec accuracy=51.768750 loss=2.003631 lr=0.010000 Epoch[005] Batch [0299]/[3759] Speed: 65.047396 samples/sec accuracy=51.697917 loss=2.006428 lr=0.010000 Epoch[005] Batch [0349]/[3759] Speed: 64.766800 samples/sec accuracy=51.857143 loss=2.006480 lr=0.010000 Epoch[005] Batch 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accuracy=52.360294 loss=1.982555 lr=0.010000 Epoch[005] Batch [0899]/[3759] Speed: 64.578222 samples/sec accuracy=52.321181 loss=1.982802 lr=0.010000 Epoch[005] Batch [0949]/[3759] Speed: 64.540139 samples/sec accuracy=52.324013 loss=1.982687 lr=0.010000 Epoch[005] Batch [0999]/[3759] Speed: 64.971575 samples/sec accuracy=52.228125 loss=1.986547 lr=0.010000 Epoch[005] Batch [1049]/[3759] Speed: 65.123909 samples/sec accuracy=52.257440 loss=1.988007 lr=0.010000 Epoch[005] Batch [1099]/[3759] Speed: 64.771469 samples/sec accuracy=52.230114 loss=1.989469 lr=0.010000 Epoch[005] Batch [1149]/[3759] Speed: 64.939605 samples/sec accuracy=52.252717 loss=1.986840 lr=0.010000 Epoch[005] Batch [1199]/[3759] Speed: 64.017102 samples/sec accuracy=52.305990 loss=1.985708 lr=0.010000 Epoch[005] Batch [1249]/[3759] Speed: 64.382622 samples/sec accuracy=52.265000 loss=1.987371 lr=0.010000 Epoch[005] Batch [1299]/[3759] Speed: 64.848286 samples/sec accuracy=52.270433 loss=1.987444 lr=0.010000 Epoch[005] 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accuracy=52.457465 loss=1.985850 lr=0.010000 Epoch[005] Batch [1849]/[3759] Speed: 64.642719 samples/sec accuracy=52.429899 loss=1.986564 lr=0.010000 Epoch[005] Batch [1899]/[3759] Speed: 64.754465 samples/sec accuracy=52.443257 loss=1.987180 lr=0.010000 Epoch[005] Batch [1949]/[3759] Speed: 65.026021 samples/sec accuracy=52.497596 loss=1.985664 lr=0.010000 Epoch[005] Batch [1999]/[3759] Speed: 64.760161 samples/sec accuracy=52.475000 loss=1.986342 lr=0.010000 Epoch[005] Batch [2049]/[3759] Speed: 64.890614 samples/sec accuracy=52.490854 loss=1.985667 lr=0.010000 Epoch[005] Batch [2099]/[3759] Speed: 64.884064 samples/sec accuracy=52.523810 loss=1.984287 lr=0.010000 Epoch[005] Batch [2149]/[3759] Speed: 64.315095 samples/sec accuracy=52.537791 loss=1.983103 lr=0.010000 Epoch[005] Batch [2199]/[3759] Speed: 65.264282 samples/sec accuracy=52.532670 loss=1.983235 lr=0.010000 Epoch[005] Batch [2249]/[3759] Speed: 64.439700 samples/sec accuracy=52.530556 loss=1.984487 lr=0.010000 Epoch[005] 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accuracy=52.539205 loss=1.985455 lr=0.010000 Epoch[005] Batch [2799]/[3759] Speed: 64.862538 samples/sec accuracy=52.533482 loss=1.986627 lr=0.010000 Epoch[005] Batch [2849]/[3759] Speed: 64.601676 samples/sec accuracy=52.541667 loss=1.986781 lr=0.010000 Epoch[005] Batch [2899]/[3759] Speed: 64.322860 samples/sec accuracy=52.545797 loss=1.986993 lr=0.010000 Epoch[005] Batch [2949]/[3759] Speed: 64.590927 samples/sec accuracy=52.568326 loss=1.986130 lr=0.010000 Epoch[005] Batch [2999]/[3759] Speed: 64.561268 samples/sec accuracy=52.585417 loss=1.986114 lr=0.010000 Epoch[005] Batch [3049]/[3759] Speed: 64.898045 samples/sec accuracy=52.613730 loss=1.985489 lr=0.010000 Epoch[005] Batch [3099]/[3759] Speed: 64.628605 samples/sec accuracy=52.610383 loss=1.985167 lr=0.010000 Epoch[005] Batch [3149]/[3759] Speed: 64.478851 samples/sec accuracy=52.611607 loss=1.985445 lr=0.010000 Epoch[005] Batch [3199]/[3759] Speed: 64.934122 samples/sec accuracy=52.621582 loss=1.984931 lr=0.010000 Epoch[005] Batch [3249]/[3759] Speed: 64.790606 samples/sec accuracy=52.608173 loss=1.984744 lr=0.010000 Epoch[005] Batch [3299]/[3759] Speed: 64.677904 samples/sec accuracy=52.603220 loss=1.985150 lr=0.010000 Epoch[005] Batch [3349]/[3759] Speed: 65.190677 samples/sec accuracy=52.612873 loss=1.984914 lr=0.010000 Epoch[005] Batch [3399]/[3759] Speed: 64.490329 samples/sec accuracy=52.623621 loss=1.984083 lr=0.010000 Epoch[005] Batch [3449]/[3759] Speed: 65.309254 samples/sec accuracy=52.633152 loss=1.983742 lr=0.010000 Epoch[005] Batch [3499]/[3759] Speed: 64.280439 samples/sec accuracy=52.641964 loss=1.984024 lr=0.010000 Epoch[005] Batch [3549]/[3759] Speed: 64.902388 samples/sec accuracy=52.627201 loss=1.985304 lr=0.010000 Epoch[005] Batch [3599]/[3759] Speed: 64.905656 samples/sec accuracy=52.628038 loss=1.985978 lr=0.010000 Epoch[005] Batch [3649]/[3759] Speed: 64.422302 samples/sec accuracy=52.625000 loss=1.986739 lr=0.010000 Epoch[005] Batch [3699]/[3759] Speed: 64.074801 samples/sec accuracy=52.627111 loss=1.986373 lr=0.010000 Epoch[005] Batch [3749]/[3759] Speed: 72.535649 samples/sec accuracy=52.636250 loss=1.986194 lr=0.010000 Batch [0049]/[0303]: acc-top1=54.250000 acc-top5=79.000000 Batch [0099]/[0303]: acc-top1=54.406250 acc-top5=79.484375 Batch [0149]/[0303]: acc-top1=54.812500 acc-top5=79.354167 Batch [0199]/[0303]: acc-top1=54.781250 acc-top5=79.195312 Batch [0249]/[0303]: acc-top1=54.712500 acc-top5=79.256250 Batch [0299]/[0303]: acc-top1=54.838542 acc-top5=79.380208 [Epoch 005] training: accuracy=52.643655 loss=1.985975 [Epoch 005] speed: 64 samples/sec time cost: 4015.160725 [Epoch 005] validation: acc-top1=54.857673 acc-top5=79.403878 loss=1.950394 Epoch[006] Batch [0049]/[3760] Speed: 43.637884 samples/sec accuracy=54.250000 loss=1.953341 lr=0.010000 Epoch[006] Batch [0099]/[3760] Speed: 64.154763 samples/sec accuracy=54.421875 loss=1.929589 lr=0.010000 Epoch[006] Batch [0149]/[3760] Speed: 64.975239 samples/sec accuracy=54.354167 loss=1.928182 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lr=0.010000 Epoch[006] Batch [2099]/[3760] Speed: 64.895552 samples/sec accuracy=54.314732 loss=1.912829 lr=0.010000 Epoch[006] Batch [2149]/[3760] Speed: 65.087719 samples/sec accuracy=54.332849 loss=1.911037 lr=0.010000 Epoch[006] Batch [2199]/[3760] Speed: 64.437916 samples/sec accuracy=54.316761 loss=1.911096 lr=0.010000 Epoch[006] Batch [2249]/[3760] Speed: 65.274767 samples/sec accuracy=54.286111 loss=1.912853 lr=0.010000 Epoch[006] Batch [2299]/[3760] Speed: 64.454622 samples/sec accuracy=54.254755 loss=1.914166 lr=0.010000 Epoch[006] Batch [2349]/[3760] Speed: 64.835839 samples/sec accuracy=54.222074 loss=1.915431 lr=0.010000 Epoch[006] Batch [2399]/[3760] Speed: 64.513532 samples/sec accuracy=54.239583 loss=1.914272 lr=0.010000 Epoch[006] Batch [2449]/[3760] Speed: 64.517672 samples/sec accuracy=54.205995 loss=1.915860 lr=0.010000 Epoch[006] Batch [2499]/[3760] Speed: 65.030199 samples/sec accuracy=54.182500 loss=1.917200 lr=0.010000 Epoch[006] Batch [2549]/[3760] Speed: 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lr=0.010000 Epoch[006] Batch [3049]/[3760] Speed: 64.701960 samples/sec accuracy=54.170594 loss=1.916163 lr=0.010000 Epoch[006] Batch [3099]/[3760] Speed: 64.682734 samples/sec accuracy=54.189516 loss=1.915064 lr=0.010000 Epoch[006] Batch [3149]/[3760] Speed: 64.648575 samples/sec accuracy=54.205853 loss=1.914637 lr=0.010000 Epoch[006] Batch [3199]/[3760] Speed: 65.357199 samples/sec accuracy=54.193359 loss=1.914964 lr=0.010000 Epoch[006] Batch [3249]/[3760] Speed: 64.704401 samples/sec accuracy=54.180769 loss=1.915521 lr=0.010000 Epoch[006] Batch [3299]/[3760] Speed: 64.638488 samples/sec accuracy=54.188447 loss=1.915331 lr=0.010000 Epoch[006] Batch [3349]/[3760] Speed: 64.898340 samples/sec accuracy=54.196362 loss=1.914500 lr=0.010000 Epoch[006] Batch [3399]/[3760] Speed: 64.762518 samples/sec accuracy=54.181066 loss=1.914512 lr=0.010000 Epoch[006] Batch [3449]/[3760] Speed: 65.605553 samples/sec accuracy=54.142663 loss=1.916216 lr=0.010000 Epoch[006] Batch [3499]/[3760] Speed: 64.263232 samples/sec accuracy=54.149554 loss=1.915725 lr=0.010000 Epoch[006] Batch [3549]/[3760] Speed: 64.924884 samples/sec accuracy=54.166373 loss=1.914769 lr=0.010000 Epoch[006] Batch [3599]/[3760] Speed: 64.526594 samples/sec accuracy=54.172743 loss=1.914664 lr=0.010000 Epoch[006] Batch [3649]/[3760] Speed: 64.714979 samples/sec accuracy=54.181935 loss=1.914256 lr=0.010000 Epoch[006] Batch [3699]/[3760] Speed: 64.826552 samples/sec accuracy=54.169764 loss=1.915190 lr=0.010000 Epoch[006] Batch [3749]/[3760] Speed: 71.869029 samples/sec accuracy=54.179583 loss=1.914621 lr=0.010000 Batch [0049]/[0303]: acc-top1=56.312500 acc-top5=80.218750 Batch [0099]/[0303]: acc-top1=55.359375 acc-top5=80.062500 Batch [0149]/[0303]: acc-top1=55.604167 acc-top5=79.916667 Batch [0199]/[0303]: acc-top1=55.343750 acc-top5=79.976562 Batch [0249]/[0303]: acc-top1=55.356250 acc-top5=80.031250 Batch [0299]/[0303]: acc-top1=55.489583 acc-top5=80.098958 [Epoch 006] training: accuracy=54.175532 loss=1.914668 [Epoch 006] speed: 64 samples/sec time cost: 4007.755798 [Epoch 006] validation: acc-top1=55.512583 acc-top5=80.110355 loss=1.953703 Epoch[007] Batch [0049]/[3760] Speed: 43.756097 samples/sec accuracy=56.375000 loss=1.848420 lr=0.010000 Epoch[007] Batch [0099]/[3760] Speed: 63.855805 samples/sec accuracy=56.640625 loss=1.822425 lr=0.010000 Epoch[007] Batch [0149]/[3760] Speed: 65.204267 samples/sec accuracy=56.218750 loss=1.838643 lr=0.010000 Epoch[007] Batch [0199]/[3760] Speed: 64.109568 samples/sec accuracy=56.156250 loss=1.846877 lr=0.010000 Epoch[007] Batch [0249]/[3760] Speed: 65.232180 samples/sec accuracy=56.243750 loss=1.837912 lr=0.010000 Epoch[007] Batch [0299]/[3760] Speed: 64.570103 samples/sec accuracy=56.192708 loss=1.839638 lr=0.010000 Epoch[007] Batch [0349]/[3760] Speed: 64.323846 samples/sec accuracy=56.138393 loss=1.834669 lr=0.010000 Epoch[007] Batch [0399]/[3760] Speed: 64.948729 samples/sec accuracy=56.093750 loss=1.829675 lr=0.010000 Epoch[007] Batch 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accuracy=55.921875 loss=1.833597 lr=0.010000 Epoch[007] Batch [0949]/[3760] Speed: 65.128240 samples/sec accuracy=55.921053 loss=1.833130 lr=0.010000 Epoch[007] Batch [0999]/[3760] Speed: 64.564021 samples/sec accuracy=55.887500 loss=1.835353 lr=0.010000 Epoch[007] Batch [1049]/[3760] Speed: 65.089554 samples/sec accuracy=55.931548 loss=1.834743 lr=0.010000 Epoch[007] Batch [1099]/[3760] Speed: 64.997353 samples/sec accuracy=55.948864 loss=1.832822 lr=0.010000 Epoch[007] Batch [1149]/[3760] Speed: 64.756136 samples/sec accuracy=55.915761 loss=1.833966 lr=0.010000 Epoch[007] Batch [1199]/[3760] Speed: 65.119983 samples/sec accuracy=55.845052 loss=1.838167 lr=0.010000 Epoch[007] Batch [1249]/[3760] Speed: 65.656308 samples/sec accuracy=55.863750 loss=1.838994 lr=0.010000 Epoch[007] Batch [1299]/[3760] Speed: 64.588452 samples/sec accuracy=55.905048 loss=1.838081 lr=0.010000 Epoch[007] Batch [1349]/[3760] Speed: 65.123938 samples/sec accuracy=55.908565 loss=1.838641 lr=0.010000 Epoch[007] 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accuracy=55.857264 loss=1.840420 lr=0.010000 Epoch[007] Batch [1899]/[3760] Speed: 65.099095 samples/sec accuracy=55.879934 loss=1.839184 lr=0.010000 Epoch[007] Batch [1949]/[3760] Speed: 64.885060 samples/sec accuracy=55.846154 loss=1.840451 lr=0.010000 Epoch[007] Batch [1999]/[3760] Speed: 64.561607 samples/sec accuracy=55.840625 loss=1.840244 lr=0.010000 Epoch[007] Batch [2049]/[3760] Speed: 64.948527 samples/sec accuracy=55.815549 loss=1.840994 lr=0.010000 Epoch[007] Batch [2099]/[3760] Speed: 64.552928 samples/sec accuracy=55.813244 loss=1.841331 lr=0.010000 Epoch[007] Batch [2149]/[3760] Speed: 65.387951 samples/sec accuracy=55.832849 loss=1.840541 lr=0.010000 Epoch[007] Batch [2199]/[3760] Speed: 64.991167 samples/sec accuracy=55.799006 loss=1.842420 lr=0.010000 Epoch[007] Batch [2249]/[3760] Speed: 64.881398 samples/sec accuracy=55.784722 loss=1.843456 lr=0.010000 Epoch[007] Batch [2299]/[3760] Speed: 65.091162 samples/sec accuracy=55.773098 loss=1.843613 lr=0.010000 Epoch[007] 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accuracy=55.657366 loss=1.847963 lr=0.010000 Epoch[007] Batch [2849]/[3760] Speed: 65.458274 samples/sec accuracy=55.665022 loss=1.847352 lr=0.010000 Epoch[007] Batch [2899]/[3760] Speed: 65.223544 samples/sec accuracy=55.664332 loss=1.846987 lr=0.010000 Epoch[007] Batch [2949]/[3760] Speed: 64.509323 samples/sec accuracy=55.659428 loss=1.847136 lr=0.010000 Epoch[007] Batch [2999]/[3760] Speed: 65.197396 samples/sec accuracy=55.621354 loss=1.848015 lr=0.010000 Epoch[007] Batch [3049]/[3760] Speed: 64.555413 samples/sec accuracy=55.612705 loss=1.848381 lr=0.010000 Epoch[007] Batch [3099]/[3760] Speed: 65.316398 samples/sec accuracy=55.602823 loss=1.848467 lr=0.010000 Epoch[007] Batch [3149]/[3760] Speed: 65.484685 samples/sec accuracy=55.593254 loss=1.849052 lr=0.010000 Epoch[007] Batch [3199]/[3760] Speed: 65.290695 samples/sec accuracy=55.606445 loss=1.848760 lr=0.010000 Epoch[007] Batch [3249]/[3760] Speed: 64.501667 samples/sec accuracy=55.618750 loss=1.849074 lr=0.010000 Epoch[007] Batch [3299]/[3760] Speed: 64.997387 samples/sec accuracy=55.597538 loss=1.849878 lr=0.010000 Epoch[007] Batch [3349]/[3760] Speed: 64.850801 samples/sec accuracy=55.591884 loss=1.850267 lr=0.010000 Epoch[007] Batch [3399]/[3760] Speed: 64.968855 samples/sec accuracy=55.589614 loss=1.850127 lr=0.010000 Epoch[007] Batch [3449]/[3760] Speed: 65.371447 samples/sec accuracy=55.615036 loss=1.849302 lr=0.010000 Epoch[007] Batch [3499]/[3760] Speed: 65.521814 samples/sec accuracy=55.636161 loss=1.848767 lr=0.010000 Epoch[007] Batch [3549]/[3760] Speed: 64.655501 samples/sec accuracy=55.627641 loss=1.848805 lr=0.010000 Epoch[007] Batch [3599]/[3760] Speed: 65.470354 samples/sec accuracy=55.630642 loss=1.848702 lr=0.010000 Epoch[007] Batch [3649]/[3760] Speed: 64.807438 samples/sec accuracy=55.636986 loss=1.848612 lr=0.010000 Epoch[007] Batch [3699]/[3760] Speed: 64.886575 samples/sec accuracy=55.611064 loss=1.849309 lr=0.010000 Epoch[007] Batch [3749]/[3760] Speed: 71.514429 samples/sec accuracy=55.615000 loss=1.849186 lr=0.010000 Batch [0049]/[0303]: acc-top1=56.156250 acc-top5=80.937500 Batch [0099]/[0303]: acc-top1=55.437500 acc-top5=80.359375 Batch [0149]/[0303]: acc-top1=56.125000 acc-top5=80.312500 Batch [0199]/[0303]: acc-top1=56.046875 acc-top5=80.242188 Batch [0249]/[0303]: acc-top1=56.081250 acc-top5=80.425000 Batch [0299]/[0303]: acc-top1=56.291667 acc-top5=80.421875 [Epoch 007] training: accuracy=55.615442 loss=1.849252 [Epoch 007] speed: 64 samples/sec time cost: 4001.459982 [Epoch 007] validation: acc-top1=56.280941 acc-top5=80.455858 loss=1.923159 Epoch[008] Batch [0049]/[3759] Speed: 43.403390 samples/sec accuracy=55.562500 loss=1.842068 lr=0.010000 Epoch[008] Batch [0099]/[3759] Speed: 63.499076 samples/sec accuracy=56.312500 loss=1.813869 lr=0.010000 Epoch[008] Batch [0149]/[3759] Speed: 65.165783 samples/sec accuracy=56.447917 loss=1.800980 lr=0.010000 Epoch[008] Batch [0199]/[3759] Speed: 64.421959 samples/sec accuracy=56.750000 loss=1.793846 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lr=0.010000 Epoch[008] Batch [2149]/[3759] Speed: 64.734702 samples/sec accuracy=56.635901 loss=1.793082 lr=0.010000 Epoch[008] Batch [2199]/[3759] Speed: 64.948704 samples/sec accuracy=56.639205 loss=1.793247 lr=0.010000 Epoch[008] Batch [2249]/[3759] Speed: 64.778251 samples/sec accuracy=56.631944 loss=1.793094 lr=0.010000 Epoch[008] Batch [2299]/[3759] Speed: 64.735111 samples/sec accuracy=56.621603 loss=1.793444 lr=0.010000 Epoch[008] Batch [2349]/[3759] Speed: 65.456544 samples/sec accuracy=56.634309 loss=1.793504 lr=0.010000 Epoch[008] Batch [2399]/[3759] Speed: 64.988005 samples/sec accuracy=56.630208 loss=1.793787 lr=0.010000 Epoch[008] Batch [2449]/[3759] Speed: 65.329286 samples/sec accuracy=56.631378 loss=1.794058 lr=0.010000 Epoch[008] Batch [2499]/[3759] Speed: 65.193562 samples/sec accuracy=56.661250 loss=1.793895 lr=0.010000 Epoch[008] Batch [2549]/[3759] Speed: 65.156695 samples/sec accuracy=56.628676 loss=1.794799 lr=0.010000 Epoch[008] Batch [2599]/[3759] Speed: 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lr=0.010000 Epoch[008] Batch [3099]/[3759] Speed: 65.179262 samples/sec accuracy=56.664315 loss=1.793776 lr=0.010000 Epoch[008] Batch [3149]/[3759] Speed: 65.514611 samples/sec accuracy=56.675595 loss=1.793789 lr=0.010000 Epoch[008] Batch [3199]/[3759] Speed: 65.281952 samples/sec accuracy=56.667969 loss=1.794090 lr=0.010000 Epoch[008] Batch [3249]/[3759] Speed: 64.546387 samples/sec accuracy=56.639904 loss=1.794824 lr=0.010000 Epoch[008] Batch [3299]/[3759] Speed: 65.430589 samples/sec accuracy=56.660985 loss=1.794649 lr=0.010000 Epoch[008] Batch [3349]/[3759] Speed: 64.974139 samples/sec accuracy=56.636194 loss=1.795642 lr=0.010000 Epoch[008] Batch [3399]/[3759] Speed: 64.855410 samples/sec accuracy=56.624540 loss=1.795740 lr=0.010000 Epoch[008] Batch [3449]/[3759] Speed: 64.706244 samples/sec accuracy=56.615489 loss=1.796293 lr=0.010000 Epoch[008] Batch [3499]/[3759] Speed: 64.851309 samples/sec accuracy=56.625000 loss=1.795728 lr=0.010000 Epoch[008] Batch [3549]/[3759] Speed: 64.927175 samples/sec accuracy=56.648327 loss=1.795465 lr=0.010000 Epoch[008] Batch [3599]/[3759] Speed: 64.986584 samples/sec accuracy=56.666667 loss=1.794639 lr=0.010000 Epoch[008] Batch [3649]/[3759] Speed: 64.441092 samples/sec accuracy=56.669521 loss=1.794337 lr=0.010000 Epoch[008] Batch [3699]/[3759] Speed: 65.069626 samples/sec accuracy=56.660895 loss=1.795024 lr=0.010000 Epoch[008] Batch [3749]/[3759] Speed: 72.693548 samples/sec accuracy=56.676667 loss=1.793953 lr=0.010000 Batch [0049]/[0303]: acc-top1=57.937500 acc-top5=80.937500 Batch [0099]/[0303]: acc-top1=57.078125 acc-top5=81.125000 Batch [0149]/[0303]: acc-top1=57.208333 acc-top5=80.833333 Batch [0199]/[0303]: acc-top1=57.125000 acc-top5=80.976562 Batch [0249]/[0303]: acc-top1=57.256250 acc-top5=81.100000 Batch [0299]/[0303]: acc-top1=57.505208 acc-top5=81.151042 [Epoch 008] training: accuracy=56.681464 loss=1.793863 [Epoch 008] speed: 64 samples/sec time cost: 3997.632363 [Epoch 008] validation: acc-top1=57.523721 acc-top5=81.162335 loss=1.871694 Epoch[009] Batch [0049]/[3760] Speed: 43.376241 samples/sec accuracy=58.906250 loss=1.658663 lr=0.010000 Epoch[009] Batch [0099]/[3760] Speed: 63.937900 samples/sec accuracy=58.453125 loss=1.692514 lr=0.010000 Epoch[009] Batch [0149]/[3760] Speed: 65.023596 samples/sec accuracy=58.229167 loss=1.709393 lr=0.010000 Epoch[009] Batch [0199]/[3760] Speed: 65.345702 samples/sec accuracy=58.460938 loss=1.701311 lr=0.010000 Epoch[009] Batch [0249]/[3760] Speed: 64.563450 samples/sec accuracy=58.525000 loss=1.697746 lr=0.010000 Epoch[009] Batch [0299]/[3760] Speed: 65.302274 samples/sec accuracy=58.427083 loss=1.699731 lr=0.010000 Epoch[009] Batch [0349]/[3760] Speed: 64.774827 samples/sec accuracy=58.325893 loss=1.704063 lr=0.010000 Epoch[009] Batch [0399]/[3760] Speed: 65.382004 samples/sec accuracy=58.105469 loss=1.707981 lr=0.010000 Epoch[009] Batch [0449]/[3760] Speed: 65.260188 samples/sec accuracy=58.107639 loss=1.714653 lr=0.010000 Epoch[009] Batch 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accuracy=57.738487 loss=1.727682 lr=0.010000 Epoch[009] Batch [0999]/[3760] Speed: 64.727182 samples/sec accuracy=57.789062 loss=1.728975 lr=0.010000 Epoch[009] Batch [1049]/[3760] Speed: 64.985841 samples/sec accuracy=57.824405 loss=1.728442 lr=0.010000 Epoch[009] Batch [1099]/[3760] Speed: 64.916735 samples/sec accuracy=57.813920 loss=1.729783 lr=0.010000 Epoch[009] Batch [1149]/[3760] Speed: 65.202065 samples/sec accuracy=57.762228 loss=1.732589 lr=0.010000 Epoch[009] Batch [1199]/[3760] Speed: 65.064802 samples/sec accuracy=57.734375 loss=1.734368 lr=0.010000 Epoch[009] Batch [1249]/[3760] Speed: 64.933507 samples/sec accuracy=57.730000 loss=1.735037 lr=0.010000 Epoch[009] Batch [1299]/[3760] Speed: 64.596613 samples/sec accuracy=57.757212 loss=1.734257 lr=0.010000 Epoch[009] Batch [1349]/[3760] Speed: 64.600955 samples/sec accuracy=57.736111 loss=1.736185 lr=0.010000 Epoch[009] Batch [1399]/[3760] Speed: 64.794871 samples/sec accuracy=57.683036 loss=1.737817 lr=0.010000 Epoch[009] 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accuracy=57.604441 loss=1.746854 lr=0.010000 Epoch[009] Batch [1949]/[3760] Speed: 65.207577 samples/sec accuracy=57.594551 loss=1.748741 lr=0.010000 Epoch[009] Batch [1999]/[3760] Speed: 64.482006 samples/sec accuracy=57.613281 loss=1.748295 lr=0.010000 Epoch[009] Batch [2049]/[3760] Speed: 65.338650 samples/sec accuracy=57.642530 loss=1.746702 lr=0.010000 Epoch[009] Batch [2099]/[3760] Speed: 64.933933 samples/sec accuracy=57.638393 loss=1.747751 lr=0.010000 Epoch[009] Batch [2149]/[3760] Speed: 64.883639 samples/sec accuracy=57.646802 loss=1.747527 lr=0.010000 Epoch[009] Batch [2199]/[3760] Speed: 65.093580 samples/sec accuracy=57.634943 loss=1.747161 lr=0.010000 Epoch[009] Batch [2249]/[3760] Speed: 64.815542 samples/sec accuracy=57.640278 loss=1.747543 lr=0.010000 Epoch[009] Batch [2299]/[3760] Speed: 64.716863 samples/sec accuracy=57.622283 loss=1.748053 lr=0.010000 Epoch[009] Batch [2349]/[3760] Speed: 65.004974 samples/sec accuracy=57.646941 loss=1.747675 lr=0.010000 Epoch[009] 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accuracy=57.622807 loss=1.750333 lr=0.010000 Epoch[009] Batch [2899]/[3760] Speed: 65.620598 samples/sec accuracy=57.603987 loss=1.750533 lr=0.010000 Epoch[009] Batch [2949]/[3760] Speed: 64.874761 samples/sec accuracy=57.626589 loss=1.749712 lr=0.010000 Epoch[009] Batch [2999]/[3760] Speed: 64.714341 samples/sec accuracy=57.632292 loss=1.749426 lr=0.010000 Epoch[009] Batch [3049]/[3760] Speed: 64.878566 samples/sec accuracy=57.651639 loss=1.749481 lr=0.010000 Epoch[009] Batch [3099]/[3760] Speed: 65.215309 samples/sec accuracy=57.653730 loss=1.749720 lr=0.010000 Epoch[009] Batch [3149]/[3760] Speed: 64.962966 samples/sec accuracy=57.637401 loss=1.750152 lr=0.010000 Epoch[009] Batch [3199]/[3760] Speed: 64.708170 samples/sec accuracy=57.634766 loss=1.750281 lr=0.010000 Epoch[009] Batch [3249]/[3760] Speed: 64.890122 samples/sec accuracy=57.610096 loss=1.751348 lr=0.010000 Epoch[009] Batch [3299]/[3760] Speed: 65.118865 samples/sec accuracy=57.612689 loss=1.751190 lr=0.010000 Epoch[009] 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[0099]/[0303]: acc-top1=57.703125 acc-top5=81.375000 Batch [0149]/[0303]: acc-top1=58.000000 acc-top5=81.364583 Batch [0199]/[0303]: acc-top1=57.820313 acc-top5=81.445312 Batch [0249]/[0303]: acc-top1=57.868750 acc-top5=81.481250 Batch [0299]/[0303]: acc-top1=58.125000 acc-top5=81.614583 [Epoch 009] training: accuracy=57.598072 loss=1.752790 [Epoch 009] speed: 64 samples/sec time cost: 4000.024483 [Epoch 009] validation: acc-top1=58.116749 acc-top5=81.626444 loss=1.858724 Epoch[010] Batch [0049]/[3760] Speed: 43.552184 samples/sec accuracy=60.406250 loss=1.625090 lr=0.010000 Epoch[010] Batch [0099]/[3760] Speed: 63.862845 samples/sec accuracy=60.109375 loss=1.649818 lr=0.010000 Epoch[010] Batch [0149]/[3760] Speed: 64.423277 samples/sec accuracy=59.447917 loss=1.663970 lr=0.010000 Epoch[010] Batch [0199]/[3760] Speed: 64.610675 samples/sec accuracy=58.937500 loss=1.671895 lr=0.010000 Epoch[010] Batch [0249]/[3760] Speed: 65.264616 samples/sec accuracy=59.068750 loss=1.667182 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lr=0.010000 Epoch[010] Batch [2199]/[3760] Speed: 64.859530 samples/sec accuracy=58.658381 loss=1.706458 lr=0.010000 Epoch[010] Batch [2249]/[3760] Speed: 65.056645 samples/sec accuracy=58.652778 loss=1.706498 lr=0.010000 Epoch[010] Batch [2299]/[3760] Speed: 65.201052 samples/sec accuracy=58.603261 loss=1.707864 lr=0.010000 Epoch[010] Batch [2349]/[3760] Speed: 65.177465 samples/sec accuracy=58.583777 loss=1.708978 lr=0.010000 Epoch[010] Batch [2399]/[3760] Speed: 65.326503 samples/sec accuracy=58.585286 loss=1.708908 lr=0.010000 Epoch[010] Batch [2449]/[3760] Speed: 64.609871 samples/sec accuracy=58.559949 loss=1.709371 lr=0.010000 Epoch[010] Batch [2499]/[3760] Speed: 65.161993 samples/sec accuracy=58.572500 loss=1.709173 lr=0.010000 Epoch[010] Batch [2549]/[3760] Speed: 65.268669 samples/sec accuracy=58.547181 loss=1.709469 lr=0.010000 Epoch[010] Batch [2599]/[3760] Speed: 64.758392 samples/sec accuracy=58.522837 loss=1.709889 lr=0.010000 Epoch[010] Batch [2649]/[3760] Speed: 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lr=0.010000 Epoch[010] Batch [3149]/[3760] Speed: 65.039145 samples/sec accuracy=58.579365 loss=1.708327 lr=0.010000 Epoch[010] Batch [3199]/[3760] Speed: 65.169898 samples/sec accuracy=58.566895 loss=1.709074 lr=0.010000 Epoch[010] Batch [3249]/[3760] Speed: 64.717673 samples/sec accuracy=58.556731 loss=1.709328 lr=0.010000 Epoch[010] Batch [3299]/[3760] Speed: 65.147221 samples/sec accuracy=58.542140 loss=1.709706 lr=0.010000 Epoch[010] Batch [3349]/[3760] Speed: 64.965799 samples/sec accuracy=58.540112 loss=1.709953 lr=0.010000 Epoch[010] Batch [3399]/[3760] Speed: 65.006787 samples/sec accuracy=58.514706 loss=1.710814 lr=0.010000 Epoch[010] Batch [3449]/[3760] Speed: 65.058993 samples/sec accuracy=58.494565 loss=1.711146 lr=0.010000 Epoch[010] Batch [3499]/[3760] Speed: 64.803394 samples/sec accuracy=58.489732 loss=1.711276 lr=0.010000 Epoch[010] Batch [3549]/[3760] Speed: 64.784392 samples/sec accuracy=58.477113 loss=1.711828 lr=0.010000 Epoch[010] Batch [3599]/[3760] Speed: 65.025453 samples/sec accuracy=58.465712 loss=1.712867 lr=0.010000 Epoch[010] Batch [3649]/[3760] Speed: 64.634973 samples/sec accuracy=58.481592 loss=1.712502 lr=0.010000 Epoch[010] Batch [3699]/[3760] Speed: 65.558559 samples/sec accuracy=58.486909 loss=1.712348 lr=0.010000 Epoch[010] Batch [3749]/[3760] Speed: 72.296604 samples/sec accuracy=58.478333 loss=1.712245 lr=0.010000 Batch [0049]/[0303]: acc-top1=57.375000 acc-top5=80.375000 Batch [0099]/[0303]: acc-top1=57.171875 acc-top5=80.656250 Batch [0149]/[0303]: acc-top1=57.354167 acc-top5=80.666667 Batch [0199]/[0303]: acc-top1=57.289062 acc-top5=80.835938 Batch [0249]/[0303]: acc-top1=57.106250 acc-top5=80.937500 Batch [0299]/[0303]: acc-top1=57.401042 acc-top5=81.046875 [Epoch 010] training: accuracy=58.479887 loss=1.712077 [Epoch 010] speed: 64 samples/sec time cost: 3995.442648 [Epoch 010] validation: acc-top1=57.379332 acc-top5=81.059200 loss=1.914449 Epoch[011] Batch [0049]/[3759] Speed: 44.382404 samples/sec accuracy=59.593750 loss=1.678591 lr=0.010000 Epoch[011] Batch [0099]/[3759] Speed: 63.641155 samples/sec accuracy=59.000000 loss=1.672032 lr=0.010000 Epoch[011] Batch [0149]/[3759] Speed: 65.179515 samples/sec accuracy=59.468750 loss=1.654567 lr=0.010000 Epoch[011] Batch [0199]/[3759] Speed: 64.818681 samples/sec accuracy=59.601563 loss=1.655228 lr=0.010000 Epoch[011] Batch [0249]/[3759] Speed: 65.238004 samples/sec accuracy=59.462500 loss=1.667589 lr=0.010000 Epoch[011] Batch [0299]/[3759] Speed: 65.203076 samples/sec accuracy=59.552083 loss=1.659221 lr=0.010000 Epoch[011] Batch [0349]/[3759] Speed: 64.312931 samples/sec accuracy=59.580357 loss=1.661416 lr=0.010000 Epoch[011] Batch [0399]/[3759] Speed: 65.256106 samples/sec accuracy=59.503906 loss=1.661374 lr=0.010000 Epoch[011] Batch [0449]/[3759] Speed: 65.258916 samples/sec accuracy=59.420139 loss=1.666398 lr=0.010000 Epoch[011] Batch [0499]/[3759] Speed: 65.200485 samples/sec accuracy=59.362500 loss=1.664509 lr=0.010000 Epoch[011] 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accuracy=59.226293 loss=1.674916 lr=0.010000 Epoch[011] Batch [2949]/[3759] Speed: 64.796806 samples/sec accuracy=59.227225 loss=1.674687 lr=0.010000 Epoch[011] Batch [2999]/[3759] Speed: 65.054285 samples/sec accuracy=59.236979 loss=1.674476 lr=0.010000 Epoch[011] Batch [3049]/[3759] Speed: 65.033027 samples/sec accuracy=59.237193 loss=1.674986 lr=0.010000 Epoch[011] Batch [3099]/[3759] Speed: 64.830977 samples/sec accuracy=59.213710 loss=1.675770 lr=0.010000 Epoch[011] Batch [3149]/[3759] Speed: 65.445532 samples/sec accuracy=59.223214 loss=1.675298 lr=0.010000 Epoch[011] Batch [3199]/[3759] Speed: 64.905851 samples/sec accuracy=59.204102 loss=1.676254 lr=0.010000 Epoch[011] Batch [3249]/[3759] Speed: 64.827952 samples/sec accuracy=59.210577 loss=1.676068 lr=0.010000 Epoch[011] Batch [3299]/[3759] Speed: 65.272474 samples/sec accuracy=59.199811 loss=1.676272 lr=0.010000 Epoch[011] Batch [3349]/[3759] Speed: 64.959932 samples/sec accuracy=59.197761 loss=1.676759 lr=0.010000 Epoch[011] 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acc-top5=81.979167 Batch [0199]/[0303]: acc-top1=58.851562 acc-top5=81.953125 Batch [0249]/[0303]: acc-top1=58.906250 acc-top5=81.862500 Batch [0299]/[0303]: acc-top1=59.041667 acc-top5=82.072917 [Epoch 011] training: accuracy=59.134743 loss=1.679144 [Epoch 011] speed: 64 samples/sec time cost: 3993.761114 [Epoch 011] validation: acc-top1=59.055281 acc-top5=82.095710 loss=1.826499 Epoch[012] Batch [0049]/[3760] Speed: 43.895257 samples/sec accuracy=60.000000 loss=1.608773 lr=0.010000 Epoch[012] Batch [0099]/[3760] Speed: 63.721156 samples/sec accuracy=59.296875 loss=1.600652 lr=0.010000 Epoch[012] Batch [0149]/[3760] Speed: 65.298669 samples/sec accuracy=59.656250 loss=1.597312 lr=0.010000 Epoch[012] Batch [0199]/[3760] Speed: 64.494677 samples/sec accuracy=59.695312 loss=1.613627 lr=0.010000 Epoch[012] Batch [0249]/[3760] Speed: 65.246440 samples/sec accuracy=59.725000 loss=1.618599 lr=0.010000 Epoch[012] Batch [0299]/[3760] Speed: 65.101487 samples/sec accuracy=59.968750 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lr=0.010000 Epoch[013] Batch [3449]/[3760] Speed: 64.843801 samples/sec accuracy=60.596920 loss=1.618446 lr=0.010000 Epoch[013] Batch [3499]/[3760] Speed: 65.041471 samples/sec accuracy=60.612500 loss=1.617833 lr=0.010000 Epoch[013] Batch [3549]/[3760] Speed: 65.190912 samples/sec accuracy=60.626761 loss=1.617375 lr=0.010000 Epoch[013] Batch [3599]/[3760] Speed: 65.462442 samples/sec accuracy=60.629774 loss=1.617340 lr=0.010000 Epoch[013] Batch [3649]/[3760] Speed: 64.601704 samples/sec accuracy=60.622860 loss=1.617690 lr=0.010000 Epoch[013] Batch [3699]/[3760] Speed: 64.558214 samples/sec accuracy=60.612753 loss=1.617833 lr=0.010000 Epoch[013] Batch [3749]/[3760] Speed: 71.782540 samples/sec accuracy=60.611667 loss=1.618599 lr=0.010000 Batch [0049]/[0303]: acc-top1=59.562500 acc-top5=82.500000 Batch [0099]/[0303]: acc-top1=59.187500 acc-top5=82.593750 Batch [0149]/[0303]: acc-top1=59.458333 acc-top5=82.291667 Batch [0199]/[0303]: acc-top1=59.414062 acc-top5=82.289062 Batch [0249]/[0303]: acc-top1=59.587500 acc-top5=82.550000 Batch [0299]/[0303]: acc-top1=59.796875 acc-top5=82.593750 [Epoch 013] training: accuracy=60.607962 loss=1.618677 [Epoch 013] speed: 64 samples/sec time cost: 3992.953745 [Epoch 013] validation: acc-top1=59.787541 acc-top5=82.580446 loss=1.783361 Epoch[014] Batch [0049]/[3759] Speed: 43.903983 samples/sec accuracy=64.031250 loss=1.486976 lr=0.010000 Epoch[014] Batch [0099]/[3759] Speed: 63.858457 samples/sec accuracy=62.906250 loss=1.510786 lr=0.010000 Epoch[014] Batch [0149]/[3759] Speed: 65.060738 samples/sec accuracy=62.864583 loss=1.517518 lr=0.010000 Epoch[014] Batch [0199]/[3759] Speed: 64.694887 samples/sec accuracy=62.484375 loss=1.533772 lr=0.010000 Epoch[014] Batch [0249]/[3759] Speed: 64.930382 samples/sec accuracy=62.287500 loss=1.543330 lr=0.010000 Epoch[014] Batch [0299]/[3759] Speed: 64.912963 samples/sec accuracy=62.015625 loss=1.553517 lr=0.010000 Epoch[014] Batch [0349]/[3759] Speed: 65.259678 samples/sec 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accuracy=61.358173 loss=1.574667 lr=0.010000 Epoch[014] Batch [1349]/[3759] Speed: 65.309732 samples/sec accuracy=61.325231 loss=1.576160 lr=0.010000 Epoch[014] Batch [1399]/[3759] Speed: 65.148269 samples/sec accuracy=61.281250 loss=1.576296 lr=0.010000 Epoch[014] Batch [1449]/[3759] Speed: 65.007032 samples/sec accuracy=61.266164 loss=1.577788 lr=0.010000 Epoch[014] Batch [1499]/[3759] Speed: 65.185320 samples/sec accuracy=61.222917 loss=1.580793 lr=0.010000 Epoch[014] Batch [1549]/[3759] Speed: 64.692681 samples/sec accuracy=61.225806 loss=1.581271 lr=0.010000 Epoch[014] Batch [1599]/[3759] Speed: 64.889504 samples/sec accuracy=61.224609 loss=1.581870 lr=0.010000 Epoch[014] Batch [1649]/[3759] Speed: 65.167845 samples/sec accuracy=61.202652 loss=1.583014 lr=0.010000 Epoch[014] Batch [1699]/[3759] Speed: 64.925795 samples/sec accuracy=61.184743 loss=1.583717 lr=0.010000 Epoch[014] Batch [1749]/[3759] Speed: 64.946182 samples/sec accuracy=61.155357 loss=1.584742 lr=0.010000 Epoch[014] 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accuracy=61.086806 loss=1.591046 lr=0.010000 Epoch[014] Batch [2299]/[3759] Speed: 65.623995 samples/sec accuracy=61.093750 loss=1.591254 lr=0.010000 Epoch[014] Batch [2349]/[3759] Speed: 65.308145 samples/sec accuracy=61.082447 loss=1.591436 lr=0.010000 Epoch[014] Batch [2399]/[3759] Speed: 64.848682 samples/sec accuracy=61.058594 loss=1.591773 lr=0.010000 Epoch[014] Batch [2449]/[3759] Speed: 65.243019 samples/sec accuracy=61.031888 loss=1.592121 lr=0.010000 Epoch[014] Batch [2499]/[3759] Speed: 64.673109 samples/sec accuracy=61.027500 loss=1.592882 lr=0.010000 Epoch[014] Batch [2549]/[3759] Speed: 65.148427 samples/sec accuracy=61.009804 loss=1.594040 lr=0.010000 Epoch[014] Batch [2599]/[3759] Speed: 64.669292 samples/sec accuracy=61.001202 loss=1.594479 lr=0.010000 Epoch[014] Batch [2649]/[3759] Speed: 65.576738 samples/sec accuracy=61.003538 loss=1.594008 lr=0.010000 Epoch[014] Batch [2699]/[3759] Speed: 64.690854 samples/sec accuracy=60.989005 loss=1.594392 lr=0.010000 Epoch[014] 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accuracy=60.931641 loss=1.597042 lr=0.010000 Epoch[014] Batch [3249]/[3759] Speed: 65.137320 samples/sec accuracy=60.931250 loss=1.597324 lr=0.010000 Epoch[014] Batch [3299]/[3759] Speed: 65.067062 samples/sec accuracy=60.952178 loss=1.597570 lr=0.010000 Epoch[014] Batch [3349]/[3759] Speed: 65.283948 samples/sec accuracy=60.934235 loss=1.597861 lr=0.010000 Epoch[014] Batch [3399]/[3759] Speed: 64.797469 samples/sec accuracy=60.940257 loss=1.597066 lr=0.010000 Epoch[014] Batch [3449]/[3759] Speed: 64.982074 samples/sec accuracy=60.910326 loss=1.597823 lr=0.010000 Epoch[014] Batch [3499]/[3759] Speed: 64.846723 samples/sec accuracy=60.913839 loss=1.597323 lr=0.010000 Epoch[014] Batch [3549]/[3759] Speed: 65.377889 samples/sec accuracy=60.901849 loss=1.597824 lr=0.010000 Epoch[014] Batch [3599]/[3759] Speed: 64.669882 samples/sec accuracy=60.922743 loss=1.597145 lr=0.010000 Epoch[014] Batch [3649]/[3759] Speed: 65.140134 samples/sec accuracy=60.910531 loss=1.597552 lr=0.010000 Epoch[014] Batch [3699]/[3759] Speed: 65.175345 samples/sec accuracy=60.896959 loss=1.598657 lr=0.010000 Epoch[014] Batch [3749]/[3759] Speed: 73.092477 samples/sec accuracy=60.886250 loss=1.598862 lr=0.010000 Batch [0049]/[0303]: acc-top1=60.656250 acc-top5=83.218750 Batch [0099]/[0303]: acc-top1=60.812500 acc-top5=83.187500 Batch [0149]/[0303]: acc-top1=61.083333 acc-top5=82.843750 Batch [0199]/[0303]: acc-top1=60.726562 acc-top5=82.984375 Batch [0249]/[0303]: acc-top1=60.550000 acc-top5=83.037500 Batch [0299]/[0303]: acc-top1=60.651042 acc-top5=83.109375 [Epoch 014] training: accuracy=60.882632 loss=1.599194 [Epoch 014] speed: 64 samples/sec time cost: 3989.825940 [Epoch 014] validation: acc-top1=60.633251 acc-top5=83.111592 loss=1.745025 Epoch[015] Batch [0049]/[3760] Speed: 43.415832 samples/sec accuracy=62.250000 loss=1.518965 lr=0.010000 Epoch[015] Batch [0099]/[3760] Speed: 63.291232 samples/sec accuracy=62.421875 loss=1.523958 lr=0.010000 Epoch[015] Batch [0149]/[3760] Speed: 65.302650 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lr=0.010000 Epoch[015] Batch [2549]/[3760] Speed: 65.197087 samples/sec accuracy=61.549632 loss=1.567531 lr=0.010000 Epoch[015] Batch [2599]/[3760] Speed: 64.841884 samples/sec accuracy=61.515625 loss=1.567988 lr=0.010000 Epoch[015] Batch [2649]/[3760] Speed: 65.132302 samples/sec accuracy=61.513561 loss=1.569166 lr=0.010000 Epoch[015] Batch [2699]/[3760] Speed: 65.551105 samples/sec accuracy=61.517940 loss=1.568638 lr=0.010000 Epoch[015] Batch [2749]/[3760] Speed: 64.907911 samples/sec accuracy=61.522159 loss=1.568443 lr=0.010000 Epoch[015] Batch [2799]/[3760] Speed: 65.384919 samples/sec accuracy=61.507254 loss=1.568501 lr=0.010000 Epoch[015] Batch [2849]/[3760] Speed: 65.165589 samples/sec accuracy=61.523026 loss=1.568568 lr=0.010000 Epoch[015] Batch [2899]/[3760] Speed: 64.612912 samples/sec accuracy=61.507543 loss=1.569328 lr=0.010000 Epoch[015] Batch [2949]/[3760] Speed: 65.823039 samples/sec accuracy=61.531250 loss=1.568624 lr=0.010000 Epoch[015] Batch [2999]/[3760] Speed: 64.472739 samples/sec accuracy=61.523958 loss=1.569259 lr=0.010000 Epoch[015] Batch [3049]/[3760] Speed: 65.561730 samples/sec accuracy=61.537910 loss=1.568941 lr=0.010000 Epoch[015] Batch [3099]/[3760] Speed: 64.780575 samples/sec accuracy=61.543851 loss=1.568857 lr=0.010000 Epoch[015] Batch [3149]/[3760] Speed: 64.760734 samples/sec accuracy=61.530258 loss=1.569646 lr=0.010000 Epoch[015] Batch [3199]/[3760] Speed: 65.295778 samples/sec accuracy=61.527832 loss=1.570933 lr=0.010000 Epoch[015] Batch [3249]/[3760] Speed: 64.988305 samples/sec accuracy=61.538462 loss=1.570599 lr=0.010000 Epoch[015] Batch [3299]/[3760] Speed: 64.938149 samples/sec accuracy=61.529356 loss=1.570755 lr=0.010000 Epoch[015] Batch [3349]/[3760] Speed: 64.673873 samples/sec accuracy=61.509795 loss=1.571505 lr=0.010000 Epoch[015] Batch [3399]/[3760] Speed: 65.295751 samples/sec accuracy=61.500919 loss=1.571859 lr=0.010000 Epoch[015] Batch [3449]/[3760] Speed: 65.178138 samples/sec accuracy=61.500906 loss=1.571246 lr=0.010000 Epoch[015] Batch [3499]/[3760] Speed: 64.226376 samples/sec accuracy=61.507143 loss=1.572082 lr=0.010000 Epoch[015] Batch [3549]/[3760] Speed: 65.351712 samples/sec accuracy=61.489877 loss=1.572683 lr=0.010000 Epoch[015] Batch [3599]/[3760] Speed: 65.126257 samples/sec accuracy=61.474392 loss=1.573065 lr=0.010000 Epoch[015] Batch [3649]/[3760] Speed: 65.657796 samples/sec accuracy=61.456764 loss=1.574018 lr=0.010000 Epoch[015] Batch [3699]/[3760] Speed: 65.131534 samples/sec accuracy=61.452280 loss=1.574551 lr=0.010000 Epoch[015] Batch [3749]/[3760] Speed: 71.696778 samples/sec accuracy=61.467917 loss=1.574280 lr=0.010000 Batch [0049]/[0303]: acc-top1=59.781250 acc-top5=82.500000 Batch [0099]/[0303]: acc-top1=59.609375 acc-top5=82.781250 Batch [0149]/[0303]: acc-top1=59.864583 acc-top5=82.718750 Batch [0199]/[0303]: acc-top1=59.687500 acc-top5=82.843750 Batch [0249]/[0303]: acc-top1=59.775000 acc-top5=82.956250 Batch [0299]/[0303]: acc-top1=59.901042 acc-top5=83.041667 [Epoch 015] training: accuracy=61.464844 loss=1.574118 [Epoch 015] speed: 64 samples/sec time cost: 3995.260007 [Epoch 015] validation: acc-top1=59.947401 acc-top5=83.070338 loss=1.781082 Epoch[016] Batch [0049]/[3760] Speed: 43.312894 samples/sec accuracy=62.625000 loss=1.531186 lr=0.010000 Epoch[016] Batch [0099]/[3760] Speed: 64.357559 samples/sec accuracy=63.093750 loss=1.505556 lr=0.010000 Epoch[016] Batch [0149]/[3760] Speed: 65.145547 samples/sec accuracy=62.979167 loss=1.509667 lr=0.010000 Epoch[016] Batch [0199]/[3760] Speed: 64.361811 samples/sec accuracy=62.859375 loss=1.523060 lr=0.010000 Epoch[016] Batch [0249]/[3760] Speed: 65.471568 samples/sec accuracy=62.768750 loss=1.531128 lr=0.010000 Epoch[016] Batch [0299]/[3760] Speed: 65.518883 samples/sec accuracy=62.760417 loss=1.532232 lr=0.010000 Epoch[016] Batch [0349]/[3760] Speed: 64.394002 samples/sec accuracy=62.687500 loss=1.533512 lr=0.010000 Epoch[016] Batch [0399]/[3760] Speed: 64.960184 samples/sec accuracy=62.742188 loss=1.529446 lr=0.010000 Epoch[016] Batch [0449]/[3760] Speed: 65.526468 samples/sec accuracy=62.638889 loss=1.529990 lr=0.010000 Epoch[016] Batch [0499]/[3760] Speed: 64.293093 samples/sec accuracy=62.656250 loss=1.531536 lr=0.010000 Epoch[016] Batch [0549]/[3760] Speed: 65.358500 samples/sec accuracy=62.559659 loss=1.534337 lr=0.010000 Epoch[016] Batch [0599]/[3760] Speed: 65.628075 samples/sec accuracy=62.460938 loss=1.535608 lr=0.010000 Epoch[016] Batch [0649]/[3760] Speed: 64.822806 samples/sec accuracy=62.473558 loss=1.533238 lr=0.010000 Epoch[016] Batch [0699]/[3760] Speed: 65.444432 samples/sec accuracy=62.386161 loss=1.536856 lr=0.010000 Epoch[016] Batch [0749]/[3760] Speed: 64.605610 samples/sec accuracy=62.354167 loss=1.540370 lr=0.010000 Epoch[016] Batch [0799]/[3760] Speed: 64.947437 samples/sec accuracy=62.302734 loss=1.543819 lr=0.010000 Epoch[016] Batch [0849]/[3760] Speed: 64.767856 samples/sec accuracy=62.295956 loss=1.541259 lr=0.010000 Epoch[016] 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accuracy=62.239583 loss=1.544789 lr=0.010000 Epoch[016] Batch [1399]/[3760] Speed: 65.567995 samples/sec accuracy=62.237723 loss=1.544994 lr=0.010000 Epoch[016] Batch [1449]/[3760] Speed: 64.876602 samples/sec accuracy=62.257543 loss=1.545065 lr=0.010000 Epoch[016] Batch [1499]/[3760] Speed: 65.286241 samples/sec accuracy=62.269792 loss=1.544531 lr=0.010000 Epoch[016] Batch [1549]/[3760] Speed: 64.943764 samples/sec accuracy=62.232863 loss=1.545225 lr=0.010000 Epoch[016] Batch [1599]/[3760] Speed: 65.158047 samples/sec accuracy=62.233398 loss=1.545885 lr=0.010000 Epoch[016] Batch [1649]/[3760] Speed: 65.490890 samples/sec accuracy=62.245265 loss=1.545472 lr=0.010000 Epoch[016] Batch [1699]/[3760] Speed: 64.929578 samples/sec accuracy=62.251838 loss=1.545237 lr=0.010000 Epoch[016] Batch [1749]/[3760] Speed: 65.502145 samples/sec accuracy=62.237500 loss=1.545293 lr=0.010000 Epoch[016] Batch [1799]/[3760] Speed: 65.330158 samples/sec accuracy=62.177951 loss=1.547064 lr=0.010000 Epoch[016] Batch [1849]/[3760] Speed: 64.673110 samples/sec accuracy=62.173142 loss=1.547920 lr=0.010000 Epoch[016] Batch [1899]/[3760] Speed: 64.758150 samples/sec accuracy=62.182566 loss=1.549219 lr=0.010000 Epoch[016] Batch [1949]/[3760] Speed: 65.186569 samples/sec accuracy=62.179487 loss=1.549598 lr=0.010000 Epoch[016] Batch [1999]/[3760] Speed: 65.273888 samples/sec accuracy=62.179688 loss=1.549607 lr=0.010000 Epoch[016] Batch [2049]/[3760] Speed: 65.030328 samples/sec accuracy=62.182165 loss=1.548771 lr=0.010000 Epoch[016] Batch [2099]/[3760] Speed: 64.904872 samples/sec accuracy=62.169643 loss=1.547804 lr=0.010000 Epoch[016] Batch [2149]/[3760] Speed: 64.924764 samples/sec accuracy=62.162064 loss=1.547453 lr=0.010000 Epoch[016] Batch [2199]/[3760] Speed: 65.022133 samples/sec accuracy=62.128551 loss=1.547913 