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_nl10_res101_seg1_kinetics400_b8_g8_inflate311_f32s2_step_epoch100_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_nl10_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_nl10_res101_seg1_kinetics400_b8_g8_inflate311_f32s2_step_epoch100_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) (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(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) ) (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) ) ) ) (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) (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=(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) (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) ) ) ) (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) (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) ) ) ) (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) (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) ) ) ) (11): Bottleneck( (bottleneck): HybridSequential( (0): Conv3D(1024 -> 256, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False) (1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=256) (2): Activation(relu) (3): Conv3D(256 -> 256, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1), bias=False) (4): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=256) (5): Activation(relu) (6): Conv3D(256 -> 1024, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False) (7): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=1024) ) (conv1): Conv3D(1024 -> 256, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False) (conv2): Conv3D(256 -> 256, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1), bias=False) (bn1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=256) (bn2): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=256) (conv3): Conv3D(256 -> 1024, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False) (bn3): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=1024) (relu): Activation(relu) ) (12): Bottleneck( (bottleneck): HybridSequential( (0): Conv3D(1024 -> 256, kernel_size=(3, 1, 1), stride=(1, 1, 1), padding=(1, 0, 0), bias=False) (1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=256) (2): Activation(relu) (3): Conv3D(256 -> 256, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1), bias=False) (4): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=256) (5): Activation(relu) (6): Conv3D(256 -> 1024, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False) (7): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=1024) ) (conv1): Conv3D(1024 -> 256, kernel_size=(3, 1, 1), stride=(1, 1, 1), padding=(1, 0, 0), bias=False) (conv2): Conv3D(256 -> 256, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1), bias=False) (bn1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=256) (bn2): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=256) (conv3): Conv3D(256 -> 1024, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False) (bn3): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=1024) (relu): Activation(relu) ) (13): Bottleneck( (bottleneck): HybridSequential( (0): Conv3D(1024 -> 256, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False) (1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=256) (2): Activation(relu) (3): Conv3D(256 -> 256, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1), bias=False) (4): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=256) (5): Activation(relu) (6): Conv3D(256 -> 1024, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False) (7): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=1024) ) (conv1): Conv3D(1024 -> 256, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False) (conv2): Conv3D(256 -> 256, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1), bias=False) (bn1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=256) (bn2): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=256) (conv3): Conv3D(256 -> 1024, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False) (bn3): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=1024) (relu): Activation(relu) ) (14): Bottleneck( (bottleneck): HybridSequential( (0): Conv3D(1024 -> 256, kernel_size=(3, 1, 1), stride=(1, 1, 1), padding=(1, 0, 0), bias=False) (1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=256) (2): Activation(relu) (3): Conv3D(256 -> 256, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1), bias=False) (4): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=256) (5): Activation(relu) (6): Conv3D(256 -> 1024, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False) (7): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=1024) (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) ) ) ) (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) (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) ) ) ) (19): Bottleneck( (bottleneck): HybridSequential( (0): Conv3D(1024 -> 256, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False) (1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=256) (2): Activation(relu) (3): Conv3D(256 -> 256, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1), bias=False) (4): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=256) (5): Activation(relu) (6): Conv3D(256 -> 1024, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False) (7): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=1024) ) (conv1): Conv3D(1024 -> 256, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False) (conv2): Conv3D(256 -> 256, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1), bias=False) (bn1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=256) (bn2): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=256) (conv3): Conv3D(256 -> 1024, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False) (bn3): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=1024) (relu): Activation(relu) ) (20): Bottleneck( (bottleneck): HybridSequential( (0): Conv3D(1024 -> 256, kernel_size=(3, 1, 1), stride=(1, 1, 1), padding=(1, 0, 0), bias=False) (1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=256) (2): Activation(relu) (3): Conv3D(256 -> 256, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1), bias=False) (4): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=256) (5): Activation(relu) (6): Conv3D(256 -> 1024, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False) (7): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=1024) ) (conv1): Conv3D(1024 -> 256, kernel_size=(3, 1, 1), stride=(1, 1, 1), padding=(1, 0, 0), bias=False) (conv2): Conv3D(256 -> 256, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1), bias=False) (bn1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=256) (bn2): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=256) (conv3): Conv3D(256 -> 1024, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False) (bn3): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=1024) (relu): Activation(relu) ) (21): Bottleneck( (bottleneck): HybridSequential( (0): Conv3D(1024 -> 256, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False) (1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=256) (2): Activation(relu) (3): Conv3D(256 -> 256, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1), bias=False) (4): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=256) (5): Activation(relu) (6): Conv3D(256 -> 1024, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False) (7): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=1024) ) (conv1): Conv3D(1024 -> 256, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False) (conv2): Conv3D(256 -> 256, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1), bias=False) (bn1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=256) (bn2): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=256) (conv3): Conv3D(256 -> 1024, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False) (bn3): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=1024) (relu): Activation(relu) ) (22): Bottleneck( (bottleneck): HybridSequential( (0): Conv3D(1024 -> 256, kernel_size=(3, 1, 1), stride=(1, 1, 1), padding=(1, 0, 0), bias=False) (1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=256) (2): Activation(relu) (3): Conv3D(256 -> 256, kernel_size=(1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1), bias=False) (4): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=256) (5): Activation(relu) (6): Conv3D(256 -> 1024, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False) (7): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=1024) (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) ) ) ) ) (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: 29.892098 samples/sec accuracy=1.187500 loss=5.916985 lr=0.010000 Epoch[000] Batch [0099]/[3759] Speed: 59.289349 samples/sec accuracy=2.000000 loss=5.779472 lr=0.010000 Epoch[000] Batch [0149]/[3759] Speed: 61.247340 samples/sec accuracy=2.979167 loss=5.633238 lr=0.010000 Epoch[000] Batch [0199]/[3759] Speed: 59.697967 samples/sec accuracy=3.843750 loss=5.488720 lr=0.010000 Epoch[000] Batch [0249]/[3759] Speed: 60.914586 samples/sec accuracy=4.612500 loss=5.357219 lr=0.010000 Epoch[000] Batch [0299]/[3759] Speed: 61.015161 samples/sec accuracy=5.484375 loss=5.242770 lr=0.010000 Epoch[000] Batch [0349]/[3759] Speed: 60.920051 samples/sec accuracy=6.348214 loss=5.139555 lr=0.010000 Epoch[000] Batch [0399]/[3759] Speed: 61.107106 samples/sec accuracy=7.011719 loss=5.059908 lr=0.010000 Epoch[000] Batch [0449]/[3759] Speed: 61.047279 samples/sec accuracy=7.583333 loss=4.992575 lr=0.010000 Epoch[000] Batch [0499]/[3759] Speed: 60.927270 samples/sec 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accuracy=23.346082 loss=3.672159 lr=0.010000 Epoch[000] Batch [3399]/[3759] Speed: 61.354732 samples/sec accuracy=23.493566 loss=3.662556 lr=0.010000 Epoch[000] Batch [3449]/[3759] Speed: 61.174470 samples/sec accuracy=23.645380 loss=3.652860 lr=0.010000 Epoch[000] Batch [3499]/[3759] Speed: 61.106251 samples/sec accuracy=23.774107 loss=3.643819 lr=0.010000 Epoch[000] Batch [3549]/[3759] Speed: 61.771399 samples/sec accuracy=23.910651 loss=3.634818 lr=0.010000 Epoch[000] Batch [3599]/[3759] Speed: 61.449560 samples/sec accuracy=24.073785 loss=3.624772 lr=0.010000 Epoch[000] Batch [3649]/[3759] Speed: 60.537780 samples/sec accuracy=24.201199 loss=3.615426 lr=0.010000 Epoch[000] Batch [3699]/[3759] Speed: 60.691510 samples/sec accuracy=24.334882 loss=3.606401 lr=0.010000 Epoch[000] Batch [3749]/[3759] Speed: 67.727753 samples/sec accuracy=24.445833 loss=3.598392 lr=0.010000 Batch [0049]/[0303]: acc-top1=36.031250 acc-top5=64.375000 Batch [0099]/[0303]: acc-top1=36.984375 acc-top5=65.312500 Batch [0149]/[0303]: acc-top1=37.333333 acc-top5=65.208333 Batch [0199]/[0303]: acc-top1=37.531250 acc-top5=65.109375 Batch [0249]/[0303]: acc-top1=37.256250 acc-top5=64.937500 Batch [0299]/[0303]: acc-top1=37.395833 acc-top5=64.958333 [Epoch 000] training: accuracy=24.474594 loss=3.596394 [Epoch 000] speed: 60 samples/sec time cost: 4265.276073 [Epoch 000] validation: acc-top1=37.365924 acc-top5=64.954620 loss=2.857056 Epoch[001] Batch [0049]/[3760] Speed: 41.292407 samples/sec accuracy=35.375000 loss=2.862741 lr=0.010000 Epoch[001] Batch [0099]/[3760] Speed: 60.647396 samples/sec accuracy=35.375000 loss=2.863801 lr=0.010000 Epoch[001] Batch [0149]/[3760] Speed: 61.916621 samples/sec accuracy=34.833333 loss=2.886872 lr=0.010000 Epoch[001] Batch [0199]/[3760] Speed: 61.087733 samples/sec accuracy=35.414062 loss=2.868818 lr=0.010000 Epoch[001] Batch [0249]/[3760] Speed: 62.458760 samples/sec accuracy=35.681250 loss=2.857844 lr=0.010000 Epoch[001] Batch [0299]/[3760] 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accuracy=38.798611 loss=2.688710 lr=0.010000 Epoch[001] Batch [3649]/[3760] Speed: 61.700942 samples/sec accuracy=38.832620 loss=2.686807 lr=0.010000 Epoch[001] Batch [3699]/[3760] Speed: 62.535602 samples/sec accuracy=38.871199 loss=2.685074 lr=0.010000 Epoch[001] Batch [3749]/[3760] Speed: 67.274428 samples/sec accuracy=38.909583 loss=2.682856 lr=0.010000 Batch [0049]/[0303]: acc-top1=44.156250 acc-top5=69.031250 Batch [0099]/[0303]: acc-top1=44.187500 acc-top5=70.343750 Batch [0149]/[0303]: acc-top1=44.187500 acc-top5=70.375000 Batch [0199]/[0303]: acc-top1=44.078125 acc-top5=70.421875 Batch [0249]/[0303]: acc-top1=43.768750 acc-top5=70.462500 Batch [0299]/[0303]: acc-top1=44.177083 acc-top5=70.578125 [Epoch 001] training: accuracy=38.914977 loss=2.682553 [Epoch 001] speed: 61 samples/sec time cost: 4196.346659 [Epoch 001] validation: acc-top1=44.188325 acc-top5=70.616749 loss=2.482384 Epoch[002] Batch [0049]/[3759] Speed: 42.458254 samples/sec accuracy=42.687500 loss=2.497113 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61.716332 samples/sec accuracy=44.702665 loss=2.388901 lr=0.010000 Epoch[002] Batch [3449]/[3759] Speed: 61.290540 samples/sec accuracy=44.722826 loss=2.387651 lr=0.010000 Epoch[002] Batch [3499]/[3759] Speed: 62.341666 samples/sec accuracy=44.720536 loss=2.387522 lr=0.010000 Epoch[002] Batch [3549]/[3759] Speed: 61.611071 samples/sec accuracy=44.749560 loss=2.386059 lr=0.010000 Epoch[002] Batch [3599]/[3759] Speed: 61.775608 samples/sec accuracy=44.766059 loss=2.384800 lr=0.010000 Epoch[002] Batch [3649]/[3759] Speed: 61.443716 samples/sec accuracy=44.785959 loss=2.384084 lr=0.010000 Epoch[002] Batch [3699]/[3759] Speed: 62.205404 samples/sec accuracy=44.797297 loss=2.383440 lr=0.010000 Epoch[002] Batch [3749]/[3759] Speed: 67.340910 samples/sec accuracy=44.823333 loss=2.382331 lr=0.010000 Batch [0049]/[0303]: acc-top1=48.687500 acc-top5=74.281250 Batch [0099]/[0303]: acc-top1=49.671875 acc-top5=75.062500 Batch [0149]/[0303]: acc-top1=49.479167 acc-top5=75.354167 Batch [0199]/[0303]: acc-top1=49.554688 acc-top5=75.460938 Batch [0249]/[0303]: acc-top1=49.200000 acc-top5=75.375000 Batch [0299]/[0303]: acc-top1=49.322917 acc-top5=75.427083 [Epoch 002] training: accuracy=44.820348 loss=2.382265 [Epoch 002] speed: 61 samples/sec time cost: 4190.704607 [Epoch 002] validation: acc-top1=49.355404 acc-top5=75.428012 loss=2.220413 Epoch[003] Batch [0049]/[3760] Speed: 41.938452 samples/sec accuracy=47.125000 loss=2.238911 lr=0.010000 Epoch[003] Batch [0099]/[3760] Speed: 60.465860 samples/sec accuracy=47.187500 loss=2.227034 lr=0.010000 Epoch[003] Batch [0149]/[3760] Speed: 62.082232 samples/sec accuracy=47.541667 loss=2.209328 lr=0.010000 Epoch[003] Batch [0199]/[3760] Speed: 61.477866 samples/sec accuracy=47.476562 loss=2.227526 lr=0.010000 Epoch[003] Batch [0249]/[3760] Speed: 61.872268 samples/sec accuracy=47.556250 loss=2.224438 lr=0.010000 Epoch[003] Batch [0299]/[3760] Speed: 61.816806 samples/sec accuracy=47.682292 loss=2.224143 lr=0.010000 Epoch[003] Batch 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accuracy=48.297517 loss=2.207199 lr=0.010000 Epoch[003] Batch [3699]/[3760] Speed: 61.598110 samples/sec accuracy=48.319679 loss=2.206691 lr=0.010000 Epoch[003] Batch [3749]/[3760] Speed: 67.384416 samples/sec accuracy=48.352083 loss=2.205780 lr=0.010000 Batch [0049]/[0303]: acc-top1=49.531250 acc-top5=75.531250 Batch [0099]/[0303]: acc-top1=51.015625 acc-top5=76.671875 Batch [0149]/[0303]: acc-top1=50.916667 acc-top5=76.677083 Batch [0199]/[0303]: acc-top1=51.062500 acc-top5=76.773438 Batch [0249]/[0303]: acc-top1=50.993750 acc-top5=76.568750 Batch [0299]/[0303]: acc-top1=51.109375 acc-top5=76.781250 [Epoch 003] training: accuracy=48.356882 loss=2.205413 [Epoch 003] speed: 61 samples/sec time cost: 4197.888897 [Epoch 003] validation: acc-top1=51.134488 acc-top5=76.799711 loss=2.106812 Epoch[004] Batch [0049]/[3760] Speed: 41.400682 samples/sec accuracy=49.875000 loss=2.101721 lr=0.010000 Epoch[004] Batch [0099]/[3760] Speed: 59.920712 samples/sec accuracy=50.015625 loss=2.097621 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[0299]/[0303]: acc-top1=53.697917 acc-top5=78.776042 [Epoch 004] training: accuracy=50.916307 loss=2.074575 [Epoch 004] speed: 61 samples/sec time cost: 4187.543952 [Epoch 004] validation: acc-top1=53.707715 acc-top5=78.774752 loss=2.035897 Epoch[005] Batch [0049]/[3759] Speed: 42.300173 samples/sec accuracy=53.500000 loss=1.986963 lr=0.010000 Epoch[005] Batch [0099]/[3759] Speed: 60.589058 samples/sec accuracy=53.296875 loss=1.970278 lr=0.010000 Epoch[005] Batch [0149]/[3759] Speed: 62.288377 samples/sec accuracy=52.656250 loss=1.979217 lr=0.010000 Epoch[005] Batch [0199]/[3759] Speed: 60.939546 samples/sec accuracy=52.656250 loss=1.973360 lr=0.010000 Epoch[005] Batch [0249]/[3759] Speed: 62.187496 samples/sec accuracy=52.637500 loss=1.979737 lr=0.010000 Epoch[005] Batch [0299]/[3759] Speed: 61.677297 samples/sec accuracy=52.687500 loss=1.986197 lr=0.010000 Epoch[005] Batch [0349]/[3759] Speed: 62.228603 samples/sec accuracy=52.450893 loss=1.994764 lr=0.010000 Epoch[005] Batch 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accuracy=52.773649 loss=1.982099 lr=0.010000 Epoch[005] Batch [3749]/[3759] Speed: 67.240336 samples/sec accuracy=52.788750 loss=1.981644 lr=0.010000 Batch [0049]/[0303]: acc-top1=55.000000 acc-top5=78.875000 Batch [0099]/[0303]: acc-top1=55.234375 acc-top5=79.687500 Batch [0149]/[0303]: acc-top1=55.208333 acc-top5=79.645833 Batch [0199]/[0303]: acc-top1=55.078125 acc-top5=79.484375 Batch [0249]/[0303]: acc-top1=54.837500 acc-top5=79.300000 Batch [0299]/[0303]: acc-top1=54.875000 acc-top5=79.354167 [Epoch 005] training: accuracy=52.781657 loss=1.981836 [Epoch 005] speed: 61 samples/sec time cost: 4188.640337 [Epoch 005] validation: acc-top1=54.888614 acc-top5=79.367781 loss=1.960066 Epoch[006] Batch [0049]/[3760] Speed: 42.368989 samples/sec accuracy=54.968750 loss=1.894536 lr=0.010000 Epoch[006] Batch [0099]/[3760] Speed: 60.274865 samples/sec accuracy=54.921875 loss=1.882782 lr=0.010000 Epoch[006] Batch [0149]/[3760] Speed: 62.250018 samples/sec accuracy=54.708333 loss=1.893918 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lr=0.010000 Epoch[006] Batch [3049]/[3760] Speed: 61.570222 samples/sec accuracy=54.375512 loss=1.908828 lr=0.010000 Epoch[006] Batch [3099]/[3760] Speed: 62.222141 samples/sec accuracy=54.388609 loss=1.908292 lr=0.010000 Epoch[006] Batch [3149]/[3760] Speed: 61.796731 samples/sec accuracy=54.412202 loss=1.907283 lr=0.010000 Epoch[006] Batch [3199]/[3760] Speed: 62.366004 samples/sec accuracy=54.445801 loss=1.906365 lr=0.010000 Epoch[006] Batch [3249]/[3760] Speed: 62.644067 samples/sec accuracy=54.450962 loss=1.905848 lr=0.010000 Epoch[006] Batch [3299]/[3760] Speed: 61.922486 samples/sec accuracy=54.461648 loss=1.906037 lr=0.010000 Epoch[006] Batch [3349]/[3760] Speed: 62.093409 samples/sec accuracy=54.455224 loss=1.905956 lr=0.010000 Epoch[006] Batch [3399]/[3760] Speed: 62.439891 samples/sec accuracy=54.445772 loss=1.906148 lr=0.010000 Epoch[006] Batch [3449]/[3760] Speed: 61.503756 samples/sec accuracy=54.447917 loss=1.905481 lr=0.010000 Epoch[006] Batch [3499]/[3760] Speed: 62.098850 samples/sec accuracy=54.446429 loss=1.905442 lr=0.010000 Epoch[006] Batch [3549]/[3760] Speed: 62.065785 samples/sec accuracy=54.447183 loss=1.905102 lr=0.010000 Epoch[006] Batch [3599]/[3760] Speed: 62.104029 samples/sec accuracy=54.447917 loss=1.904979 lr=0.010000 Epoch[006] Batch [3649]/[3760] Speed: 62.254505 samples/sec accuracy=54.437928 loss=1.905903 lr=0.010000 Epoch[006] Batch [3699]/[3760] Speed: 61.523617 samples/sec accuracy=54.450169 loss=1.905570 lr=0.010000 Epoch[006] Batch [3749]/[3760] Speed: 67.441002 samples/sec accuracy=54.448333 loss=1.905506 lr=0.010000 Batch [0049]/[0303]: acc-top1=54.468750 acc-top5=79.281250 Batch [0099]/[0303]: acc-top1=55.562500 acc-top5=80.109375 Batch [0149]/[0303]: acc-top1=55.947917 acc-top5=80.010417 Batch [0199]/[0303]: acc-top1=55.742187 acc-top5=80.101562 Batch [0249]/[0303]: acc-top1=55.456250 acc-top5=79.962500 Batch [0299]/[0303]: acc-top1=55.541667 acc-top5=79.937500 [Epoch 006] training: accuracy=54.448969 loss=1.905652 [Epoch 006] speed: 61 samples/sec time cost: 4178.978792 [Epoch 006] validation: acc-top1=55.574464 acc-top5=79.955652 loss=1.921921 Epoch[007] Batch [0049]/[3760] Speed: 41.490740 samples/sec accuracy=57.468750 loss=1.766453 lr=0.010000 Epoch[007] Batch [0099]/[3760] Speed: 60.493392 samples/sec accuracy=57.156250 loss=1.785619 lr=0.010000 Epoch[007] Batch [0149]/[3760] Speed: 62.685396 samples/sec accuracy=56.364583 loss=1.813851 lr=0.010000 Epoch[007] Batch [0199]/[3760] Speed: 61.908831 samples/sec accuracy=55.703125 loss=1.841114 lr=0.010000 Epoch[007] Batch [0249]/[3760] Speed: 62.402119 samples/sec accuracy=56.012500 loss=1.825831 lr=0.010000 Epoch[007] Batch [0299]/[3760] Speed: 61.777782 samples/sec accuracy=55.911458 loss=1.830172 lr=0.010000 Epoch[007] Batch [0349]/[3760] Speed: 62.163615 samples/sec accuracy=56.062500 loss=1.824259 lr=0.010000 Epoch[007] Batch [0399]/[3760] Speed: 61.711259 samples/sec accuracy=56.167969 loss=1.816982 lr=0.010000 Epoch[007] Batch 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accuracy=55.656250 loss=1.841182 lr=0.010000 Epoch[007] Batch [0949]/[3760] Speed: 62.464739 samples/sec accuracy=55.782895 loss=1.838948 lr=0.010000 Epoch[007] Batch [0999]/[3760] Speed: 61.734084 samples/sec accuracy=55.785938 loss=1.839760 lr=0.010000 Epoch[007] Batch [1049]/[3760] Speed: 62.204299 samples/sec accuracy=55.831845 loss=1.837387 lr=0.010000 Epoch[007] Batch [1099]/[3760] Speed: 61.882440 samples/sec accuracy=55.792614 loss=1.837825 lr=0.010000 Epoch[007] Batch [1149]/[3760] Speed: 61.922249 samples/sec accuracy=55.756793 loss=1.838382 lr=0.010000 Epoch[007] Batch [1199]/[3760] Speed: 61.295924 samples/sec accuracy=55.714844 loss=1.838927 lr=0.010000 Epoch[007] Batch [1249]/[3760] Speed: 62.143822 samples/sec accuracy=55.723750 loss=1.838984 lr=0.010000 Epoch[007] Batch [1299]/[3760] Speed: 62.019968 samples/sec accuracy=55.727163 loss=1.839167 lr=0.010000 Epoch[007] Batch [1349]/[3760] Speed: 61.880458 samples/sec accuracy=55.706019 loss=1.838437 lr=0.010000 Epoch[007] 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accuracy=55.684122 loss=1.840625 lr=0.010000 Epoch[007] Batch [1899]/[3760] Speed: 62.376672 samples/sec accuracy=55.666941 loss=1.840536 lr=0.010000 Epoch[007] Batch [1949]/[3760] Speed: 61.729202 samples/sec accuracy=55.656250 loss=1.841706 lr=0.010000 Epoch[007] Batch [1999]/[3760] Speed: 61.760901 samples/sec accuracy=55.667969 loss=1.841827 lr=0.010000 Epoch[007] Batch [2049]/[3760] Speed: 62.209783 samples/sec accuracy=55.637957 loss=1.843844 lr=0.010000 Epoch[007] Batch [2099]/[3760] Speed: 61.954465 samples/sec accuracy=55.668155 loss=1.843042 lr=0.010000 Epoch[007] Batch [2149]/[3760] Speed: 61.608027 samples/sec accuracy=55.647529 loss=1.843847 lr=0.010000 Epoch[007] Batch [2199]/[3760] Speed: 62.223131 samples/sec accuracy=55.629972 loss=1.844742 lr=0.010000 Epoch[007] Batch [2249]/[3760] Speed: 62.360322 samples/sec accuracy=55.611111 loss=1.844657 lr=0.010000 Epoch[007] Batch [2299]/[3760] Speed: 62.151460 samples/sec accuracy=55.576766 loss=1.845413 lr=0.010000 Epoch[007] 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accuracy=55.577567 loss=1.843914 lr=0.010000 Epoch[007] Batch [2849]/[3760] Speed: 61.603833 samples/sec accuracy=55.581140 loss=1.844438 lr=0.010000 Epoch[007] Batch [2899]/[3760] Speed: 62.173407 samples/sec accuracy=55.565733 loss=1.844742 lr=0.010000 Epoch[007] Batch [2949]/[3760] Speed: 61.747523 samples/sec accuracy=55.592161 loss=1.843201 lr=0.010000 Epoch[007] Batch [2999]/[3760] Speed: 62.451195 samples/sec accuracy=55.612500 loss=1.842043 lr=0.010000 Epoch[007] Batch [3049]/[3760] Speed: 61.999917 samples/sec accuracy=55.602971 loss=1.842621 lr=0.010000 Epoch[007] Batch [3099]/[3760] Speed: 62.224167 samples/sec accuracy=55.582661 loss=1.843704 lr=0.010000 Epoch[007] Batch [3149]/[3760] Speed: 62.021037 samples/sec accuracy=55.588294 loss=1.843706 lr=0.010000 Epoch[007] Batch [3199]/[3760] Speed: 61.332158 samples/sec accuracy=55.599121 loss=1.842950 lr=0.010000 Epoch[007] Batch [3249]/[3760] Speed: 62.557237 samples/sec accuracy=55.606731 loss=1.843434 lr=0.010000 Epoch[007] Batch [3299]/[3760] Speed: 61.383676 samples/sec accuracy=55.593750 loss=1.843789 lr=0.010000 Epoch[007] Batch [3349]/[3760] Speed: 62.320818 samples/sec accuracy=55.600280 loss=1.843563 lr=0.010000 Epoch[007] Batch [3399]/[3760] Speed: 62.019157 samples/sec accuracy=55.589614 loss=1.844333 lr=0.010000 Epoch[007] Batch [3449]/[3760] Speed: 62.322524 samples/sec accuracy=55.602355 loss=1.844110 lr=0.010000 Epoch[007] Batch [3499]/[3760] Speed: 62.339060 samples/sec accuracy=55.604464 loss=1.843999 lr=0.010000 Epoch[007] Batch [3549]/[3760] Speed: 61.680467 samples/sec accuracy=55.577025 loss=1.845298 lr=0.010000 Epoch[007] Batch [3599]/[3760] Speed: 62.720012 samples/sec accuracy=55.586806 loss=1.844048 lr=0.010000 Epoch[007] Batch [3649]/[3760] Speed: 61.827339 samples/sec accuracy=55.589041 loss=1.844148 lr=0.010000 Epoch[007] Batch [3699]/[3760] Speed: 62.102273 samples/sec accuracy=55.578970 loss=1.845240 lr=0.010000 Epoch[007] Batch [3749]/[3760] Speed: 66.698382 samples/sec accuracy=55.577500 loss=1.845513 lr=0.010000 Batch [0049]/[0303]: acc-top1=56.156250 acc-top5=79.062500 Batch [0099]/[0303]: acc-top1=56.656250 acc-top5=80.031250 Batch [0149]/[0303]: acc-top1=56.781250 acc-top5=80.229167 Batch [0199]/[0303]: acc-top1=56.656250 acc-top5=80.257812 Batch [0249]/[0303]: acc-top1=56.343750 acc-top5=80.356250 Batch [0299]/[0303]: acc-top1=56.453125 acc-top5=80.416667 [Epoch 007] training: accuracy=55.580535 loss=1.845426 [Epoch 007] speed: 61 samples/sec time cost: 4180.929739 [Epoch 007] validation: acc-top1=56.435644 acc-top5=80.445545 loss=1.921776 Epoch[008] Batch [0049]/[3759] Speed: 42.473820 samples/sec accuracy=56.968750 loss=1.765322 lr=0.010000 Epoch[008] Batch [0099]/[3759] Speed: 60.897835 samples/sec accuracy=57.078125 loss=1.755352 lr=0.010000 Epoch[008] Batch [0149]/[3759] Speed: 62.176996 samples/sec accuracy=56.958333 loss=1.762525 lr=0.010000 Epoch[008] Batch [0199]/[3759] Speed: 61.685399 samples/sec accuracy=56.789062 loss=1.771356 lr=0.010000 Epoch[008] Batch [0249]/[3759] Speed: 62.338634 samples/sec accuracy=56.725000 loss=1.778136 lr=0.010000 Epoch[008] Batch [0299]/[3759] Speed: 61.438367 samples/sec accuracy=56.838542 loss=1.770329 lr=0.010000 Epoch[008] Batch [0349]/[3759] Speed: 62.328443 samples/sec accuracy=56.919643 loss=1.768287 lr=0.010000 Epoch[008] Batch [0399]/[3759] Speed: 62.241452 samples/sec accuracy=56.976563 loss=1.769673 lr=0.010000 Epoch[008] Batch [0449]/[3759] Speed: 61.484865 samples/sec accuracy=56.809028 loss=1.775777 lr=0.010000 Epoch[008] Batch [0499]/[3759] Speed: 62.355848 samples/sec accuracy=56.912500 loss=1.771201 lr=0.010000 Epoch[008] Batch [0549]/[3759] Speed: 61.828862 samples/sec accuracy=56.977273 loss=1.769550 lr=0.010000 Epoch[008] Batch [0599]/[3759] Speed: 62.568964 samples/sec accuracy=56.966146 loss=1.769539 lr=0.010000 Epoch[008] Batch [0649]/[3759] Speed: 61.511140 samples/sec accuracy=56.930288 loss=1.773269 lr=0.010000 Epoch[008] Batch [0699]/[3759] Speed: 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lr=0.010000 Epoch[008] Batch [1199]/[3759] Speed: 62.409062 samples/sec accuracy=56.927083 loss=1.776731 lr=0.010000 Epoch[008] Batch [1249]/[3759] Speed: 62.052854 samples/sec accuracy=56.907500 loss=1.777058 lr=0.010000 Epoch[008] Batch [1299]/[3759] Speed: 62.436742 samples/sec accuracy=56.893029 loss=1.776948 lr=0.010000 Epoch[008] Batch [1349]/[3759] Speed: 62.194930 samples/sec accuracy=56.896991 loss=1.777827 lr=0.010000 Epoch[008] Batch [1399]/[3759] Speed: 62.135959 samples/sec accuracy=56.852679 loss=1.780081 lr=0.010000 Epoch[008] Batch [1449]/[3759] Speed: 62.067508 samples/sec accuracy=56.863147 loss=1.781032 lr=0.010000 Epoch[008] Batch [1499]/[3759] Speed: 61.967885 samples/sec accuracy=56.870833 loss=1.781728 lr=0.010000 Epoch[008] Batch [1549]/[3759] Speed: 62.354632 samples/sec accuracy=56.859879 loss=1.780652 lr=0.010000 Epoch[008] Batch [1599]/[3759] Speed: 61.931154 samples/sec accuracy=56.864258 loss=1.780413 lr=0.010000 Epoch[008] Batch [1649]/[3759] Speed: 62.201698 samples/sec accuracy=56.845644 loss=1.781411 lr=0.010000 Epoch[008] Batch [1699]/[3759] Speed: 62.305266 samples/sec accuracy=56.809743 loss=1.783651 lr=0.010000 Epoch[008] Batch [1749]/[3759] Speed: 62.022953 samples/sec accuracy=56.800000 loss=1.783427 lr=0.010000 Epoch[008] Batch [1799]/[3759] Speed: 62.577108 samples/sec accuracy=56.806424 loss=1.782933 lr=0.010000 Epoch[008] Batch [1849]/[3759] Speed: 62.551873 samples/sec accuracy=56.837838 loss=1.781958 lr=0.010000 Epoch[008] Batch [1899]/[3759] Speed: 62.303782 samples/sec accuracy=56.833059 loss=1.783417 lr=0.010000 Epoch[008] Batch [1949]/[3759] Speed: 