lr=0.010000 Epoch[016] Batch [2249]/[3760] Speed: 65.325060 samples/sec accuracy=62.120833 loss=1.548796 lr=0.010000 Epoch[016] Batch [2299]/[3760] Speed: 64.331387 samples/sec accuracy=62.116168 loss=1.548988 lr=0.010000 Epoch[016] Batch [2349]/[3760] Speed: 65.068791 samples/sec accuracy=62.132979 loss=1.548316 lr=0.010000 Epoch[016] Batch [2399]/[3760] Speed: 64.991984 samples/sec accuracy=62.137370 loss=1.547953 lr=0.010000 Epoch[016] Batch [2449]/[3760] Speed: 65.468056 samples/sec accuracy=62.113520 loss=1.548422 lr=0.010000 Epoch[016] Batch [2499]/[3760] Speed: 65.076900 samples/sec accuracy=62.118125 loss=1.548322 lr=0.010000 Epoch[016] Batch [2549]/[3760] Speed: 65.678536 samples/sec accuracy=62.095588 loss=1.549418 lr=0.010000 Epoch[016] Batch [2599]/[3760] Speed: 64.848082 samples/sec accuracy=62.082332 loss=1.549750 lr=0.010000 Epoch[016] Batch [2649]/[3760] Speed: 65.101971 samples/sec accuracy=62.068396 loss=1.549888 lr=0.010000 Epoch[016] Batch [2699]/[3760] Speed: 65.270644 samples/sec accuracy=62.068287 loss=1.550361 lr=0.010000 Epoch[016] Batch [2749]/[3760] Speed: 65.028460 samples/sec accuracy=62.061932 loss=1.550745 lr=0.010000 Epoch[016] 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accuracy=61.962500 loss=1.553249 lr=0.010000 Epoch[016] Batch [3299]/[3760] Speed: 65.197824 samples/sec accuracy=61.969697 loss=1.553257 lr=0.010000 Epoch[016] Batch [3349]/[3760] Speed: 64.738221 samples/sec accuracy=61.978545 loss=1.553257 lr=0.010000 Epoch[016] Batch [3399]/[3760] Speed: 64.954006 samples/sec accuracy=61.978860 loss=1.553578 lr=0.010000 Epoch[016] Batch [3449]/[3760] Speed: 64.599673 samples/sec accuracy=61.931159 loss=1.555147 lr=0.010000 Epoch[016] Batch [3499]/[3760] Speed: 64.973605 samples/sec accuracy=61.937946 loss=1.555252 lr=0.010000 Epoch[016] Batch [3549]/[3760] Speed: 65.900155 samples/sec accuracy=61.938380 loss=1.554941 lr=0.010000 Epoch[016] Batch [3599]/[3760] Speed: 65.325106 samples/sec accuracy=61.939670 loss=1.555111 lr=0.010000 Epoch[016] Batch [3649]/[3760] Speed: 64.518829 samples/sec accuracy=61.935788 loss=1.555126 lr=0.010000 Epoch[016] Batch [3699]/[3760] Speed: 65.102994 samples/sec accuracy=61.930743 loss=1.555935 lr=0.010000 Epoch[016] Batch [3749]/[3760] Speed: 72.260650 samples/sec accuracy=61.926667 loss=1.556126 lr=0.010000 Batch [0049]/[0303]: acc-top1=59.218750 acc-top5=82.437500 Batch [0099]/[0303]: acc-top1=59.671875 acc-top5=82.640625 Batch [0149]/[0303]: acc-top1=60.093750 acc-top5=82.489583 Batch [0199]/[0303]: acc-top1=59.757812 acc-top5=82.421875 Batch [0249]/[0303]: acc-top1=59.781250 acc-top5=82.556250 Batch [0299]/[0303]: acc-top1=59.906250 acc-top5=82.703125 [Epoch 016] training: accuracy=61.924451 loss=1.556505 [Epoch 016] speed: 64 samples/sec time cost: 3990.349752 [Epoch 016] validation: acc-top1=59.890677 acc-top5=82.750619 loss=1.810538 Epoch[017] Batch [0049]/[3759] Speed: 43.543394 samples/sec accuracy=62.312500 loss=1.514265 lr=0.010000 Epoch[017] Batch [0099]/[3759] Speed: 63.646221 samples/sec accuracy=62.546875 loss=1.509509 lr=0.010000 Epoch[017] Batch [0149]/[3759] Speed: 65.454215 samples/sec accuracy=62.822917 loss=1.492431 lr=0.010000 Epoch[017] Batch [0199]/[3759] Speed: 64.808635 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lr=0.010000 Epoch[017] Batch [0699]/[3759] Speed: 65.264044 samples/sec accuracy=62.189732 loss=1.525948 lr=0.010000 Epoch[017] Batch [0749]/[3759] Speed: 65.204073 samples/sec accuracy=62.233333 loss=1.524121 lr=0.010000 Epoch[017] Batch [0799]/[3759] Speed: 64.731964 samples/sec accuracy=62.201172 loss=1.526785 lr=0.010000 Epoch[017] Batch [0849]/[3759] Speed: 65.311469 samples/sec accuracy=62.270221 loss=1.524679 lr=0.010000 Epoch[017] Batch [0899]/[3759] Speed: 65.732749 samples/sec accuracy=62.298611 loss=1.524697 lr=0.010000 Epoch[017] Batch [0949]/[3759] Speed: 64.933661 samples/sec accuracy=62.328947 loss=1.522459 lr=0.010000 Epoch[017] Batch [0999]/[3759] Speed: 65.010363 samples/sec accuracy=62.334375 loss=1.521967 lr=0.010000 Epoch[017] Batch [1049]/[3759] Speed: 64.951147 samples/sec accuracy=62.306548 loss=1.522798 lr=0.010000 Epoch[017] Batch [1099]/[3759] Speed: 65.092894 samples/sec accuracy=62.282670 loss=1.524984 lr=0.010000 Epoch[017] Batch [1149]/[3759] Speed: 65.096082 samples/sec accuracy=62.289402 loss=1.524307 lr=0.010000 Epoch[017] Batch [1199]/[3759] Speed: 64.852817 samples/sec accuracy=62.290365 loss=1.524632 lr=0.010000 Epoch[017] Batch [1249]/[3759] Speed: 64.645174 samples/sec accuracy=62.342500 loss=1.524876 lr=0.010000 Epoch[017] Batch [1299]/[3759] Speed: 65.335279 samples/sec accuracy=62.314904 loss=1.525909 lr=0.010000 Epoch[017] Batch [1349]/[3759] Speed: 65.603717 samples/sec accuracy=62.284722 loss=1.527820 lr=0.010000 Epoch[017] Batch [1399]/[3759] Speed: 64.342391 samples/sec accuracy=62.247768 loss=1.529529 lr=0.010000 Epoch[017] Batch [1449]/[3759] Speed: 65.377738 samples/sec accuracy=62.258621 loss=1.530023 lr=0.010000 Epoch[017] Batch [1499]/[3759] Speed: 64.946396 samples/sec accuracy=62.342708 loss=1.527257 lr=0.010000 Epoch[017] Batch [1549]/[3759] Speed: 65.990586 samples/sec accuracy=62.326613 loss=1.529257 lr=0.010000 Epoch[017] Batch [1599]/[3759] Speed: 64.724058 samples/sec accuracy=62.302734 loss=1.530969 lr=0.010000 Epoch[017] Batch [1649]/[3759] Speed: 65.168420 samples/sec accuracy=62.327652 loss=1.530418 lr=0.010000 Epoch[017] Batch [1699]/[3759] Speed: 65.338712 samples/sec accuracy=62.340993 loss=1.529822 lr=0.010000 Epoch[017] Batch [1749]/[3759] Speed: 65.480497 samples/sec accuracy=62.330357 loss=1.530185 lr=0.010000 Epoch[017] Batch [1799]/[3759] Speed: 64.710065 samples/sec accuracy=62.351562 loss=1.529136 lr=0.010000 Epoch[017] Batch [1849]/[3759] Speed: 65.011251 samples/sec accuracy=62.345439 loss=1.529493 lr=0.010000 Epoch[017] Batch [1899]/[3759] Speed: 65.263325 samples/sec accuracy=62.342928 loss=1.528663 lr=0.010000 Epoch[017] Batch [1949]/[3759] Speed: 64.929207 samples/sec accuracy=62.311699 loss=1.529664 lr=0.010000 Epoch[017] Batch [1999]/[3759] Speed: 65.395336 samples/sec accuracy=62.316406 loss=1.529174 lr=0.010000 Epoch[017] Batch [2049]/[3759] Speed: 64.969458 samples/sec accuracy=62.320122 loss=1.530587 lr=0.010000 Epoch[017] Batch [2099]/[3759] Speed: 65.020300 samples/sec accuracy=62.319940 loss=1.530609 lr=0.010000 Epoch[017] Batch [2149]/[3759] Speed: 64.996410 samples/sec accuracy=62.302326 loss=1.531495 lr=0.010000 Epoch[017] Batch [2199]/[3759] Speed: 65.271449 samples/sec accuracy=62.286932 loss=1.531993 lr=0.010000 Epoch[017] Batch [2249]/[3759] Speed: 65.127852 samples/sec accuracy=62.308333 loss=1.531855 lr=0.010000 Epoch[017] Batch [2299]/[3759] Speed: 65.113331 samples/sec accuracy=62.301630 loss=1.531402 lr=0.010000 Epoch[017] Batch [2349]/[3759] Speed: 64.911240 samples/sec accuracy=62.303856 loss=1.530851 lr=0.010000 Epoch[017] Batch [2399]/[3759] Speed: 65.176579 samples/sec accuracy=62.298828 loss=1.531241 lr=0.010000 Epoch[017] Batch [2449]/[3759] Speed: 65.270617 samples/sec accuracy=62.258929 loss=1.532373 lr=0.010000 Epoch[017] Batch [2499]/[3759] Speed: 65.122192 samples/sec accuracy=62.266250 loss=1.532337 lr=0.010000 Epoch[017] Batch [2549]/[3759] Speed: 65.091580 samples/sec accuracy=62.242034 loss=1.533814 lr=0.010000 Epoch[017] Batch [2599]/[3759] Speed: 65.591219 samples/sec accuracy=62.228966 loss=1.533971 lr=0.010000 Epoch[017] Batch [2649]/[3759] Speed: 65.014401 samples/sec accuracy=62.213443 loss=1.534280 lr=0.010000 Epoch[017] Batch [2699]/[3759] Speed: 65.655815 samples/sec accuracy=62.215278 loss=1.534335 lr=0.010000 Epoch[017] Batch [2749]/[3759] Speed: 64.975128 samples/sec accuracy=62.180682 loss=1.535090 lr=0.010000 Epoch[017] Batch [2799]/[3759] Speed: 65.174293 samples/sec accuracy=62.172991 loss=1.535246 lr=0.010000 Epoch[017] Batch [2849]/[3759] Speed: 64.145414 samples/sec accuracy=62.185855 loss=1.535087 lr=0.010000 Epoch[017] Batch [2899]/[3759] Speed: 65.640317 samples/sec accuracy=62.205819 loss=1.534718 lr=0.010000 Epoch[017] Batch [2949]/[3759] Speed: 65.242872 samples/sec accuracy=62.201271 loss=1.535749 lr=0.010000 Epoch[017] Batch [2999]/[3759] Speed: 65.101844 samples/sec accuracy=62.208854 loss=1.535600 lr=0.010000 Epoch[017] Batch [3049]/[3759] Speed: 65.090116 samples/sec accuracy=62.218750 loss=1.535367 lr=0.010000 Epoch[017] Batch [3099]/[3759] Speed: 65.190530 samples/sec accuracy=62.199597 loss=1.536106 lr=0.010000 Epoch[017] Batch [3149]/[3759] Speed: 65.084012 samples/sec accuracy=62.220734 loss=1.535271 lr=0.010000 Epoch[017] Batch [3199]/[3759] Speed: 65.451769 samples/sec accuracy=62.209473 loss=1.535600 lr=0.010000 Epoch[017] Batch [3249]/[3759] Speed: 64.823380 samples/sec accuracy=62.200962 loss=1.536015 lr=0.010000 Epoch[017] Batch [3299]/[3759] Speed: 65.130778 samples/sec accuracy=62.188920 loss=1.536554 lr=0.010000 Epoch[017] Batch [3349]/[3759] Speed: 64.983030 samples/sec accuracy=62.187500 loss=1.536531 lr=0.010000 Epoch[017] Batch [3399]/[3759] Speed: 65.477234 samples/sec accuracy=62.183824 loss=1.537186 lr=0.010000 Epoch[017] Batch [3449]/[3759] Speed: 64.866655 samples/sec accuracy=62.190670 loss=1.537330 lr=0.010000 Epoch[017] Batch [3499]/[3759] Speed: 65.159292 samples/sec accuracy=62.172768 loss=1.538413 lr=0.010000 Epoch[017] Batch [3549]/[3759] Speed: 65.121188 samples/sec accuracy=62.170335 loss=1.538297 lr=0.010000 Epoch[017] Batch [3599]/[3759] Speed: 64.939827 samples/sec accuracy=62.176215 loss=1.537918 lr=0.010000 Epoch[017] Batch [3649]/[3759] Speed: 65.547939 samples/sec accuracy=62.178938 loss=1.537590 lr=0.010000 Epoch[017] Batch [3699]/[3759] Speed: 64.763807 samples/sec accuracy=62.197635 loss=1.537313 lr=0.010000 Epoch[017] Batch [3749]/[3759] Speed: 72.941194 samples/sec accuracy=62.192500 loss=1.537560 lr=0.010000 Batch [0049]/[0303]: acc-top1=60.000000 acc-top5=81.906250 Batch [0099]/[0303]: acc-top1=59.781250 acc-top5=82.359375 Batch [0149]/[0303]: acc-top1=59.843750 acc-top5=82.062500 Batch [0199]/[0303]: acc-top1=59.421875 acc-top5=82.046875 Batch [0249]/[0303]: acc-top1=59.350000 acc-top5=82.118750 Batch [0299]/[0303]: acc-top1=59.630208 acc-top5=82.322917 [Epoch 017] training: accuracy=62.189495 loss=1.537631 [Epoch 017] speed: 64 samples/sec time cost: 3984.713005 [Epoch 017] validation: acc-top1=59.653465 acc-top5=82.363861 loss=1.832020 Epoch[018] Batch [0049]/[3760] Speed: 43.698391 samples/sec accuracy=62.937500 loss=1.473922 lr=0.010000 Epoch[018] Batch [0099]/[3760] Speed: 64.117453 samples/sec accuracy=63.062500 loss=1.474811 lr=0.010000 Epoch[018] Batch [0149]/[3760] Speed: 65.511368 samples/sec accuracy=63.281250 loss=1.479440 lr=0.010000 Epoch[018] Batch [0199]/[3760] Speed: 64.796695 samples/sec accuracy=63.359375 loss=1.486666 lr=0.010000 Epoch[018] Batch [0249]/[3760] Speed: 65.303962 samples/sec accuracy=63.537500 loss=1.477046 lr=0.010000 Epoch[018] Batch [0299]/[3760] Speed: 65.234003 samples/sec accuracy=63.562500 loss=1.476100 lr=0.010000 Epoch[018] Batch [0349]/[3760] Speed: 64.762535 samples/sec accuracy=63.571429 loss=1.477790 lr=0.010000 Epoch[018] Batch [0399]/[3760] Speed: 65.345229 samples/sec accuracy=63.621094 loss=1.475439 lr=0.010000 Epoch[018] Batch [0449]/[3760] Speed: 65.171203 samples/sec 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accuracy=63.022321 loss=1.503169 lr=0.010000 Epoch[018] Batch [1449]/[3760] Speed: 65.283377 samples/sec accuracy=62.920259 loss=1.505919 lr=0.010000 Epoch[018] Batch [1499]/[3760] Speed: 65.277039 samples/sec accuracy=62.958333 loss=1.504809 lr=0.010000 Epoch[018] Batch [1549]/[3760] Speed: 64.971019 samples/sec accuracy=62.966734 loss=1.504242 lr=0.010000 Epoch[018] Batch [1599]/[3760] Speed: 65.461645 samples/sec accuracy=62.987305 loss=1.503202 lr=0.010000 Epoch[018] Batch [1649]/[3760] Speed: 65.004614 samples/sec accuracy=62.916667 loss=1.505464 lr=0.010000 Epoch[018] Batch [1699]/[3760] Speed: 65.109053 samples/sec accuracy=62.932904 loss=1.505581 lr=0.010000 Epoch[018] Batch [1749]/[3760] Speed: 64.861654 samples/sec accuracy=62.887500 loss=1.506977 lr=0.010000 Epoch[018] Batch [1799]/[3760] Speed: 65.568393 samples/sec accuracy=62.904514 loss=1.506886 lr=0.010000 Epoch[018] Batch [1849]/[3760] Speed: 65.628508 samples/sec accuracy=62.886824 loss=1.507060 lr=0.010000 Epoch[018] 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accuracy=62.884309 loss=1.509377 lr=0.010000 Epoch[018] Batch [2399]/[3760] Speed: 65.711198 samples/sec accuracy=62.887370 loss=1.510059 lr=0.010000 Epoch[018] Batch [2449]/[3760] Speed: 64.893912 samples/sec accuracy=62.862883 loss=1.510924 lr=0.010000 Epoch[018] Batch [2499]/[3760] Speed: 65.470515 samples/sec accuracy=62.840625 loss=1.511721 lr=0.010000 Epoch[018] Batch [2549]/[3760] Speed: 65.027044 samples/sec accuracy=62.813113 loss=1.512442 lr=0.010000 Epoch[018] Batch [2599]/[3760] Speed: 65.146859 samples/sec accuracy=62.801082 loss=1.512991 lr=0.010000 Epoch[018] Batch [2649]/[3760] Speed: 65.468682 samples/sec accuracy=62.770637 loss=1.513856 lr=0.010000 Epoch[018] Batch [2699]/[3760] Speed: 65.511006 samples/sec accuracy=62.758102 loss=1.514248 lr=0.010000 Epoch[018] Batch [2749]/[3760] Speed: 64.990601 samples/sec accuracy=62.732955 loss=1.514490 lr=0.010000 Epoch[018] Batch [2799]/[3760] Speed: 65.102773 samples/sec accuracy=62.733817 loss=1.514202 lr=0.010000 Epoch[018] 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accuracy=62.668087 loss=1.515870 lr=0.010000 Epoch[018] Batch [3349]/[3760] Speed: 65.233021 samples/sec accuracy=62.663713 loss=1.516033 lr=0.010000 Epoch[018] Batch [3399]/[3760] Speed: 65.338811 samples/sec accuracy=62.655331 loss=1.516786 lr=0.010000 Epoch[018] Batch [3449]/[3760] Speed: 65.584468 samples/sec accuracy=62.673913 loss=1.516477 lr=0.010000 Epoch[018] Batch [3499]/[3760] Speed: 64.821805 samples/sec accuracy=62.676786 loss=1.516267 lr=0.010000 Epoch[018] Batch [3549]/[3760] Speed: 64.740191 samples/sec accuracy=62.663292 loss=1.517340 lr=0.010000 Epoch[018] Batch [3599]/[3760] Speed: 65.161581 samples/sec accuracy=62.647569 loss=1.517847 lr=0.010000 Epoch[018] Batch [3649]/[3760] Speed: 65.272433 samples/sec accuracy=62.622860 loss=1.518761 lr=0.010000 Epoch[018] Batch [3699]/[3760] Speed: 65.397937 samples/sec accuracy=62.606841 loss=1.519445 lr=0.010000 Epoch[018] Batch [3749]/[3760] Speed: 72.675481 samples/sec accuracy=62.605417 loss=1.520051 lr=0.010000 Batch [0049]/[0303]: acc-top1=61.593750 acc-top5=84.031250 Batch [0099]/[0303]: acc-top1=60.890625 acc-top5=83.671875 Batch [0149]/[0303]: acc-top1=60.927083 acc-top5=83.302083 Batch [0199]/[0303]: acc-top1=60.578125 acc-top5=83.242188 Batch [0249]/[0303]: acc-top1=60.487500 acc-top5=83.275000 Batch [0299]/[0303]: acc-top1=60.640625 acc-top5=83.369792 [Epoch 018] training: accuracy=62.604305 loss=1.519996 [Epoch 018] speed: 64 samples/sec time cost: 3981.205272 [Epoch 018] validation: acc-top1=60.664191 acc-top5=83.405528 loss=1.741727 Epoch[019] Batch [0049]/[3760] Speed: 44.125235 samples/sec accuracy=61.906250 loss=1.504568 lr=0.010000 Epoch[019] Batch [0099]/[3760] Speed: 64.056385 samples/sec accuracy=62.734375 loss=1.484432 lr=0.010000 Epoch[019] Batch [0149]/[3760] Speed: 66.108180 samples/sec accuracy=62.937500 loss=1.493643 lr=0.010000 Epoch[019] Batch [0199]/[3760] Speed: 64.867485 samples/sec accuracy=62.921875 loss=1.492415 lr=0.010000 Epoch[019] Batch [0249]/[3760] Speed: 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66.682692 samples/sec accuracy=63.115927 loss=1.498849 lr=0.010000 Epoch[019] Batch [3149]/[3760] Speed: 66.125582 samples/sec accuracy=63.077877 loss=1.499457 lr=0.010000 Epoch[019] Batch [3199]/[3760] Speed: 66.011042 samples/sec accuracy=63.067383 loss=1.499838 lr=0.010000 Epoch[019] Batch [3249]/[3760] Speed: 66.224499 samples/sec accuracy=63.049038 loss=1.500339 lr=0.010000 Epoch[019] Batch [3299]/[3760] Speed: 66.139852 samples/sec accuracy=63.030777 loss=1.501432 lr=0.010000 Epoch[019] Batch [3349]/[3760] Speed: 66.388114 samples/sec accuracy=63.017257 loss=1.502124 lr=0.010000 Epoch[019] Batch [3399]/[3760] Speed: 66.182075 samples/sec accuracy=63.026654 loss=1.502030 lr=0.010000 Epoch[019] Batch [3449]/[3760] Speed: 65.906755 samples/sec accuracy=63.010417 loss=1.502765 lr=0.010000 Epoch[019] Batch [3499]/[3760] Speed: 65.864965 samples/sec accuracy=62.980357 loss=1.503516 lr=0.010000 Epoch[019] Batch [3549]/[3760] Speed: 65.861231 samples/sec accuracy=62.978433 loss=1.503477 lr=0.010000 Epoch[019] Batch [3599]/[3760] Speed: 65.995248 samples/sec accuracy=62.979601 loss=1.503321 lr=0.010000 Epoch[019] Batch [3649]/[3760] Speed: 65.881389 samples/sec accuracy=62.977740 loss=1.503367 lr=0.010000 Epoch[019] Batch [3699]/[3760] Speed: 65.750034 samples/sec accuracy=62.973395 loss=1.503154 lr=0.010000 Epoch[019] Batch [3749]/[3760] Speed: 72.674182 samples/sec accuracy=62.972917 loss=1.503072 lr=0.010000 Batch [0049]/[0303]: acc-top1=59.781250 acc-top5=82.812500 Batch [0099]/[0303]: acc-top1=60.218750 acc-top5=83.000000 Batch [0149]/[0303]: acc-top1=60.437500 acc-top5=82.614583 Batch [0199]/[0303]: acc-top1=60.218750 acc-top5=82.789062 Batch [0249]/[0303]: acc-top1=60.300000 acc-top5=82.881250 Batch [0299]/[0303]: acc-top1=60.651042 acc-top5=83.036458 [Epoch 019] training: accuracy=62.967088 loss=1.503222 [Epoch 019] speed: 65 samples/sec time cost: 3934.397850 [Epoch 019] validation: acc-top1=60.659035 acc-top5=83.049711 loss=1.787599 Epoch[020] Batch 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accuracy=63.671336 loss=1.472437 lr=0.010000 Epoch[020] Batch [1499]/[3759] Speed: 65.343103 samples/sec accuracy=63.648958 loss=1.473509 lr=0.010000 Epoch[020] Batch [1549]/[3759] Speed: 66.467610 samples/sec accuracy=63.617944 loss=1.475016 lr=0.010000 Epoch[020] Batch [1599]/[3759] Speed: 66.036276 samples/sec accuracy=63.592773 loss=1.476543 lr=0.010000 Epoch[020] Batch [1649]/[3759] Speed: 66.259430 samples/sec accuracy=63.526515 loss=1.478851 lr=0.010000 Epoch[020] Batch [1699]/[3759] Speed: 66.383700 samples/sec accuracy=63.529412 loss=1.479401 lr=0.010000 Epoch[020] Batch [1749]/[3759] Speed: 66.159244 samples/sec accuracy=63.521429 loss=1.479858 lr=0.010000 Epoch[020] Batch [1799]/[3759] Speed: 65.831552 samples/sec accuracy=63.517361 loss=1.480012 lr=0.010000 Epoch[020] Batch [1849]/[3759] Speed: 66.834134 samples/sec accuracy=63.523649 loss=1.480372 lr=0.010000 Epoch[020] Batch [1899]/[3759] Speed: 65.893248 samples/sec accuracy=63.518092 loss=1.480833 lr=0.010000 Epoch[020] 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accuracy=63.421224 loss=1.485454 lr=0.010000 Epoch[020] Batch [2449]/[3759] Speed: 66.612872 samples/sec accuracy=63.396684 loss=1.485305 lr=0.010000 Epoch[020] Batch [2499]/[3759] Speed: 66.540514 samples/sec accuracy=63.365625 loss=1.486768 lr=0.010000 Epoch[020] Batch [2549]/[3759] Speed: 65.713590 samples/sec accuracy=63.352328 loss=1.487572 lr=0.010000 Epoch[020] Batch [2599]/[3759] Speed: 66.528484 samples/sec accuracy=63.337740 loss=1.488287 lr=0.010000 Epoch[020] Batch [2649]/[3759] Speed: 66.188251 samples/sec accuracy=63.314858 loss=1.489016 lr=0.010000 Epoch[020] Batch [2699]/[3759] Speed: 66.388038 samples/sec accuracy=63.309028 loss=1.489720 lr=0.010000 Epoch[020] Batch [2749]/[3759] Speed: 65.737465 samples/sec accuracy=63.328409 loss=1.489227 lr=0.010000 Epoch[020] Batch [2799]/[3759] Speed: 66.019892 samples/sec accuracy=63.299665 loss=1.489800 lr=0.010000 Epoch[020] Batch [2849]/[3759] Speed: 66.155457 samples/sec accuracy=63.301535 loss=1.490381 lr=0.010000 Epoch[020] 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accuracy=63.284981 loss=1.492646 lr=0.010000 Epoch[020] Batch [3399]/[3759] Speed: 65.832970 samples/sec accuracy=63.303768 loss=1.492150 lr=0.010000 Epoch[020] Batch [3449]/[3759] Speed: 66.257745 samples/sec accuracy=63.285326 loss=1.492422 lr=0.010000 Epoch[020] Batch [3499]/[3759] Speed: 65.733388 samples/sec accuracy=63.287500 loss=1.492625 lr=0.010000 Epoch[020] Batch [3549]/[3759] Speed: 66.678241 samples/sec accuracy=63.280370 loss=1.492667 lr=0.010000 Epoch[020] Batch [3599]/[3759] Speed: 66.220255 samples/sec accuracy=63.285590 loss=1.492594 lr=0.010000 Epoch[020] Batch [3649]/[3759] Speed: 66.024673 samples/sec accuracy=63.285103 loss=1.492855 lr=0.010000 Epoch[020] Batch [3699]/[3759] Speed: 66.392860 samples/sec accuracy=63.279139 loss=1.492903 lr=0.010000 Epoch[020] Batch [3749]/[3759] Speed: 73.299480 samples/sec accuracy=63.278750 loss=1.493023 lr=0.010000 Batch [0049]/[0303]: acc-top1=60.906250 acc-top5=83.312500 Batch [0099]/[0303]: acc-top1=61.031250 acc-top5=83.593750 Batch [0149]/[0303]: acc-top1=61.156250 acc-top5=83.281250 Batch [0199]/[0303]: acc-top1=61.039062 acc-top5=83.320312 Batch [0249]/[0303]: acc-top1=61.043750 acc-top5=83.375000 Batch [0299]/[0303]: acc-top1=61.260417 acc-top5=83.468750 [Epoch 020] training: accuracy=63.280626 loss=1.492755 [Epoch 020] speed: 65 samples/sec time cost: 3929.001717 [Epoch 020] validation: acc-top1=61.236592 acc-top5=83.467409 loss=1.784772 Epoch[021] Batch [0049]/[3760] Speed: 44.143299 samples/sec accuracy=65.343750 loss=1.377702 lr=0.010000 Epoch[021] Batch [0099]/[3760] Speed: 64.390551 samples/sec accuracy=64.390625 loss=1.427726 lr=0.010000 Epoch[021] Batch [0149]/[3760] Speed: 65.599493 samples/sec accuracy=64.239583 loss=1.430091 lr=0.010000 Epoch[021] Batch [0199]/[3760] Speed: 65.288825 samples/sec accuracy=63.906250 loss=1.445596 lr=0.010000 Epoch[021] Batch [0249]/[3760] Speed: 66.839849 samples/sec accuracy=63.875000 loss=1.454107 lr=0.010000 Epoch[021] Batch [0299]/[3760] 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accuracy=63.886029 loss=1.471246 lr=0.010000 Epoch[021] Batch [1749]/[3760] Speed: 65.945519 samples/sec accuracy=63.882143 loss=1.471533 lr=0.010000 Epoch[021] Batch [1799]/[3760] Speed: 66.352379 samples/sec accuracy=63.846354 loss=1.472521 lr=0.010000 Epoch[021] Batch [1849]/[3760] Speed: 65.847365 samples/sec accuracy=63.809966 loss=1.473881 lr=0.010000 Epoch[021] Batch [1899]/[3760] Speed: 66.608290 samples/sec accuracy=63.819079 loss=1.473803 lr=0.010000 Epoch[021] Batch [1949]/[3760] Speed: 66.114262 samples/sec accuracy=63.774840 loss=1.475361 lr=0.010000 Epoch[021] Batch [1999]/[3760] Speed: 65.523852 samples/sec accuracy=63.750781 loss=1.476599 lr=0.010000 Epoch[021] Batch [2049]/[3760] Speed: 66.864359 samples/sec accuracy=63.759909 loss=1.476699 lr=0.010000 Epoch[021] Batch [2099]/[3760] Speed: 66.036918 samples/sec accuracy=63.775298 loss=1.476011 lr=0.010000 Epoch[021] Batch [2149]/[3760] Speed: 65.896223 samples/sec accuracy=63.768169 loss=1.476397 lr=0.010000 Epoch[021] 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accuracy=63.762382 loss=1.474739 lr=0.010000 Epoch[021] Batch [2699]/[3760] Speed: 66.026950 samples/sec accuracy=63.734954 loss=1.476148 lr=0.010000 Epoch[021] Batch [2749]/[3760] Speed: 66.093534 samples/sec accuracy=63.728977 loss=1.476381 lr=0.010000 Epoch[021] Batch [2799]/[3760] Speed: 66.007923 samples/sec accuracy=63.704799 loss=1.477559 lr=0.010000 Epoch[021] Batch [2849]/[3760] Speed: 65.878310 samples/sec accuracy=63.695724 loss=1.477954 lr=0.010000 Epoch[021] Batch [2899]/[3760] Speed: 65.854794 samples/sec accuracy=63.686961 loss=1.478671 lr=0.010000 Epoch[021] Batch [2949]/[3760] Speed: 66.814831 samples/sec accuracy=63.687500 loss=1.479039 lr=0.010000 Epoch[021] Batch [2999]/[3760] Speed: 65.318478 samples/sec accuracy=63.702604 loss=1.478675 lr=0.010000 Epoch[021] Batch [3049]/[3760] Speed: 66.019838 samples/sec accuracy=63.701332 loss=1.478598 lr=0.010000 Epoch[021] Batch [3099]/[3760] Speed: 65.589279 samples/sec accuracy=63.695565 loss=1.479099 lr=0.010000 Epoch[021] Batch [3149]/[3760] Speed: 66.221847 samples/sec accuracy=63.695437 loss=1.479075 lr=0.010000 Epoch[021] Batch [3199]/[3760] Speed: 66.500019 samples/sec accuracy=63.695801 loss=1.478640 lr=0.010000 Epoch[021] Batch [3249]/[3760] Speed: 66.088978 samples/sec accuracy=63.684135 loss=1.479201 lr=0.010000 Epoch[021] Batch [3299]/[3760] Speed: 65.923951 samples/sec accuracy=63.675663 loss=1.479782 lr=0.010000 Epoch[021] Batch [3349]/[3760] Speed: 65.884426 samples/sec accuracy=63.679104 loss=1.479868 lr=0.010000 Epoch[021] Batch [3399]/[3760] Speed: 66.690423 samples/sec accuracy=63.655790 loss=1.480838 lr=0.010000 Epoch[021] Batch [3449]/[3760] Speed: 65.948284 samples/sec accuracy=63.651268 loss=1.480946 lr=0.010000 Epoch[021] Batch [3499]/[3760] Speed: 66.024623 samples/sec accuracy=63.622321 loss=1.482106 lr=0.010000 Epoch[021] Batch [3549]/[3760] Speed: 66.266844 samples/sec accuracy=63.611796 loss=1.482542 lr=0.010000 Epoch[021] Batch [3599]/[3760] Speed: 66.305066 samples/sec accuracy=63.583333 loss=1.483853 lr=0.010000 Epoch[021] Batch [3649]/[3760] Speed: 65.942030 samples/sec accuracy=63.567209 loss=1.484701 lr=0.010000 Epoch[021] Batch [3699]/[3760] Speed: 66.411227 samples/sec accuracy=63.560811 loss=1.485247 lr=0.010000 Epoch[021] Batch [3749]/[3760] Speed: 72.491431 samples/sec accuracy=63.565833 loss=1.485138 lr=0.010000 Batch [0049]/[0303]: acc-top1=61.156250 acc-top5=82.968750 Batch [0099]/[0303]: acc-top1=61.281250 acc-top5=83.265625 Batch [0149]/[0303]: acc-top1=61.645833 acc-top5=83.156250 Batch [0199]/[0303]: acc-top1=61.234375 acc-top5=83.054688 Batch [0249]/[0303]: acc-top1=61.231250 acc-top5=83.181250 Batch [0299]/[0303]: acc-top1=61.479167 acc-top5=83.223958 [Epoch 021] training: accuracy=63.563414 loss=1.485316 [Epoch 021] speed: 65 samples/sec time cost: 3930.912340 [Epoch 021] validation: acc-top1=61.448020 acc-top5=83.235355 loss=1.768983 Epoch[022] Batch [0049]/[3760] Speed: 44.855283 samples/sec accuracy=64.343750 loss=1.466666 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lr=0.010000 Epoch[022] Batch [1999]/[3760] Speed: 66.024544 samples/sec accuracy=64.103906 loss=1.454545 lr=0.010000 Epoch[022] Batch [2049]/[3760] Speed: 66.803309 samples/sec accuracy=64.121189 loss=1.453885 lr=0.010000 Epoch[022] Batch [2099]/[3760] Speed: 66.247441 samples/sec accuracy=64.118304 loss=1.453678 lr=0.010000 Epoch[022] Batch [2149]/[3760] Speed: 66.374818 samples/sec accuracy=64.114826 loss=1.453843 lr=0.010000 Epoch[022] Batch [2199]/[3760] Speed: 66.096333 samples/sec accuracy=64.142045 loss=1.452930 lr=0.010000 Epoch[022] Batch [2249]/[3760] Speed: 65.675158 samples/sec accuracy=64.120833 loss=1.453963 lr=0.010000 Epoch[022] Batch [2299]/[3760] Speed: 66.377124 samples/sec accuracy=64.129076 loss=1.453252 lr=0.010000 Epoch[022] Batch [2349]/[3760] Speed: 66.312423 samples/sec accuracy=64.161569 loss=1.452808 lr=0.010000 Epoch[022] Batch [2399]/[3760] Speed: 65.934093 samples/sec accuracy=64.146484 loss=1.452776 lr=0.010000 Epoch[022] Batch [2449]/[3760] Speed: 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lr=0.010000 Epoch[022] Batch [2949]/[3760] Speed: 66.242577 samples/sec accuracy=64.010593 loss=1.457286 lr=0.010000 Epoch[022] Batch [2999]/[3760] Speed: 66.452409 samples/sec accuracy=64.019271 loss=1.457478 lr=0.010000 Epoch[022] Batch [3049]/[3760] Speed: 65.951568 samples/sec accuracy=63.991803 loss=1.458779 lr=0.010000 Epoch[022] Batch [3099]/[3760] Speed: 66.123052 samples/sec accuracy=63.969254 loss=1.459841 lr=0.010000 Epoch[022] Batch [3149]/[3760] Speed: 66.401386 samples/sec accuracy=63.964286 loss=1.459877 lr=0.010000 Epoch[022] Batch [3199]/[3760] Speed: 65.815618 samples/sec accuracy=63.939941 loss=1.460876 lr=0.010000 Epoch[022] Batch [3249]/[3760] Speed: 66.076640 samples/sec accuracy=63.937019 loss=1.461185 lr=0.010000 Epoch[022] Batch [3299]/[3760] Speed: 66.657961 samples/sec accuracy=63.927557 loss=1.461038 lr=0.010000 Epoch[022] Batch [3349]/[3760] Speed: 65.808270 samples/sec accuracy=63.931437 loss=1.461167 lr=0.010000 Epoch[022] Batch [3399]/[3760] Speed: 65.908082 samples/sec accuracy=63.939798 loss=1.461618 lr=0.010000 Epoch[022] Batch [3449]/[3760] Speed: 66.131686 samples/sec accuracy=63.933424 loss=1.461903 lr=0.010000 Epoch[022] Batch [3499]/[3760] Speed: 66.199006 samples/sec accuracy=63.921875 loss=1.462201 lr=0.010000 Epoch[022] Batch [3549]/[3760] Speed: 66.640873 samples/sec accuracy=63.899208 loss=1.462785 lr=0.010000 Epoch[022] Batch [3599]/[3760] Speed: 65.783681 samples/sec accuracy=63.893229 loss=1.462733 lr=0.010000 Epoch[022] Batch [3649]/[3760] Speed: 66.369127 samples/sec accuracy=63.887842 loss=1.463176 lr=0.010000 Epoch[022] Batch [3699]/[3760] Speed: 65.328384 samples/sec accuracy=63.878801 loss=1.463968 lr=0.010000 Epoch[022] Batch [3749]/[3760] Speed: 73.082396 samples/sec accuracy=63.864167 loss=1.464812 lr=0.010000 Batch [0049]/[0303]: acc-top1=62.187500 acc-top5=83.281250 Batch [0099]/[0303]: acc-top1=61.281250 acc-top5=83.234375 Batch [0149]/[0303]: acc-top1=61.468750 acc-top5=83.031250 Batch [0199]/[0303]: acc-top1=61.164063 acc-top5=83.039062 Batch [0249]/[0303]: acc-top1=61.337500 acc-top5=83.175000 Batch [0299]/[0303]: acc-top1=61.468750 acc-top5=83.302083 [Epoch 022] training: accuracy=63.855967 loss=1.464885 [Epoch 022] speed: 65 samples/sec time cost: 3927.303443 [Epoch 022] validation: acc-top1=61.458333 acc-top5=83.307550 loss=1.719267 Epoch[023] Batch [0049]/[3759] Speed: 44.201758 samples/sec accuracy=66.906250 loss=1.358602 lr=0.010000 Epoch[023] Batch [0099]/[3759] Speed: 64.685686 samples/sec accuracy=65.750000 loss=1.381952 lr=0.010000 Epoch[023] Batch [0149]/[3759] Speed: 65.763045 samples/sec accuracy=65.406250 loss=1.400043 lr=0.010000 Epoch[023] Batch [0199]/[3759] Speed: 65.092731 samples/sec accuracy=65.398438 loss=1.400382 lr=0.010000 Epoch[023] Batch [0249]/[3759] Speed: 66.270072 samples/sec accuracy=65.087500 loss=1.402154 lr=0.010000 Epoch[023] Batch [0299]/[3759] Speed: 65.563238 samples/sec accuracy=64.942708 loss=1.408005 lr=0.010000 Epoch[023] Batch 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accuracy=64.482422 loss=1.431004 lr=0.010000 Epoch[023] Batch [0849]/[3759] Speed: 66.672428 samples/sec accuracy=64.468750 loss=1.432922 lr=0.010000 Epoch[023] Batch [0899]/[3759] Speed: 66.061618 samples/sec accuracy=64.388889 loss=1.433732 lr=0.010000 Epoch[023] Batch [0949]/[3759] Speed: 66.013226 samples/sec accuracy=64.414474 loss=1.433500 lr=0.010000 Epoch[023] Batch [0999]/[3759] Speed: 66.587352 samples/sec accuracy=64.393750 loss=1.435366 lr=0.010000 Epoch[023] Batch [1049]/[3759] Speed: 65.949060 samples/sec accuracy=64.428571 loss=1.436804 lr=0.010000 Epoch[023] Batch [1099]/[3759] Speed: 65.867837 samples/sec accuracy=64.488636 loss=1.436710 lr=0.010000 Epoch[023] Batch [1149]/[3759] Speed: 66.571557 samples/sec accuracy=64.561141 loss=1.434001 lr=0.010000 Epoch[023] Batch [1199]/[3759] Speed: 65.944552 samples/sec accuracy=64.574219 loss=1.434542 lr=0.010000 Epoch[023] Batch [1249]/[3759] Speed: 65.981969 samples/sec accuracy=64.560000 loss=1.434468 lr=0.010000 Epoch[023] 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accuracy=64.292824 loss=1.446259 lr=0.010000 Epoch[023] Batch [2749]/[3759] Speed: 66.124648 samples/sec accuracy=64.273864 loss=1.446886 lr=0.010000 Epoch[023] Batch [2799]/[3759] Speed: 66.666213 samples/sec accuracy=64.268415 loss=1.447060 lr=0.010000 Epoch[023] Batch [2849]/[3759] Speed: 66.591144 samples/sec accuracy=64.257127 loss=1.448447 lr=0.010000 Epoch[023] Batch [2899]/[3759] Speed: 66.141147 samples/sec accuracy=64.232220 loss=1.449820 lr=0.010000 Epoch[023] Batch [2949]/[3759] Speed: 66.322378 samples/sec accuracy=64.244174 loss=1.449336 lr=0.010000 Epoch[023] Batch [2999]/[3759] Speed: 65.904820 samples/sec accuracy=64.243229 loss=1.450129 lr=0.010000 Epoch[023] Batch [3049]/[3759] Speed: 66.176589 samples/sec accuracy=64.203893 loss=1.451087 lr=0.010000 Epoch[023] Batch [3099]/[3759] Speed: 66.189645 samples/sec accuracy=64.207661 loss=1.451565 lr=0.010000 Epoch[023] Batch [3149]/[3759] Speed: 65.794521 samples/sec accuracy=64.180060 loss=1.452202 lr=0.010000 Epoch[023] 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accuracy=64.122432 loss=1.455676 lr=0.010000 Epoch[023] Batch [3699]/[3759] Speed: 66.194214 samples/sec accuracy=64.118243 loss=1.455638 lr=0.010000 Epoch[023] Batch [3749]/[3759] Speed: 73.507170 samples/sec accuracy=64.097917 loss=1.456202 lr=0.010000 Batch [0049]/[0303]: acc-top1=60.250000 acc-top5=82.406250 Batch [0099]/[0303]: acc-top1=60.703125 acc-top5=82.671875 Batch [0149]/[0303]: acc-top1=60.947917 acc-top5=82.531250 Batch [0199]/[0303]: acc-top1=60.664063 acc-top5=82.585938 Batch [0249]/[0303]: acc-top1=60.631250 acc-top5=82.943750 Batch [0299]/[0303]: acc-top1=60.812500 acc-top5=83.046875 [Epoch 023] training: accuracy=64.102404 loss=1.456157 [Epoch 023] speed: 65 samples/sec time cost: 3929.687689 [Epoch 023] validation: acc-top1=60.813738 acc-top5=83.090965 loss=1.795077 Epoch[024] Batch [0049]/[3760] Speed: 44.898948 samples/sec accuracy=64.343750 loss=1.420126 lr=0.010000 Epoch[024] Batch [0099]/[3760] Speed: 64.403850 samples/sec accuracy=65.453125 loss=1.382578 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lr=0.010000 Epoch[024] Batch [1099]/[3760] Speed: 65.631851 samples/sec accuracy=65.041193 loss=1.419669 lr=0.010000 Epoch[024] Batch [1149]/[3760] Speed: 66.399924 samples/sec accuracy=65.023098 loss=1.420111 lr=0.010000 Epoch[024] Batch [1199]/[3760] Speed: 66.576278 samples/sec accuracy=64.984375 loss=1.420997 lr=0.010000 Epoch[024] Batch [1249]/[3760] Speed: 66.843655 samples/sec accuracy=64.977500 loss=1.421054 lr=0.010000 Epoch[024] Batch [1299]/[3760] Speed: 66.463126 samples/sec accuracy=64.985577 loss=1.420121 lr=0.010000 Epoch[024] Batch [1349]/[3760] Speed: 66.208439 samples/sec accuracy=64.917824 loss=1.422327 lr=0.010000 Epoch[024] Batch [1399]/[3760] Speed: 66.086047 samples/sec accuracy=64.901786 loss=1.423162 lr=0.010000 Epoch[024] Batch [1449]/[3760] Speed: 66.556446 samples/sec accuracy=64.870690 loss=1.424380 lr=0.010000 Epoch[024] Batch [1499]/[3760] Speed: 66.036711 samples/sec accuracy=64.815625 loss=1.426198 lr=0.010000 Epoch[024] Batch [1549]/[3760] Speed: 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lr=0.010000 Epoch[024] Batch [2999]/[3760] Speed: 66.208022 samples/sec accuracy=64.466667 loss=1.438312 lr=0.010000 Epoch[024] Batch [3049]/[3760] Speed: 66.198290 samples/sec accuracy=64.472848 loss=1.438042 lr=0.010000 Epoch[024] Batch [3099]/[3760] Speed: 65.627223 samples/sec accuracy=64.452117 loss=1.439254 lr=0.010000 Epoch[024] Batch [3149]/[3760] Speed: 65.967097 samples/sec accuracy=64.442460 loss=1.439982 lr=0.010000 Epoch[024] Batch [3199]/[3760] Speed: 65.856644 samples/sec accuracy=64.421387 loss=1.441027 lr=0.010000 Epoch[024] Batch [3249]/[3760] Speed: 66.428729 samples/sec accuracy=64.409135 loss=1.441605 lr=0.010000 Epoch[024] Batch [3299]/[3760] Speed: 66.351235 samples/sec accuracy=64.375947 loss=1.442387 lr=0.010000 Epoch[024] Batch [3349]/[3760] Speed: 65.969690 samples/sec accuracy=64.360075 loss=1.443720 lr=0.010000 Epoch[024] Batch [3399]/[3760] Speed: 65.549419 samples/sec accuracy=64.355239 loss=1.443788 lr=0.010000 Epoch[024] Batch [3449]/[3760] Speed: 66.432780 samples/sec accuracy=64.352355 loss=1.444019 lr=0.010000 Epoch[024] Batch [3499]/[3760] Speed: 66.335832 samples/sec accuracy=64.342857 loss=1.444426 lr=0.010000 Epoch[024] Batch [3549]/[3760] Speed: 66.430107 samples/sec accuracy=64.319542 loss=1.445456 lr=0.010000 Epoch[024] Batch [3599]/[3760] Speed: 66.279314 samples/sec accuracy=64.297309 loss=1.446640 lr=0.010000 Epoch[024] Batch [3649]/[3760] Speed: 66.116558 samples/sec accuracy=64.280394 loss=1.447495 lr=0.010000 Epoch[024] Batch [3699]/[3760] Speed: 66.228775 samples/sec accuracy=64.279139 loss=1.447409 lr=0.010000 Epoch[024] Batch [3749]/[3760] Speed: 72.565420 samples/sec accuracy=64.281667 loss=1.447261 lr=0.010000 Batch [0049]/[0303]: acc-top1=61.500000 acc-top5=83.656250 Batch [0099]/[0303]: acc-top1=61.250000 acc-top5=83.328125 Batch [0149]/[0303]: acc-top1=61.604167 acc-top5=83.041667 Batch [0199]/[0303]: acc-top1=61.500000 acc-top5=83.132812 Batch [0249]/[0303]: acc-top1=61.418750 acc-top5=83.281250 Batch [0299]/[0303]: acc-top1=61.458333 acc-top5=83.473958 [Epoch 024] training: accuracy=64.276928 loss=1.447411 [Epoch 024] speed: 65 samples/sec time cost: 3930.551888 [Epoch 024] validation: acc-top1=61.468647 acc-top5=83.508663 loss=1.782704 Epoch[025] Batch [0049]/[3760] Speed: 44.368914 samples/sec accuracy=64.281250 loss=1.438382 lr=0.010000 Epoch[025] Batch [0099]/[3760] Speed: 64.496500 samples/sec accuracy=64.281250 loss=1.444158 lr=0.010000 Epoch[025] Batch [0149]/[3760] Speed: 65.962485 samples/sec accuracy=64.562500 loss=1.434682 lr=0.010000 Epoch[025] Batch [0199]/[3760] Speed: 65.157548 samples/sec accuracy=64.187500 loss=1.440393 lr=0.010000 Epoch[025] Batch [0249]/[3760] Speed: 66.184764 samples/sec accuracy=64.550000 loss=1.420459 lr=0.010000 Epoch[025] Batch [0299]/[3760] Speed: 65.713402 samples/sec accuracy=64.671875 loss=1.419155 lr=0.010000 Epoch[025] Batch [0349]/[3760] Speed: 65.901480 samples/sec accuracy=64.616071 loss=1.420236 lr=0.010000 Epoch[025] Batch 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accuracy=64.955882 loss=1.406532 lr=0.010000 Epoch[025] Batch [0899]/[3760] Speed: 66.004161 samples/sec accuracy=64.940972 loss=1.408769 lr=0.010000 Epoch[025] Batch [0949]/[3760] Speed: 66.362334 samples/sec accuracy=64.875000 loss=1.410836 lr=0.010000 Epoch[025] Batch [0999]/[3760] Speed: 66.466796 samples/sec accuracy=64.862500 loss=1.411380 lr=0.010000 Epoch[025] Batch [1049]/[3760] Speed: 66.000661 samples/sec accuracy=64.980655 loss=1.407478 lr=0.010000 Epoch[025] Batch [1099]/[3760] Speed: 65.840124 samples/sec accuracy=65.024148 loss=1.407655 lr=0.010000 Epoch[025] Batch [1149]/[3760] Speed: 66.198234 samples/sec accuracy=65.065217 loss=1.406913 lr=0.010000 Epoch[025] Batch [1199]/[3760] Speed: 66.216706 samples/sec accuracy=65.088542 loss=1.407004 lr=0.010000 Epoch[025] Batch [1249]/[3760] Speed: 66.380765 samples/sec accuracy=65.088750 loss=1.406482 lr=0.010000 Epoch[025] Batch [1299]/[3760] Speed: 65.929718 samples/sec accuracy=65.013221 loss=1.409526 lr=0.010000 Epoch[025] 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accuracy=64.983507 loss=1.414822 lr=0.010000 Epoch[025] Batch [1849]/[3760] Speed: 66.162696 samples/sec accuracy=64.966216 loss=1.415178 lr=0.010000 Epoch[025] Batch [1899]/[3760] Speed: 66.126976 samples/sec accuracy=64.921875 loss=1.417079 lr=0.010000 Epoch[025] Batch [1949]/[3760] Speed: 65.787068 samples/sec accuracy=64.861378 loss=1.418856 lr=0.010000 Epoch[025] Batch [1999]/[3760] Speed: 66.830587 samples/sec accuracy=64.843750 loss=1.420260 lr=0.010000 Epoch[025] Batch [2049]/[3760] Speed: 66.026904 samples/sec accuracy=64.808689 loss=1.421217 lr=0.010000 Epoch[025] Batch [2099]/[3760] Speed: 65.946989 samples/sec accuracy=64.808780 loss=1.421585 lr=0.010000 Epoch[025] Batch [2149]/[3760] Speed: 65.803101 samples/sec accuracy=64.785610 loss=1.422413 lr=0.010000 Epoch[025] Batch [2199]/[3760] Speed: 66.044229 samples/sec accuracy=64.784801 loss=1.422572 lr=0.010000 Epoch[025] Batch [2249]/[3760] Speed: 66.487434 samples/sec accuracy=64.806250 loss=1.421473 lr=0.010000 Epoch[025] 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accuracy=64.622727 loss=1.428407 lr=0.010000 Epoch[025] Batch [2799]/[3760] Speed: 66.239308 samples/sec accuracy=64.584263 loss=1.429653 lr=0.010000 Epoch[025] Batch [2849]/[3760] Speed: 65.587068 samples/sec accuracy=64.581140 loss=1.430214 lr=0.010000 Epoch[025] Batch [2899]/[3760] Speed: 66.502965 samples/sec accuracy=64.570043 loss=1.430155 lr=0.010000 Epoch[025] Batch [2949]/[3760] Speed: 65.972273 samples/sec accuracy=64.533898 loss=1.431297 lr=0.010000 Epoch[025] Batch [2999]/[3760] Speed: 65.999333 samples/sec accuracy=64.548958 loss=1.431154 lr=0.010000 Epoch[025] Batch [3049]/[3760] Speed: 66.327570 samples/sec accuracy=64.529713 loss=1.431872 lr=0.010000 Epoch[025] Batch [3099]/[3760] Speed: 65.567950 samples/sec accuracy=64.517137 loss=1.432188 lr=0.010000 Epoch[025] Batch [3149]/[3760] Speed: 65.896628 samples/sec accuracy=64.497520 loss=1.432456 lr=0.010000 Epoch[025] Batch [3199]/[3760] Speed: 66.072496 samples/sec accuracy=64.487305 loss=1.433535 lr=0.010000 Epoch[025] 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accuracy=64.403716 loss=1.437993 lr=0.010000 Epoch[025] Batch [3749]/[3760] Speed: 72.917142 samples/sec accuracy=64.395833 loss=1.438199 lr=0.010000 Batch [0049]/[0303]: acc-top1=60.812500 acc-top5=82.843750 Batch [0099]/[0303]: acc-top1=60.437500 acc-top5=83.343750 Batch [0149]/[0303]: acc-top1=60.791667 acc-top5=83.187500 Batch [0199]/[0303]: acc-top1=60.531250 acc-top5=83.187500 Batch [0249]/[0303]: acc-top1=60.675000 acc-top5=83.237500 Batch [0299]/[0303]: acc-top1=60.817708 acc-top5=83.307292 [Epoch 025] training: accuracy=64.402427 loss=1.438028 [Epoch 025] speed: 65 samples/sec time cost: 3933.867048 [Epoch 025] validation: acc-top1=60.787954 acc-top5=83.312706 loss=1.784110 Epoch[026] Batch [0049]/[3759] Speed: 44.395765 samples/sec accuracy=66.031250 loss=1.356904 lr=0.010000 Epoch[026] Batch [0099]/[3759] Speed: 64.186186 samples/sec accuracy=65.359375 loss=1.398959 lr=0.010000 Epoch[026] Batch [0149]/[3759] Speed: 65.470674 samples/sec accuracy=64.864583 loss=1.421730 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lr=0.010000 Epoch[026] Batch [1149]/[3759] Speed: 65.997009 samples/sec accuracy=65.141304 loss=1.408339 lr=0.010000 Epoch[026] Batch [1199]/[3759] Speed: 66.225230 samples/sec accuracy=65.092448 loss=1.409432 lr=0.010000 Epoch[026] Batch [1249]/[3759] Speed: 65.823901 samples/sec accuracy=65.087500 loss=1.409488 lr=0.010000 Epoch[026] Batch [1299]/[3759] Speed: 65.884091 samples/sec accuracy=65.014423 loss=1.412532 lr=0.010000 Epoch[026] Batch [1349]/[3759] Speed: 66.349648 samples/sec accuracy=64.969907 loss=1.413693 lr=0.010000 Epoch[026] Batch [1399]/[3759] Speed: 65.903426 samples/sec accuracy=64.928571 loss=1.416184 lr=0.010000 Epoch[026] Batch [1449]/[3759] Speed: 66.124218 samples/sec accuracy=64.962284 loss=1.415309 lr=0.010000 Epoch[026] Batch [1499]/[3759] Speed: 66.059904 samples/sec accuracy=64.937500 loss=1.416878 lr=0.010000 Epoch[026] Batch [1549]/[3759] Speed: 66.354742 samples/sec accuracy=64.884073 loss=1.418721 