62.426413 samples/sec accuracy=56.818109 loss=1.785405 lr=0.010000 Epoch[008] Batch [1999]/[3759] Speed: 62.459473 samples/sec accuracy=56.771094 loss=1.786242 lr=0.010000 Epoch[008] Batch [2049]/[3759] Speed: 61.959962 samples/sec accuracy=56.776677 loss=1.786962 lr=0.010000 Epoch[008] Batch [2099]/[3759] Speed: 61.936015 samples/sec accuracy=56.781994 loss=1.786545 lr=0.010000 Epoch[008] Batch [2149]/[3759] Speed: 62.402877 samples/sec accuracy=56.781250 loss=1.786577 lr=0.010000 Epoch[008] Batch [2199]/[3759] Speed: 62.138520 samples/sec accuracy=56.789773 loss=1.786883 lr=0.010000 Epoch[008] Batch [2249]/[3759] Speed: 62.313852 samples/sec accuracy=56.765278 loss=1.786986 lr=0.010000 Epoch[008] Batch [2299]/[3759] Speed: 62.318395 samples/sec accuracy=56.776495 loss=1.786689 lr=0.010000 Epoch[008] Batch [2349]/[3759] Speed: 62.189053 samples/sec accuracy=56.771277 loss=1.786804 lr=0.010000 Epoch[008] Batch [2399]/[3759] Speed: 62.537095 samples/sec accuracy=56.777995 loss=1.786144 lr=0.010000 Epoch[008] Batch [2449]/[3759] Speed: 62.198451 samples/sec accuracy=56.756378 loss=1.786743 lr=0.010000 Epoch[008] Batch [2499]/[3759] Speed: 62.396862 samples/sec accuracy=56.747500 loss=1.787366 lr=0.010000 Epoch[008] Batch [2549]/[3759] Speed: 62.048409 samples/sec accuracy=56.751225 loss=1.786926 lr=0.010000 Epoch[008] Batch [2599]/[3759] Speed: 61.877418 samples/sec accuracy=56.738582 loss=1.787819 lr=0.010000 Epoch[008] Batch [2649]/[3759] Speed: 62.040822 samples/sec accuracy=56.752358 loss=1.787446 lr=0.010000 Epoch[008] Batch [2699]/[3759] Speed: 62.370012 samples/sec accuracy=56.745949 loss=1.787792 lr=0.010000 Epoch[008] Batch [2749]/[3759] Speed: 62.337462 samples/sec accuracy=56.730114 loss=1.788898 lr=0.010000 Epoch[008] Batch [2799]/[3759] Speed: 62.020417 samples/sec accuracy=56.754464 loss=1.788217 lr=0.010000 Epoch[008] Batch [2849]/[3759] Speed: 62.417731 samples/sec accuracy=56.777412 loss=1.787153 lr=0.010000 Epoch[008] Batch [2899]/[3759] Speed: 62.856873 samples/sec accuracy=56.786638 loss=1.787389 lr=0.010000 Epoch[008] Batch [2949]/[3759] Speed: 62.244742 samples/sec accuracy=56.788136 loss=1.787387 lr=0.010000 Epoch[008] Batch [2999]/[3759] Speed: 61.535373 samples/sec accuracy=56.792708 loss=1.787134 lr=0.010000 Epoch[008] Batch [3049]/[3759] Speed: 62.506606 samples/sec accuracy=56.796619 loss=1.787225 lr=0.010000 Epoch[008] Batch [3099]/[3759] Speed: 62.185326 samples/sec accuracy=56.791331 loss=1.787067 lr=0.010000 Epoch[008] Batch [3149]/[3759] Speed: 62.061342 samples/sec accuracy=56.775298 loss=1.787634 lr=0.010000 Epoch[008] Batch [3199]/[3759] Speed: 61.776592 samples/sec accuracy=56.764160 loss=1.788065 lr=0.010000 Epoch[008] Batch [3249]/[3759] Speed: 61.682503 samples/sec accuracy=56.769231 loss=1.787716 lr=0.010000 Epoch[008] Batch [3299]/[3759] Speed: 61.964390 samples/sec accuracy=56.775095 loss=1.788087 lr=0.010000 Epoch[008] Batch [3349]/[3759] Speed: 62.244003 samples/sec accuracy=56.772854 loss=1.787776 lr=0.010000 Epoch[008] Batch [3399]/[3759] Speed: 62.404594 samples/sec accuracy=56.737592 loss=1.789099 lr=0.010000 Epoch[008] Batch [3449]/[3759] Speed: 62.186572 samples/sec accuracy=56.701993 loss=1.790350 lr=0.010000 Epoch[008] Batch [3499]/[3759] Speed: 62.745566 samples/sec accuracy=56.714732 loss=1.789903 lr=0.010000 Epoch[008] Batch [3549]/[3759] Speed: 62.247706 samples/sec accuracy=56.713028 loss=1.790196 lr=0.010000 Epoch[008] Batch [3599]/[3759] Speed: 62.058969 samples/sec accuracy=56.723958 loss=1.790414 lr=0.010000 Epoch[008] Batch [3649]/[3759] Speed: 62.425848 samples/sec accuracy=56.740154 loss=1.789967 lr=0.010000 Epoch[008] Batch [3699]/[3759] Speed: 62.407900 samples/sec accuracy=56.758024 loss=1.789461 lr=0.010000 Epoch[008] Batch [3749]/[3759] Speed: 67.871801 samples/sec accuracy=56.757083 loss=1.789160 lr=0.010000 Batch [0049]/[0303]: acc-top1=56.156250 acc-top5=79.625000 Batch [0099]/[0303]: acc-top1=57.218750 acc-top5=80.671875 Batch [0149]/[0303]: acc-top1=57.520833 acc-top5=80.791667 Batch [0199]/[0303]: acc-top1=57.484375 acc-top5=80.890625 Batch [0249]/[0303]: acc-top1=57.212500 acc-top5=80.831250 Batch [0299]/[0303]: acc-top1=57.161458 acc-top5=80.890625 [Epoch 008] training: accuracy=56.764183 loss=1.789006 [Epoch 008] speed: 61 samples/sec time cost: 4166.712499 [Epoch 008] validation: acc-top1=57.209158 acc-top5=80.925124 loss=1.894304 Epoch[009] Batch [0049]/[3760] Speed: 42.400513 samples/sec accuracy=58.000000 loss=1.728350 lr=0.010000 Epoch[009] Batch [0099]/[3760] Speed: 60.049002 samples/sec accuracy=58.328125 loss=1.714306 lr=0.010000 Epoch[009] Batch [0149]/[3760] Speed: 62.188146 samples/sec accuracy=58.385417 loss=1.715497 lr=0.010000 Epoch[009] Batch [0199]/[3760] Speed: 61.563904 samples/sec accuracy=58.515625 loss=1.709248 lr=0.010000 Epoch[009] Batch [0249]/[3760] Speed: 63.196506 samples/sec accuracy=58.350000 loss=1.721120 lr=0.010000 Epoch[009] Batch [0299]/[3760] Speed: 61.302933 samples/sec accuracy=58.156250 loss=1.727377 lr=0.010000 Epoch[009] Batch [0349]/[3760] Speed: 62.710690 samples/sec accuracy=58.308036 loss=1.715419 lr=0.010000 Epoch[009] Batch [0399]/[3760] Speed: 62.311382 samples/sec accuracy=58.203125 loss=1.721402 lr=0.010000 Epoch[009] Batch [0449]/[3760] Speed: 62.098632 samples/sec accuracy=58.319444 loss=1.718615 lr=0.010000 Epoch[009] Batch 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[0099]/[0303]: acc-top1=57.281250 acc-top5=80.906250 Batch [0149]/[0303]: acc-top1=57.864583 acc-top5=80.968750 Batch [0199]/[0303]: acc-top1=57.750000 acc-top5=81.031250 Batch [0249]/[0303]: acc-top1=57.568750 acc-top5=80.950000 Batch [0299]/[0303]: acc-top1=57.447917 acc-top5=81.020833 [Epoch 009] training: accuracy=57.785489 loss=1.742846 [Epoch 009] speed: 61 samples/sec time cost: 4166.052551 [Epoch 009] validation: acc-top1=57.451526 acc-top5=81.054043 loss=1.891288 Epoch[010] Batch [0049]/[3760] Speed: 41.970848 samples/sec accuracy=59.500000 loss=1.658844 lr=0.010000 Epoch[010] Batch [0099]/[3760] Speed: 60.578785 samples/sec accuracy=59.531250 loss=1.657341 lr=0.010000 Epoch[010] Batch [0149]/[3760] Speed: 61.379621 samples/sec accuracy=59.166667 loss=1.670442 lr=0.010000 Epoch[010] Batch [0199]/[3760] Speed: 61.046530 samples/sec accuracy=59.023438 loss=1.677747 lr=0.010000 Epoch[010] Batch [0249]/[3760] Speed: 62.873907 samples/sec accuracy=58.931250 loss=1.681202 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acc-top5=81.541667 Batch [0199]/[0303]: acc-top1=58.789062 acc-top5=81.609375 Batch [0249]/[0303]: acc-top1=58.643750 acc-top5=81.500000 Batch [0299]/[0303]: acc-top1=58.614583 acc-top5=81.635417 [Epoch 011] training: accuracy=59.399524 loss=1.667065 [Epoch 011] speed: 61 samples/sec time cost: 4163.356197 [Epoch 011] validation: acc-top1=58.678837 acc-top5=81.667698 loss=1.879740 Epoch[012] Batch [0049]/[3760] Speed: 42.115192 samples/sec accuracy=57.312500 loss=1.669758 lr=0.010000 Epoch[012] Batch [0099]/[3760] Speed: 60.495643 samples/sec accuracy=58.578125 loss=1.659925 lr=0.010000 Epoch[012] Batch [0149]/[3760] Speed: 62.357994 samples/sec accuracy=59.156250 loss=1.653671 lr=0.010000 Epoch[012] Batch [0199]/[3760] Speed: 61.169090 samples/sec accuracy=59.406250 loss=1.655334 lr=0.010000 Epoch[012] Batch [0249]/[3760] Speed: 62.498143 samples/sec accuracy=59.631250 loss=1.646883 lr=0.010000 Epoch[012] Batch [0299]/[3760] Speed: 61.596214 samples/sec accuracy=59.645833 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[0249]/[0303]: acc-top1=59.437500 acc-top5=82.293750 Batch [0299]/[0303]: acc-top1=59.432292 acc-top5=82.260417 [Epoch 013] training: accuracy=60.643285 loss=1.611220 [Epoch 013] speed: 61 samples/sec time cost: 4157.866730 [Epoch 013] validation: acc-top1=59.478135 acc-top5=82.301980 loss=1.832242 Epoch[014] Batch [0049]/[3759] Speed: 41.903022 samples/sec accuracy=62.125000 loss=1.530467 lr=0.010000 Epoch[014] Batch [0099]/[3759] Speed: 60.607211 samples/sec accuracy=61.968750 loss=1.539234 lr=0.010000 Epoch[014] Batch [0149]/[3759] Speed: 62.087512 samples/sec accuracy=61.916667 loss=1.544561 lr=0.010000 Epoch[014] Batch [0199]/[3759] Speed: 60.710808 samples/sec accuracy=61.804688 loss=1.549693 lr=0.010000 Epoch[014] Batch [0249]/[3759] Speed: 62.752386 samples/sec accuracy=61.662500 loss=1.553631 lr=0.010000 Epoch[014] Batch [0299]/[3759] Speed: 62.138835 samples/sec accuracy=61.848958 loss=1.549961 lr=0.010000 Epoch[014] Batch [0349]/[3759] Speed: 62.029398 samples/sec 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lr=0.010000 Epoch[015] Batch [3499]/[3760] Speed: 61.743042 samples/sec accuracy=61.670089 loss=1.563730 lr=0.010000 Epoch[015] Batch [3549]/[3760] Speed: 62.270500 samples/sec accuracy=61.660651 loss=1.564322 lr=0.010000 Epoch[015] Batch [3599]/[3760] Speed: 62.678210 samples/sec accuracy=61.680122 loss=1.563903 lr=0.010000 Epoch[015] Batch [3649]/[3760] Speed: 62.146252 samples/sec accuracy=61.697346 loss=1.562765 lr=0.010000 Epoch[015] Batch [3699]/[3760] Speed: 62.445154 samples/sec accuracy=61.709882 loss=1.562573 lr=0.010000 Epoch[015] Batch [3749]/[3760] Speed: 67.269494 samples/sec accuracy=61.699583 loss=1.562495 lr=0.010000 Batch [0049]/[0303]: acc-top1=59.250000 acc-top5=81.031250 Batch [0099]/[0303]: acc-top1=60.015625 acc-top5=82.281250 Batch [0149]/[0303]: acc-top1=60.625000 acc-top5=82.416667 Batch [0199]/[0303]: acc-top1=60.554688 acc-top5=82.421875 Batch [0249]/[0303]: acc-top1=60.362500 acc-top5=82.500000 Batch [0299]/[0303]: acc-top1=60.322917 acc-top5=82.562500 [Epoch 015] training: accuracy=61.695063 loss=1.562861 [Epoch 015] speed: 62 samples/sec time cost: 4153.019473 [Epoch 015] validation: acc-top1=60.349629 acc-top5=82.575289 loss=1.814311 Epoch[016] Batch [0049]/[3760] Speed: 42.343361 samples/sec accuracy=62.937500 loss=1.513195 lr=0.010000 Epoch[016] Batch [0099]/[3760] Speed: 60.859637 samples/sec accuracy=62.781250 loss=1.505317 lr=0.010000 Epoch[016] Batch [0149]/[3760] Speed: 62.453495 samples/sec accuracy=62.520833 loss=1.514292 lr=0.010000 Epoch[016] Batch [0199]/[3760] Speed: 60.909177 samples/sec accuracy=62.828125 loss=1.511843 lr=0.010000 Epoch[016] Batch [0249]/[3760] Speed: 62.276339 samples/sec accuracy=62.725000 loss=1.519000 lr=0.010000 Epoch[016] Batch [0299]/[3760] Speed: 61.539892 samples/sec accuracy=62.656250 loss=1.519787 lr=0.010000 Epoch[016] Batch [0349]/[3760] Speed: 62.545136 samples/sec accuracy=62.607143 loss=1.521681 lr=0.010000 Epoch[016] Batch [0399]/[3760] Speed: 62.055272 samples/sec accuracy=62.460938 loss=1.527519 lr=0.010000 Epoch[016] Batch [0449]/[3760] Speed: 62.106783 samples/sec accuracy=62.680556 loss=1.520533 lr=0.010000 Epoch[016] Batch [0499]/[3760] Speed: 62.350472 samples/sec accuracy=62.806250 loss=1.515503 lr=0.010000 Epoch[016] Batch [0549]/[3760] Speed: 62.547891 samples/sec accuracy=62.809659 loss=1.517735 lr=0.010000 Epoch[016] Batch [0599]/[3760] Speed: 62.704752 samples/sec accuracy=62.924479 loss=1.512392 lr=0.010000 Epoch[016] Batch [0649]/[3760] Speed: 62.200648 samples/sec accuracy=62.894231 loss=1.514057 lr=0.010000 Epoch[016] Batch [0699]/[3760] Speed: 62.636260 samples/sec accuracy=62.832589 loss=1.516963 lr=0.010000 Epoch[016] Batch [0749]/[3760] Speed: 62.719638 samples/sec accuracy=62.820833 loss=1.516810 lr=0.010000 Epoch[016] Batch [0799]/[3760] Speed: 62.716490 samples/sec accuracy=62.822266 loss=1.516572 lr=0.010000 Epoch[016] Batch [0849]/[3760] Speed: 62.110460 samples/sec accuracy=62.792279 loss=1.517980 lr=0.010000 Epoch[016] 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accuracy=62.589120 loss=1.527452 lr=0.010000 Epoch[016] Batch [1399]/[3760] Speed: 62.405317 samples/sec accuracy=62.631696 loss=1.527049 lr=0.010000 Epoch[016] Batch [1449]/[3760] Speed: 62.014662 samples/sec accuracy=62.659483 loss=1.526190 lr=0.010000 Epoch[016] Batch [1499]/[3760] Speed: 61.882377 samples/sec accuracy=62.665625 loss=1.526733 lr=0.010000 Epoch[016] Batch [1549]/[3760] Speed: 62.415703 samples/sec accuracy=62.688508 loss=1.525572 lr=0.010000 Epoch[016] Batch [1599]/[3760] Speed: 62.281157 samples/sec accuracy=62.750977 loss=1.522920 lr=0.010000 Epoch[016] Batch [1649]/[3760] Speed: 62.262792 samples/sec accuracy=62.732955 loss=1.524161 lr=0.010000 Epoch[016] Batch [1699]/[3760] Speed: 61.960243 samples/sec accuracy=62.749081 loss=1.522414 lr=0.010000 Epoch[016] Batch [1749]/[3760] Speed: 63.048278 samples/sec accuracy=62.728571 loss=1.523520 lr=0.010000 Epoch[016] Batch [1799]/[3760] Speed: 62.372850 samples/sec accuracy=62.675347 loss=1.525329 lr=0.010000 Epoch[016] 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accuracy=62.541440 loss=1.531409 lr=0.010000 Epoch[016] Batch [2349]/[3760] Speed: 62.651878 samples/sec accuracy=62.521277 loss=1.532282 lr=0.010000 Epoch[016] Batch [2399]/[3760] Speed: 62.435973 samples/sec accuracy=62.511719 loss=1.532926 lr=0.010000 Epoch[016] Batch [2449]/[3760] Speed: 62.904618 samples/sec accuracy=62.542730 loss=1.531771 lr=0.010000 Epoch[016] Batch [2499]/[3760] Speed: 62.009724 samples/sec accuracy=62.541875 loss=1.532543 lr=0.010000 Epoch[016] Batch [2549]/[3760] Speed: 62.647132 samples/sec accuracy=62.523284 loss=1.532639 lr=0.010000 Epoch[016] Batch [2599]/[3760] Speed: 62.216485 samples/sec accuracy=62.534856 loss=1.532164 lr=0.010000 Epoch[016] Batch [2649]/[3760] Speed: 62.633961 samples/sec accuracy=62.531840 loss=1.532304 lr=0.010000 Epoch[016] Batch [2699]/[3760] Speed: 62.409602 samples/sec accuracy=62.515625 loss=1.533273 lr=0.010000 Epoch[016] Batch [2749]/[3760] Speed: 62.151359 samples/sec accuracy=62.511932 loss=1.533100 lr=0.010000 Epoch[016] 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accuracy=62.440385 loss=1.534709 lr=0.010000 Epoch[016] Batch [3299]/[3760] Speed: 62.033944 samples/sec accuracy=62.422822 loss=1.535942 lr=0.010000 Epoch[016] Batch [3349]/[3760] Speed: 62.618143 samples/sec accuracy=62.417444 loss=1.535475 lr=0.010000 Epoch[016] Batch [3399]/[3760] Speed: 61.880869 samples/sec accuracy=62.422335 loss=1.535194 lr=0.010000 Epoch[016] Batch [3449]/[3760] Speed: 62.592060 samples/sec accuracy=62.401268 loss=1.535440 lr=0.010000 Epoch[016] Batch [3499]/[3760] Speed: 62.563232 samples/sec accuracy=62.398214 loss=1.535443 lr=0.010000 Epoch[016] Batch [3549]/[3760] Speed: 62.475397 samples/sec accuracy=62.392165 loss=1.535881 lr=0.010000 Epoch[016] Batch [3599]/[3760] Speed: 62.761392 samples/sec accuracy=62.380208 loss=1.536263 lr=0.010000 Epoch[016] Batch [3649]/[3760] Speed: 62.032802 samples/sec accuracy=62.364298 loss=1.537066 lr=0.010000 Epoch[016] Batch [3699]/[3760] Speed: 62.798541 samples/sec accuracy=62.375000 loss=1.537044 lr=0.010000 Epoch[016] Batch [3749]/[3760] Speed: 67.695590 samples/sec accuracy=62.343750 loss=1.538272 lr=0.010000 Batch [0049]/[0303]: acc-top1=60.187500 acc-top5=82.093750 Batch [0099]/[0303]: acc-top1=60.375000 acc-top5=82.640625 Batch [0149]/[0303]: acc-top1=60.416667 acc-top5=82.468750 Batch [0199]/[0303]: acc-top1=60.320312 acc-top5=82.468750 Batch [0249]/[0303]: acc-top1=60.081250 acc-top5=82.487500 Batch [0299]/[0303]: acc-top1=60.000000 acc-top5=82.541667 [Epoch 016] training: accuracy=62.343334 loss=1.538383 [Epoch 016] speed: 62 samples/sec time cost: 4156.251593 [Epoch 016] validation: acc-top1=59.983498 acc-top5=82.539191 loss=1.805104 Epoch[017] Batch [0049]/[3759] Speed: 42.443188 samples/sec accuracy=62.687500 loss=1.462289 lr=0.010000 Epoch[017] Batch [0099]/[3759] Speed: 60.905756 samples/sec accuracy=62.312500 loss=1.500672 lr=0.010000 Epoch[017] Batch [0149]/[3759] Speed: 62.766407 samples/sec accuracy=62.520833 loss=1.487753 lr=0.010000 Epoch[017] Batch [0199]/[3759] Speed: 61.170566 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lr=0.010000 Epoch[017] Batch [3549]/[3759] Speed: 62.789213 samples/sec accuracy=62.490317 loss=1.526500 lr=0.010000 Epoch[017] Batch [3599]/[3759] Speed: 62.673431 samples/sec accuracy=62.483941 loss=1.527202 lr=0.010000 Epoch[017] Batch [3649]/[3759] Speed: 62.559880 samples/sec accuracy=62.462329 loss=1.528469 lr=0.010000 Epoch[017] Batch [3699]/[3759] Speed: 62.221651 samples/sec accuracy=62.447635 loss=1.528876 lr=0.010000 Epoch[017] Batch [3749]/[3759] Speed: 68.053759 samples/sec accuracy=62.448333 loss=1.528710 lr=0.010000 Batch [0049]/[0303]: acc-top1=59.437500 acc-top5=81.812500 Batch [0099]/[0303]: acc-top1=60.718750 acc-top5=82.703125 Batch [0149]/[0303]: acc-top1=61.083333 acc-top5=83.000000 Batch [0199]/[0303]: acc-top1=61.039062 acc-top5=83.109375 Batch [0249]/[0303]: acc-top1=60.943750 acc-top5=83.050000 Batch [0299]/[0303]: acc-top1=60.812500 acc-top5=83.104167 [Epoch 017] training: accuracy=62.449704 loss=1.528627 [Epoch 017] speed: 62 samples/sec time cost: 4149.510916 [Epoch 017] validation: acc-top1=60.839521 acc-top5=83.127063 loss=1.798129 Epoch[018] Batch [0049]/[3760] Speed: 42.407270 samples/sec accuracy=62.343750 loss=1.487178 lr=0.010000 Epoch[018] Batch [0099]/[3760] Speed: 60.878806 samples/sec accuracy=63.453125 loss=1.452987 lr=0.010000 Epoch[018] Batch [0149]/[3760] Speed: 62.107231 samples/sec accuracy=62.489583 loss=1.476730 lr=0.010000 Epoch[018] Batch [0199]/[3760] Speed: 60.767905 samples/sec accuracy=63.000000 loss=1.468831 lr=0.010000 Epoch[018] Batch [0249]/[3760] Speed: 62.618976 samples/sec accuracy=63.112500 loss=1.478313 lr=0.010000 Epoch[018] Batch [0299]/[3760] Speed: 62.107379 samples/sec accuracy=63.218750 loss=1.477280 lr=0.010000 Epoch[018] Batch [0349]/[3760] Speed: 62.536433 samples/sec accuracy=63.486607 loss=1.472877 lr=0.010000 Epoch[018] Batch [0399]/[3760] Speed: 61.946357 samples/sec accuracy=63.457031 loss=1.474114 lr=0.010000 Epoch[018] Batch [0449]/[3760] Speed: 62.349689 samples/sec 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accuracy=62.996686 loss=1.502490 lr=0.010000 Epoch[018] Batch [3349]/[3760] Speed: 62.640194 samples/sec accuracy=62.989272 loss=1.502918 lr=0.010000 Epoch[018] Batch [3399]/[3760] Speed: 62.750295 samples/sec accuracy=62.947151 loss=1.504664 lr=0.010000 Epoch[018] Batch [3449]/[3760] Speed: 62.745315 samples/sec accuracy=62.935688 loss=1.504694 lr=0.010000 Epoch[018] Batch [3499]/[3760] Speed: 62.419813 samples/sec accuracy=62.912946 loss=1.505156 lr=0.010000 Epoch[018] Batch [3549]/[3760] Speed: 62.543455 samples/sec accuracy=62.916373 loss=1.505051 lr=0.010000 Epoch[018] Batch [3599]/[3760] Speed: 62.973899 samples/sec accuracy=62.906250 loss=1.505455 lr=0.010000 Epoch[018] Batch [3649]/[3760] Speed: 62.477714 samples/sec accuracy=62.893408 loss=1.505665 lr=0.010000 Epoch[018] Batch [3699]/[3760] Speed: 62.679371 samples/sec accuracy=62.876689 loss=1.506599 lr=0.010000 Epoch[018] Batch [3749]/[3760] Speed: 67.330220 samples/sec accuracy=62.871667 loss=1.506522 lr=0.010000 Batch [0049]/[0303]: acc-top1=59.968750 acc-top5=82.468750 Batch [0099]/[0303]: acc-top1=60.453125 acc-top5=83.421875 Batch [0149]/[0303]: acc-top1=60.781250 acc-top5=83.322917 Batch [0199]/[0303]: acc-top1=60.898438 acc-top5=83.351562 Batch [0249]/[0303]: acc-top1=60.606250 acc-top5=83.143750 Batch [0299]/[0303]: acc-top1=60.593750 acc-top5=83.114583 [Epoch 018] training: accuracy=62.873172 loss=1.506678 [Epoch 018] speed: 62 samples/sec time cost: 4145.520712 [Epoch 018] validation: acc-top1=60.597153 acc-top5=83.137376 loss=1.773953 Epoch[019] Batch [0049]/[3760] Speed: 42.205760 samples/sec accuracy=64.250000 loss=1.446838 lr=0.010000 Epoch[019] Batch [0099]/[3760] Speed: 61.260936 samples/sec accuracy=64.343750 loss=1.457265 lr=0.010000 Epoch[019] Batch [0149]/[3760] Speed: 62.255367 samples/sec accuracy=64.552083 loss=1.439786 lr=0.010000 Epoch[019] Batch [0199]/[3760] Speed: 60.686452 samples/sec accuracy=64.468750 loss=1.432795 lr=0.010000 Epoch[019] Batch [0249]/[3760] Speed: 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lr=0.010000 Epoch[019] Batch [3599]/[3760] Speed: 62.704224 samples/sec accuracy=63.250000 loss=1.491374 lr=0.010000 Epoch[019] Batch [3649]/[3760] Speed: 62.253980 samples/sec accuracy=63.231164 loss=1.492466 lr=0.010000 Epoch[019] Batch [3699]/[3760] Speed: 62.368240 samples/sec accuracy=63.220861 loss=1.493080 lr=0.010000 Epoch[019] Batch [3749]/[3760] Speed: 67.638970 samples/sec accuracy=63.207500 loss=1.493868 lr=0.010000 Batch [0049]/[0303]: acc-top1=59.250000 acc-top5=82.125000 Batch [0099]/[0303]: acc-top1=60.562500 acc-top5=82.625000 Batch [0149]/[0303]: acc-top1=60.875000 acc-top5=82.822917 Batch [0199]/[0303]: acc-top1=60.937500 acc-top5=83.085938 Batch [0249]/[0303]: acc-top1=60.587500 acc-top5=82.943750 Batch [0299]/[0303]: acc-top1=60.614583 acc-top5=82.984375 [Epoch 019] training: accuracy=63.196476 loss=1.494310 [Epoch 019] speed: 62 samples/sec time cost: 4152.961324 [Epoch 019] validation: acc-top1=60.628094 acc-top5=83.029084 loss=1.782790 Epoch[020] Batch 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accuracy=63.685168 loss=1.477168 lr=0.010000 Epoch[020] Batch [3399]/[3759] Speed: 62.573347 samples/sec accuracy=63.698989 loss=1.476701 lr=0.010000 Epoch[020] Batch [3449]/[3759] Speed: 62.798165 samples/sec accuracy=63.661232 loss=1.477952 lr=0.010000 Epoch[020] Batch [3499]/[3759] Speed: 62.301254 samples/sec accuracy=63.655357 loss=1.477700 lr=0.010000 Epoch[020] Batch [3549]/[3759] Speed: 62.665860 samples/sec accuracy=63.647007 loss=1.477917 lr=0.010000 Epoch[020] Batch [3599]/[3759] Speed: 62.612941 samples/sec accuracy=63.631510 loss=1.478004 lr=0.010000 Epoch[020] Batch [3649]/[3759] Speed: 62.348509 samples/sec accuracy=63.627568 loss=1.477865 lr=0.010000 Epoch[020] Batch [3699]/[3759] Speed: 62.479511 samples/sec accuracy=63.601774 loss=1.478800 lr=0.010000 Epoch[020] Batch [3749]/[3759] Speed: 67.852777 samples/sec accuracy=63.579167 loss=1.479209 lr=0.010000 Batch [0049]/[0303]: acc-top1=61.312500 acc-top5=82.500000 Batch [0099]/[0303]: acc-top1=61.890625 acc-top5=83.531250 Batch [0149]/[0303]: acc-top1=62.291667 acc-top5=83.541667 Batch [0199]/[0303]: acc-top1=62.164062 acc-top5=83.515625 Batch [0249]/[0303]: acc-top1=62.006250 acc-top5=83.606250 Batch [0299]/[0303]: acc-top1=61.859375 acc-top5=83.755208 [Epoch 020] training: accuracy=63.574505 loss=1.479559 [Epoch 020] speed: 62 samples/sec time cost: 4144.246863 [Epoch 020] validation: acc-top1=61.870875 acc-top5=83.787129 loss=1.742431 Epoch[021] Batch [0049]/[3760] Speed: 41.786505 samples/sec accuracy=64.437500 loss=1.447042 lr=0.010000 Epoch[021] Batch [0099]/[3760] Speed: 60.351539 samples/sec accuracy=65.171875 loss=1.419663 lr=0.010000 Epoch[021] Batch [0149]/[3760] Speed: 62.160212 samples/sec accuracy=64.979167 loss=1.427080 lr=0.010000 Epoch[021] Batch [0199]/[3760] Speed: 61.263660 samples/sec accuracy=64.921875 loss=1.429963 lr=0.010000 Epoch[021] Batch [0249]/[3760] Speed: 62.127715 samples/sec accuracy=65.112500 loss=1.423208 lr=0.010000 Epoch[021] Batch [0299]/[3760] 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accuracy=63.813368 loss=1.467335 lr=0.010000 Epoch[021] Batch [3649]/[3760] Speed: 62.359279 samples/sec accuracy=63.818921 loss=1.466855 lr=0.010000 Epoch[021] Batch [3699]/[3760] Speed: 62.884642 samples/sec accuracy=63.807010 loss=1.467154 lr=0.010000 Epoch[021] Batch [3749]/[3760] Speed: 67.564047 samples/sec accuracy=63.805833 loss=1.467229 lr=0.010000 Batch [0049]/[0303]: acc-top1=60.937500 acc-top5=82.500000 Batch [0099]/[0303]: acc-top1=61.390625 acc-top5=83.453125 Batch [0149]/[0303]: acc-top1=61.625000 acc-top5=83.343750 Batch [0199]/[0303]: acc-top1=61.812500 acc-top5=83.492188 Batch [0249]/[0303]: acc-top1=61.431250 acc-top5=83.643750 Batch [0299]/[0303]: acc-top1=61.421875 acc-top5=83.739583 [Epoch 021] training: accuracy=63.797374 loss=1.467407 [Epoch 021] speed: 62 samples/sec time cost: 4147.548477 [Epoch 021] validation: acc-top1=61.448020 acc-top5=83.761345 loss=1.750819 Epoch[022] Batch [0049]/[3760] Speed: 41.831360 samples/sec accuracy=64.593750 loss=1.412341 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lr=0.010000 Epoch[022] Batch [2949]/[3760] Speed: 62.201352 samples/sec accuracy=64.324153 loss=1.448845 lr=0.010000 Epoch[022] Batch [2999]/[3760] Speed: 62.232431 samples/sec accuracy=64.312500 loss=1.449160 lr=0.010000 Epoch[022] Batch [3049]/[3760] Speed: 62.229293 samples/sec accuracy=64.291496 loss=1.449831 lr=0.010000 Epoch[022] Batch [3099]/[3760] Speed: 62.223869 samples/sec accuracy=64.304435 loss=1.449869 lr=0.010000 Epoch[022] Batch [3149]/[3760] Speed: 62.450116 samples/sec accuracy=64.301587 loss=1.450035 lr=0.010000 Epoch[022] Batch [3199]/[3760] Speed: 62.158214 samples/sec accuracy=64.270020 loss=1.450962 lr=0.010000 Epoch[022] Batch [3249]/[3760] Speed: 62.574301 samples/sec accuracy=64.262500 loss=1.450827 lr=0.010000 Epoch[022] Batch [3299]/[3760] Speed: 63.092502 samples/sec accuracy=64.239583 loss=1.452084 lr=0.010000 Epoch[022] Batch [3349]/[3760] Speed: 62.603119 samples/sec accuracy=64.223414 loss=1.452955 lr=0.010000 Epoch[022] Batch [3399]/[3760] Speed: 62.299326 samples/sec accuracy=64.190717 loss=1.453465 lr=0.010000 Epoch[022] Batch [3449]/[3760] Speed: 62.361911 samples/sec accuracy=64.182971 loss=1.453767 lr=0.010000 Epoch[022] Batch [3499]/[3760] Speed: 62.446826 samples/sec accuracy=64.179018 loss=1.454098 lr=0.010000 Epoch[022] Batch [3549]/[3760] Speed: 62.722592 samples/sec accuracy=64.170775 loss=1.454884 lr=0.010000 Epoch[022] Batch [3599]/[3760] Speed: 62.133860 samples/sec accuracy=64.160590 loss=1.455717 lr=0.010000 Epoch[022] Batch [3649]/[3760] Speed: 62.837564 samples/sec accuracy=64.136986 loss=1.456822 lr=0.010000 Epoch[022] Batch [3699]/[3760] Speed: 62.575448 samples/sec accuracy=64.121622 loss=1.457852 lr=0.010000 Epoch[022] Batch [3749]/[3760] Speed: 67.780230 samples/sec accuracy=64.118750 loss=1.457878 lr=0.010000 Batch [0049]/[0303]: acc-top1=59.187500 acc-top5=82.468750 Batch [0099]/[0303]: acc-top1=61.125000 acc-top5=83.312500 Batch [0149]/[0303]: acc-top1=61.510417 acc-top5=83.312500 Batch [0199]/[0303]: acc-top1=61.570312 acc-top5=83.453125 Batch [0249]/[0303]: acc-top1=61.443750 acc-top5=83.437500 Batch [0299]/[0303]: acc-top1=61.312500 acc-top5=83.640625 [Epoch 022] training: accuracy=64.119016 loss=1.457775 [Epoch 022] speed: 62 samples/sec time cost: 4145.579773 [Epoch 022] validation: acc-top1=61.350041 acc-top5=83.632426 loss=1.766662 Epoch[023] Batch [0049]/[3759] Speed: 42.812426 samples/sec accuracy=65.031250 loss=1.418318 lr=0.010000 Epoch[023] Batch [0099]/[3759] Speed: 60.444904 samples/sec accuracy=64.625000 loss=1.427635 lr=0.010000 Epoch[023] Batch [0149]/[3759] Speed: 62.481750 samples/sec accuracy=64.697917 loss=1.422698 lr=0.010000 Epoch[023] Batch [0199]/[3759] Speed: 61.653192 samples/sec accuracy=64.773438 loss=1.414073 lr=0.010000 Epoch[023] Batch [0249]/[3759] Speed: 62.552108 samples/sec accuracy=64.406250 loss=1.427861 lr=0.010000 Epoch[023] Batch [0299]/[3759] Speed: 61.883781 samples/sec accuracy=64.614583 loss=1.425709 lr=0.010000 Epoch[023] Batch 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accuracy=64.290240 loss=1.445604 lr=0.010000 Epoch[023] Batch [3699]/[3759] Speed: 62.618306 samples/sec accuracy=64.306166 loss=1.445325 lr=0.010000 Epoch[023] Batch [3749]/[3759] Speed: 67.486190 samples/sec accuracy=64.292500 loss=1.445886 lr=0.010000 Batch [0049]/[0303]: acc-top1=59.718750 acc-top5=83.156250 Batch [0099]/[0303]: acc-top1=61.468750 acc-top5=83.937500 Batch [0149]/[0303]: acc-top1=61.781250 acc-top5=83.718750 Batch [0199]/[0303]: acc-top1=61.781250 acc-top5=83.718750 Batch [0249]/[0303]: acc-top1=61.418750 acc-top5=83.493750 Batch [0299]/[0303]: acc-top1=61.421875 acc-top5=83.494792 [Epoch 023] training: accuracy=64.288624 loss=1.445871 [Epoch 023] speed: 62 samples/sec time cost: 4146.716873 [Epoch 023] validation: acc-top1=61.437706 acc-top5=83.503507 loss=1.776315 Epoch[024] Batch [0049]/[3760] Speed: 42.466369 samples/sec accuracy=65.968750 loss=1.386133 lr=0.010000 Epoch[024] Batch [0099]/[3760] Speed: 60.492345 samples/sec accuracy=65.968750 loss=1.369589 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lr=0.010000 Epoch[024] Batch [2999]/[3760] Speed: 62.599411 samples/sec accuracy=64.601562 loss=1.428539 lr=0.010000 Epoch[024] Batch [3049]/[3760] Speed: 62.167535 samples/sec accuracy=64.596824 loss=1.428865 lr=0.010000 Epoch[024] Batch [3099]/[3760] Speed: 62.618044 samples/sec accuracy=64.590222 loss=1.429875 lr=0.010000 Epoch[024] Batch [3149]/[3760] Speed: 61.884145 samples/sec accuracy=64.582341 loss=1.430462 lr=0.010000 Epoch[024] Batch [3199]/[3760] Speed: 62.356417 samples/sec accuracy=64.585449 loss=1.430824 lr=0.010000 Epoch[024] Batch [3249]/[3760] Speed: 62.000920 samples/sec accuracy=64.573077 loss=1.431123 lr=0.010000 Epoch[024] Batch [3299]/[3760] Speed: 62.209769 samples/sec accuracy=64.573864 loss=1.430922 lr=0.010000 