lr=0.010000 Epoch[026] Batch [1599]/[3759] Speed: 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lr=0.010000 Epoch[026] Batch [2099]/[3759] Speed: 65.836151 samples/sec accuracy=64.866071 loss=1.419760 lr=0.010000 Epoch[026] Batch [2149]/[3759] Speed: 65.711796 samples/sec accuracy=64.824128 loss=1.421468 lr=0.010000 Epoch[026] Batch [2199]/[3759] Speed: 66.235368 samples/sec accuracy=64.774148 loss=1.422492 lr=0.010000 Epoch[026] Batch [2249]/[3759] Speed: 65.679613 samples/sec accuracy=64.772917 loss=1.422610 lr=0.010000 Epoch[026] Batch [2299]/[3759] Speed: 66.042923 samples/sec accuracy=64.786005 loss=1.422321 lr=0.010000 Epoch[026] Batch [2349]/[3759] Speed: 65.982197 samples/sec accuracy=64.771941 loss=1.423637 lr=0.010000 Epoch[026] Batch [2399]/[3759] Speed: 65.578805 samples/sec accuracy=64.763672 loss=1.424325 lr=0.010000 Epoch[026] Batch [2449]/[3759] Speed: 65.719044 samples/sec accuracy=64.765944 loss=1.424743 lr=0.010000 Epoch[026] Batch [2499]/[3759] Speed: 65.875990 samples/sec accuracy=64.774375 loss=1.423928 lr=0.010000 Epoch[026] Batch [2549]/[3759] Speed: 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lr=0.010000 Epoch[026] Batch [3049]/[3759] Speed: 66.287032 samples/sec accuracy=64.748463 loss=1.426563 lr=0.010000 Epoch[026] Batch [3099]/[3759] Speed: 66.097321 samples/sec accuracy=64.747984 loss=1.426936 lr=0.010000 Epoch[026] Batch [3149]/[3759] Speed: 66.539995 samples/sec accuracy=64.749504 loss=1.426887 lr=0.010000 Epoch[026] Batch [3199]/[3759] Speed: 65.980933 samples/sec accuracy=64.728027 loss=1.427310 lr=0.010000 Epoch[026] Batch [3249]/[3759] Speed: 65.947004 samples/sec accuracy=64.722596 loss=1.427315 lr=0.010000 Epoch[026] Batch [3299]/[3759] Speed: 66.113213 samples/sec accuracy=64.723011 loss=1.427776 lr=0.010000 Epoch[026] Batch [3349]/[3759] Speed: 66.154603 samples/sec accuracy=64.714552 loss=1.427819 lr=0.010000 Epoch[026] Batch [3399]/[3759] Speed: 65.952682 samples/sec accuracy=64.696232 loss=1.428541 lr=0.010000 Epoch[026] Batch [3449]/[3759] Speed: 65.932244 samples/sec accuracy=64.679801 loss=1.429479 lr=0.010000 Epoch[026] Batch [3499]/[3759] Speed: 65.862215 samples/sec accuracy=64.673214 loss=1.429590 lr=0.010000 Epoch[026] Batch [3549]/[3759] Speed: 66.311012 samples/sec accuracy=64.674296 loss=1.429653 lr=0.010000 Epoch[026] Batch [3599]/[3759] Speed: 66.515824 samples/sec accuracy=64.661892 loss=1.430663 lr=0.010000 Epoch[026] Batch [3649]/[3759] Speed: 65.550404 samples/sec accuracy=64.655822 loss=1.430978 lr=0.010000 Epoch[026] Batch [3699]/[3759] Speed: 66.310439 samples/sec accuracy=64.658361 loss=1.430413 lr=0.010000 Epoch[026] Batch [3749]/[3759] Speed: 73.328662 samples/sec accuracy=64.656667 loss=1.430536 lr=0.010000 Batch [0049]/[0303]: acc-top1=60.000000 acc-top5=82.593750 Batch [0099]/[0303]: acc-top1=60.609375 acc-top5=82.968750 Batch [0149]/[0303]: acc-top1=61.010417 acc-top5=82.864583 Batch [0199]/[0303]: acc-top1=60.484375 acc-top5=82.882812 Batch [0249]/[0303]: acc-top1=60.406250 acc-top5=83.087500 Batch [0299]/[0303]: acc-top1=60.583333 acc-top5=83.223958 [Epoch 026] training: accuracy=64.652334 loss=1.430696 [Epoch 026] speed: 65 samples/sec time cost: 3935.056370 [Epoch 026] validation: acc-top1=60.597153 acc-top5=83.235355 loss=1.787753 Epoch[027] Batch [0049]/[3760] Speed: 44.740747 samples/sec accuracy=65.968750 loss=1.390065 lr=0.010000 Epoch[027] Batch [0099]/[3760] Speed: 63.861507 samples/sec accuracy=66.015625 loss=1.373955 lr=0.010000 Epoch[027] Batch [0149]/[3760] Speed: 66.209411 samples/sec accuracy=66.250000 loss=1.367787 lr=0.010000 Epoch[027] Batch [0199]/[3760] Speed: 65.550645 samples/sec accuracy=66.250000 loss=1.365433 lr=0.010000 Epoch[027] Batch [0249]/[3760] Speed: 66.293909 samples/sec accuracy=66.218750 loss=1.366610 lr=0.010000 Epoch[027] Batch [0299]/[3760] Speed: 65.648539 samples/sec accuracy=66.005208 loss=1.369900 lr=0.010000 Epoch[027] Batch [0349]/[3760] Speed: 66.291283 samples/sec accuracy=66.066964 loss=1.365945 lr=0.010000 Epoch[027] Batch [0399]/[3760] Speed: 65.982499 samples/sec accuracy=66.152344 loss=1.368369 lr=0.010000 Epoch[027] Batch 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accuracy=65.663194 loss=1.383038 lr=0.010000 Epoch[027] Batch [0949]/[3760] Speed: 66.336112 samples/sec accuracy=65.667763 loss=1.384136 lr=0.010000 Epoch[027] Batch [0999]/[3760] Speed: 65.616049 samples/sec accuracy=65.631250 loss=1.386596 lr=0.010000 Epoch[027] Batch [1049]/[3760] Speed: 66.140741 samples/sec accuracy=65.625000 loss=1.387240 lr=0.010000 Epoch[027] Batch [1099]/[3760] Speed: 65.737248 samples/sec accuracy=65.565341 loss=1.388465 lr=0.010000 Epoch[027] Batch [1149]/[3760] Speed: 66.145427 samples/sec accuracy=65.506793 loss=1.390918 lr=0.010000 Epoch[027] Batch [1199]/[3760] Speed: 65.635985 samples/sec accuracy=65.477865 loss=1.391505 lr=0.010000 Epoch[027] Batch [1249]/[3760] Speed: 66.070651 samples/sec accuracy=65.428750 loss=1.394480 lr=0.010000 Epoch[027] Batch [1299]/[3760] Speed: 66.529762 samples/sec accuracy=65.429087 loss=1.395242 lr=0.010000 Epoch[027] Batch [1349]/[3760] Speed: 66.392370 samples/sec accuracy=65.393519 loss=1.398190 lr=0.010000 Epoch[027] 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accuracy=65.322635 loss=1.401713 lr=0.010000 Epoch[027] Batch [1899]/[3760] Speed: 66.252306 samples/sec accuracy=65.315789 loss=1.401177 lr=0.010000 Epoch[027] Batch [1949]/[3760] Speed: 66.337295 samples/sec accuracy=65.304487 loss=1.402084 lr=0.010000 Epoch[027] Batch [1999]/[3760] Speed: 65.596962 samples/sec accuracy=65.302344 loss=1.401555 lr=0.010000 Epoch[027] Batch [2049]/[3760] Speed: 66.335258 samples/sec accuracy=65.286585 loss=1.402700 lr=0.010000 Epoch[027] Batch [2099]/[3760] Speed: 65.693207 samples/sec accuracy=65.259673 loss=1.403433 lr=0.010000 Epoch[027] Batch [2149]/[3760] Speed: 65.694546 samples/sec accuracy=65.239099 loss=1.403704 lr=0.010000 Epoch[027] Batch [2199]/[3760] Speed: 66.301277 samples/sec accuracy=65.199574 loss=1.405764 lr=0.010000 Epoch[027] Batch [2249]/[3760] Speed: 65.943900 samples/sec accuracy=65.154861 loss=1.407370 lr=0.010000 Epoch[027] Batch [2299]/[3760] Speed: 66.056726 samples/sec accuracy=65.135190 loss=1.407855 lr=0.010000 Epoch[027] 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accuracy=65.063058 loss=1.411166 lr=0.010000 Epoch[027] Batch [2849]/[3760] Speed: 65.845266 samples/sec accuracy=65.056469 loss=1.411724 lr=0.010000 Epoch[027] Batch [2899]/[3760] Speed: 66.324490 samples/sec accuracy=65.048491 loss=1.412440 lr=0.010000 Epoch[027] Batch [2949]/[3760] Speed: 65.671425 samples/sec accuracy=65.047140 loss=1.412883 lr=0.010000 Epoch[027] Batch [2999]/[3760] Speed: 66.490947 samples/sec accuracy=65.039583 loss=1.413065 lr=0.010000 Epoch[027] Batch [3049]/[3760] Speed: 66.153411 samples/sec accuracy=65.027152 loss=1.413702 lr=0.010000 Epoch[027] Batch [3099]/[3760] Speed: 66.094962 samples/sec accuracy=65.009073 loss=1.414426 lr=0.010000 Epoch[027] Batch [3149]/[3760] Speed: 65.907228 samples/sec accuracy=64.984623 loss=1.415550 lr=0.010000 Epoch[027] Batch [3199]/[3760] Speed: 65.704751 samples/sec accuracy=64.983398 loss=1.415737 lr=0.010000 Epoch[027] Batch [3249]/[3760] Speed: 66.566137 samples/sec accuracy=64.969231 loss=1.416181 lr=0.010000 Epoch[027] Batch [3299]/[3760] Speed: 66.192341 samples/sec accuracy=64.974432 loss=1.416334 lr=0.010000 Epoch[027] Batch [3349]/[3760] Speed: 66.465397 samples/sec accuracy=64.953825 loss=1.416655 lr=0.010000 Epoch[027] Batch [3399]/[3760] Speed: 65.509213 samples/sec accuracy=64.954504 loss=1.416855 lr=0.010000 Epoch[027] Batch [3449]/[3760] Speed: 66.017690 samples/sec accuracy=64.942029 loss=1.417634 lr=0.010000 Epoch[027] Batch [3499]/[3760] Speed: 65.885841 samples/sec accuracy=64.956250 loss=1.417213 lr=0.010000 Epoch[027] Batch [3549]/[3760] Speed: 66.490293 samples/sec accuracy=64.952025 loss=1.417445 lr=0.010000 Epoch[027] Batch [3599]/[3760] Speed: 66.273511 samples/sec accuracy=64.944878 loss=1.417891 lr=0.010000 Epoch[027] Batch [3649]/[3760] Speed: 65.922270 samples/sec accuracy=64.946918 loss=1.417489 lr=0.010000 Epoch[027] Batch [3699]/[3760] Speed: 66.226943 samples/sec accuracy=64.924831 loss=1.418545 lr=0.010000 Epoch[027] Batch [3749]/[3760] Speed: 72.517050 samples/sec accuracy=64.917917 loss=1.419205 lr=0.010000 Batch [0049]/[0303]: acc-top1=60.843750 acc-top5=82.750000 Batch [0099]/[0303]: acc-top1=60.671875 acc-top5=83.218750 Batch [0149]/[0303]: acc-top1=61.093750 acc-top5=83.104167 Batch [0199]/[0303]: acc-top1=60.867188 acc-top5=83.000000 Batch [0249]/[0303]: acc-top1=61.062500 acc-top5=83.150000 Batch [0299]/[0303]: acc-top1=61.203125 acc-top5=83.276042 [Epoch 027] training: accuracy=64.913979 loss=1.419251 [Epoch 027] speed: 65 samples/sec time cost: 3935.222133 [Epoch 027] validation: acc-top1=61.200495 acc-top5=83.297236 loss=1.796644 Epoch[028] Batch [0049]/[3760] Speed: 44.593564 samples/sec accuracy=65.437500 loss=1.421524 lr=0.010000 Epoch[028] Batch [0099]/[3760] Speed: 64.168429 samples/sec accuracy=65.531250 loss=1.397547 lr=0.010000 Epoch[028] Batch [0149]/[3760] Speed: 65.973507 samples/sec accuracy=65.489583 loss=1.400885 lr=0.010000 Epoch[028] Batch [0199]/[3760] Speed: 65.500554 samples/sec accuracy=65.664062 loss=1.391106 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lr=0.010000 Epoch[028] Batch [2149]/[3760] Speed: 65.705469 samples/sec accuracy=65.228924 loss=1.405638 lr=0.010000 Epoch[028] Batch [2199]/[3760] Speed: 66.418542 samples/sec accuracy=65.220881 loss=1.405458 lr=0.010000 Epoch[028] Batch [2249]/[3760] Speed: 65.523545 samples/sec accuracy=65.209028 loss=1.406705 lr=0.010000 Epoch[028] Batch [2299]/[3760] Speed: 66.463534 samples/sec accuracy=65.184103 loss=1.406993 lr=0.010000 Epoch[028] Batch [2349]/[3760] Speed: 65.697193 samples/sec accuracy=65.168218 loss=1.407112 lr=0.010000 Epoch[028] Batch [2399]/[3760] Speed: 66.395034 samples/sec accuracy=65.169271 loss=1.407757 lr=0.010000 Epoch[028] Batch [2449]/[3760] Speed: 65.596361 samples/sec accuracy=65.137755 loss=1.408484 lr=0.010000 Epoch[028] Batch [2499]/[3760] Speed: 66.222273 samples/sec accuracy=65.117500 loss=1.408765 lr=0.010000 Epoch[028] Batch [2549]/[3760] Speed: 66.061385 samples/sec accuracy=65.124387 loss=1.409248 lr=0.010000 Epoch[028] Batch [2599]/[3760] Speed: 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lr=0.010000 Epoch[028] Batch [3099]/[3760] Speed: 65.897945 samples/sec accuracy=65.112399 loss=1.411599 lr=0.010000 Epoch[028] Batch [3149]/[3760] Speed: 66.099056 samples/sec accuracy=65.075893 loss=1.412996 lr=0.010000 Epoch[028] Batch [3199]/[3760] Speed: 66.007803 samples/sec accuracy=65.053223 loss=1.413545 lr=0.010000 Epoch[028] Batch [3249]/[3760] Speed: 65.965571 samples/sec accuracy=65.043269 loss=1.413948 lr=0.010000 Epoch[028] Batch [3299]/[3760] Speed: 66.247529 samples/sec accuracy=65.040246 loss=1.414512 lr=0.010000 Epoch[028] Batch [3349]/[3760] Speed: 66.195308 samples/sec accuracy=65.047575 loss=1.414733 lr=0.010000 Epoch[028] Batch [3399]/[3760] Speed: 65.888218 samples/sec accuracy=65.018842 loss=1.415794 lr=0.010000 Epoch[028] Batch [3449]/[3760] Speed: 66.301256 samples/sec accuracy=65.005888 loss=1.416106 lr=0.010000 Epoch[028] Batch [3499]/[3760] Speed: 65.952221 samples/sec accuracy=65.002679 loss=1.416016 lr=0.010000 Epoch[028] Batch [3549]/[3760] Speed: 65.856273 samples/sec accuracy=64.992958 loss=1.416406 lr=0.010000 Epoch[028] Batch [3599]/[3760] Speed: 66.010593 samples/sec accuracy=64.984375 loss=1.416617 lr=0.010000 Epoch[028] Batch [3649]/[3760] Speed: 66.226764 samples/sec accuracy=64.987586 loss=1.416820 lr=0.010000 Epoch[028] Batch [3699]/[3760] Speed: 66.046665 samples/sec accuracy=64.981841 loss=1.417058 lr=0.010000 Epoch[028] Batch [3749]/[3760] Speed: 72.826147 samples/sec accuracy=64.975417 loss=1.417074 lr=0.010000 Batch [0049]/[0303]: acc-top1=61.062500 acc-top5=82.531250 Batch [0099]/[0303]: acc-top1=61.265625 acc-top5=83.250000 Batch [0149]/[0303]: acc-top1=61.666667 acc-top5=83.010417 Batch [0199]/[0303]: acc-top1=61.273438 acc-top5=83.250000 Batch [0249]/[0303]: acc-top1=61.393750 acc-top5=83.481250 Batch [0299]/[0303]: acc-top1=61.677083 acc-top5=83.567708 [Epoch 028] training: accuracy=64.977560 loss=1.417136 [Epoch 028] speed: 65 samples/sec time cost: 3934.817128 [Epoch 028] validation: acc-top1=61.680074 acc-top5=83.586015 loss=1.795300 Epoch[029] Batch [0049]/[3759] Speed: 44.956595 samples/sec accuracy=66.218750 loss=1.410420 lr=0.010000 Epoch[029] Batch [0099]/[3759] Speed: 63.982843 samples/sec accuracy=67.265625 loss=1.349067 lr=0.010000 Epoch[029] Batch [0149]/[3759] Speed: 65.493063 samples/sec accuracy=66.604167 loss=1.355217 lr=0.010000 Epoch[029] Batch [0199]/[3759] Speed: 65.340660 samples/sec accuracy=66.851562 loss=1.349957 lr=0.010000 Epoch[029] Batch [0249]/[3759] Speed: 66.480985 samples/sec accuracy=66.787500 loss=1.348027 lr=0.010000 Epoch[029] Batch [0299]/[3759] Speed: 65.998670 samples/sec accuracy=66.697917 loss=1.348766 lr=0.010000 Epoch[029] Batch [0349]/[3759] Speed: 65.757647 samples/sec accuracy=66.584821 loss=1.354182 lr=0.010000 Epoch[029] Batch [0399]/[3759] Speed: 66.005973 samples/sec accuracy=66.589844 loss=1.352780 lr=0.010000 Epoch[029] Batch [0449]/[3759] Speed: 65.932507 samples/sec accuracy=66.649306 loss=1.352237 lr=0.010000 Epoch[029] Batch 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accuracy=66.097039 loss=1.371797 lr=0.010000 Epoch[029] Batch [0999]/[3759] Speed: 65.253418 samples/sec accuracy=66.048438 loss=1.373964 lr=0.010000 Epoch[029] Batch [1049]/[3759] Speed: 66.223759 samples/sec accuracy=66.023810 loss=1.373985 lr=0.010000 Epoch[029] Batch [1099]/[3759] Speed: 65.380261 samples/sec accuracy=65.958807 loss=1.375705 lr=0.010000 Epoch[029] Batch [1149]/[3759] Speed: 66.384046 samples/sec accuracy=65.978261 loss=1.376282 lr=0.010000 Epoch[029] Batch [1199]/[3759] Speed: 66.272455 samples/sec accuracy=65.881510 loss=1.379957 lr=0.010000 Epoch[029] Batch [1249]/[3759] Speed: 66.123458 samples/sec accuracy=65.848750 loss=1.381264 lr=0.010000 Epoch[029] Batch [1299]/[3759] Speed: 66.296472 samples/sec accuracy=65.814904 loss=1.382333 lr=0.010000 Epoch[029] Batch [1349]/[3759] Speed: 65.567063 samples/sec accuracy=65.857639 loss=1.381973 lr=0.010000 Epoch[029] Batch [1399]/[3759] Speed: 66.507054 samples/sec accuracy=65.824777 loss=1.383867 lr=0.010000 Epoch[029] 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accuracy=65.530428 loss=1.395619 lr=0.010000 Epoch[029] Batch [1949]/[3759] Speed: 66.379138 samples/sec accuracy=65.504808 loss=1.396865 lr=0.010000 Epoch[029] Batch [1999]/[3759] Speed: 66.278501 samples/sec accuracy=65.523438 loss=1.396397 lr=0.010000 Epoch[029] Batch [2049]/[3759] Speed: 65.773466 samples/sec accuracy=65.512195 loss=1.396285 lr=0.010000 Epoch[029] Batch [2099]/[3759] Speed: 66.099196 samples/sec accuracy=65.534970 loss=1.396106 lr=0.010000 Epoch[029] Batch [2149]/[3759] Speed: 65.729536 samples/sec accuracy=65.519622 loss=1.396612 lr=0.010000 Epoch[029] Batch [2199]/[3759] Speed: 66.058020 samples/sec accuracy=65.514915 loss=1.397176 lr=0.010000 Epoch[029] Batch [2249]/[3759] Speed: 66.445618 samples/sec accuracy=65.565278 loss=1.395532 lr=0.010000 Epoch[029] Batch [2299]/[3759] Speed: 65.908647 samples/sec accuracy=65.548913 loss=1.395667 lr=0.010000 Epoch[029] Batch [2349]/[3759] Speed: 66.698031 samples/sec accuracy=65.535239 loss=1.396330 lr=0.010000 Epoch[029] 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accuracy=65.435307 loss=1.402174 lr=0.010000 Epoch[029] Batch [2899]/[3759] Speed: 66.231385 samples/sec accuracy=65.414871 loss=1.402762 lr=0.010000 Epoch[029] Batch [2949]/[3759] Speed: 66.545526 samples/sec accuracy=65.422140 loss=1.402266 lr=0.010000 Epoch[029] Batch [2999]/[3759] Speed: 65.539902 samples/sec accuracy=65.429688 loss=1.402819 lr=0.010000 Epoch[029] Batch [3049]/[3759] Speed: 65.695580 samples/sec accuracy=65.404713 loss=1.403271 lr=0.010000 Epoch[029] Batch [3099]/[3759] Speed: 65.675728 samples/sec accuracy=65.404234 loss=1.403101 lr=0.010000 Epoch[029] Batch [3149]/[3759] Speed: 66.470592 samples/sec accuracy=65.378968 loss=1.403835 lr=0.010000 Epoch[029] Batch [3199]/[3759] Speed: 65.801783 samples/sec accuracy=65.350586 loss=1.405058 lr=0.010000 Epoch[029] Batch [3249]/[3759] Speed: 65.753991 samples/sec accuracy=65.344712 loss=1.405486 lr=0.010000 Epoch[029] Batch [3299]/[3759] Speed: 66.173678 samples/sec accuracy=65.337595 loss=1.405828 lr=0.010000 Epoch[029] Batch [3349]/[3759] Speed: 65.938239 samples/sec accuracy=65.326026 loss=1.406223 lr=0.010000 Epoch[029] Batch [3399]/[3759] Speed: 66.438996 samples/sec accuracy=65.315717 loss=1.406444 lr=0.010000 Epoch[029] Batch [3449]/[3759] Speed: 65.746307 samples/sec accuracy=65.296196 loss=1.408052 lr=0.010000 Epoch[029] Batch [3499]/[3759] Speed: 65.996827 samples/sec accuracy=65.301786 loss=1.408105 lr=0.010000 Epoch[029] Batch [3549]/[3759] Speed: 66.222494 samples/sec accuracy=65.290053 loss=1.408038 lr=0.010000 Epoch[029] Batch [3599]/[3759] Speed: 65.879407 samples/sec accuracy=65.276476 loss=1.408209 lr=0.010000 Epoch[029] Batch [3649]/[3759] Speed: 66.379863 samples/sec accuracy=65.252140 loss=1.408952 lr=0.010000 Epoch[029] Batch [3699]/[3759] Speed: 66.093200 samples/sec accuracy=65.220017 loss=1.409654 lr=0.010000 Epoch[029] Batch [3749]/[3759] Speed: 73.514978 samples/sec accuracy=65.195833 loss=1.410363 lr=0.010000 Batch [0049]/[0303]: acc-top1=60.343750 acc-top5=82.750000 Batch [0099]/[0303]: acc-top1=60.375000 acc-top5=83.234375 Batch [0149]/[0303]: acc-top1=61.072917 acc-top5=83.041667 Batch [0199]/[0303]: acc-top1=61.062500 acc-top5=83.062500 Batch [0249]/[0303]: acc-top1=61.006250 acc-top5=83.318750 Batch [0299]/[0303]: acc-top1=61.369792 acc-top5=83.369792 [Epoch 029] training: accuracy=65.191457 loss=1.410581 [Epoch 029] speed: 65 samples/sec time cost: 3933.880303 [Epoch 029] validation: acc-top1=61.355198 acc-top5=83.384901 loss=1.768311 Epoch[030] Batch [0049]/[3760] Speed: 44.089985 samples/sec accuracy=66.312500 loss=1.354538 lr=0.010000 Epoch[030] Batch [0099]/[3760] Speed: 63.752808 samples/sec accuracy=65.875000 loss=1.374634 lr=0.010000 Epoch[030] Batch [0149]/[3760] Speed: 66.195396 samples/sec accuracy=65.937500 loss=1.370392 lr=0.010000 Epoch[030] Batch [0199]/[3760] Speed: 64.865837 samples/sec accuracy=66.031250 loss=1.364858 lr=0.010000 Epoch[030] Batch [0249]/[3760] Speed: 65.701288 samples/sec accuracy=65.956250 loss=1.361860 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lr=0.010000 Epoch[030] Batch [1249]/[3760] Speed: 65.804679 samples/sec accuracy=65.801250 loss=1.381892 lr=0.010000 Epoch[030] Batch [1299]/[3760] Speed: 66.237293 samples/sec accuracy=65.774038 loss=1.383167 lr=0.010000 Epoch[030] Batch [1349]/[3760] Speed: 65.774367 samples/sec accuracy=65.766204 loss=1.382564 lr=0.010000 Epoch[030] Batch [1399]/[3760] Speed: 66.236810 samples/sec accuracy=65.779018 loss=1.381918 lr=0.010000 Epoch[030] Batch [1449]/[3760] Speed: 66.057537 samples/sec accuracy=65.775862 loss=1.382548 lr=0.010000 Epoch[030] Batch [1499]/[3760] Speed: 65.860853 samples/sec accuracy=65.673958 loss=1.385955 lr=0.010000 Epoch[030] Batch [1549]/[3760] Speed: 66.412738 samples/sec accuracy=65.607863 loss=1.388423 lr=0.010000 Epoch[030] Batch [1599]/[3760] Speed: 65.800000 samples/sec accuracy=65.583984 loss=1.389285 lr=0.010000 Epoch[030] Batch [1649]/[3760] Speed: 66.297668 samples/sec accuracy=65.588068 loss=1.389715 lr=0.010000 Epoch[030] Batch [1699]/[3760] Speed: 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lr=0.010000 Epoch[030] Batch [2199]/[3760] Speed: 65.775653 samples/sec accuracy=65.386364 loss=1.396752 lr=0.010000 Epoch[030] Batch [2249]/[3760] Speed: 66.156689 samples/sec accuracy=65.393750 loss=1.396709 lr=0.010000 Epoch[030] Batch [2299]/[3760] Speed: 65.714960 samples/sec accuracy=65.391984 loss=1.396567 lr=0.010000 Epoch[030] Batch [2349]/[3760] Speed: 66.139832 samples/sec accuracy=65.400931 loss=1.396471 lr=0.010000 Epoch[030] Batch [2399]/[3760] Speed: 65.864895 samples/sec accuracy=65.398438 loss=1.397624 lr=0.010000 Epoch[030] Batch [2449]/[3760] Speed: 66.078054 samples/sec accuracy=65.390306 loss=1.397557 lr=0.010000 Epoch[030] Batch [2499]/[3760] Speed: 65.814438 samples/sec accuracy=65.356875 loss=1.398814 lr=0.010000 Epoch[030] Batch [2549]/[3760] Speed: 66.357519 samples/sec accuracy=65.366422 loss=1.398995 lr=0.010000 Epoch[030] Batch [2599]/[3760] Speed: 66.090856 samples/sec accuracy=65.395433 loss=1.397755 lr=0.010000 Epoch[030] Batch [2649]/[3760] Speed: 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lr=0.010000 Epoch[030] Batch [3149]/[3760] Speed: 65.988239 samples/sec accuracy=65.335317 loss=1.403566 lr=0.010000 Epoch[030] Batch [3199]/[3760] Speed: 65.573535 samples/sec accuracy=65.333496 loss=1.403736 lr=0.010000 Epoch[030] Batch [3249]/[3760] Speed: 65.868011 samples/sec accuracy=65.317788 loss=1.403822 lr=0.010000 Epoch[030] Batch [3299]/[3760] Speed: 65.973870 samples/sec accuracy=65.290720 loss=1.404946 lr=0.010000 Epoch[030] Batch [3349]/[3760] Speed: 65.584969 samples/sec accuracy=65.292910 loss=1.404854 lr=0.010000 Epoch[030] Batch [3399]/[3760] Speed: 65.932412 samples/sec accuracy=65.305147 loss=1.405069 lr=0.010000 Epoch[030] Batch [3449]/[3760] Speed: 65.971047 samples/sec accuracy=65.292120 loss=1.404891 lr=0.010000 Epoch[030] Batch [3499]/[3760] Speed: 66.136446 samples/sec accuracy=65.292857 loss=1.405607 lr=0.010000 Epoch[030] Batch [3549]/[3760] Speed: 65.989363 samples/sec accuracy=65.264525 loss=1.406722 lr=0.010000 Epoch[030] Batch [3599]/[3760] Speed: 65.899361 samples/sec accuracy=65.259115 loss=1.406783 lr=0.010000 Epoch[030] Batch [3649]/[3760] Speed: 66.067390 samples/sec accuracy=65.266267 loss=1.406164 lr=0.010000 Epoch[030] Batch [3699]/[3760] Speed: 65.737219 samples/sec accuracy=65.271115 loss=1.406081 lr=0.010000 Epoch[030] Batch [3749]/[3760] Speed: 72.750823 samples/sec accuracy=65.257917 loss=1.406483 lr=0.010000 Batch [0049]/[0303]: acc-top1=61.468750 acc-top5=83.562500 Batch [0099]/[0303]: acc-top1=61.546875 acc-top5=83.687500 Batch [0149]/[0303]: acc-top1=61.729167 acc-top5=83.562500 Batch [0199]/[0303]: acc-top1=61.109375 acc-top5=83.500000 Batch [0249]/[0303]: acc-top1=61.100000 acc-top5=83.612500 Batch [0299]/[0303]: acc-top1=61.437500 acc-top5=83.755208 [Epoch 030] training: accuracy=65.257646 loss=1.406581 [Epoch 030] speed: 65 samples/sec time cost: 3939.824112 [Epoch 030] validation: acc-top1=61.468647 acc-top5=83.781972 loss=1.768973 Epoch[031] Batch [0049]/[3760] Speed: 45.028766 samples/sec accuracy=67.312500 loss=1.346050 lr=0.010000 Epoch[031] Batch [0099]/[3760] Speed: 64.422560 samples/sec accuracy=66.875000 loss=1.339602 lr=0.010000 Epoch[031] Batch [0149]/[3760] Speed: 65.646520 samples/sec accuracy=66.562500 loss=1.348605 lr=0.010000 Epoch[031] Batch [0199]/[3760] Speed: 64.818837 samples/sec accuracy=66.515625 loss=1.349279 lr=0.010000 Epoch[031] Batch [0249]/[3760] Speed: 65.924314 samples/sec accuracy=66.168750 loss=1.359795 lr=0.010000 Epoch[031] Batch [0299]/[3760] Speed: 65.587393 samples/sec accuracy=66.291667 loss=1.350619 lr=0.010000 Epoch[031] Batch [0349]/[3760] Speed: 66.380935 samples/sec accuracy=66.294643 loss=1.354665 lr=0.010000 Epoch[031] Batch [0399]/[3760] Speed: 66.096028 samples/sec accuracy=66.214844 loss=1.358698 lr=0.010000 Epoch[031] Batch [0449]/[3760] Speed: 66.044097 samples/sec accuracy=66.472222 loss=1.350940 lr=0.010000 Epoch[031] Batch [0499]/[3760] Speed: 66.207717 samples/sec accuracy=66.431250 loss=1.353227 lr=0.010000 Epoch[031] 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accuracy=66.348438 loss=1.355889 lr=0.010000 Epoch[031] Batch [1049]/[3760] Speed: 65.731357 samples/sec accuracy=66.375000 loss=1.355310 lr=0.010000 Epoch[031] Batch [1099]/[3760] Speed: 65.967271 samples/sec accuracy=66.366477 loss=1.355083 lr=0.010000 Epoch[031] Batch [1149]/[3760] Speed: 66.063024 samples/sec accuracy=66.331522 loss=1.359268 lr=0.010000 Epoch[031] Batch [1199]/[3760] Speed: 66.074681 samples/sec accuracy=66.226562 loss=1.361524 lr=0.010000 Epoch[031] Batch [1249]/[3760] Speed: 66.264461 samples/sec accuracy=66.195000 loss=1.361986 lr=0.010000 Epoch[031] Batch [1299]/[3760] Speed: 66.249140 samples/sec accuracy=66.217548 loss=1.363277 lr=0.010000 Epoch[031] Batch [1349]/[3760] Speed: 65.623067 samples/sec accuracy=66.243056 loss=1.362058 lr=0.010000 Epoch[031] Batch [1399]/[3760] Speed: 65.713382 samples/sec accuracy=66.224330 loss=1.361743 lr=0.010000 Epoch[031] Batch [1449]/[3760] Speed: 66.147539 samples/sec accuracy=66.178879 loss=1.363974 lr=0.010000 Epoch[031] 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accuracy=65.940705 loss=1.375152 lr=0.010000 Epoch[031] Batch [1999]/[3760] Speed: 66.389463 samples/sec accuracy=65.914844 loss=1.375519 lr=0.010000 Epoch[031] Batch [2049]/[3760] Speed: 65.912692 samples/sec accuracy=65.933689 loss=1.375100 lr=0.010000 Epoch[031] Batch [2099]/[3760] Speed: 65.667312 samples/sec accuracy=65.893601 loss=1.376303 lr=0.010000 Epoch[031] Batch [2149]/[3760] Speed: 66.138168 samples/sec accuracy=65.884448 loss=1.377058 lr=0.010000 Epoch[031] Batch [2199]/[3760] Speed: 65.433703 samples/sec accuracy=65.876420 loss=1.377688 lr=0.010000 Epoch[031] Batch [2249]/[3760] Speed: 66.326364 samples/sec accuracy=65.873611 loss=1.378318 lr=0.010000 Epoch[031] Batch [2299]/[3760] Speed: 65.570047 samples/sec accuracy=65.810462 loss=1.381217 lr=0.010000 Epoch[031] Batch [2349]/[3760] Speed: 66.281418 samples/sec accuracy=65.801862 loss=1.381657 lr=0.010000 Epoch[031] Batch [2399]/[3760] Speed: 66.086902 samples/sec accuracy=65.799479 loss=1.381742 lr=0.010000 Epoch[031] Batch [2449]/[3760] Speed: 65.765031 samples/sec accuracy=65.792730 loss=1.381918 lr=0.010000 Epoch[031] Batch [2499]/[3760] Speed: 65.441030 samples/sec accuracy=65.768125 loss=1.383160 lr=0.010000 Epoch[031] Batch [2549]/[3760] Speed: 66.229409 samples/sec accuracy=65.776348 loss=1.382535 lr=0.010000 Epoch[031] Batch [2599]/[3760] Speed: 65.944377 samples/sec accuracy=65.765024 loss=1.382865 lr=0.010000 Epoch[031] Batch [2649]/[3760] Speed: 65.662807 samples/sec accuracy=65.738797 loss=1.383562 lr=0.010000 Epoch[031] Batch [2699]/[3760] Speed: 65.712230 samples/sec accuracy=65.734954 loss=1.383348 lr=0.010000 Epoch[031] Batch [2749]/[3760] Speed: 65.945938 samples/sec accuracy=65.703409 loss=1.384810 lr=0.010000 Epoch[031] Batch [2799]/[3760] Speed: 65.853013 samples/sec accuracy=65.686384 loss=1.384272 lr=0.010000 Epoch[031] Batch [2849]/[3760] Speed: 65.843532 samples/sec accuracy=65.667763 loss=1.384697 lr=0.010000 Epoch[031] Batch [2899]/[3760] Speed: 66.007044 samples/sec accuracy=65.655172 loss=1.385506 lr=0.010000 Epoch[031] Batch [2949]/[3760] Speed: 65.736686 samples/sec accuracy=65.642479 loss=1.386652 lr=0.010000 Epoch[031] Batch [2999]/[3760] Speed: 66.011694 samples/sec accuracy=65.652083 loss=1.386761 lr=0.010000 Epoch[031] Batch [3049]/[3760] Speed: 66.146613 samples/sec accuracy=65.623463 loss=1.388088 lr=0.010000 Epoch[031] Batch [3099]/[3760] Speed: 66.093674 samples/sec accuracy=65.621976 loss=1.388998 lr=0.010000 Epoch[031] Batch [3149]/[3760] Speed: 66.485300 samples/sec accuracy=65.601687 loss=1.390113 lr=0.010000 Epoch[031] Batch [3199]/[3760] Speed: 65.424346 samples/sec accuracy=65.593262 loss=1.390334 lr=0.010000 Epoch[031] Batch [3249]/[3760] Speed: 66.149366 samples/sec accuracy=65.595192 loss=1.390137 lr=0.010000 Epoch[031] Batch [3299]/[3760] Speed: 65.724483 samples/sec accuracy=65.577652 loss=1.391199 lr=0.010000 Epoch[031] Batch [3349]/[3760] Speed: 66.090385 samples/sec accuracy=65.570896 loss=1.392272 lr=0.010000 Epoch[031] Batch [3399]/[3760] Speed: 65.885761 samples/sec accuracy=65.547335 loss=1.393040 lr=0.010000 Epoch[031] Batch [3449]/[3760] Speed: 65.585520 samples/sec accuracy=65.528533 loss=1.394286 lr=0.010000 Epoch[031] Batch [3499]/[3760] Speed: 65.956164 samples/sec accuracy=65.525893 loss=1.394161 lr=0.010000 Epoch[031] Batch [3549]/[3760] Speed: 66.136115 samples/sec accuracy=65.528169 loss=1.394300 lr=0.010000 Epoch[031] Batch [3599]/[3760] Speed: 65.844489 samples/sec accuracy=65.517795 loss=1.394980 lr=0.010000 Epoch[031] Batch [3649]/[3760] Speed: 66.340904 samples/sec accuracy=65.509846 loss=1.394935 lr=0.010000 Epoch[031] Batch [3699]/[3760] Speed: 65.525065 samples/sec accuracy=65.495777 loss=1.395238 lr=0.010000 Epoch[031] Batch [3749]/[3760] Speed: 72.894472 samples/sec accuracy=65.462083 loss=1.396338 lr=0.010000 Batch [0049]/[0303]: acc-top1=61.843750 acc-top5=84.031250 Batch [0099]/[0303]: acc-top1=61.484375 acc-top5=83.843750 Batch [0149]/[0303]: acc-top1=61.447917 acc-top5=83.604167 Batch [0199]/[0303]: acc-top1=61.453125 acc-top5=83.554688 Batch [0249]/[0303]: acc-top1=61.506250 acc-top5=83.593750 Batch [0299]/[0303]: acc-top1=61.703125 acc-top5=83.718750 [Epoch 031] training: accuracy=65.454621 loss=1.396393 [Epoch 031] speed: 65 samples/sec time cost: 3941.629843 [Epoch 031] validation: acc-top1=61.731642 acc-top5=83.730404 loss=1.804925 Epoch[032] Batch [0049]/[3759] Speed: 44.410665 samples/sec accuracy=65.875000 loss=1.364539 lr=0.010000 Epoch[032] Batch [0099]/[3759] Speed: 64.315214 samples/sec accuracy=66.468750 loss=1.357837 lr=0.010000 Epoch[032] Batch [0149]/[3759] Speed: 65.467836 samples/sec accuracy=66.614583 loss=1.351757 lr=0.010000 Epoch[032] Batch [0199]/[3759] Speed: 65.423222 samples/sec accuracy=66.414062 loss=1.350885 lr=0.010000 Epoch[032] Batch [0249]/[3759] Speed: 66.913224 samples/sec accuracy=66.506250 loss=1.346398 lr=0.010000 Epoch[032] Batch [0299]/[3759] Speed: 66.037299 samples/sec accuracy=66.437500 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accuracy=66.018750 loss=1.372139 lr=0.010000 Epoch[032] Batch [1299]/[3759] Speed: 66.257007 samples/sec accuracy=66.003606 loss=1.372957 lr=0.010000 Epoch[032] Batch [1349]/[3759] Speed: 66.183628 samples/sec accuracy=65.966435 loss=1.375483 lr=0.010000 Epoch[032] Batch [1399]/[3759] Speed: 66.256273 samples/sec accuracy=65.880580 loss=1.378818 lr=0.010000 Epoch[032] Batch [1449]/[3759] Speed: 66.219249 samples/sec accuracy=65.875000 loss=1.378761 lr=0.010000 Epoch[032] Batch [1499]/[3759] Speed: 66.377447 samples/sec accuracy=65.828125 loss=1.378998 lr=0.010000 Epoch[032] Batch [1549]/[3759] Speed: 66.079453 samples/sec accuracy=65.846774 loss=1.378605 lr=0.010000 Epoch[032] Batch [1599]/[3759] Speed: 65.654261 samples/sec accuracy=65.885742 loss=1.376631 lr=0.010000 Epoch[032] Batch [1649]/[3759] Speed: 66.813550 samples/sec accuracy=65.873106 loss=1.378245 lr=0.010000 Epoch[032] Batch [1699]/[3759] Speed: 65.341496 samples/sec accuracy=65.886949 loss=1.378215 lr=0.010000 Epoch[032] 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accuracy=65.733665 loss=1.385416 lr=0.010000 Epoch[032] Batch [2249]/[3759] Speed: 66.316160 samples/sec accuracy=65.739583 loss=1.385668 lr=0.010000 Epoch[032] Batch [2299]/[3759] Speed: 65.954169 samples/sec accuracy=65.711277 loss=1.386394 lr=0.010000 Epoch[032] Batch [2349]/[3759] Speed: 66.010476 samples/sec accuracy=65.700798 loss=1.386838 lr=0.010000 Epoch[032] Batch [2399]/[3759] Speed: 65.969590 samples/sec accuracy=65.682943 loss=1.387792 lr=0.010000 Epoch[032] Batch [2449]/[3759] Speed: 65.856944 samples/sec accuracy=65.668367 loss=1.387901 lr=0.010000 Epoch[032] Batch [2499]/[3759] Speed: 66.154890 samples/sec accuracy=65.628125 loss=1.388707 lr=0.010000 Epoch[032] Batch [2549]/[3759] Speed: 66.137462 samples/sec accuracy=65.613971 loss=1.388641 lr=0.010000 Epoch[032] Batch [2599]/[3759] Speed: 66.342048 samples/sec accuracy=65.621394 loss=1.388807 lr=0.010000 Epoch[032] Batch [2649]/[3759] Speed: 66.267577 samples/sec accuracy=65.640330 loss=1.388630 lr=0.010000 Epoch[032] 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accuracy=65.590278 loss=1.390662 lr=0.010000 Epoch[032] Batch [3199]/[3759] Speed: 66.534555 samples/sec accuracy=65.579102 loss=1.391074 lr=0.010000 Epoch[032] Batch [3249]/[3759] Speed: 66.106383 samples/sec accuracy=65.559135 loss=1.391946 lr=0.010000 Epoch[032] Batch [3299]/[3759] Speed: 65.856459 samples/sec accuracy=65.532670 loss=1.392647 lr=0.010000 Epoch[032] Batch [3349]/[3759] Speed: 66.372117 samples/sec accuracy=65.516791 loss=1.393275 lr=0.010000 Epoch[032] Batch [3399]/[3759] Speed: 65.679002 samples/sec accuracy=65.514246 loss=1.393137 lr=0.010000 Epoch[032] Batch [3449]/[3759] Speed: 66.113386 samples/sec accuracy=65.505435 loss=1.393197 lr=0.010000 Epoch[032] Batch [3499]/[3759] Speed: 65.787865 samples/sec accuracy=65.514286 loss=1.392940 lr=0.010000 Epoch[032] Batch [3549]/[3759] Speed: 66.265486 samples/sec accuracy=65.497799 loss=1.393160 lr=0.010000 Epoch[032] Batch [3599]/[3759] Speed: 66.301787 samples/sec accuracy=65.483941 loss=1.393930 lr=0.010000 Epoch[032] Batch [3649]/[3759] Speed: 66.256330 samples/sec accuracy=65.512414 loss=1.393276 lr=0.010000 Epoch[032] Batch [3699]/[3759] Speed: 66.115499 samples/sec accuracy=65.509291 loss=1.393340 lr=0.010000 Epoch[032] Batch [3749]/[3759] Speed: 73.279578 samples/sec accuracy=65.498333 loss=1.393909 lr=0.010000 Batch [0049]/[0303]: acc-top1=61.375000 acc-top5=83.031250 Batch [0099]/[0303]: acc-top1=61.156250 acc-top5=83.406250 Batch [0149]/[0303]: acc-top1=61.375000 acc-top5=83.187500 Batch [0199]/[0303]: acc-top1=61.226562 acc-top5=83.257812 Batch [0249]/[0303]: acc-top1=61.450000 acc-top5=83.518750 Batch [0299]/[0303]: acc-top1=61.765625 acc-top5=83.593750 [Epoch 032] training: accuracy=65.502378 loss=1.393720 [Epoch 032] speed: 65 samples/sec time cost: 3931.255124 [Epoch 032] validation: acc-top1=61.772896 acc-top5=83.616955 loss=1.812134 Epoch[033] Batch [0049]/[3760] Speed: 44.433522 samples/sec accuracy=67.343750 loss=1.310064 lr=0.010000 Epoch[033] Batch [0099]/[3760] Speed: 64.808220 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lr=0.010000 Epoch[033] Batch [0599]/[3760] Speed: 66.196816 samples/sec accuracy=66.510417 loss=1.346411 lr=0.010000 Epoch[033] Batch [0649]/[3760] Speed: 66.045497 samples/sec accuracy=66.490385 loss=1.347444 lr=0.010000 Epoch[033] Batch [0699]/[3760] Speed: 66.096730 samples/sec accuracy=66.488839 loss=1.346992 lr=0.010000 Epoch[033] Batch [0749]/[3760] Speed: 66.362838 samples/sec accuracy=66.547917 loss=1.345449 lr=0.010000 Epoch[033] Batch [0799]/[3760] Speed: 66.363232 samples/sec accuracy=66.517578 loss=1.347823 lr=0.010000 Epoch[033] Batch [0849]/[3760] Speed: 66.136915 samples/sec accuracy=66.501838 loss=1.349514 lr=0.010000 Epoch[033] Batch [0899]/[3760] Speed: 65.972208 samples/sec accuracy=66.519097 loss=1.347197 lr=0.010000 Epoch[033] Batch [0949]/[3760] Speed: 66.490249 samples/sec accuracy=66.486842 loss=1.350830 lr=0.010000 Epoch[033] Batch [0999]/[3760] Speed: 65.754509 samples/sec accuracy=66.432813 loss=1.352514 lr=0.010000 Epoch[033] Batch [1049]/[3760] Speed: 66.239918 samples/sec accuracy=66.372024 loss=1.354320 lr=0.010000 Epoch[033] Batch [1099]/[3760] Speed: 66.271466 samples/sec accuracy=66.342330 loss=1.356463 lr=0.010000 Epoch[033] Batch [1149]/[3760] Speed: 65.892656 samples/sec accuracy=66.251359 loss=1.359084 lr=0.010000 Epoch[033] Batch [1199]/[3760] Speed: 66.177487 samples/sec accuracy=66.255208 loss=1.359926 lr=0.010000 Epoch[033] Batch [1249]/[3760] Speed: 66.151261 samples/sec accuracy=66.267500 loss=1.359678 lr=0.010000 Epoch[033] Batch [1299]/[3760] Speed: 66.044027 samples/sec accuracy=66.268029 loss=1.359751 lr=0.010000 Epoch[033] Batch [1349]/[3760] Speed: 66.338792 samples/sec accuracy=66.243056 loss=1.359991 lr=0.010000 Epoch[033] Batch [1399]/[3760] Speed: 66.229504 samples/sec accuracy=66.219866 loss=1.361529 lr=0.010000 Epoch[033] Batch [1449]/[3760] Speed: 65.910761 samples/sec accuracy=66.209052 loss=1.362292 lr=0.010000 Epoch[033] Batch [1499]/[3760] Speed: 66.166385 samples/sec accuracy=66.248958 loss=1.360646 lr=0.010000 Epoch[033] Batch [1549]/[3760] Speed: 66.472315 samples/sec accuracy=66.229839 loss=1.362018 lr=0.010000 Epoch[033] Batch [1599]/[3760] Speed: 66.168379 samples/sec accuracy=66.232422 loss=1.362702 lr=0.010000 Epoch[033] Batch [1649]/[3760] Speed: 66.006296 samples/sec accuracy=66.232955 loss=1.363302 lr=0.010000 Epoch[033] Batch [1699]/[3760] Speed: 66.464756 samples/sec accuracy=66.219669 loss=1.363961 lr=0.010000 Epoch[033] Batch [1749]/[3760] Speed: 66.287783 samples/sec accuracy=66.180357 loss=1.365300 lr=0.010000 Epoch[033] Batch [1799]/[3760] Speed: 66.196570 samples/sec accuracy=66.156250 loss=1.366581 lr=0.010000 Epoch[033] Batch [1849]/[3760] Speed: 65.994650 samples/sec accuracy=66.095439 loss=1.368935 lr=0.010000 Epoch[033] Batch [1899]/[3760] Speed: 66.626861 samples/sec accuracy=66.088816 loss=1.369127 lr=0.010000 Epoch[033] Batch [1949]/[3760] Speed: 66.117299 samples/sec accuracy=66.078526 loss=1.371275 lr=0.010000 Epoch[033] Batch [1999]/[3760] Speed: 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lr=0.010000 Epoch[033] Batch [2499]/[3760] Speed: 65.733407 samples/sec accuracy=65.900625 loss=1.378610 lr=0.010000 Epoch[033] Batch [2549]/[3760] Speed: 66.385616 samples/sec accuracy=65.879902 loss=1.378905 lr=0.010000 Epoch[033] Batch [2599]/[3760] Speed: 66.235100 samples/sec accuracy=65.874399 loss=1.379541 lr=0.010000 Epoch[033] Batch [2649]/[3760] Speed: 66.148016 samples/sec accuracy=65.877358 loss=1.379862 lr=0.010000 Epoch[033] Batch [2699]/[3760] Speed: 66.106029 samples/sec accuracy=65.863426 loss=1.380521 lr=0.010000 Epoch[033] Batch [2749]/[3760] Speed: 65.509395 samples/sec accuracy=65.871591 loss=1.380147 lr=0.010000 Epoch[033] Batch [2799]/[3760] Speed: 65.777279 samples/sec accuracy=65.872210 loss=1.380350 lr=0.010000 Epoch[033] Batch [2849]/[3760] Speed: 66.199236 samples/sec accuracy=65.844846 loss=1.380995 lr=0.010000 Epoch[033] Batch [2899]/[3760] Speed: 65.800365 samples/sec accuracy=65.822737 loss=1.382038 lr=0.010000 Epoch[033] Batch [2949]/[3760] Speed: 66.628959 samples/sec accuracy=65.810911 loss=1.382982 lr=0.010000 Epoch[033] Batch [2999]/[3760] Speed: 66.204227 samples/sec accuracy=65.798437 loss=1.382925 lr=0.010000 Epoch[033] Batch [3049]/[3760] Speed: 66.326220 samples/sec accuracy=65.785861 loss=1.383769 lr=0.010000 Epoch[033] Batch [3099]/[3760] Speed: 65.675231 samples/sec accuracy=65.754032 loss=1.384590 lr=0.010000 Epoch[033] Batch [3149]/[3760] Speed: 65.990939 samples/sec accuracy=65.725694 loss=1.385564 lr=0.010000 Epoch[033] Batch [3199]/[3760] Speed: 65.542032 samples/sec accuracy=65.730469 loss=1.385031 lr=0.010000 Epoch[033] Batch [3249]/[3760] Speed: 66.027075 samples/sec accuracy=65.706250 loss=1.385953 lr=0.010000 Epoch[033] Batch [3299]/[3760] Speed: 66.505047 samples/sec accuracy=65.681818 loss=1.387063 lr=0.010000 Epoch[033] Batch [3349]/[3760] Speed: 65.930182 samples/sec accuracy=65.658116 loss=1.387611 lr=0.010000 Epoch[033] Batch [3399]/[3760] Speed: 66.088279 samples/sec accuracy=65.624081 loss=1.388828 lr=0.010000 Epoch[033] Batch [3449]/[3760] Speed: 66.413228 samples/sec accuracy=65.603714 loss=1.389891 lr=0.010000 Epoch[033] Batch [3499]/[3760] Speed: 65.900146 samples/sec accuracy=65.610268 loss=1.389646 lr=0.010000 Epoch[033] Batch [3549]/[3760] Speed: 66.165820 samples/sec accuracy=65.606954 loss=1.389810 lr=0.010000 Epoch[033] Batch [3599]/[3760] Speed: 65.820672 samples/sec accuracy=65.587674 loss=1.390078 lr=0.010000 Epoch[033] Batch [3649]/[3760] Speed: 66.292684 samples/sec accuracy=65.595034 loss=1.390403 lr=0.010000 Epoch[033] Batch [3699]/[3760] Speed: 65.979667 samples/sec accuracy=65.589527 loss=1.390747 lr=0.010000 Epoch[033] Batch [3749]/[3760] Speed: 73.031611 samples/sec accuracy=65.570417 loss=1.391561 lr=0.010000 Batch [0049]/[0303]: acc-top1=61.625000 acc-top5=83.281250 Batch [0099]/[0303]: acc-top1=61.062500 acc-top5=83.453125 Batch [0149]/[0303]: acc-top1=61.500000 acc-top5=83.041667 Batch [0199]/[0303]: acc-top1=61.359375 acc-top5=83.117188 Batch [0249]/[0303]: acc-top1=61.331250 acc-top5=83.312500 Batch [0299]/[0303]: acc-top1=61.562500 acc-top5=83.479167 [Epoch 033] training: accuracy=65.556848 loss=1.391954 [Epoch 033] speed: 65 samples/sec time cost: 3933.213904 [Epoch 033] validation: acc-top1=61.571782 acc-top5=83.493193 loss=1.759780 Epoch[034] Batch [0049]/[3759] Speed: 44.739822 samples/sec accuracy=67.000000 loss=1.301824 lr=0.010000 Epoch[034] Batch [0099]/[3759] Speed: 63.817996 samples/sec accuracy=65.921875 loss=1.365476 lr=0.010000 Epoch[034] Batch [0149]/[3759] Speed: 66.310003 samples/sec accuracy=66.250000 loss=1.342170 lr=0.010000 Epoch[034] Batch [0199]/[3759] Speed: 65.042080 samples/sec accuracy=66.445312 loss=1.343681 lr=0.010000 Epoch[034] Batch [0249]/[3759] Speed: 66.455681 samples/sec accuracy=66.437500 loss=1.340506 lr=0.010000 Epoch[034] Batch [0299]/[3759] Speed: 65.887092 samples/sec accuracy=66.359375 loss=1.348158 lr=0.010000 Epoch[034] Batch [0349]/[3759] Speed: 66.462798 samples/sec accuracy=66.419643 loss=1.345088 lr=0.010000 Epoch[034] Batch [0399]/[3759] Speed: 66.281452 samples/sec accuracy=66.535156 loss=1.341654 lr=0.010000 Epoch[034] Batch [0449]/[3759] Speed: 65.427289 samples/sec accuracy=66.614583 loss=1.340517 lr=0.010000 Epoch[034] Batch [0499]/[3759] Speed: 66.265733 samples/sec accuracy=66.759375 loss=1.338624 lr=0.010000 Epoch[034] Batch [0549]/[3759] Speed: 66.080007 samples/sec accuracy=66.664773 loss=1.341652 lr=0.010000 Epoch[034] Batch [0599]/[3759] Speed: 66.577778 samples/sec accuracy=66.611979 loss=1.346234 lr=0.010000 Epoch[034] Batch [0649]/[3759] Speed: 65.600350 samples/sec accuracy=66.620192 loss=1.345839 lr=0.010000 Epoch[034] Batch [0699]/[3759] Speed: 66.200560 samples/sec accuracy=66.477679 loss=1.348336 lr=0.010000 Epoch[034] Batch [0749]/[3759] Speed: 65.546089 samples/sec accuracy=66.479167 loss=1.347016 lr=0.010000 Epoch[034] Batch [0799]/[3759] Speed: 65.955906 samples/sec accuracy=66.501953 loss=1.347678 lr=0.010000 Epoch[034] Batch [0849]/[3759] Speed: 66.578868 samples/sec accuracy=66.454044 loss=1.347882 lr=0.010000 Epoch[034] Batch [0899]/[3759] Speed: 66.432813 samples/sec accuracy=66.373264 loss=1.352838 lr=0.010000 Epoch[034] Batch [0949]/[3759] Speed: 66.154177 samples/sec