Epoch[024] Batch [3349]/[3760] Speed: 62.231888 samples/sec accuracy=64.550373 loss=1.431997 lr=0.010000 Epoch[024] Batch [3399]/[3760] Speed: 62.843000 samples/sec accuracy=64.562500 loss=1.431441 lr=0.010000 Epoch[024] Batch [3449]/[3760] Speed: 62.452377 samples/sec accuracy=64.559783 loss=1.431431 lr=0.010000 Epoch[024] Batch [3499]/[3760] Speed: 62.075172 samples/sec accuracy=64.551786 loss=1.431623 lr=0.010000 Epoch[024] Batch [3549]/[3760] Speed: 62.441648 samples/sec accuracy=64.531690 loss=1.432672 lr=0.010000 Epoch[024] Batch [3599]/[3760] Speed: 62.029871 samples/sec accuracy=64.548177 loss=1.432588 lr=0.010000 Epoch[024] Batch [3649]/[3760] Speed: 62.707320 samples/sec accuracy=64.532962 loss=1.432960 lr=0.010000 Epoch[024] Batch [3699]/[3760] Speed: 62.423834 samples/sec accuracy=64.528294 loss=1.432989 lr=0.010000 Epoch[024] Batch [3749]/[3760] Speed: 67.439457 samples/sec accuracy=64.530833 loss=1.433253 lr=0.010000 Batch [0049]/[0303]: acc-top1=60.281250 acc-top5=82.406250 Batch [0099]/[0303]: acc-top1=61.453125 acc-top5=83.343750 Batch [0149]/[0303]: acc-top1=62.041667 acc-top5=83.531250 Batch [0199]/[0303]: acc-top1=62.156250 acc-top5=83.570312 Batch [0249]/[0303]: acc-top1=62.037500 acc-top5=83.606250 Batch [0299]/[0303]: acc-top1=62.026042 acc-top5=83.645833 [Epoch 024] training: accuracy=64.536237 loss=1.433072 [Epoch 024] speed: 62 samples/sec time cost: 4145.388091 [Epoch 024] validation: acc-top1=62.041048 acc-top5=83.668523 loss=1.776275 Epoch[025] Batch [0049]/[3760] Speed: 42.583415 samples/sec accuracy=65.875000 loss=1.369782 lr=0.010000 Epoch[025] Batch [0099]/[3760] Speed: 60.793603 samples/sec accuracy=65.406250 loss=1.362118 lr=0.010000 Epoch[025] Batch [0149]/[3760] Speed: 62.252823 samples/sec accuracy=65.312500 loss=1.376887 lr=0.010000 Epoch[025] Batch [0199]/[3760] Speed: 60.770405 samples/sec accuracy=65.539062 loss=1.368702 lr=0.010000 Epoch[025] Batch [0249]/[3760] Speed: 62.625073 samples/sec accuracy=65.418750 loss=1.378056 lr=0.010000 Epoch[025] Batch [0299]/[3760] Speed: 61.546259 samples/sec accuracy=65.333333 loss=1.388048 lr=0.010000 Epoch[025] Batch [0349]/[3760] Speed: 62.629022 samples/sec accuracy=65.138393 loss=1.393991 lr=0.010000 Epoch[025] Batch 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accuracy=65.000000 loss=1.410301 lr=0.010000 Epoch[025] Batch [1849]/[3760] Speed: 62.330818 samples/sec accuracy=64.967061 loss=1.411780 lr=0.010000 Epoch[025] Batch [1899]/[3760] Speed: 62.757697 samples/sec accuracy=64.951480 loss=1.412434 lr=0.010000 Epoch[025] Batch [1949]/[3760] Speed: 62.348578 samples/sec accuracy=64.951122 loss=1.411920 lr=0.010000 Epoch[025] Batch [1999]/[3760] Speed: 62.421576 samples/sec accuracy=64.961719 loss=1.412460 lr=0.010000 Epoch[025] Batch [2049]/[3760] Speed: 62.759581 samples/sec accuracy=64.960366 loss=1.412951 lr=0.010000 Epoch[025] Batch [2099]/[3760] Speed: 62.600292 samples/sec accuracy=64.967262 loss=1.412997 lr=0.010000 Epoch[025] Batch [2149]/[3760] Speed: 62.437016 samples/sec accuracy=64.941860 loss=1.413479 lr=0.010000 Epoch[025] Batch [2199]/[3760] Speed: 61.950542 samples/sec accuracy=64.938920 loss=1.413771 lr=0.010000 Epoch[025] Batch [2249]/[3760] Speed: 63.152195 samples/sec accuracy=64.900000 loss=1.414949 lr=0.010000 Epoch[025] 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accuracy=64.797159 loss=1.419195 lr=0.010000 Epoch[025] Batch [2799]/[3760] Speed: 62.227658 samples/sec accuracy=64.790179 loss=1.419435 lr=0.010000 Epoch[025] Batch [2849]/[3760] Speed: 62.274967 samples/sec accuracy=64.767544 loss=1.420528 lr=0.010000 Epoch[025] Batch [2899]/[3760] Speed: 62.249676 samples/sec accuracy=64.742457 loss=1.420817 lr=0.010000 Epoch[025] Batch [2949]/[3760] Speed: 62.238771 samples/sec accuracy=64.728814 loss=1.421509 lr=0.010000 Epoch[025] Batch [2999]/[3760] Speed: 61.892613 samples/sec accuracy=64.719271 loss=1.421970 lr=0.010000 Epoch[025] Batch [3049]/[3760] Speed: 61.837629 samples/sec accuracy=64.705943 loss=1.422685 lr=0.010000 Epoch[025] Batch [3099]/[3760] Speed: 62.444335 samples/sec accuracy=64.705141 loss=1.423220 lr=0.010000 Epoch[025] Batch [3149]/[3760] Speed: 62.005381 samples/sec accuracy=64.694444 loss=1.423880 lr=0.010000 Epoch[025] Batch [3199]/[3760] Speed: 62.191076 samples/sec accuracy=64.691895 loss=1.424638 lr=0.010000 Epoch[025] 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accuracy=64.626689 loss=1.428724 lr=0.010000 Epoch[025] Batch [3749]/[3760] Speed: 67.375508 samples/sec accuracy=64.643750 loss=1.428313 lr=0.010000 Batch [0049]/[0303]: acc-top1=60.531250 acc-top5=82.718750 Batch [0099]/[0303]: acc-top1=61.750000 acc-top5=83.312500 Batch [0149]/[0303]: acc-top1=62.072917 acc-top5=83.520833 Batch [0199]/[0303]: acc-top1=61.984375 acc-top5=83.484375 Batch [0249]/[0303]: acc-top1=61.625000 acc-top5=83.381250 Batch [0299]/[0303]: acc-top1=61.489583 acc-top5=83.526042 [Epoch 025] training: accuracy=64.643035 loss=1.428463 [Epoch 025] speed: 62 samples/sec time cost: 4147.466890 [Epoch 025] validation: acc-top1=61.540842 acc-top5=83.565388 loss=1.824810 Epoch[026] Batch [0049]/[3759] Speed: 41.944281 samples/sec accuracy=65.312500 loss=1.403761 lr=0.010000 Epoch[026] Batch [0099]/[3759] Speed: 60.593781 samples/sec accuracy=65.421875 loss=1.372381 lr=0.010000 Epoch[026] Batch [0149]/[3759] Speed: 62.252507 samples/sec accuracy=66.156250 loss=1.354840 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lr=0.010000 Epoch[026] Batch [2099]/[3759] Speed: 62.214312 samples/sec accuracy=65.198661 loss=1.402914 lr=0.010000 Epoch[026] Batch [2149]/[3759] Speed: 62.891740 samples/sec accuracy=65.205669 loss=1.403067 lr=0.010000 Epoch[026] Batch [2199]/[3759] Speed: 62.500541 samples/sec accuracy=65.194602 loss=1.403919 lr=0.010000 Epoch[026] Batch [2249]/[3759] Speed: 62.718582 samples/sec accuracy=65.186806 loss=1.403999 lr=0.010000 Epoch[026] Batch [2299]/[3759] Speed: 62.420392 samples/sec accuracy=65.161005 loss=1.405494 lr=0.010000 Epoch[026] Batch [2349]/[3759] Speed: 62.159968 samples/sec accuracy=65.148271 loss=1.405888 lr=0.010000 Epoch[026] Batch [2399]/[3759] Speed: 62.384145 samples/sec accuracy=65.156250 loss=1.406025 lr=0.010000 Epoch[026] Batch [2449]/[3759] Speed: 62.593235 samples/sec accuracy=65.151148 loss=1.406740 lr=0.010000 Epoch[026] Batch [2499]/[3759] Speed: 61.873492 samples/sec accuracy=65.140000 loss=1.407502 lr=0.010000 Epoch[026] Batch [2549]/[3759] Speed: 62.385036 samples/sec accuracy=65.132966 loss=1.407617 lr=0.010000 Epoch[026] Batch [2599]/[3759] Speed: 62.359669 samples/sec accuracy=65.115986 loss=1.408410 lr=0.010000 Epoch[026] Batch [2649]/[3759] Speed: 62.470394 samples/sec accuracy=65.117335 loss=1.409496 lr=0.010000 Epoch[026] Batch [2699]/[3759] Speed: 62.621575 samples/sec accuracy=65.096644 loss=1.410772 lr=0.010000 Epoch[026] Batch [2749]/[3759] Speed: 62.259961 samples/sec accuracy=65.098295 loss=1.411333 lr=0.010000 Epoch[026] Batch [2799]/[3759] Speed: 62.119740 samples/sec accuracy=65.070312 loss=1.412626 lr=0.010000 Epoch[026] Batch [2849]/[3759] Speed: 62.455400 samples/sec accuracy=65.095943 loss=1.412421 lr=0.010000 Epoch[026] Batch [2899]/[3759] Speed: 62.737580 samples/sec accuracy=65.066272 loss=1.413642 lr=0.010000 Epoch[026] Batch [2949]/[3759] Speed: 61.730159 samples/sec accuracy=65.039195 loss=1.414774 lr=0.010000 Epoch[026] Batch [2999]/[3759] Speed: 62.104098 samples/sec accuracy=65.048958 loss=1.414426 lr=0.010000 Epoch[026] Batch [3049]/[3759] Speed: 62.523539 samples/sec accuracy=65.027664 loss=1.416038 lr=0.010000 Epoch[026] Batch [3099]/[3759] Speed: 62.269198 samples/sec accuracy=65.025706 loss=1.416093 lr=0.010000 Epoch[026] Batch [3149]/[3759] Speed: 62.643106 samples/sec accuracy=64.989583 loss=1.417803 lr=0.010000 Epoch[026] Batch [3199]/[3759] Speed: 62.502093 samples/sec accuracy=64.985352 loss=1.418360 lr=0.010000 Epoch[026] Batch [3249]/[3759] Speed: 62.393206 samples/sec accuracy=64.974038 loss=1.418632 lr=0.010000 Epoch[026] Batch [3299]/[3759] Speed: 62.218519 samples/sec accuracy=64.965436 loss=1.419140 lr=0.010000 Epoch[026] Batch [3349]/[3759] Speed: 62.679535 samples/sec accuracy=64.941698 loss=1.419993 lr=0.010000 Epoch[026] Batch [3399]/[3759] Speed: 62.618681 samples/sec accuracy=64.915901 loss=1.421083 lr=0.010000 Epoch[026] Batch [3449]/[3759] Speed: 62.182271 samples/sec accuracy=64.904438 loss=1.421425 lr=0.010000 Epoch[026] Batch [3499]/[3759] Speed: 62.325330 samples/sec accuracy=64.916071 loss=1.420985 lr=0.010000 Epoch[026] Batch [3549]/[3759] Speed: 61.966129 samples/sec accuracy=64.905370 loss=1.422100 lr=0.010000 Epoch[026] Batch [3599]/[3759] Speed: 62.950277 samples/sec accuracy=64.875000 loss=1.422961 lr=0.010000 Epoch[026] Batch [3649]/[3759] Speed: 62.345793 samples/sec accuracy=64.860017 loss=1.423493 lr=0.010000 Epoch[026] Batch [3699]/[3759] Speed: 61.755571 samples/sec accuracy=64.851774 loss=1.423777 lr=0.010000 Epoch[026] Batch [3749]/[3759] Speed: 67.932234 samples/sec accuracy=64.848750 loss=1.423685 lr=0.010000 Batch [0049]/[0303]: acc-top1=61.000000 acc-top5=83.500000 Batch [0099]/[0303]: acc-top1=61.687500 acc-top5=83.890625 Batch [0149]/[0303]: acc-top1=62.031250 acc-top5=83.697917 Batch [0199]/[0303]: acc-top1=62.101562 acc-top5=83.718750 Batch [0249]/[0303]: acc-top1=61.756250 acc-top5=83.543750 Batch [0299]/[0303]: acc-top1=61.864583 acc-top5=83.645833 [Epoch 026] training: accuracy=64.847699 loss=1.423881 [Epoch 026] speed: 62 samples/sec time cost: 4147.676244 [Epoch 026] validation: acc-top1=61.876031 acc-top5=83.668523 loss=1.760328 Epoch[027] Batch [0049]/[3760] Speed: 42.114622 samples/sec accuracy=66.843750 loss=1.349514 lr=0.010000 Epoch[027] Batch [0099]/[3760] Speed: 60.301177 samples/sec accuracy=67.265625 loss=1.323592 lr=0.010000 Epoch[027] Batch [0149]/[3760] Speed: 62.231974 samples/sec accuracy=67.156250 loss=1.333711 lr=0.010000 Epoch[027] Batch [0199]/[3760] Speed: 60.923073 samples/sec accuracy=66.734375 loss=1.343349 lr=0.010000 Epoch[027] Batch [0249]/[3760] Speed: 62.496683 samples/sec accuracy=66.462500 loss=1.349073 lr=0.010000 Epoch[027] Batch [0299]/[3760] Speed: 62.174973 samples/sec accuracy=66.453125 loss=1.347584 lr=0.010000 Epoch[027] Batch [0349]/[3760] Speed: 62.348734 samples/sec accuracy=66.303571 loss=1.353878 lr=0.010000 Epoch[027] Batch [0399]/[3760] Speed: 62.235363 samples/sec accuracy=66.203125 loss=1.358491 lr=0.010000 Epoch[027] Batch 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accuracy=65.039167 loss=1.407883 lr=0.010000 Batch [0049]/[0303]: acc-top1=60.250000 acc-top5=82.937500 Batch [0099]/[0303]: acc-top1=61.531250 acc-top5=83.609375 Batch [0149]/[0303]: acc-top1=61.906250 acc-top5=83.697917 Batch [0199]/[0303]: acc-top1=62.054688 acc-top5=83.859375 Batch [0249]/[0303]: acc-top1=61.693750 acc-top5=83.775000 Batch [0299]/[0303]: acc-top1=61.703125 acc-top5=83.750000 [Epoch 027] training: accuracy=65.042387 loss=1.407762 [Epoch 027] speed: 62 samples/sec time cost: 4150.425340 [Epoch 027] validation: acc-top1=61.705858 acc-top5=83.766502 loss=1.778576 Epoch[028] Batch [0049]/[3760] Speed: 41.996828 samples/sec accuracy=67.156250 loss=1.301306 lr=0.010000 Epoch[028] Batch [0099]/[3760] Speed: 61.170732 samples/sec accuracy=67.578125 loss=1.299886 lr=0.010000 Epoch[028] Batch [0149]/[3760] Speed: 62.181395 samples/sec accuracy=66.739583 loss=1.339997 lr=0.010000 Epoch[028] Batch [0199]/[3760] Speed: 61.438329 samples/sec accuracy=66.437500 loss=1.353420 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lr=0.010000 Epoch[028] Batch [2149]/[3760] Speed: 62.679681 samples/sec accuracy=65.582122 loss=1.390305 lr=0.010000 Epoch[028] Batch [2199]/[3760] Speed: 62.430519 samples/sec accuracy=65.583097 loss=1.390241 lr=0.010000 Epoch[028] Batch [2249]/[3760] Speed: 62.556463 samples/sec accuracy=65.567361 loss=1.391170 lr=0.010000 Epoch[028] Batch [2299]/[3760] Speed: 62.587308 samples/sec accuracy=65.556386 loss=1.391433 lr=0.010000 Epoch[028] Batch [2349]/[3760] Speed: 62.502683 samples/sec accuracy=65.532580 loss=1.392076 lr=0.010000 Epoch[028] Batch [2399]/[3760] Speed: 62.135191 samples/sec accuracy=65.523438 loss=1.392356 lr=0.010000 Epoch[028] Batch [2449]/[3760] Speed: 62.338040 samples/sec accuracy=65.505740 loss=1.392658 lr=0.010000 Epoch[028] Batch [2499]/[3760] Speed: 62.528515 samples/sec accuracy=65.476875 loss=1.393496 lr=0.010000 Epoch[028] Batch [2549]/[3760] Speed: 62.650047 samples/sec accuracy=65.458333 loss=1.394319 lr=0.010000 Epoch[028] Batch [2599]/[3760] Speed: 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lr=0.010000 Epoch[028] Batch [3099]/[3760] Speed: 62.606114 samples/sec accuracy=65.313508 loss=1.399873 lr=0.010000 Epoch[028] Batch [3149]/[3760] Speed: 62.405176 samples/sec accuracy=65.313988 loss=1.399834 lr=0.010000 Epoch[028] Batch [3199]/[3760] Speed: 61.983358 samples/sec accuracy=65.309082 loss=1.400100 lr=0.010000 Epoch[028] Batch [3249]/[3760] Speed: 62.270653 samples/sec accuracy=65.277404 loss=1.401366 lr=0.010000 Epoch[028] Batch [3299]/[3760] Speed: 61.861676 samples/sec accuracy=65.267519 loss=1.401090 lr=0.010000 Epoch[028] Batch [3349]/[3760] Speed: 62.569212 samples/sec accuracy=65.263526 loss=1.400819 lr=0.010000 Epoch[028] Batch [3399]/[3760] Speed: 62.078660 samples/sec accuracy=65.246783 loss=1.401447 lr=0.010000 Epoch[028] Batch [3449]/[3760] Speed: 62.487705 samples/sec accuracy=65.251812 loss=1.400964 lr=0.010000 Epoch[028] Batch [3499]/[3760] Speed: 62.156482 samples/sec accuracy=65.235268 loss=1.401486 lr=0.010000 Epoch[028] Batch [3549]/[3760] Speed: 62.433686 samples/sec accuracy=65.227113 loss=1.401949 lr=0.010000 Epoch[028] Batch [3599]/[3760] Speed: 62.366518 samples/sec accuracy=65.217448 loss=1.402472 lr=0.010000 Epoch[028] Batch [3649]/[3760] Speed: 62.576470 samples/sec accuracy=65.202483 loss=1.403444 lr=0.010000 Epoch[028] Batch [3699]/[3760] Speed: 62.233643 samples/sec accuracy=65.207770 loss=1.403481 lr=0.010000 Epoch[028] Batch [3749]/[3760] Speed: 67.424177 samples/sec accuracy=65.218750 loss=1.402637 lr=0.010000 Batch [0049]/[0303]: acc-top1=61.031250 acc-top5=82.375000 Batch [0099]/[0303]: acc-top1=62.000000 acc-top5=83.328125 Batch [0149]/[0303]: acc-top1=61.979167 acc-top5=83.489583 Batch [0199]/[0303]: acc-top1=61.781250 acc-top5=83.578125 Batch [0249]/[0303]: acc-top1=61.662500 acc-top5=83.575000 Batch [0299]/[0303]: acc-top1=61.625000 acc-top5=83.609375 [Epoch 028] training: accuracy=65.216506 loss=1.402754 [Epoch 028] speed: 62 samples/sec time cost: 4150.067995 [Epoch 028] validation: acc-top1=61.654290 acc-top5=83.627269 loss=1.793003 Epoch[029] Batch [0049]/[3759] Speed: 42.297584 samples/sec accuracy=66.031250 loss=1.374260 lr=0.010000 Epoch[029] Batch [0099]/[3759] Speed: 60.900398 samples/sec accuracy=65.875000 loss=1.375205 lr=0.010000 Epoch[029] Batch [0149]/[3759] Speed: 62.098456 samples/sec accuracy=66.020833 loss=1.371229 lr=0.010000 Epoch[029] Batch [0199]/[3759] Speed: 60.974993 samples/sec accuracy=66.164062 loss=1.360429 lr=0.010000 Epoch[029] Batch [0249]/[3759] Speed: 62.747509 samples/sec accuracy=66.200000 loss=1.363662 lr=0.010000 Epoch[029] Batch [0299]/[3759] Speed: 61.579127 samples/sec accuracy=66.265625 loss=1.361812 lr=0.010000 Epoch[029] Batch [0349]/[3759] Speed: 62.800508 samples/sec accuracy=66.214286 loss=1.368482 lr=0.010000 Epoch[029] Batch [0399]/[3759] Speed: 62.226345 samples/sec accuracy=66.187500 loss=1.366484 lr=0.010000 Epoch[029] Batch [0449]/[3759] Speed: 62.220419 samples/sec accuracy=66.180556 loss=1.365492 lr=0.010000 Epoch[029] Batch 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[0099]/[0303]: acc-top1=60.781250 acc-top5=83.578125 Batch [0149]/[0303]: acc-top1=61.625000 acc-top5=83.802083 Batch [0199]/[0303]: acc-top1=61.710937 acc-top5=83.953125 Batch [0249]/[0303]: acc-top1=61.631250 acc-top5=83.906250 Batch [0299]/[0303]: acc-top1=61.541667 acc-top5=83.937500 [Epoch 029] training: accuracy=65.318236 loss=1.400073 [Epoch 029] speed: 61 samples/sec time cost: 4150.122814 [Epoch 029] validation: acc-top1=61.556312 acc-top5=83.962459 loss=1.789777 Epoch[030] Batch [0049]/[3760] Speed: 42.637352 samples/sec accuracy=65.812500 loss=1.335711 lr=0.010000 Epoch[030] Batch [0099]/[3760] Speed: 60.698527 samples/sec accuracy=65.593750 loss=1.344374 lr=0.010000 Epoch[030] Batch [0149]/[3760] Speed: 61.726818 samples/sec accuracy=65.697917 loss=1.351873 lr=0.010000 Epoch[030] Batch [0199]/[3760] Speed: 61.025541 samples/sec accuracy=66.015625 loss=1.346620 lr=0.010000 Epoch[030] Batch [0249]/[3760] Speed: 62.822348 samples/sec accuracy=66.200000 loss=1.344848 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lr=0.010000 Epoch[030] Batch [3149]/[3760] Speed: 62.661800 samples/sec accuracy=65.660218 loss=1.387430 lr=0.010000 Epoch[030] Batch [3199]/[3760] Speed: 62.299681 samples/sec accuracy=65.656250 loss=1.387519 lr=0.010000 Epoch[030] Batch [3249]/[3760] Speed: 62.548136 samples/sec accuracy=65.636538 loss=1.388431 lr=0.010000 Epoch[030] Batch [3299]/[3760] Speed: 62.377636 samples/sec accuracy=65.637784 loss=1.388678 lr=0.010000 Epoch[030] Batch [3349]/[3760] Speed: 62.600203 samples/sec accuracy=65.632929 loss=1.388957 lr=0.010000 Epoch[030] Batch [3399]/[3760] Speed: 62.357689 samples/sec accuracy=65.617647 loss=1.389116 lr=0.010000 Epoch[030] Batch [3449]/[3760] Speed: 62.636973 samples/sec accuracy=65.612319 loss=1.389316 lr=0.010000 Epoch[030] Batch [3499]/[3760] Speed: 61.980900 samples/sec accuracy=65.598214 loss=1.389773 lr=0.010000 Epoch[030] Batch [3549]/[3760] Speed: 62.576576 samples/sec accuracy=65.582746 loss=1.389676 lr=0.010000 Epoch[030] Batch [3599]/[3760] Speed: 62.528215 samples/sec accuracy=65.572483 loss=1.390098 lr=0.010000 Epoch[030] Batch [3649]/[3760] Speed: 62.390290 samples/sec accuracy=65.555223 loss=1.390652 lr=0.010000 Epoch[030] Batch [3699]/[3760] Speed: 62.550813 samples/sec accuracy=65.538429 loss=1.391555 lr=0.010000 Epoch[030] Batch [3749]/[3760] Speed: 67.325279 samples/sec accuracy=65.518750 loss=1.392197 lr=0.010000 Batch [0049]/[0303]: acc-top1=60.312500 acc-top5=82.218750 Batch [0099]/[0303]: acc-top1=61.593750 acc-top5=82.968750 Batch [0149]/[0303]: acc-top1=62.020833 acc-top5=83.229167 Batch [0199]/[0303]: acc-top1=61.953125 acc-top5=83.398438 Batch [0249]/[0303]: acc-top1=61.687500 acc-top5=83.293750 Batch [0299]/[0303]: acc-top1=61.588542 acc-top5=83.265625 [Epoch 030] training: accuracy=65.517786 loss=1.392422 [Epoch 030] speed: 62 samples/sec time cost: 4148.864289 [Epoch 030] validation: acc-top1=61.602723 acc-top5=83.281766 loss=1.813581 Epoch[031] Batch [0049]/[3760] Speed: 42.341155 samples/sec 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acc-top5=83.510417 Batch [0199]/[0303]: acc-top1=62.164062 acc-top5=83.640625 Batch [0249]/[0303]: acc-top1=61.968750 acc-top5=83.587500 Batch [0299]/[0303]: acc-top1=61.906250 acc-top5=83.614583 [Epoch 031] training: accuracy=65.551031 loss=1.391227 [Epoch 031] speed: 62 samples/sec time cost: 4150.975056 [Epoch 031] validation: acc-top1=61.917285 acc-top5=83.627269 loss=1.768165 Epoch[032] Batch [0049]/[3759] Speed: 42.092237 samples/sec accuracy=68.000000 loss=1.292652 lr=0.010000 Epoch[032] Batch [0099]/[3759] Speed: 60.664200 samples/sec accuracy=67.750000 loss=1.298693 lr=0.010000 Epoch[032] Batch [0149]/[3759] Speed: 62.007412 samples/sec accuracy=67.708333 loss=1.300386 lr=0.010000 Epoch[032] Batch [0199]/[3759] Speed: 61.054723 samples/sec accuracy=67.429688 loss=1.305799 lr=0.010000 Epoch[032] Batch [0249]/[3759] Speed: 62.496397 samples/sec accuracy=67.237500 loss=1.311991 lr=0.010000 Epoch[032] Batch [0299]/[3759] Speed: 61.732543 samples/sec accuracy=66.932292 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accuracy=65.856534 loss=1.375237 lr=0.010000 Epoch[032] Batch [2249]/[3759] Speed: 62.094821 samples/sec accuracy=65.861806 loss=1.375564 lr=0.010000 Epoch[032] Batch [2299]/[3759] Speed: 62.159051 samples/sec accuracy=65.850543 loss=1.376089 lr=0.010000 Epoch[032] Batch [2349]/[3759] Speed: 62.351897 samples/sec accuracy=65.859707 loss=1.376161 lr=0.010000 Epoch[032] Batch [2399]/[3759] Speed: 62.072403 samples/sec accuracy=65.848307 loss=1.375737 lr=0.010000 Epoch[032] Batch [2449]/[3759] Speed: 62.435862 samples/sec accuracy=65.846301 loss=1.376337 lr=0.010000 Epoch[032] Batch [2499]/[3759] Speed: 61.823177 samples/sec accuracy=65.816875 loss=1.376854 lr=0.010000 Epoch[032] Batch [2549]/[3759] Speed: 62.537797 samples/sec accuracy=65.830270 loss=1.376765 lr=0.010000 Epoch[032] Batch [2599]/[3759] Speed: 61.825116 samples/sec accuracy=65.805889 loss=1.376954 lr=0.010000 Epoch[032] Batch [2649]/[3759] Speed: 62.522775 samples/sec accuracy=65.810142 loss=1.376396 lr=0.010000 Epoch[032] 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accuracy=65.796131 loss=1.378173 lr=0.010000 Epoch[032] Batch [3199]/[3759] Speed: 62.442165 samples/sec accuracy=65.788086 loss=1.378398 lr=0.010000 Epoch[032] Batch [3249]/[3759] Speed: 62.247139 samples/sec accuracy=65.789904 loss=1.378518 lr=0.010000 Epoch[032] Batch [3299]/[3759] Speed: 62.987465 samples/sec accuracy=65.760417 loss=1.380176 lr=0.010000 Epoch[032] Batch [3349]/[3759] Speed: 62.155241 samples/sec accuracy=65.743470 loss=1.380243 lr=0.010000 Epoch[032] Batch [3399]/[3759] Speed: 62.559643 samples/sec accuracy=65.724265 loss=1.381440 lr=0.010000 Epoch[032] Batch [3449]/[3759] Speed: 62.276283 samples/sec accuracy=65.713315 loss=1.381351 lr=0.010000 Epoch[032] Batch [3499]/[3759] Speed: 62.562376 samples/sec accuracy=65.692411 loss=1.382208 lr=0.010000 Epoch[032] Batch [3549]/[3759] Speed: 62.150540 samples/sec accuracy=65.681778 loss=1.382625 lr=0.010000 Epoch[032] Batch [3599]/[3759] Speed: 63.042154 samples/sec accuracy=65.676649 loss=1.382521 lr=0.010000 Epoch[032] Batch [3649]/[3759] Speed: 61.955124 samples/sec accuracy=65.643408 loss=1.383556 lr=0.010000 Epoch[032] Batch [3699]/[3759] Speed: 62.755114 samples/sec accuracy=65.633446 loss=1.383530 lr=0.010000 Epoch[032] Batch [3749]/[3759] Speed: 68.151293 samples/sec accuracy=65.620000 loss=1.383904 lr=0.010000 Batch [0049]/[0303]: acc-top1=60.375000 acc-top5=81.781250 Batch [0099]/[0303]: acc-top1=61.453125 acc-top5=82.671875 Batch [0149]/[0303]: acc-top1=61.729167 acc-top5=82.979167 Batch [0199]/[0303]: acc-top1=61.875000 acc-top5=83.218750 Batch [0249]/[0303]: acc-top1=61.393750 acc-top5=83.268750 Batch [0299]/[0303]: acc-top1=61.437500 acc-top5=83.338542 [Epoch 032] training: accuracy=65.621675 loss=1.383839 [Epoch 032] speed: 62 samples/sec time cost: 4151.070209 [Epoch 032] validation: acc-top1=61.448020 acc-top5=83.343647 loss=1.848973 Epoch[033] Batch [0049]/[3760] Speed: 42.282239 samples/sec accuracy=67.531250 loss=1.303877 lr=0.010000 Epoch[033] Batch [0099]/[3760] Speed: 59.974646 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lr=0.010000 Epoch[033] Batch [3449]/[3760] Speed: 61.803036 samples/sec accuracy=65.918025 loss=1.376268 lr=0.010000 Epoch[033] Batch [3499]/[3760] Speed: 62.472762 samples/sec accuracy=65.898214 loss=1.376995 lr=0.010000 Epoch[033] Batch [3549]/[3760] Speed: 62.555271 samples/sec accuracy=65.909331 loss=1.377585 lr=0.010000 Epoch[033] Batch [3599]/[3760] Speed: 62.379119 samples/sec accuracy=65.878906 loss=1.378869 lr=0.010000 Epoch[033] Batch [3649]/[3760] Speed: 62.065956 samples/sec accuracy=65.865154 loss=1.379118 lr=0.010000 Epoch[033] Batch [3699]/[3760] Speed: 62.112351 samples/sec accuracy=65.859375 loss=1.379471 lr=0.010000 Epoch[033] Batch [3749]/[3760] Speed: 66.911892 samples/sec accuracy=65.855833 loss=1.379463 lr=0.010000 Batch [0049]/[0303]: acc-top1=60.843750 acc-top5=82.906250 Batch [0099]/[0303]: acc-top1=61.656250 acc-top5=83.890625 Batch [0149]/[0303]: acc-top1=62.322917 acc-top5=83.864583 Batch [0199]/[0303]: acc-top1=62.453125 acc-top5=83.953125 Batch [0249]/[0303]: acc-top1=62.418750 acc-top5=83.893750 Batch [0299]/[0303]: acc-top1=62.317708 acc-top5=83.864583 [Epoch 033] training: accuracy=65.856882 loss=1.379470 [Epoch 033] speed: 62 samples/sec time cost: 4148.573598 [Epoch 033] validation: acc-top1=62.386551 acc-top5=83.885107 loss=1.809335 Epoch[034] Batch [0049]/[3759] Speed: 43.255572 samples/sec accuracy=67.156250 loss=1.336188 lr=0.010000 Epoch[034] Batch [0099]/[3759] Speed: 60.763839 samples/sec accuracy=67.687500 loss=1.308254 lr=0.010000 Epoch[034] Batch [0149]/[3759] Speed: 62.586727 samples/sec accuracy=67.979167 loss=1.298408 lr=0.010000 Epoch[034] Batch [0199]/[3759] Speed: 62.024375 samples/sec accuracy=67.523438 loss=1.303383 lr=0.010000 Epoch[034] Batch [0249]/[3759] Speed: 62.701173 samples/sec accuracy=67.418750 loss=1.308547 lr=0.010000 Epoch[034] Batch [0299]/[3759] Speed: 62.517403 samples/sec accuracy=67.380208 loss=1.307145 lr=0.010000 Epoch[034] Batch [0349]/[3759] Speed: 62.550466 samples/sec 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accuracy=66.741587 loss=1.339816 lr=0.010000 Epoch[034] Batch [1349]/[3759] Speed: 62.484840 samples/sec accuracy=66.738426 loss=1.340599 lr=0.010000 Epoch[034] Batch [1399]/[3759] Speed: 62.222162 samples/sec accuracy=66.717634 loss=1.341567 lr=0.010000 Epoch[034] Batch [1449]/[3759] Speed: 61.881389 samples/sec accuracy=66.757543 loss=1.340446 lr=0.010000 Epoch[034] Batch [1499]/[3759] Speed: 62.177895 samples/sec accuracy=66.718750 loss=1.342539 lr=0.010000 Epoch[034] Batch [1549]/[3759] Speed: 62.715265 samples/sec accuracy=66.698589 loss=1.343270 lr=0.010000 Epoch[034] Batch [1599]/[3759] Speed: 62.372789 samples/sec accuracy=66.653320 loss=1.345496 lr=0.010000 Epoch[034] Batch [1649]/[3759] Speed: 62.304823 samples/sec accuracy=66.568182 loss=1.348340 lr=0.010000 Epoch[034] Batch [1699]/[3759] Speed: 62.525416 samples/sec accuracy=66.525735 loss=1.350645 lr=0.010000 Epoch[034] Batch [1749]/[3759] Speed: 62.576280 samples/sec accuracy=66.536607 loss=1.351250 lr=0.010000 Epoch[034] 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accuracy=66.356250 loss=1.355895 lr=0.010000 Epoch[034] Batch [2299]/[3759] Speed: 62.623788 samples/sec accuracy=66.344429 loss=1.356254 lr=0.010000 Epoch[034] Batch [2349]/[3759] Speed: 62.214011 samples/sec accuracy=66.311170 loss=1.357362 lr=0.010000 Epoch[034] Batch [2399]/[3759] Speed: 62.599630 samples/sec accuracy=66.286458 loss=1.358436 lr=0.010000 Epoch[034] Batch [2449]/[3759] Speed: 62.709001 samples/sec accuracy=66.285077 loss=1.358642 lr=0.010000 Epoch[034] Batch [2499]/[3759] Speed: 62.712059 samples/sec accuracy=66.312500 loss=1.357642 lr=0.010000 Epoch[034] Batch [2549]/[3759] Speed: 62.150828 samples/sec accuracy=66.297181 loss=1.358841 lr=0.010000 Epoch[034] Batch [2599]/[3759] Speed: 63.155247 samples/sec accuracy=66.283053 loss=1.358695 lr=0.010000 Epoch[034] Batch [2649]/[3759] Speed: 62.612903 samples/sec accuracy=66.270637 loss=1.359001 lr=0.010000 Epoch[034] Batch [2699]/[3759] Speed: 62.047691 samples/sec accuracy=66.281250 loss=1.358954 lr=0.010000 Epoch[034] 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accuracy=66.121582 loss=1.364888 lr=0.010000 Epoch[034] Batch [3249]/[3759] Speed: 62.137332 samples/sec accuracy=66.093750 loss=1.365427 lr=0.010000 Epoch[034] Batch [3299]/[3759] Speed: 62.254212 samples/sec accuracy=66.073864 loss=1.366455 lr=0.010000 Epoch[034] Batch [3349]/[3759] Speed: 62.521180 samples/sec accuracy=66.075093 loss=1.365932 lr=0.010000 Epoch[034] Batch [3399]/[3759] Speed: 62.635637 samples/sec accuracy=66.061581 loss=1.366812 lr=0.010000 Epoch[034] Batch [3449]/[3759] Speed: 62.571413 samples/sec accuracy=66.054801 loss=1.366621 lr=0.010000 Epoch[034] Batch [3499]/[3759] Speed: 62.422294 samples/sec accuracy=66.040625 loss=1.366998 lr=0.010000 Epoch[034] Batch [3549]/[3759] Speed: 62.245207 samples/sec accuracy=66.030810 loss=1.368159 lr=0.010000 Epoch[034] Batch [3599]/[3759] Speed: 62.696361 samples/sec accuracy=66.019965 loss=1.368595 lr=0.010000 Epoch[034] Batch [3649]/[3759] Speed: 62.471896 samples/sec accuracy=66.017979 loss=1.368426 lr=0.010000 Epoch[034] Batch [3699]/[3759] Speed: 62.755371 samples/sec accuracy=65.992399 loss=1.369040 lr=0.010000 Epoch[034] Batch [3749]/[3759] Speed: 67.685946 samples/sec accuracy=65.970417 loss=1.370245 lr=0.010000 Batch [0049]/[0303]: acc-top1=59.843750 acc-top5=81.718750 Batch [0099]/[0303]: acc-top1=60.812500 acc-top5=82.437500 Batch [0149]/[0303]: acc-top1=60.947917 acc-top5=82.489583 Batch [0199]/[0303]: acc-top1=61.007812 acc-top5=82.703125 Batch [0249]/[0303]: acc-top1=60.868750 acc-top5=82.612500 Batch [0299]/[0303]: acc-top1=60.796875 acc-top5=82.703125 [Epoch 034] training: accuracy=65.972084 loss=1.370170 [Epoch 034] speed: 62 samples/sec time cost: 4144.375113 [Epoch 034] validation: acc-top1=60.772483 acc-top5=82.729992 loss=1.884068 Epoch[035] Batch [0049]/[3760] Speed: 42.367696 samples/sec accuracy=68.125000 loss=1.284223 lr=0.010000 Epoch[035] Batch [0099]/[3760] Speed: 61.054828 samples/sec accuracy=67.500000 loss=1.295241 lr=0.010000 Epoch[035] Batch [0149]/[3760] Speed: 61.846757 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lr=0.010000 Epoch[035] Batch [3499]/[3760] Speed: 62.488616 samples/sec accuracy=66.012946 loss=1.366523 lr=0.010000 Epoch[035] Batch [3549]/[3760] Speed: 61.959256 samples/sec accuracy=66.008363 loss=1.366563 lr=0.010000 Epoch[035] Batch [3599]/[3760] Speed: 61.646058 samples/sec accuracy=66.013021 loss=1.366100 lr=0.010000 Epoch[035] Batch [3649]/[3760] Speed: 62.173138 samples/sec accuracy=66.020548 loss=1.365993 lr=0.010000 Epoch[035] Batch [3699]/[3760] Speed: 61.492522 samples/sec accuracy=66.025760 loss=1.365686 lr=0.010000 Epoch[035] Batch [3749]/[3760] Speed: 67.649267 samples/sec accuracy=66.013750 loss=1.365921 lr=0.010000 Batch [0049]/[0303]: acc-top1=61.156250 acc-top5=83.312500 Batch [0099]/[0303]: acc-top1=62.234375 acc-top5=83.718750 Batch [0149]/[0303]: acc-top1=62.197917 acc-top5=83.562500 Batch [0199]/[0303]: acc-top1=62.140625 acc-top5=83.593750 Batch [0249]/[0303]: acc-top1=61.818750 acc-top5=83.587500 Batch [0299]/[0303]: acc-top1=61.776042 acc-top5=83.713542 [Epoch 035] training: accuracy=66.008145 loss=1.366074 [Epoch 035] speed: 61 samples/sec time cost: 4153.370150 [Epoch 035] validation: acc-top1=61.814150 acc-top5=83.725248 loss=1.805953 Epoch[036] Batch [0049]/[3760] Speed: 42.676373 samples/sec accuracy=67.593750 loss=1.338307 lr=0.010000 Epoch[036] Batch [0099]/[3760] Speed: 60.782779 samples/sec accuracy=67.750000 loss=1.306612 lr=0.010000 Epoch[036] Batch [0149]/[3760] Speed: 62.781695 samples/sec accuracy=67.812500 loss=1.298797 lr=0.010000 Epoch[036] Batch [0199]/[3760] Speed: 61.204071 samples/sec accuracy=67.570312 loss=1.303756 lr=0.010000 Epoch[036] Batch [0249]/[3760] Speed: 62.761475 samples/sec accuracy=67.606250 loss=1.303557 lr=0.010000 Epoch[036] Batch [0299]/[3760] Speed: 62.121925 samples/sec accuracy=67.385417 loss=1.315409 lr=0.010000 Epoch[036] Batch [0349]/[3760] Speed: 62.572735 samples/sec accuracy=67.183036 loss=1.319264 lr=0.010000 Epoch[036] Batch [0399]/[3760] Speed: 62.298073 samples/sec accuracy=67.253906 loss=1.315353 lr=0.010000 Epoch[036] Batch [0449]/[3760] Speed: 62.348416 samples/sec accuracy=67.260417 loss=1.318334 lr=0.010000 Epoch[036] Batch [0499]/[3760] Speed: 62.481931 samples/sec accuracy=67.209375 loss=1.321786 lr=0.010000 Epoch[036] Batch [0549]/[3760] Speed: 62.431159 samples/sec accuracy=67.167614 loss=1.322845 lr=0.010000 Epoch[036] Batch [0599]/[3760] Speed: 62.547839 samples/sec accuracy=67.164062 loss=1.325654 lr=0.010000 Epoch[036] Batch [0649]/[3760] Speed: 61.989763 samples/sec accuracy=67.194712 loss=1.323428 lr=0.010000 Epoch[036] Batch [0699]/[3760] Speed: 62.513203 samples/sec accuracy=67.227679 loss=1.323145 lr=0.010000 Epoch[036] Batch [0749]/[3760] Speed: 62.200610 samples/sec accuracy=67.147917 loss=1.323402 lr=0.010000 Epoch[036] Batch [0799]/[3760] Speed: 62.288534 samples/sec accuracy=67.066406 loss=1.326738 lr=0.010000 Epoch[036] Batch [0849]/[3760] Speed: 62.397173 samples/sec accuracy=67.007353 loss=1.331449 lr=0.010000 Epoch[036] 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accuracy=66.997685 loss=1.332999 lr=0.010000 Epoch[036] Batch [1399]/[3760] Speed: 61.858700 samples/sec accuracy=66.972098 loss=1.333162 lr=0.010000 Epoch[036] Batch [1449]/[3760] Speed: 63.352792 samples/sec accuracy=66.945043 loss=1.334361 lr=0.010000 Epoch[036] Batch [1499]/[3760] Speed: 61.987517 samples/sec accuracy=66.919792 loss=1.334591 lr=0.010000 Epoch[036] Batch [1549]/[3760] Speed: 62.326259 samples/sec accuracy=66.854839 loss=1.336825 lr=0.010000 Epoch[036] Batch [1599]/[3760] Speed: 62.649247 samples/sec accuracy=66.833008 loss=1.338132 lr=0.010000 Epoch[036] Batch [1649]/[3760] Speed: 61.977222 samples/sec accuracy=66.849432 loss=1.337978 lr=0.010000 Epoch[036] Batch [1699]/[3760] Speed: 62.942186 samples/sec accuracy=66.811581 loss=1.340428 lr=0.010000 Epoch[036] Batch [1749]/[3760] Speed: 62.390725 samples/sec accuracy=66.786607 loss=1.341145 lr=0.010000 Epoch[036] Batch [1799]/[3760] Speed: 62.360087 samples/sec accuracy=66.758681 loss=1.341758 lr=0.010000 Epoch[036] 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accuracy=66.516984 loss=1.352915 lr=0.010000 Epoch[036] Batch [2349]/[3760] Speed: 62.774318 samples/sec accuracy=66.500665 loss=1.352692 lr=0.010000 Epoch[036] Batch [2399]/[3760] Speed: 62.879704 samples/sec accuracy=66.489583 loss=1.353333 lr=0.010000 Epoch[036] Batch [2449]/[3760] Speed: 62.066493 samples/sec accuracy=66.496173 loss=1.352990 lr=0.010000 Epoch[036] Batch [2499]/[3760] Speed: 62.799849 samples/sec accuracy=66.475625 loss=1.353642 lr=0.010000 Epoch[036] Batch [2549]/[3760] Speed: 62.065943 samples/sec accuracy=66.470588 loss=1.353834 lr=0.010000 Epoch[036] Batch [2599]/[3760] Speed: 62.775897 samples/sec accuracy=66.438101 loss=1.355124 lr=0.010000 Epoch[036] Batch [2649]/[3760] Speed: 62.308248 samples/sec accuracy=66.406840 loss=1.356532 lr=0.010000 Epoch[036] Batch [2699]/[3760] Speed: 62.376148 samples/sec accuracy=66.399306 loss=1.357472 lr=0.010000 Epoch[036] Batch [2749]/[3760] Speed: 62.064995 samples/sec accuracy=66.383523 loss=1.357802 lr=0.010000 Epoch[036] Batch [2799]/[3760] Speed: 62.724101 samples/sec accuracy=66.391183 loss=1.357773 lr=0.010000 Epoch[036] Batch [2849]/[3760] Speed: 62.037421 samples/sec accuracy=66.378289 loss=1.358745 lr=0.010000 Epoch[036] Batch [2899]/[3760] Speed: 62.926456 samples/sec accuracy=66.358836 loss=1.359519 lr=0.010000 Epoch[036] Batch [2949]/[3760] Speed: 62.228371 samples/sec accuracy=66.340042 loss=1.360983 lr=0.010000 Epoch[036] Batch [2999]/[3760] Speed: 62.629115 samples/sec accuracy=66.327083 loss=1.361246 lr=0.010000 Epoch[036] Batch [3049]/[3760] Speed: 63.025206 samples/sec accuracy=66.324795 loss=1.361733 lr=0.010000 Epoch[036] Batch [3099]/[3760] Speed: 62.170014 samples/sec accuracy=66.319052 loss=1.361984 lr=0.010000 Epoch[036] Batch [3149]/[3760] Speed: 62.309316 samples/sec accuracy=66.293155 loss=1.363303 lr=0.010000 Epoch[036] Batch [3199]/[3760] Speed: 62.980425 samples/sec accuracy=66.294922 loss=1.363324 lr=0.010000 Epoch[036] Batch [3249]/[3760] Speed: 62.559370 samples/sec accuracy=66.274038 loss=1.364567 lr=0.010000 Epoch[036] Batch [3299]/[3760] Speed: 62.159390 samples/sec accuracy=66.263731 loss=1.364964 lr=0.010000 Epoch[036] Batch [3349]/[3760] Speed: 62.350605 samples/sec accuracy=66.256530 loss=1.365146 lr=0.010000 Epoch[036] Batch [3399]/[3760] Speed: 62.194136 samples/sec accuracy=66.239430 loss=1.365554 lr=0.010000 Epoch[036] Batch [3449]/[3760] Speed: 62.580942 samples/sec accuracy=66.247283 loss=1.365544 lr=0.010000 Epoch[036] Batch [3499]/[3760] Speed: 62.287891 samples/sec accuracy=66.228125 loss=1.366355 lr=0.010000 Epoch[036] Batch [3549]/[3760] Speed: 63.034136 samples/sec accuracy=66.224472 loss=1.366702 lr=0.010000 Epoch[036] Batch [3599]/[3760] Speed: 62.488328 samples/sec accuracy=66.226562 loss=1.366821 lr=0.010000 Epoch[036] Batch [3649]/[3760] Speed: 62.624244 samples/sec accuracy=66.196490 loss=1.367634 lr=0.010000 Epoch[036] Batch [3699]/[3760] Speed: 62.284318 samples/sec accuracy=66.187922 loss=1.367919 lr=0.010000 Epoch[036] Batch [3749]/[3760] Speed: 67.702957 samples/sec accuracy=66.177083 loss=1.368204 lr=0.010000 Batch [0049]/[0303]: acc-top1=60.968750 acc-top5=83.062500 Batch [0099]/[0303]: acc-top1=61.890625 acc-top5=83.687500 Batch [0149]/[0303]: acc-top1=62.135417 acc-top5=83.541667 Batch [0199]/[0303]: acc-top1=62.046875 acc-top5=83.695312 Batch [0249]/[0303]: acc-top1=61.825000 acc-top5=83.700000 Batch [0299]/[0303]: acc-top1=61.848958 acc-top5=83.760417 [Epoch 036] training: accuracy=66.173122 loss=1.368259 [Epoch 036] speed: 62 samples/sec time cost: 4146.431006 [Epoch 036] validation: acc-top1=61.906972 acc-top5=83.771658 loss=1.775130 Epoch[037] Batch [0049]/[3759] Speed: 41.987930 samples/sec accuracy=67.156250 loss=1.273179 lr=0.010000 Epoch[037] Batch [0099]/[3759] Speed: 60.765463 samples/sec accuracy=67.468750 loss=1.294880 lr=0.010000 Epoch[037] Batch [0149]/[3759] Speed: 62.203264 samples/sec accuracy=67.864583 loss=1.300994 lr=0.010000 Epoch[037] Batch [0199]/[3759] Speed: 61.256768 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lr=0.010000 Epoch[037] Batch [2599]/[3759] Speed: 62.796255 samples/sec accuracy=66.637019 loss=1.346234 lr=0.010000 Epoch[037] Batch [2649]/[3759] Speed: 62.132870 samples/sec accuracy=66.651533 loss=1.346210 lr=0.010000 Epoch[037] Batch [2699]/[3759] Speed: 62.660120 samples/sec accuracy=66.637731 loss=1.346874 lr=0.010000 Epoch[037] Batch [2749]/[3759] Speed: 62.305177 samples/sec accuracy=66.646591 loss=1.346330 lr=0.010000 Epoch[037] Batch [2799]/[3759] Speed: 62.193674 samples/sec accuracy=66.633929 loss=1.347251 lr=0.010000 Epoch[037] Batch [2849]/[3759] Speed: 62.628563 samples/sec accuracy=66.594846 loss=1.348425 lr=0.010000 Epoch[037] Batch [2899]/[3759] Speed: 62.571545 samples/sec accuracy=66.594289 loss=1.348517 lr=0.010000 Epoch[037] Batch [2949]/[3759] Speed: 62.796176 samples/sec accuracy=66.591631 loss=1.349236 lr=0.010000 Epoch[037] Batch [2999]/[3759] Speed: 62.211925 samples/sec accuracy=66.564583 loss=1.350163 lr=0.010000 Epoch[037] Batch [3049]/[3759] Speed: 62.299976 samples/sec accuracy=66.557889 loss=1.350496 lr=0.010000 Epoch[037] Batch [3099]/[3759] Speed: 62.381418 samples/sec accuracy=66.554435 loss=1.350715 lr=0.010000 Epoch[037] Batch [3149]/[3759] Speed: 62.091279 samples/sec accuracy=66.540675 loss=1.351011 lr=0.010000 Epoch[037] Batch [3199]/[3759] Speed: 62.790770 samples/sec accuracy=66.527344 loss=1.351417 lr=0.010000 Epoch[037] Batch [3249]/[3759] Speed: 62.196999 samples/sec accuracy=66.523077 loss=1.352148 lr=0.010000 Epoch[037] Batch [3299]/[3759] Speed: 62.246095 samples/sec accuracy=66.506155 loss=1.352610 lr=0.010000 Epoch[037] Batch [3349]/[3759] Speed: 62.141745 samples/sec accuracy=66.486007 loss=1.352974 lr=0.010000 Epoch[037] Batch [3399]/[3759] Speed: 62.202735 samples/sec accuracy=66.479320 loss=1.353661 lr=0.010000 Epoch[037] Batch [3449]/[3759] Speed: 62.375318 samples/sec accuracy=66.442935 loss=1.354729 lr=0.010000 Epoch[037] Batch [3499]/[3759] Speed: 62.291140 samples/sec accuracy=66.440625 loss=1.354664 lr=0.010000 Epoch[037] Batch [3549]/[3759] Speed: 62.886130 samples/sec accuracy=66.448504 loss=1.354695 lr=0.010000 Epoch[037] Batch [3599]/[3759] Speed: 62.057383 samples/sec accuracy=66.441406 loss=1.354574 lr=0.010000 Epoch[037] Batch [3649]/[3759] Speed: 62.729649 samples/sec accuracy=66.431507 loss=1.354726 lr=0.010000 Epoch[037] Batch [3699]/[3759] Speed: 62.616309 samples/sec accuracy=66.422297 loss=1.355202 lr=0.010000 Epoch[037] Batch [3749]/[3759] Speed: 67.571275 samples/sec accuracy=66.416250 loss=1.355916 lr=0.010000 Batch [0049]/[0303]: acc-top1=60.437500 acc-top5=82.937500 Batch [0099]/[0303]: acc-top1=61.375000 acc-top5=83.609375 Batch [0149]/[0303]: acc-top1=61.500000 acc-top5=83.583333 Batch [0199]/[0303]: acc-top1=61.703125 acc-top5=83.828125 Batch [0249]/[0303]: acc-top1=61.543750 acc-top5=83.731250 Batch [0299]/[0303]: acc-top1=61.635417 acc-top5=83.817708 [Epoch 037] training: accuracy=66.418097 loss=1.356004 [Epoch 037] speed: 62 samples/sec time cost: 4147.179039 [Epoch 037] validation: acc-top1=61.685231 acc-top5=83.838696 loss=1.779946 Epoch[038] Batch [0049]/[3760] Speed: 42.322072 samples/sec accuracy=68.437500 loss=1.259898 lr=0.010000 Epoch[038] Batch [0099]/[3760] Speed: 60.955483 samples/sec accuracy=67.515625 loss=1.287298 lr=0.010000 Epoch[038] Batch [0149]/[3760] Speed: 62.622033 samples/sec accuracy=66.604167 loss=1.335391 lr=0.010000 Epoch[038] Batch [0199]/[3760] Speed: 61.910123 samples/sec accuracy=66.812500 loss=1.322268 lr=0.010000 Epoch[038] Batch [0249]/[3760] Speed: 62.640704 samples/sec accuracy=67.143750 loss=1.308651 lr=0.010000 Epoch[038] Batch [0299]/[3760] Speed: 62.232723 samples/sec accuracy=67.291667 loss=1.307737 lr=0.010000 Epoch[038] Batch [0349]/[3760] Speed: 62.895580 samples/sec accuracy=67.187500 loss=1.308557 lr=0.010000 Epoch[038] Batch [0399]/[3760] Speed: 62.538081 samples/sec accuracy=67.324219 loss=1.303789 lr=0.010000 Epoch[038] Batch [0449]/[3760] Speed: 62.505988 samples/sec accuracy=67.326389 loss=1.304219 lr=0.010000 Epoch[038] Batch [0499]/[3760] Speed: 62.432894 samples/sec accuracy=67.262500 loss=1.306635 lr=0.010000 Epoch[038] Batch [0549]/[3760] Speed: 62.373072 samples/sec accuracy=67.125000 loss=1.311017 lr=0.010000 Epoch[038] Batch [0599]/[3760] Speed: 62.630788 samples/sec accuracy=67.127604 loss=1.312511 lr=0.010000 Epoch[038] Batch [0649]/[3760] Speed: 62.298447 samples/sec accuracy=66.927885 loss=1.318663 lr=0.010000 Epoch[038] Batch [0699]/[3760] Speed: 62.349654 samples/sec accuracy=66.850446 loss=1.320950 lr=0.010000 Epoch[038] Batch [0749]/[3760] Speed: 62.934394 samples/sec accuracy=66.920833 loss=1.317757 lr=0.010000 Epoch[038] Batch [0799]/[3760] Speed: 61.767812 samples/sec accuracy=66.898438 loss=1.317889 lr=0.010000 Epoch[038] Batch [0849]/[3760] Speed: 62.286989 samples/sec accuracy=66.898897 loss=1.319962 lr=0.010000 Epoch[038] Batch [0899]/[3760] Speed: 62.486250 samples/sec accuracy=66.840278 loss=1.322270 lr=0.010000 Epoch[038] 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accuracy=66.790179 loss=1.331565 lr=0.010000 Epoch[038] Batch [1449]/[3760] Speed: 62.015595 samples/sec accuracy=66.760776 loss=1.332076 lr=0.010000 Epoch[038] Batch [1499]/[3760] Speed: 62.864145 samples/sec accuracy=66.745833 loss=1.332332 lr=0.010000 Epoch[038] Batch [1549]/[3760] Speed: 62.702851 samples/sec accuracy=66.743952 loss=1.332926 lr=0.010000 Epoch[038] Batch [1599]/[3760] Speed: 62.604532 samples/sec accuracy=66.708984 loss=1.334707 lr=0.010000 Epoch[038] Batch [1649]/[3760] Speed: 62.633380 samples/sec accuracy=66.679924 loss=1.335762 lr=0.010000 Epoch[038] Batch [1699]/[3760] Speed: 62.854254 samples/sec accuracy=66.663603 loss=1.335777 lr=0.010000 Epoch[038] Batch [1749]/[3760] Speed: 62.213381 samples/sec accuracy=66.678571 loss=1.335272 lr=0.010000 Epoch[038] Batch [1799]/[3760] Speed: 62.615659 samples/sec accuracy=66.678819 loss=1.334968 lr=0.010000 Epoch[038] Batch [1849]/[3760] Speed: 62.380958 samples/sec accuracy=66.662162 loss=1.334717 lr=0.010000 Epoch[038] 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accuracy=66.529920 loss=1.340961 lr=0.010000 Epoch[038] Batch [2399]/[3760] Speed: 62.401597 samples/sec accuracy=66.541016 loss=1.340657 lr=0.010000 Epoch[038] Batch [2449]/[3760] Speed: 62.619046 samples/sec accuracy=66.478954 loss=1.343043 lr=0.010000 Epoch[038] Batch [2499]/[3760] Speed: 61.852879 samples/sec accuracy=66.483125 loss=1.342928 lr=0.010000 Epoch[038] Batch [2549]/[3760] Speed: 62.567976 samples/sec accuracy=66.487745 loss=1.342544 lr=0.010000 Epoch[038] Batch [2599]/[3760] Speed: 62.473147 samples/sec accuracy=66.493389 loss=1.343282 lr=0.010000 Epoch[038] Batch [2649]/[3760] Speed: 61.968627 samples/sec accuracy=66.474646 loss=1.344730 lr=0.010000 Epoch[038] Batch [2699]/[3760] Speed: 62.224302 samples/sec accuracy=66.449653 loss=1.345425 lr=0.010000 Epoch[038] Batch [2749]/[3760] Speed: 62.666732 samples/sec accuracy=66.425000 loss=1.346779 lr=0.010000 Epoch[038] Batch [2799]/[3760] Speed: 61.772753 samples/sec accuracy=66.419085 loss=1.347047 lr=0.010000 Epoch[038] 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accuracy=66.342330 loss=1.351269 lr=0.010000 Epoch[038] Batch [3349]/[3760] Speed: 62.545524 samples/sec accuracy=66.352612 loss=1.351174 lr=0.010000 Epoch[038] Batch [3399]/[3760] Speed: 62.386161 samples/sec accuracy=66.334559 loss=1.352145 lr=0.010000 Epoch[038] Batch [3449]/[3760] Speed: 62.179292 samples/sec accuracy=66.330163 loss=1.352110 lr=0.010000 Epoch[038] Batch [3499]/[3760] Speed: 61.992951 samples/sec accuracy=66.313393 loss=1.352879 lr=0.010000 Epoch[038] Batch [3549]/[3760] Speed: 62.121704 samples/sec accuracy=66.302817 loss=1.352997 lr=0.010000 Epoch[038] Batch [3599]/[3760] Speed: 62.376723 samples/sec accuracy=66.286458 loss=1.353547 lr=0.010000 Epoch[038] Batch [3649]/[3760] Speed: 62.273351 samples/sec accuracy=66.266695 loss=1.354043 lr=0.010000 Epoch[038] Batch [3699]/[3760] Speed: 62.255931 samples/sec accuracy=66.263936 loss=1.353937 lr=0.010000 Epoch[038] Batch [3749]/[3760] Speed: 67.332433 samples/sec accuracy=66.280417 loss=1.353524 lr=0.010000 Batch [0049]/[0303]: acc-top1=60.187500 acc-top5=82.625000 Batch [0099]/[0303]: acc-top1=61.062500 acc-top5=83.093750 Batch [0149]/[0303]: acc-top1=62.145833 acc-top5=83.343750 Batch [0199]/[0303]: acc-top1=62.398438 acc-top5=83.664062 Batch [0249]/[0303]: acc-top1=62.175000 acc-top5=83.587500 Batch [0299]/[0303]: acc-top1=62.182292 acc-top5=83.692708 [Epoch 038] training: accuracy=66.274518 loss=1.353914 [Epoch 038] speed: 62 samples/sec time cost: 4147.095500 [Epoch 038] validation: acc-top1=62.216378 acc-top5=83.730404 loss=1.810826 Epoch[039] Batch [0049]/[3760] Speed: 41.863432 samples/sec accuracy=69.156250 loss=1.249882 lr=0.010000 Epoch[039] Batch [0099]/[3760] Speed: 61.203037 samples/sec accuracy=68.515625 loss=1.266068 lr=0.010000 Epoch[039] Batch [0149]/[3760] Speed: 62.788159 samples/sec accuracy=67.906250 loss=1.293017 lr=0.010000 Epoch[039] Batch [0199]/[3760] Speed: 61.150000 samples/sec accuracy=67.679688 loss=1.304900 lr=0.010000 Epoch[039] Batch [0249]/[3760] Speed: 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lr=0.010000 Epoch[039] Batch [3599]/[3760] Speed: 62.857007 samples/sec accuracy=66.449219 loss=1.348098 lr=0.010000 Epoch[039] Batch [3649]/[3760] Speed: 62.311841 samples/sec accuracy=66.429366 loss=1.348717 lr=0.010000 Epoch[039] Batch [3699]/[3760] Speed: 62.325575 samples/sec accuracy=66.408784 loss=1.349637 lr=0.010000 Epoch[039] Batch [3749]/[3760] Speed: 66.894552 samples/sec accuracy=66.393750 loss=1.350490 lr=0.001000 Batch [0049]/[0303]: acc-top1=60.531250 acc-top5=82.125000 Batch [0099]/[0303]: acc-top1=61.781250 acc-top5=83.375000 Batch [0149]/[0303]: acc-top1=61.833333 acc-top5=83.416667 Batch [0199]/[0303]: acc-top1=61.937500 acc-top5=83.695312 Batch [0249]/[0303]: acc-top1=61.731250 acc-top5=83.600000 Batch [0299]/[0303]: acc-top1=61.635417 acc-top5=83.739583 [Epoch 039] training: accuracy=66.390043 loss=1.350752 [Epoch 039] speed: 62 samples/sec time cost: 4151.432296 [Epoch 039] validation: acc-top1=61.638820 acc-top5=83.766502 loss=1.792469 Epoch[040] Batch 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accuracy=70.440625 loss=1.179970 lr=0.001000 Epoch[040] Batch [0549]/[3759] Speed: 62.541270 samples/sec accuracy=70.488636 loss=1.172439 lr=0.001000 Epoch[040] Batch [0599]/[3759] Speed: 62.707073 samples/sec accuracy=70.700521 loss=1.165141 lr=0.001000 Epoch[040] Batch [0649]/[3759] Speed: 61.778827 samples/sec accuracy=70.790865 loss=1.163847 lr=0.001000 Epoch[040] Batch [0699]/[3759] Speed: 62.994314 samples/sec accuracy=70.872768 loss=1.160008 lr=0.001000 Epoch[040] Batch [0749]/[3759] Speed: 62.132157 samples/sec accuracy=70.995833 loss=1.153854 lr=0.001000 Epoch[040] Batch [0799]/[3759] Speed: 62.162071 samples/sec accuracy=71.126953 loss=1.149330 lr=0.001000 Epoch[040] Batch [0849]/[3759] Speed: 62.358734 samples/sec accuracy=71.205882 loss=1.145801 lr=0.001000 Epoch[040] Batch [0899]/[3759] Speed: 62.294239 samples/sec accuracy=71.314236 loss=1.141840 lr=0.001000 Epoch[040] Batch [0949]/[3759] Speed: 62.216013 samples/sec accuracy=71.370066 loss=1.139175 lr=0.001000 Epoch[040] 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accuracy=71.873922 loss=1.116099 lr=0.001000 Epoch[040] Batch [1499]/[3759] Speed: 62.537264 samples/sec accuracy=71.902083 loss=1.113732 lr=0.001000 Epoch[040] Batch [1549]/[3759] Speed: 62.304206 samples/sec accuracy=71.981855 loss=1.111260 lr=0.001000 Epoch[040] Batch [1599]/[3759] Speed: 62.205582 samples/sec accuracy=72.077148 loss=1.108370 lr=0.001000 Epoch[040] Batch [1649]/[3759] Speed: 62.593641 samples/sec accuracy=72.108902 loss=1.106775 lr=0.001000 Epoch[040] Batch [1699]/[3759] Speed: 62.375714 samples/sec accuracy=72.170037 loss=1.104562 lr=0.001000 Epoch[040] Batch [1749]/[3759] Speed: 62.373435 samples/sec accuracy=72.160714 loss=1.104258 lr=0.001000 Epoch[040] Batch [1799]/[3759] Speed: 62.492553 samples/sec accuracy=72.213542 loss=1.102303 lr=0.001000 Epoch[040] Batch [1849]/[3759] Speed: 62.535249 samples/sec accuracy=72.271959 loss=1.099819 lr=0.001000 Epoch[040] Batch [1899]/[3759] Speed: 62.039612 samples/sec accuracy=72.312500 loss=1.098443 lr=0.001000 Epoch[040] 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accuracy=72.712240 loss=1.082940 lr=0.001000 Epoch[040] Batch [2449]/[3759] Speed: 62.361916 samples/sec accuracy=72.753827 loss=1.080607 lr=0.001000 Epoch[040] Batch [2499]/[3759] Speed: 62.123985 samples/sec accuracy=72.783750 loss=1.079913 lr=0.001000 Epoch[040] Batch [2549]/[3759] Speed: 62.053348 samples/sec accuracy=72.808824 loss=1.078933 lr=0.001000 Epoch[040] Batch [2599]/[3759] Speed: 62.515467 samples/sec accuracy=72.844952 loss=1.077600 lr=0.001000 Epoch[040] Batch [2649]/[3759] Speed: 62.525617 samples/sec accuracy=72.862618 loss=1.076531 lr=0.001000 Epoch[040] Batch [2699]/[3759] Speed: 62.548048 samples/sec accuracy=72.896991 loss=1.074586 lr=0.001000 Epoch[040] Batch [2749]/[3759] Speed: 61.604342 samples/sec accuracy=72.905114 loss=1.073832 lr=0.001000 Epoch[040] Batch [2799]/[3759] Speed: 62.743001 samples/sec accuracy=72.910714 loss=1.073236 lr=0.001000 Epoch[040] Batch [2849]/[3759] Speed: 62.018870 samples/sec accuracy=72.941338 loss=1.072319 lr=0.001000 Epoch[040] 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accuracy=73.083022 loss=1.065000 lr=0.001000 Epoch[040] Batch [3399]/[3759] Speed: 62.235125 samples/sec accuracy=73.088235 loss=1.064576 lr=0.001000 Epoch[040] Batch [3449]/[3759] Speed: 62.178445 samples/sec accuracy=73.114583 loss=1.063693 lr=0.001000 Epoch[040] Batch [3499]/[3759] Speed: 62.094383 samples/sec accuracy=73.149554 loss=1.062201 lr=0.001000 Epoch[040] Batch [3549]/[3759] Speed: 62.298937 samples/sec accuracy=73.158011 loss=1.061552 lr=0.001000 Epoch[040] Batch [3599]/[3759] Speed: 62.532417 samples/sec accuracy=73.180556 loss=1.060609 lr=0.001000 Epoch[040] Batch [3649]/[3759] Speed: 62.091349 samples/sec accuracy=73.199058 loss=1.059955 lr=0.001000 Epoch[040] Batch [3699]/[3759] Speed: 62.522312 samples/sec accuracy=73.218328 loss=1.058901 lr=0.001000 Epoch[040] Batch [3749]/[3759] Speed: 67.809630 samples/sec accuracy=73.218333 loss=1.058703 lr=0.001000 Batch [0049]/[0303]: acc-top1=66.343750 acc-top5=85.625000 Batch [0099]/[0303]: acc-top1=67.734375 acc-top5=86.562500 Batch [0149]/[0303]: acc-top1=67.385417 acc-top5=86.552083 Batch [0199]/[0303]: acc-top1=67.476562 acc-top5=86.695312 Batch [0249]/[0303]: acc-top1=67.337500 acc-top5=86.656250 Batch [0299]/[0303]: acc-top1=67.218750 acc-top5=86.677083 [Epoch 040] training: accuracy=73.220521 loss=1.058539 [Epoch 040] speed: 62 samples/sec time cost: 4151.149271 [Epoch 040] validation: acc-top1=67.208127 acc-top5=86.690388 loss=1.565766 Epoch[041] Batch [0049]/[3760] Speed: 42.732661 samples/sec accuracy=75.156250 loss=0.964468 lr=0.001000 Epoch[041] Batch [0099]/[3760] Speed: 61.071734 samples/sec accuracy=75.062500 loss=0.969722 lr=0.001000 Epoch[041] Batch [0149]/[3760] Speed: 62.199522 samples/sec accuracy=75.562500 loss=0.967148 lr=0.001000 Epoch[041] Batch [0199]/[3760] Speed: 61.307281 samples/sec accuracy=75.250000 loss=0.978583 lr=0.001000 Epoch[041] Batch [0249]/[3760] Speed: 62.903628 samples/sec accuracy=75.137500 loss=0.980618 lr=0.001000 Epoch[041] Batch [0299]/[3760] 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accuracy=75.410590 loss=0.967239 lr=0.001000 Epoch[041] Batch [3649]/[3760] Speed: 61.936101 samples/sec accuracy=75.416952 loss=0.966892 lr=0.001000 Epoch[041] Batch [3699]/[3760] Speed: 61.848581 samples/sec accuracy=75.424409 loss=0.966480 lr=0.001000 Epoch[041] Batch [3749]/[3760] Speed: 67.759858 samples/sec accuracy=75.418333 loss=0.966668 lr=0.001000 Batch [0049]/[0303]: acc-top1=65.937500 acc-top5=86.343750 Batch [0099]/[0303]: acc-top1=67.625000 acc-top5=87.109375 Batch [0149]/[0303]: acc-top1=67.708333 acc-top5=87.010417 Batch [0199]/[0303]: acc-top1=67.796875 acc-top5=87.171875 Batch [0249]/[0303]: acc-top1=67.706250 acc-top5=87.225000 Batch [0299]/[0303]: acc-top1=67.682292 acc-top5=87.161458 [Epoch 041] training: accuracy=75.423870 loss=0.966262 [Epoch 041] speed: 62 samples/sec time cost: 4151.284013 [Epoch 041] validation: acc-top1=67.687706 acc-top5=87.169967 loss=1.559235 Epoch[042] Batch [0049]/[3760] Speed: 42.429164 samples/sec accuracy=77.718750 loss=0.904275 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62.239108 samples/sec accuracy=76.295956 loss=0.930762 lr=0.001000 Epoch[042] Batch [3449]/[3760] Speed: 62.352963 samples/sec accuracy=76.297554 loss=0.930423 lr=0.001000 Epoch[042] Batch [3499]/[3760] Speed: 61.797612 samples/sec accuracy=76.312054 loss=0.930231 lr=0.001000 Epoch[042] Batch [3549]/[3760] Speed: 62.753514 samples/sec accuracy=76.316901 loss=0.929962 lr=0.001000 Epoch[042] Batch [3599]/[3760] Speed: 62.074634 samples/sec accuracy=76.312934 loss=0.929919 lr=0.001000 Epoch[042] Batch [3649]/[3760] Speed: 62.630399 samples/sec accuracy=76.297517 loss=0.930243 lr=0.001000 Epoch[042] Batch [3699]/[3760] Speed: 61.872826 samples/sec accuracy=76.298986 loss=0.930397 lr=0.001000 Epoch[042] Batch [3749]/[3760] Speed: 67.611570 samples/sec accuracy=76.304583 loss=0.929843 lr=0.001000 Batch [0049]/[0303]: acc-top1=65.937500 acc-top5=86.562500 Batch [0099]/[0303]: acc-top1=67.546875 acc-top5=87.062500 Batch [0149]/[0303]: acc-top1=67.864583 acc-top5=87.020833 Batch [0199]/[0303]: acc-top1=67.992188 acc-top5=87.117188 Batch [0249]/[0303]: acc-top1=67.862500 acc-top5=87.143750 Batch [0299]/[0303]: acc-top1=67.807292 acc-top5=87.083333 [Epoch 042] training: accuracy=76.312749 loss=0.929772 [Epoch 042] speed: 61 samples/sec time cost: 4153.655200 [Epoch 042] validation: acc-top1=67.826939 acc-top5=87.092616 loss=1.557026 Epoch[043] Batch [0049]/[3759] Speed: 42.150090 samples/sec accuracy=76.843750 loss=0.896006 lr=0.001000 Epoch[043] Batch [0099]/[3759] Speed: 60.694437 samples/sec accuracy=76.906250 loss=0.891817 lr=0.001000 Epoch[043] Batch [0149]/[3759] Speed: 62.543714 samples/sec accuracy=77.239583 loss=0.885373 lr=0.001000 Epoch[043] Batch [0199]/[3759] Speed: 61.555138 samples/sec accuracy=77.046875 loss=0.894777 lr=0.001000 Epoch[043] Batch [0249]/[3759] Speed: 62.890073 samples/sec accuracy=77.137500 loss=0.896802 lr=0.001000 Epoch[043] Batch [0299]/[3759] Speed: 61.853107 samples/sec accuracy=76.906250 loss=0.901734 lr=0.001000 Epoch[043] Batch 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accuracy=76.913099 loss=0.903477 lr=0.001000 Epoch[043] Batch [3699]/[3759] Speed: 62.515417 samples/sec accuracy=76.896537 loss=0.903576 lr=0.001000 Epoch[043] Batch [3749]/[3759] Speed: 67.801525 samples/sec accuracy=76.873750 loss=0.904249 lr=0.001000 Batch [0049]/[0303]: acc-top1=65.968750 acc-top5=86.687500 Batch [0099]/[0303]: acc-top1=67.890625 acc-top5=87.140625 Batch [0149]/[0303]: acc-top1=67.854167 acc-top5=87.052083 Batch [0199]/[0303]: acc-top1=68.023438 acc-top5=87.125000 Batch [0249]/[0303]: acc-top1=67.837500 acc-top5=87.262500 Batch [0299]/[0303]: acc-top1=67.817708 acc-top5=87.234375 [Epoch 043] training: accuracy=76.878408 loss=0.904064 [Epoch 043] speed: 62 samples/sec time cost: 4148.675007 [Epoch 043] validation: acc-top1=67.826939 acc-top5=87.242162 loss=1.549061 Epoch[044] Batch [0049]/[3760] Speed: 42.179688 samples/sec accuracy=76.250000 loss=0.926062 lr=0.001000 Epoch[044] Batch [0099]/[3760] Speed: 60.898793 samples/sec accuracy=77.500000 loss=0.887584 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lr=0.001000 Epoch[044] Batch [2999]/[3760] Speed: 62.023277 samples/sec accuracy=77.304688 loss=0.890124 lr=0.001000 Epoch[044] Batch [3049]/[3760] Speed: 62.213680 samples/sec accuracy=77.304303 loss=0.890099 lr=0.001000 Epoch[044] Batch [3099]/[3760] Speed: 61.828010 samples/sec accuracy=77.314012 loss=0.889644 lr=0.001000 Epoch[044] Batch [3149]/[3760] Speed: 62.497290 samples/sec accuracy=77.310516 loss=0.889394 lr=0.001000 Epoch[044] Batch [3199]/[3760] Speed: 62.154997 samples/sec accuracy=77.314941 loss=0.889401 lr=0.001000 Epoch[044] Batch [3249]/[3760] Speed: 62.536980 samples/sec accuracy=77.325000 loss=0.889151 lr=0.001000 Epoch[044] Batch [3299]/[3760] Speed: 62.560332 samples/sec accuracy=77.320076 loss=0.889271 lr=0.001000 Epoch[044] Batch [3349]/[3760] Speed: 62.473755 samples/sec accuracy=77.318097 