accuracy=66.366776 loss=1.354714 lr=0.010000 Epoch[034] Batch [0999]/[3759] Speed: 66.093543 samples/sec accuracy=66.331250 loss=1.357851 lr=0.010000 Epoch[034] Batch [1049]/[3759] Speed: 66.238933 samples/sec accuracy=66.337798 loss=1.358191 lr=0.010000 Epoch[034] Batch [1099]/[3759] Speed: 66.314723 samples/sec accuracy=66.367898 loss=1.356425 lr=0.010000 Epoch[034] Batch [1149]/[3759] Speed: 66.274684 samples/sec accuracy=66.294837 loss=1.358619 lr=0.010000 Epoch[034] Batch [1199]/[3759] Speed: 66.289172 samples/sec accuracy=66.316406 loss=1.357297 lr=0.010000 Epoch[034] Batch [1249]/[3759] Speed: 66.283636 samples/sec accuracy=66.287500 loss=1.359544 lr=0.010000 Epoch[034] Batch [1299]/[3759] Speed: 66.516614 samples/sec accuracy=66.276442 loss=1.359165 lr=0.010000 Epoch[034] Batch [1349]/[3759] Speed: 65.882048 samples/sec accuracy=66.226852 loss=1.361569 lr=0.010000 Epoch[034] Batch [1399]/[3759] Speed: 65.986441 samples/sec accuracy=66.220982 loss=1.360299 lr=0.010000 Epoch[034] Batch [1449]/[3759] Speed: 66.289962 samples/sec accuracy=66.157328 loss=1.363617 lr=0.010000 Epoch[034] Batch [1499]/[3759] Speed: 65.666079 samples/sec accuracy=66.122917 loss=1.366190 lr=0.010000 Epoch[034] Batch [1549]/[3759] Speed: 66.399548 samples/sec accuracy=66.085685 loss=1.367838 lr=0.010000 Epoch[034] Batch [1599]/[3759] Speed: 65.923623 samples/sec accuracy=66.073242 loss=1.369188 lr=0.010000 Epoch[034] Batch [1649]/[3759] Speed: 66.538285 samples/sec accuracy=66.118371 loss=1.368839 lr=0.010000 Epoch[034] Batch [1699]/[3759] Speed: 66.435894 samples/sec accuracy=66.078125 loss=1.369078 lr=0.010000 Epoch[034] Batch [1749]/[3759] Speed: 66.181776 samples/sec accuracy=66.048214 loss=1.370605 lr=0.010000 Epoch[034] Batch [1799]/[3759] Speed: 66.147501 samples/sec accuracy=65.979167 loss=1.373347 lr=0.010000 Epoch[034] Batch [1849]/[3759] Speed: 65.740549 samples/sec accuracy=66.005068 loss=1.372834 lr=0.010000 Epoch[034] Batch [1899]/[3759] Speed: 66.311736 samples/sec accuracy=66.019737 loss=1.372151 lr=0.010000 Epoch[034] Batch [1949]/[3759] Speed: 65.857765 samples/sec accuracy=65.966346 loss=1.374303 lr=0.010000 Epoch[034] Batch [1999]/[3759] Speed: 65.840820 samples/sec accuracy=65.960156 loss=1.373812 lr=0.010000 Epoch[034] Batch [2049]/[3759] Speed: 66.161519 samples/sec accuracy=65.950457 loss=1.374124 lr=0.010000 Epoch[034] Batch [2099]/[3759] Speed: 65.911021 samples/sec accuracy=65.903274 loss=1.375577 lr=0.010000 Epoch[034] Batch [2149]/[3759] Speed: 66.259568 samples/sec accuracy=65.889535 loss=1.376471 lr=0.010000 Epoch[034] Batch [2199]/[3759] Speed: 66.252953 samples/sec accuracy=65.875000 loss=1.377064 lr=0.010000 Epoch[034] Batch [2249]/[3759] Speed: 66.159385 samples/sec accuracy=65.883333 loss=1.376907 lr=0.010000 Epoch[034] Batch [2299]/[3759] Speed: 66.445481 samples/sec accuracy=65.913723 loss=1.375384 lr=0.010000 Epoch[034] Batch [2349]/[3759] Speed: 65.956213 samples/sec accuracy=65.912899 loss=1.375706 lr=0.010000 Epoch[034] Batch [2399]/[3759] Speed: 66.066310 samples/sec accuracy=65.902995 loss=1.376368 lr=0.010000 Epoch[034] Batch [2449]/[3759] Speed: 66.051498 samples/sec accuracy=65.872449 loss=1.377921 lr=0.010000 Epoch[034] Batch [2499]/[3759] Speed: 65.755258 samples/sec accuracy=65.850000 loss=1.378524 lr=0.010000 Epoch[034] Batch [2549]/[3759] Speed: 66.872751 samples/sec accuracy=65.851103 loss=1.378261 lr=0.010000 Epoch[034] Batch [2599]/[3759] Speed: 65.657821 samples/sec accuracy=65.819712 loss=1.378952 lr=0.010000 Epoch[034] Batch [2649]/[3759] Speed: 65.955032 samples/sec accuracy=65.818396 loss=1.379039 lr=0.010000 Epoch[034] Batch [2699]/[3759] Speed: 66.104824 samples/sec accuracy=65.788773 loss=1.379486 lr=0.010000 Epoch[034] Batch [2749]/[3759] Speed: 66.022204 samples/sec accuracy=65.771591 loss=1.379653 lr=0.010000 Epoch[034] Batch [2799]/[3759] Speed: 66.097437 samples/sec accuracy=65.753906 loss=1.380377 lr=0.010000 Epoch[034] Batch [2849]/[3759] Speed: 65.903305 samples/sec accuracy=65.743421 loss=1.381321 lr=0.010000 Epoch[034] Batch [2899]/[3759] Speed: 66.318160 samples/sec accuracy=65.740841 loss=1.381744 lr=0.010000 Epoch[034] Batch [2949]/[3759] Speed: 65.851010 samples/sec accuracy=65.743114 loss=1.381586 lr=0.010000 Epoch[034] Batch [2999]/[3759] Speed: 66.226547 samples/sec accuracy=65.764062 loss=1.380744 lr=0.010000 Epoch[034] Batch [3049]/[3759] Speed: 66.080985 samples/sec accuracy=65.778176 loss=1.380578 lr=0.010000 Epoch[034] Batch [3099]/[3759] Speed: 66.732363 samples/sec accuracy=65.756552 loss=1.381508 lr=0.010000 Epoch[034] Batch [3149]/[3759] Speed: 65.950850 samples/sec accuracy=65.753968 loss=1.382231 lr=0.010000 Epoch[034] Batch [3199]/[3759] Speed: 66.282349 samples/sec accuracy=65.748047 loss=1.382816 lr=0.010000 Epoch[034] Batch [3249]/[3759] Speed: 65.782497 samples/sec accuracy=65.737019 loss=1.383257 lr=0.010000 Epoch[034] Batch [3299]/[3759] Speed: 65.896294 samples/sec accuracy=65.730114 loss=1.383216 lr=0.010000 Epoch[034] Batch [3349]/[3759] Speed: 66.390727 samples/sec accuracy=65.699160 loss=1.384123 lr=0.010000 Epoch[034] Batch [3399]/[3759] Speed: 66.070374 samples/sec accuracy=65.678309 loss=1.384549 lr=0.010000 Epoch[034] Batch [3449]/[3759] Speed: 66.178006 samples/sec accuracy=65.677083 loss=1.383856 lr=0.010000 Epoch[034] Batch [3499]/[3759] Speed: 66.295578 samples/sec accuracy=65.675000 loss=1.384395 lr=0.010000 Epoch[034] Batch [3549]/[3759] Speed: 66.595625 samples/sec accuracy=65.638204 loss=1.385702 lr=0.010000 Epoch[034] Batch [3599]/[3759] Speed: 65.647215 samples/sec accuracy=65.642795 loss=1.385678 lr=0.010000 Epoch[034] Batch [3649]/[3759] Speed: 66.151949 samples/sec accuracy=65.639127 loss=1.386226 lr=0.010000 Epoch[034] Batch [3699]/[3759] Speed: 65.918126 samples/sec accuracy=65.646959 loss=1.386156 lr=0.010000 Epoch[034] Batch [3749]/[3759] Speed: 73.448053 samples/sec accuracy=65.626667 loss=1.387121 lr=0.010000 Batch [0049]/[0303]: acc-top1=61.000000 acc-top5=82.843750 Batch [0099]/[0303]: acc-top1=61.062500 acc-top5=83.296875 Batch [0149]/[0303]: acc-top1=60.968750 acc-top5=83.260417 Batch [0199]/[0303]: acc-top1=60.812500 acc-top5=83.132812 Batch [0249]/[0303]: acc-top1=60.856250 acc-top5=83.300000 Batch [0299]/[0303]: acc-top1=61.093750 acc-top5=83.390625 [Epoch 034] training: accuracy=65.625831 loss=1.387390 [Epoch 034] speed: 65 samples/sec time cost: 3930.385452 [Epoch 034] validation: acc-top1=61.076733 acc-top5=83.395215 loss=1.791187 Epoch[035] Batch [0049]/[3760] Speed: 44.455279 samples/sec accuracy=67.062500 loss=1.338802 lr=0.010000 Epoch[035] Batch [0099]/[3760] Speed: 64.723351 samples/sec accuracy=66.984375 loss=1.308526 lr=0.010000 Epoch[035] Batch [0149]/[3760] Speed: 65.921568 samples/sec accuracy=67.062500 loss=1.318971 lr=0.010000 Epoch[035] Batch [0199]/[3760] Speed: 65.813200 samples/sec accuracy=67.031250 loss=1.321668 lr=0.010000 Epoch[035] Batch [0249]/[3760] Speed: 66.515565 samples/sec accuracy=66.737500 loss=1.331154 lr=0.010000 Epoch[035] Batch [0299]/[3760] Speed: 65.464809 samples/sec accuracy=66.682292 loss=1.332310 lr=0.010000 Epoch[035] Batch [0349]/[3760] Speed: 66.247974 samples/sec accuracy=66.558036 loss=1.329977 lr=0.010000 Epoch[035] Batch [0399]/[3760] Speed: 66.184603 samples/sec accuracy=66.347656 loss=1.341902 lr=0.010000 Epoch[035] Batch [0449]/[3760] Speed: 66.328552 samples/sec accuracy=66.246528 loss=1.346773 lr=0.010000 Epoch[035] Batch [0499]/[3760] Speed: 66.297993 samples/sec accuracy=66.256250 loss=1.345615 lr=0.010000 Epoch[035] Batch [0549]/[3760] Speed: 65.676421 samples/sec accuracy=66.230114 loss=1.348728 lr=0.010000 Epoch[035] Batch [0599]/[3760] Speed: 65.405227 samples/sec accuracy=66.226562 loss=1.350312 lr=0.010000 Epoch[035] Batch [0649]/[3760] Speed: 66.029829 samples/sec accuracy=66.189904 loss=1.351153 lr=0.010000 Epoch[035] Batch [0699]/[3760] Speed: 66.399049 samples/sec accuracy=66.122768 loss=1.355878 lr=0.010000 Epoch[035] Batch [0749]/[3760] Speed: 66.386647 samples/sec accuracy=66.141667 loss=1.356418 lr=0.010000 Epoch[035] Batch [0799]/[3760] Speed: 66.062303 samples/sec accuracy=66.109375 loss=1.359068 lr=0.010000 Epoch[035] Batch [0849]/[3760] Speed: 66.067292 samples/sec accuracy=66.112132 loss=1.358123 lr=0.010000 Epoch[035] Batch [0899]/[3760] Speed: 66.250182 samples/sec accuracy=66.161458 loss=1.358757 lr=0.010000 Epoch[035] Batch [0949]/[3760] Speed: 66.583303 samples/sec accuracy=66.149671 loss=1.358026 lr=0.010000 Epoch[035] Batch [0999]/[3760] Speed: 65.818141 samples/sec accuracy=66.160938 loss=1.357262 lr=0.010000 Epoch[035] Batch [1049]/[3760] Speed: 67.139851 samples/sec accuracy=66.217262 loss=1.355617 lr=0.010000 Epoch[035] Batch [1099]/[3760] Speed: 66.147690 samples/sec accuracy=66.230114 loss=1.355333 lr=0.010000 Epoch[035] Batch [1149]/[3760] Speed: 66.101280 samples/sec accuracy=66.236413 loss=1.353968 lr=0.010000 Epoch[035] Batch [1199]/[3760] Speed: 66.425539 samples/sec accuracy=66.221354 loss=1.354647 lr=0.010000 Epoch[035] Batch [1249]/[3760] Speed: 66.346857 samples/sec accuracy=66.167500 loss=1.356148 lr=0.010000 Epoch[035] Batch [1299]/[3760] Speed: 66.246855 samples/sec accuracy=66.171875 loss=1.356152 lr=0.010000 Epoch[035] Batch [1349]/[3760] Speed: 66.085746 samples/sec accuracy=66.148148 loss=1.356669 lr=0.010000 Epoch[035] Batch [1399]/[3760] Speed: 66.066564 samples/sec accuracy=66.158482 loss=1.356118 lr=0.010000 Epoch[035] Batch [1449]/[3760] Speed: 66.601266 samples/sec accuracy=66.115302 loss=1.358043 lr=0.010000 Epoch[035] Batch [1499]/[3760] Speed: 65.777647 samples/sec accuracy=66.118750 loss=1.357593 lr=0.010000 Epoch[035] Batch [1549]/[3760] Speed: 66.462429 samples/sec accuracy=66.102823 loss=1.359029 lr=0.010000 Epoch[035] Batch [1599]/[3760] Speed: 65.940693 samples/sec accuracy=66.090820 loss=1.360088 lr=0.010000 Epoch[035] Batch [1649]/[3760] Speed: 66.062573 samples/sec accuracy=66.089015 loss=1.361437 lr=0.010000 Epoch[035] Batch [1699]/[3760] Speed: 66.730333 samples/sec accuracy=66.089154 loss=1.361995 lr=0.010000 Epoch[035] Batch [1749]/[3760] Speed: 65.608165 samples/sec accuracy=66.103571 loss=1.361844 lr=0.010000 Epoch[035] Batch [1799]/[3760] Speed: 66.377769 samples/sec accuracy=66.094618 loss=1.362218 lr=0.010000 Epoch[035] Batch [1849]/[3760] Speed: 65.781700 samples/sec accuracy=66.102196 loss=1.361944 lr=0.010000 Epoch[035] Batch [1899]/[3760] Speed: 66.524436 samples/sec accuracy=66.060855 loss=1.363318 lr=0.010000 Epoch[035] Batch [1949]/[3760] Speed: 65.946827 samples/sec accuracy=66.047276 loss=1.363772 lr=0.010000 Epoch[035] Batch [1999]/[3760] Speed: 66.121707 samples/sec accuracy=66.033594 loss=1.363781 lr=0.010000 Epoch[035] Batch [2049]/[3760] Speed: 66.037642 samples/sec accuracy=66.018293 loss=1.364986 lr=0.010000 Epoch[035] Batch [2099]/[3760] Speed: 66.301728 samples/sec accuracy=66.016369 loss=1.365332 lr=0.010000 Epoch[035] Batch [2149]/[3760] Speed: 66.141352 samples/sec accuracy=66.020349 loss=1.365358 lr=0.010000 Epoch[035] Batch [2199]/[3760] Speed: 66.593964 samples/sec accuracy=66.029830 loss=1.365763 lr=0.010000 Epoch[035] Batch [2249]/[3760] Speed: 66.060689 samples/sec accuracy=66.024306 loss=1.365701 lr=0.010000 Epoch[035] Batch [2299]/[3760] Speed: 66.278000 samples/sec accuracy=66.014946 loss=1.366557 lr=0.010000 Epoch[035] Batch [2349]/[3760] Speed: 66.516044 samples/sec accuracy=65.978059 loss=1.368379 lr=0.010000 Epoch[035] Batch [2399]/[3760] Speed: 66.367645 samples/sec accuracy=65.938151 loss=1.369140 lr=0.010000 Epoch[035] Batch [2449]/[3760] Speed: 66.120395 samples/sec accuracy=65.924107 loss=1.369485 lr=0.010000 Epoch[035] Batch [2499]/[3760] Speed: 66.269147 samples/sec accuracy=65.890625 loss=1.370638 lr=0.010000 Epoch[035] Batch [2549]/[3760] Speed: 65.968465 samples/sec accuracy=65.878676 loss=1.371096 lr=0.010000 Epoch[035] Batch [2599]/[3760] Speed: 65.950924 samples/sec accuracy=65.849760 loss=1.371367 lr=0.010000 Epoch[035] Batch [2649]/[3760] Speed: 66.298408 samples/sec accuracy=65.824882 loss=1.372133 lr=0.010000 Epoch[035] Batch [2699]/[3760] Speed: 65.958996 samples/sec accuracy=65.847801 loss=1.371586 lr=0.010000 Epoch[035] Batch [2749]/[3760] Speed: 65.985844 samples/sec accuracy=65.814773 loss=1.373054 lr=0.010000 Epoch[035] Batch [2799]/[3760] Speed: 65.736742 samples/sec accuracy=65.803571 loss=1.373384 lr=0.010000 Epoch[035] Batch [2849]/[3760] Speed: 66.572568 samples/sec accuracy=65.815241 loss=1.373068 lr=0.010000 Epoch[035] Batch [2899]/[3760] Speed: 65.566966 samples/sec accuracy=65.816272 loss=1.373746 lr=0.010000 Epoch[035] Batch [2949]/[3760] Speed: 66.423735 samples/sec accuracy=65.814619 loss=1.373870 lr=0.010000 Epoch[035] Batch [2999]/[3760] Speed: 65.762719 samples/sec accuracy=65.818229 loss=1.373603 lr=0.010000 Epoch[035] Batch [3049]/[3760] Speed: 66.950925 samples/sec accuracy=65.807889 loss=1.374097 lr=0.010000 Epoch[035] Batch [3099]/[3760] Speed: 65.699361 samples/sec accuracy=65.813004 loss=1.373515 lr=0.010000 Epoch[035] Batch [3149]/[3760] Speed: 65.803000 samples/sec accuracy=65.794643 loss=1.374613 lr=0.010000 Epoch[035] Batch [3199]/[3760] Speed: 66.388743 samples/sec accuracy=65.786621 loss=1.374789 lr=0.010000 Epoch[035] Batch [3249]/[3760] Speed: 66.222438 samples/sec accuracy=65.766346 loss=1.375410 lr=0.010000 Epoch[035] Batch [3299]/[3760] Speed: 65.709533 samples/sec accuracy=65.750947 loss=1.375998 lr=0.010000 Epoch[035] Batch [3349]/[3760] Speed: 66.251666 samples/sec accuracy=65.757929 loss=1.376445 lr=0.010000 Epoch[035] Batch [3399]/[3760] Speed: 66.561773 samples/sec accuracy=65.744945 loss=1.377017 lr=0.010000 Epoch[035] Batch [3449]/[3760] Speed: 66.148908 samples/sec accuracy=65.744565 loss=1.377099 lr=0.010000 Epoch[035] Batch [3499]/[3760] Speed: 65.610428 samples/sec accuracy=65.729464 loss=1.378042 lr=0.010000 Epoch[035] Batch [3549]/[3760] Speed: 66.266490 samples/sec accuracy=65.714349 loss=1.378420 lr=0.010000 Epoch[035] Batch [3599]/[3760] Speed: 65.713299 samples/sec accuracy=65.698351 loss=1.379275 lr=0.010000 Epoch[035] Batch [3649]/[3760] Speed: 66.167025 samples/sec accuracy=65.702055 loss=1.379211 lr=0.010000 Epoch[035] Batch [3699]/[3760] Speed: 65.841886 samples/sec accuracy=65.698057 loss=1.379480 lr=0.010000 Epoch[035] Batch [3749]/[3760] Speed: 73.614433 samples/sec accuracy=65.674167 loss=1.380581 lr=0.010000 Batch [0049]/[0303]: acc-top1=62.156250 acc-top5=83.562500 Batch [0099]/[0303]: acc-top1=61.703125 acc-top5=83.468750 Batch [0149]/[0303]: acc-top1=61.729167 acc-top5=83.031250 Batch [0199]/[0303]: acc-top1=61.429688 acc-top5=82.992188 Batch [0249]/[0303]: acc-top1=61.450000 acc-top5=83.168750 Batch [0299]/[0303]: acc-top1=61.520833 acc-top5=83.343750 [Epoch 035] training: accuracy=65.669880 loss=1.380803 [Epoch 035] speed: 65 samples/sec time cost: 3929.921830 [Epoch 035] validation: acc-top1=61.499587 acc-top5=83.359117 loss=1.773608 Epoch[036] Batch [0049]/[3760] Speed: 45.047624 samples/sec accuracy=67.062500 loss=1.303890 lr=0.010000 Epoch[036] Batch [0099]/[3760] Speed: 64.252813 samples/sec accuracy=68.000000 loss=1.292217 lr=0.010000 Epoch[036] Batch [0149]/[3760] Speed: 66.317445 samples/sec accuracy=67.645833 loss=1.312558 lr=0.010000 Epoch[036] Batch [0199]/[3760] Speed: 64.877113 samples/sec accuracy=67.257812 loss=1.319192 lr=0.010000 Epoch[036] Batch [0249]/[3760] Speed: 66.614885 samples/sec accuracy=67.331250 loss=1.318080 lr=0.010000 Epoch[036] Batch [0299]/[3760] Speed: 65.424665 samples/sec accuracy=67.177083 loss=1.324964 lr=0.010000 Epoch[036] Batch [0349]/[3760] Speed: 66.645513 samples/sec accuracy=67.031250 loss=1.328664 lr=0.010000 Epoch[036] Batch [0399]/[3760] Speed: 65.911877 samples/sec accuracy=66.941406 loss=1.333127 lr=0.010000 Epoch[036] Batch [0449]/[3760] Speed: 65.996535 samples/sec accuracy=66.892361 loss=1.334394 lr=0.010000 Epoch[036] Batch [0499]/[3760] Speed: 66.442553 samples/sec accuracy=66.759375 loss=1.337094 lr=0.010000 Epoch[036] Batch [0549]/[3760] Speed: 66.029222 samples/sec accuracy=66.821023 loss=1.335501 lr=0.010000 Epoch[036] Batch [0599]/[3760] Speed: 65.968146 samples/sec accuracy=66.773438 loss=1.336112 lr=0.010000 Epoch[036] Batch [0649]/[3760] Speed: 66.097243 samples/sec accuracy=66.701923 loss=1.339741 lr=0.010000 Epoch[036] Batch [0699]/[3760] Speed: 66.358719 samples/sec accuracy=66.705357 loss=1.340350 lr=0.010000 Epoch[036] Batch [0749]/[3760] Speed: 65.777225 samples/sec accuracy=66.712500 loss=1.340003 lr=0.010000 Epoch[036] Batch [0799]/[3760] Speed: 65.633780 samples/sec accuracy=66.609375 loss=1.343072 lr=0.010000 Epoch[036] Batch [0849]/[3760] Speed: 66.234961 samples/sec accuracy=66.683824 loss=1.340384 lr=0.010000 Epoch[036] Batch [0899]/[3760] Speed: 66.123787 samples/sec accuracy=66.630208 loss=1.342476 lr=0.010000 Epoch[036] Batch [0949]/[3760] Speed: 66.493433 samples/sec accuracy=66.521382 loss=1.345513 lr=0.010000 Epoch[036] Batch [0999]/[3760] Speed: 66.157527 samples/sec accuracy=66.485938 loss=1.347283 lr=0.010000 Epoch[036] Batch [1049]/[3760] Speed: 66.069457 samples/sec accuracy=66.444940 loss=1.348467 lr=0.010000 Epoch[036] Batch [1099]/[3760] Speed: 65.747690 samples/sec accuracy=66.399148 loss=1.349286 lr=0.010000 Epoch[036] Batch [1149]/[3760] Speed: 66.261642 samples/sec accuracy=66.379076 loss=1.351379 lr=0.010000 Epoch[036] Batch [1199]/[3760] Speed: 66.298473 samples/sec accuracy=66.428385 loss=1.349973 lr=0.010000 Epoch[036] Batch [1249]/[3760] Speed: 65.996804 samples/sec accuracy=66.450000 loss=1.349392 lr=0.010000 Epoch[036] Batch [1299]/[3760] Speed: 66.076270 samples/sec accuracy=66.419471 loss=1.349932 lr=0.010000 Epoch[036] Batch [1349]/[3760] Speed: 66.249588 samples/sec accuracy=66.428241 loss=1.350166 lr=0.010000 Epoch[036] Batch [1399]/[3760] Speed: 66.498005 samples/sec accuracy=66.352679 loss=1.351449 lr=0.010000 Epoch[036] Batch [1449]/[3760] Speed: 66.071583 samples/sec accuracy=66.324353 loss=1.353493 lr=0.010000 Epoch[036] Batch [1499]/[3760] Speed: 65.731192 samples/sec accuracy=66.298958 loss=1.354426 lr=0.010000 Epoch[036] Batch [1549]/[3760] Speed: 66.530357 samples/sec accuracy=66.259073 loss=1.356273 lr=0.010000 Epoch[036] Batch [1599]/[3760] Speed: 65.958989 samples/sec accuracy=66.246094 loss=1.357344 lr=0.010000 Epoch[036] Batch [1649]/[3760] Speed: 65.997678 samples/sec accuracy=66.208333 loss=1.358429 lr=0.010000 Epoch[036] Batch [1699]/[3760] Speed: 66.542615 samples/sec accuracy=66.211397 loss=1.357734 lr=0.010000 Epoch[036] Batch [1749]/[3760] Speed: 66.463036 samples/sec accuracy=66.183036 loss=1.358368 lr=0.010000 Epoch[036] Batch [1799]/[3760] Speed: 66.048937 samples/sec accuracy=66.184028 loss=1.357491 lr=0.010000 Epoch[036] Batch [1849]/[3760] Speed: 65.988937 samples/sec accuracy=66.130068 loss=1.359861 lr=0.010000 Epoch[036] Batch [1899]/[3760] Speed: 66.244765 samples/sec accuracy=66.146382 loss=1.359930 lr=0.010000 Epoch[036] Batch [1949]/[3760] Speed: 66.356946 samples/sec accuracy=66.135417 loss=1.361413 lr=0.010000 Epoch[036] Batch [1999]/[3760] Speed: 65.741568 samples/sec accuracy=66.159375 loss=1.361426 lr=0.010000 Epoch[036] Batch [2049]/[3760] Speed: 66.245429 samples/sec accuracy=66.177591 loss=1.361301 lr=0.010000 Epoch[036] Batch [2099]/[3760] Speed: 66.507875 samples/sec accuracy=66.142857 loss=1.362740 lr=0.010000 Epoch[036] Batch [2149]/[3760] Speed: 65.944854 samples/sec accuracy=66.148983 loss=1.361677 lr=0.010000 Epoch[036] Batch [2199]/[3760] Speed: 65.840460 samples/sec accuracy=66.128551 loss=1.363492 lr=0.010000 Epoch[036] Batch [2249]/[3760] Speed: 66.736109 samples/sec accuracy=66.143750 loss=1.363140 lr=0.010000 Epoch[036] Batch [2299]/[3760] Speed: 65.519609 samples/sec accuracy=66.103261 loss=1.363640 lr=0.010000 Epoch[036] Batch [2349]/[3760] Speed: 66.463192 samples/sec accuracy=66.124335 loss=1.362954 lr=0.010000 Epoch[036] Batch [2399]/[3760] Speed: 66.052617 samples/sec accuracy=66.129557 loss=1.362842 lr=0.010000 Epoch[036] Batch [2449]/[3760] Speed: 66.057585 samples/sec accuracy=66.123724 loss=1.362316 lr=0.010000 Epoch[036] Batch [2499]/[3760] Speed: 66.322640 samples/sec accuracy=66.102500 loss=1.363206 lr=0.010000 Epoch[036] Batch [2549]/[3760] Speed: 66.464541 samples/sec accuracy=66.096201 loss=1.363496 lr=0.010000 Epoch[036] Batch [2599]/[3760] Speed: 65.943166 samples/sec accuracy=66.063101 loss=1.364327 lr=0.010000 Epoch[036] Batch [2649]/[3760] Speed: 66.374107 samples/sec accuracy=66.035967 loss=1.365584 lr=0.010000 Epoch[036] Batch [2699]/[3760] Speed: 65.933980 samples/sec accuracy=66.023148 loss=1.365978 lr=0.010000 Epoch[036] Batch [2749]/[3760] Speed: 66.070186 samples/sec accuracy=65.989205 loss=1.367403 lr=0.010000 Epoch[036] Batch [2799]/[3760] Speed: 65.858011 samples/sec accuracy=65.965402 loss=1.368414 lr=0.010000 Epoch[036] Batch [2849]/[3760] Speed: 66.691371 samples/sec accuracy=65.940241 loss=1.369026 lr=0.010000 Epoch[036] Batch [2899]/[3760] Speed: 66.286372 samples/sec accuracy=65.944504 loss=1.368409 lr=0.010000 Epoch[036] Batch [2949]/[3760] Speed: 65.812305 samples/sec accuracy=65.941737 loss=1.369158 lr=0.010000 Epoch[036] Batch [2999]/[3760] Speed: 66.346006 samples/sec accuracy=65.948437 loss=1.369008 lr=0.010000 Epoch[036] Batch [3049]/[3760] Speed: 66.252072 samples/sec accuracy=65.921107 loss=1.370040 lr=0.010000 Epoch[036] Batch [3099]/[3760] Speed: 66.169058 samples/sec accuracy=65.911290 loss=1.370952 lr=0.010000 Epoch[036] Batch [3149]/[3760] Speed: 65.550180 samples/sec accuracy=65.903274 loss=1.372317 lr=0.010000 Epoch[036] Batch [3199]/[3760] Speed: 66.699604 samples/sec accuracy=65.901855 loss=1.372715 lr=0.010000 Epoch[036] Batch [3249]/[3760] Speed: 66.159627 samples/sec accuracy=65.901923 loss=1.373105 lr=0.010000 Epoch[036] Batch [3299]/[3760] Speed: 66.650203 samples/sec accuracy=65.907670 loss=1.372995 lr=0.010000 Epoch[036] Batch [3349]/[3760] Speed: 66.480040 samples/sec accuracy=65.900187 loss=1.374050 lr=0.010000 Epoch[036] Batch [3399]/[3760] Speed: 66.298158 samples/sec accuracy=65.895221 loss=1.373525 lr=0.010000 Epoch[036] Batch [3449]/[3760] Speed: 66.052075 samples/sec accuracy=65.891304 loss=1.373885 lr=0.010000 Epoch[036] Batch [3499]/[3760] Speed: 65.625520 samples/sec accuracy=65.892857 loss=1.374222 lr=0.010000 Epoch[036] Batch [3549]/[3760] Speed: 66.069544 samples/sec accuracy=65.876320 loss=1.374471 lr=0.010000 Epoch[036] Batch [3599]/[3760] Speed: 66.446741 samples/sec accuracy=65.845486 loss=1.375187 lr=0.010000 Epoch[036] Batch [3649]/[3760] Speed: 65.980268 samples/sec accuracy=65.826199 loss=1.375674 lr=0.010000 Epoch[036] Batch [3699]/[3760] Speed: 66.254847 samples/sec accuracy=65.809122 loss=1.376578 lr=0.010000 Epoch[036] Batch [3749]/[3760] Speed: 73.087939 samples/sec accuracy=65.809167 loss=1.376825 lr=0.010000 Batch [0049]/[0303]: acc-top1=61.156250 acc-top5=83.093750 Batch [0099]/[0303]: acc-top1=61.203125 acc-top5=83.593750 Batch [0149]/[0303]: acc-top1=61.843750 acc-top5=83.395833 Batch [0199]/[0303]: acc-top1=61.648438 acc-top5=83.343750 Batch [0249]/[0303]: acc-top1=61.681250 acc-top5=83.356250 Batch [0299]/[0303]: acc-top1=62.000000 acc-top5=83.390625 [Epoch 036] training: accuracy=65.806184 loss=1.376940 [Epoch 036] speed: 65 samples/sec time cost: 3931.778116 [Epoch 036] validation: acc-top1=62.015264 acc-top5=83.415842 loss=1.803610 Epoch[037] Batch [0049]/[3759] Speed: 45.081641 samples/sec accuracy=67.218750 loss=1.310742 lr=0.010000 Epoch[037] Batch [0099]/[3759] Speed: 64.730158 samples/sec accuracy=66.515625 loss=1.331714 lr=0.010000 Epoch[037] Batch [0149]/[3759] Speed: 66.468344 samples/sec accuracy=66.562500 loss=1.330903 lr=0.010000 Epoch[037] Batch [0199]/[3759] Speed: 65.221121 samples/sec accuracy=66.484375 loss=1.338785 lr=0.010000 Epoch[037] Batch [0249]/[3759] Speed: 66.802144 samples/sec accuracy=66.493750 loss=1.338496 lr=0.010000 Epoch[037] Batch [0299]/[3759] Speed: 65.633885 samples/sec accuracy=66.510417 loss=1.335209 lr=0.010000 Epoch[037] Batch [0349]/[3759] Speed: 66.392336 samples/sec accuracy=66.361607 loss=1.338208 lr=0.010000 Epoch[037] Batch [0399]/[3759] Speed: 66.211692 samples/sec accuracy=66.347656 loss=1.339693 lr=0.010000 Epoch[037] Batch [0449]/[3759] Speed: 65.394603 samples/sec accuracy=66.461806 loss=1.338572 lr=0.010000 Epoch[037] Batch [0499]/[3759] Speed: 66.820419 samples/sec accuracy=66.440625 loss=1.341791 lr=0.010000 Epoch[037] Batch [0549]/[3759] Speed: 65.825940 samples/sec accuracy=66.474432 loss=1.340814 lr=0.010000 Epoch[037] Batch [0599]/[3759] Speed: 66.519550 samples/sec accuracy=66.520833 loss=1.340687 lr=0.010000 Epoch[037] Batch [0649]/[3759] Speed: 66.463846 samples/sec accuracy=66.514423 loss=1.340734 lr=0.010000 Epoch[037] Batch [0699]/[3759] Speed: 66.217382 samples/sec accuracy=66.401786 loss=1.347605 lr=0.010000 Epoch[037] Batch [0749]/[3759] Speed: 66.630312 samples/sec accuracy=66.395833 loss=1.348695 lr=0.010000 Epoch[037] Batch [0799]/[3759] Speed: 66.040405 samples/sec accuracy=66.400391 loss=1.349688 lr=0.010000 Epoch[037] Batch [0849]/[3759] Speed: 66.524435 samples/sec accuracy=66.459559 loss=1.347601 lr=0.010000 Epoch[037] Batch [0899]/[3759] Speed: 65.765419 samples/sec accuracy=66.460069 loss=1.348659 lr=0.010000 Epoch[037] Batch [0949]/[3759] Speed: 66.482120 samples/sec accuracy=66.435855 loss=1.349254 lr=0.010000 Epoch[037] Batch [0999]/[3759] Speed: 66.266228 samples/sec accuracy=66.421875 loss=1.348820 lr=0.010000 Epoch[037] Batch [1049]/[3759] Speed: 66.710510 samples/sec accuracy=66.401786 loss=1.348920 lr=0.010000 Epoch[037] Batch [1099]/[3759] Speed: 66.452107 samples/sec accuracy=66.308239 loss=1.351774 lr=0.010000 Epoch[037] Batch [1149]/[3759] Speed: 66.043618 samples/sec accuracy=66.304348 loss=1.352133 lr=0.010000 Epoch[037] Batch [1199]/[3759] Speed: 66.491901 samples/sec accuracy=66.324219 loss=1.352498 lr=0.010000 Epoch[037] Batch [1249]/[3759] Speed: 65.818855 samples/sec accuracy=66.356250 loss=1.350505 lr=0.010000 Epoch[037] Batch [1299]/[3759] Speed: 66.059675 samples/sec accuracy=66.359375 loss=1.351516 lr=0.010000 Epoch[037] Batch [1349]/[3759] Speed: 66.396489 samples/sec accuracy=66.297454 loss=1.352537 lr=0.010000 Epoch[037] Batch [1399]/[3759] Speed: 65.876879 samples/sec accuracy=66.316964 loss=1.351737 lr=0.010000 Epoch[037] Batch [1449]/[3759] Speed: 65.929528 samples/sec accuracy=66.298491 loss=1.353283 lr=0.010000 Epoch[037] Batch [1499]/[3759] Speed: 66.202233 samples/sec accuracy=66.307292 loss=1.353730 lr=0.010000 Epoch[037] Batch [1549]/[3759] Speed: 65.914249 samples/sec accuracy=66.289315 loss=1.353929 lr=0.010000 Epoch[037] Batch [1599]/[3759] Speed: 66.312094 samples/sec accuracy=66.267578 loss=1.355470 lr=0.010000 Epoch[037] Batch [1649]/[3759] Speed: 66.129165 samples/sec accuracy=66.224432 loss=1.355752 lr=0.010000 Epoch[037] Batch [1699]/[3759] Speed: 66.268944 samples/sec accuracy=66.217831 loss=1.356184 lr=0.010000 Epoch[037] Batch [1749]/[3759] Speed: 66.365822 samples/sec accuracy=66.208036 loss=1.357036 lr=0.010000 Epoch[037] Batch [1799]/[3759] Speed: 66.236547 samples/sec accuracy=66.179688 loss=1.358654 lr=0.010000 Epoch[037] Batch [1849]/[3759] Speed: 66.283903 samples/sec accuracy=66.148649 loss=1.359877 lr=0.010000 Epoch[037] Batch [1899]/[3759] Speed: 66.186871 samples/sec accuracy=66.136513 loss=1.359816 lr=0.010000 Epoch[037] Batch [1949]/[3759] Speed: 66.121090 samples/sec accuracy=66.137019 loss=1.360348 lr=0.010000 Epoch[037] Batch [1999]/[3759] Speed: 66.164804 samples/sec accuracy=66.185156 loss=1.359048 lr=0.010000 Epoch[037] Batch [2049]/[3759] Speed: 66.370021 samples/sec accuracy=66.175305 loss=1.359544 lr=0.010000 Epoch[037] Batch [2099]/[3759] Speed: 66.109254 samples/sec accuracy=66.159970 loss=1.360366 lr=0.010000 Epoch[037] Batch [2149]/[3759] Speed: 66.591968 samples/sec accuracy=66.138808 loss=1.361728 lr=0.010000 Epoch[037] Batch [2199]/[3759] Speed: 66.072057 samples/sec accuracy=66.125000 loss=1.362611 lr=0.010000 Epoch[037] Batch [2249]/[3759] Speed: 66.129321 samples/sec accuracy=66.110417 loss=1.362311 lr=0.010000 Epoch[037] Batch [2299]/[3759] Speed: 65.798400 samples/sec accuracy=66.112772 loss=1.362132 lr=0.010000 Epoch[037] Batch [2349]/[3759] Speed: 66.053254 samples/sec accuracy=66.117686 loss=1.361832 lr=0.010000 Epoch[037] Batch [2399]/[3759] Speed: 66.264389 samples/sec accuracy=66.098307 loss=1.361801 lr=0.010000 Epoch[037] Batch [2449]/[3759] Speed: 65.789276 samples/sec accuracy=66.111607 loss=1.361444 lr=0.010000 Epoch[037] Batch [2499]/[3759] Speed: 65.935672 samples/sec accuracy=66.093750 loss=1.362161 lr=0.010000 Epoch[037] Batch [2549]/[3759] Speed: 65.829297 samples/sec accuracy=66.090074 loss=1.362767 lr=0.010000 Epoch[037] Batch [2599]/[3759] Speed: 66.372788 samples/sec accuracy=66.086538 loss=1.363044 lr=0.010000 Epoch[037] Batch [2649]/[3759] Speed: 66.321813 samples/sec accuracy=66.065448 loss=1.364014 lr=0.010000 Epoch[037] Batch [2699]/[3759] Speed: 66.719173 samples/sec accuracy=66.043981 loss=1.364825 lr=0.010000 Epoch[037] Batch [2749]/[3759] Speed: 65.978254 samples/sec accuracy=66.039773 loss=1.365257 lr=0.010000 Epoch[037] Batch [2799]/[3759] Speed: 66.348836 samples/sec accuracy=66.007812 loss=1.366323 lr=0.010000 Epoch[037] Batch [2849]/[3759] Speed: 66.497939 samples/sec accuracy=66.004386 loss=1.366347 lr=0.010000 Epoch[037] Batch [2899]/[3759] Speed: 66.037294 samples/sec accuracy=65.991918 loss=1.366744 lr=0.010000 Epoch[037] Batch [2949]/[3759] Speed: 65.836332 samples/sec accuracy=65.958686 loss=1.368402 lr=0.010000 Epoch[037] Batch [2999]/[3759] Speed: 66.224512 samples/sec accuracy=65.941146 loss=1.369307 lr=0.010000 Epoch[037] Batch [3049]/[3759] Speed: 66.458682 samples/sec accuracy=65.955430 loss=1.368710 lr=0.010000 Epoch[037] Batch [3099]/[3759] Speed: 65.762698 samples/sec accuracy=65.971270 loss=1.368144 lr=0.010000 Epoch[037] Batch [3149]/[3759] Speed: 66.696118 samples/sec accuracy=65.965774 loss=1.368572 lr=0.010000 Epoch[037] Batch [3199]/[3759] Speed: 65.615145 samples/sec accuracy=65.964844 loss=1.369021 lr=0.010000 Epoch[037] Batch [3249]/[3759] Speed: 66.429291 samples/sec accuracy=65.972596 loss=1.368607 lr=0.010000 Epoch[037] Batch [3299]/[3759] Speed: 65.977069 samples/sec accuracy=65.987216 loss=1.367741 lr=0.010000 Epoch[037] Batch [3349]/[3759] Speed: 66.753043 samples/sec accuracy=65.974813 loss=1.368813 lr=0.010000 Epoch[037] Batch [3399]/[3759] Speed: 65.954151 samples/sec accuracy=65.976103 loss=1.369829 lr=0.010000 Epoch[037] Batch [3449]/[3759] Speed: 66.591057 samples/sec accuracy=65.942029 loss=1.371206 lr=0.010000 Epoch[037] Batch [3499]/[3759] Speed: 65.731965 samples/sec accuracy=65.936161 loss=1.371405 lr=0.010000 Epoch[037] Batch [3549]/[3759] Speed: 66.262134 samples/sec accuracy=65.930018 loss=1.371972 lr=0.010000 Epoch[037] Batch [3599]/[3759] Speed: 66.449147 samples/sec accuracy=65.929688 loss=1.371837 lr=0.010000 Epoch[037] Batch [3649]/[3759] Speed: 66.384075 samples/sec accuracy=65.912243 loss=1.372168 lr=0.010000 Epoch[037] Batch [3699]/[3759] Speed: 65.850807 samples/sec accuracy=65.897804 loss=1.372757 lr=0.010000 Epoch[037] Batch [3749]/[3759] Speed: 74.228990 samples/sec accuracy=65.882083 loss=1.373395 lr=0.010000 Batch [0049]/[0303]: acc-top1=60.312500 acc-top5=83.437500 Batch [0099]/[0303]: acc-top1=60.515625 acc-top5=83.468750 Batch [0149]/[0303]: acc-top1=61.093750 acc-top5=83.031250 Batch [0199]/[0303]: acc-top1=60.750000 acc-top5=82.906250 Batch [0249]/[0303]: acc-top1=60.687500 acc-top5=83.062500 Batch [0299]/[0303]: acc-top1=60.932292 acc-top5=83.109375 [Epoch 037] training: accuracy=65.883130 loss=1.373302 [Epoch 037] speed: 65 samples/sec time cost: 3926.148362 [Epoch 037] validation: acc-top1=60.901403 acc-top5=83.116749 loss=1.852098 Epoch[038] Batch [0049]/[3760] Speed: 44.111739 samples/sec accuracy=66.156250 loss=1.368470 lr=0.010000 Epoch[038] Batch [0099]/[3760] Speed: 65.175240 samples/sec accuracy=66.828125 loss=1.327445 lr=0.010000 Epoch[038] Batch [0149]/[3760] Speed: 65.965784 samples/sec accuracy=66.968750 loss=1.331916 lr=0.010000 Epoch[038] Batch [0199]/[3760] Speed: 65.093973 samples/sec accuracy=66.835938 loss=1.331128 lr=0.010000 Epoch[038] Batch [0249]/[3760] Speed: 66.273415 samples/sec accuracy=67.168750 loss=1.319941 lr=0.010000 Epoch[038] Batch [0299]/[3760] Speed: 65.631529 samples/sec accuracy=67.052083 loss=1.324538 lr=0.010000 Epoch[038] Batch [0349]/[3760] Speed: 66.811490 samples/sec accuracy=66.933036 loss=1.326264 lr=0.010000 Epoch[038] Batch [0399]/[3760] Speed: 65.502434 samples/sec accuracy=67.035156 loss=1.324025 lr=0.010000 Epoch[038] Batch [0449]/[3760] Speed: 66.133257 samples/sec accuracy=66.951389 loss=1.329759 lr=0.010000 Epoch[038] Batch [0499]/[3760] Speed: 66.201192 samples/sec accuracy=66.731250 loss=1.336715 lr=0.010000 Epoch[038] Batch [0549]/[3760] Speed: 65.824251 samples/sec accuracy=66.644886 loss=1.337600 lr=0.010000 Epoch[038] Batch [0599]/[3760] Speed: 66.788577 samples/sec accuracy=66.690104 loss=1.338287 lr=0.010000 Epoch[038] Batch [0649]/[3760] Speed: 65.907285 samples/sec accuracy=66.718750 loss=1.337949 lr=0.010000 Epoch[038] Batch [0699]/[3760] Speed: 66.970928 samples/sec accuracy=66.691964 loss=1.340087 lr=0.010000 Epoch[038] Batch [0749]/[3760] Speed: 65.890393 samples/sec accuracy=66.560417 loss=1.343896 lr=0.010000 Epoch[038] Batch [0799]/[3760] Speed: 66.351828 samples/sec accuracy=66.498047 loss=1.346822 lr=0.010000 Epoch[038] Batch [0849]/[3760] Speed: 66.286812 samples/sec accuracy=66.481618 loss=1.348410 lr=0.010000 Epoch[038] Batch [0899]/[3760] Speed: 66.046491 samples/sec accuracy=66.458333 loss=1.348188 lr=0.010000 Epoch[038] 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accuracy=66.487723 loss=1.347247 lr=0.010000 Epoch[038] Batch [1449]/[3760] Speed: 66.239553 samples/sec accuracy=66.420259 loss=1.350433 lr=0.010000 Epoch[038] Batch [1499]/[3760] Speed: 66.176673 samples/sec accuracy=66.413542 loss=1.352265 lr=0.010000 Epoch[038] Batch [1549]/[3760] Speed: 65.629201 samples/sec accuracy=66.413306 loss=1.352439 lr=0.010000 Epoch[038] Batch [1599]/[3760] Speed: 66.484128 samples/sec accuracy=66.420898 loss=1.353236 lr=0.010000 Epoch[038] Batch [1649]/[3760] Speed: 66.275933 samples/sec accuracy=66.408144 loss=1.353169 lr=0.010000 Epoch[038] Batch [1699]/[3760] Speed: 66.294200 samples/sec accuracy=66.366728 loss=1.355132 lr=0.010000 Epoch[038] Batch [1749]/[3760] Speed: 66.209250 samples/sec accuracy=66.342857 loss=1.356379 lr=0.010000 Epoch[038] Batch [1799]/[3760] Speed: 65.626683 samples/sec accuracy=66.346354 loss=1.356289 lr=0.010000 Epoch[038] Batch [1849]/[3760] Speed: 66.762598 samples/sec accuracy=66.370777 loss=1.355052 lr=0.010000 Epoch[038] 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accuracy=66.210106 loss=1.358623 lr=0.010000 Epoch[038] Batch [2399]/[3760] Speed: 65.934676 samples/sec accuracy=66.201172 loss=1.358684 lr=0.010000 Epoch[038] Batch [2449]/[3760] Speed: 66.070418 samples/sec accuracy=66.213648 loss=1.357752 lr=0.010000 Epoch[038] Batch [2499]/[3760] Speed: 65.336487 samples/sec accuracy=66.228750 loss=1.357368 lr=0.010000 Epoch[038] Batch [2549]/[3760] Speed: 66.471143 samples/sec accuracy=66.239583 loss=1.357266 lr=0.010000 Epoch[038] Batch [2599]/[3760] Speed: 66.148817 samples/sec accuracy=66.245192 loss=1.357629 lr=0.010000 Epoch[038] Batch [2649]/[3760] Speed: 66.616162 samples/sec accuracy=66.238208 loss=1.357891 lr=0.010000 Epoch[038] Batch [2699]/[3760] Speed: 65.909343 samples/sec accuracy=66.245370 loss=1.357693 lr=0.010000 Epoch[038] Batch [2749]/[3760] Speed: 66.627574 samples/sec accuracy=66.205114 loss=1.358859 lr=0.010000 Epoch[038] Batch [2799]/[3760] Speed: 66.496597 samples/sec accuracy=66.223214 loss=1.358318 lr=0.010000 Epoch[038] Batch [2849]/[3760] Speed: 65.903988 samples/sec accuracy=66.204496 loss=1.358807 lr=0.010000 Epoch[038] Batch [2899]/[3760] Speed: 66.221496 samples/sec accuracy=66.188578 loss=1.359473 lr=0.010000 Epoch[038] Batch [2949]/[3760] Speed: 65.709330 samples/sec accuracy=66.174788 loss=1.360614 lr=0.010000 Epoch[038] Batch [2999]/[3760] Speed: 65.881256 samples/sec accuracy=66.183333 loss=1.360716 lr=0.010000 Epoch[038] Batch [3049]/[3760] Speed: 66.007638 samples/sec accuracy=66.192623 loss=1.361052 lr=0.010000 Epoch[038] Batch [3099]/[3760] Speed: 65.798421 samples/sec accuracy=66.193044 loss=1.361554 lr=0.010000 Epoch[038] Batch [3149]/[3760] Speed: 66.688446 samples/sec accuracy=66.154266 loss=1.362940 lr=0.010000 Epoch[038] Batch [3199]/[3760] Speed: 65.693397 samples/sec accuracy=66.173828 loss=1.362352 lr=0.010000 Epoch[038] Batch [3249]/[3760] Speed: 66.388237 samples/sec accuracy=66.153365 loss=1.363156 lr=0.010000 Epoch[038] Batch [3299]/[3760] Speed: 66.751899 samples/sec accuracy=66.163352 loss=1.362919 lr=0.010000 Epoch[038] Batch [3349]/[3760] Speed: 65.930530 samples/sec accuracy=66.154384 loss=1.363064 lr=0.010000 Epoch[038] Batch [3399]/[3760] Speed: 65.703971 samples/sec accuracy=66.153952 loss=1.363171 lr=0.010000 Epoch[038] Batch [3449]/[3760] Speed: 66.072271 samples/sec accuracy=66.142663 loss=1.363596 lr=0.010000 Epoch[038] Batch [3499]/[3760] Speed: 66.001423 samples/sec accuracy=66.133929 loss=1.364426 lr=0.010000 Epoch[038] Batch [3549]/[3760] Speed: 66.529180 samples/sec accuracy=66.125000 loss=1.364710 lr=0.010000 Epoch[038] Batch [3599]/[3760] Speed: 65.950532 samples/sec accuracy=66.107639 loss=1.365789 lr=0.010000 Epoch[038] Batch [3649]/[3760] Speed: 66.015972 samples/sec accuracy=66.098887 loss=1.366296 lr=0.010000 Epoch[038] Batch [3699]/[3760] Speed: 66.330608 samples/sec accuracy=66.106419 loss=1.366518 lr=0.010000 Epoch[038] Batch [3749]/[3760] Speed: 73.446560 samples/sec accuracy=66.095833 loss=1.367571 lr=0.010000 Batch [0049]/[0303]: acc-top1=60.875000 acc-top5=83.000000 Batch [0099]/[0303]: acc-top1=60.765625 acc-top5=83.156250 Batch [0149]/[0303]: acc-top1=61.281250 acc-top5=83.062500 Batch [0199]/[0303]: acc-top1=61.195312 acc-top5=83.109375 Batch [0249]/[0303]: acc-top1=61.131250 acc-top5=83.181250 Batch [0299]/[0303]: acc-top1=61.270833 acc-top5=83.317708 [Epoch 038] training: accuracy=66.091257 loss=1.367767 [Epoch 038] speed: 65 samples/sec time cost: 3931.697932 [Epoch 038] validation: acc-top1=61.272690 acc-top5=83.338490 loss=1.768510 Epoch[039] Batch [0049]/[3760] Speed: 44.746807 samples/sec accuracy=66.500000 loss=1.343865 lr=0.010000 Epoch[039] Batch [0099]/[3760] Speed: 64.365089 samples/sec accuracy=66.937500 loss=1.318500 lr=0.010000 Epoch[039] Batch [0149]/[3760] Speed: 65.781343 samples/sec accuracy=67.229167 loss=1.321061 lr=0.010000 Epoch[039] Batch [0199]/[3760] Speed: 65.578424 samples/sec accuracy=67.453125 loss=1.310272 lr=0.010000 Epoch[039] Batch [0249]/[3760] Speed: 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lr=0.010000 Epoch[039] Batch [0749]/[3760] Speed: 66.668113 samples/sec accuracy=67.129167 loss=1.330705 lr=0.010000 Epoch[039] Batch [0799]/[3760] Speed: 65.988942 samples/sec accuracy=66.968750 loss=1.334495 lr=0.010000 Epoch[039] Batch [0849]/[3760] Speed: 66.413307 samples/sec accuracy=66.895221 loss=1.336292 lr=0.010000 Epoch[039] Batch [0899]/[3760] Speed: 65.907051 samples/sec accuracy=66.927083 loss=1.337154 lr=0.010000 Epoch[039] Batch [0949]/[3760] Speed: 66.717560 samples/sec accuracy=66.929276 loss=1.338328 lr=0.010000 Epoch[039] Batch [0999]/[3760] Speed: 65.581147 samples/sec accuracy=66.910938 loss=1.339296 lr=0.010000 Epoch[039] Batch [1049]/[3760] Speed: 66.135019 samples/sec accuracy=66.883929 loss=1.340871 lr=0.010000 Epoch[039] Batch [1099]/[3760] Speed: 66.343359 samples/sec accuracy=66.830966 loss=1.343816 lr=0.010000 Epoch[039] Batch [1149]/[3760] Speed: 66.056571 samples/sec accuracy=66.802989 loss=1.345010 lr=0.010000 Epoch[039] Batch [1199]/[3760] Speed: 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lr=0.010000 Epoch[039] Batch [1699]/[3760] Speed: 66.442529 samples/sec accuracy=66.625919 loss=1.347586 lr=0.010000 Epoch[039] Batch [1749]/[3760] Speed: 65.816576 samples/sec accuracy=66.622321 loss=1.348375 lr=0.010000 Epoch[039] Batch [1799]/[3760] Speed: 65.936200 samples/sec accuracy=66.655382 loss=1.347474 lr=0.010000 Epoch[039] Batch [1849]/[3760] Speed: 66.094404 samples/sec accuracy=66.631757 loss=1.348206 lr=0.010000 Epoch[039] Batch [1899]/[3760] Speed: 66.488038 samples/sec accuracy=66.594572 loss=1.349285 lr=0.010000 Epoch[039] Batch [1949]/[3760] Speed: 66.132027 samples/sec accuracy=66.557692 loss=1.350860 lr=0.010000 Epoch[039] Batch [1999]/[3760] Speed: 66.332856 samples/sec accuracy=66.586719 loss=1.350024 lr=0.010000 Epoch[039] Batch [2049]/[3760] Speed: 65.963221 samples/sec accuracy=66.563262 loss=1.350605 lr=0.010000 Epoch[039] Batch [2099]/[3760] Speed: 66.405843 samples/sec accuracy=66.507440 loss=1.352062 lr=0.010000 Epoch[039] Batch [2149]/[3760] Speed: 66.082851 samples/sec accuracy=66.531250 loss=1.351961 lr=0.010000 Epoch[039] Batch [2199]/[3760] Speed: 66.230670 samples/sec accuracy=66.511364 loss=1.352339 lr=0.010000 Epoch[039] Batch [2249]/[3760] Speed: 66.573489 samples/sec accuracy=66.505556 loss=1.352787 lr=0.010000 Epoch[039] Batch [2299]/[3760] Speed: 65.986822 samples/sec accuracy=66.476902 loss=1.353667 lr=0.010000 Epoch[039] Batch [2349]/[3760] Speed: 65.473297 samples/sec accuracy=66.467420 loss=1.354210 lr=0.010000 Epoch[039] Batch [2399]/[3760] Speed: 66.117807 samples/sec accuracy=66.427083 loss=1.356482 lr=0.010000 Epoch[039] Batch [2449]/[3760] Speed: 65.856409 samples/sec accuracy=66.447704 loss=1.356629 lr=0.010000 Epoch[039] Batch [2499]/[3760] Speed: 66.222901 samples/sec accuracy=66.438125 loss=1.355908 lr=0.010000 Epoch[039] Batch [2549]/[3760] Speed: 65.641092 samples/sec accuracy=66.410539 loss=1.356936 lr=0.010000 Epoch[039] Batch [2599]/[3760] Speed: 66.433211 samples/sec accuracy=66.409255 loss=1.356789 lr=0.010000 Epoch[039] Batch [2649]/[3760] Speed: 65.930657 samples/sec accuracy=66.383844 loss=1.357600 lr=0.010000 Epoch[039] Batch [2699]/[3760] Speed: 65.964720 samples/sec accuracy=66.358218 loss=1.358114 lr=0.010000 Epoch[039] Batch [2749]/[3760] Speed: 66.026121 samples/sec accuracy=66.355114 loss=1.358390 lr=0.010000 Epoch[039] Batch [2799]/[3760] Speed: 65.699867 samples/sec accuracy=66.315290 loss=1.359751 