loss=0.889540 lr=0.001000 Epoch[044] Batch [3399]/[3760] Speed: 62.077049 samples/sec accuracy=77.332261 loss=0.888942 lr=0.001000 Epoch[044] Batch [3449]/[3760] Speed: 62.361004 samples/sec accuracy=77.345109 loss=0.888219 lr=0.001000 Epoch[044] Batch [3499]/[3760] Speed: 62.082291 samples/sec accuracy=77.338393 loss=0.888127 lr=0.001000 Epoch[044] Batch [3549]/[3760] Speed: 62.553482 samples/sec accuracy=77.347711 loss=0.888017 lr=0.001000 Epoch[044] Batch [3599]/[3760] Speed: 62.013582 samples/sec accuracy=77.348958 loss=0.887711 lr=0.001000 Epoch[044] Batch [3649]/[3760] Speed: 62.187717 samples/sec accuracy=77.338185 loss=0.888138 lr=0.001000 Epoch[044] Batch [3699]/[3760] Speed: 62.417594 samples/sec accuracy=77.339105 loss=0.888251 lr=0.001000 Epoch[044] Batch [3749]/[3760] Speed: 67.192626 samples/sec accuracy=77.345833 loss=0.887593 lr=0.001000 Batch [0049]/[0303]: acc-top1=66.218750 acc-top5=86.812500 Batch [0099]/[0303]: acc-top1=67.562500 acc-top5=87.328125 Batch [0149]/[0303]: acc-top1=67.854167 acc-top5=87.208333 Batch [0199]/[0303]: acc-top1=68.117188 acc-top5=87.351562 Batch [0249]/[0303]: acc-top1=68.056250 acc-top5=87.368750 Batch [0299]/[0303]: acc-top1=67.963542 acc-top5=87.406250 [Epoch 044] training: accuracy=77.340426 loss=0.887706 [Epoch 044] speed: 61 samples/sec time cost: 4154.715004 [Epoch 044] validation: acc-top1=67.966172 acc-top5=87.412335 loss=1.551549 Epoch[045] Batch [0049]/[3760] Speed: 42.829213 samples/sec accuracy=78.531250 loss=0.817523 lr=0.001000 Epoch[045] Batch [0099]/[3760] Speed: 60.816811 samples/sec accuracy=78.500000 loss=0.821682 lr=0.001000 Epoch[045] Batch [0149]/[3760] Speed: 62.111080 samples/sec accuracy=78.156250 loss=0.837304 lr=0.001000 Epoch[045] Batch [0199]/[3760] Speed: 61.452418 samples/sec accuracy=78.257812 loss=0.832797 lr=0.001000 Epoch[045] Batch [0249]/[3760] Speed: 62.668417 samples/sec accuracy=78.381250 loss=0.833980 lr=0.001000 Epoch[045] Batch [0299]/[3760] Speed: 61.822739 samples/sec accuracy=78.291667 loss=0.839409 lr=0.001000 Epoch[045] Batch [0349]/[3760] Speed: 62.562091 samples/sec accuracy=78.107143 loss=0.846150 lr=0.001000 Epoch[045] Batch 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accuracy=77.882353 loss=0.860586 lr=0.001000 Epoch[045] Batch [0899]/[3760] Speed: 62.096372 samples/sec accuracy=77.885417 loss=0.861148 lr=0.001000 Epoch[045] Batch [0949]/[3760] Speed: 62.335763 samples/sec accuracy=77.870066 loss=0.861900 lr=0.001000 Epoch[045] Batch [0999]/[3760] Speed: 61.994364 samples/sec accuracy=77.904688 loss=0.860506 lr=0.001000 Epoch[045] Batch [1049]/[3760] Speed: 62.250715 samples/sec accuracy=77.906250 loss=0.860279 lr=0.001000 Epoch[045] Batch [1099]/[3760] Speed: 62.459762 samples/sec accuracy=77.894886 loss=0.860310 lr=0.001000 Epoch[045] Batch [1149]/[3760] Speed: 62.049560 samples/sec accuracy=77.918478 loss=0.860914 lr=0.001000 Epoch[045] Batch [1199]/[3760] Speed: 62.130879 samples/sec accuracy=77.846354 loss=0.863461 lr=0.001000 Epoch[045] Batch [1249]/[3760] Speed: 62.003317 samples/sec accuracy=77.815000 loss=0.865131 lr=0.001000 Epoch[045] Batch [1299]/[3760] Speed: 62.511686 samples/sec accuracy=77.818510 loss=0.865290 lr=0.001000 Epoch[045] 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accuracy=77.813368 loss=0.867084 lr=0.001000 Epoch[045] Batch [1849]/[3760] Speed: 62.103638 samples/sec accuracy=77.806588 loss=0.867195 lr=0.001000 Epoch[045] Batch [1899]/[3760] Speed: 62.754147 samples/sec accuracy=77.818257 loss=0.867508 lr=0.001000 Epoch[045] Batch [1949]/[3760] Speed: 62.569613 samples/sec accuracy=77.819712 loss=0.867354 lr=0.001000 Epoch[045] Batch [1999]/[3760] Speed: 62.230076 samples/sec accuracy=77.824219 loss=0.867376 lr=0.001000 Epoch[045] Batch [2049]/[3760] Speed: 62.552499 samples/sec accuracy=77.804116 loss=0.868369 lr=0.001000 Epoch[045] Batch [2099]/[3760] Speed: 62.576191 samples/sec accuracy=77.813988 loss=0.868643 lr=0.001000 Epoch[045] Batch [2149]/[3760] Speed: 62.250919 samples/sec accuracy=77.789971 loss=0.869543 lr=0.001000 Epoch[045] Batch [2199]/[3760] Speed: 62.066637 samples/sec accuracy=77.781960 loss=0.869999 lr=0.001000 Epoch[045] Batch [2249]/[3760] Speed: 62.760119 samples/sec accuracy=77.815278 loss=0.868788 lr=0.001000 Epoch[045] 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accuracy=77.819886 loss=0.869434 lr=0.001000 Epoch[045] Batch [2799]/[3760] Speed: 62.450688 samples/sec accuracy=77.803013 loss=0.870205 lr=0.001000 Epoch[045] Batch [2849]/[3760] Speed: 62.146309 samples/sec accuracy=77.815241 loss=0.869802 lr=0.001000 Epoch[045] Batch [2899]/[3760] Speed: 62.612729 samples/sec accuracy=77.810345 loss=0.870107 lr=0.001000 Epoch[045] Batch [2949]/[3760] Speed: 61.470752 samples/sec accuracy=77.810911 loss=0.870082 lr=0.001000 Epoch[045] Batch [2999]/[3760] Speed: 62.922271 samples/sec accuracy=77.814063 loss=0.870077 lr=0.001000 Epoch[045] Batch [3049]/[3760] Speed: 62.187598 samples/sec accuracy=77.808402 loss=0.870762 lr=0.001000 Epoch[045] Batch [3099]/[3760] Speed: 62.363925 samples/sec accuracy=77.810988 loss=0.870397 lr=0.001000 Epoch[045] Batch [3149]/[3760] Speed: 61.699254 samples/sec accuracy=77.804067 loss=0.870634 lr=0.001000 Epoch[045] Batch [3199]/[3760] Speed: 62.383342 samples/sec accuracy=77.812012 loss=0.870656 lr=0.001000 Epoch[045] 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accuracy=77.781250 loss=0.871102 lr=0.001000 Epoch[045] Batch [3749]/[3760] Speed: 67.800925 samples/sec accuracy=77.784167 loss=0.870876 lr=0.001000 Batch [0049]/[0303]: acc-top1=66.500000 acc-top5=86.312500 Batch [0099]/[0303]: acc-top1=68.390625 acc-top5=87.140625 Batch [0149]/[0303]: acc-top1=68.500000 acc-top5=87.052083 Batch [0199]/[0303]: acc-top1=68.468750 acc-top5=87.203125 Batch [0249]/[0303]: acc-top1=68.406250 acc-top5=87.218750 Batch [0299]/[0303]: acc-top1=68.432292 acc-top5=87.161458 [Epoch 045] training: accuracy=77.785904 loss=0.870928 [Epoch 045] speed: 62 samples/sec time cost: 4152.159401 [Epoch 045] validation: acc-top1=68.445751 acc-top5=87.169967 loss=1.553817 Epoch[046] Batch [0049]/[3759] Speed: 42.519052 samples/sec accuracy=77.031250 loss=0.907349 lr=0.001000 Epoch[046] Batch [0099]/[3759] Speed: 60.562909 samples/sec accuracy=77.718750 loss=0.858144 lr=0.001000 Epoch[046] Batch [0149]/[3759] Speed: 62.068132 samples/sec accuracy=78.239583 loss=0.855789 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lr=0.001000 Epoch[046] Batch [1149]/[3759] Speed: 62.522503 samples/sec accuracy=78.168478 loss=0.855389 lr=0.001000 Epoch[046] Batch [1199]/[3759] Speed: 62.079324 samples/sec accuracy=78.165365 loss=0.856592 lr=0.001000 Epoch[046] Batch [1249]/[3759] Speed: 62.822420 samples/sec accuracy=78.245000 loss=0.853195 lr=0.001000 Epoch[046] Batch [1299]/[3759] Speed: 62.104195 samples/sec accuracy=78.265625 loss=0.851370 lr=0.001000 Epoch[046] Batch [1349]/[3759] Speed: 62.848316 samples/sec accuracy=78.230324 loss=0.852836 lr=0.001000 Epoch[046] Batch [1399]/[3759] Speed: 62.489636 samples/sec accuracy=78.275670 loss=0.850856 lr=0.001000 Epoch[046] Batch [1449]/[3759] Speed: 62.017435 samples/sec accuracy=78.308190 loss=0.849538 lr=0.001000 Epoch[046] Batch [1499]/[3759] Speed: 62.627113 samples/sec accuracy=78.329167 loss=0.849461 lr=0.001000 Epoch[046] Batch [1549]/[3759] Speed: 62.421652 samples/sec accuracy=78.331653 loss=0.849099 lr=0.001000 Epoch[046] Batch [1599]/[3759] Speed: 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lr=0.001000 Epoch[046] Batch [2099]/[3759] Speed: 62.248511 samples/sec accuracy=78.320685 loss=0.849478 lr=0.001000 Epoch[046] Batch [2149]/[3759] Speed: 62.819646 samples/sec accuracy=78.312500 loss=0.849678 lr=0.001000 Epoch[046] Batch [2199]/[3759] Speed: 62.187773 samples/sec accuracy=78.313920 loss=0.849702 lr=0.001000 Epoch[046] Batch [2249]/[3759] Speed: 62.133865 samples/sec accuracy=78.300694 loss=0.850273 lr=0.001000 Epoch[046] Batch [2299]/[3759] Speed: 62.525206 samples/sec accuracy=78.308424 loss=0.849534 lr=0.001000 Epoch[046] Batch [2349]/[3759] Speed: 62.477202 samples/sec accuracy=78.319814 loss=0.849303 lr=0.001000 Epoch[046] Batch [2399]/[3759] Speed: 62.365141 samples/sec accuracy=78.307292 loss=0.849007 lr=0.001000 Epoch[046] Batch [2449]/[3759] Speed: 62.585022 samples/sec accuracy=78.288265 loss=0.849446 lr=0.001000 Epoch[046] Batch [2499]/[3759] Speed: 62.353160 samples/sec accuracy=78.270000 loss=0.849603 lr=0.001000 Epoch[046] Batch [2549]/[3759] Speed: 62.081515 samples/sec accuracy=78.264706 loss=0.850017 lr=0.001000 Epoch[046] Batch [2599]/[3759] Speed: 62.515730 samples/sec accuracy=78.264423 loss=0.850458 lr=0.001000 Epoch[046] Batch [2649]/[3759] Speed: 62.258080 samples/sec accuracy=78.271816 loss=0.849899 lr=0.001000 Epoch[046] Batch [2699]/[3759] Speed: 62.635325 samples/sec accuracy=78.255208 loss=0.850693 lr=0.001000 Epoch[046] Batch [2749]/[3759] Speed: 61.818896 samples/sec accuracy=78.241477 loss=0.850994 lr=0.001000 Epoch[046] Batch [2799]/[3759] Speed: 62.702836 samples/sec accuracy=78.241071 loss=0.851000 lr=0.001000 Epoch[046] Batch [2849]/[3759] Speed: 62.547238 samples/sec accuracy=78.224781 loss=0.851304 lr=0.001000 Epoch[046] Batch [2899]/[3759] Speed: 62.471753 samples/sec accuracy=78.214440 loss=0.852013 lr=0.001000 Epoch[046] Batch [2949]/[3759] Speed: 62.362372 samples/sec accuracy=78.206038 loss=0.852314 lr=0.001000 Epoch[046] Batch [2999]/[3759] Speed: 62.165668 samples/sec accuracy=78.191667 loss=0.852523 lr=0.001000 Epoch[046] Batch [3049]/[3759] Speed: 62.586801 samples/sec accuracy=78.190574 loss=0.852540 lr=0.001000 Epoch[046] Batch [3099]/[3759] Speed: 62.515509 samples/sec accuracy=78.188004 loss=0.852974 lr=0.001000 Epoch[046] Batch [3149]/[3759] Speed: 62.540872 samples/sec accuracy=78.182540 loss=0.852933 lr=0.001000 Epoch[046] Batch [3199]/[3759] Speed: 61.893656 samples/sec accuracy=78.187988 loss=0.852923 lr=0.001000 Epoch[046] Batch [3249]/[3759] Speed: 62.428426 samples/sec accuracy=78.197596 loss=0.852660 lr=0.001000 Epoch[046] Batch [3299]/[3759] Speed: 62.259678 samples/sec accuracy=78.205966 loss=0.852247 lr=0.001000 Epoch[046] Batch [3349]/[3759] Speed: 62.668777 samples/sec accuracy=78.201959 loss=0.852239 lr=0.001000 Epoch[046] Batch [3399]/[3759] Speed: 62.021839 samples/sec accuracy=78.201287 loss=0.852315 lr=0.001000 Epoch[046] Batch [3449]/[3759] Speed: 62.586228 samples/sec accuracy=78.192482 loss=0.852502 lr=0.001000 Epoch[046] Batch [3499]/[3759] Speed: 62.167337 samples/sec accuracy=78.199554 loss=0.852645 lr=0.001000 Epoch[046] Batch [3549]/[3759] Speed: 62.743570 samples/sec accuracy=78.187940 loss=0.853239 lr=0.001000 Epoch[046] Batch [3599]/[3759] Speed: 62.002404 samples/sec accuracy=78.186198 loss=0.853018 lr=0.001000 Epoch[046] Batch [3649]/[3759] Speed: 62.625775 samples/sec accuracy=78.173801 loss=0.853691 lr=0.001000 Epoch[046] Batch [3699]/[3759] Speed: 62.625277 samples/sec accuracy=78.186655 loss=0.853434 lr=0.001000 Epoch[046] Batch [3749]/[3759] Speed: 67.805277 samples/sec accuracy=78.182917 loss=0.854118 lr=0.001000 Batch [0049]/[0303]: acc-top1=66.781250 acc-top5=86.687500 Batch [0099]/[0303]: acc-top1=68.312500 acc-top5=87.125000 Batch [0149]/[0303]: acc-top1=68.541667 acc-top5=87.010417 Batch [0199]/[0303]: acc-top1=68.414062 acc-top5=87.062500 Batch [0249]/[0303]: acc-top1=68.256250 acc-top5=87.112500 Batch [0299]/[0303]: acc-top1=68.208333 acc-top5=87.145833 [Epoch 046] training: accuracy=78.187350 loss=0.854104 [Epoch 046] speed: 62 samples/sec time cost: 4149.338876 [Epoch 046] validation: acc-top1=68.224010 acc-top5=87.164810 loss=1.557035 Epoch[047] Batch [0049]/[3760] Speed: 42.304322 samples/sec accuracy=79.062500 loss=0.799024 lr=0.001000 Epoch[047] Batch [0099]/[3760] Speed: 60.600079 samples/sec accuracy=78.828125 loss=0.810638 lr=0.001000 Epoch[047] Batch [0149]/[3760] Speed: 61.983899 samples/sec accuracy=78.770833 loss=0.810468 lr=0.001000 Epoch[047] Batch [0199]/[3760] Speed: 61.187159 samples/sec accuracy=78.765625 loss=0.822443 lr=0.001000 Epoch[047] Batch [0249]/[3760] Speed: 62.845525 samples/sec accuracy=78.981250 loss=0.820778 lr=0.001000 Epoch[047] Batch [0299]/[3760] Speed: 62.077002 samples/sec accuracy=78.979167 loss=0.822062 lr=0.001000 Epoch[047] Batch [0349]/[3760] Speed: 63.009049 samples/sec accuracy=78.915179 loss=0.823907 lr=0.001000 Epoch[047] Batch [0399]/[3760] Speed: 62.598208 samples/sec accuracy=78.847656 loss=0.824298 lr=0.001000 Epoch[047] Batch 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accuracy=78.835069 loss=0.829196 lr=0.001000 Epoch[047] Batch [0949]/[3760] Speed: 62.169991 samples/sec accuracy=78.838816 loss=0.829168 lr=0.001000 Epoch[047] Batch [0999]/[3760] Speed: 62.039582 samples/sec accuracy=78.829688 loss=0.829156 lr=0.001000 Epoch[047] Batch [1049]/[3760] Speed: 62.553079 samples/sec accuracy=78.776786 loss=0.830805 lr=0.001000 Epoch[047] Batch [1099]/[3760] Speed: 62.078974 samples/sec accuracy=78.782670 loss=0.831009 lr=0.001000 Epoch[047] Batch [1149]/[3760] Speed: 62.225933 samples/sec accuracy=78.794837 loss=0.830068 lr=0.001000 Epoch[047] Batch [1199]/[3760] Speed: 62.295322 samples/sec accuracy=78.799479 loss=0.830438 lr=0.001000 Epoch[047] Batch [1249]/[3760] Speed: 62.697361 samples/sec accuracy=78.760000 loss=0.830353 lr=0.001000 Epoch[047] Batch [1299]/[3760] Speed: 62.080905 samples/sec accuracy=78.735577 loss=0.831607 lr=0.001000 Epoch[047] Batch [1349]/[3760] Speed: 61.654408 samples/sec accuracy=78.716435 loss=0.834013 lr=0.001000 Epoch[047] 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accuracy=78.675781 loss=0.835405 lr=0.001000 Epoch[047] Batch [2849]/[3760] Speed: 62.095974 samples/sec accuracy=78.657346 loss=0.836031 lr=0.001000 Epoch[047] Batch [2899]/[3760] Speed: 62.393610 samples/sec accuracy=78.654634 loss=0.835754 lr=0.001000 Epoch[047] Batch [2949]/[3760] Speed: 61.275411 samples/sec accuracy=78.670021 loss=0.835870 lr=0.001000 Epoch[047] Batch [2999]/[3760] Speed: 62.951205 samples/sec accuracy=78.662500 loss=0.836148 lr=0.001000 Epoch[047] Batch [3049]/[3760] Speed: 61.643332 samples/sec accuracy=78.637295 loss=0.836846 lr=0.001000 Epoch[047] Batch [3099]/[3760] Speed: 62.815191 samples/sec accuracy=78.640625 loss=0.836824 lr=0.001000 Epoch[047] Batch [3149]/[3760] Speed: 61.913456 samples/sec accuracy=78.634425 loss=0.836871 lr=0.001000 Epoch[047] Batch [3199]/[3760] Speed: 62.685095 samples/sec accuracy=78.656250 loss=0.836004 lr=0.001000 Epoch[047] Batch [3249]/[3760] Speed: 62.160771 samples/sec accuracy=78.662019 loss=0.835829 lr=0.001000 Epoch[047] 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accuracy=78.572917 loss=0.838311 lr=0.001000 Batch [0049]/[0303]: acc-top1=66.937500 acc-top5=86.875000 Batch [0099]/[0303]: acc-top1=68.859375 acc-top5=87.234375 Batch [0149]/[0303]: acc-top1=68.864583 acc-top5=87.229167 Batch [0199]/[0303]: acc-top1=68.914062 acc-top5=87.390625 Batch [0249]/[0303]: acc-top1=68.618750 acc-top5=87.462500 Batch [0299]/[0303]: acc-top1=68.505208 acc-top5=87.453125 [Epoch 047] training: accuracy=78.580452 loss=0.837998 [Epoch 047] speed: 61 samples/sec time cost: 4154.314030 [Epoch 047] validation: acc-top1=68.512789 acc-top5=87.458746 loss=1.564761 Epoch[048] Batch [0049]/[3760] Speed: 42.546353 samples/sec accuracy=78.656250 loss=0.839847 lr=0.001000 Epoch[048] Batch [0099]/[3760] Speed: 60.997521 samples/sec accuracy=79.359375 loss=0.819250 lr=0.001000 Epoch[048] Batch [0149]/[3760] Speed: 61.832188 samples/sec accuracy=79.343750 loss=0.814895 lr=0.001000 Epoch[048] Batch [0199]/[3760] Speed: 61.176552 samples/sec accuracy=79.343750 loss=0.817135 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lr=0.001000 Epoch[048] Batch [2149]/[3760] Speed: 62.695414 samples/sec accuracy=79.012355 loss=0.822034 lr=0.001000 Epoch[048] Batch [2199]/[3760] Speed: 62.119103 samples/sec accuracy=79.012074 loss=0.821769 lr=0.001000 Epoch[048] Batch [2249]/[3760] Speed: 62.412937 samples/sec accuracy=79.030556 loss=0.820690 lr=0.001000 Epoch[048] Batch [2299]/[3760] Speed: 62.629348 samples/sec accuracy=79.012908 loss=0.821041 lr=0.001000 Epoch[048] Batch [2349]/[3760] Speed: 62.122545 samples/sec accuracy=79.005984 loss=0.821576 lr=0.001000 Epoch[048] Batch [2399]/[3760] Speed: 62.559082 samples/sec accuracy=78.985026 loss=0.821785 lr=0.001000 Epoch[048] Batch [2449]/[3760] Speed: 62.163526 samples/sec accuracy=78.992985 loss=0.821106 lr=0.001000 Epoch[048] Batch [2499]/[3760] Speed: 62.268761 samples/sec accuracy=78.998125 loss=0.820932 lr=0.001000 Epoch[048] Batch [2549]/[3760] Speed: 62.493500 samples/sec accuracy=79.009191 loss=0.820400 lr=0.001000 Epoch[048] Batch [2599]/[3760] Speed: 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lr=0.001000 Epoch[048] Batch [3099]/[3760] Speed: 62.152513 samples/sec accuracy=78.935484 loss=0.822409 lr=0.001000 Epoch[048] Batch [3149]/[3760] Speed: 62.304943 samples/sec accuracy=78.928571 loss=0.822914 lr=0.001000 Epoch[048] Batch [3199]/[3760] Speed: 62.477195 samples/sec accuracy=78.925293 loss=0.823279 lr=0.001000 Epoch[048] Batch [3249]/[3760] Speed: 62.415101 samples/sec accuracy=78.935577 loss=0.823276 lr=0.001000 Epoch[048] Batch [3299]/[3760] Speed: 62.159802 samples/sec accuracy=78.924716 loss=0.823816 lr=0.001000 Epoch[048] Batch [3349]/[3760] Speed: 62.501517 samples/sec accuracy=78.915578 loss=0.823886 lr=0.001000 Epoch[048] Batch [3399]/[3760] Speed: 62.540723 samples/sec accuracy=78.897518 loss=0.824491 lr=0.001000 Epoch[048] Batch [3449]/[3760] Speed: 62.741710 samples/sec accuracy=78.895380 loss=0.824500 lr=0.001000 Epoch[048] Batch [3499]/[3760] Speed: 62.145449 samples/sec accuracy=78.882589 loss=0.824836 lr=0.001000 Epoch[048] Batch [3549]/[3760] Speed: 62.325570 samples/sec accuracy=78.893926 loss=0.824740 lr=0.001000 Epoch[048] Batch [3599]/[3760] Speed: 61.934140 samples/sec accuracy=78.895833 loss=0.824870 lr=0.001000 Epoch[048] Batch [3649]/[3760] Speed: 62.357569 samples/sec accuracy=78.895120 loss=0.824907 lr=0.001000 Epoch[048] Batch [3699]/[3760] Speed: 62.880264 samples/sec accuracy=78.912584 loss=0.824730 lr=0.001000 Epoch[048] Batch [3749]/[3760] Speed: 67.687116 samples/sec accuracy=78.916667 loss=0.824872 lr=0.001000 Batch [0049]/[0303]: acc-top1=67.062500 acc-top5=86.593750 Batch [0099]/[0303]: acc-top1=68.625000 acc-top5=87.031250 Batch [0149]/[0303]: acc-top1=68.645833 acc-top5=86.885417 Batch [0199]/[0303]: acc-top1=68.781250 acc-top5=87.062500 Batch [0249]/[0303]: acc-top1=68.581250 acc-top5=87.125000 Batch [0299]/[0303]: acc-top1=68.562500 acc-top5=87.125000 [Epoch 048] training: accuracy=78.905834 loss=0.825377 [Epoch 048] speed: 62 samples/sec time cost: 4152.909436 [Epoch 048] validation: acc-top1=68.564356 acc-top5=87.123556 loss=1.554933 Epoch[049] Batch [0049]/[3759] Speed: 42.570024 samples/sec accuracy=80.000000 loss=0.775195 lr=0.001000 Epoch[049] Batch [0099]/[3759] Speed: 61.135622 samples/sec accuracy=79.781250 loss=0.799423 lr=0.001000 Epoch[049] Batch [0149]/[3759] Speed: 62.537647 samples/sec accuracy=80.041667 loss=0.781828 lr=0.001000 Epoch[049] Batch [0199]/[3759] Speed: 61.260729 samples/sec accuracy=79.585938 loss=0.792509 lr=0.001000 Epoch[049] Batch [0249]/[3759] Speed: 62.873619 samples/sec accuracy=79.537500 loss=0.795902 lr=0.001000 Epoch[049] Batch [0299]/[3759] Speed: 61.642206 samples/sec accuracy=79.656250 loss=0.794640 lr=0.001000 Epoch[049] Batch [0349]/[3759] Speed: 62.833085 samples/sec accuracy=79.598214 loss=0.795348 lr=0.001000 Epoch[049] Batch [0399]/[3759] Speed: 62.711211 samples/sec accuracy=79.667969 loss=0.796726 lr=0.001000 Epoch[049] Batch [0449]/[3759] Speed: 62.774911 samples/sec accuracy=79.652778 loss=0.794952 lr=0.001000 Epoch[049] Batch 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accuracy=79.197368 loss=0.811384 lr=0.001000 Epoch[049] Batch [2899]/[3759] Speed: 62.304261 samples/sec accuracy=79.185345 loss=0.811294 lr=0.001000 Epoch[049] Batch [2949]/[3759] Speed: 62.773361 samples/sec accuracy=79.171081 loss=0.811373 lr=0.001000 Epoch[049] Batch [2999]/[3759] Speed: 62.493873 samples/sec accuracy=79.178125 loss=0.810949 lr=0.001000 Epoch[049] Batch [3049]/[3759] Speed: 62.432372 samples/sec accuracy=79.170082 loss=0.811548 lr=0.001000 Epoch[049] Batch [3099]/[3759] Speed: 62.204927 samples/sec accuracy=79.172379 loss=0.811771 lr=0.001000 Epoch[049] Batch [3149]/[3759] Speed: 62.046177 samples/sec accuracy=79.172619 loss=0.811680 lr=0.001000 Epoch[049] Batch [3199]/[3759] Speed: 62.541699 samples/sec accuracy=79.161621 loss=0.811823 lr=0.001000 Epoch[049] Batch [3249]/[3759] Speed: 62.742809 samples/sec accuracy=79.145673 loss=0.812546 lr=0.001000 Epoch[049] Batch [3299]/[3759] Speed: 62.503805 samples/sec accuracy=79.164299 loss=0.811949 lr=0.001000 Epoch[049] 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[0099]/[0303]: acc-top1=68.203125 acc-top5=86.593750 Batch [0149]/[0303]: acc-top1=68.229167 acc-top5=86.552083 Batch [0199]/[0303]: acc-top1=68.406250 acc-top5=86.906250 Batch [0249]/[0303]: acc-top1=68.218750 acc-top5=87.012500 Batch [0299]/[0303]: acc-top1=68.197917 acc-top5=87.057292 [Epoch 049] training: accuracy=79.096003 loss=0.815012 [Epoch 049] speed: 62 samples/sec time cost: 4146.320130 [Epoch 049] validation: acc-top1=68.198226 acc-top5=87.061675 loss=1.578566 Epoch[050] Batch [0049]/[3760] Speed: 42.331036 samples/sec accuracy=81.031250 loss=0.745509 lr=0.001000 Epoch[050] Batch [0099]/[3760] Speed: 60.550124 samples/sec accuracy=80.515625 loss=0.767775 lr=0.001000 Epoch[050] Batch [0149]/[3760] Speed: 62.365989 samples/sec accuracy=80.114583 loss=0.781254 lr=0.001000 Epoch[050] Batch [0199]/[3760] Speed: 61.282440 samples/sec accuracy=80.070312 loss=0.781460 lr=0.001000 Epoch[050] Batch [0249]/[3760] Speed: 63.161778 samples/sec accuracy=80.231250 loss=0.775364 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lr=0.001000 Epoch[050] Batch [3149]/[3760] Speed: 62.046630 samples/sec accuracy=79.491567 loss=0.802427 lr=0.001000 Epoch[050] Batch [3199]/[3760] Speed: 62.412096 samples/sec accuracy=79.490234 loss=0.802254 lr=0.001000 Epoch[050] Batch [3249]/[3760] Speed: 62.523434 samples/sec accuracy=79.491346 loss=0.802492 lr=0.001000 Epoch[050] Batch [3299]/[3760] Speed: 62.365833 samples/sec accuracy=79.500000 loss=0.802223 lr=0.001000 Epoch[050] Batch [3349]/[3760] Speed: 62.790194 samples/sec accuracy=79.510728 loss=0.801792 lr=0.001000 Epoch[050] Batch [3399]/[3760] Speed: 62.389099 samples/sec accuracy=79.515165 loss=0.801337 lr=0.001000 Epoch[050] Batch [3449]/[3760] Speed: 62.136996 samples/sec accuracy=79.507699 loss=0.801860 lr=0.001000 Epoch[050] Batch [3499]/[3760] Speed: 62.107417 samples/sec accuracy=79.501339 loss=0.801793 lr=0.001000 Epoch[050] Batch [3549]/[3760] Speed: 62.891813 samples/sec accuracy=79.506162 loss=0.801435 lr=0.001000 Epoch[050] Batch [3599]/[3760] Speed: 62.028511 samples/sec accuracy=79.504774 loss=0.801531 lr=0.001000 Epoch[050] Batch [3649]/[3760] Speed: 62.191475 samples/sec accuracy=79.500000 loss=0.801826 lr=0.001000 Epoch[050] Batch [3699]/[3760] Speed: 62.433635 samples/sec accuracy=79.495777 loss=0.801812 lr=0.001000 Epoch[050] Batch [3749]/[3760] Speed: 67.163675 samples/sec accuracy=79.500833 loss=0.801835 lr=0.001000 Batch [0049]/[0303]: acc-top1=66.968750 acc-top5=87.093750 Batch [0099]/[0303]: acc-top1=68.500000 acc-top5=87.437500 Batch [0149]/[0303]: acc-top1=68.552083 acc-top5=87.125000 Batch [0199]/[0303]: acc-top1=68.453125 acc-top5=87.132812 Batch [0249]/[0303]: acc-top1=68.262500 acc-top5=87.162500 Batch [0299]/[0303]: acc-top1=68.302083 acc-top5=87.145833 [Epoch 050] training: accuracy=79.498836 loss=0.801953 [Epoch 050] speed: 62 samples/sec time cost: 4151.053346 [Epoch 050] validation: acc-top1=68.291048 acc-top5=87.159653 loss=1.591633 Epoch[051] Batch [0049]/[3760] Speed: 43.063437 samples/sec accuracy=79.625000 loss=0.829407 lr=0.001000 Epoch[051] Batch [0099]/[3760] Speed: 60.337892 samples/sec accuracy=79.734375 loss=0.799459 lr=0.001000 Epoch[051] Batch [0149]/[3760] Speed: 62.080845 samples/sec accuracy=79.812500 loss=0.791107 lr=0.001000 Epoch[051] Batch [0199]/[3760] Speed: 60.589888 samples/sec accuracy=79.609375 loss=0.801621 lr=0.001000 Epoch[051] Batch [0249]/[3760] Speed: 62.899427 samples/sec accuracy=79.806250 loss=0.794399 lr=0.001000 Epoch[051] Batch [0299]/[3760] Speed: 61.997642 samples/sec accuracy=79.932292 loss=0.788589 lr=0.001000 Epoch[051] Batch [0349]/[3760] Speed: 61.886391 samples/sec accuracy=79.941964 loss=0.786370 lr=0.001000 Epoch[051] Batch [0399]/[3760] Speed: 62.939192 samples/sec accuracy=79.996094 loss=0.780860 lr=0.001000 Epoch[051] Batch [0449]/[3760] Speed: 62.440074 samples/sec accuracy=79.923611 loss=0.785872 lr=0.001000 Epoch[051] Batch [0499]/[3760] Speed: 62.451007 samples/sec accuracy=79.868750 loss=0.789055 lr=0.001000 Epoch[051] 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acc-top5=86.958333 Batch [0199]/[0303]: acc-top1=68.828125 acc-top5=87.234375 Batch [0249]/[0303]: acc-top1=68.662500 acc-top5=87.275000 Batch [0299]/[0303]: acc-top1=68.619792 acc-top5=87.218750 [Epoch 051] training: accuracy=79.787650 loss=0.790962 [Epoch 051] speed: 62 samples/sec time cost: 4149.008912 [Epoch 051] validation: acc-top1=68.646865 acc-top5=87.226691 loss=1.572973 Epoch[052] Batch [0049]/[3759] Speed: 42.728508 samples/sec accuracy=81.468750 loss=0.752325 lr=0.001000 Epoch[052] Batch [0099]/[3759] Speed: 61.534919 samples/sec accuracy=80.921875 loss=0.756875 lr=0.001000 Epoch[052] Batch [0149]/[3759] Speed: 61.937677 samples/sec accuracy=80.531250 loss=0.762824 lr=0.001000 Epoch[052] Batch [0199]/[3759] Speed: 61.413420 samples/sec accuracy=80.367188 loss=0.759017 lr=0.001000 Epoch[052] Batch [0249]/[3759] Speed: 62.641618 samples/sec accuracy=80.412500 loss=0.758723 lr=0.001000 Epoch[052] Batch [0299]/[3759] Speed: 60.783284 samples/sec accuracy=80.296875 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accuracy=79.875992 loss=0.782686 lr=0.001000 Epoch[052] Batch [3199]/[3759] Speed: 62.141848 samples/sec accuracy=79.848145 loss=0.783663 lr=0.001000 Epoch[052] Batch [3249]/[3759] Speed: 62.228694 samples/sec accuracy=79.847115 loss=0.783529 lr=0.001000 Epoch[052] Batch [3299]/[3759] Speed: 62.486605 samples/sec accuracy=79.839489 loss=0.783779 lr=0.001000 Epoch[052] Batch [3349]/[3759] Speed: 62.363598 samples/sec accuracy=79.848881 loss=0.783381 lr=0.001000 Epoch[052] Batch [3399]/[3759] Speed: 62.446121 samples/sec accuracy=79.845129 loss=0.783890 lr=0.001000 Epoch[052] Batch [3449]/[3759] Speed: 62.050209 samples/sec accuracy=79.851449 loss=0.783642 lr=0.001000 Epoch[052] Batch [3499]/[3759] Speed: 63.006655 samples/sec accuracy=79.831250 loss=0.784370 lr=0.001000 Epoch[052] Batch [3549]/[3759] Speed: 62.370856 samples/sec accuracy=79.840229 loss=0.784172 lr=0.001000 Epoch[052] Batch [3599]/[3759] Speed: 62.699270 samples/sec accuracy=79.841580 loss=0.784395 lr=0.001000 Epoch[052] Batch [3649]/[3759] Speed: 62.459595 samples/sec accuracy=79.830908 loss=0.784317 lr=0.001000 Epoch[052] Batch [3699]/[3759] Speed: 62.505173 samples/sec accuracy=79.841216 loss=0.784166 lr=0.001000 Epoch[052] Batch [3749]/[3759] Speed: 67.281211 samples/sec accuracy=79.845417 loss=0.784096 lr=0.001000 Batch [0049]/[0303]: acc-top1=66.312500 acc-top5=86.343750 Batch [0099]/[0303]: acc-top1=68.109375 acc-top5=86.906250 Batch [0149]/[0303]: acc-top1=68.364583 acc-top5=86.781250 Batch [0199]/[0303]: acc-top1=68.492188 acc-top5=87.085938 Batch [0249]/[0303]: acc-top1=68.175000 acc-top5=87.100000 Batch [0299]/[0303]: acc-top1=68.166667 acc-top5=87.109375 [Epoch 052] training: accuracy=79.851274 loss=0.783912 [Epoch 052] speed: 62 samples/sec time cost: 4149.989839 [Epoch 052] validation: acc-top1=68.198226 acc-top5=87.118399 loss=1.598549 Epoch[053] Batch [0049]/[3760] Speed: 42.134483 samples/sec accuracy=80.500000 loss=0.780111 lr=0.001000 Epoch[053] Batch [0099]/[3760] Speed: 60.700460 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[0249]/[0303]: acc-top1=68.081250 acc-top5=87.068750 Batch [0299]/[0303]: acc-top1=68.109375 acc-top5=87.078125 [Epoch 053] training: accuracy=80.270944 loss=0.769832 [Epoch 053] speed: 62 samples/sec time cost: 4145.292815 [Epoch 053] validation: acc-top1=68.126031 acc-top5=87.092616 loss=1.612981 Epoch[054] Batch [0049]/[3760] Speed: 42.118694 samples/sec accuracy=81.468750 loss=0.719781 lr=0.001000 Epoch[054] Batch [0099]/[3760] Speed: 60.966683 samples/sec accuracy=81.671875 loss=0.708299 lr=0.001000 Epoch[054] Batch [0149]/[3760] Speed: 62.244341 samples/sec accuracy=80.906250 loss=0.738508 lr=0.001000 Epoch[054] Batch [0199]/[3760] Speed: 61.750458 samples/sec accuracy=80.890625 loss=0.734965 lr=0.001000 Epoch[054] Batch [0249]/[3760] Speed: 62.309420 samples/sec accuracy=80.756250 loss=0.739421 lr=0.001000 Epoch[054] Batch [0299]/[3760] Speed: 61.641082 samples/sec accuracy=80.817708 loss=0.740595 lr=0.001000 Epoch[054] Batch [0349]/[3760] Speed: 62.724656 samples/sec 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accuracy=80.424316 loss=0.759735 lr=0.001000 Epoch[054] Batch [3249]/[3760] Speed: 62.065920 samples/sec accuracy=80.417788 loss=0.760060 lr=0.001000 Epoch[054] Batch [3299]/[3760] Speed: 62.563883 samples/sec accuracy=80.433239 loss=0.759571 lr=0.001000 Epoch[054] Batch [3349]/[3760] Speed: 62.207966 samples/sec accuracy=80.421642 loss=0.759636 lr=0.001000 Epoch[054] Batch [3399]/[3760] Speed: 62.371239 samples/sec accuracy=80.420496 loss=0.759855 lr=0.001000 Epoch[054] Batch [3449]/[3760] Speed: 62.298715 samples/sec accuracy=80.422101 loss=0.759883 lr=0.001000 Epoch[054] Batch [3499]/[3760] Speed: 62.904743 samples/sec accuracy=80.413839 loss=0.759787 lr=0.001000 Epoch[054] Batch [3549]/[3760] Speed: 62.357994 samples/sec accuracy=80.397887 loss=0.760487 lr=0.001000 Epoch[054] Batch [3599]/[3760] Speed: 62.334638 samples/sec accuracy=80.404948 loss=0.760121 lr=0.001000 Epoch[054] Batch [3649]/[3760] Speed: 62.593034 samples/sec accuracy=80.409247 loss=0.760311 lr=0.001000 Epoch[054] 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lr=0.001000 Epoch[055] Batch [3499]/[3759] Speed: 62.201106 samples/sec accuracy=80.444643 loss=0.758787 lr=0.001000 Epoch[055] Batch [3549]/[3759] Speed: 62.419547 samples/sec accuracy=80.450264 loss=0.758778 lr=0.001000 Epoch[055] Batch [3599]/[3759] Speed: 61.905095 samples/sec accuracy=80.463108 loss=0.758589 lr=0.001000 Epoch[055] Batch [3649]/[3759] Speed: 62.819002 samples/sec accuracy=80.470890 loss=0.758209 lr=0.001000 Epoch[055] Batch [3699]/[3759] Speed: 62.518562 samples/sec accuracy=80.472128 loss=0.758289 lr=0.001000 Epoch[055] Batch [3749]/[3759] Speed: 67.290853 samples/sec accuracy=80.469583 loss=0.758070 lr=0.001000 Batch [0049]/[0303]: acc-top1=66.562500 acc-top5=86.843750 Batch [0099]/[0303]: acc-top1=68.187500 acc-top5=87.218750 Batch [0149]/[0303]: acc-top1=68.520833 acc-top5=87.291667 Batch [0199]/[0303]: acc-top1=68.617188 acc-top5=87.437500 Batch [0249]/[0303]: acc-top1=68.437500 acc-top5=87.443750 Batch [0299]/[0303]: acc-top1=68.401042 acc-top5=87.385417 [Epoch 055] training: accuracy=80.473115 loss=0.758021 [Epoch 055] speed: 62 samples/sec time cost: 4147.179090 [Epoch 055] validation: acc-top1=68.414810 acc-top5=87.396865 loss=1.598438 Epoch[056] Batch [0049]/[3760] Speed: 42.091571 samples/sec accuracy=80.343750 loss=0.768673 lr=0.001000 Epoch[056] Batch [0099]/[3760] Speed: 60.733255 samples/sec accuracy=81.062500 loss=0.725886 lr=0.001000 Epoch[056] Batch [0149]/[3760] Speed: 61.641969 samples/sec accuracy=81.114583 loss=0.728480 lr=0.001000 Epoch[056] Batch [0199]/[3760] Speed: 60.960666 samples/sec accuracy=81.179688 loss=0.726813 lr=0.001000 Epoch[056] Batch [0249]/[3760] Speed: 62.789427 samples/sec accuracy=81.118750 loss=0.735253 lr=0.001000 Epoch[056] Batch [0299]/[3760] Speed: 61.968779 samples/sec accuracy=81.031250 loss=0.736608 lr=0.001000 Epoch[056] Batch [0349]/[3760] Speed: 62.477738 samples/sec accuracy=81.089286 loss=0.739371 lr=0.001000 Epoch[056] Batch [0399]/[3760] Speed: 61.965576 samples/sec accuracy=80.957031 loss=0.744260 lr=0.001000 Epoch[056] Batch [0449]/[3760] Speed: 61.734104 samples/sec accuracy=80.968750 loss=0.742131 lr=0.001000 Epoch[056] Batch [0499]/[3760] Speed: 62.691786 samples/sec accuracy=80.903125 loss=0.745569 lr=0.001000 Epoch[056] Batch [0549]/[3760] Speed: 62.380288 samples/sec accuracy=80.897727 loss=0.745265 lr=0.001000 Epoch[056] Batch [0599]/[3760] Speed: 62.746949 samples/sec accuracy=80.942708 loss=0.742124 lr=0.001000 Epoch[056] Batch [0649]/[3760] Speed: 62.780264 samples/sec accuracy=80.915865 loss=0.742928 lr=0.001000 Epoch[056] Batch [0699]/[3760] Speed: 62.180941 samples/sec accuracy=80.841518 loss=0.743965 lr=0.001000 Epoch[056] Batch [0749]/[3760] Speed: 62.727260 samples/sec accuracy=80.856250 loss=0.744014 lr=0.001000 Epoch[056] Batch [0799]/[3760] Speed: 62.462217 samples/sec accuracy=80.755859 loss=0.746972 lr=0.001000 Epoch[056] Batch [0849]/[3760] Speed: 62.229162 samples/sec accuracy=80.748162 loss=0.748054 lr=0.001000 Epoch[056] 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accuracy=80.884259 loss=0.740763 lr=0.001000 Epoch[056] Batch [1399]/[3760] Speed: 62.247993 samples/sec accuracy=80.901786 loss=0.740423 lr=0.001000 Epoch[056] Batch [1449]/[3760] Speed: 62.312699 samples/sec accuracy=80.894397 loss=0.739837 lr=0.001000 Epoch[056] Batch [1499]/[3760] Speed: 61.999447 samples/sec accuracy=80.918750 loss=0.740243 lr=0.001000 Epoch[056] Batch [1549]/[3760] Speed: 62.549024 samples/sec accuracy=80.906250 loss=0.741271 lr=0.001000 Epoch[056] Batch [1599]/[3760] Speed: 61.720977 samples/sec accuracy=80.929688 loss=0.741530 lr=0.001000 Epoch[056] Batch [1649]/[3760] Speed: 62.380540 samples/sec accuracy=80.906250 loss=0.741514 lr=0.001000 Epoch[056] Batch [1699]/[3760] Speed: 62.527668 samples/sec accuracy=80.908088 loss=0.741199 lr=0.001000 Epoch[056] Batch [1749]/[3760] Speed: 62.156133 samples/sec accuracy=80.917857 loss=0.741069 lr=0.001000 Epoch[056] Batch [1799]/[3760] Speed: 62.804066 samples/sec accuracy=80.953125 loss=0.740686 lr=0.001000 Epoch[056] 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accuracy=80.974185 loss=0.739871 lr=0.001000 Epoch[056] Batch [2349]/[3760] Speed: 62.200088 samples/sec accuracy=80.949468 loss=0.740759 lr=0.001000 Epoch[056] Batch [2399]/[3760] Speed: 62.029447 samples/sec accuracy=80.952474 loss=0.740404 lr=0.001000 Epoch[056] Batch [2449]/[3760] Speed: 62.561061 samples/sec accuracy=80.924745 loss=0.741650 lr=0.001000 Epoch[056] Batch [2499]/[3760] Speed: 62.214738 samples/sec accuracy=80.932500 loss=0.741287 lr=0.001000 Epoch[056] Batch [2549]/[3760] Speed: 62.269610 samples/sec accuracy=80.913603 loss=0.742398 lr=0.001000 Epoch[056] Batch [2599]/[3760] Speed: 62.315294 samples/sec accuracy=80.895433 loss=0.742888 lr=0.001000 Epoch[056] Batch [2649]/[3760] Speed: 62.472680 samples/sec accuracy=80.882075 loss=0.743307 lr=0.001000 Epoch[056] Batch [2699]/[3760] Speed: 62.387891 samples/sec accuracy=80.894097 loss=0.742859 lr=0.001000 Epoch[056] Batch [2749]/[3760] Speed: 62.404914 samples/sec accuracy=80.882955 loss=0.742851 lr=0.001000 Epoch[056] 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accuracy=80.846154 loss=0.744462 lr=0.001000 Epoch[056] Batch [3299]/[3760] Speed: 62.048218 samples/sec accuracy=80.846117 loss=0.744887 lr=0.001000 Epoch[056] Batch [3349]/[3760] Speed: 62.509413 samples/sec accuracy=80.845149 loss=0.744768 lr=0.001000 Epoch[056] Batch [3399]/[3760] Speed: 62.291534 samples/sec accuracy=80.849724 loss=0.744436 lr=0.001000 Epoch[056] Batch [3449]/[3760] Speed: 62.119613 samples/sec accuracy=80.833786 loss=0.744940 lr=0.001000 Epoch[056] Batch [3499]/[3760] Speed: 63.030153 samples/sec accuracy=80.822321 loss=0.744995 lr=0.001000 Epoch[056] Batch [3549]/[3760] Speed: 61.817888 samples/sec accuracy=80.813820 loss=0.744801 lr=0.001000 Epoch[056] Batch [3599]/[3760] Speed: 62.330714 samples/sec accuracy=80.823351 loss=0.744793 lr=0.001000 Epoch[056] Batch [3649]/[3760] Speed: 62.020965 samples/sec accuracy=80.822774 loss=0.744907 lr=0.001000 Epoch[056] Batch [3699]/[3760] Speed: 62.219447 samples/sec accuracy=80.823057 loss=0.744735 lr=0.001000 Epoch[056] Batch [3749]/[3760] Speed: 67.322622 samples/sec accuracy=80.811667 loss=0.745336 lr=0.001000 Batch [0049]/[0303]: acc-top1=66.375000 acc-top5=86.218750 Batch [0099]/[0303]: acc-top1=67.734375 acc-top5=86.859375 Batch [0149]/[0303]: acc-top1=68.156250 acc-top5=86.739583 Batch [0199]/[0303]: acc-top1=68.382812 acc-top5=86.898438 Batch [0249]/[0303]: acc-top1=68.368750 acc-top5=86.931250 Batch [0299]/[0303]: acc-top1=68.348958 acc-top5=86.979167 [Epoch 056] training: accuracy=80.803690 loss=0.745460 [Epoch 056] speed: 61 samples/sec time cost: 4152.557527 [Epoch 056] validation: acc-top1=68.363243 acc-top5=86.984323 loss=1.625633 Epoch[057] Batch [0049]/[3760] Speed: 42.185592 samples/sec accuracy=81.656250 loss=0.709486 lr=0.001000 Epoch[057] Batch [0099]/[3760] Speed: 60.902974 samples/sec accuracy=81.640625 loss=0.711364 lr=0.001000 Epoch[057] Batch [0149]/[3760] Speed: 61.784302 samples/sec accuracy=81.916667 loss=0.699068 lr=0.001000 Epoch[057] Batch [0199]/[3760] Speed: 60.810613 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lr=0.001000 Epoch[057] Batch [3549]/[3760] Speed: 61.881919 samples/sec accuracy=81.046215 loss=0.737436 lr=0.001000 Epoch[057] Batch [3599]/[3760] Speed: 62.683319 samples/sec accuracy=81.036892 loss=0.737771 lr=0.001000 Epoch[057] Batch [3649]/[3760] Speed: 62.266608 samples/sec accuracy=81.038527 loss=0.737888 lr=0.001000 Epoch[057] Batch [3699]/[3760] Speed: 62.527090 samples/sec accuracy=81.029983 loss=0.737810 lr=0.001000 Epoch[057] Batch [3749]/[3760] Speed: 67.367151 samples/sec accuracy=81.027917 loss=0.737955 lr=0.001000 Batch [0049]/[0303]: acc-top1=66.875000 acc-top5=86.718750 Batch [0099]/[0303]: acc-top1=68.343750 acc-top5=86.859375 Batch [0149]/[0303]: acc-top1=68.354167 acc-top5=86.781250 Batch [0199]/[0303]: acc-top1=68.609375 acc-top5=87.023438 Batch [0249]/[0303]: acc-top1=68.393750 acc-top5=87.018750 Batch [0299]/[0303]: acc-top1=68.364583 acc-top5=87.078125 [Epoch 057] training: accuracy=81.026430 loss=0.738006 [Epoch 057] speed: 61 samples/sec time cost: 4151.899158 [Epoch 057] validation: acc-top1=68.378713 acc-top5=87.077145 loss=1.611030 Epoch[058] Batch [0049]/[3759] Speed: 42.538933 samples/sec accuracy=81.750000 loss=0.711373 lr=0.001000 Epoch[058] Batch [0099]/[3759] Speed: 60.965480 samples/sec accuracy=80.906250 loss=0.732076 lr=0.001000 Epoch[058] Batch [0149]/[3759] Speed: 61.691687 samples/sec accuracy=80.947917 loss=0.713709 lr=0.001000 Epoch[058] Batch [0199]/[3759] Speed: 61.137559 samples/sec accuracy=81.070312 loss=0.714558 lr=0.001000 Epoch[058] Batch [0249]/[3759] Speed: 62.355190 samples/sec accuracy=81.343750 loss=0.711741 lr=0.001000 Epoch[058] Batch [0299]/[3759] Speed: 61.882827 samples/sec accuracy=81.265625 loss=0.713715 lr=0.001000 Epoch[058] Batch [0349]/[3759] Speed: 62.568200 samples/sec accuracy=81.174107 loss=0.718628 lr=0.001000 Epoch[058] Batch [0399]/[3759] Speed: 62.249665 samples/sec accuracy=81.285156 loss=0.716253 lr=0.001000 Epoch[058] Batch [0449]/[3759] Speed: 62.701119 samples/sec 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accuracy=81.440848 loss=0.722901 lr=0.001000 Epoch[058] Batch [1449]/[3759] Speed: 62.856730 samples/sec accuracy=81.433190 loss=0.723502 lr=0.001000 Epoch[058] Batch [1499]/[3759] Speed: 61.847792 samples/sec accuracy=81.432292 loss=0.723721 lr=0.001000 Epoch[058] Batch [1549]/[3759] Speed: 62.221273 samples/sec accuracy=81.417339 loss=0.724683 lr=0.001000 Epoch[058] Batch [1599]/[3759] Speed: 62.093840 samples/sec accuracy=81.416016 loss=0.725708 lr=0.001000 Epoch[058] Batch [1649]/[3759] Speed: 62.309924 samples/sec accuracy=81.421402 loss=0.725300 lr=0.001000 Epoch[058] Batch [1699]/[3759] Speed: 62.086201 samples/sec accuracy=81.408088 loss=0.725965 lr=0.001000 Epoch[058] Batch [1749]/[3759] Speed: 62.389446 samples/sec accuracy=81.409821 loss=0.725429 lr=0.001000 Epoch[058] Batch [1799]/[3759] Speed: 62.246072 samples/sec accuracy=81.411458 loss=0.725290 lr=0.001000 Epoch[058] Batch [1849]/[3759] Speed: 62.153744 samples/sec accuracy=81.402872 loss=0.725743 lr=0.001000 Epoch[058] 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accuracy=81.376330 loss=0.726426 lr=0.001000 Epoch[058] Batch [2399]/[3759] Speed: 61.835053 samples/sec accuracy=81.384766 loss=0.726044 lr=0.001000 Epoch[058] Batch [2449]/[3759] Speed: 62.359838 samples/sec accuracy=81.374362 loss=0.725822 lr=0.001000 Epoch[058] Batch [2499]/[3759] Speed: 62.246476 samples/sec accuracy=81.365000 loss=0.726314 lr=0.001000 Epoch[058] Batch [2549]/[3759] Speed: 62.279827 samples/sec accuracy=81.371936 loss=0.725996 lr=0.001000 Epoch[058] Batch [2599]/[3759] Speed: 61.925783 samples/sec accuracy=81.366587 loss=0.725797 lr=0.001000 Epoch[058] Batch [2649]/[3759] Speed: 62.249871 samples/sec accuracy=81.372642 loss=0.725861 lr=0.001000 Epoch[058] Batch [2699]/[3759] Speed: 62.589225 samples/sec accuracy=81.402778 loss=0.725247 lr=0.001000 Epoch[058] Batch [2749]/[3759] Speed: 62.391573 samples/sec accuracy=81.409091 loss=0.724942 lr=0.001000 Epoch[058] Batch [2799]/[3759] Speed: 62.262509 samples/sec accuracy=81.412946 loss=0.724471 lr=0.001000 Epoch[058] 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accuracy=81.308712 loss=0.727611 lr=0.001000 Epoch[058] Batch [3349]/[3759] Speed: 62.288357 samples/sec accuracy=81.300840 loss=0.727947 lr=0.001000 Epoch[058] Batch [3399]/[3759] Speed: 62.647574 samples/sec accuracy=81.300551 loss=0.727564 lr=0.001000 Epoch[058] Batch [3449]/[3759] Speed: 62.340181 samples/sec accuracy=81.295743 loss=0.728189 lr=0.001000 Epoch[058] Batch [3499]/[3759] Speed: 62.298363 samples/sec accuracy=81.300446 loss=0.727683 lr=0.001000 Epoch[058] Batch [3549]/[3759] Speed: 62.409820 samples/sec accuracy=81.295775 loss=0.727464 lr=0.001000 Epoch[058] Batch [3599]/[3759] Speed: 62.621926 samples/sec accuracy=81.289931 loss=0.727666 lr=0.001000 Epoch[058] Batch [3649]/[3759] Speed: 61.860568 samples/sec accuracy=81.280394 loss=0.727806 lr=0.001000 Epoch[058] Batch [3699]/[3759] Speed: 62.406954 samples/sec accuracy=81.266047 loss=0.728509 lr=0.001000 Epoch[058] Batch [3749]/[3759] Speed: 67.959007 samples/sec accuracy=81.261667 loss=0.728650 lr=0.001000 Batch [0049]/[0303]: acc-top1=66.687500 acc-top5=86.250000 Batch [0099]/[0303]: acc-top1=68.015625 acc-top5=86.734375 Batch [0149]/[0303]: acc-top1=68.177083 acc-top5=86.875000 Batch [0199]/[0303]: acc-top1=68.375000 acc-top5=87.085938 Batch [0249]/[0303]: acc-top1=68.131250 acc-top5=87.075000 Batch [0299]/[0303]: acc-top1=68.078125 acc-top5=87.026042 [Epoch 058] training: accuracy=81.264548 loss=0.728466 [Epoch 058] speed: 61 samples/sec time cost: 4150.044602 [Epoch 058] validation: acc-top1=68.095091 acc-top5=87.035891 loss=1.623106 Epoch[059] Batch [0049]/[3760] Speed: 42.470903 samples/sec accuracy=80.906250 loss=0.726174 lr=0.001000 Epoch[059] Batch [0099]/[3760] Speed: 61.068011 samples/sec accuracy=81.750000 loss=0.698349 lr=0.001000 Epoch[059] Batch [0149]/[3760] Speed: 61.799755 samples/sec accuracy=81.677083 loss=0.704960 lr=0.001000 Epoch[059] Batch [0199]/[3760] Speed: 60.803905 samples/sec accuracy=81.625000 loss=0.709608 lr=0.001000 Epoch[059] Batch [0249]/[3760] Speed: 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lr=0.001000 Epoch[059] Batch [3599]/[3760] Speed: 62.819732 samples/sec accuracy=81.394097 loss=0.722789 lr=0.001000 Epoch[059] Batch [3649]/[3760] Speed: 62.767566 samples/sec accuracy=81.395976 loss=0.722552 lr=0.001000 Epoch[059] Batch [3699]/[3760] Speed: 62.626877 samples/sec accuracy=81.392736 loss=0.722702 lr=0.001000 Epoch[059] Batch [3749]/[3760] Speed: 67.578415 samples/sec accuracy=81.378333 loss=0.722930 lr=0.001000 Batch [0049]/[0303]: acc-top1=66.531250 acc-top5=86.375000 Batch [0099]/[0303]: acc-top1=67.828125 acc-top5=86.984375 Batch [0149]/[0303]: acc-top1=67.989583 acc-top5=86.854167 Batch [0199]/[0303]: acc-top1=68.125000 acc-top5=87.046875 Batch [0249]/[0303]: acc-top1=67.825000 acc-top5=87.000000 Batch [0299]/[0303]: acc-top1=67.890625 acc-top5=87.052083 [Epoch 059] training: accuracy=81.381732 loss=0.722666 [Epoch 059] speed: 61 samples/sec time cost: 4153.574892 [Epoch 059] validation: acc-top1=67.904290 acc-top5=87.051361 loss=1.636578 Epoch[060] Batch 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accuracy=81.592351 loss=0.715189 lr=0.001000 Epoch[060] Batch [3399]/[3760] Speed: 62.097772 samples/sec accuracy=81.579963 loss=0.715790 lr=0.001000 Epoch[060] Batch [3449]/[3760] Speed: 62.273035 samples/sec accuracy=81.570199 loss=0.716279 lr=0.001000 Epoch[060] Batch [3499]/[3760] Speed: 62.501921 samples/sec accuracy=81.572321 loss=0.716214 lr=0.001000 Epoch[060] Batch [3549]/[3760] Speed: 61.863522 samples/sec accuracy=81.566021 loss=0.716551 lr=0.001000 Epoch[060] Batch [3599]/[3760] Speed: 62.664896 samples/sec accuracy=81.584201 loss=0.715930 lr=0.001000 Epoch[060] Batch [3649]/[3760] Speed: 62.600797 samples/sec accuracy=81.582192 loss=0.716061 lr=0.001000 Epoch[060] Batch [3699]/[3760] Speed: 62.327799 samples/sec accuracy=81.579814 loss=0.716428 lr=0.001000 Epoch[060] Batch [3749]/[3760] Speed: 67.117866 samples/sec accuracy=81.571667 loss=0.716810 lr=0.001000 Batch [0049]/[0303]: acc-top1=65.843750 acc-top5=86.093750 Batch [0099]/[0303]: acc-top1=67.546875 acc-top5=86.734375 Batch [0149]/[0303]: acc-top1=67.770833 acc-top5=86.645833 Batch [0199]/[0303]: acc-top1=68.007812 acc-top5=86.789062 Batch [0249]/[0303]: acc-top1=67.618750 acc-top5=86.731250 Batch [0299]/[0303]: acc-top1=67.588542 acc-top5=86.776042 [Epoch 060] training: accuracy=81.572889 loss=0.716731 [Epoch 060] speed: 62 samples/sec time cost: 4149.401208 [Epoch 060] validation: acc-top1=67.605198 acc-top5=86.772896 loss=1.659866 Epoch[061] Batch [0049]/[3759] Speed: 41.697686 samples/sec accuracy=82.218750 loss=0.674386 lr=0.001000 Epoch[061] Batch [0099]/[3759] Speed: 61.017391 samples/sec accuracy=82.062500 loss=0.694551 lr=0.001000 Epoch[061] Batch [0149]/[3759] Speed: 61.856090 samples/sec accuracy=82.302083 loss=0.693583 lr=0.001000 Epoch[061] Batch [0199]/[3759] Speed: 61.564820 samples/sec accuracy=82.359375 loss=0.694636 lr=0.001000 Epoch[061] Batch [0249]/[3759] Speed: 62.683523 samples/sec accuracy=82.356250 loss=0.693576 lr=0.001000 Epoch[061] Batch [0299]/[3759] 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accuracy=81.937934 loss=0.700791 lr=0.001000 Epoch[061] Batch [3649]/[3759] Speed: 62.557738 samples/sec accuracy=81.946918 loss=0.700713 lr=0.001000 Epoch[061] Batch [3699]/[3759] Speed: 62.284758 samples/sec accuracy=81.944257 loss=0.700656 lr=0.001000 Epoch[061] Batch [3749]/[3759] Speed: 67.110689 samples/sec accuracy=81.936667 loss=0.700829 lr=0.001000 Batch [0049]/[0303]: acc-top1=66.250000 acc-top5=86.281250 Batch [0099]/[0303]: acc-top1=67.640625 acc-top5=86.718750 Batch [0149]/[0303]: acc-top1=67.927083 acc-top5=86.635417 Batch [0199]/[0303]: acc-top1=67.976562 acc-top5=86.781250 Batch [0249]/[0303]: acc-top1=67.800000 acc-top5=86.762500 Batch [0299]/[0303]: acc-top1=67.734375 acc-top5=86.822917 [Epoch 061] training: accuracy=81.936685 loss=0.700921 [Epoch 061] speed: 61 samples/sec time cost: 4150.207777 [Epoch 061] validation: acc-top1=67.739274 acc-top5=86.819307 loss=1.661839 Epoch[062] Batch [0049]/[3760] Speed: 42.652793 samples/sec accuracy=82.500000 loss=0.682662 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62.254863 samples/sec accuracy=82.022059 loss=0.699298 lr=0.001000 Epoch[062] Batch [3449]/[3760] Speed: 62.425823 samples/sec accuracy=82.016757 loss=0.699744 lr=0.001000 Epoch[062] Batch [3499]/[3760] Speed: 62.127919 samples/sec accuracy=82.021429 loss=0.699501 lr=0.001000 Epoch[062] Batch [3549]/[3760] Speed: 62.646206 samples/sec accuracy=82.014525 loss=0.699839 lr=0.001000 Epoch[062] Batch [3599]/[3760] Speed: 62.547876 samples/sec accuracy=82.026042 loss=0.699962 lr=0.001000 Epoch[062] Batch [3649]/[3760] Speed: 62.121438 samples/sec accuracy=82.015411 loss=0.700341 lr=0.001000 Epoch[062] Batch [3699]/[3760] Speed: 62.328474 samples/sec accuracy=82.013514 loss=0.700907 lr=0.001000 Epoch[062] Batch [3749]/[3760] Speed: 67.598754 samples/sec accuracy=82.008333 loss=0.701079 lr=0.001000 Batch [0049]/[0303]: acc-top1=66.281250 acc-top5=86.187500 Batch [0099]/[0303]: acc-top1=67.796875 acc-top5=86.796875 Batch [0149]/[0303]: acc-top1=68.041667 acc-top5=86.802083 Batch [0199]/[0303]: acc-top1=68.078125 acc-top5=86.921875 Batch [0249]/[0303]: acc-top1=67.756250 acc-top5=86.925000 Batch [0299]/[0303]: acc-top1=67.895833 acc-top5=86.942708 [Epoch 062] training: accuracy=82.005485 loss=0.701174 [Epoch 062] speed: 62 samples/sec time cost: 4150.685594 [Epoch 062] validation: acc-top1=67.883663 acc-top5=86.943069 loss=1.649730 Epoch[063] Batch [0049]/[3760] Speed: 42.139305 samples/sec accuracy=82.031250 loss=0.692962 lr=0.001000 Epoch[063] Batch [0099]/[3760] Speed: 60.106206 samples/sec accuracy=82.437500 loss=0.680283 lr=0.001000 Epoch[063] Batch [0149]/[3760] Speed: 62.241903 samples/sec accuracy=82.531250 loss=0.668399 lr=0.001000 Epoch[063] Batch [0199]/[3760] Speed: 61.111341 samples/sec accuracy=82.429688 loss=0.669370 lr=0.001000 Epoch[063] Batch [0249]/[3760] Speed: 62.610283 samples/sec accuracy=82.456250 loss=0.674417 lr=0.001000 Epoch[063] Batch [0299]/[3760] Speed: 61.687437 samples/sec accuracy=82.411458 loss=0.675064 lr=0.001000 Epoch[063] Batch 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accuracy=82.123843 loss=0.689452 lr=0.001000 Epoch[063] Batch [2749]/[3760] Speed: 62.359598 samples/sec accuracy=82.120455 loss=0.689771 lr=0.001000 Epoch[063] Batch [2799]/[3760] Speed: 62.599811 samples/sec accuracy=82.112723 loss=0.690319 lr=0.001000 Epoch[063] Batch [2849]/[3760] Speed: 62.040486 samples/sec accuracy=82.114583 loss=0.690209 lr=0.001000 Epoch[063] Batch [2899]/[3760] Speed: 62.843387 samples/sec accuracy=82.110991 loss=0.690081 lr=0.001000 Epoch[063] Batch [2949]/[3760] Speed: 62.316780 samples/sec accuracy=82.111229 loss=0.690474 lr=0.001000 Epoch[063] Batch [2999]/[3760] Speed: 62.725490 samples/sec accuracy=82.108333 loss=0.690693 lr=0.001000 Epoch[063] Batch [3049]/[3760] Speed: 62.311420 samples/sec accuracy=82.090164 loss=0.690886 lr=0.001000 Epoch[063] Batch [3099]/[3760] Speed: 62.087117 samples/sec accuracy=82.087198 loss=0.691237 lr=0.001000 Epoch[063] Batch [3149]/[3760] Speed: 62.808050 samples/sec accuracy=82.083829 loss=0.691488 lr=0.001000 Epoch[063] 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accuracy=82.144692 loss=0.690367 lr=0.001000 Epoch[063] Batch [3699]/[3760] Speed: 62.499788 samples/sec accuracy=82.147382 loss=0.690538 lr=0.001000 Epoch[063] Batch [3749]/[3760] Speed: 67.370420 samples/sec accuracy=82.132083 loss=0.691045 lr=0.001000 Batch [0049]/[0303]: acc-top1=66.406250 acc-top5=86.218750 Batch [0099]/[0303]: acc-top1=67.859375 acc-top5=86.625000 Batch [0149]/[0303]: acc-top1=68.072917 acc-top5=86.593750 Batch [0199]/[0303]: acc-top1=67.929688 acc-top5=86.625000 Batch [0249]/[0303]: acc-top1=67.787500 acc-top5=86.656250 Batch [0299]/[0303]: acc-top1=67.760417 acc-top5=86.708333 [Epoch 063] training: accuracy=82.135555 loss=0.691094 [Epoch 063] speed: 62 samples/sec time cost: 4149.174229 [Epoch 063] validation: acc-top1=67.754744 acc-top5=86.726485 loss=1.688058 Epoch[064] Batch [0049]/[3759] Speed: 42.284523 samples/sec accuracy=83.250000 loss=0.661822 lr=0.001000 Epoch[064] Batch [0099]/[3759] Speed: 60.629814 samples/sec accuracy=83.546875 loss=0.657473 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lr=0.001000 Epoch[064] Batch [2999]/[3759] Speed: 62.444497 samples/sec accuracy=82.485937 loss=0.679216 lr=0.001000 Epoch[064] Batch [3049]/[3759] Speed: 62.630066 samples/sec accuracy=82.494877 loss=0.678762 lr=0.001000 Epoch[064] Batch [3099]/[3759] Speed: 62.320377 samples/sec accuracy=82.483871 loss=0.679213 lr=0.001000 Epoch[064] Batch [3149]/[3759] Speed: 62.465939 samples/sec accuracy=82.502480 loss=0.678557 lr=0.001000 Epoch[064] Batch [3199]/[3759] Speed: 62.556022 samples/sec accuracy=82.502441 loss=0.679113 lr=0.001000 Epoch[064] Batch [3249]/[3759] Speed: 62.463191 samples/sec accuracy=82.503846 loss=0.679184 lr=0.001000 Epoch[064] Batch [3299]/[3759] Speed: 62.695331 samples/sec accuracy=82.494318 loss=0.679525 lr=0.001000 Epoch[064] Batch [3349]/[3759] Speed: 61.721740 samples/sec accuracy=82.491138 loss=0.679313 lr=0.001000 Epoch[064] Batch [3399]/[3759] Speed: 62.689880 samples/sec accuracy=82.490349 loss=0.679344 lr=0.001000 Epoch[064] Batch [3449]/[3759] Speed: 61.881119 samples/sec accuracy=82.495924 loss=0.679181 lr=0.001000 Epoch[064] Batch [3499]/[3759] Speed: 62.072903 samples/sec accuracy=82.500446 loss=0.678781 lr=0.001000 Epoch[064] Batch [3549]/[3759] Speed: 62.388791 samples/sec accuracy=82.493838 loss=0.678761 lr=0.001000 Epoch[064] Batch [3599]/[3759] Speed: 62.799476 samples/sec accuracy=82.479601 loss=0.679434 lr=0.001000 Epoch[064] Batch [3649]/[3759] Speed: 62.454164 samples/sec accuracy=82.471318 loss=0.679433 lr=0.001000 Epoch[064] Batch [3699]/[3759] Speed: 62.312796 samples/sec accuracy=82.464105 loss=0.679775 lr=0.001000 Epoch[064] Batch [3749]/[3759] Speed: 67.685469 samples/sec accuracy=82.477917 loss=0.679502 lr=0.001000 Batch [0049]/[0303]: acc-top1=66.125000 acc-top5=85.625000 Batch [0099]/[0303]: acc-top1=67.750000 acc-top5=86.796875 Batch [0149]/[0303]: acc-top1=67.812500 acc-top5=86.697917 Batch [0199]/[0303]: acc-top1=67.796875 acc-top5=86.750000 Batch [0249]/[0303]: acc-top1=67.562500 acc-top5=86.756250 Batch [0299]/[0303]: acc-top1=67.619792 acc-top5=86.739583 [Epoch 064] training: accuracy=82.479549 loss=0.679462 [Epoch 064] speed: 62 samples/sec time cost: 4148.671568 [Epoch 064] validation: acc-top1=67.605198 acc-top5=86.757426 loss=1.688508 Epoch[065] Batch [0049]/[3760] Speed: 42.190633 samples/sec accuracy=82.281250 loss=0.682650 lr=0.001000 Epoch[065] Batch [0099]/[3760] Speed: 60.464237 samples/sec accuracy=83.140625 loss=0.655443 lr=0.001000 Epoch[065] Batch [0149]/[3760] Speed: 62.029847 samples/sec accuracy=82.729167 loss=0.666383 lr=0.001000 Epoch[065] Batch [0199]/[3760] Speed: 60.412689 samples/sec accuracy=82.968750 loss=0.659144 lr=0.001000 Epoch[065] Batch [0249]/[3760] Speed: 62.873358 samples/sec accuracy=82.912500 loss=0.661497 lr=0.001000 Epoch[065] Batch [0299]/[3760] Speed: 61.060414 samples/sec accuracy=83.057292 loss=0.656027 lr=0.001000 Epoch[065] Batch [0349]/[3760] Speed: 62.382794 samples/sec accuracy=83.174107 loss=0.651824 lr=0.001000 Epoch[065] Batch 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accuracy=82.643182 loss=0.672515 lr=0.001000 Epoch[065] Batch [2799]/[3760] Speed: 61.933700 samples/sec accuracy=82.646205 loss=0.671975 lr=0.001000 Epoch[065] Batch [2849]/[3760] Speed: 62.736906 samples/sec accuracy=82.654057 loss=0.671886 lr=0.001000 Epoch[065] Batch [2899]/[3760] Speed: 62.384478 samples/sec accuracy=82.656250 loss=0.671587 lr=0.001000 Epoch[065] Batch [2949]/[3760] Speed: 62.698649 samples/sec accuracy=82.637182 loss=0.672185 lr=0.001000 Epoch[065] Batch [2999]/[3760] Speed: 62.501161 samples/sec accuracy=82.610938 loss=0.673268 lr=0.001000 Epoch[065] Batch [3049]/[3760] Speed: 62.442365 samples/sec accuracy=82.588115 loss=0.674275 lr=0.001000 Epoch[065] Batch [3099]/[3760] Speed: 62.756447 samples/sec accuracy=82.573085 loss=0.675165 lr=0.001000 Epoch[065] Batch [3149]/[3760] Speed: 62.376180 samples/sec accuracy=82.563492 loss=0.675396 lr=0.001000 Epoch[065] Batch [3199]/[3760] Speed: 62.164902 samples/sec accuracy=82.553711 loss=0.675635 lr=0.001000 Epoch[065] 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accuracy=82.557010 loss=0.677420 lr=0.001000 Epoch[065] Batch [3749]/[3760] Speed: 67.092124 samples/sec accuracy=82.552083 loss=0.677364 lr=0.001000 Batch [0049]/[0303]: acc-top1=66.687500 acc-top5=85.406250 Batch [0099]/[0303]: acc-top1=68.125000 acc-top5=86.359375 Batch [0149]/[0303]: acc-top1=68.156250 acc-top5=86.375000 Batch [0199]/[0303]: acc-top1=68.070312 acc-top5=86.460938 Batch [0249]/[0303]: acc-top1=67.831250 acc-top5=86.575000 Batch [0299]/[0303]: acc-top1=67.828125 acc-top5=86.614583 [Epoch 065] training: accuracy=82.552776 loss=0.677225 [Epoch 065] speed: 61 samples/sec time cost: 4151.459972 [Epoch 065] validation: acc-top1=67.780528 acc-top5=86.613036 loss=1.701968 Epoch[066] Batch [0049]/[3759] Speed: 42.813032 samples/sec accuracy=82.812500 loss=0.656494 lr=0.001000 Epoch[066] Batch [0099]/[3759] Speed: 60.492911 samples/sec accuracy=83.328125 loss=0.645269 lr=0.001000 Epoch[066] Batch [0149]/[3759] Speed: 62.184223 samples/sec accuracy=83.104167 loss=0.649698 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lr=0.001000 Epoch[066] Batch [2099]/[3759] Speed: 61.970686 samples/sec accuracy=82.757440 loss=0.666308 lr=0.001000 Epoch[066] Batch [2149]/[3759] Speed: 62.684972 samples/sec accuracy=82.731831 loss=0.667056 lr=0.001000 Epoch[066] Batch [2199]/[3759] Speed: 62.627753 samples/sec accuracy=82.708097 loss=0.667503 lr=0.001000 Epoch[066] Batch [2249]/[3759] Speed: 61.958461 samples/sec accuracy=82.703472 loss=0.667123 lr=0.001000 Epoch[066] Batch [2299]/[3759] Speed: 62.668648 samples/sec accuracy=82.718071 loss=0.666814 lr=0.001000 Epoch[066] Batch [2349]/[3759] Speed: 62.333628 samples/sec accuracy=82.713431 loss=0.667542 lr=0.001000 Epoch[066] Batch [2399]/[3759] Speed: 62.971069 