lr=0.010000 Epoch[039] Batch [2849]/[3760] Speed: 66.270136 samples/sec accuracy=66.307018 loss=1.360522 lr=0.010000 Epoch[039] Batch [2899]/[3760] Speed: 65.648561 samples/sec accuracy=66.301185 loss=1.361074 lr=0.010000 Epoch[039] Batch [2949]/[3760] Speed: 65.904679 samples/sec accuracy=66.298199 loss=1.361663 lr=0.010000 Epoch[039] Batch [2999]/[3760] Speed: 66.293233 samples/sec accuracy=66.282813 loss=1.362324 lr=0.010000 Epoch[039] Batch [3049]/[3760] Speed: 66.326912 samples/sec accuracy=66.285861 loss=1.362706 lr=0.010000 Epoch[039] Batch [3099]/[3760] Speed: 65.820643 samples/sec accuracy=66.270665 loss=1.363447 lr=0.010000 Epoch[039] Batch [3149]/[3760] Speed: 66.519090 samples/sec accuracy=66.264881 loss=1.364163 lr=0.010000 Epoch[039] Batch [3199]/[3760] Speed: 66.188394 samples/sec accuracy=66.268555 loss=1.364169 lr=0.010000 Epoch[039] Batch [3249]/[3760] Speed: 66.116253 samples/sec accuracy=66.264904 loss=1.364499 lr=0.010000 Epoch[039] Batch [3299]/[3760] Speed: 65.902547 samples/sec accuracy=66.225852 loss=1.366223 lr=0.010000 Epoch[039] Batch [3349]/[3760] Speed: 66.278641 samples/sec accuracy=66.195429 loss=1.366884 lr=0.010000 Epoch[039] Batch [3399]/[3760] Speed: 66.068333 samples/sec accuracy=66.211857 loss=1.366600 lr=0.010000 Epoch[039] Batch [3449]/[3760] Speed: 66.559282 samples/sec accuracy=66.187047 loss=1.367448 lr=0.010000 Epoch[039] Batch [3499]/[3760] Speed: 66.124005 samples/sec accuracy=66.169196 loss=1.368007 lr=0.010000 Epoch[039] Batch [3549]/[3760] Speed: 66.038569 samples/sec accuracy=66.162412 loss=1.368019 lr=0.010000 Epoch[039] Batch [3599]/[3760] Speed: 66.085003 samples/sec accuracy=66.155382 loss=1.368226 lr=0.010000 Epoch[039] Batch [3649]/[3760] Speed: 66.067210 samples/sec accuracy=66.166524 loss=1.367880 lr=0.010000 Epoch[039] Batch [3699]/[3760] Speed: 65.778429 samples/sec accuracy=66.133024 loss=1.368763 lr=0.010000 Epoch[039] Batch [3749]/[3760] Speed: 73.015609 samples/sec accuracy=66.132083 loss=1.368938 lr=0.001000 Batch [0049]/[0303]: acc-top1=60.250000 acc-top5=83.281250 Batch [0099]/[0303]: acc-top1=60.390625 acc-top5=83.484375 Batch [0149]/[0303]: acc-top1=60.906250 acc-top5=83.218750 Batch [0199]/[0303]: acc-top1=60.875000 acc-top5=83.234375 Batch [0249]/[0303]: acc-top1=61.031250 acc-top5=83.431250 Batch [0299]/[0303]: acc-top1=61.104167 acc-top5=83.494792 [Epoch 039] training: accuracy=66.122839 loss=1.369283 [Epoch 039] speed: 65 samples/sec time cost: 3934.246449 [Epoch 039] validation: acc-top1=61.076733 acc-top5=83.503507 loss=1.820559 Epoch[040] Batch 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accuracy=72.854011 loss=1.081634 lr=0.001000 Epoch[040] Batch [3399]/[3759] Speed: 65.814558 samples/sec accuracy=72.856158 loss=1.081617 lr=0.001000 Epoch[040] Batch [3449]/[3759] Speed: 65.929062 samples/sec accuracy=72.857790 loss=1.080994 lr=0.001000 Epoch[040] Batch [3499]/[3759] Speed: 65.958256 samples/sec accuracy=72.888839 loss=1.079649 lr=0.001000 Epoch[040] Batch [3549]/[3759] Speed: 66.230682 samples/sec accuracy=72.917254 loss=1.078385 lr=0.001000 Epoch[040] Batch [3599]/[3759] Speed: 66.350603 samples/sec accuracy=72.916667 loss=1.078105 lr=0.001000 Epoch[040] Batch [3649]/[3759] Speed: 66.275307 samples/sec accuracy=72.939212 loss=1.077393 lr=0.001000 Epoch[040] Batch [3699]/[3759] Speed: 66.224602 samples/sec accuracy=72.938767 loss=1.077535 lr=0.001000 Epoch[040] Batch [3749]/[3759] Speed: 73.586230 samples/sec accuracy=72.957083 loss=1.076643 lr=0.001000 Batch [0049]/[0303]: acc-top1=65.781250 acc-top5=86.531250 Batch [0099]/[0303]: acc-top1=66.078125 acc-top5=86.875000 Batch [0149]/[0303]: acc-top1=66.718750 acc-top5=86.531250 Batch [0199]/[0303]: acc-top1=66.585938 acc-top5=86.429688 Batch [0249]/[0303]: acc-top1=66.593750 acc-top5=86.543750 Batch [0299]/[0303]: acc-top1=66.937500 acc-top5=86.609375 [Epoch 040] training: accuracy=72.964469 loss=1.076276 [Epoch 040] speed: 65 samples/sec time cost: 3928.910508 [Epoch 040] validation: acc-top1=66.909035 acc-top5=86.607880 loss=1.565003 Epoch[041] Batch [0049]/[3760] Speed: 44.543839 samples/sec accuracy=74.093750 loss=1.012558 lr=0.001000 Epoch[041] Batch [0099]/[3760] Speed: 64.424196 samples/sec accuracy=74.640625 loss=0.999178 lr=0.001000 Epoch[041] Batch [0149]/[3760] Speed: 66.126830 samples/sec accuracy=74.468750 loss=1.007141 lr=0.001000 Epoch[041] Batch [0199]/[3760] Speed: 65.236829 samples/sec accuracy=74.617188 loss=1.004548 lr=0.001000 Epoch[041] Batch [0249]/[3760] Speed: 66.257270 samples/sec accuracy=74.681250 loss=0.999484 lr=0.001000 Epoch[041] Batch [0299]/[3760] 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accuracy=74.765330 loss=0.993730 lr=0.001000 Epoch[041] Batch [2699]/[3760] Speed: 65.853385 samples/sec accuracy=74.756944 loss=0.994081 lr=0.001000 Epoch[041] Batch [2749]/[3760] Speed: 66.464226 samples/sec accuracy=74.777841 loss=0.993403 lr=0.001000 Epoch[041] Batch [2799]/[3760] Speed: 66.445545 samples/sec accuracy=74.789062 loss=0.992850 lr=0.001000 Epoch[041] Batch [2849]/[3760] Speed: 65.914230 samples/sec accuracy=74.804276 loss=0.992227 lr=0.001000 Epoch[041] Batch [2899]/[3760] Speed: 65.609252 samples/sec accuracy=74.817349 loss=0.992233 lr=0.001000 Epoch[041] Batch [2949]/[3760] Speed: 66.180299 samples/sec accuracy=74.819915 loss=0.991889 lr=0.001000 Epoch[041] Batch [2999]/[3760] Speed: 65.943954 samples/sec accuracy=74.822396 loss=0.991868 lr=0.001000 Epoch[041] Batch [3049]/[3760] Speed: 66.487350 samples/sec accuracy=74.842213 loss=0.991034 lr=0.001000 Epoch[041] Batch [3099]/[3760] Speed: 66.101579 samples/sec accuracy=74.874496 loss=0.989968 lr=0.001000 Epoch[041] 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accuracy=74.896267 loss=0.990031 lr=0.001000 Epoch[041] Batch [3649]/[3760] Speed: 65.715703 samples/sec accuracy=74.907106 loss=0.989476 lr=0.001000 Epoch[041] Batch [3699]/[3760] Speed: 65.989477 samples/sec accuracy=74.908361 loss=0.989061 lr=0.001000 Epoch[041] Batch [3749]/[3760] Speed: 73.123976 samples/sec accuracy=74.923333 loss=0.988643 lr=0.001000 Batch [0049]/[0303]: acc-top1=66.968750 acc-top5=86.781250 Batch [0099]/[0303]: acc-top1=67.046875 acc-top5=87.000000 Batch [0149]/[0303]: acc-top1=67.552083 acc-top5=86.625000 Batch [0199]/[0303]: acc-top1=67.437500 acc-top5=86.593750 Batch [0249]/[0303]: acc-top1=67.268750 acc-top5=86.781250 Batch [0299]/[0303]: acc-top1=67.609375 acc-top5=86.947917 [Epoch 041] training: accuracy=74.921875 loss=0.988834 [Epoch 041] speed: 65 samples/sec time cost: 3935.119689 [Epoch 041] validation: acc-top1=67.584571 acc-top5=86.953383 loss=1.554249 Epoch[042] Batch [0049]/[3760] Speed: 44.558959 samples/sec accuracy=75.750000 loss=0.930318 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acc-top1=67.382812 acc-top5=86.531250 Batch [0249]/[0303]: acc-top1=67.343750 acc-top5=86.662500 Batch [0299]/[0303]: acc-top1=67.677083 acc-top5=86.770833 [Epoch 042] training: accuracy=75.861868 loss=0.950108 [Epoch 042] speed: 65 samples/sec time cost: 3937.278822 [Epoch 042] validation: acc-top1=67.682550 acc-top5=86.783210 loss=1.563203 Epoch[043] Batch [0049]/[3759] Speed: 44.297235 samples/sec accuracy=76.718750 loss=0.932877 lr=0.001000 Epoch[043] Batch [0099]/[3759] Speed: 63.962089 samples/sec accuracy=76.890625 loss=0.915126 lr=0.001000 Epoch[043] Batch [0149]/[3759] Speed: 66.782953 samples/sec accuracy=76.791667 loss=0.919800 lr=0.001000 Epoch[043] Batch [0199]/[3759] Speed: 65.270620 samples/sec accuracy=76.468750 loss=0.928840 lr=0.001000 Epoch[043] Batch [0249]/[3759] Speed: 65.923328 samples/sec accuracy=76.512500 loss=0.923588 lr=0.001000 Epoch[043] Batch [0299]/[3759] Speed: 65.539088 samples/sec accuracy=76.432292 loss=0.934595 lr=0.001000 Epoch[043] Batch 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accuracy=76.457620 loss=0.924549 lr=0.001000 Epoch[043] Batch [3699]/[3759] Speed: 66.096375 samples/sec accuracy=76.451014 loss=0.924803 lr=0.001000 Epoch[043] Batch [3749]/[3759] Speed: 73.763301 samples/sec accuracy=76.430417 loss=0.925488 lr=0.001000 Batch [0049]/[0303]: acc-top1=66.687500 acc-top5=86.812500 Batch [0099]/[0303]: acc-top1=67.203125 acc-top5=87.109375 Batch [0149]/[0303]: acc-top1=67.447917 acc-top5=86.875000 Batch [0199]/[0303]: acc-top1=67.289062 acc-top5=86.835938 Batch [0249]/[0303]: acc-top1=67.250000 acc-top5=86.893750 Batch [0299]/[0303]: acc-top1=67.619792 acc-top5=87.036458 [Epoch 043] training: accuracy=76.428239 loss=0.925573 [Epoch 043] speed: 65 samples/sec time cost: 3936.976155 [Epoch 043] validation: acc-top1=67.610355 acc-top5=87.077145 loss=1.543192 Epoch[044] Batch [0049]/[3760] Speed: 44.534161 samples/sec accuracy=78.562500 loss=0.832876 lr=0.001000 Epoch[044] Batch [0099]/[3760] Speed: 63.972448 samples/sec accuracy=78.265625 loss=0.849676 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[0299]/[0303]: acc-top1=67.895833 acc-top5=86.937500 [Epoch 044] training: accuracy=76.962267 loss=0.909052 [Epoch 044] speed: 65 samples/sec time cost: 3940.068292 [Epoch 044] validation: acc-top1=67.899134 acc-top5=86.953383 loss=1.531474 Epoch[045] Batch [0049]/[3760] Speed: 44.987026 samples/sec accuracy=78.000000 loss=0.854779 lr=0.001000 Epoch[045] Batch [0099]/[3760] Speed: 63.887898 samples/sec accuracy=78.250000 loss=0.857417 lr=0.001000 Epoch[045] Batch [0149]/[3760] Speed: 65.808288 samples/sec accuracy=78.010417 loss=0.859262 lr=0.001000 Epoch[045] Batch [0199]/[3760] Speed: 64.898534 samples/sec accuracy=77.929688 loss=0.868348 lr=0.001000 Epoch[045] Batch [0249]/[3760] Speed: 66.270825 samples/sec accuracy=77.825000 loss=0.875942 lr=0.001000 Epoch[045] Batch [0299]/[3760] Speed: 65.071307 samples/sec accuracy=77.770833 loss=0.874606 lr=0.001000 Epoch[045] Batch [0349]/[3760] Speed: 66.432622 samples/sec accuracy=77.651786 loss=0.880647 lr=0.001000 Epoch[045] Batch 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accuracy=77.384549 loss=0.893951 lr=0.001000 Epoch[045] Batch [1849]/[3760] Speed: 65.735833 samples/sec accuracy=77.363176 loss=0.894696 lr=0.001000 Epoch[045] Batch [1899]/[3760] Speed: 65.791465 samples/sec accuracy=77.384046 loss=0.893671 lr=0.001000 Epoch[045] Batch [1949]/[3760] Speed: 65.883060 samples/sec accuracy=77.381410 loss=0.893075 lr=0.001000 Epoch[045] Batch [1999]/[3760] Speed: 65.862514 samples/sec accuracy=77.362500 loss=0.892688 lr=0.001000 Epoch[045] Batch [2049]/[3760] Speed: 66.352181 samples/sec accuracy=77.377287 loss=0.892080 lr=0.001000 Epoch[045] Batch [2099]/[3760] Speed: 65.565820 samples/sec accuracy=77.365327 loss=0.892246 lr=0.001000 Epoch[045] Batch [2149]/[3760] Speed: 66.603371 samples/sec accuracy=77.356831 loss=0.892633 lr=0.001000 Epoch[045] Batch [2199]/[3760] Speed: 65.630567 samples/sec accuracy=77.333807 loss=0.893800 lr=0.001000 Epoch[045] Batch [2249]/[3760] Speed: 66.030888 samples/sec accuracy=77.315972 loss=0.894328 lr=0.001000 Epoch[045] 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accuracy=77.331818 loss=0.892196 lr=0.001000 Epoch[045] Batch [2799]/[3760] Speed: 66.355076 samples/sec accuracy=77.322545 loss=0.892166 lr=0.001000 Epoch[045] Batch [2849]/[3760] Speed: 65.538321 samples/sec accuracy=77.315241 loss=0.892364 lr=0.001000 Epoch[045] Batch [2899]/[3760] Speed: 65.850148 samples/sec accuracy=77.319504 loss=0.891605 lr=0.001000 Epoch[045] Batch [2949]/[3760] Speed: 65.852932 samples/sec accuracy=77.328919 loss=0.890756 lr=0.001000 Epoch[045] Batch [2999]/[3760] Speed: 66.196329 samples/sec accuracy=77.325521 loss=0.891286 lr=0.001000 Epoch[045] Batch [3049]/[3760] Speed: 66.007046 samples/sec accuracy=77.330430 loss=0.891065 lr=0.001000 Epoch[045] Batch [3099]/[3760] Speed: 65.823819 samples/sec accuracy=77.339214 loss=0.890329 lr=0.001000 Epoch[045] Batch [3149]/[3760] Speed: 65.793017 samples/sec accuracy=77.330357 loss=0.891019 lr=0.001000 Epoch[045] Batch [3199]/[3760] Speed: 65.327451 samples/sec accuracy=77.337402 loss=0.890544 lr=0.001000 Epoch[045] 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accuracy=77.332770 loss=0.890672 lr=0.001000 Epoch[045] Batch [3749]/[3760] Speed: 73.035568 samples/sec accuracy=77.329167 loss=0.890585 lr=0.001000 Batch [0049]/[0303]: acc-top1=67.781250 acc-top5=86.625000 Batch [0099]/[0303]: acc-top1=67.796875 acc-top5=86.968750 Batch [0149]/[0303]: acc-top1=68.072917 acc-top5=86.729167 Batch [0199]/[0303]: acc-top1=67.859375 acc-top5=86.742188 Batch [0249]/[0303]: acc-top1=67.843750 acc-top5=86.893750 Batch [0299]/[0303]: acc-top1=68.187500 acc-top5=87.098958 [Epoch 045] training: accuracy=77.331699 loss=0.890445 [Epoch 045] speed: 65 samples/sec time cost: 3940.916924 [Epoch 045] validation: acc-top1=68.182756 acc-top5=87.113243 loss=1.551332 Epoch[046] Batch [0049]/[3759] Speed: 44.849790 samples/sec accuracy=76.406250 loss=0.925064 lr=0.001000 Epoch[046] Batch [0099]/[3759] Speed: 64.516758 samples/sec accuracy=77.109375 loss=0.885660 lr=0.001000 Epoch[046] Batch [0149]/[3759] Speed: 66.019071 samples/sec accuracy=77.572917 loss=0.878855 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65.715255 samples/sec accuracy=77.778571 loss=0.873081 lr=0.001000 Epoch[046] Batch [3549]/[3759] Speed: 66.033996 samples/sec accuracy=77.786532 loss=0.873191 lr=0.001000 Epoch[046] Batch [3599]/[3759] Speed: 65.650849 samples/sec accuracy=77.796007 loss=0.872815 lr=0.001000 Epoch[046] Batch [3649]/[3759] Speed: 66.088811 samples/sec accuracy=77.794949 loss=0.873252 lr=0.001000 Epoch[046] Batch [3699]/[3759] Speed: 65.962078 samples/sec accuracy=77.782517 loss=0.873958 lr=0.001000 Epoch[046] Batch [3749]/[3759] Speed: 74.260281 samples/sec accuracy=77.779583 loss=0.873851 lr=0.001000 Batch [0049]/[0303]: acc-top1=67.687500 acc-top5=86.625000 Batch [0099]/[0303]: acc-top1=67.640625 acc-top5=86.875000 Batch [0149]/[0303]: acc-top1=67.927083 acc-top5=86.666667 Batch [0199]/[0303]: acc-top1=67.718750 acc-top5=86.523438 Batch [0249]/[0303]: acc-top1=67.825000 acc-top5=86.687500 Batch [0299]/[0303]: acc-top1=68.114583 acc-top5=86.817708 [Epoch 046] training: accuracy=77.777916 loss=0.873885 [Epoch 046] speed: 65 samples/sec time cost: 3935.846570 [Epoch 046] validation: acc-top1=68.110561 acc-top5=86.850248 loss=1.555797 Epoch[047] Batch [0049]/[3760] Speed: 44.772212 samples/sec accuracy=79.281250 loss=0.809781 lr=0.001000 Epoch[047] Batch [0099]/[3760] Speed: 64.864993 samples/sec accuracy=78.718750 loss=0.834825 lr=0.001000 Epoch[047] Batch [0149]/[3760] Speed: 66.235054 samples/sec accuracy=78.645833 loss=0.832353 lr=0.001000 Epoch[047] Batch [0199]/[3760] Speed: 65.154276 samples/sec accuracy=78.843750 loss=0.827086 lr=0.001000 Epoch[047] Batch [0249]/[3760] Speed: 66.053256 samples/sec accuracy=78.693750 loss=0.827482 lr=0.001000 Epoch[047] Batch [0299]/[3760] Speed: 64.957841 samples/sec accuracy=78.531250 loss=0.836445 lr=0.001000 Epoch[047] Batch [0349]/[3760] Speed: 66.980462 samples/sec accuracy=78.732143 loss=0.831867 lr=0.001000 Epoch[047] Batch [0399]/[3760] Speed: 65.965405 samples/sec accuracy=78.742188 loss=0.830540 lr=0.001000 Epoch[047] Batch 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accuracy=78.578125 loss=0.839814 lr=0.001000 Epoch[047] Batch [0949]/[3760] Speed: 65.764974 samples/sec accuracy=78.458882 loss=0.843562 lr=0.001000 Epoch[047] Batch [0999]/[3760] Speed: 65.370408 samples/sec accuracy=78.485938 loss=0.843267 lr=0.001000 Epoch[047] Batch [1049]/[3760] Speed: 65.905699 samples/sec accuracy=78.422619 loss=0.844494 lr=0.001000 Epoch[047] Batch [1099]/[3760] Speed: 66.021540 samples/sec accuracy=78.346591 loss=0.847346 lr=0.001000 Epoch[047] Batch [1149]/[3760] Speed: 66.223468 samples/sec accuracy=78.320652 loss=0.847958 lr=0.001000 Epoch[047] Batch [1199]/[3760] Speed: 65.931285 samples/sec accuracy=78.322917 loss=0.849385 lr=0.001000 Epoch[047] Batch [1249]/[3760] Speed: 65.885337 samples/sec accuracy=78.345000 loss=0.848544 lr=0.001000 Epoch[047] Batch [1299]/[3760] Speed: 66.374011 samples/sec accuracy=78.317308 loss=0.849267 lr=0.001000 Epoch[047] Batch [1349]/[3760] Speed: 66.053779 samples/sec accuracy=78.320602 loss=0.848418 lr=0.001000 Epoch[047] 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accuracy=78.224662 loss=0.852764 lr=0.001000 Epoch[047] Batch [1899]/[3760] Speed: 65.675161 samples/sec accuracy=78.205592 loss=0.852888 lr=0.001000 Epoch[047] Batch [1949]/[3760] Speed: 66.087566 samples/sec accuracy=78.204327 loss=0.853646 lr=0.001000 Epoch[047] Batch [1999]/[3760] Speed: 65.853069 samples/sec accuracy=78.232031 loss=0.853201 lr=0.001000 Epoch[047] Batch [2049]/[3760] Speed: 66.699549 samples/sec accuracy=78.231707 loss=0.852955 lr=0.001000 Epoch[047] Batch [2099]/[3760] Speed: 66.114175 samples/sec accuracy=78.230655 loss=0.852591 lr=0.001000 Epoch[047] Batch [2149]/[3760] Speed: 65.159476 samples/sec accuracy=78.212936 loss=0.853366 lr=0.001000 Epoch[047] Batch [2199]/[3760] Speed: 66.173152 samples/sec accuracy=78.211648 loss=0.853683 lr=0.001000 Epoch[047] Batch [2249]/[3760] Speed: 65.946585 samples/sec accuracy=78.231944 loss=0.853037 lr=0.001000 Epoch[047] Batch [2299]/[3760] Speed: 66.523037 samples/sec accuracy=78.215353 loss=0.853764 lr=0.001000 Epoch[047] 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accuracy=78.129464 loss=0.855397 lr=0.001000 Epoch[047] Batch [2849]/[3760] Speed: 65.363303 samples/sec accuracy=78.122807 loss=0.855612 lr=0.001000 Epoch[047] Batch [2899]/[3760] Speed: 66.060533 samples/sec accuracy=78.133082 loss=0.855158 lr=0.001000 Epoch[047] Batch [2949]/[3760] Speed: 65.999717 samples/sec accuracy=78.118644 loss=0.855492 lr=0.001000 Epoch[047] Batch [2999]/[3760] Speed: 66.335350 samples/sec accuracy=78.121354 loss=0.855448 lr=0.001000 Epoch[047] Batch [3049]/[3760] Speed: 66.252118 samples/sec accuracy=78.117316 loss=0.855040 lr=0.001000 Epoch[047] Batch [3099]/[3760] Speed: 66.346978 samples/sec accuracy=78.117440 loss=0.854969 lr=0.001000 Epoch[047] Batch [3149]/[3760] Speed: 65.830367 samples/sec accuracy=78.098710 loss=0.855807 lr=0.001000 Epoch[047] Batch [3199]/[3760] Speed: 65.986918 samples/sec accuracy=78.085449 loss=0.856320 lr=0.001000 Epoch[047] Batch [3249]/[3760] Speed: 65.573594 samples/sec accuracy=78.076923 loss=0.856738 lr=0.001000 Epoch[047] 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accuracy=78.025000 loss=0.858363 lr=0.001000 Batch [0049]/[0303]: acc-top1=66.500000 acc-top5=86.625000 Batch [0099]/[0303]: acc-top1=67.250000 acc-top5=86.984375 Batch [0149]/[0303]: acc-top1=67.666667 acc-top5=86.739583 Batch [0199]/[0303]: acc-top1=67.523438 acc-top5=86.765625 Batch [0249]/[0303]: acc-top1=67.693750 acc-top5=86.831250 Batch [0299]/[0303]: acc-top1=67.989583 acc-top5=86.994792 [Epoch 047] training: accuracy=78.021941 loss=0.858230 [Epoch 047] speed: 65 samples/sec time cost: 3937.598686 [Epoch 047] validation: acc-top1=67.981642 acc-top5=87.035891 loss=1.550070 Epoch[048] Batch [0049]/[3760] Speed: 44.774169 samples/sec accuracy=78.218750 loss=0.854379 lr=0.001000 Epoch[048] Batch [0099]/[3760] Speed: 64.270629 samples/sec accuracy=78.812500 loss=0.846619 lr=0.001000 Epoch[048] Batch [0149]/[3760] Speed: 66.174675 samples/sec accuracy=79.114583 loss=0.841725 lr=0.001000 Epoch[048] Batch [0199]/[3760] Speed: 65.135161 samples/sec accuracy=79.078125 loss=0.839403 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acc-top5=87.149340 loss=1.555679 Epoch[049] Batch [0049]/[3759] Speed: 44.088072 samples/sec accuracy=79.187500 loss=0.828411 lr=0.001000 Epoch[049] Batch [0099]/[3759] Speed: 64.698195 samples/sec accuracy=79.250000 loss=0.821680 lr=0.001000 Epoch[049] Batch [0149]/[3759] Speed: 65.950418 samples/sec accuracy=78.791667 loss=0.832937 lr=0.001000 Epoch[049] Batch [0199]/[3759] Speed: 65.009040 samples/sec accuracy=78.710938 loss=0.840928 lr=0.001000 Epoch[049] Batch [0249]/[3759] Speed: 66.444203 samples/sec accuracy=78.775000 loss=0.839876 lr=0.001000 Epoch[049] Batch [0299]/[3759] Speed: 65.443472 samples/sec accuracy=78.796875 loss=0.837575 lr=0.001000 Epoch[049] Batch [0349]/[3759] Speed: 66.317177 samples/sec accuracy=78.754464 loss=0.836226 lr=0.001000 Epoch[049] Batch [0399]/[3759] Speed: 66.126103 samples/sec accuracy=78.859375 loss=0.835731 lr=0.001000 Epoch[049] Batch [0449]/[3759] Speed: 66.259796 samples/sec accuracy=78.878472 loss=0.837764 lr=0.001000 Epoch[049] Batch 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accuracy=78.688048 loss=0.835602 lr=0.001000 Epoch[049] Batch [2899]/[3759] Speed: 65.508406 samples/sec accuracy=78.706358 loss=0.835457 lr=0.001000 Epoch[049] Batch [2949]/[3759] Speed: 66.103253 samples/sec accuracy=78.684322 loss=0.836136 lr=0.001000 Epoch[049] Batch [2999]/[3759] Speed: 65.518420 samples/sec accuracy=78.680729 loss=0.836484 lr=0.001000 Epoch[049] Batch [3049]/[3759] Speed: 65.895033 samples/sec accuracy=78.684939 loss=0.836589 lr=0.001000 Epoch[049] Batch [3099]/[3759] Speed: 66.137085 samples/sec accuracy=78.670867 loss=0.837211 lr=0.001000 Epoch[049] Batch [3149]/[3759] Speed: 65.770389 samples/sec accuracy=78.655754 loss=0.837742 lr=0.001000 Epoch[049] Batch [3199]/[3759] Speed: 66.038199 samples/sec accuracy=78.659180 loss=0.837419 lr=0.001000 Epoch[049] Batch [3249]/[3759] Speed: 66.205737 samples/sec accuracy=78.653365 loss=0.837580 lr=0.001000 Epoch[049] Batch [3299]/[3759] Speed: 65.562420 samples/sec accuracy=78.633049 loss=0.837998 lr=0.001000 Epoch[049] 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[0099]/[0303]: acc-top1=67.562500 acc-top5=87.296875 Batch [0149]/[0303]: acc-top1=68.010417 acc-top5=86.958333 Batch [0199]/[0303]: acc-top1=67.843750 acc-top5=86.804688 Batch [0249]/[0303]: acc-top1=67.937500 acc-top5=86.812500 Batch [0299]/[0303]: acc-top1=68.317708 acc-top5=87.005208 [Epoch 049] training: accuracy=78.586393 loss=0.838152 [Epoch 049] speed: 65 samples/sec time cost: 3933.614940 [Epoch 049] validation: acc-top1=68.291048 acc-top5=87.025578 loss=1.549099 Epoch[050] Batch [0049]/[3760] Speed: 43.964594 samples/sec accuracy=77.593750 loss=0.860754 lr=0.001000 Epoch[050] Batch [0099]/[3760] Speed: 64.661043 samples/sec accuracy=78.218750 loss=0.838348 lr=0.001000 Epoch[050] Batch [0149]/[3760] Speed: 65.964696 samples/sec accuracy=78.177083 loss=0.840475 lr=0.001000 Epoch[050] Batch [0199]/[3760] Speed: 65.620058 samples/sec accuracy=78.703125 loss=0.828819 lr=0.001000 Epoch[050] Batch [0249]/[3760] Speed: 66.213062 samples/sec accuracy=78.806250 loss=0.830221 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lr=0.001000 Epoch[050] Batch [3149]/[3760] Speed: 66.497905 samples/sec accuracy=78.940972 loss=0.823269 lr=0.001000 Epoch[050] Batch [3199]/[3760] Speed: 65.751594 samples/sec accuracy=78.941406 loss=0.823322 lr=0.001000 Epoch[050] Batch [3249]/[3760] Speed: 66.144263 samples/sec accuracy=78.937981 loss=0.823187 lr=0.001000 Epoch[050] Batch [3299]/[3760] Speed: 66.294760 samples/sec accuracy=78.920928 loss=0.823536 lr=0.001000 Epoch[050] Batch [3349]/[3760] Speed: 65.960445 samples/sec accuracy=78.939366 loss=0.822957 lr=0.001000 Epoch[050] Batch [3399]/[3760] Speed: 66.002840 samples/sec accuracy=78.935662 loss=0.822986 lr=0.001000 Epoch[050] Batch [3449]/[3760] Speed: 66.039200 samples/sec accuracy=78.947464 loss=0.822627 lr=0.001000 Epoch[050] Batch [3499]/[3760] Speed: 66.225082 samples/sec accuracy=78.938839 loss=0.822638 lr=0.001000 Epoch[050] Batch [3549]/[3760] Speed: 66.133611 samples/sec accuracy=78.927817 loss=0.823006 lr=0.001000 Epoch[050] Batch [3599]/[3760] Speed: 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accuracy=79.812500 loss=0.786517 lr=0.001000 Epoch[051] Batch [0099]/[3760] Speed: 64.677406 samples/sec accuracy=79.390625 loss=0.798333 lr=0.001000 Epoch[051] Batch [0149]/[3760] Speed: 65.820927 samples/sec accuracy=79.479167 loss=0.802009 lr=0.001000 Epoch[051] Batch [0199]/[3760] Speed: 65.540806 samples/sec accuracy=79.500000 loss=0.809736 lr=0.001000 Epoch[051] Batch [0249]/[3760] Speed: 66.720785 samples/sec accuracy=79.668750 loss=0.805069 lr=0.001000 Epoch[051] Batch [0299]/[3760] Speed: 65.263868 samples/sec accuracy=79.567708 loss=0.804621 lr=0.001000 Epoch[051] Batch [0349]/[3760] Speed: 66.574319 samples/sec accuracy=79.500000 loss=0.806674 lr=0.001000 Epoch[051] Batch [0399]/[3760] Speed: 66.397390 samples/sec accuracy=79.464844 loss=0.805359 lr=0.001000 Epoch[051] Batch [0449]/[3760] Speed: 65.999544 samples/sec accuracy=79.309028 loss=0.811339 lr=0.001000 Epoch[051] Batch [0499]/[3760] Speed: 66.243184 samples/sec accuracy=79.309375 loss=0.813430 lr=0.001000 Epoch[051] 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accuracy=79.326562 loss=0.811631 lr=0.001000 Epoch[051] Batch [1049]/[3760] Speed: 66.449776 samples/sec accuracy=79.296131 loss=0.812784 lr=0.001000 Epoch[051] Batch [1099]/[3760] Speed: 66.029774 samples/sec accuracy=79.276989 loss=0.812557 lr=0.001000 Epoch[051] Batch [1149]/[3760] Speed: 65.968712 samples/sec accuracy=79.305707 loss=0.810834 lr=0.001000 Epoch[051] Batch [1199]/[3760] Speed: 66.667857 samples/sec accuracy=79.276042 loss=0.811317 lr=0.001000 Epoch[051] Batch [1249]/[3760] Speed: 65.753920 samples/sec accuracy=79.272500 loss=0.811681 lr=0.001000 Epoch[051] Batch [1299]/[3760] Speed: 66.259348 samples/sec accuracy=79.268029 loss=0.811391 lr=0.001000 Epoch[051] Batch [1349]/[3760] Speed: 65.943369 samples/sec accuracy=79.266204 loss=0.812067 lr=0.001000 Epoch[051] Batch [1399]/[3760] Speed: 65.878740 samples/sec accuracy=79.279018 loss=0.811847 lr=0.001000 Epoch[051] Batch [1449]/[3760] Speed: 66.472282 samples/sec accuracy=79.267241 loss=0.812091 lr=0.001000 Epoch[051] 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accuracy=79.225962 loss=0.813243 lr=0.001000 Epoch[051] Batch [1999]/[3760] Speed: 66.394832 samples/sec accuracy=79.225000 loss=0.813177 lr=0.001000 Epoch[051] Batch [2049]/[3760] Speed: 65.432748 samples/sec accuracy=79.190549 loss=0.814315 lr=0.001000 Epoch[051] Batch [2099]/[3760] Speed: 66.383442 samples/sec accuracy=79.152530 loss=0.815596 lr=0.001000 Epoch[051] Batch [2149]/[3760] Speed: 65.853912 samples/sec accuracy=79.163517 loss=0.815081 lr=0.001000 Epoch[051] Batch [2199]/[3760] Speed: 66.547496 samples/sec accuracy=79.168324 loss=0.814647 lr=0.001000 Epoch[051] Batch [2249]/[3760] Speed: 65.547034 samples/sec accuracy=79.180556 loss=0.814266 lr=0.001000 Epoch[051] Batch [2299]/[3760] Speed: 66.239985 samples/sec accuracy=79.164402 loss=0.814786 lr=0.001000 Epoch[051] Batch [2349]/[3760] Speed: 66.188993 samples/sec accuracy=79.169548 loss=0.814396 lr=0.001000 Epoch[051] Batch [2399]/[3760] Speed: 65.952331 samples/sec accuracy=79.194661 loss=0.813778 lr=0.001000 Epoch[051] 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accuracy=79.153556 loss=0.813531 lr=0.001000 Epoch[051] Batch [2949]/[3760] Speed: 66.308030 samples/sec accuracy=79.137712 loss=0.814041 lr=0.001000 Epoch[051] Batch [2999]/[3760] Speed: 66.488796 samples/sec accuracy=79.139062 loss=0.813918 lr=0.001000 Epoch[051] Batch [3049]/[3760] Speed: 66.173092 samples/sec accuracy=79.143955 loss=0.813995 lr=0.001000 Epoch[051] Batch [3099]/[3760] Speed: 65.996662 samples/sec accuracy=79.144153 loss=0.814077 lr=0.001000 Epoch[051] Batch [3149]/[3760] Speed: 66.442256 samples/sec accuracy=79.130952 loss=0.814558 lr=0.001000 Epoch[051] Batch [3199]/[3760] Speed: 66.315461 samples/sec accuracy=79.131348 loss=0.814772 lr=0.001000 Epoch[051] Batch [3249]/[3760] Speed: 65.667788 samples/sec accuracy=79.128846 loss=0.814639 lr=0.001000 Epoch[051] Batch [3299]/[3760] Speed: 66.497682 samples/sec accuracy=79.135417 loss=0.814684 lr=0.001000 Epoch[051] Batch [3349]/[3760] Speed: 65.827883 samples/sec accuracy=79.137127 loss=0.814503 lr=0.001000 Epoch[051] Batch [3399]/[3760] Speed: 66.310283 samples/sec accuracy=79.127298 loss=0.814894 lr=0.001000 Epoch[051] Batch [3449]/[3760] Speed: 66.416636 samples/sec accuracy=79.132699 loss=0.814832 lr=0.001000 Epoch[051] Batch [3499]/[3760] Speed: 66.117769 samples/sec accuracy=79.123661 loss=0.814816 lr=0.001000 Epoch[051] Batch [3549]/[3760] Speed: 66.161659 samples/sec accuracy=79.125880 loss=0.814402 lr=0.001000 Epoch[051] Batch [3599]/[3760] Speed: 66.289689 samples/sec accuracy=79.117188 loss=0.814735 lr=0.001000 Epoch[051] Batch [3649]/[3760] Speed: 66.298035 samples/sec accuracy=79.116010 loss=0.815079 lr=0.001000 Epoch[051] Batch [3699]/[3760] Speed: 65.953806 samples/sec accuracy=79.110642 loss=0.815333 lr=0.001000 Epoch[051] Batch [3749]/[3760] Speed: 72.760922 samples/sec accuracy=79.106250 loss=0.815352 lr=0.001000 Batch [0049]/[0303]: acc-top1=67.687500 acc-top5=87.000000 Batch [0099]/[0303]: acc-top1=67.687500 acc-top5=87.078125 Batch [0149]/[0303]: acc-top1=67.947917 acc-top5=87.041667 Batch [0199]/[0303]: acc-top1=67.781250 acc-top5=86.859375 Batch [0249]/[0303]: acc-top1=67.887500 acc-top5=86.912500 Batch [0299]/[0303]: acc-top1=68.250000 acc-top5=87.161458 [Epoch 051] training: accuracy=79.102809 loss=0.815548 [Epoch 051] speed: 65 samples/sec time cost: 3929.586132 [Epoch 051] validation: acc-top1=68.198226 acc-top5=87.185437 loss=1.571412 Epoch[052] Batch [0049]/[3759] Speed: 44.893086 samples/sec accuracy=80.062500 loss=0.791047 lr=0.001000 Epoch[052] Batch [0099]/[3759] Speed: 64.839703 samples/sec accuracy=79.984375 loss=0.802467 lr=0.001000 Epoch[052] Batch [0149]/[3759] Speed: 66.058015 samples/sec accuracy=79.354167 loss=0.814546 lr=0.001000 Epoch[052] Batch [0199]/[3759] Speed: 65.481615 samples/sec accuracy=79.554688 loss=0.811549 lr=0.001000 Epoch[052] Batch [0249]/[3759] Speed: 66.214766 samples/sec accuracy=79.750000 loss=0.802123 lr=0.001000 Epoch[052] Batch [0299]/[3759] Speed: 65.803016 samples/sec accuracy=79.729167 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lr=0.001000 Epoch[053] Batch [3449]/[3760] Speed: 66.422517 samples/sec accuracy=79.732790 loss=0.791097 lr=0.001000 Epoch[053] Batch [3499]/[3760] Speed: 66.192510 samples/sec accuracy=79.718750 loss=0.791763 lr=0.001000 Epoch[053] Batch [3549]/[3760] Speed: 66.017275 samples/sec accuracy=79.713028 loss=0.791639 lr=0.001000 Epoch[053] Batch [3599]/[3760] Speed: 66.031126 samples/sec accuracy=79.708333 loss=0.791861 lr=0.001000 Epoch[053] Batch [3649]/[3760] Speed: 66.538718 samples/sec accuracy=79.703767 loss=0.792163 lr=0.001000 Epoch[053] Batch [3699]/[3760] Speed: 66.190747 samples/sec accuracy=79.703970 loss=0.792091 lr=0.001000 Epoch[053] Batch [3749]/[3760] Speed: 72.666075 samples/sec accuracy=79.700000 loss=0.792065 lr=0.001000 Batch [0049]/[0303]: acc-top1=67.531250 acc-top5=86.812500 Batch [0099]/[0303]: acc-top1=67.265625 acc-top5=87.062500 Batch [0149]/[0303]: acc-top1=67.729167 acc-top5=86.781250 Batch [0199]/[0303]: acc-top1=67.593750 acc-top5=86.656250 Batch [0249]/[0303]: acc-top1=67.593750 acc-top5=86.668750 Batch [0299]/[0303]: acc-top1=67.864583 acc-top5=86.796875 [Epoch 053] training: accuracy=79.694980 loss=0.792271 [Epoch 053] speed: 65 samples/sec time cost: 3929.184404 [Epoch 053] validation: acc-top1=67.868193 acc-top5=86.814150 loss=1.566856 Epoch[054] Batch [0049]/[3760] Speed: 44.771373 samples/sec accuracy=79.062500 loss=0.816888 lr=0.001000 Epoch[054] Batch [0099]/[3760] Speed: 64.757329 samples/sec accuracy=79.296875 loss=0.820115 lr=0.001000 Epoch[054] Batch [0149]/[3760] Speed: 66.170790 samples/sec accuracy=79.468750 loss=0.802894 lr=0.001000 Epoch[054] Batch [0199]/[3760] Speed: 65.279073 samples/sec accuracy=79.585938 loss=0.802784 lr=0.001000 Epoch[054] Batch [0249]/[3760] Speed: 66.036247 samples/sec accuracy=79.881250 loss=0.793788 lr=0.001000 Epoch[054] Batch [0299]/[3760] Speed: 66.116128 samples/sec accuracy=80.276042 loss=0.780330 lr=0.001000 Epoch[054] Batch [0349]/[3760] Speed: 66.236199 samples/sec 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accuracy=79.972656 loss=0.781833 lr=0.001000 Epoch[054] Batch [3249]/[3760] Speed: 66.001416 samples/sec accuracy=79.975481 loss=0.781981 lr=0.001000 Epoch[054] Batch [3299]/[3760] Speed: 66.722727 samples/sec accuracy=79.961648 loss=0.782644 lr=0.001000 Epoch[054] Batch [3349]/[3760] Speed: 66.038215 samples/sec accuracy=79.939832 loss=0.782966 lr=0.001000 Epoch[054] Batch [3399]/[3760] Speed: 66.531325 samples/sec accuracy=79.934743 loss=0.782982 lr=0.001000 Epoch[054] Batch [3449]/[3760] Speed: 65.984402 samples/sec accuracy=79.928895 loss=0.783516 lr=0.001000 Epoch[054] Batch [3499]/[3760] Speed: 65.855171 samples/sec accuracy=79.927232 loss=0.783586 lr=0.001000 Epoch[054] Batch [3549]/[3760] Speed: 66.528190 samples/sec accuracy=79.917694 loss=0.783795 lr=0.001000 Epoch[054] Batch [3599]/[3760] Speed: 65.902221 samples/sec accuracy=79.908420 loss=0.784047 lr=0.001000 Epoch[054] Batch [3649]/[3760] Speed: 66.585033 samples/sec accuracy=79.893408 loss=0.784457 lr=0.001000 Epoch[054] Batch [3699]/[3760] Speed: 66.554487 samples/sec accuracy=79.866132 loss=0.785233 lr=0.001000 Epoch[054] Batch [3749]/[3760] Speed: 72.272321 samples/sec accuracy=79.867917 loss=0.785222 lr=0.001000 Batch [0049]/[0303]: acc-top1=67.687500 acc-top5=86.312500 Batch [0099]/[0303]: acc-top1=67.609375 acc-top5=86.703125 Batch [0149]/[0303]: acc-top1=67.843750 acc-top5=86.593750 Batch [0199]/[0303]: acc-top1=67.609375 acc-top5=86.671875 Batch [0249]/[0303]: acc-top1=67.675000 acc-top5=86.800000 Batch [0299]/[0303]: acc-top1=68.078125 acc-top5=86.953125 [Epoch 054] training: accuracy=79.872424 loss=0.784951 [Epoch 054] speed: 65 samples/sec time cost: 3928.306723 [Epoch 054] validation: acc-top1=68.084777 acc-top5=86.974010 loss=1.587701 Epoch[055] Batch [0049]/[3759] Speed: 45.278356 samples/sec accuracy=80.562500 loss=0.748325 lr=0.001000 Epoch[055] Batch [0099]/[3759] Speed: 64.219662 samples/sec accuracy=79.546875 loss=0.784100 lr=0.001000 Epoch[055] Batch [0149]/[3759] Speed: 66.611716 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lr=0.001000 Epoch[055] Batch [2549]/[3759] Speed: 66.378006 samples/sec accuracy=80.238971 loss=0.768548 lr=0.001000 Epoch[055] Batch [2599]/[3759] Speed: 66.212845 samples/sec accuracy=80.219952 loss=0.769275 lr=0.001000 Epoch[055] Batch [2649]/[3759] Speed: 66.130992 samples/sec accuracy=80.214033 loss=0.770104 lr=0.001000 Epoch[055] Batch [2699]/[3759] Speed: 66.109940 samples/sec accuracy=80.231481 loss=0.769608 lr=0.001000 Epoch[055] Batch [2749]/[3759] Speed: 65.590771 samples/sec accuracy=80.215909 loss=0.770165 lr=0.001000 Epoch[055] Batch [2799]/[3759] Speed: 66.084200 samples/sec accuracy=80.209263 loss=0.770280 lr=0.001000 Epoch[055] Batch [2849]/[3759] Speed: 65.956198 samples/sec accuracy=80.199561 loss=0.770304 lr=0.001000 Epoch[055] Batch [2899]/[3759] Speed: 66.117922 samples/sec accuracy=80.191810 loss=0.770831 lr=0.001000 Epoch[055] Batch [2949]/[3759] Speed: 66.176441 samples/sec accuracy=80.183792 loss=0.770771 lr=0.001000 Epoch[055] Batch [2999]/[3759] Speed: 65.939147 samples/sec accuracy=80.152604 loss=0.771699 lr=0.001000 Epoch[055] Batch [3049]/[3759] Speed: 66.655274 samples/sec accuracy=80.157275 loss=0.771752 lr=0.001000 Epoch[055] Batch [3099]/[3759] Speed: 66.329476 samples/sec accuracy=80.148185 loss=0.772294 lr=0.001000 Epoch[055] Batch [3149]/[3759] Speed: 65.701281 samples/sec accuracy=80.152282 loss=0.772458 lr=0.001000 Epoch[055] Batch [3199]/[3759] Speed: 66.734977 samples/sec accuracy=80.159180 loss=0.772338 lr=0.001000 Epoch[055] Batch [3249]/[3759] Speed: 66.147941 samples/sec accuracy=80.149519 loss=0.772717 lr=0.001000 Epoch[055] Batch [3299]/[3759] Speed: 66.216562 samples/sec accuracy=80.147727 loss=0.772732 lr=0.001000 Epoch[055] Batch [3349]/[3759] Speed: 66.036883 samples/sec accuracy=80.146922 loss=0.772742 lr=0.001000 Epoch[055] Batch [3399]/[3759] Speed: 66.388804 samples/sec accuracy=80.130974 loss=0.773195 lr=0.001000 Epoch[055] Batch [3449]/[3759] Speed: 66.317332 samples/sec accuracy=80.125453 loss=0.773444 lr=0.001000 Epoch[055] Batch [3499]/[3759] Speed: 65.521710 samples/sec accuracy=80.134821 loss=0.772818 lr=0.001000 Epoch[055] Batch [3549]/[3759] Speed: 66.193404 samples/sec accuracy=80.119278 loss=0.773168 lr=0.001000 Epoch[055] Batch [3599]/[3759] Speed: 65.716329 samples/sec accuracy=80.105035 loss=0.773412 lr=0.001000 Epoch[055] Batch [3649]/[3759] Speed: 66.542143 samples/sec accuracy=80.102740 loss=0.773982 lr=0.001000 Epoch[055] Batch [3699]/[3759] Speed: 65.759449 samples/sec accuracy=80.097551 loss=0.774719 lr=0.001000 Epoch[055] Batch [3749]/[3759] Speed: 74.262115 samples/sec accuracy=80.098333 loss=0.774142 lr=0.001000 Batch [0049]/[0303]: acc-top1=67.468750 acc-top5=86.781250 Batch [0099]/[0303]: acc-top1=67.703125 acc-top5=86.937500 Batch [0149]/[0303]: acc-top1=67.927083 acc-top5=86.708333 Batch [0199]/[0303]: acc-top1=67.742188 acc-top5=86.617188 Batch [0249]/[0303]: acc-top1=67.793750 acc-top5=86.700000 Batch [0299]/[0303]: acc-top1=68.119792 acc-top5=86.802083 [Epoch 055] training: accuracy=80.096934 loss=0.774062 [Epoch 055] speed: 65 samples/sec time cost: 3927.589310 [Epoch 055] validation: acc-top1=68.115718 acc-top5=86.824464 loss=1.582635 Epoch[056] Batch [0049]/[3760] Speed: 44.231609 samples/sec accuracy=79.937500 loss=0.779200 lr=0.001000 Epoch[056] Batch [0099]/[3760] Speed: 64.557143 samples/sec accuracy=80.906250 loss=0.740708 lr=0.001000 Epoch[056] Batch [0149]/[3760] Speed: 66.107849 samples/sec accuracy=81.187500 loss=0.729930 lr=0.001000 Epoch[056] Batch [0199]/[3760] Speed: 65.573698 samples/sec accuracy=81.257812 loss=0.727813 lr=0.001000 Epoch[056] Batch [0249]/[3760] Speed: 66.286439 samples/sec accuracy=81.025000 loss=0.739233 lr=0.001000 Epoch[056] Batch [0299]/[3760] Speed: 65.914726 samples/sec accuracy=80.713542 loss=0.753961 lr=0.001000 Epoch[056] Batch [0349]/[3760] Speed: 65.443251 samples/sec accuracy=80.691964 loss=0.757373 lr=0.001000 Epoch[056] Batch [0399]/[3760] Speed: 66.289104 samples/sec 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accuracy=80.201923 loss=0.770819 lr=0.001000 Epoch[056] Batch [3299]/[3760] Speed: 66.163263 samples/sec accuracy=80.218277 loss=0.770530 lr=0.001000 Epoch[056] Batch [3349]/[3760] Speed: 65.492017 samples/sec accuracy=80.221549 loss=0.770807 lr=0.001000 Epoch[056] Batch [3399]/[3760] Speed: 66.339176 samples/sec accuracy=80.220129 loss=0.770979 lr=0.001000 Epoch[056] Batch [3449]/[3760] Speed: 65.372642 samples/sec accuracy=80.193388 loss=0.771704 lr=0.001000 Epoch[056] Batch [3499]/[3760] Speed: 66.613783 samples/sec accuracy=80.209375 loss=0.771200 lr=0.001000 Epoch[056] Batch [3549]/[3760] Speed: 66.489344 samples/sec accuracy=80.194102 loss=0.771213 lr=0.001000 Epoch[056] Batch [3599]/[3760] Speed: 65.763629 samples/sec accuracy=80.187066 loss=0.771451 lr=0.001000 Epoch[056] Batch [3649]/[3760] Speed: 66.206675 samples/sec accuracy=80.177654 loss=0.771914 lr=0.001000 Epoch[056] Batch [3699]/[3760] Speed: 66.372453 samples/sec accuracy=80.191301 loss=0.771264 lr=0.001000 Epoch[056] Batch [3749]/[3760] Speed: 72.748308 samples/sec accuracy=80.197500 loss=0.771429 lr=0.001000 Batch [0049]/[0303]: acc-top1=67.125000 acc-top5=86.625000 Batch [0099]/[0303]: acc-top1=67.000000 acc-top5=86.859375 Batch [0149]/[0303]: acc-top1=67.500000 acc-top5=86.708333 Batch [0199]/[0303]: acc-top1=67.500000 acc-top5=86.687500 Batch [0249]/[0303]: acc-top1=67.487500 acc-top5=86.781250 Batch [0299]/[0303]: acc-top1=67.828125 acc-top5=86.968750 [Epoch 056] training: accuracy=80.199884 loss=0.771361 [Epoch 056] speed: 65 samples/sec time cost: 3931.212883 [Epoch 056] validation: acc-top1=67.832096 acc-top5=86.999794 loss=1.576534 Epoch[057] Batch [0049]/[3760] Speed: 44.624094 samples/sec accuracy=81.218750 loss=0.711206 lr=0.001000 Epoch[057] Batch [0099]/[3760] Speed: 64.364685 samples/sec accuracy=80.671875 loss=0.736176 lr=0.001000 Epoch[057] Batch [0149]/[3760] Speed: 65.560031 samples/sec accuracy=81.062500 loss=0.725950 lr=0.001000 Epoch[057] Batch [0199]/[3760] Speed: 65.270138 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66.091242 samples/sec accuracy=80.724702 loss=0.749500 lr=0.001000 Epoch[057] Batch [2149]/[3760] Speed: 65.979278 samples/sec accuracy=80.720203 loss=0.749765 lr=0.001000 Epoch[057] Batch [2199]/[3760] Speed: 66.384866 samples/sec accuracy=80.735085 loss=0.749483 lr=0.001000 Epoch[057] Batch [2249]/[3760] Speed: 66.075474 samples/sec accuracy=80.715278 loss=0.749700 lr=0.001000 Epoch[057] Batch [2299]/[3760] Speed: 66.034898 samples/sec accuracy=80.711957 loss=0.750022 lr=0.001000 Epoch[057] Batch [2349]/[3760] Speed: 66.085989 samples/sec accuracy=80.739362 loss=0.749247 lr=0.001000 Epoch[057] Batch [2399]/[3760] Speed: 65.541158 samples/sec accuracy=80.720703 loss=0.749983 lr=0.001000 Epoch[057] Batch [2449]/[3760] Speed: 66.600891 samples/sec accuracy=80.723214 loss=0.749713 lr=0.001000 Epoch[057] Batch [2499]/[3760] Speed: 65.547102 samples/sec accuracy=80.743125 loss=0.749204 lr=0.001000 Epoch[057] Batch [2549]/[3760] Speed: 65.916795 samples/sec accuracy=80.745711 loss=0.749268 lr=0.001000 Epoch[057] Batch [2599]/[3760] Speed: 66.159690 samples/sec accuracy=80.728365 loss=0.749656 lr=0.001000 Epoch[057] Batch [2649]/[3760] Speed: 66.234496 samples/sec accuracy=80.735259 loss=0.749795 lr=0.001000 Epoch[057] Batch [2699]/[3760] Speed: 65.973700 samples/sec accuracy=80.720486 loss=0.750203 lr=0.001000 Epoch[057] Batch [2749]/[3760] Speed: 66.199177 samples/sec accuracy=80.685795 loss=0.751655 lr=0.001000 Epoch[057] Batch [2799]/[3760] Speed: 66.025738 samples/sec accuracy=80.685826 loss=0.751678 lr=0.001000 Epoch[057] Batch [2849]/[3760] Speed: 66.306333 samples/sec accuracy=80.669956 loss=0.751605 lr=0.001000 Epoch[057] Batch [2899]/[3760] Speed: 66.080082 samples/sec accuracy=80.674030 loss=0.751776 lr=0.001000 Epoch[057] Batch [2949]/[3760] Speed: 66.420722 samples/sec accuracy=80.671081 loss=0.751900 lr=0.001000 Epoch[057] Batch [2999]/[3760] Speed: 65.888482 samples/sec accuracy=80.678125 loss=0.751596 lr=0.001000 Epoch[057] Batch [3049]/[3760] Speed: 66.836295 samples/sec accuracy=80.679816 loss=0.751860 lr=0.001000 Epoch[057] Batch [3099]/[3760] Speed: 66.162389 samples/sec accuracy=80.670363 loss=0.752491 lr=0.001000 Epoch[057] Batch [3149]/[3760] Speed: 65.797236 samples/sec accuracy=80.670139 loss=0.752346 lr=0.001000 Epoch[057] Batch [3199]/[3760] Speed: 65.884181 samples/sec accuracy=80.687988 loss=0.752189 lr=0.001000 Epoch[057] Batch [3249]/[3760] Speed: 66.212212 samples/sec accuracy=80.694712 loss=0.752139 lr=0.001000 Epoch[057] Batch [3299]/[3760] Speed: 66.184661 samples/sec accuracy=80.703598 loss=0.751900 lr=0.001000 Epoch[057] Batch [3349]/[3760] Speed: 66.353504 samples/sec accuracy=80.694963 loss=0.752530 lr=0.001000 Epoch[057] Batch [3399]/[3760] Speed: 65.814862 samples/sec accuracy=80.692555 loss=0.752773 lr=0.001000 Epoch[057] Batch [3449]/[3760] Speed: 66.357284 samples/sec accuracy=80.683424 loss=0.753474 lr=0.001000 Epoch[057] Batch [3499]/[3760] Speed: 66.230405 samples/sec accuracy=80.670982 loss=0.753916 lr=0.001000 Epoch[057] Batch [3549]/[3760] Speed: 65.741672 samples/sec accuracy=80.657130 loss=0.754402 lr=0.001000 Epoch[057] Batch [3599]/[3760] Speed: 66.660155 samples/sec accuracy=80.650608 loss=0.754796 lr=0.001000 Epoch[057] Batch [3649]/[3760] Speed: 66.124171 samples/sec accuracy=80.654538 loss=0.755066 lr=0.001000 Epoch[057] Batch [3699]/[3760] Speed: 66.015007 samples/sec accuracy=80.642736 loss=0.755156 lr=0.001000 Epoch[057] Batch [3749]/[3760] Speed: 72.945204 samples/sec accuracy=80.636667 loss=0.755505 lr=0.001000 Batch [0049]/[0303]: acc-top1=67.750000 acc-top5=87.156250 Batch [0099]/[0303]: acc-top1=67.171875 acc-top5=86.984375 Batch [0149]/[0303]: acc-top1=67.854167 acc-top5=86.552083 Batch [0199]/[0303]: acc-top1=67.687500 acc-top5=86.507812 Batch [0249]/[0303]: acc-top1=67.631250 acc-top5=86.656250 Batch [0299]/[0303]: acc-top1=68.046875 acc-top5=86.786458 [Epoch 057] training: accuracy=80.634142 loss=0.755526 [Epoch 057] speed: 65 samples/sec time cost: 3932.141272 [Epoch 057] validation: acc-top1=68.058993 acc-top5=86.798680 loss=1.590857 Epoch[058] Batch [0049]/[3759] Speed: 44.544089 samples/sec accuracy=80.562500 loss=0.765203 lr=0.001000 Epoch[058] Batch [0099]/[3759] Speed: 64.344200 samples/sec accuracy=80.468750 loss=0.749728 lr=0.001000 Epoch[058] Batch [0149]/[3759] Speed: 66.021811 samples/sec accuracy=80.552083 loss=0.745333 lr=0.001000 Epoch[058] Batch [0199]/[3759] Speed: 64.974957 samples/sec accuracy=80.531250 loss=0.749258 lr=0.001000 Epoch[058] Batch [0249]/[3759] Speed: 66.304978 samples/sec accuracy=80.631250 loss=0.745446 lr=0.001000 Epoch[058] Batch [0299]/[3759] Speed: 65.544427 samples/sec accuracy=80.833333 loss=0.740999 lr=0.001000 Epoch[058] Batch [0349]/[3759] Speed: 66.915837 samples/sec accuracy=80.861607 loss=0.744633 lr=0.001000 Epoch[058] Batch [0399]/[3759] Speed: 65.857567 samples/sec accuracy=80.984375 loss=0.738789 lr=0.001000 Epoch[058] Batch [0449]/[3759] Speed: 66.231145 samples/sec 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[0049]/[0303]: acc-top1=67.875000 acc-top5=86.687500 Batch [0099]/[0303]: acc-top1=67.640625 acc-top5=86.984375 Batch [0149]/[0303]: acc-top1=67.979167 acc-top5=86.833333 Batch [0199]/[0303]: acc-top1=67.992188 acc-top5=86.765625 Batch [0249]/[0303]: acc-top1=68.006250 acc-top5=86.812500 Batch [0299]/[0303]: acc-top1=68.348958 acc-top5=86.984375 [Epoch 058] training: accuracy=80.790686 loss=0.747903 [Epoch 058] speed: 65 samples/sec time cost: 3930.802374 [Epoch 058] validation: acc-top1=68.337459 acc-top5=86.979167 loss=1.623280 Epoch[059] Batch [0049]/[3760] Speed: 44.512643 samples/sec accuracy=81.375000 loss=0.711247 lr=0.001000 Epoch[059] Batch [0099]/[3760] Speed: 64.857834 samples/sec accuracy=81.546875 loss=0.718117 lr=0.001000 Epoch[059] Batch [0149]/[3760] Speed: 65.917009 samples/sec accuracy=81.479167 loss=0.724358 lr=0.001000 Epoch[059] Batch [0199]/[3760] Speed: 65.332195 samples/sec accuracy=81.500000 loss=0.723928 lr=0.001000 Epoch[059] Batch [0249]/[3760] Speed: 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lr=0.001000 Epoch[059] Batch [3599]/[3760] Speed: 65.916497 samples/sec accuracy=80.850260 loss=0.744511 lr=0.001000 Epoch[059] Batch [3649]/[3760] Speed: 66.552265 samples/sec accuracy=80.845462 loss=0.744609 lr=0.001000 Epoch[059] Batch [3699]/[3760] Speed: 66.263896 samples/sec accuracy=80.847973 loss=0.744727 lr=0.001000 Epoch[059] Batch [3749]/[3760] Speed: 72.785868 samples/sec accuracy=80.850000 loss=0.744889 lr=0.001000 Batch [0049]/[0303]: acc-top1=67.250000 acc-top5=86.875000 Batch [0099]/[0303]: acc-top1=67.218750 acc-top5=86.843750 Batch [0149]/[0303]: acc-top1=67.479167 acc-top5=86.510417 Batch [0199]/[0303]: acc-top1=67.531250 acc-top5=86.609375 Batch [0249]/[0303]: acc-top1=67.587500 acc-top5=86.637500 Batch [0299]/[0303]: acc-top1=67.994792 acc-top5=86.734375 [Epoch 059] training: accuracy=80.846493 loss=0.745216 [Epoch 059] speed: 65 samples/sec time cost: 3934.885475 [Epoch 059] validation: acc-top1=67.961015 acc-top5=86.741955 loss=1.620185 Epoch[060] Batch 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accuracy=81.093284 loss=0.736746 lr=0.001000 Epoch[060] Batch [3399]/[3760] Speed: 65.990830 samples/sec accuracy=81.092831 loss=0.736193 lr=0.001000 Epoch[060] Batch [3449]/[3760] Speed: 65.727598 samples/sec accuracy=81.096014 loss=0.736107 lr=0.001000 Epoch[060] Batch [3499]/[3760] Speed: 66.353252 samples/sec accuracy=81.090625 loss=0.736164 lr=0.001000 Epoch[060] Batch [3549]/[3760] Speed: 66.105523 samples/sec accuracy=81.094630 loss=0.735946 lr=0.001000 Epoch[060] Batch [3599]/[3760] Speed: 65.350400 samples/sec accuracy=81.074219 loss=0.736749 lr=0.001000 Epoch[060] Batch [3649]/[3760] Speed: 66.303202 samples/sec accuracy=81.057791 loss=0.736663 lr=0.001000 Epoch[060] Batch [3699]/[3760] Speed: 65.795902 samples/sec accuracy=81.049831 loss=0.736983 lr=0.001000 Epoch[060] Batch [3749]/[3760] Speed: 72.865891 samples/sec accuracy=81.043333 loss=0.737443 lr=0.001000 Batch [0049]/[0303]: acc-top1=67.000000 acc-top5=87.125000 Batch [0099]/[0303]: acc-top1=66.937500 acc-top5=86.968750 Batch [0149]/[0303]: acc-top1=67.364583 acc-top5=86.843750 Batch [0199]/[0303]: acc-top1=67.437500 acc-top5=86.828125 Batch [0249]/[0303]: acc-top1=67.506250 acc-top5=86.793750 Batch [0299]/[0303]: acc-top1=67.776042 acc-top5=86.901042 [Epoch 060] training: accuracy=81.040974 loss=0.737448 [Epoch 060] speed: 65 samples/sec time cost: 3935.941876 [Epoch 060] validation: acc-top1=67.765058 acc-top5=86.917285 loss=1.615935 Epoch[061] Batch [0049]/[3759] Speed: 44.443981 samples/sec accuracy=82.750000 loss=0.698824 lr=0.001000 Epoch[061] Batch [0099]/[3759] Speed: 63.764449 samples/sec accuracy=82.250000 loss=0.706974 lr=0.001000 Epoch[061] Batch [0149]/[3759] Speed: 66.342549 samples/sec accuracy=81.989583 loss=0.716239 lr=0.001000 Epoch[061] Batch [0199]/[3759] Speed: 64.840845 samples/sec accuracy=81.968750 loss=0.710730 lr=0.001000 Epoch[061] Batch [0249]/[3759] Speed: 66.111282 samples/sec accuracy=82.106250 loss=0.701477 lr=0.001000 Epoch[061] Batch [0299]/[3759] 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accuracy=81.345920 loss=0.727260 lr=0.001000 Epoch[061] Batch [3649]/[3759] Speed: 66.204699 samples/sec accuracy=81.335188 loss=0.727628 lr=0.001000 Epoch[061] Batch [3699]/[3759] Speed: 65.897668 samples/sec accuracy=81.344172 loss=0.727623 lr=0.001000 Epoch[061] Batch [3749]/[3759] Speed: 73.612387 samples/sec accuracy=81.328333 loss=0.727883 lr=0.001000 Batch [0049]/[0303]: acc-top1=67.843750 acc-top5=86.406250 Batch [0099]/[0303]: acc-top1=67.687500 acc-top5=86.500000 Batch [0149]/[0303]: acc-top1=67.833333 acc-top5=86.322917 Batch [0199]/[0303]: acc-top1=67.687500 acc-top5=86.359375 Batch [0249]/[0303]: acc-top1=67.681250 acc-top5=86.456250 Batch [0299]/[0303]: acc-top1=68.052083 acc-top5=86.552083 [Epoch 061] training: accuracy=81.332718 loss=0.727805 [Epoch 061] speed: 65 samples/sec time cost: 3936.641986 [Epoch 061] validation: acc-top1=68.048680 acc-top5=86.566625 loss=1.638898 Epoch[062] Batch [0049]/[3760] Speed: 44.766608 samples/sec accuracy=81.156250 loss=0.719207 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acc-top1=67.687500 acc-top5=86.812500 Batch [0249]/[0303]: acc-top1=67.700000 acc-top5=86.925000 Batch [0299]/[0303]: acc-top1=67.968750 acc-top5=87.005208 [Epoch 062] training: accuracy=81.473570 loss=0.718111 [Epoch 062] speed: 65 samples/sec time cost: 3936.709194 [Epoch 062] validation: acc-top1=67.930074 acc-top5=87.030734 loss=1.638185 Epoch[063] Batch [0049]/[3760] Speed: 44.625427 samples/sec accuracy=82.843750 loss=0.684521 lr=0.001000 Epoch[063] Batch [0099]/[3760] Speed: 64.515605 samples/sec accuracy=82.171875 loss=0.691686 lr=0.001000 Epoch[063] Batch [0149]/[3760] Speed: 65.843576 samples/sec accuracy=81.937500 loss=0.701572 lr=0.001000 Epoch[063] Batch [0199]/[3760] Speed: 64.550013 samples/sec accuracy=81.648438 loss=0.710465 lr=0.001000 Epoch[063] Batch [0249]/[3760] Speed: 66.276065 samples/sec accuracy=81.637500 loss=0.714773 lr=0.001000 Epoch[063] Batch [0299]/[3760] Speed: 65.406702 samples/sec accuracy=81.781250 loss=0.709315 lr=0.001000 Epoch[063] Batch 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accuracy=81.692637 loss=0.712616 lr=0.001000 Epoch[063] Batch [3699]/[3760] Speed: 66.459618 samples/sec accuracy=81.698902 loss=0.712520 lr=0.001000 Epoch[063] Batch [3749]/[3760] Speed: 72.845908 samples/sec accuracy=81.707917 loss=0.712400 lr=0.001000 Batch [0049]/[0303]: acc-top1=67.343750 acc-top5=86.812500 Batch [0099]/[0303]: acc-top1=67.234375 acc-top5=86.890625 Batch [0149]/[0303]: acc-top1=67.541667 acc-top5=86.614583 Batch [0199]/[0303]: acc-top1=67.476562 acc-top5=86.640625 Batch [0249]/[0303]: acc-top1=67.543750 acc-top5=86.656250 Batch [0299]/[0303]: acc-top1=67.828125 acc-top5=86.828125 [Epoch 063] training: accuracy=81.697972 loss=0.712675 [Epoch 063] speed: 65 samples/sec time cost: 3939.214296 [Epoch 063] validation: acc-top1=67.826939 acc-top5=86.839934 loss=1.655310 Epoch[064] Batch [0049]/[3759] Speed: 44.243890 samples/sec accuracy=81.000000 loss=0.726832 lr=0.001000 Epoch[064] Batch [0099]/[3759] Speed: 64.464620 samples/sec accuracy=81.109375 loss=0.720292 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[0299]/[0303]: acc-top1=67.838542 acc-top5=86.973958 [Epoch 064] training: accuracy=81.781641 loss=0.706542 [Epoch 064] speed: 65 samples/sec time cost: 3936.770666 [Epoch 064] validation: acc-top1=67.837252 acc-top5=86.994637 loss=1.633160 Epoch[065] Batch [0049]/[3760] Speed: 44.845222 samples/sec accuracy=82.187500 loss=0.712372 lr=0.001000 Epoch[065] Batch [0099]/[3760] Speed: 64.330542 samples/sec accuracy=82.109375 loss=0.706772 lr=0.001000 Epoch[065] Batch [0149]/[3760] Speed: 65.579001 samples/sec accuracy=81.906250 loss=0.709339 lr=0.001000 Epoch[065] Batch [0199]/[3760] Speed: 64.536862 samples/sec accuracy=81.968750 loss=0.708274 lr=0.001000 Epoch[065] Batch [0249]/[3760] Speed: 66.672934 samples/sec accuracy=82.131250 loss=0.704454 lr=0.001000 Epoch[065] Batch [0299]/[3760] Speed: 65.379783 samples/sec accuracy=82.411458 loss=0.695353 lr=0.001000 Epoch[065] Batch [0349]/[3760] Speed: 66.401023 samples/sec accuracy=82.602679 loss=0.691286 lr=0.001000 Epoch[065] Batch 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accuracy=82.004545 loss=0.696639 lr=0.001000 Epoch[065] Batch [2799]/[3760] Speed: 65.489405 samples/sec accuracy=81.994978 loss=0.697303 lr=0.001000 Epoch[065] Batch [2849]/[3760] Speed: 66.011578 samples/sec accuracy=81.983004 loss=0.697503 lr=0.001000 Epoch[065] Batch [2899]/[3760] Speed: 65.760504 samples/sec accuracy=81.976832 loss=0.697339 lr=0.001000 Epoch[065] Batch [2949]/[3760] Speed: 66.075619 samples/sec accuracy=81.994174 loss=0.696772 lr=0.001000 Epoch[065] Batch [2999]/[3760] Speed: 66.583683 samples/sec accuracy=81.984375 loss=0.696543 lr=0.001000 Epoch[065] Batch [3049]/[3760] Speed: 65.709042 samples/sec accuracy=81.975410 loss=0.696901 lr=0.001000 Epoch[065] Batch [3099]/[3760] Speed: 66.324016 samples/sec accuracy=81.987399 loss=0.696547 lr=0.001000 Epoch[065] Batch [3149]/[3760] Speed: 66.001609 samples/sec accuracy=81.963790 loss=0.697427 lr=0.001000 Epoch[065] Batch [3199]/[3760] Speed: 66.356484 samples/sec accuracy=81.958496 loss=0.698176 lr=0.001000 Epoch[065] 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accuracy=81.932855 loss=0.699430 lr=0.001000 Epoch[065] Batch [3749]/[3760] Speed: 73.605874 samples/sec accuracy=81.930833 loss=0.699532 lr=0.001000 Batch [0049]/[0303]: acc-top1=67.406250 acc-top5=86.531250 Batch [0099]/[0303]: acc-top1=67.109375 acc-top5=86.796875 Batch [0149]/[0303]: acc-top1=67.364583 acc-top5=86.635417 Batch [0199]/[0303]: acc-top1=67.265625 acc-top5=86.601562 Batch [0249]/[0303]: acc-top1=67.275000 acc-top5=86.662500 Batch [0299]/[0303]: acc-top1=67.572917 acc-top5=86.817708 [Epoch 065] training: accuracy=81.930269 loss=0.699515 [Epoch 065] speed: 65 samples/sec time cost: 3938.812612 [Epoch 065] validation: acc-top1=67.584571 acc-top5=86.824464 loss=1.659697 Epoch[066] Batch [0049]/[3759] Speed: 44.532704 samples/sec accuracy=83.156250 loss=0.644792 lr=0.001000 Epoch[066] Batch [0099]/[3759] Speed: 63.586030 samples/sec accuracy=82.593750 loss=0.671950 lr=0.001000 Epoch[066] Batch [0149]/[3759] Speed: 66.682610 samples/sec accuracy=82.593750 loss=0.677722 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[Epoch 066] speed: 65 samples/sec time cost: 3936.764119 [Epoch 066] validation: acc-top1=67.677393 acc-top5=86.602723 loss=1.677927 Epoch[067] Batch [0049]/[3760] Speed: 44.603044 samples/sec accuracy=82.343750 loss=0.684352 lr=0.001000 Epoch[067] Batch [0099]/[3760] Speed: 64.042380 samples/sec accuracy=82.984375 loss=0.656669 lr=0.001000 Epoch[067] Batch [0149]/[3760] Speed: 65.764587 samples/sec accuracy=83.156250 loss=0.654132 lr=0.001000 Epoch[067] Batch [0199]/[3760] Speed: 65.113488 samples/sec accuracy=82.953125 loss=0.662018 lr=0.001000 Epoch[067] Batch [0249]/[3760] Speed: 66.767552 samples/sec accuracy=82.987500 loss=0.660363 lr=0.001000 Epoch[067] Batch [0299]/[3760] Speed: 66.089310 samples/sec accuracy=82.947917 loss=0.663204 lr=0.001000 Epoch[067] Batch [0349]/[3760] Speed: 66.365971 samples/sec accuracy=82.937500 loss=0.664243 lr=0.001000 Epoch[067] Batch [0399]/[3760] Speed: 65.906104 samples/sec accuracy=83.000000 loss=0.661518 lr=0.001000 Epoch[067] Batch 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accuracy=82.440290 loss=0.682714 lr=0.001000 Epoch[067] Batch [2849]/[3760] Speed: 66.015255 samples/sec accuracy=82.438048 loss=0.682946 lr=0.001000 Epoch[067] Batch [2899]/[3760] Speed: 66.010755 samples/sec accuracy=82.422953 loss=0.683614 lr=0.001000 Epoch[067] Batch [2949]/[3760] Speed: 66.332487 samples/sec accuracy=82.416314 loss=0.684214 lr=0.001000 Epoch[067] Batch [2999]/[3760] Speed: 65.967843 samples/sec accuracy=82.415104 loss=0.684085 lr=0.001000 Epoch[067] Batch [3049]/[3760] Speed: 66.151133 samples/sec accuracy=82.417520 loss=0.684303 lr=0.001000 Epoch[067] Batch [3099]/[3760] Speed: 66.316304 samples/sec accuracy=82.386593 loss=0.685274 lr=0.001000 Epoch[067] Batch [3149]/[3760] Speed: 65.408729 samples/sec accuracy=82.385417 loss=0.685356 lr=0.001000 Epoch[067] Batch [3199]/[3760] Speed: 66.631983 samples/sec accuracy=82.362305 loss=0.686203 lr=0.001000 Epoch[067] Batch [3249]/[3760] Speed: 66.341233 samples/sec accuracy=82.349519 loss=0.686867 lr=0.001000 Epoch[067] 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accuracy=82.319167 loss=0.687875 lr=0.001000 Batch [0049]/[0303]: acc-top1=67.906250 acc-top5=86.156250 Batch [0099]/[0303]: acc-top1=67.250000 acc-top5=86.312500 Batch [0149]/[0303]: acc-top1=67.572917 acc-top5=86.291667 Batch [0199]/[0303]: acc-top1=67.453125 acc-top5=86.500000 Batch [0249]/[0303]: acc-top1=67.437500 acc-top5=86.568750 Batch [0299]/[0303]: acc-top1=67.687500 acc-top5=86.697917 [Epoch 067] training: accuracy=82.318816 loss=0.687915 [Epoch 067] speed: 65 samples/sec time cost: 3934.220851 [Epoch 067] validation: acc-top1=67.703177 acc-top5=86.705858 loss=1.662226 Epoch[068] Batch [0049]/[3760] Speed: 43.883409 samples/sec accuracy=83.156250 loss=0.669422 lr=0.001000 Epoch[068] Batch [0099]/[3760] Speed: 64.912666 samples/sec accuracy=82.875000 loss=0.668786 lr=0.001000 Epoch[068] Batch [0149]/[3760] Speed: 66.157991 samples/sec accuracy=82.958333 loss=0.661269 lr=0.001000 Epoch[068] Batch [0199]/[3760] Speed: 65.688397 samples/sec accuracy=82.835938 loss=0.659634 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acc-top5=86.618193 loss=1.682558 Epoch[069] Batch [0049]/[3759] Speed: 44.335966 samples/sec accuracy=82.156250 loss=0.682146 lr=0.001000 Epoch[069] Batch [0099]/[3759] Speed: 64.888696 samples/sec accuracy=82.718750 loss=0.671706 lr=0.001000 Epoch[069] Batch [0149]/[3759] Speed: 66.019044 samples/sec accuracy=83.083333 loss=0.662254 lr=0.001000 Epoch[069] Batch [0199]/[3759] Speed: 65.704751 samples/sec accuracy=83.210938 loss=0.657052 lr=0.001000 Epoch[069] Batch [0249]/[3759] Speed: 66.415325 samples/sec accuracy=83.156250 loss=0.659728 lr=0.001000 Epoch[069] Batch [0299]/[3759] Speed: 65.545480 samples/sec accuracy=83.119792 loss=0.657503 lr=0.001000 Epoch[069] Batch [0349]/[3759] Speed: 66.098250 samples/sec accuracy=83.053571 loss=0.661604 lr=0.001000 Epoch[069] Batch [0399]/[3759] Speed: 66.052581 samples/sec accuracy=83.050781 loss=0.662903 lr=0.001000 Epoch[069] Batch [0449]/[3759] Speed: 65.165411 samples/sec accuracy=82.888889 loss=0.669050 lr=0.001000 Epoch[069] Batch 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accuracy=82.632675 loss=0.673868 lr=0.001000 Epoch[069] Batch [2899]/[3759] Speed: 66.310472 samples/sec accuracy=82.636315 loss=0.673738 lr=0.001000 Epoch[069] Batch [2949]/[3759] Speed: 66.576405 samples/sec accuracy=82.632945 loss=0.673594 lr=0.001000 Epoch[069] Batch [2999]/[3759] Speed: 65.884050 samples/sec accuracy=82.615104 loss=0.674599 lr=0.001000 Epoch[069] Batch [3049]/[3759] Speed: 66.000844 samples/sec accuracy=82.608607 loss=0.675245 lr=0.001000 Epoch[069] Batch [3099]/[3759] Speed: 66.173785 samples/sec accuracy=82.599294 loss=0.675350 lr=0.001000 Epoch[069] Batch [3149]/[3759] Speed: 66.294201 samples/sec accuracy=82.608631 loss=0.675481 lr=0.001000 Epoch[069] Batch [3199]/[3759] Speed: 65.756458 samples/sec accuracy=82.600098 loss=0.675379 lr=0.001000 Epoch[069] Batch [3249]/[3759] Speed: 66.406924 samples/sec accuracy=82.613462 loss=0.675017 lr=0.001000 Epoch[069] Batch [3299]/[3759] Speed: 66.654122 samples/sec accuracy=82.609848 loss=0.675157 lr=0.001000 Epoch[069] 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[0099]/[0303]: acc-top1=66.890625 acc-top5=86.171875 Batch [0149]/[0303]: acc-top1=67.312500 acc-top5=86.031250 Batch [0199]/[0303]: acc-top1=67.179688 acc-top5=86.234375 Batch [0249]/[0303]: acc-top1=67.143750 acc-top5=86.275000 Batch [0299]/[0303]: acc-top1=67.520833 acc-top5=86.427083 [Epoch 069] training: accuracy=82.588039 loss=0.677577 [Epoch 069] speed: 65 samples/sec time cost: 3934.075424 [Epoch 069] validation: acc-top1=67.496906 acc-top5=86.442863 loss=1.694230 Epoch[070] Batch [0049]/[3760] Speed: 44.387856 samples/sec accuracy=82.500000 loss=0.673163 lr=0.001000 Epoch[070] Batch [0099]/[3760] Speed: 64.692928 samples/sec accuracy=82.500000 loss=0.662818 lr=0.001000 Epoch[070] Batch [0149]/[3760] Speed: 66.929057 samples/sec accuracy=83.052083 loss=0.657051 lr=0.001000 Epoch[070] Batch [0199]/[3760] Speed: 65.199844 samples/sec accuracy=82.906250 loss=0.657262 lr=0.001000 Epoch[070] Batch [0249]/[3760] Speed: 66.564608 samples/sec accuracy=83.012500 loss=0.654336 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accuracy=83.120690 loss=0.656171 lr=0.001000 Epoch[071] Batch [2949]/[3760] Speed: 66.201471 samples/sec accuracy=83.126059 loss=0.656178 lr=0.001000 Epoch[071] Batch [2999]/[3760] Speed: 65.664340 samples/sec accuracy=83.132292 loss=0.655644 lr=0.001000 Epoch[071] Batch [3049]/[3760] Speed: 66.370971 samples/sec accuracy=83.122951 loss=0.655560 lr=0.001000 Epoch[071] Batch [3099]/[3760] Speed: 66.019319 samples/sec accuracy=83.122984 loss=0.655834 lr=0.001000 Epoch[071] Batch [3149]/[3760] Speed: 66.309829 samples/sec accuracy=83.120536 loss=0.655939 lr=0.001000 Epoch[071] Batch [3199]/[3760] Speed: 66.058846 samples/sec accuracy=83.096191 loss=0.656547 lr=0.001000 Epoch[071] Batch [3249]/[3760] Speed: 66.629427 samples/sec accuracy=83.113462 loss=0.655776 lr=0.001000 Epoch[071] Batch [3299]/[3760] Speed: 66.096078 samples/sec accuracy=83.112689 loss=0.655755 lr=0.001000 Epoch[071] Batch [3349]/[3760] Speed: 66.118759 samples/sec accuracy=83.116138 loss=0.655696 lr=0.001000 Epoch[071] 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acc-top5=86.500000 Batch [0199]/[0303]: acc-top1=67.343750 acc-top5=86.390625 Batch [0249]/[0303]: acc-top1=67.112500 acc-top5=86.400000 Batch [0299]/[0303]: acc-top1=67.473958 acc-top5=86.593750 [Epoch 071] training: accuracy=83.058511 loss=0.657011 [Epoch 071] speed: 65 samples/sec time cost: 3927.320624 [Epoch 071] validation: acc-top1=67.460809 acc-top5=86.613036 loss=1.720339 Epoch[072] Batch [0049]/[3759] Speed: 45.402133 samples/sec accuracy=84.625000 loss=0.603425 lr=0.001000 Epoch[072] Batch [0099]/[3759] Speed: 64.303389 samples/sec accuracy=84.140625 loss=0.621712 lr=0.001000 Epoch[072] Batch [0149]/[3759] Speed: 66.040614 samples/sec accuracy=83.854167 loss=0.629866 lr=0.001000 Epoch[072] Batch [0199]/[3759] Speed: 65.781536 samples/sec accuracy=84.015625 loss=0.628816 lr=0.001000 Epoch[072] Batch [0249]/[3759] Speed: 65.973578 samples/sec accuracy=83.918750 loss=0.631133 lr=0.001000 Epoch[072] Batch [0299]/[3759] Speed: 65.450763 samples/sec accuracy=83.723958 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lr=0.001000 Epoch[073] Batch [3449]/[3760] Speed: 66.316366 samples/sec accuracy=83.216486 loss=0.652292 lr=0.001000 Epoch[073] Batch [3499]/[3760] Speed: 65.599737 samples/sec accuracy=83.199554 loss=0.652681 lr=0.001000 Epoch[073] Batch [3549]/[3760] Speed: 66.529462 samples/sec accuracy=83.178697 loss=0.653104 lr=0.001000 Epoch[073] Batch [3599]/[3760] Speed: 66.130057 samples/sec accuracy=83.178385 loss=0.652968 lr=0.001000 Epoch[073] Batch [3649]/[3760] Speed: 65.975094 samples/sec accuracy=83.165240 loss=0.653544 lr=0.001000 Epoch[073] Batch [3699]/[3760] Speed: 65.892011 samples/sec accuracy=83.160051 loss=0.653868 lr=0.001000 Epoch[073] Batch [3749]/[3760] Speed: 73.663399 samples/sec accuracy=83.154167 loss=0.654068 lr=0.001000 Batch [0049]/[0303]: acc-top1=67.531250 acc-top5=85.750000 Batch [0099]/[0303]: acc-top1=66.937500 acc-top5=86.328125 Batch [0149]/[0303]: acc-top1=67.322917 acc-top5=86.218750 Batch [0199]/[0303]: acc-top1=67.351562 acc-top5=86.359375 Batch [0249]/[0303]: acc-top1=67.356250 acc-top5=86.400000 Batch [0299]/[0303]: acc-top1=67.635417 acc-top5=86.593750 [Epoch 073] training: accuracy=83.155336 loss=0.654008 [Epoch 073] speed: 65 samples/sec time cost: 3933.174348 [Epoch 073] validation: acc-top1=67.615512 acc-top5=86.623350 loss=1.704517 Epoch[074] Batch [0049]/[3760] Speed: 44.121214 samples/sec accuracy=84.218750 loss=0.630210 lr=0.001000 Epoch[074] Batch [0099]/[3760] Speed: 64.475145 samples/sec accuracy=83.656250 loss=0.647253 lr=0.001000 Epoch[074] Batch [0149]/[3760] Speed: 66.589270 samples/sec accuracy=83.947917 loss=0.632622 lr=0.001000 Epoch[074] Batch [0199]/[3760] Speed: 65.253273 samples/sec accuracy=83.703125 loss=0.639613 lr=0.001000 Epoch[074] Batch [0249]/[3760] Speed: 65.939429 samples/sec accuracy=83.762500 loss=0.643079 lr=0.001000 Epoch[074] Batch [0299]/[3760] Speed: 65.534833 samples/sec accuracy=83.796875 loss=0.634010 lr=0.001000 Epoch[074] Batch [0349]/[3760] Speed: 65.705211 samples/sec 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accuracy=83.483398 loss=0.641142 lr=0.001000 Epoch[074] Batch [3249]/[3760] Speed: 66.615370 samples/sec accuracy=83.499038 loss=0.640744 lr=0.001000 Epoch[074] Batch [3299]/[3760] Speed: 66.305124 samples/sec accuracy=83.493845 loss=0.641215 lr=0.001000 Epoch[074] Batch [3349]/[3760] Speed: 66.342208 samples/sec accuracy=83.494403 loss=0.641105 lr=0.001000 Epoch[074] Batch [3399]/[3760] Speed: 66.541808 samples/sec accuracy=83.483456 loss=0.641395 lr=0.001000 Epoch[074] Batch [3449]/[3760] Speed: 66.026841 samples/sec accuracy=83.478714 loss=0.641276 lr=0.001000 Epoch[074] Batch [3499]/[3760] Speed: 65.973549 samples/sec accuracy=83.462054 loss=0.641713 lr=0.001000 Epoch[074] Batch [3549]/[3760] Speed: 65.881504 samples/sec accuracy=83.461708 loss=0.641647 lr=0.001000 Epoch[074] Batch [3599]/[3760] Speed: 66.691351 samples/sec accuracy=83.459635 loss=0.641652 lr=0.001000 Epoch[074] Batch [3649]/[3760] Speed: 66.124294 samples/sec accuracy=83.462757 loss=0.641391 lr=0.001000 Epoch[074] Batch [3699]/[3760] Speed: 66.353938 samples/sec accuracy=83.457770 loss=0.641724 lr=0.001000 Epoch[074] Batch [3749]/[3760] Speed: 73.443179 samples/sec accuracy=83.440833 loss=0.642024 lr=0.001000 Batch [0049]/[0303]: acc-top1=66.375000 acc-top5=85.906250 Batch [0099]/[0303]: acc-top1=66.312500 acc-top5=85.984375 Batch [0149]/[0303]: acc-top1=66.687500 acc-top5=85.927083 Batch [0199]/[0303]: acc-top1=66.585938 acc-top5=85.953125 Batch [0249]/[0303]: acc-top1=66.675000 acc-top5=85.962500 Batch [0299]/[0303]: acc-top1=67.046875 acc-top5=86.072917 [Epoch 074] training: accuracy=83.439162 loss=0.642148 [Epoch 074] speed: 65 samples/sec time cost: 3931.046834 [Epoch 074] validation: acc-top1=67.058581 acc-top5=86.092203 loss=1.762446 Epoch[075] Batch [0049]/[3759] Speed: 44.467171 samples/sec accuracy=82.968750 loss=0.672576 lr=0.001000 Epoch[075] Batch [0099]/[3759] Speed: 64.748445 samples/sec accuracy=83.484375 loss=0.648724 lr=0.001000 Epoch[075] Batch [0149]/[3759] Speed: 66.103675 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lr=0.001000 Epoch[075] Batch [2549]/[3759] Speed: 66.218434 samples/sec accuracy=83.525123 loss=0.639153 lr=0.001000 Epoch[075] Batch [2599]/[3759] Speed: 65.854770 samples/sec accuracy=83.533053 loss=0.638332 lr=0.001000 Epoch[075] Batch [2649]/[3759] Speed: 65.731600 samples/sec accuracy=83.524175 loss=0.638482 lr=0.001000 Epoch[075] Batch [2699]/[3759] Speed: 66.178669 samples/sec accuracy=83.516782 loss=0.638546 lr=0.001000 Epoch[075] Batch [2749]/[3759] Speed: 66.158442 samples/sec accuracy=83.502273 loss=0.639152 lr=0.001000 Epoch[075] Batch [2799]/[3759] Speed: 65.996733 samples/sec accuracy=83.489397 loss=0.639535 lr=0.001000 Epoch[075] Batch [2849]/[3759] Speed: 66.118820 samples/sec accuracy=83.479715 loss=0.639948 lr=0.001000 Epoch[075] Batch [2899]/[3759] Speed: 66.099077 samples/sec accuracy=83.463362 loss=0.640315 lr=0.001000 Epoch[075] Batch [2949]/[3759] Speed: 65.851991 samples/sec accuracy=83.487818 loss=0.639410 lr=0.001000 Epoch[075] Batch [2999]/[3759] Speed: 65.294121 samples/sec accuracy=83.499479 loss=0.639493 lr=0.001000 Epoch[075] Batch [3049]/[3759] Speed: 66.172532 samples/sec accuracy=83.496414 loss=0.639462 lr=0.001000 Epoch[075] Batch [3099]/[3759] Speed: 65.693156 samples/sec accuracy=83.492440 loss=0.639498 lr=0.001000 Epoch[075] Batch [3149]/[3759] Speed: 65.520581 samples/sec accuracy=83.490575 loss=0.639488 lr=0.001000 Epoch[075] Batch [3199]/[3759] Speed: 66.929108 samples/sec accuracy=83.490234 loss=0.639363 lr=0.001000 Epoch[075] Batch [3249]/[3759] Speed: 66.043886 samples/sec accuracy=83.483173 loss=0.639298 lr=0.001000 Epoch[075] Batch [3299]/[3759] Speed: 65.803281 samples/sec accuracy=83.476326 loss=0.639357 lr=0.001000 Epoch[075] Batch [3349]/[3759] Speed: 66.404668 samples/sec accuracy=83.472015 loss=0.639275 lr=0.001000 Epoch[075] Batch [3399]/[3759] Speed: 66.063958 samples/sec accuracy=83.472426 loss=0.639112 lr=0.001000 Epoch[075] Batch [3449]/[3759] Speed: 66.044839 samples/sec accuracy=83.476902 loss=0.639049 lr=0.001000 Epoch[075] Batch [3499]/[3759] Speed: 66.182459 samples/sec accuracy=83.460714 loss=0.639403 lr=0.001000 Epoch[075] Batch [3549]/[3759] Speed: 65.957461 samples/sec accuracy=83.454665 loss=0.639543 lr=0.001000 Epoch[075] Batch [3599]/[3759] Speed: 66.182579 samples/sec accuracy=83.451823 loss=0.640071 lr=0.001000 Epoch[075] Batch [3649]/[3759] Speed: 66.095098 samples/sec accuracy=83.433647 loss=0.640672 lr=0.001000 Epoch[075] Batch [3699]/[3759] Speed: 65.540451 samples/sec accuracy=83.432855 loss=0.640747 lr=0.001000 Epoch[075] Batch [3749]/[3759] Speed: 73.973752 samples/sec accuracy=83.418750 loss=0.641277 lr=0.001000 Batch [0049]/[0303]: acc-top1=66.750000 acc-top5=85.625000 Batch [0099]/[0303]: acc-top1=66.875000 acc-top5=86.031250 Batch [0149]/[0303]: acc-top1=66.947917 acc-top5=86.010417 Batch [0199]/[0303]: acc-top1=66.898438 acc-top5=86.117188 Batch [0249]/[0303]: acc-top1=66.887500 acc-top5=86.193750 Batch [0299]/[0303]: acc-top1=67.244792 acc-top5=86.354167 [Epoch 075] training: accuracy=83.418545 loss=0.641348 [Epoch 075] speed: 65 samples/sec time cost: 3934.161566 [Epoch 075] validation: acc-top1=67.259695 acc-top5=86.386139 loss=1.701571 Epoch[076] Batch [0049]/[3760] Speed: 45.096829 samples/sec accuracy=85.125000 loss=0.581744 lr=0.001000 Epoch[076] Batch [0099]/[3760] Speed: 64.424770 samples/sec accuracy=84.890625 loss=0.594817 lr=0.001000 Epoch[076] Batch [0149]/[3760] Speed: 66.523182 samples/sec accuracy=84.406250 loss=0.607593 lr=0.001000 Epoch[076] Batch [0199]/[3760] Speed: 64.897047 samples/sec accuracy=84.023438 loss=0.618145 lr=0.001000 Epoch[076] Batch [0249]/[3760] Speed: 66.443537 samples/sec accuracy=84.056250 loss=0.611979 lr=0.001000 Epoch[076] Batch [0299]/[3760] Speed: 64.874327 samples/sec accuracy=84.145833 loss=0.612686 lr=0.001000 Epoch[076] Batch [0349]/[3760] Speed: 66.358796 samples/sec accuracy=84.102679 loss=0.610862 lr=0.001000 Epoch[076] Batch [0399]/[3760] Speed: 65.996320 samples/sec accuracy=84.058594 loss=0.612039 lr=0.001000 Epoch[076] Batch [0449]/[3760] Speed: 65.626459 samples/sec accuracy=83.927083 loss=0.616785 lr=0.001000 Epoch[076] Batch [0499]/[3760] Speed: 66.296527 samples/sec accuracy=84.046875 loss=0.614164 lr=0.001000 Epoch[076] Batch [0549]/[3760] Speed: 66.008433 samples/sec accuracy=84.034091 loss=0.614827 lr=0.001000 Epoch[076] Batch [0599]/[3760] Speed: 66.691764 samples/sec accuracy=83.989583 loss=0.618871 lr=0.001000 Epoch[076] Batch [0649]/[3760] Speed: 65.619398 samples/sec accuracy=84.045673 loss=0.617685 lr=0.001000 Epoch[076] Batch [0699]/[3760] Speed: 65.808897 samples/sec accuracy=84.011161 loss=0.620333 lr=0.001000 Epoch[076] Batch [0749]/[3760] Speed: 65.958897 samples/sec accuracy=83.931250 loss=0.622315 lr=0.001000 Epoch[076] Batch [0799]/[3760] Speed: 65.462244 samples/sec accuracy=83.894531 loss=0.624901 lr=0.001000 Epoch[076] Batch [0849]/[3760] Speed: 65.490861 samples/sec accuracy=83.959559 loss=0.623610 lr=0.001000 Epoch[076] 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Batch [3749]/[3760] Speed: 73.106746 samples/sec accuracy=83.730417 loss=0.632036 lr=0.001000 Batch [0049]/[0303]: acc-top1=67.000000 acc-top5=85.656250 Batch [0099]/[0303]: acc-top1=66.656250 acc-top5=85.875000 Batch [0149]/[0303]: acc-top1=66.895833 acc-top5=85.656250 Batch [0199]/[0303]: acc-top1=66.742188 acc-top5=85.796875 Batch [0249]/[0303]: acc-top1=66.687500 acc-top5=85.862500 Batch [0299]/[0303]: acc-top1=66.947917 acc-top5=85.984375 [Epoch 076] training: accuracy=83.727144 loss=0.632154 [Epoch 076] speed: 65 samples/sec time cost: 3934.353971 [Epoch 076] validation: acc-top1=66.924505 acc-top5=85.999381 loss=1.766802 Epoch[077] Batch [0049]/[3760] Speed: 45.013123 samples/sec accuracy=84.000000 loss=0.650784 lr=0.001000 Epoch[077] Batch [0099]/[3760] Speed: 64.344338 samples/sec accuracy=83.828125 loss=0.640004 lr=0.001000 Epoch[077] Batch [0149]/[3760] Speed: 65.939924 samples/sec accuracy=83.906250 loss=0.627253 lr=0.001000 Epoch[077] Batch [0199]/[3760] Speed: 64.722579 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lr=0.001000 Epoch[077] Batch [2599]/[3760] Speed: 65.663776 samples/sec accuracy=83.986178 loss=0.627190 lr=0.001000 Epoch[077] Batch [2649]/[3760] Speed: 66.722230 samples/sec accuracy=83.983491 loss=0.626863 lr=0.001000 Epoch[077] Batch [2699]/[3760] Speed: 66.405244 samples/sec accuracy=83.988426 loss=0.626813 lr=0.001000 Epoch[077] Batch [2749]/[3760] Speed: 65.821096 samples/sec accuracy=83.979545 loss=0.626998 lr=0.001000 Epoch[077] Batch [2799]/[3760] Speed: 65.575202 samples/sec accuracy=83.970982 loss=0.627416 lr=0.001000 Epoch[077] Batch [2849]/[3760] Speed: 66.523950 samples/sec accuracy=83.950110 loss=0.627484 lr=0.001000 Epoch[077] Batch [2899]/[3760] Speed: 65.932138 samples/sec accuracy=83.943966 loss=0.627421 lr=0.001000 Epoch[077] Batch [2949]/[3760] Speed: 66.403099 samples/sec accuracy=83.953390 loss=0.626669 lr=0.001000 Epoch[077] Batch [2999]/[3760] Speed: 65.972854 samples/sec accuracy=83.941146 loss=0.627288 lr=0.001000 Epoch[077] Batch [3049]/[3760] Speed: 66.355743 samples/sec accuracy=83.911373 loss=0.628377 lr=0.001000 Epoch[077] Batch [3099]/[3760] Speed: 65.841458 samples/sec accuracy=83.889113 loss=0.629072 lr=0.001000 Epoch[077] Batch [3149]/[3760] Speed: 66.352358 samples/sec accuracy=83.887401 loss=0.629255 lr=0.001000 Epoch[077] Batch [3199]/[3760] Speed: 65.694297 samples/sec accuracy=83.891113 loss=0.629399 lr=0.001000 Epoch[077] Batch [3249]/[3760] Speed: 66.554703 samples/sec accuracy=83.872115 loss=0.629883 lr=0.001000 Epoch[077] Batch [3299]/[3760] Speed: 66.143701 samples/sec accuracy=83.875473 loss=0.629856 lr=0.001000 Epoch[077] Batch [3349]/[3760] Speed: 65.732591 samples/sec accuracy=83.867071 loss=0.629900 lr=0.001000 Epoch[077] Batch [3399]/[3760] Speed: 66.386632 samples/sec accuracy=83.861213 loss=0.630214 lr=0.001000 Epoch[077] Batch [3449]/[3760] Speed: 66.169429 samples/sec accuracy=83.861413 loss=0.630315 lr=0.001000 Epoch[077] Batch [3499]/[3760] Speed: 66.128931 samples/sec accuracy=83.847321 loss=0.630507 lr=0.001000 Epoch[077] Batch [3549]/[3760] Speed: 65.544929 samples/sec accuracy=83.843310 loss=0.630460 lr=0.001000 Epoch[077] Batch [3599]/[3760] Speed: 65.765888 samples/sec accuracy=83.840712 loss=0.630511 lr=0.001000 Epoch[077] Batch [3649]/[3760] Speed: 65.811537 samples/sec accuracy=83.822774 loss=0.631060 lr=0.001000 Epoch[077] Batch [3699]/[3760] Speed: 65.952921 samples/sec accuracy=83.823902 loss=0.630863 lr=0.001000 Epoch[077] Batch [3749]/[3760] Speed: 72.694511 samples/sec accuracy=83.812917 loss=0.631195 lr=0.001000 Batch [0049]/[0303]: acc-top1=66.468750 acc-top5=86.031250 Batch [0099]/[0303]: acc-top1=66.437500 acc-top5=86.281250 Batch [0149]/[0303]: acc-top1=66.843750 acc-top5=86.052083 Batch [0199]/[0303]: acc-top1=66.734375 acc-top5=86.023438 Batch [0249]/[0303]: acc-top1=66.850000 acc-top5=86.181250 Batch [0299]/[0303]: acc-top1=67.270833 acc-top5=86.375000 [Epoch 077] training: accuracy=83.813996 loss=0.631080 [Epoch 077] speed: 65 samples/sec time cost: 3936.587914 [Epoch 077] validation: acc-top1=67.285479 acc-top5=86.396452 loss=1.756087 Epoch[078] Batch [0049]/[3759] Speed: 44.307447 samples/sec accuracy=84.343750 loss=0.602538 lr=0.001000 Epoch[078] Batch [0099]/[3759] Speed: 64.455629 samples/sec accuracy=84.828125 loss=0.580487 lr=0.001000 Epoch[078] Batch [0149]/[3759] Speed: 66.039740 samples/sec accuracy=84.989583 loss=0.575970 lr=0.001000 Epoch[078] Batch [0199]/[3759] Speed: 65.755216 samples/sec accuracy=84.929688 loss=0.580143 lr=0.001000 Epoch[078] Batch [0249]/[3759] Speed: 66.298306 samples/sec accuracy=84.862500 loss=0.586576 lr=0.001000 Epoch[078] Batch [0299]/[3759] Speed: 65.606463 samples/sec accuracy=84.828125 loss=0.588857 lr=0.001000 Epoch[078] Batch [0349]/[3759] Speed: 66.086737 samples/sec accuracy=84.718750 loss=0.595235 lr=0.001000 Epoch[078] Batch [0399]/[3759] Speed: 65.657013 samples/sec accuracy=84.644531 loss=0.596560 lr=0.001000 Epoch[078] Batch [0449]/[3759] Speed: 66.072897 samples/sec 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[0049]/[0303]: acc-top1=66.250000 acc-top5=85.468750 Batch [0099]/[0303]: acc-top1=66.343750 acc-top5=85.828125 Batch [0149]/[0303]: acc-top1=66.645833 acc-top5=85.822917 Batch [0199]/[0303]: acc-top1=66.617188 acc-top5=85.929688 Batch [0249]/[0303]: acc-top1=66.781250 acc-top5=86.068750 Batch [0299]/[0303]: acc-top1=67.187500 acc-top5=86.218750 [Epoch 078] training: accuracy=83.907788 loss=0.622830 [Epoch 078] speed: 65 samples/sec time cost: 3942.440484 [Epoch 078] validation: acc-top1=67.172030 acc-top5=86.246906 loss=1.777184 Epoch[079] Batch [0049]/[3760] Speed: 43.753204 samples/sec accuracy=84.156250 loss=0.608676 lr=0.001000 Epoch[079] Batch [0099]/[3760] Speed: 63.504955 samples/sec accuracy=84.187500 loss=0.604303 lr=0.001000 Epoch[079] Batch [0149]/[3760] Speed: 65.383762 samples/sec accuracy=84.437500 loss=0.592437 lr=0.001000 Epoch[079] Batch [0199]/[3760] Speed: 65.318228 samples/sec accuracy=84.546875 loss=0.593781 lr=0.001000 Epoch[079] Batch [0249]/[3760] Speed: 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lr=0.001000 Epoch[079] Batch [3599]/[3760] Speed: 65.962332 samples/sec accuracy=84.005208 loss=0.619954 lr=0.001000 Epoch[079] Batch [3649]/[3760] Speed: 66.081942 samples/sec accuracy=84.015839 loss=0.619824 lr=0.001000 Epoch[079] Batch [3699]/[3760] Speed: 65.582454 samples/sec accuracy=84.010557 loss=0.619698 lr=0.001000 Epoch[079] Batch [3749]/[3760] Speed: 72.613810 samples/sec accuracy=83.995833 loss=0.619888 lr=0.000100 Batch [0049]/[0303]: acc-top1=66.562500 acc-top5=85.875000 Batch [0099]/[0303]: acc-top1=66.531250 acc-top5=86.046875 Batch [0149]/[0303]: acc-top1=66.541667 acc-top5=85.927083 Batch [0199]/[0303]: acc-top1=66.546875 acc-top5=86.000000 Batch [0249]/[0303]: acc-top1=66.568750 acc-top5=86.018750 Batch [0299]/[0303]: acc-top1=66.921875 acc-top5=86.187500 [Epoch 079] training: accuracy=83.995180 loss=0.619947 [Epoch 079] speed: 65 samples/sec time cost: 3941.253012 [Epoch 079] validation: acc-top1=66.924505 acc-top5=86.210809 loss=1.779825 Epoch[080] Batch 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accuracy=85.469401 loss=0.565338 lr=0.000100 Epoch[080] Batch [2449]/[3760] Speed: 65.262316 samples/sec accuracy=85.470663 loss=0.565056 lr=0.000100 Epoch[080] Batch [2499]/[3760] Speed: 66.533519 samples/sec accuracy=85.481875 loss=0.564685 lr=0.000100 Epoch[080] Batch [2549]/[3760] Speed: 66.293939 samples/sec accuracy=85.468137 loss=0.565055 lr=0.000100 Epoch[080] Batch [2599]/[3760] Speed: 66.016481 samples/sec accuracy=85.493389 loss=0.564048 lr=0.000100 Epoch[080] Batch [2649]/[3760] Speed: 65.817631 samples/sec accuracy=85.486439 loss=0.564339 lr=0.000100 Epoch[080] Batch [2699]/[3760] Speed: 66.103882 samples/sec accuracy=85.499421 loss=0.563961 lr=0.000100 Epoch[080] Batch [2749]/[3760] Speed: 66.070757 samples/sec accuracy=85.503977 loss=0.563851 lr=0.000100 Epoch[080] Batch [2799]/[3760] Speed: 66.019270 samples/sec accuracy=85.494978 loss=0.564090 lr=0.000100 Epoch[080] Batch [2849]/[3760] Speed: 65.773673 samples/sec accuracy=85.501096 loss=0.563593 lr=0.000100 Epoch[080] 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accuracy=85.566231 loss=0.561592 lr=0.000100 Epoch[080] Batch [3399]/[3760] Speed: 65.220871 samples/sec accuracy=85.560202 loss=0.561661 lr=0.000100 Epoch[080] Batch [3449]/[3760] Speed: 66.188218 samples/sec accuracy=85.565217 loss=0.561727 lr=0.000100 Epoch[080] Batch [3499]/[3760] Speed: 65.458187 samples/sec accuracy=85.561607 loss=0.561702 lr=0.000100 Epoch[080] Batch [3549]/[3760] Speed: 66.262257 samples/sec accuracy=85.564261 loss=0.561528 lr=0.000100 Epoch[080] Batch [3599]/[3760] Speed: 65.189227 samples/sec accuracy=85.569878 loss=0.561190 lr=0.000100 Epoch[080] Batch [3649]/[3760] Speed: 66.042371 samples/sec accuracy=85.579623 loss=0.561078 lr=0.000100 Epoch[080] Batch [3699]/[3760] Speed: 65.799435 samples/sec accuracy=85.571368 loss=0.561203 lr=0.000100 Epoch[080] Batch [3749]/[3760] Speed: 73.246760 samples/sec accuracy=85.576667 loss=0.561510 lr=0.000100 Batch [0049]/[0303]: acc-top1=67.406250 acc-top5=85.812500 Batch [0099]/[0303]: acc-top1=67.125000 acc-top5=86.265625 Batch [0149]/[0303]: acc-top1=67.239583 acc-top5=86.250000 Batch [0199]/[0303]: acc-top1=67.375000 acc-top5=86.320312 Batch [0249]/[0303]: acc-top1=67.375000 acc-top5=86.425000 Batch [0299]/[0303]: acc-top1=67.770833 acc-top5=86.619792 [Epoch 080] training: accuracy=85.570146 loss=0.561774 [Epoch 080] speed: 65 samples/sec time cost: 3939.871070 [Epoch 080] validation: acc-top1=67.765058 acc-top5=86.633663 loss=1.744762 Epoch[081] Batch [0049]/[3759] Speed: 45.048470 samples/sec accuracy=85.593750 loss=0.565407 lr=0.000100 Epoch[081] Batch [0099]/[3759] Speed: 63.959121 samples/sec accuracy=86.546875 loss=0.530466 lr=0.000100 Epoch[081] Batch [0149]/[3759] Speed: 66.243430 samples/sec accuracy=86.354167 loss=0.543311 lr=0.000100 Epoch[081] Batch [0199]/[3759] Speed: 65.031941 samples/sec accuracy=86.476562 loss=0.539534 lr=0.000100 Epoch[081] Batch [0249]/[3759] Speed: 66.135184 samples/sec accuracy=86.300000 loss=0.542066 lr=0.000100 Epoch[081] Batch [0299]/[3759] 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accuracy=86.286765 loss=0.535427 lr=0.000100 Epoch[081] Batch [1749]/[3759] Speed: 65.812520 samples/sec accuracy=86.276786 loss=0.535498 lr=0.000100 Epoch[081] Batch [1799]/[3759] Speed: 65.824044 samples/sec accuracy=86.273438 loss=0.535550 lr=0.000100 Epoch[081] Batch [1849]/[3759] Speed: 65.782685 samples/sec accuracy=86.252534 loss=0.536238 lr=0.000100 Epoch[081] Batch [1899]/[3759] Speed: 66.139257 samples/sec accuracy=86.267270 loss=0.536510 lr=0.000100 Epoch[081] Batch [1949]/[3759] Speed: 65.333959 samples/sec accuracy=86.259615 loss=0.536943 lr=0.000100 Epoch[081] Batch [1999]/[3759] Speed: 66.048908 samples/sec accuracy=86.263281 loss=0.536921 lr=0.000100 Epoch[081] Batch [2049]/[3759] Speed: 65.814294 samples/sec accuracy=86.286585 loss=0.536353 lr=0.000100 Epoch[081] Batch [2099]/[3759] Speed: 65.894997 samples/sec accuracy=86.265625 loss=0.537094 lr=0.000100 Epoch[081] Batch [2149]/[3759] Speed: 65.319693 samples/sec accuracy=86.260901 loss=0.537250 lr=0.000100 Epoch[081] 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accuracy=86.264151 loss=0.537664 lr=0.000100 Epoch[081] Batch [2699]/[3759] Speed: 65.833096 samples/sec accuracy=86.265046 loss=0.538147 lr=0.000100 Epoch[081] Batch [2749]/[3759] Speed: 65.336890 samples/sec accuracy=86.266477 loss=0.538004 lr=0.000100 Epoch[081] Batch [2799]/[3759] Speed: 66.046066 samples/sec accuracy=86.251674 loss=0.538419 lr=0.000100 Epoch[081] Batch [2849]/[3759] Speed: 66.237072 samples/sec accuracy=86.239035 loss=0.539256 lr=0.000100 Epoch[081] Batch [2899]/[3759] Speed: 66.505846 samples/sec accuracy=86.245151 loss=0.538985 lr=0.000100 Epoch[081] Batch [2949]/[3759] Speed: 65.349626 samples/sec accuracy=86.240996 loss=0.539167 lr=0.000100 Epoch[081] Batch [2999]/[3759] Speed: 66.047784 samples/sec accuracy=86.237500 loss=0.538716 lr=0.000100 Epoch[081] Batch [3049]/[3759] Speed: 65.786736 samples/sec accuracy=86.242828 loss=0.538958 lr=0.000100 Epoch[081] Batch [3099]/[3759] Speed: 66.543136 samples/sec accuracy=86.244960 loss=0.538620 lr=0.000100 Epoch[081] Batch [3149]/[3759] Speed: 65.331510 samples/sec accuracy=86.236111 loss=0.538701 lr=0.000100 Epoch[081] Batch [3199]/[3759] Speed: 66.032051 samples/sec accuracy=86.246582 loss=0.538332 lr=0.000100 Epoch[081] Batch [3249]/[3759] Speed: 65.785872 samples/sec accuracy=86.241346 loss=0.538482 lr=0.000100 Epoch[081] Batch [3299]/[3759] Speed: 65.331767 samples/sec accuracy=86.241477 loss=0.538541 lr=0.000100 Epoch[081] Batch [3349]/[3759] Speed: 66.079021 samples/sec accuracy=86.243470 loss=0.538456 lr=0.000100 Epoch[081] Batch [3399]/[3759] Speed: 65.981419 samples/sec accuracy=86.245864 loss=0.538167 lr=0.000100 Epoch[081] Batch [3449]/[3759] Speed: 65.948937 samples/sec accuracy=86.239130 loss=0.538525 lr=0.000100 Epoch[081] Batch [3499]/[3759] Speed: 66.026365 samples/sec accuracy=86.240179 loss=0.538379 lr=0.000100 Epoch[081] Batch [3549]/[3759] Speed: 65.593615 samples/sec accuracy=86.239437 loss=0.538406 lr=0.000100 Epoch[081] Batch [3599]/[3759] Speed: 65.803172 samples/sec accuracy=86.233507 loss=0.538583 lr=0.000100 Epoch[081] Batch [3649]/[3759] Speed: 65.837308 samples/sec accuracy=86.220462 loss=0.538783 lr=0.000100 Epoch[081] Batch [3699]/[3759] Speed: 64.993972 samples/sec accuracy=86.233108 loss=0.538254 lr=0.000100 Epoch[081] Batch [3749]/[3759] Speed: 74.036855 samples/sec accuracy=86.230833 loss=0.538150 lr=0.000100 Batch [0049]/[0303]: acc-top1=66.875000 acc-top5=85.875000 Batch [0099]/[0303]: acc-top1=66.812500 acc-top5=86.203125 Batch [0149]/[0303]: acc-top1=67.177083 acc-top5=86.072917 Batch [0199]/[0303]: acc-top1=67.273438 acc-top5=86.210938 Batch [0249]/[0303]: acc-top1=67.381250 acc-top5=86.275000 Batch [0299]/[0303]: acc-top1=67.791667 acc-top5=86.520833 [Epoch 081] training: accuracy=86.232209 loss=0.538110 [Epoch 081] speed: 65 samples/sec time cost: 3939.395964 [Epoch 081] validation: acc-top1=67.785685 acc-top5=86.520215 loss=1.748143 Epoch[082] Batch [0049]/[3760] Speed: 44.055729 samples/sec accuracy=86.687500 loss=0.523139 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acc-top1=67.343750 acc-top5=86.203125 Batch [0249]/[0303]: acc-top1=67.400000 acc-top5=86.281250 Batch [0299]/[0303]: acc-top1=67.838542 acc-top5=86.526042 [Epoch 082] training: accuracy=86.447806 loss=0.530722 [Epoch 082] speed: 65 samples/sec time cost: 3937.542717 [Epoch 082] validation: acc-top1=67.832096 acc-top5=86.540842 loss=1.756741 Epoch[083] Batch [0049]/[3760] Speed: 44.591170 samples/sec accuracy=86.000000 loss=0.548508 lr=0.000100 Epoch[083] Batch [0099]/[3760] Speed: 64.078210 samples/sec accuracy=86.203125 loss=0.540715 lr=0.000100 Epoch[083] Batch [0149]/[3760] Speed: 66.660460 samples/sec accuracy=86.427083 loss=0.529298 lr=0.000100 Epoch[083] Batch [0199]/[3760] Speed: 65.363558 samples/sec accuracy=86.320312 loss=0.538292 lr=0.000100 Epoch[083] Batch [0249]/[3760] Speed: 66.963803 samples/sec accuracy=86.281250 loss=0.537248 lr=0.000100 Epoch[083] Batch [0299]/[3760] Speed: 64.795270 samples/sec accuracy=86.411458 loss=0.533160 lr=0.000100 Epoch[083] Batch 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accuracy=86.398438 loss=0.531649 lr=0.000100 Epoch[083] Batch [0849]/[3760] Speed: 65.895683 samples/sec accuracy=86.398897 loss=0.530626 lr=0.000100 Epoch[083] Batch [0899]/[3760] Speed: 65.857429 samples/sec accuracy=86.423611 loss=0.530529 lr=0.000100 Epoch[083] Batch [0949]/[3760] Speed: 65.962516 samples/sec accuracy=86.444079 loss=0.529392 lr=0.000100 Epoch[083] Batch [0999]/[3760] Speed: 66.396787 samples/sec accuracy=86.509375 loss=0.528286 lr=0.000100 Epoch[083] Batch [1049]/[3760] Speed: 66.110546 samples/sec accuracy=86.569940 loss=0.525982 lr=0.000100 Epoch[083] Batch [1099]/[3760] Speed: 66.244555 samples/sec accuracy=86.576705 loss=0.525115 lr=0.000100 Epoch[083] Batch [1149]/[3760] Speed: 65.899644 samples/sec accuracy=86.580163 loss=0.524955 lr=0.000100 Epoch[083] Batch [1199]/[3760] Speed: 66.378439 samples/sec accuracy=86.606771 loss=0.524756 lr=0.000100 Epoch[083] Batch [1249]/[3760] Speed: 65.951872 samples/sec accuracy=86.585000 loss=0.525059 lr=0.000100 Epoch[083] 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accuracy=86.621429 loss=0.523768 lr=0.000100 Epoch[083] Batch [1799]/[3760] Speed: 66.266860 samples/sec accuracy=86.591146 loss=0.524496 lr=0.000100 Epoch[083] Batch [1849]/[3760] Speed: 65.677896 samples/sec accuracy=86.579392 loss=0.525168 lr=0.000100 Epoch[083] Batch [1899]/[3760] Speed: 65.914806 samples/sec accuracy=86.580592 loss=0.525568 lr=0.000100 Epoch[083] Batch [1949]/[3760] Speed: 65.985847 samples/sec accuracy=86.584135 loss=0.526113 lr=0.000100 Epoch[083] Batch [1999]/[3760] Speed: 65.930125 samples/sec accuracy=86.570312 loss=0.526360 lr=0.000100 Epoch[083] Batch [2049]/[3760] Speed: 66.556685 samples/sec accuracy=86.595274 loss=0.525450 lr=0.000100 Epoch[083] Batch [2099]/[3760] Speed: 65.321838 samples/sec accuracy=86.627232 loss=0.524409 lr=0.000100 Epoch[083] Batch [2149]/[3760] Speed: 66.283250 samples/sec accuracy=86.643895 loss=0.523419 lr=0.000100 Epoch[083] Batch [2199]/[3760] Speed: 66.141727 samples/sec accuracy=86.644176 loss=0.523236 lr=0.000100 Epoch[083] 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accuracy=86.563079 loss=0.526673 lr=0.000100 Epoch[083] Batch [2749]/[3760] Speed: 66.210826 samples/sec accuracy=86.575568 loss=0.526164 lr=0.000100 Epoch[083] Batch [2799]/[3760] Speed: 65.467925 samples/sec accuracy=86.571987 loss=0.526167 lr=0.000100 Epoch[083] Batch [2849]/[3760] Speed: 66.109137 samples/sec accuracy=86.587171 loss=0.525852 lr=0.000100 Epoch[083] Batch [2899]/[3760] Speed: 65.609100 samples/sec accuracy=86.583513 loss=0.526239 lr=0.000100 Epoch[083] Batch [2949]/[3760] Speed: 66.083934 samples/sec accuracy=86.584216 loss=0.526157 lr=0.000100 Epoch[083] Batch [2999]/[3760] Speed: 65.795975 samples/sec accuracy=86.591667 loss=0.526053 lr=0.000100 Epoch[083] Batch [3049]/[3760] Speed: 65.264275 samples/sec accuracy=86.596824 loss=0.525751 lr=0.000100 Epoch[083] Batch [3099]/[3760] Speed: 66.494721 samples/sec accuracy=86.598286 loss=0.525823 lr=0.000100 Epoch[083] Batch [3149]/[3760] Speed: 66.404539 samples/sec accuracy=86.591766 loss=0.525649 lr=0.000100 Epoch[083] 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accuracy=86.583476 loss=0.525024 lr=0.000100 Epoch[083] Batch [3699]/[3760] Speed: 66.483404 samples/sec accuracy=86.586571 loss=0.524993 lr=0.000100 Epoch[083] Batch [3749]/[3760] Speed: 73.026824 samples/sec accuracy=86.591667 loss=0.525006 lr=0.000100 Batch [0049]/[0303]: acc-top1=67.468750 acc-top5=86.406250 Batch [0099]/[0303]: acc-top1=67.000000 acc-top5=86.406250 Batch [0149]/[0303]: acc-top1=67.312500 acc-top5=86.343750 Batch [0199]/[0303]: acc-top1=67.429688 acc-top5=86.335938 Batch [0249]/[0303]: acc-top1=67.381250 acc-top5=86.406250 Batch [0299]/[0303]: acc-top1=67.807292 acc-top5=86.598958 [Epoch 083] training: accuracy=86.587849 loss=0.525139 [Epoch 083] speed: 65 samples/sec time cost: 3938.298070 [Epoch 083] validation: acc-top1=67.806312 acc-top5=86.607880 loss=1.767942 Epoch[084] Batch [0049]/[3759] Speed: 44.931928 samples/sec accuracy=86.875000 loss=0.525926 lr=0.000100 Epoch[084] Batch [0099]/[3759] Speed: 63.680454 samples/sec accuracy=86.937500 loss=0.511055 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[0299]/[0303]: acc-top1=67.916667 acc-top5=86.531250 [Epoch 084] training: accuracy=86.730181 loss=0.518510 [Epoch 084] speed: 65 samples/sec time cost: 3939.553974 [Epoch 084] validation: acc-top1=67.909447 acc-top5=86.540842 loss=1.777543 Epoch[085] Batch [0049]/[3760] Speed: 44.544608 samples/sec accuracy=86.437500 loss=0.531472 lr=0.000100 Epoch[085] Batch [0099]/[3760] Speed: 63.995065 samples/sec accuracy=86.687500 loss=0.517962 lr=0.000100 Epoch[085] Batch [0149]/[3760] Speed: 65.769839 samples/sec accuracy=86.958333 loss=0.509247 lr=0.000100 Epoch[085] Batch [0199]/[3760] Speed: 65.145576 samples/sec accuracy=87.312500 loss=0.498387 lr=0.000100 Epoch[085] Batch [0249]/[3760] Speed: 66.677881 samples/sec accuracy=87.268750 loss=0.497505 lr=0.000100 Epoch[085] Batch [0299]/[3760] Speed: 65.369540 samples/sec accuracy=87.161458 loss=0.500313 lr=0.000100 Epoch[085] Batch [0349]/[3760] Speed: 66.142875 samples/sec accuracy=87.013393 loss=0.502424 lr=0.000100 Epoch[085] Batch 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accuracy=87.004340 loss=0.507352 lr=0.000100 Epoch[085] Batch [1849]/[3760] Speed: 65.968306 samples/sec accuracy=87.006757 loss=0.507057 lr=0.000100 Epoch[085] Batch [1899]/[3760] Speed: 66.181997 samples/sec accuracy=86.998355 loss=0.507474 lr=0.000100 Epoch[085] Batch [1949]/[3760] Speed: 65.686472 samples/sec accuracy=86.983173 loss=0.508086 lr=0.000100 Epoch[085] Batch [1999]/[3760] Speed: 66.222516 samples/sec accuracy=86.971094 loss=0.508433 lr=0.000100 Epoch[085] Batch [2049]/[3760] Speed: 66.398017 samples/sec accuracy=86.979421 loss=0.507295 lr=0.000100 Epoch[085] Batch [2099]/[3760] Speed: 66.316730 samples/sec accuracy=86.944196 loss=0.508646 lr=0.000100 Epoch[085] Batch [2149]/[3760] Speed: 66.278047 samples/sec accuracy=86.963663 loss=0.507964 lr=0.000100 Epoch[085] Batch [2199]/[3760] Speed: 65.894059 samples/sec accuracy=86.963068 loss=0.507845 lr=0.000100 Epoch[085] Batch [2249]/[3760] Speed: 66.204590 samples/sec accuracy=86.946528 loss=0.508393 lr=0.000100 Epoch[085] 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accuracy=86.941477 loss=0.509348 lr=0.000100 Epoch[085] Batch [2799]/[3760] Speed: 66.852234 samples/sec accuracy=86.933036 loss=0.509508 lr=0.000100 Epoch[085] Batch [2849]/[3760] Speed: 65.367397 samples/sec accuracy=86.929276 loss=0.509593 lr=0.000100 Epoch[085] Batch [2899]/[3760] Speed: 65.960729 samples/sec accuracy=86.917026 loss=0.510481 lr=0.000100 Epoch[085] Batch [2949]/[3760] Speed: 65.839846 samples/sec accuracy=86.902013 loss=0.510974 lr=0.000100 Epoch[085] Batch [2999]/[3760] Speed: 65.853946 samples/sec accuracy=86.913542 loss=0.510847 lr=0.000100 Epoch[085] Batch [3049]/[3760] Speed: 65.901841 samples/sec accuracy=86.890369 loss=0.511087 lr=0.000100 Epoch[085] Batch [3099]/[3760] Speed: 65.957037 samples/sec accuracy=86.888609 loss=0.511186 lr=0.000100 Epoch[085] Batch [3149]/[3760] Speed: 66.044595 samples/sec accuracy=86.873512 loss=0.511461 lr=0.000100 Epoch[085] Batch [3199]/[3760] Speed: 66.442048 samples/sec accuracy=86.870117 loss=0.511671 lr=0.000100 Epoch[085] 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accuracy=86.870777 loss=0.511395 lr=0.000100 Epoch[085] Batch [3749]/[3760] Speed: 72.528165 samples/sec accuracy=86.870833 loss=0.511269 lr=0.000100 Batch [0049]/[0303]: acc-top1=67.625000 acc-top5=86.468750 Batch [0099]/[0303]: acc-top1=67.343750 acc-top5=86.546875 Batch [0149]/[0303]: acc-top1=67.604167 acc-top5=86.364583 Batch [0199]/[0303]: acc-top1=67.585938 acc-top5=86.382812 Batch [0249]/[0303]: acc-top1=67.562500 acc-top5=86.437500 Batch [0299]/[0303]: acc-top1=67.916667 acc-top5=86.598958 [Epoch 085] training: accuracy=86.874169 loss=0.511179 [Epoch 085] speed: 65 samples/sec time cost: 3935.554598 [Epoch 085] validation: acc-top1=67.924917 acc-top5=86.613036 loss=1.752915 Epoch[086] Batch [0049]/[3760] Speed: 44.591288 samples/sec accuracy=87.906250 loss=0.452269 lr=0.000100 Epoch[086] Batch [0099]/[3760] Speed: 64.172794 samples/sec accuracy=87.578125 loss=0.470217 lr=0.000100 Epoch[086] Batch [0149]/[3760] Speed: 65.640491 samples/sec accuracy=87.354167 loss=0.484669 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lr=0.000100 Epoch[086] Batch [2099]/[3760] Speed: 66.020400 samples/sec accuracy=86.992560 loss=0.505732 lr=0.000100 Epoch[086] Batch [2149]/[3760] Speed: 65.990617 samples/sec accuracy=87.003634 loss=0.505425 lr=0.000100 Epoch[086] Batch [2199]/[3760] Speed: 65.763071 samples/sec accuracy=87.004972 loss=0.505761 lr=0.000100 Epoch[086] Batch [2249]/[3760] Speed: 65.968220 samples/sec accuracy=87.018056 loss=0.505404 lr=0.000100 Epoch[086] Batch [2299]/[3760] Speed: 65.579870 samples/sec accuracy=87.023098 loss=0.505732 lr=0.000100 Epoch[086] Batch [2349]/[3760] Speed: 66.641972 samples/sec accuracy=87.010638 loss=0.505803 lr=0.000100 Epoch[086] Batch [2399]/[3760] Speed: 66.498013 samples/sec accuracy=87.005859 loss=0.505675 lr=0.000100 Epoch[086] Batch [2449]/[3760] Speed: 65.165959 samples/sec accuracy=87.022321 loss=0.505267 lr=0.000100 Epoch[086] Batch [2499]/[3760] Speed: 66.332648 samples/sec accuracy=87.026250 loss=0.505266 lr=0.000100 Epoch[086] Batch [2549]/[3760] Speed: 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lr=0.000100 Epoch[086] Batch [3049]/[3760] Speed: 64.750870 samples/sec accuracy=86.986680 loss=0.506790 lr=0.000100 Epoch[086] Batch [3099]/[3760] Speed: 66.612812 samples/sec accuracy=86.997480 loss=0.506721 lr=0.000100 Epoch[086] Batch [3149]/[3760] Speed: 65.917921 samples/sec accuracy=87.000000 loss=0.506699 lr=0.000100 Epoch[086] Batch [3199]/[3760] Speed: 66.310954 samples/sec accuracy=87.005371 loss=0.506386 lr=0.000100 Epoch[086] Batch [3249]/[3760] Speed: 65.894415 samples/sec accuracy=86.997596 loss=0.506509 lr=0.000100 Epoch[086] Batch [3299]/[3760] Speed: 66.123041 samples/sec accuracy=86.987216 loss=0.506750 lr=0.000100 Epoch[086] Batch [3349]/[3760] Speed: 66.238922 samples/sec accuracy=86.992071 loss=0.506670 lr=0.000100 Epoch[086] Batch [3399]/[3760] Speed: 65.713252 samples/sec accuracy=86.990349 loss=0.506608 lr=0.000100 Epoch[086] Batch [3449]/[3760] Speed: 66.159010 samples/sec accuracy=86.991395 loss=0.505928 lr=0.000100 Epoch[086] Batch [3499]/[3760] Speed: 65.685931 samples/sec accuracy=86.984375 loss=0.506087 lr=0.000100 Epoch[086] Batch [3549]/[3760] Speed: 66.242854 samples/sec accuracy=86.978873 loss=0.506298 lr=0.000100 Epoch[086] Batch [3599]/[3760] Speed: 65.860167 samples/sec accuracy=86.990017 loss=0.506127 lr=0.000100 Epoch[086] Batch [3649]/[3760] Speed: 65.946934 samples/sec accuracy=86.985873 loss=0.506137 lr=0.000100 Epoch[086] Batch [3699]/[3760] Speed: 65.955914 samples/sec accuracy=86.974662 loss=0.506508 lr=0.000100 Epoch[086] Batch [3749]/[3760] Speed: 72.752755 samples/sec accuracy=86.959583 loss=0.506984 lr=0.000100 Batch [0049]/[0303]: acc-top1=67.343750 acc-top5=86.187500 Batch [0099]/[0303]: acc-top1=67.328125 acc-top5=86.218750 Batch [0149]/[0303]: acc-top1=67.593750 acc-top5=86.270833 Batch [0199]/[0303]: acc-top1=67.562500 acc-top5=86.320312 Batch [0249]/[0303]: acc-top1=67.537500 acc-top5=86.425000 Batch [0299]/[0303]: acc-top1=67.906250 acc-top5=86.635417 [Epoch 086] training: accuracy=86.957281 loss=0.506965 [Epoch 086] speed: 65 samples/sec time cost: 3932.630694 [Epoch 086] validation: acc-top1=67.904290 acc-top5=86.649134 loss=1.775750 Epoch[087] Batch [0049]/[3759] Speed: 44.123697 samples/sec accuracy=87.437500 loss=0.493761 lr=0.000100 Epoch[087] Batch [0099]/[3759] Speed: 63.840894 samples/sec accuracy=87.515625 loss=0.491786 lr=0.000100 Epoch[087] Batch [0149]/[3759] Speed: 66.262206 samples/sec accuracy=87.625000 loss=0.489247 lr=0.000100 Epoch[087] Batch [0199]/[3759] Speed: 65.605763 samples/sec accuracy=87.257812 loss=0.500618 lr=0.000100 Epoch[087] Batch [0249]/[3759] Speed: 66.408939 samples/sec accuracy=87.337500 loss=0.499285 lr=0.000100 Epoch[087] Batch [0299]/[3759] Speed: 65.682443 samples/sec accuracy=87.354167 loss=0.501920 lr=0.000100 Epoch[087] Batch [0349]/[3759] Speed: 65.665452 samples/sec accuracy=87.410714 loss=0.497442 lr=0.000100 Epoch[087] Batch [0399]/[3759] Speed: 66.406655 samples/sec accuracy=87.339844 loss=0.499219 lr=0.000100 Epoch[087] Batch 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accuracy=87.279514 loss=0.500066 lr=0.000100 Epoch[087] Batch [0949]/[3759] Speed: 66.024514 samples/sec accuracy=87.174342 loss=0.503079 lr=0.000100 Epoch[087] Batch [0999]/[3759] Speed: 65.879610 samples/sec accuracy=87.142188 loss=0.503998 lr=0.000100 Epoch[087] Batch [1049]/[3759] Speed: 66.526859 samples/sec accuracy=87.111607 loss=0.505231 lr=0.000100 Epoch[087] Batch [1099]/[3759] Speed: 66.075641 samples/sec accuracy=87.095170 loss=0.506679 lr=0.000100 Epoch[087] Batch [1149]/[3759] Speed: 65.800583 samples/sec accuracy=87.099185 loss=0.506684 lr=0.000100 Epoch[087] Batch [1199]/[3759] Speed: 66.433203 samples/sec accuracy=87.108073 loss=0.506906 lr=0.000100 Epoch[087] Batch [1249]/[3759] Speed: 65.964868 samples/sec accuracy=87.067500 loss=0.508395 lr=0.000100 Epoch[087] Batch [1299]/[3759] Speed: 66.532091 samples/sec accuracy=87.034856 loss=0.509160 lr=0.000100 Epoch[087] Batch [1349]/[3759] Speed: 66.243474 samples/sec accuracy=87.059028 loss=0.509455 lr=0.000100 Epoch[087] Batch [1399]/[3759] Speed: 66.557023 samples/sec accuracy=87.063616 loss=0.509779 lr=0.000100 Epoch[087] Batch [1449]/[3759] Speed: 66.101576 samples/sec accuracy=87.062500 loss=0.509396 lr=0.000100 Epoch[087] Batch [1499]/[3759] Speed: 65.890338 samples/sec accuracy=87.076042 loss=0.509003 lr=0.000100 Epoch[087] Batch [1549]/[3759] Speed: 65.573244 samples/sec accuracy=87.078629 loss=0.508876 lr=0.000100 Epoch[087] Batch [1599]/[3759] Speed: 66.812010 samples/sec accuracy=87.047852 loss=0.509452 lr=0.000100 Epoch[087] Batch [1649]/[3759] Speed: 66.064892 samples/sec accuracy=87.061553 loss=0.509421 lr=0.000100 Epoch[087] Batch [1699]/[3759] Speed: 66.263104 samples/sec accuracy=87.063419 loss=0.509161 lr=0.000100 Epoch[087] Batch [1749]/[3759] Speed: 65.378959 samples/sec accuracy=87.094643 loss=0.507881 lr=0.000100 Epoch[087] Batch [1799]/[3759] Speed: 66.725585 samples/sec accuracy=87.089410 loss=0.508638 lr=0.000100 Epoch[087] Batch [1849]/[3759] Speed: 66.171073 samples/sec accuracy=87.081081 loss=0.509196 lr=0.000100 Epoch[087] Batch [1899]/[3759] Speed: 66.242963 samples/sec accuracy=87.083059 loss=0.509180 lr=0.000100 Epoch[087] Batch [1949]/[3759] Speed: 66.636739 samples/sec accuracy=87.061699 loss=0.509560 lr=0.000100 Epoch[087] Batch [1999]/[3759] Speed: 65.660690 samples/sec accuracy=87.053906 loss=0.509405 lr=0.000100 Epoch[087] Batch [2049]/[3759] Speed: 66.271847 samples/sec accuracy=87.034299 loss=0.510017 lr=0.000100 Epoch[087] Batch [2099]/[3759] Speed: 66.327034 samples/sec accuracy=87.045387 loss=0.509559 lr=0.000100 Epoch[087] Batch [2149]/[3759] Speed: 66.275509 samples/sec accuracy=87.087936 loss=0.508311 lr=0.000100 Epoch[087] Batch [2199]/[3759] Speed: 65.815743 samples/sec accuracy=87.108665 loss=0.507586 lr=0.000100 Epoch[087] Batch [2249]/[3759] Speed: 66.617754 samples/sec accuracy=87.096528 loss=0.508466 lr=0.000100 Epoch[087] Batch [2299]/[3759] Speed: 65.864518 samples/sec accuracy=87.105299 loss=0.507791 lr=0.000100 Epoch[087] Batch [2349]/[3759] Speed: 66.089110 samples/sec accuracy=87.117686 loss=0.507692 lr=0.000100 Epoch[087] Batch [2399]/[3759] Speed: 66.575084 samples/sec accuracy=87.115885 loss=0.507738 lr=0.000100 Epoch[087] Batch [2449]/[3759] Speed: 66.399774 samples/sec accuracy=87.105867 loss=0.507581 lr=0.000100 Epoch[087] Batch [2499]/[3759] Speed: 66.086078 samples/sec accuracy=87.115000 loss=0.506867 lr=0.000100 Epoch[087] Batch [2549]/[3759] Speed: 66.050946 samples/sec accuracy=87.102941 loss=0.507465 lr=0.000100 Epoch[087] Batch [2599]/[3759] Speed: 66.179309 samples/sec accuracy=87.108774 loss=0.507534 lr=0.000100 Epoch[087] Batch [2649]/[3759] Speed: 65.991389 samples/sec accuracy=87.110259 loss=0.507644 lr=0.000100 Epoch[087] Batch [2699]/[3759] Speed: 66.373255 samples/sec accuracy=87.107060 loss=0.507317 lr=0.000100 Epoch[087] Batch [2749]/[3759] Speed: 66.062692 samples/sec accuracy=87.114205 loss=0.506846 lr=0.000100 Epoch[087] Batch [2799]/[3759] Speed: 65.803236 samples/sec accuracy=87.121652 loss=0.506642 lr=0.000100 Epoch[087] Batch [2849]/[3759] Speed: 66.008951 samples/sec accuracy=87.120066 loss=0.507149 lr=0.000100 Epoch[087] Batch [2899]/[3759] Speed: 66.528165 samples/sec accuracy=87.100216 loss=0.507932 lr=0.000100 Epoch[087] Batch [2949]/[3759] Speed: 65.690027 samples/sec accuracy=87.095869 loss=0.508400 lr=0.000100 Epoch[087] Batch [2999]/[3759] Speed: 65.744555 samples/sec accuracy=87.109896 loss=0.508016 lr=0.000100 Epoch[087] Batch [3049]/[3759] Speed: 66.367815 samples/sec accuracy=87.108094 loss=0.508063 lr=0.000100 Epoch[087] Batch [3099]/[3759] Speed: 66.410697 samples/sec accuracy=87.087702 loss=0.508444 lr=0.000100 Epoch[087] Batch [3149]/[3759] Speed: 65.969928 samples/sec accuracy=87.094246 loss=0.508524 lr=0.000100 Epoch[087] Batch [3199]/[3759] Speed: 65.855575 samples/sec accuracy=87.096191 loss=0.508174 lr=0.000100 Epoch[087] Batch [3249]/[3759] Speed: 66.753863 samples/sec accuracy=87.074038 loss=0.508663 lr=0.000100 Epoch[087] Batch [3299]/[3759] Speed: 65.986554 samples/sec accuracy=87.084754 loss=0.508399 lr=0.000100 Epoch[087] Batch [3349]/[3759] Speed: 66.554003 samples/sec accuracy=87.067164 loss=0.509167 lr=0.000100 Epoch[087] Batch [3399]/[3759] Speed: 66.088424 samples/sec accuracy=87.059283 loss=0.509360 lr=0.000100 Epoch[087] Batch [3449]/[3759] Speed: 66.087918 samples/sec accuracy=87.067482 loss=0.509241 lr=0.000100 Epoch[087] Batch [3499]/[3759] Speed: 66.215925 samples/sec accuracy=87.067411 loss=0.509201 lr=0.000100 Epoch[087] Batch [3549]/[3759] Speed: 66.350485 samples/sec accuracy=87.064261 loss=0.509344 lr=0.000100 Epoch[087] Batch [3599]/[3759] Speed: 65.943019 samples/sec accuracy=87.073351 loss=0.508991 lr=0.000100 Epoch[087] Batch [3649]/[3759] Speed: 66.167936 samples/sec accuracy=87.068065 loss=0.508925 lr=0.000100 Epoch[087] Batch [3699]/[3759] Speed: 66.309176 samples/sec accuracy=87.059966 loss=0.509038 lr=0.000100 Epoch[087] Batch [3749]/[3759] Speed: 73.642384 samples/sec accuracy=87.047917 loss=0.509307 lr=0.000100 Batch [0049]/[0303]: acc-top1=67.156250 acc-top5=86.375000 Batch [0099]/[0303]: acc-top1=67.109375 acc-top5=86.421875 Batch [0149]/[0303]: acc-top1=67.312500 acc-top5=86.270833 Batch [0199]/[0303]: acc-top1=67.234375 acc-top5=86.351562 Batch [0249]/[0303]: acc-top1=67.312500 acc-top5=86.375000 Batch [0299]/[0303]: acc-top1=67.687500 acc-top5=86.562500 [Epoch 087] training: accuracy=87.047336 loss=0.509325 [Epoch 087] speed: 65 samples/sec time cost: 3928.706688 [Epoch 087] validation: acc-top1=67.687706 acc-top5=86.566625 loss=1.753480 Epoch[088] Batch [0049]/[3760] Speed: 44.942329 samples/sec accuracy=87.625000 loss=0.488110 lr=0.000100 Epoch[088] Batch [0099]/[3760] Speed: 64.174253 samples/sec accuracy=86.828125 loss=0.515465 lr=0.000100 Epoch[088] Batch [0149]/[3760] Speed: 66.019214 samples/sec accuracy=87.000000 loss=0.512640 lr=0.000100 Epoch[088] Batch [0199]/[3760] Speed: 65.470902 samples/sec accuracy=86.859375 loss=0.516886 lr=0.000100 Epoch[088] Batch [0249]/[3760] Speed: 66.361283 samples/sec accuracy=86.925000 loss=0.516671 lr=0.000100 Epoch[088] Batch [0299]/[3760] Speed: 65.644087 samples/sec accuracy=87.031250 loss=0.513828 lr=0.000100 Epoch[088] Batch [0349]/[3760] Speed: 66.152640 samples/sec accuracy=87.071429 loss=0.508874 lr=0.000100 Epoch[088] Batch [0399]/[3760] Speed: 66.750178 samples/sec accuracy=87.179688 loss=0.506235 lr=0.000100 Epoch[088] Batch [0449]/[3760] Speed: 65.948974 samples/sec accuracy=87.211806 loss=0.505959 lr=0.000100 Epoch[088] Batch [0499]/[3760] Speed: 66.275479 samples/sec accuracy=87.218750 loss=0.506886 lr=0.000100 Epoch[088] Batch [0549]/[3760] Speed: 66.333219 samples/sec accuracy=87.136364 loss=0.507281 lr=0.000100 Epoch[088] Batch [0599]/[3760] Speed: 66.404405 samples/sec accuracy=87.078125 loss=0.507630 lr=0.000100 Epoch[088] Batch [0649]/[3760] Speed: 65.527503 samples/sec accuracy=87.055288 loss=0.508488 lr=0.000100 Epoch[088] Batch [0699]/[3760] Speed: 66.140199 samples/sec accuracy=87.066964 loss=0.509101 lr=0.000100 Epoch[088] Batch [0749]/[3760] Speed: 66.309883 samples/sec accuracy=87.070833 loss=0.508671 lr=0.000100 Epoch[088] Batch [0799]/[3760] Speed: 66.063104 samples/sec accuracy=87.052734 loss=0.510921 lr=0.000100 Epoch[088] Batch [0849]/[3760] Speed: 66.493268 samples/sec accuracy=87.047794 loss=0.510945 lr=0.000100 Epoch[088] Batch [0899]/[3760] Speed: 65.028648 samples/sec accuracy=87.090278 loss=0.509509 lr=0.000100 Epoch[088] Batch [0949]/[3760] Speed: 66.202091 samples/sec accuracy=87.108553 loss=0.508845 lr=0.000100 Epoch[088] Batch [0999]/[3760] Speed: 66.191757 samples/sec accuracy=87.126563 loss=0.508688 lr=0.000100 Epoch[088] Batch [1049]/[3760] Speed: 66.072652 samples/sec accuracy=87.049107 loss=0.510460 lr=0.000100 Epoch[088] Batch [1099]/[3760] Speed: 65.812986 samples/sec accuracy=87.053977 loss=0.510364 lr=0.000100 Epoch[088] Batch [1149]/[3760] Speed: 66.201129 samples/sec accuracy=87.048913 loss=0.509703 lr=0.000100 Epoch[088] Batch [1199]/[3760] Speed: 66.517310 samples/sec accuracy=87.063802 loss=0.509485 lr=0.000100 Epoch[088] Batch [1249]/[3760] Speed: 66.316125 samples/sec accuracy=87.072500 loss=0.508976 lr=0.000100 Epoch[088] Batch [1299]/[3760] Speed: 65.853342 samples/sec accuracy=87.075721 loss=0.508396 lr=0.000100 Epoch[088] Batch [1349]/[3760] Speed: 65.627170 samples/sec accuracy=87.038194 loss=0.508184 lr=0.000100 Epoch[088] Batch [1399]/[3760] Speed: 65.542802 samples/sec accuracy=87.022321 loss=0.508738 lr=0.000100 Epoch[088] Batch [1449]/[3760] Speed: 66.322402 samples/sec accuracy=87.044181 loss=0.507534 lr=0.000100 Epoch[088] Batch [1499]/[3760] Speed: 66.135654 samples/sec accuracy=87.032292 loss=0.507906 lr=0.000100 Epoch[088] Batch [1549]/[3760] Speed: 66.135224 samples/sec accuracy=87.022177 loss=0.507701 lr=0.000100 Epoch[088] Batch [1599]/[3760] Speed: 65.782721 samples/sec accuracy=87.024414 loss=0.507037 lr=0.000100 Epoch[088] Batch [1649]/[3760] Speed: 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acc-top5=86.551155 loss=1.762393 Epoch[089] Batch [0049]/[3760] Speed: 44.070072 samples/sec accuracy=87.343750 loss=0.510687 lr=0.000100 Epoch[089] Batch [0099]/[3760] Speed: 64.696869 samples/sec accuracy=86.953125 loss=0.514998 lr=0.000100 Epoch[089] Batch [0149]/[3760] Speed: 66.069320 samples/sec accuracy=86.958333 loss=0.516159 lr=0.000100 Epoch[089] Batch [0199]/[3760] Speed: 65.517790 samples/sec accuracy=87.234375 loss=0.505906 lr=0.000100 Epoch[089] Batch [0249]/[3760] Speed: 66.279803 samples/sec accuracy=87.087500 loss=0.511282 lr=0.000100 Epoch[089] Batch [0299]/[3760] Speed: 65.262909 samples/sec accuracy=87.093750 loss=0.508971 lr=0.000100 Epoch[089] Batch [0349]/[3760] Speed: 66.237768 samples/sec accuracy=87.200893 loss=0.504329 lr=0.000100 Epoch[089] Batch [0399]/[3760] Speed: 66.253237 samples/sec accuracy=87.195312 loss=0.503921 lr=0.000100 Epoch[089] Batch [0449]/[3760] Speed: 65.573486 samples/sec accuracy=87.131944 loss=0.505106 lr=0.000100 Epoch[089] Batch 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accuracy=87.279605 loss=0.497075 lr=0.000100 Epoch[089] Batch [0999]/[3760] Speed: 66.196954 samples/sec accuracy=87.309375 loss=0.495803 lr=0.000100 Epoch[089] Batch [1049]/[3760] Speed: 65.812054 samples/sec accuracy=87.322917 loss=0.495363 lr=0.000100 Epoch[089] Batch [1099]/[3760] Speed: 65.654434 samples/sec accuracy=87.340909 loss=0.495195 lr=0.000100 Epoch[089] Batch [1149]/[3760] Speed: 66.238987 samples/sec accuracy=87.308424 loss=0.496627 lr=0.000100 Epoch[089] Batch [1199]/[3760] Speed: 66.528037 samples/sec accuracy=87.308594 loss=0.496364 lr=0.000100 Epoch[089] Batch [1249]/[3760] Speed: 66.571204 samples/sec accuracy=87.305000 loss=0.496299 lr=0.000100 Epoch[089] Batch [1299]/[3760] Speed: 65.480829 samples/sec accuracy=87.330529 loss=0.494978 lr=0.000100 Epoch[089] Batch [1349]/[3760] Speed: 66.180134 samples/sec accuracy=87.290509 loss=0.496367 lr=0.000100 Epoch[089] Batch [1399]/[3760] Speed: 66.119079 samples/sec accuracy=87.263393 loss=0.497530 lr=0.000100 Epoch[089] 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accuracy=87.152138 loss=0.500897 lr=0.000100 Epoch[089] Batch [1949]/[3760] Speed: 66.013958 samples/sec accuracy=87.145833 loss=0.501017 lr=0.000100 Epoch[089] Batch [1999]/[3760] Speed: 66.241316 samples/sec accuracy=87.150781 loss=0.500284 lr=0.000100 Epoch[089] Batch [2049]/[3760] Speed: 66.497428 samples/sec accuracy=87.128811 loss=0.501117 lr=0.000100 Epoch[089] Batch [2099]/[3760] Speed: 66.126753 samples/sec accuracy=87.144345 loss=0.500466 lr=0.000100 Epoch[089] Batch [2149]/[3760] Speed: 65.400221 samples/sec accuracy=87.147529 loss=0.500245 lr=0.000100 Epoch[089] Batch [2199]/[3760] Speed: 66.251121 samples/sec accuracy=87.150568 loss=0.500329 lr=0.000100 Epoch[089] Batch [2249]/[3760] Speed: 66.208727 samples/sec accuracy=87.161806 loss=0.499886 lr=0.000100 Epoch[089] Batch [2299]/[3760] Speed: 66.061598 samples/sec accuracy=87.133832 loss=0.500945 lr=0.000100 Epoch[089] Batch [2349]/[3760] Speed: 65.352655 samples/sec accuracy=87.116356 loss=0.501818 lr=0.000100 Epoch[089] 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accuracy=87.159539 loss=0.501226 lr=0.000100 Epoch[089] Batch [2899]/[3760] Speed: 66.537524 samples/sec accuracy=87.179418 loss=0.500661 lr=0.000100 Epoch[089] Batch [2949]/[3760] Speed: 66.622939 samples/sec accuracy=87.177966 loss=0.500744 lr=0.000100 Epoch[089] Batch [2999]/[3760] Speed: 66.062825 samples/sec accuracy=87.181771 loss=0.501123 lr=0.000100 Epoch[089] Batch [3049]/[3760] Speed: 65.779348 samples/sec accuracy=87.171107 loss=0.501845 lr=0.000100 Epoch[089] Batch [3099]/[3760] Speed: 66.203820 samples/sec accuracy=87.169355 loss=0.501983 lr=0.000100 Epoch[089] Batch [3149]/[3760] Speed: 66.249586 samples/sec accuracy=87.156250 loss=0.502568 lr=0.000100 Epoch[089] Batch [3199]/[3760] Speed: 65.582154 samples/sec accuracy=87.153809 loss=0.502855 lr=0.000100 Epoch[089] Batch [3249]/[3760] Speed: 66.359759 samples/sec accuracy=87.148558 loss=0.502788 lr=0.000100 Epoch[089] Batch [3299]/[3760] Speed: 66.067531 samples/sec accuracy=87.148201 loss=0.502606 lr=0.000100 Epoch[089] Batch [3349]/[3760] Speed: 66.547192 samples/sec accuracy=87.151119 loss=0.502263 lr=0.000100 Epoch[089] Batch [3399]/[3760] Speed: 65.955950 samples/sec accuracy=87.151195 loss=0.502024 lr=0.000100 Epoch[089] Batch [3449]/[3760] Speed: 65.722279 samples/sec accuracy=87.151268 loss=0.502052 lr=0.000100 Epoch[089] Batch [3499]/[3760] Speed: 66.315859 samples/sec accuracy=87.152232 loss=0.502085 lr=0.000100 Epoch[089] Batch [3549]/[3760] Speed: 66.177546 samples/sec accuracy=87.154049 loss=0.502009 lr=0.000100 Epoch[089] Batch [3599]/[3760] Speed: 66.401524 samples/sec accuracy=87.158854 loss=0.501710 lr=0.000100 Epoch[089] Batch [3649]/[3760] Speed: 65.841362 samples/sec accuracy=87.166952 loss=0.501364 lr=0.000100 Epoch[089] Batch [3699]/[3760] Speed: 66.024063 samples/sec accuracy=87.173142 loss=0.501248 lr=0.000100 Epoch[089] Batch [3749]/[3760] Speed: 73.897865 samples/sec accuracy=87.174167 loss=0.501395 lr=0.000100 Batch [0049]/[0303]: acc-top1=67.500000 acc-top5=86.312500 Batch [0099]/[0303]: acc-top1=67.375000 acc-top5=86.343750 Batch [0149]/[0303]: acc-top1=67.531250 acc-top5=86.281250 Batch [0199]/[0303]: acc-top1=67.531250 acc-top5=86.351562 Batch [0249]/[0303]: acc-top1=67.612500 acc-top5=86.400000 Batch [0299]/[0303]: acc-top1=67.968750 acc-top5=86.578125 [Epoch 089] training: accuracy=87.172955 loss=0.501355 [Epoch 089] speed: 65 samples/sec time cost: 3931.057770 [Epoch 089] validation: acc-top1=67.981642 acc-top5=86.587252 loss=1.777467 Epoch[090] Batch [0049]/[3759] Speed: 44.131549 samples/sec accuracy=87.781250 loss=0.476475 lr=0.000100 Epoch[090] Batch [0099]/[3759] Speed: 64.168813 samples/sec accuracy=87.718750 loss=0.489297 lr=0.000100 Epoch[090] Batch [0149]/[3759] Speed: 65.761107 samples/sec accuracy=87.666667 loss=0.491543 lr=0.000100 Epoch[090] Batch [0199]/[3759] Speed: 65.643100 samples/sec accuracy=87.718750 loss=0.489027 lr=0.000100 Epoch[090] Batch [0249]/[3759] Speed: 66.746047 samples/sec accuracy=87.756250 loss=0.484095 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lr=0.000100 Epoch[090] Batch [3149]/[3759] Speed: 66.148242 samples/sec accuracy=87.338294 loss=0.496630 lr=0.000100 Epoch[090] Batch [3199]/[3759] Speed: 65.944202 samples/sec accuracy=87.344238 loss=0.496572 lr=0.000100 Epoch[090] Batch [3249]/[3759] Speed: 66.344610 samples/sec accuracy=87.354327 loss=0.496454 lr=0.000100 Epoch[090] Batch [3299]/[3759] Speed: 65.534839 samples/sec accuracy=87.357481 loss=0.496621 lr=0.000100 Epoch[090] Batch [3349]/[3759] Speed: 66.628357 samples/sec accuracy=87.365672 loss=0.496368 lr=0.000100 Epoch[090] Batch [3399]/[3759] Speed: 66.087427 samples/sec accuracy=87.369026 loss=0.496317 lr=0.000100 Epoch[090] Batch [3449]/[3759] Speed: 66.364809 samples/sec accuracy=87.372736 loss=0.496415 lr=0.000100 Epoch[090] Batch [3499]/[3759] Speed: 65.646400 samples/sec accuracy=87.376786 loss=0.496053 lr=0.000100 Epoch[090] Batch [3549]/[3759] Speed: 66.043260 samples/sec accuracy=87.367518 loss=0.496368 lr=0.000100 Epoch[090] Batch [3599]/[3759] Speed: 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accuracy=86.968750 loss=0.513863 lr=0.000100 Epoch[091] Batch [0099]/[3760] Speed: 64.557300 samples/sec accuracy=86.718750 loss=0.511079 lr=0.000100 Epoch[091] Batch [0149]/[3760] Speed: 65.542623 samples/sec accuracy=86.750000 loss=0.507380 lr=0.000100 Epoch[091] Batch [0199]/[3760] Speed: 65.998475 samples/sec accuracy=86.976562 loss=0.501266 lr=0.000100 Epoch[091] Batch [0249]/[3760] Speed: 66.644807 samples/sec accuracy=87.318750 loss=0.492003 lr=0.000100 Epoch[091] Batch [0299]/[3760] Speed: 65.218116 samples/sec accuracy=87.531250 loss=0.485620 lr=0.000100 Epoch[091] Batch [0349]/[3760] Speed: 65.500267 samples/sec accuracy=87.500000 loss=0.483830 lr=0.000100 Epoch[091] Batch [0399]/[3760] Speed: 66.654297 samples/sec accuracy=87.437500 loss=0.490400 lr=0.000100 Epoch[091] Batch [0449]/[3760] Speed: 66.029648 samples/sec accuracy=87.461806 loss=0.492928 lr=0.000100 Epoch[091] Batch [0499]/[3760] Speed: 66.443590 samples/sec accuracy=87.412500 loss=0.494426 lr=0.000100 Epoch[091] 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accuracy=87.234375 loss=0.499764 lr=0.000100 Epoch[091] Batch [1049]/[3760] Speed: 66.450925 samples/sec accuracy=87.199405 loss=0.501109 lr=0.000100 Epoch[091] Batch [1099]/[3760] Speed: 66.659492 samples/sec accuracy=87.207386 loss=0.501419 lr=0.000100 Epoch[091] Batch [1149]/[3760] Speed: 66.001631 samples/sec accuracy=87.182065 loss=0.501694 lr=0.000100 Epoch[091] Batch [1199]/[3760] Speed: 65.820434 samples/sec accuracy=87.244792 loss=0.498997 lr=0.000100 Epoch[091] Batch [1249]/[3760] Speed: 65.931785 samples/sec accuracy=87.217500 loss=0.499596 lr=0.000100 Epoch[091] Batch [1299]/[3760] Speed: 66.648327 samples/sec accuracy=87.176683 loss=0.500856 lr=0.000100 Epoch[091] Batch [1349]/[3760] Speed: 65.873052 samples/sec accuracy=87.200231 loss=0.500859 lr=0.000100 Epoch[091] Batch [1399]/[3760] Speed: 66.347183 samples/sec accuracy=87.189732 loss=0.501562 lr=0.000100 Epoch[091] Batch [1449]/[3760] Speed: 65.679381 samples/sec accuracy=87.162716 loss=0.502272 lr=0.000100 Epoch[091] 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accuracy=87.292468 loss=0.495748 lr=0.000100 Epoch[091] Batch [1999]/[3760] Speed: 66.227076 samples/sec accuracy=87.310937 loss=0.494779 lr=0.000100 Epoch[091] Batch [2049]/[3760] Speed: 66.406361 samples/sec accuracy=87.323171 loss=0.494486 lr=0.000100 Epoch[091] Batch [2099]/[3760] Speed: 65.769830 samples/sec accuracy=87.326637 loss=0.493995 lr=0.000100 Epoch[091] Batch [2149]/[3760] Speed: 65.964965 samples/sec accuracy=87.329215 loss=0.494484 lr=0.000100 Epoch[091] Batch [2199]/[3760] Speed: 66.117620 samples/sec accuracy=87.338068 loss=0.494067 lr=0.000100 Epoch[091] Batch [2249]/[3760] Speed: 65.658538 samples/sec accuracy=87.343750 loss=0.493677 lr=0.000100 Epoch[091] Batch [2299]/[3760] Speed: 65.844780 samples/sec accuracy=87.325408 loss=0.494356 lr=0.000100 Epoch[091] Batch [2349]/[3760] Speed: 66.084579 samples/sec accuracy=87.315160 loss=0.494970 lr=0.000100 Epoch[091] Batch [2399]/[3760] Speed: 66.309027 samples/sec accuracy=87.309896 loss=0.495190 lr=0.000100 Epoch[091] 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accuracy=87.355603 loss=0.495158 lr=0.000100 Epoch[091] Batch [2949]/[3760] Speed: 65.486715 samples/sec accuracy=87.336335 loss=0.495762 lr=0.000100 Epoch[091] Batch [2999]/[3760] Speed: 66.376843 samples/sec accuracy=87.346354 loss=0.495803 lr=0.000100 Epoch[091] Batch [3049]/[3760] Speed: 65.655604 samples/sec accuracy=87.351434 loss=0.495324 lr=0.000100 Epoch[091] Batch [3099]/[3760] Speed: 66.039713 samples/sec accuracy=87.337198 loss=0.495812 lr=0.000100 Epoch[091] Batch [3149]/[3760] Speed: 65.350435 samples/sec accuracy=87.336806 loss=0.495839 lr=0.000100 Epoch[091] Batch [3199]/[3760] Speed: 66.603254 samples/sec accuracy=87.347656 loss=0.495373 lr=0.000100 Epoch[091] Batch [3249]/[3760] Speed: 66.742924 samples/sec accuracy=87.340865 loss=0.495555 lr=0.000100 Epoch[091] Batch [3299]/[3760] Speed: 66.239237 samples/sec accuracy=87.328125 loss=0.496163 lr=0.000100 Epoch[091] Batch [3349]/[3760] Speed: 66.525523 samples/sec accuracy=87.333022 loss=0.495959 lr=0.000100 Epoch[091] Batch [3399]/[3760] Speed: 65.819848 samples/sec accuracy=87.327665 loss=0.495999 lr=0.000100 Epoch[091] Batch [3449]/[3760] Speed: 66.263391 samples/sec accuracy=87.321105 loss=0.496025 lr=0.000100 Epoch[091] Batch [3499]/[3760] Speed: 66.202697 samples/sec accuracy=87.316964 loss=0.496168 lr=0.000100 Epoch[091] Batch [3549]/[3760] Speed: 66.046527 samples/sec accuracy=87.334947 loss=0.495745 lr=0.000100 Epoch[091] Batch [3599]/[3760] Speed: 66.295316 samples/sec accuracy=87.334201 loss=0.495613 lr=0.000100 Epoch[091] Batch [3649]/[3760] Speed: 66.464539 samples/sec accuracy=87.329623 loss=0.495648 lr=0.000100 Epoch[091] Batch [3699]/[3760] Speed: 65.934411 samples/sec accuracy=87.333615 loss=0.495592 lr=0.000100 Epoch[091] Batch [3749]/[3760] Speed: 72.632353 samples/sec accuracy=87.339167 loss=0.495614 lr=0.000100 Batch [0049]/[0303]: acc-top1=67.750000 acc-top5=86.531250 Batch [0099]/[0303]: acc-top1=67.453125 acc-top5=86.468750 Batch [0149]/[0303]: acc-top1=67.593750 acc-top5=86.281250 Batch [0199]/[0303]: acc-top1=67.507812 acc-top5=86.304688 Batch [0249]/[0303]: acc-top1=67.468750 acc-top5=86.412500 Batch [0299]/[0303]: acc-top1=67.838542 acc-top5=86.593750 [Epoch 091] training: accuracy=87.333777 loss=0.495689 [Epoch 091] speed: 65 samples/sec time cost: 3929.282251 [Epoch 091] validation: acc-top1=67.852723 acc-top5=86.597566 loss=1.775650 Epoch[092] Batch [0049]/[3760] Speed: 44.886664 samples/sec accuracy=86.968750 loss=0.512508 lr=0.000100 Epoch[092] Batch [0099]/[3760] Speed: 65.015997 samples/sec accuracy=86.750000 loss=0.521884 lr=0.000100 Epoch[092] Batch [0149]/[3760] Speed: 66.279356 samples/sec accuracy=86.906250 loss=0.516097 lr=0.000100 Epoch[092] Batch [0199]/[3760] Speed: 65.690379 samples/sec accuracy=87.023438 loss=0.510997 lr=0.000100 Epoch[092] Batch [0249]/[3760] Speed: 65.654278 samples/sec accuracy=87.143750 loss=0.510676 lr=0.000100 Epoch[092] Batch [0299]/[3760] Speed: 66.447729 samples/sec accuracy=87.171875 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accuracy=87.378750 loss=0.496378 lr=0.000100 Epoch[092] Batch [1299]/[3760] Speed: 66.335959 samples/sec accuracy=87.401442 loss=0.495005 lr=0.000100 Epoch[092] Batch [1349]/[3760] Speed: 66.010641 samples/sec accuracy=87.406250 loss=0.494758 lr=0.000100 Epoch[092] Batch [1399]/[3760] Speed: 66.480831 samples/sec accuracy=87.429688 loss=0.493601 lr=0.000100 Epoch[092] Batch [1449]/[3760] Speed: 65.710678 samples/sec accuracy=87.461207 loss=0.493315 lr=0.000100 Epoch[092] Batch [1499]/[3760] Speed: 66.051437 samples/sec accuracy=87.464583 loss=0.493478 lr=0.000100 Epoch[092] Batch [1549]/[3760] Speed: 66.136785 samples/sec accuracy=87.416331 loss=0.495054 lr=0.000100 Epoch[092] Batch [1599]/[3760] Speed: 66.501255 samples/sec accuracy=87.416016 loss=0.495424 lr=0.000100 Epoch[092] Batch [1649]/[3760] Speed: 66.146302 samples/sec accuracy=87.428977 loss=0.494584 lr=0.000100 Epoch[092] Batch [1699]/[3760] Speed: 66.091314 samples/sec accuracy=87.446691 loss=0.493955 lr=0.000100 Epoch[092] 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accuracy=87.419034 loss=0.493712 lr=0.000100 Epoch[092] Batch [2249]/[3760] Speed: 66.331855 samples/sec accuracy=87.422917 loss=0.493465 lr=0.000100 Epoch[092] Batch [2299]/[3760] Speed: 65.962257 samples/sec accuracy=87.426630 loss=0.493207 lr=0.000100 Epoch[092] Batch [2349]/[3760] Speed: 65.983045 samples/sec accuracy=87.419548 loss=0.493696 lr=0.000100 Epoch[092] Batch [2399]/[3760] Speed: 65.669700 samples/sec accuracy=87.401042 loss=0.494002 lr=0.000100 Epoch[092] Batch [2449]/[3760] Speed: 66.290660 samples/sec accuracy=87.413903 loss=0.493907 lr=0.000100 Epoch[092] Batch [2499]/[3760] Speed: 65.954707 samples/sec accuracy=87.448125 loss=0.492815 lr=0.000100 Epoch[092] Batch [2549]/[3760] Speed: 66.365086 samples/sec accuracy=87.449142 loss=0.492941 lr=0.000100 Epoch[092] Batch [2599]/[3760] Speed: 65.546840 samples/sec accuracy=87.448918 loss=0.493158 lr=0.000100 Epoch[092] Batch [2649]/[3760] Speed: 65.769142 samples/sec accuracy=87.425708 loss=0.493689 lr=0.000100 Epoch[092] 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accuracy=87.375496 loss=0.494891 lr=0.000100 Epoch[092] Batch [3199]/[3760] Speed: 66.271097 samples/sec accuracy=87.369629 loss=0.495085 lr=0.000100 Epoch[092] Batch [3249]/[3760] Speed: 65.293661 samples/sec accuracy=87.375962 loss=0.494899 lr=0.000100 Epoch[092] Batch [3299]/[3760] Speed: 65.830295 samples/sec accuracy=87.383996 loss=0.494755 lr=0.000100 Epoch[092] Batch [3349]/[3760] Speed: 65.956659 samples/sec accuracy=87.381063 loss=0.494849 lr=0.000100 Epoch[092] Batch [3399]/[3760] Speed: 65.714120 samples/sec accuracy=87.384651 loss=0.494614 lr=0.000100 Epoch[092] Batch [3449]/[3760] Speed: 65.703566 samples/sec accuracy=87.377717 loss=0.494913 lr=0.000100 Epoch[092] Batch [3499]/[3760] Speed: 65.096229 samples/sec accuracy=87.369643 loss=0.495151 lr=0.000100 Epoch[092] Batch [3549]/[3760] Speed: 66.021967 samples/sec accuracy=87.361356 loss=0.495370 lr=0.000100 Epoch[092] Batch [3599]/[3760] Speed: 65.989661 samples/sec accuracy=87.356771 loss=0.495379 lr=0.000100 Epoch[092] Batch [3649]/[3760] Speed: 66.543797 samples/sec accuracy=87.357449 loss=0.495500 lr=0.000100 Epoch[092] Batch [3699]/[3760] Speed: 66.112214 samples/sec accuracy=87.348818 loss=0.495630 lr=0.000100 Epoch[092] Batch [3749]/[3760] Speed: 72.936167 samples/sec accuracy=87.353333 loss=0.495455 lr=0.000100 Batch [0049]/[0303]: acc-top1=67.875000 acc-top5=86.625000 Batch [0099]/[0303]: acc-top1=67.484375 acc-top5=86.453125 Batch [0149]/[0303]: acc-top1=67.718750 acc-top5=86.281250 Batch [0199]/[0303]: acc-top1=67.664062 acc-top5=86.328125 Batch [0249]/[0303]: acc-top1=67.631250 acc-top5=86.393750 Batch [0299]/[0303]: acc-top1=67.937500 acc-top5=86.625000 [Epoch 092] training: accuracy=87.349983 loss=0.495652 [Epoch 092] speed: 65 samples/sec time cost: 3928.064709 [Epoch 092] validation: acc-top1=67.930074 acc-top5=86.623350 loss=1.766523 Epoch[093] Batch [0049]/[3759] Speed: 44.514931 samples/sec accuracy=87.093750 loss=0.488842 lr=0.000100 Epoch[093] Batch [0099]/[3759] Speed: 64.269986 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lr=0.000100 Epoch[093] Batch [0599]/[3759] Speed: 66.493823 samples/sec accuracy=87.291667 loss=0.493914 lr=0.000100 Epoch[093] Batch [0649]/[3759] Speed: 65.595859 samples/sec accuracy=87.281250 loss=0.495009 lr=0.000100 Epoch[093] Batch [0699]/[3759] Speed: 66.137340 samples/sec accuracy=87.272321 loss=0.494360 lr=0.000100 Epoch[093] Batch [0749]/[3759] Speed: 66.031337 samples/sec accuracy=87.262500 loss=0.495554 lr=0.000100 Epoch[093] Batch [0799]/[3759] Speed: 65.716132 samples/sec accuracy=87.251953 loss=0.495503 lr=0.000100 Epoch[093] Batch [0849]/[3759] Speed: 65.629537 samples/sec accuracy=87.233456 loss=0.496733 lr=0.000100 Epoch[093] Batch [0899]/[3759] Speed: 65.940168 samples/sec accuracy=87.239583 loss=0.497315 lr=0.000100 Epoch[093] Batch [0949]/[3759] Speed: 66.381438 samples/sec accuracy=87.251645 loss=0.496677 lr=0.000100 Epoch[093] Batch [0999]/[3759] Speed: 65.620331 samples/sec accuracy=87.278125 loss=0.495292 lr=0.000100 Epoch[093] Batch [1049]/[3759] Speed: 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lr=0.000100 Epoch[093] Batch [1549]/[3759] Speed: 66.246525 samples/sec accuracy=87.369960 loss=0.493729 lr=0.000100 Epoch[093] Batch [1599]/[3759] Speed: 65.975148 samples/sec accuracy=87.375977 loss=0.493418 lr=0.000100 Epoch[093] Batch [1649]/[3759] Speed: 66.314182 samples/sec accuracy=87.330492 loss=0.494779 lr=0.000100 Epoch[093] Batch [1699]/[3759] Speed: 65.119075 samples/sec accuracy=87.343750 loss=0.494611 lr=0.000100 Epoch[093] Batch [1749]/[3759] Speed: 67.367879 samples/sec accuracy=87.340179 loss=0.494894 lr=0.000100 Epoch[093] Batch [1799]/[3759] Speed: 66.179804 samples/sec accuracy=87.345486 loss=0.495327 lr=0.000100 Epoch[093] Batch [1849]/[3759] Speed: 66.482278 samples/sec accuracy=87.336149 loss=0.495836 lr=0.000100 Epoch[093] Batch [1899]/[3759] Speed: 66.385804 samples/sec accuracy=87.329770 loss=0.496296 lr=0.000100 Epoch[093] Batch [1949]/[3759] Speed: 65.412679 samples/sec accuracy=87.306090 loss=0.497188 lr=0.000100 Epoch[093] Batch [1999]/[3759] Speed: 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lr=0.000100 Epoch[093] Batch [2499]/[3759] Speed: 65.836907 samples/sec accuracy=87.393125 loss=0.494718 lr=0.000100 Epoch[093] Batch [2549]/[3759] Speed: 65.597225 samples/sec accuracy=87.387868 loss=0.494869 lr=0.000100 Epoch[093] Batch [2599]/[3759] Speed: 65.708759 samples/sec accuracy=87.400240 loss=0.494761 lr=0.000100 Epoch[093] Batch [2649]/[3759] Speed: 66.196817 samples/sec accuracy=87.412736 loss=0.493994 lr=0.000100 Epoch[093] Batch [2699]/[3759] Speed: 66.079111 samples/sec accuracy=87.399306 loss=0.494650 lr=0.000100 Epoch[093] Batch [2749]/[3759] Speed: 66.008853 samples/sec accuracy=87.418750 loss=0.493912 lr=0.000100 Epoch[093] Batch [2799]/[3759] Speed: 65.202621 samples/sec accuracy=87.427455 loss=0.493611 lr=0.000100 Epoch[093] Batch [2849]/[3759] Speed: 66.274734 samples/sec accuracy=87.430921 loss=0.493583 lr=0.000100 Epoch[093] Batch [2899]/[3759] Speed: 66.042264 samples/sec accuracy=87.426724 loss=0.493726 lr=0.000100 Epoch[093] Batch [2949]/[3759] Speed: 66.129622 samples/sec accuracy=87.443856 loss=0.493452 lr=0.000100 Epoch[093] Batch [2999]/[3759] Speed: 66.146903 samples/sec accuracy=87.449479 loss=0.493091 lr=0.000100 Epoch[093] Batch [3049]/[3759] Speed: 65.666493 samples/sec accuracy=87.434426 loss=0.493329 lr=0.000100 Epoch[093] Batch [3099]/[3759] Speed: 66.212713 samples/sec accuracy=87.445060 loss=0.492776 lr=0.000100 Epoch[093] Batch [3149]/[3759] Speed: 65.574717 samples/sec accuracy=87.441468 loss=0.493058 lr=0.000100 Epoch[093] Batch [3199]/[3759] Speed: 66.486996 samples/sec accuracy=87.433105 loss=0.493315 lr=0.000100 Epoch[093] Batch [3249]/[3759] Speed: 64.856018 samples/sec accuracy=87.432212 loss=0.493314 lr=0.000100 Epoch[093] Batch [3299]/[3759] Speed: 66.584615 samples/sec accuracy=87.430398 loss=0.493516 lr=0.000100 Epoch[093] Batch [3349]/[3759] Speed: 66.159972 samples/sec accuracy=87.442164 loss=0.493460 lr=0.000100 Epoch[093] Batch [3399]/[3759] Speed: 66.188834 samples/sec accuracy=87.437040 loss=0.493576 lr=0.000100 Epoch[093] Batch [3449]/[3759] Speed: 65.753408 samples/sec accuracy=87.433877 loss=0.493266 lr=0.000100 Epoch[093] Batch [3499]/[3759] Speed: 65.939096 samples/sec accuracy=87.432143 loss=0.493171 lr=0.000100 Epoch[093] Batch [3549]/[3759] Speed: 65.942072 samples/sec accuracy=87.423856 loss=0.493421 lr=0.000100 Epoch[093] Batch [3599]/[3759] Speed: 65.849438 samples/sec accuracy=87.403646 loss=0.493807 lr=0.000100 Epoch[093] Batch [3649]/[3759] Speed: 66.301632 samples/sec accuracy=87.407106 loss=0.493693 lr=0.000100 Epoch[093] Batch [3699]/[3759] Speed: 65.767013 samples/sec accuracy=87.415118 loss=0.493352 lr=0.000100 Epoch[093] Batch [3749]/[3759] Speed: 73.451326 samples/sec accuracy=87.411667 loss=0.493382 lr=0.000100 Batch [0049]/[0303]: acc-top1=67.750000 acc-top5=86.218750 Batch [0099]/[0303]: acc-top1=67.656250 acc-top5=86.250000 Batch [0149]/[0303]: acc-top1=67.635417 acc-top5=86.072917 Batch [0199]/[0303]: acc-top1=67.585938 acc-top5=86.156250 Batch [0249]/[0303]: acc-top1=67.506250 acc-top5=86.293750 Batch [0299]/[0303]: acc-top1=67.911458 acc-top5=86.500000 [Epoch 093] training: accuracy=87.410631 loss=0.493475 [Epoch 093] speed: 65 samples/sec time cost: 3931.705718 [Epoch 093] validation: acc-top1=67.914604 acc-top5=86.504744 loss=1.791155 Epoch[094] Batch [0049]/[3760] Speed: 44.226222 samples/sec accuracy=87.281250 loss=0.511305 lr=0.000100 Epoch[094] Batch [0099]/[3760] Speed: 64.272837 samples/sec accuracy=87.328125 loss=0.487705 lr=0.000100 Epoch[094] Batch [0149]/[3760] Speed: 65.451303 samples/sec accuracy=87.229167 loss=0.495464 lr=0.000100 Epoch[094] Batch [0199]/[3760] Speed: 65.328139 samples/sec accuracy=87.187500 loss=0.499169 lr=0.000100 Epoch[094] Batch [0249]/[3760] Speed: 66.463781 samples/sec accuracy=87.281250 loss=0.500940 lr=0.000100 Epoch[094] Batch [0299]/[3760] Speed: 65.699149 samples/sec accuracy=87.109375 loss=0.504065 lr=0.000100 Epoch[094] Batch [0349]/[3760] Speed: 66.410338 samples/sec accuracy=87.267857 loss=0.498351 lr=0.000100 Epoch[094] Batch [0399]/[3760] Speed: 65.462424 samples/sec accuracy=87.179688 loss=0.499593 lr=0.000100 Epoch[094] Batch [0449]/[3760] Speed: 66.042636 samples/sec accuracy=87.184028 loss=0.499081 lr=0.000100 Epoch[094] Batch [0499]/[3760] Speed: 66.002125 samples/sec accuracy=87.225000 loss=0.497438 lr=0.000100 Epoch[094] Batch [0549]/[3760] Speed: 65.955749 samples/sec accuracy=87.323864 loss=0.495288 lr=0.000100 Epoch[094] Batch [0599]/[3760] Speed: 66.187495 samples/sec accuracy=87.328125 loss=0.496006 lr=0.000100 Epoch[094] Batch [0649]/[3760] Speed: 65.957280 samples/sec accuracy=87.360577 loss=0.494638 lr=0.000100 Epoch[094] Batch [0699]/[3760] Speed: 66.547646 samples/sec accuracy=87.359375 loss=0.495455 lr=0.000100 Epoch[094] Batch [0749]/[3760] Speed: 65.819561 samples/sec accuracy=87.452083 loss=0.492275 lr=0.000100 Epoch[094] Batch [0799]/[3760] Speed: 65.221442 samples/sec accuracy=87.513672 loss=0.491323 lr=0.000100 Epoch[094] 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accuracy=87.509615 loss=0.489642 lr=0.000100 Epoch[094] Batch [1349]/[3760] Speed: 65.897626 samples/sec accuracy=87.547454 loss=0.489109 lr=0.000100 Epoch[094] Batch [1399]/[3760] Speed: 66.374330 samples/sec accuracy=87.590402 loss=0.487492 lr=0.000100 Epoch[094] Batch [1449]/[3760] Speed: 66.196823 samples/sec accuracy=87.609914 loss=0.486895 lr=0.000100 Epoch[094] Batch [1499]/[3760] Speed: 65.613211 samples/sec accuracy=87.617708 loss=0.486289 lr=0.000100 Epoch[094] Batch [1549]/[3760] Speed: 66.314561 samples/sec accuracy=87.604839 loss=0.487009 lr=0.000100 Epoch[094] Batch [1599]/[3760] Speed: 65.722229 samples/sec accuracy=87.590820 loss=0.487504 lr=0.000100 Epoch[094] Batch [1649]/[3760] Speed: 66.553612 samples/sec accuracy=87.580492 loss=0.487514 lr=0.000100 Epoch[094] Batch [1699]/[3760] Speed: 65.645073 samples/sec accuracy=87.560662 loss=0.488293 lr=0.000100 Epoch[094] Batch [1749]/[3760] Speed: 65.742194 samples/sec accuracy=87.550000 loss=0.488724 lr=0.000100 Epoch[094] 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accuracy=87.543750 loss=0.488990 lr=0.000100 Epoch[094] Batch [2299]/[3760] Speed: 66.215191 samples/sec accuracy=87.549592 loss=0.488827 lr=0.000100 Epoch[094] Batch [2349]/[3760] Speed: 65.179739 samples/sec accuracy=87.535239 loss=0.489364 lr=0.000100 Epoch[094] Batch [2399]/[3760] Speed: 66.472599 samples/sec accuracy=87.546875 loss=0.488789 lr=0.000100 Epoch[094] Batch [2449]/[3760] Speed: 65.861243 samples/sec accuracy=87.566964 loss=0.488152 lr=0.000100 Epoch[094] Batch [2499]/[3760] Speed: 66.187509 samples/sec accuracy=87.558125 loss=0.488147 lr=0.000100 Epoch[094] Batch [2549]/[3760] Speed: 66.490542 samples/sec accuracy=87.534314 loss=0.488462 lr=0.000100 Epoch[094] Batch [2599]/[3760] Speed: 65.361996 samples/sec accuracy=87.539663 loss=0.488230 lr=0.000100 Epoch[094] Batch [2649]/[3760] Speed: 65.664486 samples/sec accuracy=87.545991 loss=0.488290 lr=0.000100 Epoch[094] Batch [2699]/[3760] Speed: 66.198286 samples/sec accuracy=87.544560 loss=0.488528 lr=0.000100 Epoch[094] 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accuracy=87.528809 loss=0.487851 lr=0.000100 Epoch[094] Batch [3249]/[3760] Speed: 65.115462 samples/sec accuracy=87.536538 loss=0.487727 lr=0.000100 Epoch[094] Batch [3299]/[3760] Speed: 65.664582 samples/sec accuracy=87.534564 loss=0.487676 lr=0.000100 Epoch[094] Batch [3349]/[3760] Speed: 66.685435 samples/sec accuracy=87.548507 loss=0.487322 lr=0.000100 Epoch[094] Batch [3399]/[3760] Speed: 65.844083 samples/sec accuracy=87.544577 loss=0.487699 lr=0.000100 Epoch[094] Batch [3449]/[3760] Speed: 64.662860 samples/sec accuracy=87.551630 loss=0.487384 lr=0.000100 Epoch[094] Batch [3499]/[3760] Speed: 66.650951 samples/sec accuracy=87.545982 loss=0.487393 lr=0.000100 Epoch[094] Batch [3549]/[3760] Speed: 65.750387 samples/sec accuracy=87.544894 loss=0.487136 lr=0.000100 Epoch[094] Batch [3599]/[3760] Speed: 65.768907 samples/sec accuracy=87.548611 loss=0.487027 lr=0.000100 Epoch[094] Batch [3649]/[3760] Speed: 65.934113 samples/sec accuracy=87.542380 loss=0.487090 lr=0.000100 Epoch[094] Batch [3699]/[3760] Speed: 65.410162 samples/sec accuracy=87.539696 loss=0.487413 lr=0.000100 Epoch[094] Batch [3749]/[3760] Speed: 72.953044 samples/sec accuracy=87.525417 loss=0.487862 lr=0.000100 Batch [0049]/[0303]: acc-top1=67.312500 acc-top5=86.125000 Batch [0099]/[0303]: acc-top1=67.109375 acc-top5=86.218750 Batch [0149]/[0303]: acc-top1=67.218750 acc-top5=86.052083 Batch [0199]/[0303]: acc-top1=67.187500 acc-top5=86.148438 Batch [0249]/[0303]: acc-top1=67.193750 acc-top5=86.150000 Batch [0299]/[0303]: acc-top1=67.598958 acc-top5=86.302083 [Epoch 094] training: accuracy=87.527842 loss=0.487798 [Epoch 094] speed: 65 samples/sec time cost: 3936.165714 [Epoch 094] validation: acc-top1=67.594884 acc-top5=86.313944 loss=1.791224 Epoch[095] Batch [0049]/[3760] Speed: 44.495525 samples/sec accuracy=87.093750 loss=0.493983 lr=0.000100 Epoch[095] Batch [0099]/[3760] Speed: 64.765088 samples/sec accuracy=87.515625 loss=0.478774 lr=0.000100 Epoch[095] Batch [0149]/[3760] Speed: 65.359159 samples/sec accuracy=87.906250 loss=0.469224 lr=0.000100 Epoch[095] Batch [0199]/[3760] Speed: 64.796745 samples/sec accuracy=87.632812 loss=0.482415 lr=0.000100 Epoch[095] Batch [0249]/[3760] Speed: 66.735858 samples/sec accuracy=87.675000 loss=0.484813 lr=0.000100 Epoch[095] Batch [0299]/[3760] Speed: 65.425352 samples/sec accuracy=87.739583 loss=0.484600 lr=0.000100 Epoch[095] Batch [0349]/[3760] Speed: 66.071813 samples/sec accuracy=87.705357 loss=0.482997 lr=0.000100 Epoch[095] Batch [0399]/[3760] Speed: 65.193684 samples/sec accuracy=87.703125 loss=0.480003 lr=0.000100 Epoch[095] Batch [0449]/[3760] Speed: 65.952653 samples/sec accuracy=87.652778 loss=0.481521 lr=0.000100 Epoch[095] Batch [0499]/[3760] Speed: 65.986060 samples/sec accuracy=87.793750 loss=0.476747 lr=0.000100 Epoch[095] Batch [0549]/[3760] Speed: 66.399788 samples/sec accuracy=87.767045 loss=0.477900 lr=0.000100 Epoch[095] Batch [0599]/[3760] Speed: 65.561484 samples/sec accuracy=87.729167 loss=0.477881 lr=0.000100 Epoch[095] Batch [0649]/[3760] Speed: 66.207097 samples/sec accuracy=87.716346 loss=0.478900 lr=0.000100 Epoch[095] Batch [0699]/[3760] Speed: 65.681124 samples/sec accuracy=87.683036 loss=0.480184 lr=0.000100 Epoch[095] Batch [0749]/[3760] Speed: 65.616780 samples/sec accuracy=87.727083 loss=0.478707 lr=0.000100 Epoch[095] Batch [0799]/[3760] Speed: 65.446593 samples/sec accuracy=87.738281 loss=0.478744 lr=0.000100 Epoch[095] Batch [0849]/[3760] Speed: 66.467813 samples/sec accuracy=87.729779 loss=0.479634 lr=0.000100 Epoch[095] Batch [0899]/[3760] Speed: 65.740034 samples/sec accuracy=87.699653 loss=0.480705 lr=0.000100 Epoch[095] Batch [0949]/[3760] Speed: 66.002664 samples/sec accuracy=87.751645 loss=0.478824 lr=0.000100 Epoch[095] Batch [0999]/[3760] Speed: 66.070561 samples/sec accuracy=87.720312 loss=0.479269 lr=0.000100 Epoch[095] Batch [1049]/[3760] Speed: 65.866299 samples/sec accuracy=87.669643 loss=0.481112 lr=0.000100 Epoch[095] Batch [1099]/[3760] Speed: 65.762086 samples/sec accuracy=87.670455 loss=0.481663 lr=0.000100 Epoch[095] Batch [1149]/[3760] Speed: 66.168789 samples/sec accuracy=87.686141 loss=0.481619 lr=0.000100 Epoch[095] Batch [1199]/[3760] Speed: 65.875691 samples/sec accuracy=87.695312 loss=0.481959 lr=0.000100 Epoch[095] Batch [1249]/[3760] Speed: 65.661399 samples/sec accuracy=87.692500 loss=0.481244 lr=0.000100 Epoch[095] Batch [1299]/[3760] Speed: 66.272381 samples/sec accuracy=87.703125 loss=0.481202 lr=0.000100 Epoch[095] Batch [1349]/[3760] Speed: 66.095157 samples/sec accuracy=87.731481 loss=0.480399 lr=0.000100 Epoch[095] Batch [1399]/[3760] Speed: 66.354402 samples/sec accuracy=87.745536 loss=0.480871 lr=0.000100 Epoch[095] Batch [1449]/[3760] Speed: 65.618947 samples/sec accuracy=87.745690 loss=0.480880 lr=0.000100 Epoch[095] Batch [1499]/[3760] Speed: 65.525942 samples/sec accuracy=87.739583 loss=0.481314 lr=0.000100 Epoch[095] Batch [1549]/[3760] Speed: 66.275111 samples/sec accuracy=87.711694 loss=0.482854 lr=0.000100 Epoch[095] Batch [1599]/[3760] Speed: 65.814075 samples/sec accuracy=87.735352 loss=0.482364 lr=0.000100 Epoch[095] Batch [1649]/[3760] Speed: 66.470564 samples/sec accuracy=87.737689 loss=0.482041 lr=0.000100 Epoch[095] Batch [1699]/[3760] Speed: 65.284850 samples/sec accuracy=87.763787 loss=0.481566 lr=0.000100 Epoch[095] Batch [1749]/[3760] Speed: 66.160945 samples/sec accuracy=87.776786 loss=0.481234 lr=0.000100 Epoch[095] Batch [1799]/[3760] Speed: 65.722587 samples/sec accuracy=87.789062 loss=0.481005 lr=0.000100 Epoch[095] Batch [1849]/[3760] Speed: 65.872905 samples/sec accuracy=87.782095 loss=0.481255 lr=0.000100 Epoch[095] Batch [1899]/[3760] Speed: 66.365775 samples/sec accuracy=87.768092 loss=0.481575 lr=0.000100 Epoch[095] Batch [1949]/[3760] Speed: 65.958789 samples/sec accuracy=87.741186 loss=0.482923 lr=0.000100 Epoch[095] Batch [1999]/[3760] Speed: 66.019318 samples/sec accuracy=87.742188 loss=0.482795 lr=0.000100 Epoch[095] Batch [2049]/[3760] Speed: 65.733833 samples/sec accuracy=87.729421 loss=0.483496 lr=0.000100 Epoch[095] Batch [2099]/[3760] Speed: 65.528812 samples/sec accuracy=87.731399 loss=0.483077 lr=0.000100 Epoch[095] Batch [2149]/[3760] Speed: 65.531276 samples/sec accuracy=87.706395 loss=0.483646 lr=0.000100 Epoch[095] Batch [2199]/[3760] Speed: 66.130400 samples/sec accuracy=87.688920 loss=0.483871 lr=0.000100 Epoch[095] Batch [2249]/[3760] Speed: 66.005472 samples/sec accuracy=87.691667 loss=0.483531 lr=0.000100 Epoch[095] Batch [2299]/[3760] Speed: 66.273232 samples/sec accuracy=87.691576 loss=0.483876 lr=0.000100 Epoch[095] Batch [2349]/[3760] Speed: 66.153717 samples/sec accuracy=87.706117 loss=0.483725 lr=0.000100 Epoch[095] Batch [2399]/[3760] Speed: 65.497485 samples/sec accuracy=87.697266 loss=0.484035 lr=0.000100 Epoch[095] Batch [2449]/[3760] Speed: 65.986048 samples/sec accuracy=87.684949 loss=0.484059 lr=0.000100 Epoch[095] Batch [2499]/[3760] Speed: 66.367217 samples/sec accuracy=87.705625 loss=0.483330 lr=0.000100 Epoch[095] Batch [2549]/[3760] Speed: 65.802807 samples/sec accuracy=87.698529 loss=0.483389 lr=0.000100 Epoch[095] Batch [2599]/[3760] Speed: 65.247568 samples/sec accuracy=87.706731 loss=0.483230 lr=0.000100 Epoch[095] Batch [2649]/[3760] Speed: 65.707838 samples/sec accuracy=87.708726 loss=0.482875 lr=0.000100 Epoch[095] Batch [2699]/[3760] Speed: 66.490245 samples/sec accuracy=87.712384 loss=0.482452 lr=0.000100 Epoch[095] Batch [2749]/[3760] Speed: 65.710031 samples/sec accuracy=87.701136 loss=0.482652 lr=0.000100 Epoch[095] Batch [2799]/[3760] Speed: 66.267606 samples/sec accuracy=87.708705 loss=0.482632 lr=0.000100 Epoch[095] Batch [2849]/[3760] Speed: 65.699033 samples/sec accuracy=87.702851 loss=0.482846 lr=0.000100 Epoch[095] Batch [2899]/[3760] Speed: 66.439526 samples/sec accuracy=87.692888 loss=0.483037 lr=0.000100 Epoch[095] Batch [2949]/[3760] Speed: 65.761097 samples/sec accuracy=87.698623 loss=0.482705 lr=0.000100 Epoch[095] Batch [2999]/[3760] Speed: 65.770216 samples/sec accuracy=87.688021 loss=0.483021 lr=0.000100 Epoch[095] Batch [3049]/[3760] Speed: 65.489509 samples/sec accuracy=87.686988 loss=0.482992 lr=0.000100 Epoch[095] Batch [3099]/[3760] Speed: 65.852202 samples/sec accuracy=87.683468 loss=0.482914 lr=0.000100 Epoch[095] Batch [3149]/[3760] Speed: 66.258171 samples/sec accuracy=87.683532 loss=0.483175 lr=0.000100 Epoch[095] Batch [3199]/[3760] Speed: 65.703665 samples/sec accuracy=87.664551 loss=0.483901 lr=0.000100 Epoch[095] Batch [3249]/[3760] Speed: 66.187608 samples/sec accuracy=87.662500 loss=0.483854 lr=0.000100 Epoch[095] Batch [3299]/[3760] Speed: 65.466154 samples/sec accuracy=87.667614 loss=0.484022 lr=0.000100 Epoch[095] Batch [3349]/[3760] Speed: 65.934232 samples/sec accuracy=87.671175 loss=0.483941 lr=0.000100 Epoch[095] Batch [3399]/[3760] Speed: 65.809481 samples/sec accuracy=87.657629 loss=0.484284 lr=0.000100 Epoch[095] Batch [3449]/[3760] Speed: 66.443950 samples/sec accuracy=87.646739 loss=0.484665 lr=0.000100 Epoch[095] Batch [3499]/[3760] Speed: 65.653276 samples/sec accuracy=87.659375 loss=0.484517 lr=0.000100 Epoch[095] Batch [3549]/[3760] Speed: 66.065885 samples/sec accuracy=87.640405 loss=0.485193 lr=0.000100 Epoch[095] Batch [3599]/[3760] Speed: 66.295366 samples/sec accuracy=87.635417 loss=0.485298 lr=0.000100 Epoch[095] Batch [3649]/[3760] Speed: 65.533475 samples/sec accuracy=87.625856 loss=0.485725 lr=0.000100 Epoch[095] Batch [3699]/[3760] Speed: 65.398503 samples/sec accuracy=87.632179 loss=0.485524 lr=0.000100 Epoch[095] Batch [3749]/[3760] Speed: 73.876328 samples/sec accuracy=87.632083 loss=0.485675 lr=0.000100 Batch [0049]/[0303]: acc-top1=67.062500 acc-top5=86.312500 Batch [0099]/[0303]: acc-top1=67.140625 acc-top5=86.296875 Batch [0149]/[0303]: acc-top1=67.458333 acc-top5=86.166667 Batch [0199]/[0303]: acc-top1=67.429688 acc-top5=86.203125 Batch [0249]/[0303]: acc-top1=67.475000 acc-top5=86.262500 Batch [0299]/[0303]: acc-top1=67.776042 acc-top5=86.494792 [Epoch 095] training: accuracy=87.637965 loss=0.485404 [Epoch 095] speed: 65 samples/sec time cost: 3939.347159 [Epoch 095] validation: acc-top1=67.780528 acc-top5=86.494431 loss=1.798365 Epoch[096] Batch [0049]/[3759] Speed: 44.047301 samples/sec accuracy=87.406250 loss=0.462416 lr=0.000100 Epoch[096] Batch [0099]/[3759] Speed: 64.616344 samples/sec accuracy=87.125000 loss=0.481701 lr=0.000100 Epoch[096] Batch [0149]/[3759] Speed: 65.713405 samples/sec accuracy=87.145833 loss=0.485349 lr=0.000100 Epoch[096] Batch [0199]/[3759] Speed: 64.826800 samples/sec accuracy=87.164062 loss=0.487096 lr=0.000100 Epoch[096] Batch [0249]/[3759] Speed: 66.444513 samples/sec accuracy=87.262500 loss=0.486988 lr=0.000100 Epoch[096] Batch [0299]/[3759] Speed: 66.007410 samples/sec accuracy=87.234375 loss=0.490348 lr=0.000100 Epoch[096] Batch [0349]/[3759] Speed: 66.195198 samples/sec accuracy=87.267857 loss=0.487434 lr=0.000100 Epoch[096] Batch [0399]/[3759] Speed: 65.438227 samples/sec accuracy=87.367188 loss=0.485854 lr=0.000100 Epoch[096] Batch [0449]/[3759] Speed: 65.914605 samples/sec accuracy=87.423611 loss=0.483710 lr=0.000100 Epoch[096] Batch [0499]/[3759] Speed: 66.422489 samples/sec accuracy=87.490625 loss=0.482061 lr=0.000100 Epoch[096] Batch [0549]/[3759] Speed: 66.102951 samples/sec accuracy=87.502841 loss=0.482593 lr=0.000100 Epoch[096] Batch [0599]/[3759] Speed: 66.032504 samples/sec accuracy=87.567708 loss=0.480716 lr=0.000100 Epoch[096] Batch [0649]/[3759] Speed: 65.474643 samples/sec accuracy=87.538462 loss=0.481757 lr=0.000100 Epoch[096] Batch [0699]/[3759] Speed: 66.043984 samples/sec accuracy=87.604911 loss=0.479692 lr=0.000100 Epoch[096] Batch [0749]/[3759] Speed: 65.878886 samples/sec accuracy=87.650000 loss=0.477082 lr=0.000100 Epoch[096] Batch [0799]/[3759] Speed: 65.533633 samples/sec accuracy=87.626953 loss=0.477877 lr=0.000100 Epoch[096] Batch [0849]/[3759] Speed: 65.821079 samples/sec accuracy=87.604779 loss=0.478807 lr=0.000100 Epoch[096] 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accuracy=87.709491 loss=0.477781 lr=0.000100 Epoch[096] Batch [1399]/[3759] Speed: 65.867457 samples/sec accuracy=87.706473 loss=0.478362 lr=0.000100 Epoch[096] Batch [1449]/[3759] Speed: 66.509325 samples/sec accuracy=87.730603 loss=0.477643 lr=0.000100 Epoch[096] Batch [1499]/[3759] Speed: 66.166033 samples/sec accuracy=87.751042 loss=0.477436 lr=0.000100 Epoch[096] Batch [1549]/[3759] Speed: 65.747633 samples/sec accuracy=87.754032 loss=0.477698 lr=0.000100 Epoch[096] Batch [1599]/[3759] Speed: 66.299957 samples/sec accuracy=87.767578 loss=0.477477 lr=0.000100 Epoch[096] Batch [1649]/[3759] Speed: 65.704878 samples/sec accuracy=87.758523 loss=0.477491 lr=0.000100 Epoch[096] Batch [1699]/[3759] Speed: 66.199021 samples/sec accuracy=87.759191 loss=0.477690 lr=0.000100 Epoch[096] Batch [1749]/[3759] Speed: 65.469706 samples/sec accuracy=87.762500 loss=0.477724 lr=0.000100 Epoch[096] Batch [1799]/[3759] Speed: 66.049915 samples/sec accuracy=87.755208 loss=0.477887 lr=0.000100 Epoch[096] 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accuracy=87.713315 loss=0.479826 lr=0.000100 Epoch[096] Batch [2349]/[3759] Speed: 66.157992 samples/sec accuracy=87.722739 loss=0.479467 lr=0.000100 Epoch[096] Batch [2399]/[3759] Speed: 66.339279 samples/sec accuracy=87.726562 loss=0.479314 lr=0.000100 Epoch[096] Batch [2449]/[3759] Speed: 65.781842 samples/sec accuracy=87.716199 loss=0.479985 lr=0.000100 Epoch[096] Batch [2499]/[3759] Speed: 66.185529 samples/sec accuracy=87.726875 loss=0.479680 lr=0.000100 Epoch[096] Batch [2549]/[3759] Speed: 66.026273 samples/sec accuracy=87.717525 loss=0.480017 lr=0.000100 Epoch[096] Batch [2599]/[3759] Speed: 66.071363 samples/sec accuracy=87.702524 loss=0.481024 lr=0.000100 Epoch[096] Batch [2649]/[3759] Speed: 64.890037 samples/sec accuracy=87.704599 loss=0.481163 lr=0.000100 Epoch[096] Batch [2699]/[3759] Speed: 66.711507 samples/sec accuracy=87.707176 loss=0.481137 lr=0.000100 Epoch[096] Batch [2749]/[3759] Speed: 65.649611 samples/sec accuracy=87.706818 loss=0.481332 lr=0.000100 Epoch[096] 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accuracy=87.681250 loss=0.481890 lr=0.000100 Epoch[096] Batch [3299]/[3759] Speed: 66.197803 samples/sec accuracy=87.680398 loss=0.481969 lr=0.000100 Epoch[096] Batch [3349]/[3759] Speed: 65.747026 samples/sec accuracy=87.680504 loss=0.481910 lr=0.000100 Epoch[096] Batch [3399]/[3759] Speed: 66.110978 samples/sec accuracy=87.670956 loss=0.482513 lr=0.000100 Epoch[096] Batch [3449]/[3759] Speed: 66.079393 samples/sec accuracy=87.678442 loss=0.482533 lr=0.000100 Epoch[096] Batch [3499]/[3759] Speed: 65.875454 samples/sec accuracy=87.674554 loss=0.482763 lr=0.000100 Epoch[096] Batch [3549]/[3759] Speed: 65.410776 samples/sec accuracy=87.678257 loss=0.482748 lr=0.000100 Epoch[096] Batch [3599]/[3759] Speed: 66.417171 samples/sec accuracy=87.688802 loss=0.482648 lr=0.000100 Epoch[096] Batch [3649]/[3759] Speed: 65.730214 samples/sec accuracy=87.696918 loss=0.482675 lr=0.000100 Epoch[096] Batch [3699]/[3759] Speed: 66.327061 samples/sec accuracy=87.676098 loss=0.483487 lr=0.000100 Epoch[096] Batch [3749]/[3759] Speed: 74.009224 samples/sec accuracy=87.661250 loss=0.484199 lr=0.000100 Batch [0049]/[0303]: acc-top1=67.218750 acc-top5=86.281250 Batch [0099]/[0303]: acc-top1=67.390625 acc-top5=86.140625 Batch [0149]/[0303]: acc-top1=67.562500 acc-top5=86.052083 Batch [0199]/[0303]: acc-top1=67.460938 acc-top5=86.164062 Batch [0249]/[0303]: acc-top1=67.493750 acc-top5=86.181250 Batch [0299]/[0303]: acc-top1=67.791667 acc-top5=86.421875 [Epoch 096] training: accuracy=87.657954 loss=0.484221 [Epoch 096] speed: 65 samples/sec time cost: 3937.996727 [Epoch 096] validation: acc-top1=67.795998 acc-top5=86.437706 loss=1.784224 Epoch[097] Batch [0049]/[3760] Speed: 44.135257 samples/sec accuracy=86.937500 loss=0.515885 lr=0.000100 Epoch[097] Batch [0099]/[3760] Speed: 64.271870 samples/sec accuracy=87.359375 loss=0.498178 lr=0.000100 Epoch[097] Batch [0149]/[3760] Speed: 66.155439 samples/sec accuracy=87.812500 loss=0.482173 lr=0.000100 Epoch[097] Batch [0199]/[3760] Speed: 65.193984 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lr=0.000100 Epoch[097] Batch [1649]/[3760] Speed: 66.716856 samples/sec accuracy=87.660985 loss=0.483011 lr=0.000100 Epoch[097] Batch [1699]/[3760] Speed: 65.925003 samples/sec accuracy=87.673713 loss=0.482582 lr=0.000100 Epoch[097] Batch [1749]/[3760] Speed: 65.941066 samples/sec accuracy=87.685714 loss=0.482497 lr=0.000100 Epoch[097] Batch [1799]/[3760] Speed: 66.386387 samples/sec accuracy=87.679688 loss=0.482083 lr=0.000100 Epoch[097] Batch [1849]/[3760] Speed: 65.944445 samples/sec accuracy=87.684966 loss=0.482370 lr=0.000100 Epoch[097] Batch [1899]/[3760] Speed: 65.607290 samples/sec accuracy=87.673520 loss=0.482446 lr=0.000100 Epoch[097] Batch [1949]/[3760] Speed: 66.358478 samples/sec accuracy=87.672276 loss=0.482783 lr=0.000100 Epoch[097] Batch [1999]/[3760] Speed: 65.915307 samples/sec accuracy=87.683594 loss=0.482240 lr=0.000100 Epoch[097] Batch [2049]/[3760] Speed: 65.828170 samples/sec accuracy=87.673780 loss=0.482569 lr=0.000100 Epoch[097] Batch [2099]/[3760] Speed: 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lr=0.000100 Epoch[097] Batch [2599]/[3760] Speed: 66.130154 samples/sec accuracy=87.686899 loss=0.480949 lr=0.000100 Epoch[097] Batch [2649]/[3760] Speed: 66.350524 samples/sec accuracy=87.682783 loss=0.480967 lr=0.000100 Epoch[097] Batch [2699]/[3760] Speed: 66.136081 samples/sec accuracy=87.686343 loss=0.480696 lr=0.000100 Epoch[097] Batch [2749]/[3760] Speed: 65.336316 samples/sec accuracy=87.682955 loss=0.480810 lr=0.000100 Epoch[097] Batch [2799]/[3760] Speed: 65.951255 samples/sec accuracy=87.677455 loss=0.480946 lr=0.000100 Epoch[097] Batch [2849]/[3760] Speed: 65.846367 samples/sec accuracy=87.674890 loss=0.481065 lr=0.000100 Epoch[097] Batch [2899]/[3760] Speed: 66.402415 samples/sec accuracy=87.677802 loss=0.480628 lr=0.000100 Epoch[097] Batch [2949]/[3760] Speed: 65.364338 samples/sec accuracy=87.672669 loss=0.480716 lr=0.000100 Epoch[097] Batch [2999]/[3760] Speed: 65.480462 samples/sec accuracy=87.676042 loss=0.480443 lr=0.000100 Epoch[097] Batch [3049]/[3760] Speed: 65.547561 samples/sec accuracy=87.677254 loss=0.480417 lr=0.000100 Epoch[097] Batch [3099]/[3760] Speed: 66.142267 samples/sec accuracy=87.676915 loss=0.480525 lr=0.000100 Epoch[097] Batch [3149]/[3760] Speed: 65.938286 samples/sec accuracy=87.690972 loss=0.480115 lr=0.000100 Epoch[097] Batch [3199]/[3760] Speed: 65.234009 samples/sec accuracy=87.672852 loss=0.480500 lr=0.000100 Epoch[097] Batch [3249]/[3760] Speed: 66.414096 samples/sec accuracy=87.664904 loss=0.480802 lr=0.000100 Epoch[097] Batch [3299]/[3760] Speed: 66.084926 samples/sec accuracy=87.673295 loss=0.480333 lr=0.000100 Epoch[097] Batch [3349]/[3760] Speed: 66.319348 samples/sec accuracy=87.669776 loss=0.480677 lr=0.000100 Epoch[097] Batch [3399]/[3760] Speed: 65.935633 samples/sec accuracy=87.668199 loss=0.480826 lr=0.000100 Epoch[097] Batch [3449]/[3760] Speed: 65.181661 samples/sec accuracy=87.661685 loss=0.481049 lr=0.000100 Epoch[097] Batch [3499]/[3760] Speed: 66.178897 samples/sec accuracy=87.656696 loss=0.481080 lr=0.000100 Epoch[097] Batch [3549]/[3760] Speed: 66.069976 samples/sec accuracy=87.672535 loss=0.480425 lr=0.000100 Epoch[097] Batch [3599]/[3760] Speed: 66.304039 samples/sec accuracy=87.676215 loss=0.480399 lr=0.000100 Epoch[097] Batch [3649]/[3760] Speed: 65.894092 samples/sec accuracy=87.678510 loss=0.480280 lr=0.000100 Epoch[097] Batch [3699]/[3760] Speed: 65.592427 samples/sec accuracy=87.676098 loss=0.480665 lr=0.000100 Epoch[097] Batch [3749]/[3760] Speed: 73.503782 samples/sec accuracy=87.683333 loss=0.480246 lr=0.000100 Batch [0049]/[0303]: acc-top1=67.468750 acc-top5=86.281250 Batch [0099]/[0303]: acc-top1=67.312500 acc-top5=86.234375 Batch [0149]/[0303]: acc-top1=67.489583 acc-top5=86.052083 Batch [0199]/[0303]: acc-top1=67.382812 acc-top5=86.078125 Batch [0249]/[0303]: acc-top1=67.412500 acc-top5=86.118750 Batch [0299]/[0303]: acc-top1=67.718750 acc-top5=86.328125 [Epoch 097] training: accuracy=87.678690 loss=0.480307 [Epoch 097] speed: 65 samples/sec time cost: 3939.356797 [Epoch 097] validation: acc-top1=67.713490 acc-top5=86.329414 loss=1.817835 Epoch[098] Batch [0049]/[3759] Speed: 44.263748 samples/sec accuracy=86.875000 loss=0.516056 lr=0.000100 Epoch[098] Batch [0099]/[3759] Speed: 64.680338 samples/sec accuracy=87.000000 loss=0.508321 lr=0.000100 Epoch[098] Batch [0149]/[3759] Speed: 65.341165 samples/sec accuracy=87.197917 loss=0.500404 lr=0.000100 Epoch[098] Batch [0199]/[3759] Speed: 64.555210 samples/sec accuracy=87.328125 loss=0.498615 lr=0.000100 Epoch[098] Batch [0249]/[3759] Speed: 66.851493 samples/sec accuracy=87.443750 loss=0.495199 lr=0.000100 Epoch[098] Batch [0299]/[3759] Speed: 65.027597 samples/sec accuracy=87.536458 loss=0.492986 lr=0.000100 Epoch[098] Batch [0349]/[3759] Speed: 65.898995 samples/sec accuracy=87.473214 loss=0.494867 lr=0.000100 Epoch[098] Batch [0399]/[3759] Speed: 66.167591 samples/sec accuracy=87.460938 loss=0.494331 lr=0.000100 Epoch[098] Batch [0449]/[3759] Speed: 65.714179 samples/sec accuracy=87.472222 loss=0.493141 lr=0.000100 Epoch[098] Batch [0499]/[3759] Speed: 65.995897 samples/sec accuracy=87.496875 loss=0.490896 lr=0.000100 Epoch[098] Batch [0549]/[3759] Speed: 66.003155 samples/sec accuracy=87.571023 loss=0.488258 lr=0.000100 Epoch[098] Batch [0599]/[3759] Speed: 65.529839 samples/sec accuracy=87.606771 loss=0.487118 lr=0.000100 Epoch[098] Batch [0649]/[3759] Speed: 65.884845 samples/sec accuracy=87.634615 loss=0.486298 lr=0.000100 Epoch[098] Batch [0699]/[3759] Speed: 65.983178 samples/sec accuracy=87.660714 loss=0.485686 lr=0.000100 Epoch[098] Batch [0749]/[3759] Speed: 66.089187 samples/sec accuracy=87.714583 loss=0.484665 lr=0.000100 Epoch[098] Batch [0799]/[3759] Speed: 65.328750 samples/sec accuracy=87.744141 loss=0.483639 lr=0.000100 Epoch[098] Batch [0849]/[3759] Speed: 66.604588 samples/sec accuracy=87.735294 loss=0.482788 lr=0.000100 Epoch[098] Batch [0899]/[3759] Speed: 66.038883 samples/sec accuracy=87.711806 loss=0.484553 lr=0.000100 Epoch[098] 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accuracy=87.677455 loss=0.484545 lr=0.000100 Epoch[098] Batch [1449]/[3759] Speed: 65.892871 samples/sec accuracy=87.679957 loss=0.484520 lr=0.000100 Epoch[098] Batch [1499]/[3759] Speed: 65.733104 samples/sec accuracy=87.680208 loss=0.484264 lr=0.000100 Epoch[098] Batch [1549]/[3759] Speed: 66.282023 samples/sec accuracy=87.655242 loss=0.485054 lr=0.000100 Epoch[098] Batch [1599]/[3759] Speed: 66.077466 samples/sec accuracy=87.652344 loss=0.484941 lr=0.000100 Epoch[098] Batch [1649]/[3759] Speed: 66.156980 samples/sec accuracy=87.645833 loss=0.485337 lr=0.000100 Epoch[098] Batch [1699]/[3759] Speed: 66.067530 samples/sec accuracy=87.671875 loss=0.484035 lr=0.000100 Epoch[098] Batch [1749]/[3759] Speed: 65.664738 samples/sec accuracy=87.668750 loss=0.484353 lr=0.000100 Epoch[098] Batch [1799]/[3759] Speed: 65.773507 samples/sec accuracy=87.692708 loss=0.483404 lr=0.000100 Epoch[098] Batch [1849]/[3759] Speed: 66.341529 samples/sec accuracy=87.681588 loss=0.483890 lr=0.000100 Epoch[098] 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accuracy=87.682181 loss=0.483899 lr=0.000100 Epoch[098] Batch [2399]/[3759] Speed: 66.228159 samples/sec accuracy=87.678385 loss=0.483645 lr=0.000100 Epoch[098] Batch [2449]/[3759] Speed: 65.449320 samples/sec accuracy=87.677296 loss=0.483694 lr=0.000100 Epoch[098] Batch [2499]/[3759] Speed: 66.271910 samples/sec accuracy=87.695000 loss=0.482908 lr=0.000100 Epoch[098] Batch [2549]/[3759] Speed: 66.016503 samples/sec accuracy=87.698529 loss=0.482685 lr=0.000100 Epoch[098] Batch [2599]/[3759] Speed: 66.515449 samples/sec accuracy=87.695913 loss=0.482642 lr=0.000100 Epoch[098] Batch [2649]/[3759] Speed: 65.186831 samples/sec accuracy=87.696344 loss=0.482377 lr=0.000100 Epoch[098] Batch [2699]/[3759] Speed: 66.084878 samples/sec accuracy=87.701968 loss=0.482491 lr=0.000100 Epoch[098] Batch [2749]/[3759] Speed: 66.215280 samples/sec accuracy=87.688636 loss=0.483033 lr=0.000100 Epoch[098] Batch [2799]/[3759] Speed: 66.231414 samples/sec accuracy=87.699219 loss=0.482684 lr=0.000100 Epoch[098] 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accuracy=87.705492 loss=0.481660 lr=0.000100 Epoch[098] Batch [3349]/[3759] Speed: 65.674117 samples/sec accuracy=87.700560 loss=0.482044 lr=0.000100 Epoch[098] Batch [3399]/[3759] Speed: 65.554481 samples/sec accuracy=87.689798 loss=0.482264 lr=0.000100 Epoch[098] Batch [3449]/[3759] Speed: 66.442826 samples/sec accuracy=87.706975 loss=0.481824 lr=0.000100 Epoch[098] Batch [3499]/[3759] Speed: 65.806239 samples/sec accuracy=87.706696 loss=0.481854 lr=0.000100 Epoch[098] Batch [3549]/[3759] Speed: 66.398328 samples/sec accuracy=87.702465 loss=0.481904 lr=0.000100 Epoch[098] Batch [3599]/[3759] Speed: 65.752521 samples/sec accuracy=87.690104 loss=0.482105 lr=0.000100 Epoch[098] Batch [3649]/[3759] Speed: 66.214225 samples/sec accuracy=87.709760 loss=0.481568 lr=0.000100 Epoch[098] Batch [3699]/[3759] Speed: 66.327764 samples/sec accuracy=87.716216 loss=0.481146 lr=0.000100 Epoch[098] Batch [3749]/[3759] Speed: 73.275660 samples/sec accuracy=87.711667 loss=0.481381 lr=0.000100 Batch [0049]/[0303]: acc-top1=67.593750 acc-top5=86.093750 Batch [0099]/[0303]: acc-top1=67.343750 acc-top5=86.093750 Batch [0149]/[0303]: acc-top1=67.510417 acc-top5=86.145833 Batch [0199]/[0303]: acc-top1=67.390625 acc-top5=86.156250 Batch [0249]/[0303]: acc-top1=67.456250 acc-top5=86.225000 Batch [0299]/[0303]: acc-top1=67.723958 acc-top5=86.406250 [Epoch 098] training: accuracy=87.718642 loss=0.481190 [Epoch 098] speed: 65 samples/sec time cost: 3936.626318 [Epoch 098] validation: acc-top1=67.728960 acc-top5=86.411922 loss=1.793634 Epoch[099] Batch [0049]/[3760] Speed: 44.022514 samples/sec accuracy=87.187500 loss=0.505176 lr=0.000100 Epoch[099] Batch [0099]/[3760] Speed: 64.570794 samples/sec accuracy=87.750000 loss=0.480381 lr=0.000100 Epoch[099] Batch [0149]/[3760] Speed: 66.300399 samples/sec accuracy=87.562500 loss=0.481359 lr=0.000100 Epoch[099] Batch [0199]/[3760] Speed: 64.883811 samples/sec accuracy=87.554688 loss=0.484114 lr=0.000100 Epoch[099] Batch [0249]/[3760] Speed: 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lr=0.000100 Epoch[099] Batch [1699]/[3760] Speed: 66.313485 samples/sec accuracy=87.847426 loss=0.477760 lr=0.000100 Epoch[099] Batch [1749]/[3760] Speed: 65.782394 samples/sec accuracy=87.833929 loss=0.478445 lr=0.000100 Epoch[099] Batch [1799]/[3760] Speed: 65.998034 samples/sec accuracy=87.813368 loss=0.479527 lr=0.000100 Epoch[099] Batch [1849]/[3760] Speed: 66.135786 samples/sec accuracy=87.801520 loss=0.479823 lr=0.000100 Epoch[099] Batch [1899]/[3760] Speed: 65.664149 samples/sec accuracy=87.815789 loss=0.479275 lr=0.000100 Epoch[099] Batch [1949]/[3760] Speed: 65.691569 samples/sec accuracy=87.814103 loss=0.478839 lr=0.000100 Epoch[099] Batch [1999]/[3760] Speed: 66.301862 samples/sec accuracy=87.820312 loss=0.479224 lr=0.000100 Epoch[099] Batch [2049]/[3760] Speed: 65.784115 samples/sec accuracy=87.814024 loss=0.479011 lr=0.000100 Epoch[099] Batch [2099]/[3760] Speed: 66.225920 samples/sec accuracy=87.804315 loss=0.479084 lr=0.000100 Epoch[099] Batch [2149]/[3760] Speed: 65.762284 samples/sec accuracy=87.787791 loss=0.479680 lr=0.000100 Epoch[099] Batch [2199]/[3760] Speed: 65.884089 samples/sec accuracy=87.781960 loss=0.479786 lr=0.000100 Epoch[099] Batch [2249]/[3760] Speed: 66.132224 samples/sec accuracy=87.763889 loss=0.479826 lr=0.000100 Epoch[099] Batch [2299]/[3760] Speed: 66.117624 samples/sec accuracy=87.775136 loss=0.479775 lr=0.000100 Epoch[099] Batch [2349]/[3760] Speed: 65.845399 samples/sec accuracy=87.769282 loss=0.479528 lr=0.000100 Epoch[099] Batch [2399]/[3760] Speed: 66.118543 samples/sec accuracy=87.764323 loss=0.479814 lr=0.000100 Epoch[099] Batch [2449]/[3760] Speed: 66.156616 samples/sec accuracy=87.757653 loss=0.479623 lr=0.000100 Epoch[099] Batch [2499]/[3760] Speed: 65.898901 samples/sec accuracy=87.751250 loss=0.480089 lr=0.000100 Epoch[099] Batch [2549]/[3760] Speed: 66.067804 samples/sec accuracy=87.769608 loss=0.479481 lr=0.000100 Epoch[099] Batch [2599]/[3760] Speed: 65.509709 samples/sec accuracy=87.769231 loss=0.479299 lr=0.000100 Epoch[099] Batch [2649]/[3760] Speed: 66.260073 samples/sec accuracy=87.750000 loss=0.480179 lr=0.000100 Epoch[099] Batch [2699]/[3760] Speed: 65.458306 samples/sec accuracy=87.780093 loss=0.479216 lr=0.000100 Epoch[099] Batch [2749]/[3760] Speed: 66.393893 samples/sec accuracy=87.769318 loss=0.479544 lr=0.000100 Epoch[099] Batch [2799]/[3760] Speed: 66.242076 samples/sec accuracy=87.767857 loss=0.479609 lr=0.000100 Epoch[099] Batch [2849]/[3760] Speed: 65.618197 samples/sec accuracy=87.754386 loss=0.479982 lr=0.000100 Epoch[099] Batch [2899]/[3760] Speed: 65.971617 samples/sec accuracy=87.768858 loss=0.479788 lr=0.000100 Epoch[099] Batch [2949]/[3760] Speed: 66.019705 samples/sec accuracy=87.754237 loss=0.480118 lr=0.000100 Epoch[099] Batch [2999]/[3760] Speed: 66.035303 samples/sec accuracy=87.759375 loss=0.479783 lr=0.000100 Epoch[099] Batch [3049]/[3760] Speed: 65.607855 samples/sec accuracy=87.759734 loss=0.479871 lr=0.000100 Epoch[099] Batch [3099]/[3760] Speed: 65.983951 samples/sec accuracy=87.762601 loss=0.479890 lr=0.000100 Epoch[099] Batch [3149]/[3760] Speed: 65.707812 samples/sec accuracy=87.765873 loss=0.479933 lr=0.000100 Epoch[099] Batch [3199]/[3760] Speed: 65.925400 samples/sec accuracy=87.765137 loss=0.479815 lr=0.000100 Epoch[099] Batch [3249]/[3760] Speed: 66.725993 samples/sec accuracy=87.764423 loss=0.479468 lr=0.000100 Epoch[099] Batch [3299]/[3760] Speed: 65.411961 samples/sec accuracy=87.766572 loss=0.479647 lr=0.000100 Epoch[099] Batch [3349]/[3760] Speed: 65.798446 samples/sec accuracy=87.762593 loss=0.479770 lr=0.000100 Epoch[099] Batch [3399]/[3760] Speed: 66.406668 samples/sec accuracy=87.767004 loss=0.479466 lr=0.000100 Epoch[099] Batch [3449]/[3760] Speed: 65.901456 samples/sec accuracy=87.764040 loss=0.479811 lr=0.000100 Epoch[099] Batch [3499]/[3760] Speed: 64.960569 samples/sec accuracy=87.762500 loss=0.479677 lr=0.000100 Epoch[099] Batch [3549]/[3760] Speed: 66.591343 samples/sec accuracy=87.761004 loss=0.479681 lr=0.000100 Epoch[099] Batch [3599]/[3760] Speed: 66.085431 samples/sec accuracy=87.765191 loss=0.479295 lr=0.000100 Epoch[099] Batch [3649]/[3760] Speed: 65.766709 samples/sec accuracy=87.776969 loss=0.478690 lr=0.000100 Epoch[099] Batch [3699]/[3760] Speed: 65.752122 samples/sec accuracy=87.767736 loss=0.478704 lr=0.000100 Epoch[099] Batch [3749]/[3760] Speed: 72.161720 samples/sec accuracy=87.768333 loss=0.478645 lr=0.000100 Batch [0049]/[0303]: acc-top1=67.281250 acc-top5=86.031250 Batch [0099]/[0303]: acc-top1=67.265625 acc-top5=86.156250 Batch [0149]/[0303]: acc-top1=67.468750 acc-top5=86.041667 Batch [0199]/[0303]: acc-top1=67.320312 acc-top5=86.093750 Batch [0249]/[0303]: acc-top1=67.337500 acc-top5=86.218750 Batch [0299]/[0303]: acc-top1=67.588542 acc-top5=86.406250 [Epoch 099] training: accuracy=87.771360 loss=0.478536 [Epoch 099] speed: 65 samples/sec time cost: 3938.850777 [Epoch 099] validation: acc-top1=67.574257 acc-top5=86.432550 loss=1.808336