samples/sec accuracy=82.715495 loss=0.667512 lr=0.001000 Epoch[066] Batch [2449]/[3759] Speed: 62.266965 samples/sec accuracy=82.718112 loss=0.667292 lr=0.001000 Epoch[066] Batch [2499]/[3759] Speed: 62.384334 samples/sec accuracy=82.708125 loss=0.667443 lr=0.001000 Epoch[066] Batch [2549]/[3759] Speed: 62.862833 samples/sec accuracy=82.710784 loss=0.667616 lr=0.001000 Epoch[066] Batch [2599]/[3759] Speed: 62.349260 samples/sec accuracy=82.718750 loss=0.667727 lr=0.001000 Epoch[066] Batch [2649]/[3759] Speed: 62.033137 samples/sec accuracy=82.723467 loss=0.667103 lr=0.001000 Epoch[066] Batch [2699]/[3759] Speed: 62.424552 samples/sec accuracy=82.715278 loss=0.667376 lr=0.001000 Epoch[066] Batch [2749]/[3759] Speed: 62.338416 samples/sec accuracy=82.719886 loss=0.667158 lr=0.001000 Epoch[066] Batch [2799]/[3759] Speed: 62.508830 samples/sec accuracy=82.718192 loss=0.667454 lr=0.001000 Epoch[066] Batch [2849]/[3759] Speed: 62.318166 samples/sec accuracy=82.712719 loss=0.667983 lr=0.001000 Epoch[066] Batch [2899]/[3759] Speed: 61.653786 samples/sec accuracy=82.705280 loss=0.668393 lr=0.001000 Epoch[066] Batch [2949]/[3759] Speed: 63.068748 samples/sec accuracy=82.715572 loss=0.667979 lr=0.001000 Epoch[066] Batch [2999]/[3759] Speed: 61.947398 samples/sec accuracy=82.728125 loss=0.667509 lr=0.001000 Epoch[066] Batch [3049]/[3759] Speed: 62.594975 samples/sec accuracy=82.735143 loss=0.667623 lr=0.001000 Epoch[066] Batch [3099]/[3759] Speed: 62.296992 samples/sec accuracy=82.746472 loss=0.667634 lr=0.001000 Epoch[066] Batch [3149]/[3759] Speed: 62.676870 samples/sec accuracy=82.733631 loss=0.668228 lr=0.001000 Epoch[066] Batch [3199]/[3759] Speed: 62.809412 samples/sec accuracy=82.727539 loss=0.668305 lr=0.001000 Epoch[066] Batch [3249]/[3759] Speed: 61.992672 samples/sec accuracy=82.719231 loss=0.668599 lr=0.001000 Epoch[066] Batch [3299]/[3759] Speed: 62.553210 samples/sec accuracy=82.713068 loss=0.668759 lr=0.001000 Epoch[066] Batch [3349]/[3759] Speed: 62.510267 samples/sec accuracy=82.708022 loss=0.668991 lr=0.001000 Epoch[066] Batch [3399]/[3759] Speed: 62.467238 samples/sec accuracy=82.684743 loss=0.669600 lr=0.001000 Epoch[066] Batch [3449]/[3759] Speed: 62.490543 samples/sec accuracy=82.686594 loss=0.669733 lr=0.001000 Epoch[066] Batch [3499]/[3759] Speed: 62.115674 samples/sec accuracy=82.682143 loss=0.669664 lr=0.001000 Epoch[066] Batch [3549]/[3759] Speed: 62.868393 samples/sec accuracy=82.688820 loss=0.669491 lr=0.001000 Epoch[066] Batch [3599]/[3759] Speed: 62.216692 samples/sec accuracy=82.678819 loss=0.669714 lr=0.001000 Epoch[066] Batch [3649]/[3759] Speed: 63.008327 samples/sec accuracy=82.672945 loss=0.670213 lr=0.001000 Epoch[066] Batch [3699]/[3759] Speed: 62.357740 samples/sec accuracy=82.670186 loss=0.670455 lr=0.001000 Epoch[066] Batch [3749]/[3759] Speed: 67.906823 samples/sec accuracy=82.661667 loss=0.670567 lr=0.001000 Batch [0049]/[0303]: acc-top1=66.468750 acc-top5=85.562500 Batch [0099]/[0303]: acc-top1=67.625000 acc-top5=86.500000 Batch [0149]/[0303]: acc-top1=67.666667 acc-top5=86.541667 Batch [0199]/[0303]: acc-top1=67.812500 acc-top5=86.617188 Batch [0249]/[0303]: acc-top1=67.600000 acc-top5=86.618750 Batch [0299]/[0303]: acc-top1=67.541667 acc-top5=86.614583 [Epoch 066] training: accuracy=82.656208 loss=0.670878 [Epoch 066] speed: 62 samples/sec time cost: 4142.227501 [Epoch 066] validation: acc-top1=67.543317 acc-top5=86.623350 loss=1.709326 Epoch[067] Batch [0049]/[3760] Speed: 42.417809 samples/sec accuracy=82.718750 loss=0.686324 lr=0.001000 Epoch[067] Batch [0099]/[3760] Speed: 60.880095 samples/sec accuracy=82.984375 loss=0.670281 lr=0.001000 Epoch[067] Batch [0149]/[3760] Speed: 62.379245 samples/sec accuracy=83.302083 loss=0.651061 lr=0.001000 Epoch[067] Batch [0199]/[3760] Speed: 61.670219 samples/sec accuracy=83.554688 loss=0.643053 lr=0.001000 Epoch[067] Batch [0249]/[3760] Speed: 62.174491 samples/sec accuracy=83.631250 loss=0.643160 lr=0.001000 Epoch[067] Batch [0299]/[3760] Speed: 62.190594 samples/sec accuracy=83.510417 loss=0.646300 lr=0.001000 Epoch[067] Batch [0349]/[3760] Speed: 62.699982 samples/sec accuracy=83.357143 loss=0.649349 lr=0.001000 Epoch[067] Batch [0399]/[3760] Speed: 62.405739 samples/sec accuracy=83.363281 loss=0.649211 lr=0.001000 Epoch[067] Batch 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accuracy=82.818333 loss=0.666419 lr=0.001000 Batch [0049]/[0303]: acc-top1=65.968750 acc-top5=85.500000 Batch [0099]/[0303]: acc-top1=67.531250 acc-top5=86.437500 Batch [0149]/[0303]: acc-top1=67.791667 acc-top5=86.427083 Batch [0199]/[0303]: acc-top1=67.835938 acc-top5=86.578125 Batch [0249]/[0303]: acc-top1=67.581250 acc-top5=86.550000 Batch [0299]/[0303]: acc-top1=67.697917 acc-top5=86.536458 [Epoch 067] training: accuracy=82.818733 loss=0.666225 [Epoch 067] speed: 62 samples/sec time cost: 4146.327968 [Epoch 067] validation: acc-top1=67.692863 acc-top5=86.551155 loss=1.712821 Epoch[068] Batch [0049]/[3760] Speed: 42.700262 samples/sec accuracy=83.968750 loss=0.627956 lr=0.001000 Epoch[068] Batch [0099]/[3760] Speed: 60.878044 samples/sec accuracy=84.187500 loss=0.624544 lr=0.001000 Epoch[068] Batch [0149]/[3760] Speed: 62.308970 samples/sec accuracy=84.208333 loss=0.623035 lr=0.001000 Epoch[068] Batch [0199]/[3760] Speed: 60.896761 samples/sec accuracy=83.875000 loss=0.632907 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lr=0.001000 Epoch[068] Batch [3099]/[3760] Speed: 62.149041 samples/sec accuracy=83.115927 loss=0.656535 lr=0.001000 Epoch[068] Batch [3149]/[3760] Speed: 62.691683 samples/sec accuracy=83.113095 loss=0.656747 lr=0.001000 Epoch[068] Batch [3199]/[3760] Speed: 62.533842 samples/sec accuracy=83.112305 loss=0.656513 lr=0.001000 Epoch[068] Batch [3249]/[3760] Speed: 62.266532 samples/sec accuracy=83.098077 loss=0.656866 lr=0.001000 Epoch[068] Batch [3299]/[3760] Speed: 62.782666 samples/sec accuracy=83.119318 loss=0.656163 lr=0.001000 Epoch[068] Batch [3349]/[3760] Speed: 62.630946 samples/sec accuracy=83.123134 loss=0.655932 lr=0.001000 Epoch[068] Batch [3399]/[3760] Speed: 62.273231 samples/sec accuracy=83.119945 loss=0.655971 lr=0.001000 Epoch[068] Batch [3449]/[3760] Speed: 62.639240 samples/sec accuracy=83.109601 loss=0.656546 lr=0.001000 Epoch[068] Batch [3499]/[3760] Speed: 61.969635 samples/sec accuracy=83.111607 loss=0.656567 lr=0.001000 Epoch[068] Batch [3549]/[3760] Speed: 63.049467 samples/sec accuracy=83.091109 loss=0.657122 lr=0.001000 Epoch[068] Batch [3599]/[3760] Speed: 62.223690 samples/sec accuracy=83.085503 loss=0.657403 lr=0.001000 Epoch[068] Batch [3649]/[3760] Speed: 62.192329 samples/sec accuracy=83.103168 loss=0.656907 lr=0.001000 Epoch[068] Batch [3699]/[3760] Speed: 62.230169 samples/sec accuracy=83.102196 loss=0.656463 lr=0.001000 Epoch[068] Batch [3749]/[3760] Speed: 67.304449 samples/sec accuracy=83.101667 loss=0.656694 lr=0.001000 Batch [0049]/[0303]: acc-top1=67.062500 acc-top5=85.343750 Batch [0099]/[0303]: acc-top1=68.046875 acc-top5=86.390625 Batch [0149]/[0303]: acc-top1=68.145833 acc-top5=86.322917 Batch [0199]/[0303]: acc-top1=68.296875 acc-top5=86.523438 Batch [0249]/[0303]: acc-top1=67.962500 acc-top5=86.518750 Batch [0299]/[0303]: acc-top1=67.895833 acc-top5=86.515625 [Epoch 068] training: accuracy=83.105469 loss=0.656712 [Epoch 068] speed: 62 samples/sec time cost: 4146.654720 [Epoch 068] validation: acc-top1=67.878507 acc-top5=86.530528 loss=1.719864 Epoch[069] Batch [0049]/[3759] Speed: 42.207861 samples/sec accuracy=83.718750 loss=0.632301 lr=0.001000 Epoch[069] Batch [0099]/[3759] Speed: 61.021330 samples/sec accuracy=83.765625 loss=0.627484 lr=0.001000 Epoch[069] Batch [0149]/[3759] Speed: 62.317066 samples/sec accuracy=83.635417 loss=0.636798 lr=0.001000 Epoch[069] Batch [0199]/[3759] Speed: 61.233193 samples/sec accuracy=83.507812 loss=0.636868 lr=0.001000 Epoch[069] Batch [0249]/[3759] Speed: 62.792269 samples/sec accuracy=83.668750 loss=0.634619 lr=0.001000 Epoch[069] Batch [0299]/[3759] Speed: 61.977017 samples/sec accuracy=83.588542 loss=0.637549 lr=0.001000 Epoch[069] Batch [0349]/[3759] Speed: 63.123998 samples/sec accuracy=83.633929 loss=0.635704 lr=0.001000 Epoch[069] Batch [0399]/[3759] Speed: 61.601524 samples/sec accuracy=83.632812 loss=0.635892 lr=0.001000 Epoch[069] Batch [0449]/[3759] Speed: 63.029349 samples/sec accuracy=83.718750 loss=0.635090 lr=0.001000 Epoch[069] Batch 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accuracy=83.299890 loss=0.646616 lr=0.001000 Epoch[069] Batch [2899]/[3759] Speed: 61.907371 samples/sec accuracy=83.286099 loss=0.646843 lr=0.001000 Epoch[069] Batch [2949]/[3759] Speed: 62.763164 samples/sec accuracy=83.289195 loss=0.646606 lr=0.001000 Epoch[069] Batch [2999]/[3759] Speed: 62.623835 samples/sec accuracy=83.281250 loss=0.646815 lr=0.001000 Epoch[069] Batch [3049]/[3759] Speed: 62.183012 samples/sec accuracy=83.290984 loss=0.646528 lr=0.001000 Epoch[069] Batch [3099]/[3759] Speed: 62.727572 samples/sec accuracy=83.280242 loss=0.647064 lr=0.001000 Epoch[069] Batch [3149]/[3759] Speed: 62.411674 samples/sec accuracy=83.278770 loss=0.646940 lr=0.001000 Epoch[069] Batch [3199]/[3759] Speed: 62.595499 samples/sec accuracy=83.272949 loss=0.647449 lr=0.001000 Epoch[069] Batch [3249]/[3759] Speed: 62.209027 samples/sec accuracy=83.266346 loss=0.647457 lr=0.001000 Epoch[069] Batch [3299]/[3759] Speed: 63.003641 samples/sec accuracy=83.256155 loss=0.648017 lr=0.001000 Epoch[069] 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[0099]/[0303]: acc-top1=68.218750 acc-top5=86.312500 Batch [0149]/[0303]: acc-top1=68.145833 acc-top5=86.500000 Batch [0199]/[0303]: acc-top1=68.242188 acc-top5=86.570312 Batch [0249]/[0303]: acc-top1=67.925000 acc-top5=86.518750 Batch [0299]/[0303]: acc-top1=67.812500 acc-top5=86.520833 [Epoch 069] training: accuracy=83.217777 loss=0.648977 [Epoch 069] speed: 62 samples/sec time cost: 4142.280754 [Epoch 069] validation: acc-top1=67.790842 acc-top5=86.556312 loss=1.710779 Epoch[070] Batch [0049]/[3760] Speed: 42.947336 samples/sec accuracy=84.375000 loss=0.599878 lr=0.001000 Epoch[070] Batch [0099]/[3760] Speed: 61.251694 samples/sec accuracy=84.250000 loss=0.612965 lr=0.001000 Epoch[070] Batch [0149]/[3760] Speed: 62.413496 samples/sec accuracy=84.072917 loss=0.626619 lr=0.001000 Epoch[070] Batch [0199]/[3760] Speed: 61.087111 samples/sec accuracy=83.921875 loss=0.624797 lr=0.001000 Epoch[070] Batch [0249]/[3760] Speed: 63.090459 samples/sec accuracy=83.400000 loss=0.642552 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lr=0.001000 Epoch[070] Batch [3149]/[3760] Speed: 62.773665 samples/sec accuracy=83.375496 loss=0.648463 lr=0.001000 Epoch[070] Batch [3199]/[3760] Speed: 62.681263 samples/sec accuracy=83.375977 loss=0.648375 lr=0.001000 Epoch[070] Batch [3249]/[3760] Speed: 62.432853 samples/sec accuracy=83.371154 loss=0.648642 lr=0.001000 Epoch[070] Batch [3299]/[3760] Speed: 61.811402 samples/sec accuracy=83.380682 loss=0.648433 lr=0.001000 Epoch[070] Batch [3349]/[3760] Speed: 63.388840 samples/sec accuracy=83.389459 loss=0.648146 lr=0.001000 Epoch[070] Batch [3399]/[3760] Speed: 62.299002 samples/sec accuracy=83.389246 loss=0.647928 lr=0.001000 Epoch[070] Batch [3449]/[3760] Speed: 62.427736 samples/sec accuracy=83.370471 loss=0.648830 lr=0.001000 Epoch[070] Batch [3499]/[3760] Speed: 62.348945 samples/sec accuracy=83.361161 loss=0.648785 lr=0.001000 Epoch[070] Batch [3549]/[3760] Speed: 62.450380 samples/sec accuracy=83.357394 loss=0.648984 lr=0.001000 Epoch[070] Batch [3599]/[3760] Speed: 62.072289 samples/sec accuracy=83.353299 loss=0.648950 lr=0.001000 Epoch[070] Batch [3649]/[3760] Speed: 63.298015 samples/sec accuracy=83.362158 loss=0.648743 lr=0.001000 Epoch[070] Batch [3699]/[3760] Speed: 61.873521 samples/sec accuracy=83.369088 loss=0.648539 lr=0.001000 Epoch[070] Batch [3749]/[3760] Speed: 67.757305 samples/sec accuracy=83.357917 loss=0.648993 lr=0.001000 Batch [0049]/[0303]: acc-top1=66.500000 acc-top5=85.531250 Batch [0099]/[0303]: acc-top1=67.828125 acc-top5=86.359375 Batch [0149]/[0303]: acc-top1=68.041667 acc-top5=86.364583 Batch [0199]/[0303]: acc-top1=68.007812 acc-top5=86.601562 Batch [0249]/[0303]: acc-top1=67.687500 acc-top5=86.525000 Batch [0299]/[0303]: acc-top1=67.697917 acc-top5=86.520833 [Epoch 070] training: accuracy=83.356882 loss=0.648951 [Epoch 070] speed: 62 samples/sec time cost: 4138.970456 [Epoch 070] validation: acc-top1=67.708333 acc-top5=86.530528 loss=1.739753 Epoch[071] Batch [0049]/[3760] Speed: 42.305822 samples/sec accuracy=84.125000 loss=0.620062 lr=0.001000 Epoch[071] Batch [0099]/[3760] Speed: 60.319746 samples/sec accuracy=84.000000 loss=0.619040 lr=0.001000 Epoch[071] Batch [0149]/[3760] Speed: 62.558390 samples/sec accuracy=83.989583 loss=0.621802 lr=0.001000 Epoch[071] Batch [0199]/[3760] Speed: 61.038021 samples/sec accuracy=83.960938 loss=0.612661 lr=0.001000 Epoch[071] Batch [0249]/[3760] Speed: 62.297526 samples/sec accuracy=84.068750 loss=0.612396 lr=0.001000 Epoch[071] Batch [0299]/[3760] Speed: 61.468515 samples/sec accuracy=84.052083 loss=0.614491 lr=0.001000 Epoch[071] Batch [0349]/[3760] Speed: 62.595257 samples/sec accuracy=83.852679 loss=0.621354 lr=0.001000 Epoch[071] Batch [0399]/[3760] Speed: 61.983145 samples/sec accuracy=83.792969 loss=0.624615 lr=0.001000 Epoch[071] Batch [0449]/[3760] Speed: 62.320523 samples/sec accuracy=83.881944 loss=0.623975 lr=0.001000 Epoch[071] Batch [0499]/[3760] Speed: 62.322043 samples/sec accuracy=84.021875 loss=0.621655 lr=0.001000 Epoch[071] 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acc-top5=86.500000 Batch [0199]/[0303]: acc-top1=68.210938 acc-top5=86.546875 Batch [0249]/[0303]: acc-top1=67.950000 acc-top5=86.481250 Batch [0299]/[0303]: acc-top1=67.895833 acc-top5=86.432292 [Epoch 071] training: accuracy=83.377244 loss=0.643344 [Epoch 071] speed: 62 samples/sec time cost: 4149.033631 [Epoch 071] validation: acc-top1=67.893977 acc-top5=86.442863 loss=1.743758 Epoch[072] Batch [0049]/[3759] Speed: 42.257613 samples/sec accuracy=84.531250 loss=0.598832 lr=0.001000 Epoch[072] Batch [0099]/[3759] Speed: 59.787354 samples/sec accuracy=83.968750 loss=0.626467 lr=0.001000 Epoch[072] Batch [0149]/[3759] Speed: 62.867795 samples/sec accuracy=84.041667 loss=0.631280 lr=0.001000 Epoch[072] Batch [0199]/[3759] Speed: 60.756485 samples/sec accuracy=83.773438 loss=0.636713 lr=0.001000 Epoch[072] Batch [0249]/[3759] Speed: 62.553618 samples/sec accuracy=83.825000 loss=0.634889 lr=0.001000 Epoch[072] Batch [0299]/[3759] Speed: 61.596550 samples/sec accuracy=83.786458 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accuracy=83.551587 loss=0.634642 lr=0.001000 Epoch[072] Batch [3199]/[3759] Speed: 62.226913 samples/sec accuracy=83.541016 loss=0.635214 lr=0.001000 Epoch[072] Batch [3249]/[3759] Speed: 62.194152 samples/sec accuracy=83.520192 loss=0.635882 lr=0.001000 Epoch[072] Batch [3299]/[3759] Speed: 62.544844 samples/sec accuracy=83.534091 loss=0.635375 lr=0.001000 Epoch[072] Batch [3349]/[3759] Speed: 62.461850 samples/sec accuracy=83.531716 loss=0.635248 lr=0.001000 Epoch[072] Batch [3399]/[3759] Speed: 61.994953 samples/sec accuracy=83.537224 loss=0.635228 lr=0.001000 Epoch[072] Batch [3449]/[3759] Speed: 62.557924 samples/sec accuracy=83.527627 loss=0.635940 lr=0.001000 Epoch[072] Batch [3499]/[3759] Speed: 62.841711 samples/sec accuracy=83.519643 loss=0.636138 lr=0.001000 Epoch[072] Batch [3549]/[3759] Speed: 61.882747 samples/sec accuracy=83.510123 loss=0.636602 lr=0.001000 Epoch[072] Batch [3599]/[3759] Speed: 62.389245 samples/sec accuracy=83.515191 loss=0.636530 lr=0.001000 Epoch[072] 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[0249]/[0303]: acc-top1=67.462500 acc-top5=86.143750 Batch [0299]/[0303]: acc-top1=67.421875 acc-top5=86.187500 [Epoch 073] training: accuracy=83.794880 loss=0.630446 [Epoch 073] speed: 62 samples/sec time cost: 4146.819963 [Epoch 073] validation: acc-top1=67.404084 acc-top5=86.200495 loss=1.742144 Epoch[074] Batch [0049]/[3760] Speed: 41.762375 samples/sec accuracy=85.031250 loss=0.571800 lr=0.001000 Epoch[074] Batch [0099]/[3760] Speed: 61.117364 samples/sec accuracy=84.609375 loss=0.593943 lr=0.001000 Epoch[074] Batch [0149]/[3760] Speed: 61.918385 samples/sec accuracy=84.333333 loss=0.610054 lr=0.001000 Epoch[074] Batch [0199]/[3760] Speed: 61.586199 samples/sec accuracy=84.312500 loss=0.610105 lr=0.001000 Epoch[074] Batch [0249]/[3760] Speed: 62.902576 samples/sec accuracy=84.337500 loss=0.614017 lr=0.001000 Epoch[074] Batch [0299]/[3760] Speed: 61.711934 samples/sec accuracy=84.416667 loss=0.615068 lr=0.001000 Epoch[074] Batch [0349]/[3760] Speed: 62.942987 samples/sec 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accuracy=84.175481 loss=0.615736 lr=0.001000 Epoch[074] Batch [1349]/[3760] Speed: 62.026215 samples/sec accuracy=84.168981 loss=0.615791 lr=0.001000 Epoch[074] Batch [1399]/[3760] Speed: 62.568059 samples/sec accuracy=84.113839 loss=0.617720 lr=0.001000 Epoch[074] Batch [1449]/[3760] Speed: 62.267851 samples/sec accuracy=84.143319 loss=0.616468 lr=0.001000 Epoch[074] Batch [1499]/[3760] Speed: 62.830711 samples/sec accuracy=84.134375 loss=0.616852 lr=0.001000 Epoch[074] Batch [1549]/[3760] Speed: 62.454884 samples/sec accuracy=84.115927 loss=0.617375 lr=0.001000 Epoch[074] Batch [1599]/[3760] Speed: 62.462210 samples/sec accuracy=84.168945 loss=0.616048 lr=0.001000 Epoch[074] Batch [1649]/[3760] Speed: 62.817843 samples/sec accuracy=84.158144 loss=0.616628 lr=0.001000 Epoch[074] Batch [1699]/[3760] Speed: 62.463024 samples/sec accuracy=84.120404 loss=0.618614 lr=0.001000 Epoch[074] Batch [1749]/[3760] Speed: 62.405642 samples/sec accuracy=84.100000 loss=0.619337 lr=0.001000 Epoch[074] 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accuracy=84.019444 loss=0.622118 lr=0.001000 Epoch[074] Batch [2299]/[3760] Speed: 62.140386 samples/sec accuracy=84.042799 loss=0.621951 lr=0.001000 Epoch[074] Batch [2349]/[3760] Speed: 62.412524 samples/sec accuracy=84.022606 loss=0.622836 lr=0.001000 Epoch[074] Batch [2399]/[3760] Speed: 62.064564 samples/sec accuracy=84.024089 loss=0.622912 lr=0.001000 Epoch[074] Batch [2449]/[3760] Speed: 62.554858 samples/sec accuracy=84.017219 loss=0.623600 lr=0.001000 Epoch[074] Batch [2499]/[3760] Speed: 62.330387 samples/sec accuracy=84.015625 loss=0.623260 lr=0.001000 Epoch[074] Batch [2549]/[3760] Speed: 61.969234 samples/sec accuracy=83.993260 loss=0.624224 lr=0.001000 Epoch[074] Batch [2599]/[3760] Speed: 62.600872 samples/sec accuracy=83.997596 loss=0.623798 lr=0.001000 Epoch[074] Batch [2649]/[3760] Speed: 62.032064 samples/sec accuracy=83.999410 loss=0.623521 lr=0.001000 Epoch[074] Batch [2699]/[3760] Speed: 62.601657 samples/sec accuracy=83.998843 loss=0.623523 lr=0.001000 Epoch[074] 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accuracy=83.950684 loss=0.625673 lr=0.001000 Epoch[074] Batch [3249]/[3760] Speed: 62.905296 samples/sec accuracy=83.933654 loss=0.625683 lr=0.001000 Epoch[074] Batch [3299]/[3760] Speed: 62.058595 samples/sec accuracy=83.938447 loss=0.625510 lr=0.001000 Epoch[074] Batch [3349]/[3760] Speed: 62.599145 samples/sec accuracy=83.930504 loss=0.625691 lr=0.001000 Epoch[074] Batch [3399]/[3760] Speed: 61.816489 samples/sec accuracy=83.937500 loss=0.625591 lr=0.001000 Epoch[074] Batch [3449]/[3760] Speed: 62.579323 samples/sec accuracy=83.927536 loss=0.626146 lr=0.001000 Epoch[074] Batch [3499]/[3760] Speed: 62.258536 samples/sec accuracy=83.930804 loss=0.625967 lr=0.001000 Epoch[074] Batch [3549]/[3760] Speed: 61.970388 samples/sec accuracy=83.920335 loss=0.626492 lr=0.001000 Epoch[074] Batch [3599]/[3760] Speed: 62.631586 samples/sec accuracy=83.920573 loss=0.626524 lr=0.001000 Epoch[074] Batch [3649]/[3760] Speed: 62.383676 samples/sec accuracy=83.904110 loss=0.626562 lr=0.001000 Epoch[074] 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lr=0.001000 Epoch[075] Batch [3499]/[3759] Speed: 61.874322 samples/sec accuracy=84.083482 loss=0.616191 lr=0.001000 Epoch[075] Batch [3549]/[3759] Speed: 62.434605 samples/sec accuracy=84.085827 loss=0.616302 lr=0.001000 Epoch[075] Batch [3599]/[3759] Speed: 62.519898 samples/sec accuracy=84.076389 loss=0.616575 lr=0.001000 Epoch[075] Batch [3649]/[3759] Speed: 62.168668 samples/sec accuracy=84.059503 loss=0.617254 lr=0.001000 Epoch[075] Batch [3699]/[3759] Speed: 62.293040 samples/sec accuracy=84.054054 loss=0.617285 lr=0.001000 Epoch[075] Batch [3749]/[3759] Speed: 67.290381 samples/sec accuracy=84.053750 loss=0.617165 lr=0.001000 Batch [0049]/[0303]: acc-top1=66.031250 acc-top5=85.718750 Batch [0099]/[0303]: acc-top1=67.500000 acc-top5=86.390625 Batch [0149]/[0303]: acc-top1=67.447917 acc-top5=86.562500 Batch [0199]/[0303]: acc-top1=67.554688 acc-top5=86.601562 Batch [0249]/[0303]: acc-top1=67.250000 acc-top5=86.468750 Batch [0299]/[0303]: acc-top1=67.348958 acc-top5=86.416667 [Epoch 075] training: accuracy=84.053688 loss=0.617161 [Epoch 075] speed: 62 samples/sec time cost: 4149.462029 [Epoch 075] validation: acc-top1=67.331889 acc-top5=86.448020 loss=1.794420 Epoch[076] Batch [0049]/[3760] Speed: 42.189516 samples/sec accuracy=85.687500 loss=0.557884 lr=0.001000 Epoch[076] Batch [0099]/[3760] Speed: 60.506075 samples/sec accuracy=85.375000 loss=0.574534 lr=0.001000 Epoch[076] Batch [0149]/[3760] Speed: 62.580024 samples/sec accuracy=85.104167 loss=0.583121 lr=0.001000 Epoch[076] Batch [0199]/[3760] Speed: 61.252790 samples/sec accuracy=85.093750 loss=0.579249 lr=0.001000 Epoch[076] Batch [0249]/[3760] Speed: 62.973593 samples/sec accuracy=84.925000 loss=0.583164 lr=0.001000 Epoch[076] Batch [0299]/[3760] Speed: 61.769939 samples/sec accuracy=85.072917 loss=0.577586 lr=0.001000 Epoch[076] Batch [0349]/[3760] Speed: 62.762499 samples/sec accuracy=85.000000 loss=0.581033 lr=0.001000 Epoch[076] Batch [0399]/[3760] Speed: 61.912884 samples/sec 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accuracy=84.618056 loss=0.595680 lr=0.001000 Epoch[076] Batch [1399]/[3760] Speed: 62.674287 samples/sec accuracy=84.618304 loss=0.595706 lr=0.001000 Epoch[076] Batch [1449]/[3760] Speed: 62.134783 samples/sec accuracy=84.601293 loss=0.596080 lr=0.001000 Epoch[076] Batch [1499]/[3760] Speed: 62.354100 samples/sec accuracy=84.586458 loss=0.595871 lr=0.001000 Epoch[076] Batch [1549]/[3760] Speed: 62.558956 samples/sec accuracy=84.562500 loss=0.596849 lr=0.001000 Epoch[076] Batch [1599]/[3760] Speed: 62.284615 samples/sec accuracy=84.559570 loss=0.596754 lr=0.001000 Epoch[076] Batch [1649]/[3760] Speed: 62.524019 samples/sec accuracy=84.545455 loss=0.597820 lr=0.001000 Epoch[076] Batch [1699]/[3760] Speed: 61.998430 samples/sec accuracy=84.518382 loss=0.598189 lr=0.001000 Epoch[076] Batch [1749]/[3760] Speed: 62.713445 samples/sec accuracy=84.525893 loss=0.597730 lr=0.001000 Epoch[076] Batch [1799]/[3760] Speed: 62.416771 samples/sec accuracy=84.539062 loss=0.597873 lr=0.001000 Epoch[076] 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accuracy=84.487092 loss=0.600753 lr=0.001000 Epoch[076] Batch [2349]/[3760] Speed: 62.010562 samples/sec accuracy=84.487367 loss=0.600260 lr=0.001000 Epoch[076] Batch [2399]/[3760] Speed: 61.951371 samples/sec accuracy=84.462891 loss=0.600497 lr=0.001000 Epoch[076] Batch [2449]/[3760] Speed: 62.906804 samples/sec accuracy=84.486607 loss=0.600044 lr=0.001000 Epoch[076] Batch [2499]/[3760] Speed: 62.004204 samples/sec accuracy=84.460625 loss=0.601107 lr=0.001000 Epoch[076] Batch [2549]/[3760] Speed: 62.732832 samples/sec accuracy=84.441789 loss=0.601625 lr=0.001000 Epoch[076] Batch [2599]/[3760] Speed: 61.915729 samples/sec accuracy=84.427284 loss=0.602213 lr=0.001000 Epoch[076] Batch [2649]/[3760] Speed: 62.776678 samples/sec accuracy=84.428066 loss=0.602459 lr=0.001000 Epoch[076] Batch [2699]/[3760] Speed: 62.293047 samples/sec accuracy=84.413773 loss=0.603110 lr=0.001000 Epoch[076] Batch [2749]/[3760] Speed: 62.155277 samples/sec accuracy=84.420455 loss=0.603024 lr=0.001000 Epoch[076] 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accuracy=84.351923 loss=0.605360 lr=0.001000 Epoch[076] Batch [3299]/[3760] Speed: 62.063754 samples/sec accuracy=84.329545 loss=0.606338 lr=0.001000 Epoch[076] Batch [3349]/[3760] Speed: 62.975588 samples/sec accuracy=84.322295 loss=0.607037 lr=0.001000 Epoch[076] Batch [3399]/[3760] Speed: 62.316346 samples/sec accuracy=84.333640 loss=0.606659 lr=0.001000 Epoch[076] Batch [3449]/[3760] Speed: 62.373452 samples/sec accuracy=84.335145 loss=0.606537 lr=0.001000 Epoch[076] Batch [3499]/[3760] Speed: 62.634320 samples/sec accuracy=84.327679 loss=0.606612 lr=0.001000 Epoch[076] Batch [3549]/[3760] Speed: 62.150689 samples/sec accuracy=84.318222 loss=0.607133 lr=0.001000 Epoch[076] Batch [3599]/[3760] Speed: 61.876542 samples/sec accuracy=84.330729 loss=0.606482 lr=0.001000 Epoch[076] Batch [3649]/[3760] Speed: 62.650112 samples/sec accuracy=84.325342 loss=0.606727 lr=0.001000 Epoch[076] Batch [3699]/[3760] Speed: 62.334037 samples/sec accuracy=84.310389 loss=0.607030 lr=0.001000 Epoch[076] Batch [3749]/[3760] Speed: 67.180451 samples/sec accuracy=84.307500 loss=0.607290 lr=0.001000 Batch [0049]/[0303]: acc-top1=66.281250 acc-top5=85.500000 Batch [0099]/[0303]: acc-top1=67.703125 acc-top5=86.546875 Batch [0149]/[0303]: acc-top1=67.468750 acc-top5=86.458333 Batch [0199]/[0303]: acc-top1=67.398438 acc-top5=86.414062 Batch [0249]/[0303]: acc-top1=67.225000 acc-top5=86.337500 Batch [0299]/[0303]: acc-top1=67.302083 acc-top5=86.385417 [Epoch 076] training: accuracy=84.307680 loss=0.607264 [Epoch 076] speed: 61 samples/sec time cost: 4151.279960 [Epoch 076] validation: acc-top1=67.275165 acc-top5=86.396452 loss=1.806877 Epoch[077] Batch [0049]/[3760] Speed: 42.455702 samples/sec accuracy=84.375000 loss=0.582094 lr=0.001000 Epoch[077] Batch [0099]/[3760] Speed: 60.334852 samples/sec accuracy=84.718750 loss=0.583865 lr=0.001000 Epoch[077] Batch [0149]/[3760] Speed: 61.835161 samples/sec accuracy=84.729167 loss=0.580703 lr=0.001000 Epoch[077] Batch [0199]/[3760] Speed: 60.826099 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lr=0.001000 Epoch[077] Batch [3549]/[3760] Speed: 62.693765 samples/sec accuracy=84.280810 loss=0.606687 lr=0.001000 Epoch[077] Batch [3599]/[3760] Speed: 62.103138 samples/sec accuracy=84.267361 loss=0.607099 lr=0.001000 Epoch[077] Batch [3649]/[3760] Speed: 62.325766 samples/sec accuracy=84.270976 loss=0.607019 lr=0.001000 Epoch[077] Batch [3699]/[3760] Speed: 62.217027 samples/sec accuracy=84.263936 loss=0.607174 lr=0.001000 Epoch[077] Batch [3749]/[3760] Speed: 66.838107 samples/sec accuracy=84.278333 loss=0.606956 lr=0.001000 Batch [0049]/[0303]: acc-top1=65.625000 acc-top5=85.218750 Batch [0099]/[0303]: acc-top1=67.171875 acc-top5=86.281250 Batch [0149]/[0303]: acc-top1=67.364583 acc-top5=86.322917 Batch [0199]/[0303]: acc-top1=67.570312 acc-top5=86.406250 Batch [0249]/[0303]: acc-top1=67.281250 acc-top5=86.287500 Batch [0299]/[0303]: acc-top1=67.375000 acc-top5=86.364583 [Epoch 077] training: accuracy=84.279422 loss=0.606915 [Epoch 077] speed: 61 samples/sec time cost: 4153.882423 [Epoch 077] validation: acc-top1=67.373144 acc-top5=86.355198 loss=1.781458 Epoch[078] Batch [0049]/[3759] Speed: 42.141586 samples/sec accuracy=84.656250 loss=0.569464 lr=0.001000 Epoch[078] Batch [0099]/[3759] Speed: 61.093137 samples/sec accuracy=84.640625 loss=0.588475 lr=0.001000 Epoch[078] Batch [0149]/[3759] Speed: 61.847689 samples/sec accuracy=84.916667 loss=0.581781 lr=0.001000 Epoch[078] Batch [0199]/[3759] Speed: 61.350660 samples/sec accuracy=84.976562 loss=0.583409 lr=0.001000 Epoch[078] Batch [0249]/[3759] Speed: 62.756184 samples/sec accuracy=85.087500 loss=0.580221 lr=0.001000 Epoch[078] Batch [0299]/[3759] Speed: 61.298736 samples/sec accuracy=85.161458 loss=0.578558 lr=0.001000 Epoch[078] Batch [0349]/[3759] Speed: 62.581015 samples/sec accuracy=85.013393 loss=0.584189 lr=0.001000 Epoch[078] Batch [0399]/[3759] Speed: 62.337296 samples/sec accuracy=84.882812 loss=0.586683 lr=0.001000 Epoch[078] Batch [0449]/[3759] Speed: 62.150060 samples/sec 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accuracy=84.679688 loss=0.594639 lr=0.001000 Epoch[078] Batch [1449]/[3759] Speed: 62.801349 samples/sec accuracy=84.679957 loss=0.594580 lr=0.001000 Epoch[078] Batch [1499]/[3759] Speed: 62.064367 samples/sec accuracy=84.651042 loss=0.595051 lr=0.001000 Epoch[078] Batch [1549]/[3759] Speed: 62.782976 samples/sec accuracy=84.658266 loss=0.594638 lr=0.001000 Epoch[078] Batch [1599]/[3759] Speed: 62.774470 samples/sec accuracy=84.666992 loss=0.594749 lr=0.001000 Epoch[078] Batch [1649]/[3759] Speed: 62.401973 samples/sec accuracy=84.669508 loss=0.593959 lr=0.001000 Epoch[078] Batch [1699]/[3759] Speed: 62.782711 samples/sec accuracy=84.670037 loss=0.592944 lr=0.001000 Epoch[078] Batch [1749]/[3759] Speed: 61.845337 samples/sec accuracy=84.650893 loss=0.593716 lr=0.001000 Epoch[078] Batch [1799]/[3759] Speed: 62.471035 samples/sec accuracy=84.657118 loss=0.593512 lr=0.001000 Epoch[078] Batch [1849]/[3759] Speed: 62.128071 samples/sec accuracy=84.646959 loss=0.593368 lr=0.001000 Epoch[078] 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accuracy=84.459280 loss=0.602269 lr=0.001000 Epoch[078] Batch [3349]/[3759] Speed: 62.495104 samples/sec accuracy=84.437966 loss=0.603110 lr=0.001000 Epoch[078] Batch [3399]/[3759] Speed: 62.256435 samples/sec accuracy=84.437500 loss=0.603118 lr=0.001000 Epoch[078] Batch [3449]/[3759] Speed: 62.631206 samples/sec accuracy=84.436594 loss=0.602835 lr=0.001000 Epoch[078] Batch [3499]/[3759] Speed: 62.440642 samples/sec accuracy=84.420982 loss=0.603182 lr=0.001000 Epoch[078] Batch [3549]/[3759] Speed: 62.221188 samples/sec accuracy=84.426056 loss=0.603143 lr=0.001000 Epoch[078] Batch [3599]/[3759] Speed: 62.061730 samples/sec accuracy=84.430556 loss=0.602817 lr=0.001000 Epoch[078] Batch [3649]/[3759] Speed: 62.889081 samples/sec accuracy=84.429366 loss=0.603113 lr=0.001000 Epoch[078] Batch [3699]/[3759] Speed: 62.863148 samples/sec accuracy=84.434966 loss=0.602980 lr=0.001000 Epoch[078] Batch [3749]/[3759] Speed: 67.726150 samples/sec accuracy=84.432083 loss=0.602989 lr=0.001000 Batch [0049]/[0303]: acc-top1=65.968750 acc-top5=85.781250 Batch [0099]/[0303]: acc-top1=67.156250 acc-top5=86.453125 Batch [0149]/[0303]: acc-top1=67.468750 acc-top5=86.510417 Batch [0199]/[0303]: acc-top1=67.492188 acc-top5=86.570312 Batch [0249]/[0303]: acc-top1=67.362500 acc-top5=86.437500 Batch [0299]/[0303]: acc-top1=67.401042 acc-top5=86.437500 [Epoch 078] training: accuracy=84.429037 loss=0.603082 [Epoch 078] speed: 62 samples/sec time cost: 4147.419923 [Epoch 078] validation: acc-top1=67.388614 acc-top5=86.453177 loss=1.787237 Epoch[079] Batch [0049]/[3760] Speed: 41.971651 samples/sec accuracy=84.187500 loss=0.609977 lr=0.001000 Epoch[079] Batch [0099]/[3760] Speed: 60.327936 samples/sec accuracy=84.390625 loss=0.597608 lr=0.001000 Epoch[079] Batch [0149]/[3760] Speed: 62.324203 samples/sec accuracy=84.375000 loss=0.604266 lr=0.001000 Epoch[079] Batch [0199]/[3760] Speed: 61.062286 samples/sec accuracy=84.476562 loss=0.602909 lr=0.001000 Epoch[079] Batch [0249]/[3760] Speed: 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lr=0.001000 Epoch[079] Batch [3599]/[3760] Speed: 62.343391 samples/sec accuracy=84.576389 loss=0.599130 lr=0.001000 Epoch[079] Batch [3649]/[3760] Speed: 62.115459 samples/sec accuracy=84.564640 loss=0.599357 lr=0.001000 Epoch[079] Batch [3699]/[3760] Speed: 62.122269 samples/sec accuracy=84.567990 loss=0.599292 lr=0.001000 Epoch[079] Batch [3749]/[3760] Speed: 67.346378 samples/sec accuracy=84.560000 loss=0.599737 lr=0.000100 Batch [0049]/[0303]: acc-top1=65.937500 acc-top5=85.312500 Batch [0099]/[0303]: acc-top1=67.328125 acc-top5=86.078125 Batch [0149]/[0303]: acc-top1=67.020833 acc-top5=86.072917 Batch [0199]/[0303]: acc-top1=67.257812 acc-top5=86.257812 Batch [0249]/[0303]: acc-top1=67.125000 acc-top5=86.243750 Batch [0299]/[0303]: acc-top1=67.104167 acc-top5=86.281250 [Epoch 079] training: accuracy=84.550366 loss=0.599989 [Epoch 079] speed: 61 samples/sec time cost: 4152.574609 [Epoch 079] validation: acc-top1=67.084365 acc-top5=86.303630 loss=1.782744 Epoch[080] Batch 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accuracy=85.606250 loss=0.567248 lr=0.000100 Epoch[080] Batch [0549]/[3760] Speed: 61.944516 samples/sec accuracy=85.713068 loss=0.564269 lr=0.000100 Epoch[080] Batch [0599]/[3760] Speed: 62.615197 samples/sec accuracy=85.765625 loss=0.561521 lr=0.000100 Epoch[080] Batch [0649]/[3760] Speed: 62.265622 samples/sec accuracy=85.747596 loss=0.561403 lr=0.000100 Epoch[080] Batch [0699]/[3760] Speed: 62.175770 samples/sec accuracy=85.734375 loss=0.561046 lr=0.000100 Epoch[080] Batch [0749]/[3760] Speed: 61.503771 samples/sec accuracy=85.781250 loss=0.557509 lr=0.000100 Epoch[080] Batch [0799]/[3760] Speed: 63.028566 samples/sec accuracy=85.822266 loss=0.554795 lr=0.000100 Epoch[080] Batch [0849]/[3760] Speed: 61.944567 samples/sec accuracy=85.825368 loss=0.554852 lr=0.000100 Epoch[080] Batch [0899]/[3760] Speed: 62.306642 samples/sec accuracy=85.796875 loss=0.554782 lr=0.000100 Epoch[080] Batch [0949]/[3760] Speed: 62.535160 samples/sec accuracy=85.728618 loss=0.557233 lr=0.000100 Epoch[080] 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accuracy=85.955819 loss=0.549366 lr=0.000100 Epoch[080] Batch [1499]/[3760] Speed: 62.634732 samples/sec accuracy=85.959375 loss=0.549478 lr=0.000100 Epoch[080] Batch [1549]/[3760] Speed: 62.264843 samples/sec accuracy=85.947581 loss=0.549184 lr=0.000100 Epoch[080] Batch [1599]/[3760] Speed: 62.430103 samples/sec accuracy=85.948242 loss=0.549571 lr=0.000100 Epoch[080] Batch [1649]/[3760] Speed: 62.237611 samples/sec accuracy=85.949811 loss=0.550146 lr=0.000100 Epoch[080] Batch [1699]/[3760] Speed: 62.708473 samples/sec accuracy=85.973346 loss=0.549460 lr=0.000100 Epoch[080] Batch [1749]/[3760] Speed: 62.365743 samples/sec accuracy=85.957143 loss=0.550248 lr=0.000100 Epoch[080] Batch [1799]/[3760] Speed: 62.314841 samples/sec accuracy=85.973090 loss=0.548949 lr=0.000100 Epoch[080] Batch [1849]/[3760] Speed: 61.806791 samples/sec accuracy=85.979730 loss=0.549258 lr=0.000100 Epoch[080] Batch [1899]/[3760] Speed: 62.979243 samples/sec accuracy=86.003289 loss=0.548348 lr=0.000100 Epoch[080] 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accuracy=86.042969 loss=0.546201 lr=0.000100 Epoch[080] Batch [2449]/[3760] Speed: 61.970963 samples/sec accuracy=86.054209 loss=0.545881 lr=0.000100 Epoch[080] Batch [2499]/[3760] Speed: 62.194901 samples/sec accuracy=86.067500 loss=0.544946 lr=0.000100 Epoch[080] Batch [2549]/[3760] Speed: 62.112290 samples/sec accuracy=86.077206 loss=0.544431 lr=0.000100 Epoch[080] Batch [2599]/[3760] Speed: 62.068180 samples/sec accuracy=86.094351 loss=0.543469 lr=0.000100 Epoch[080] Batch [2649]/[3760] Speed: 62.692757 samples/sec accuracy=86.103774 loss=0.543455 lr=0.000100 Epoch[080] Batch [2699]/[3760] Speed: 61.802593 samples/sec accuracy=86.118056 loss=0.543109 lr=0.000100 Epoch[080] Batch [2749]/[3760] Speed: 62.369529 samples/sec accuracy=86.131250 loss=0.542432 lr=0.000100 Epoch[080] Batch [2799]/[3760] Speed: 61.875201 samples/sec accuracy=86.144531 loss=0.541961 lr=0.000100 Epoch[080] Batch [2849]/[3760] Speed: 62.174028 samples/sec accuracy=86.149671 loss=0.541819 lr=0.000100 Epoch[080] Batch [2899]/[3760] Speed: 62.192825 samples/sec accuracy=86.139547 loss=0.541812 lr=0.000100 Epoch[080] Batch [2949]/[3760] Speed: 62.393585 samples/sec accuracy=86.138771 loss=0.542089 lr=0.000100 Epoch[080] Batch [2999]/[3760] Speed: 62.150776 samples/sec accuracy=86.144271 loss=0.541956 lr=0.000100 Epoch[080] Batch [3049]/[3760] Speed: 61.972172 samples/sec accuracy=86.139344 loss=0.541865 lr=0.000100 Epoch[080] Batch [3099]/[3760] Speed: 63.036401 samples/sec accuracy=86.144657 loss=0.541357 lr=0.000100 Epoch[080] Batch [3149]/[3760] Speed: 62.449371 samples/sec accuracy=86.168651 loss=0.540563 lr=0.000100 Epoch[080] Batch [3199]/[3760] Speed: 62.553268 samples/sec accuracy=86.171387 loss=0.540743 lr=0.000100 Epoch[080] Batch [3249]/[3760] Speed: 62.706945 samples/sec accuracy=86.181250 loss=0.540088 lr=0.000100 Epoch[080] Batch [3299]/[3760] Speed: 62.215570 samples/sec accuracy=86.185606 loss=0.539987 lr=0.000100 Epoch[080] Batch [3349]/[3760] Speed: 62.661479 samples/sec accuracy=86.194963 loss=0.539679 lr=0.000100 Epoch[080] Batch [3399]/[3760] Speed: 62.296525 samples/sec accuracy=86.202665 loss=0.539484 lr=0.000100 Epoch[080] Batch [3449]/[3760] Speed: 62.232445 samples/sec accuracy=86.202899 loss=0.539395 lr=0.000100 Epoch[080] Batch [3499]/[3760] Speed: 61.970950 samples/sec accuracy=86.213393 loss=0.538957 lr=0.000100 Epoch[080] Batch [3549]/[3760] Speed: 62.261115 samples/sec accuracy=86.211268 loss=0.538822 lr=0.000100 Epoch[080] Batch [3599]/[3760] Speed: 62.272450 samples/sec accuracy=86.212674 loss=0.538267 lr=0.000100 Epoch[080] Batch [3649]/[3760] Speed: 62.238167 samples/sec accuracy=86.217466 loss=0.538048 lr=0.000100 Epoch[080] Batch [3699]/[3760] Speed: 62.548283 samples/sec accuracy=86.213260 loss=0.538134 lr=0.000100 Epoch[080] Batch [3749]/[3760] Speed: 67.214311 samples/sec accuracy=86.225000 loss=0.537616 lr=0.000100 Batch [0049]/[0303]: acc-top1=66.843750 acc-top5=85.625000 Batch [0099]/[0303]: acc-top1=68.000000 acc-top5=86.484375 Batch [0149]/[0303]: acc-top1=67.916667 acc-top5=86.645833 Batch [0199]/[0303]: acc-top1=68.054688 acc-top5=86.835938 Batch [0249]/[0303]: acc-top1=67.962500 acc-top5=86.750000 Batch [0299]/[0303]: acc-top1=67.947917 acc-top5=86.682292 [Epoch 080] training: accuracy=86.221742 loss=0.537815 [Epoch 080] speed: 61 samples/sec time cost: 4155.840200 [Epoch 080] validation: acc-top1=67.935231 acc-top5=86.685231 loss=1.756060 Epoch[081] Batch [0049]/[3759] Speed: 42.540641 samples/sec accuracy=87.843750 loss=0.493817 lr=0.000100 Epoch[081] Batch [0099]/[3759] Speed: 60.683942 samples/sec accuracy=86.921875 loss=0.518928 lr=0.000100 Epoch[081] Batch [0149]/[3759] Speed: 61.837744 samples/sec accuracy=86.947917 loss=0.519621 lr=0.000100 Epoch[081] Batch [0199]/[3759] Speed: 61.544038 samples/sec accuracy=87.164062 loss=0.514622 lr=0.000100 Epoch[081] Batch [0249]/[3759] Speed: 63.103899 samples/sec accuracy=87.100000 loss=0.511005 lr=0.000100 Epoch[081] Batch [0299]/[3759] 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accuracy=86.825955 loss=0.513415 lr=0.000100 Epoch[081] Batch [3649]/[3759] Speed: 62.269642 samples/sec accuracy=86.827483 loss=0.513348 lr=0.000100 Epoch[081] Batch [3699]/[3759] Speed: 62.615899 samples/sec accuracy=86.830236 loss=0.513126 lr=0.000100 Epoch[081] Batch [3749]/[3759] Speed: 67.607909 samples/sec accuracy=86.830833 loss=0.512995 lr=0.000100 Batch [0049]/[0303]: acc-top1=67.125000 acc-top5=85.875000 Batch [0099]/[0303]: acc-top1=68.000000 acc-top5=86.718750 Batch [0149]/[0303]: acc-top1=68.125000 acc-top5=86.729167 Batch [0199]/[0303]: acc-top1=68.132812 acc-top5=86.828125 Batch [0249]/[0303]: acc-top1=67.962500 acc-top5=86.750000 Batch [0299]/[0303]: acc-top1=67.979167 acc-top5=86.677083 [Epoch 081] training: accuracy=86.832435 loss=0.512886 [Epoch 081] speed: 62 samples/sec time cost: 4143.817207 [Epoch 081] validation: acc-top1=67.971328 acc-top5=86.695545 loss=1.759752 Epoch[082] Batch [0049]/[3760] Speed: 43.406516 samples/sec accuracy=86.875000 loss=0.497517 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lr=0.000100 Epoch[082] Batch [2949]/[3760] Speed: 61.841598 samples/sec accuracy=87.051907 loss=0.505206 lr=0.000100 Epoch[082] Batch [2999]/[3760] Speed: 62.228582 samples/sec accuracy=87.041667 loss=0.505339 lr=0.000100 Epoch[082] Batch [3049]/[3760] Speed: 62.168983 samples/sec accuracy=87.058402 loss=0.504977 lr=0.000100 Epoch[082] Batch [3099]/[3760] Speed: 62.657957 samples/sec accuracy=87.039819 loss=0.505658 lr=0.000100 Epoch[082] Batch [3149]/[3760] Speed: 62.025562 samples/sec accuracy=87.040179 loss=0.505397 lr=0.000100 Epoch[082] Batch [3199]/[3760] Speed: 62.572841 samples/sec accuracy=87.037109 loss=0.505476 lr=0.000100 Epoch[082] Batch [3249]/[3760] Speed: 62.368817 samples/sec accuracy=87.067308 loss=0.504411 lr=0.000100 Epoch[082] Batch [3299]/[3760] Speed: 62.411794 samples/sec accuracy=87.062973 loss=0.504669 lr=0.000100 Epoch[082] Batch [3349]/[3760] Speed: 61.941377 samples/sec accuracy=87.055504 loss=0.504685 lr=0.000100 Epoch[082] Batch [3399]/[3760] Speed: 62.267603 samples/sec accuracy=87.053309 loss=0.504877 lr=0.000100 Epoch[082] Batch [3449]/[3760] Speed: 62.558388 samples/sec accuracy=87.041667 loss=0.505141 lr=0.000100 Epoch[082] Batch [3499]/[3760] Speed: 61.693487 samples/sec accuracy=87.051339 loss=0.504851 lr=0.000100 Epoch[082] Batch [3549]/[3760] Speed: 62.129561 samples/sec accuracy=87.058539 loss=0.504555 lr=0.000100 Epoch[082] Batch [3599]/[3760] Speed: 62.686650 samples/sec accuracy=87.057292 loss=0.504528 lr=0.000100 Epoch[082] Batch [3649]/[3760] Speed: 62.491997 samples/sec accuracy=87.051370 loss=0.504942 lr=0.000100 Epoch[082] Batch [3699]/[3760] Speed: 62.687956 samples/sec accuracy=87.035051 loss=0.505362 lr=0.000100 Epoch[082] Batch [3749]/[3760] Speed: 67.623384 samples/sec accuracy=87.032917 loss=0.505251 lr=0.000100 Batch [0049]/[0303]: acc-top1=66.750000 acc-top5=85.500000 Batch [0099]/[0303]: acc-top1=67.843750 acc-top5=86.421875 Batch [0149]/[0303]: acc-top1=68.072917 acc-top5=86.541667 Batch [0199]/[0303]: acc-top1=68.218750 acc-top5=86.687500 Batch [0249]/[0303]: acc-top1=68.050000 acc-top5=86.662500 Batch [0299]/[0303]: acc-top1=68.015625 acc-top5=86.598958 [Epoch 082] training: accuracy=87.028757 loss=0.505117 [Epoch 082] speed: 62 samples/sec time cost: 4143.885660 [Epoch 082] validation: acc-top1=68.007426 acc-top5=86.613036 loss=1.778401 Epoch[083] Batch [0049]/[3760] Speed: 42.759611 samples/sec accuracy=87.343750 loss=0.492107 lr=0.000100 Epoch[083] Batch [0099]/[3760] Speed: 61.169125 samples/sec accuracy=86.953125 loss=0.513207 lr=0.000100 Epoch[083] Batch [0149]/[3760] Speed: 61.737577 samples/sec accuracy=86.854167 loss=0.514721 lr=0.000100 Epoch[083] Batch [0199]/[3760] Speed: 61.470954 samples/sec accuracy=86.898438 loss=0.506953 lr=0.000100 Epoch[083] Batch [0249]/[3760] Speed: 62.751490 samples/sec accuracy=86.775000 loss=0.508443 lr=0.000100 Epoch[083] Batch [0299]/[3760] Speed: 61.795862 samples/sec accuracy=86.812500 loss=0.507323 lr=0.000100 Epoch[083] Batch 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accuracy=87.188079 loss=0.497819 lr=0.000100 Epoch[083] Batch [2749]/[3760] Speed: 62.524984 samples/sec accuracy=87.181818 loss=0.498285 lr=0.000100 Epoch[083] Batch [2799]/[3760] Speed: 62.400976 samples/sec accuracy=87.171317 loss=0.498522 lr=0.000100 Epoch[083] Batch [2849]/[3760] Speed: 61.780889 samples/sec accuracy=87.170504 loss=0.498731 lr=0.000100 Epoch[083] Batch [2899]/[3760] Speed: 62.518638 samples/sec accuracy=87.190733 loss=0.498044 lr=0.000100 Epoch[083] Batch [2949]/[3760] Speed: 62.361028 samples/sec accuracy=87.184852 loss=0.497738 lr=0.000100 Epoch[083] Batch [2999]/[3760] Speed: 62.312668 samples/sec accuracy=87.178646 loss=0.498275 lr=0.000100 Epoch[083] Batch [3049]/[3760] Speed: 62.152744 samples/sec accuracy=87.183402 loss=0.498197 lr=0.000100 Epoch[083] Batch [3099]/[3760] Speed: 62.170096 samples/sec accuracy=87.200101 loss=0.498239 lr=0.000100 Epoch[083] Batch [3149]/[3760] Speed: 62.800006 samples/sec accuracy=87.213790 loss=0.497912 lr=0.000100 Epoch[083] 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accuracy=87.193493 loss=0.498757 lr=0.000100 Epoch[083] Batch [3699]/[3760] Speed: 62.513141 samples/sec accuracy=87.186655 loss=0.498983 lr=0.000100 Epoch[083] Batch [3749]/[3760] Speed: 66.992645 samples/sec accuracy=87.182917 loss=0.499110 lr=0.000100 Batch [0049]/[0303]: acc-top1=66.843750 acc-top5=85.750000 Batch [0099]/[0303]: acc-top1=68.046875 acc-top5=86.578125 Batch [0149]/[0303]: acc-top1=68.270833 acc-top5=86.552083 Batch [0199]/[0303]: acc-top1=68.429688 acc-top5=86.593750 Batch [0249]/[0303]: acc-top1=68.193750 acc-top5=86.593750 Batch [0299]/[0303]: acc-top1=68.098958 acc-top5=86.557292 [Epoch 083] training: accuracy=87.176695 loss=0.499393 [Epoch 083] speed: 62 samples/sec time cost: 4146.271127 [Epoch 083] validation: acc-top1=68.089934 acc-top5=86.587252 loss=1.763675 Epoch[084] Batch [0049]/[3759] Speed: 42.979865 samples/sec accuracy=87.062500 loss=0.505289 lr=0.000100 Epoch[084] Batch [0099]/[3759] Speed: 60.357944 samples/sec accuracy=87.218750 loss=0.501850 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lr=0.000100 Epoch[084] Batch [1099]/[3759] Speed: 61.804967 samples/sec accuracy=87.292614 loss=0.495182 lr=0.000100 Epoch[084] Batch [1149]/[3759] Speed: 62.560681 samples/sec accuracy=87.323370 loss=0.494157 lr=0.000100 Epoch[084] Batch [1199]/[3759] Speed: 61.622033 samples/sec accuracy=87.324219 loss=0.494429 lr=0.000100 Epoch[084] Batch [1249]/[3759] Speed: 62.517277 samples/sec accuracy=87.335000 loss=0.494173 lr=0.000100 Epoch[084] Batch [1299]/[3759] Speed: 62.888953 samples/sec accuracy=87.332933 loss=0.494785 lr=0.000100 Epoch[084] Batch [1349]/[3759] Speed: 61.852603 samples/sec accuracy=87.329861 loss=0.494464 lr=0.000100 Epoch[084] Batch [1399]/[3759] Speed: 62.703504 samples/sec accuracy=87.318080 loss=0.494411 lr=0.000100 Epoch[084] Batch [1449]/[3759] Speed: 62.782792 samples/sec accuracy=87.320043 loss=0.494239 lr=0.000100 Epoch[084] Batch [1499]/[3759] Speed: 62.066065 samples/sec accuracy=87.321875 loss=0.494272 lr=0.000100 Epoch[084] Batch [1549]/[3759] Speed: 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lr=0.000100 Epoch[084] Batch [2049]/[3759] Speed: 62.472512 samples/sec accuracy=87.333841 loss=0.495166 lr=0.000100 Epoch[084] Batch [2099]/[3759] Speed: 62.537883 samples/sec accuracy=87.335565 loss=0.494665 lr=0.000100 Epoch[084] Batch [2149]/[3759] Speed: 62.877641 samples/sec accuracy=87.337936 loss=0.494439 lr=0.000100 Epoch[084] Batch [2199]/[3759] Speed: 62.215235 samples/sec accuracy=87.338068 loss=0.494271 lr=0.000100 Epoch[084] Batch [2249]/[3759] Speed: 62.562257 samples/sec accuracy=87.342361 loss=0.493964 lr=0.000100 Epoch[084] Batch [2299]/[3759] Speed: 62.335498 samples/sec accuracy=87.338315 loss=0.494221 lr=0.000100 Epoch[084] Batch [2349]/[3759] Speed: 62.473621 samples/sec accuracy=87.331782 loss=0.494886 lr=0.000100 Epoch[084] Batch [2399]/[3759] Speed: 62.813212 samples/sec accuracy=87.323568 loss=0.495073 lr=0.000100 Epoch[084] Batch [2449]/[3759] Speed: 62.916938 samples/sec accuracy=87.309311 loss=0.495726 lr=0.000100 Epoch[084] Batch [2499]/[3759] Speed: 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lr=0.000100 Epoch[084] Batch [2999]/[3759] Speed: 62.546795 samples/sec accuracy=87.302083 loss=0.496307 lr=0.000100 Epoch[084] Batch [3049]/[3759] Speed: 62.606289 samples/sec accuracy=87.308402 loss=0.496235 lr=0.000100 Epoch[084] Batch [3099]/[3759] Speed: 62.099066 samples/sec accuracy=87.303427 loss=0.496417 lr=0.000100 Epoch[084] Batch [3149]/[3759] Speed: 62.397099 samples/sec accuracy=87.312004 loss=0.496055 lr=0.000100 Epoch[084] Batch [3199]/[3759] Speed: 62.458965 samples/sec accuracy=87.299316 loss=0.496355 lr=0.000100 Epoch[084] Batch [3249]/[3759] Speed: 62.261888 samples/sec accuracy=87.294231 loss=0.496389 lr=0.000100 Epoch[084] Batch [3299]/[3759] Speed: 62.664377 samples/sec accuracy=87.303977 loss=0.496030 lr=0.000100 Epoch[084] Batch [3349]/[3759] Speed: 62.133308 samples/sec accuracy=87.307369 loss=0.495723 lr=0.000100 Epoch[084] Batch [3399]/[3759] Speed: 62.373469 samples/sec accuracy=87.301011 loss=0.495948 lr=0.000100 Epoch[084] Batch [3449]/[3759] Speed: 62.059092 samples/sec accuracy=87.311594 loss=0.495576 lr=0.000100 Epoch[084] Batch [3499]/[3759] Speed: 62.424625 samples/sec accuracy=87.328125 loss=0.495238 lr=0.000100 Epoch[084] Batch [3549]/[3759] Speed: 62.094711 samples/sec accuracy=87.323944 loss=0.495030 lr=0.000100 Epoch[084] Batch [3599]/[3759] Speed: 62.415253 samples/sec accuracy=87.340712 loss=0.494425 lr=0.000100 Epoch[084] Batch [3649]/[3759] Speed: 62.133629 samples/sec accuracy=87.348887 loss=0.494238 lr=0.000100 Epoch[084] Batch [3699]/[3759] Speed: 62.868895 samples/sec accuracy=87.356841 loss=0.494228 lr=0.000100 Epoch[084] Batch [3749]/[3759] Speed: 67.498441 samples/sec accuracy=87.356250 loss=0.494149 lr=0.000100 Batch [0049]/[0303]: acc-top1=66.687500 acc-top5=86.000000 Batch [0099]/[0303]: acc-top1=68.156250 acc-top5=86.718750 Batch [0149]/[0303]: acc-top1=68.177083 acc-top5=86.833333 Batch [0199]/[0303]: acc-top1=68.367188 acc-top5=86.867188 Batch [0249]/[0303]: acc-top1=68.100000 acc-top5=86.800000 Batch [0299]/[0303]: acc-top1=67.937500 acc-top5=86.781250 [Epoch 084] training: accuracy=87.353684 loss=0.494255 [Epoch 084] speed: 62 samples/sec time cost: 4146.456462 [Epoch 084] validation: acc-top1=67.924917 acc-top5=86.798680 loss=1.778088 Epoch[085] Batch [0049]/[3760] Speed: 42.929356 samples/sec accuracy=87.312500 loss=0.491225 lr=0.000100 Epoch[085] Batch [0099]/[3760] Speed: 60.748446 samples/sec accuracy=87.437500 loss=0.490527 lr=0.000100 Epoch[085] Batch [0149]/[3760] Speed: 62.593581 samples/sec accuracy=87.593750 loss=0.483146 lr=0.000100 Epoch[085] Batch [0199]/[3760] Speed: 61.578504 samples/sec accuracy=87.468750 loss=0.487265 lr=0.000100 Epoch[085] Batch [0249]/[3760] Speed: 62.671127 samples/sec accuracy=87.531250 loss=0.486275 lr=0.000100 Epoch[085] Batch [0299]/[3760] Speed: 61.379332 samples/sec accuracy=87.562500 loss=0.485995 lr=0.000100 Epoch[085] Batch [0349]/[3760] Speed: 62.779802 samples/sec accuracy=87.651786 loss=0.480118 lr=0.000100 Epoch[085] Batch 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accuracy=87.468750 loss=0.486519 lr=0.000100 Epoch[085] Batch [0899]/[3760] Speed: 62.503040 samples/sec accuracy=87.446181 loss=0.487411 lr=0.000100 Epoch[085] Batch [0949]/[3760] Speed: 62.644194 samples/sec accuracy=87.412829 loss=0.488612 lr=0.000100 Epoch[085] Batch [0999]/[3760] Speed: 62.145110 samples/sec accuracy=87.453125 loss=0.487039 lr=0.000100 Epoch[085] Batch [1049]/[3760] Speed: 63.098749 samples/sec accuracy=87.415179 loss=0.487781 lr=0.000100 Epoch[085] Batch [1099]/[3760] Speed: 61.821215 samples/sec accuracy=87.416193 loss=0.488182 lr=0.000100 Epoch[085] Batch [1149]/[3760] Speed: 62.714821 samples/sec accuracy=87.387228 loss=0.489693 lr=0.000100 Epoch[085] Batch [1199]/[3760] Speed: 62.865298 samples/sec accuracy=87.412760 loss=0.489570 lr=0.000100 Epoch[085] Batch [1249]/[3760] Speed: 62.226313 samples/sec accuracy=87.381250 loss=0.490129 lr=0.000100 Epoch[085] Batch [1299]/[3760] Speed: 63.467009 samples/sec accuracy=87.371394 loss=0.490758 lr=0.000100 Epoch[085] 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accuracy=87.515625 loss=0.487683 lr=0.000100 Epoch[085] Batch [1849]/[3760] Speed: 62.528930 samples/sec accuracy=87.494088 loss=0.488381 lr=0.000100 Epoch[085] Batch [1899]/[3760] Speed: 61.939311 samples/sec accuracy=87.500000 loss=0.488609 lr=0.000100 Epoch[085] Batch [1949]/[3760] Speed: 62.852319 samples/sec accuracy=87.495192 loss=0.488766 lr=0.000100 Epoch[085] Batch [1999]/[3760] Speed: 62.086756 samples/sec accuracy=87.505469 loss=0.488718 lr=0.000100 Epoch[085] Batch [2049]/[3760] Speed: 62.654446 samples/sec accuracy=87.498476 loss=0.489034 lr=0.000100 Epoch[085] Batch [2099]/[3760] Speed: 62.677682 samples/sec accuracy=87.498512 loss=0.489179 lr=0.000100 Epoch[085] Batch [2149]/[3760] Speed: 62.797518 samples/sec accuracy=87.483285 loss=0.489099 lr=0.000100 Epoch[085] Batch [2199]/[3760] Speed: 62.619862 samples/sec accuracy=87.500710 loss=0.488553 lr=0.000100 Epoch[085] Batch [2249]/[3760] Speed: 62.112077 samples/sec accuracy=87.495139 loss=0.488660 lr=0.000100 Epoch[085] 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accuracy=87.472159 loss=0.489331 lr=0.000100 Epoch[085] Batch [2799]/[3760] Speed: 62.135944 samples/sec accuracy=87.456473 loss=0.489425 lr=0.000100 Epoch[085] Batch [2849]/[3760] Speed: 62.391853 samples/sec accuracy=87.450658 loss=0.489694 lr=0.000100 Epoch[085] Batch [2899]/[3760] Speed: 62.867807 samples/sec accuracy=87.434806 loss=0.490169 lr=0.000100 Epoch[085] Batch [2949]/[3760] Speed: 62.876297 samples/sec accuracy=87.433792 loss=0.490067 lr=0.000100 Epoch[085] Batch [2999]/[3760] Speed: 62.064201 samples/sec accuracy=87.439583 loss=0.490117 lr=0.000100 Epoch[085] Batch [3049]/[3760] Speed: 62.810944 samples/sec accuracy=87.456967 loss=0.489423 lr=0.000100 Epoch[085] Batch [3099]/[3760] Speed: 62.470731 samples/sec accuracy=87.459173 loss=0.489140 lr=0.000100 Epoch[085] Batch [3149]/[3760] Speed: 62.455738 samples/sec accuracy=87.459325 loss=0.489203 lr=0.000100 Epoch[085] Batch [3199]/[3760] Speed: 62.679308 samples/sec accuracy=87.461426 loss=0.489239 lr=0.000100 Epoch[085] Batch [3249]/[3760] Speed: 62.260115 samples/sec accuracy=87.462500 loss=0.489489 lr=0.000100 Epoch[085] Batch [3299]/[3760] Speed: 62.009423 samples/sec accuracy=87.456439 loss=0.490002 lr=0.000100 Epoch[085] Batch [3349]/[3760] Speed: 61.702646 samples/sec accuracy=87.452892 loss=0.489814 lr=0.000100 Epoch[085] Batch [3399]/[3760] Speed: 62.421398 samples/sec accuracy=87.436581 loss=0.490375 lr=0.000100 Epoch[085] Batch [3449]/[3760] Speed: 62.220161 samples/sec accuracy=87.439312 loss=0.490547 lr=0.000100 Epoch[085] Batch [3499]/[3760] Speed: 62.497030 samples/sec accuracy=87.456696 loss=0.490098 lr=0.000100 Epoch[085] Batch [3549]/[3760] Speed: 62.252989 samples/sec accuracy=87.447623 loss=0.490574 lr=0.000100 Epoch[085] Batch [3599]/[3760] Speed: 62.391983 samples/sec accuracy=87.449653 loss=0.490549 lr=0.000100 Epoch[085] Batch [3649]/[3760] Speed: 62.363624 samples/sec accuracy=87.455051 loss=0.490629 lr=0.000100 Epoch[085] Batch [3699]/[3760] Speed: 62.520074 samples/sec accuracy=87.455659 loss=0.490630 lr=0.000100 Epoch[085] Batch [3749]/[3760] Speed: 67.519743 samples/sec accuracy=87.443333 loss=0.490920 lr=0.000100 Batch [0049]/[0303]: acc-top1=66.718750 acc-top5=85.656250 Batch [0099]/[0303]: acc-top1=67.906250 acc-top5=86.578125 Batch [0149]/[0303]: acc-top1=67.916667 acc-top5=86.604167 Batch [0199]/[0303]: acc-top1=68.070312 acc-top5=86.601562 Batch [0249]/[0303]: acc-top1=67.881250 acc-top5=86.562500 Batch [0299]/[0303]: acc-top1=67.838542 acc-top5=86.520833 [Epoch 085] training: accuracy=87.440160 loss=0.491026 [Epoch 085] speed: 62 samples/sec time cost: 4143.373271 [Epoch 085] validation: acc-top1=67.832096 acc-top5=86.551155 loss=1.796368 Epoch[086] Batch [0049]/[3760] Speed: 42.492504 samples/sec accuracy=88.187500 loss=0.473521 lr=0.000100 Epoch[086] Batch [0099]/[3760] Speed: 60.652288 samples/sec accuracy=88.015625 loss=0.482838 lr=0.000100 Epoch[086] Batch [0149]/[3760] Speed: 61.760203 samples/sec accuracy=87.697917 loss=0.487754 lr=0.000100 Epoch[086] Batch [0199]/[3760] Speed: 60.937487 samples/sec accuracy=87.656250 loss=0.486982 lr=0.000100 Epoch[086] Batch [0249]/[3760] Speed: 62.932299 samples/sec accuracy=87.593750 loss=0.483838 lr=0.000100 Epoch[086] Batch [0299]/[3760] Speed: 61.751232 samples/sec accuracy=87.541667 loss=0.487111 lr=0.000100 Epoch[086] Batch [0349]/[3760] Speed: 62.244473 samples/sec accuracy=87.544643 loss=0.484154 lr=0.000100 Epoch[086] Batch [0399]/[3760] Speed: 62.393128 samples/sec accuracy=87.457031 loss=0.487338 lr=0.000100 Epoch[086] Batch [0449]/[3760] Speed: 62.798932 samples/sec accuracy=87.572917 loss=0.482680 lr=0.000100 Epoch[086] Batch [0499]/[3760] Speed: 61.741242 samples/sec accuracy=87.615625 loss=0.481255 lr=0.000100 Epoch[086] Batch [0549]/[3760] Speed: 62.283686 samples/sec accuracy=87.605114 loss=0.482310 lr=0.000100 Epoch[086] Batch [0599]/[3760] Speed: 63.010606 samples/sec accuracy=87.630208 loss=0.483694 lr=0.000100 Epoch[086] Batch [0649]/[3760] Speed: 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lr=0.000100 Epoch[086] Batch [1149]/[3760] Speed: 62.342788 samples/sec accuracy=87.540761 loss=0.488399 lr=0.000100 Epoch[086] Batch [1199]/[3760] Speed: 62.281394 samples/sec accuracy=87.515625 loss=0.489176 lr=0.000100 Epoch[086] Batch [1249]/[3760] Speed: 62.431334 samples/sec accuracy=87.548750 loss=0.488532 lr=0.000100 Epoch[086] Batch [1299]/[3760] Speed: 62.465962 samples/sec accuracy=87.552885 loss=0.488589 lr=0.000100 Epoch[086] Batch [1349]/[3760] Speed: 62.509973 samples/sec accuracy=87.557870 loss=0.488801 lr=0.000100 Epoch[086] Batch [1399]/[3760] Speed: 62.592724 samples/sec accuracy=87.571429 loss=0.488172 lr=0.000100 Epoch[086] Batch [1449]/[3760] Speed: 62.351932 samples/sec accuracy=87.547414 loss=0.489236 lr=0.000100 Epoch[086] Batch [1499]/[3760] Speed: 62.633268 samples/sec accuracy=87.569792 loss=0.488117 lr=0.000100 Epoch[086] Batch [1549]/[3760] Speed: 61.871701 samples/sec accuracy=87.589718 loss=0.487202 lr=0.000100 Epoch[086] Batch [1599]/[3760] Speed: 62.653341 samples/sec accuracy=87.603516 loss=0.487097 lr=0.000100 Epoch[086] Batch [1649]/[3760] Speed: 62.139961 samples/sec accuracy=87.607955 loss=0.485729 lr=0.000100 Epoch[086] Batch [1699]/[3760] Speed: 62.444108 samples/sec accuracy=87.592831 loss=0.485494 lr=0.000100 Epoch[086] Batch [1749]/[3760] Speed: 62.556696 samples/sec accuracy=87.590179 loss=0.484671 lr=0.000100 Epoch[086] Batch [1799]/[3760] Speed: 62.242053 samples/sec accuracy=87.590278 loss=0.485171 lr=0.000100 Epoch[086] Batch [1849]/[3760] Speed: 62.532985 samples/sec accuracy=87.560811 loss=0.486812 lr=0.000100 Epoch[086] Batch [1899]/[3760] Speed: 62.551823 samples/sec accuracy=87.560033 loss=0.486574 lr=0.000100 Epoch[086] Batch [1949]/[3760] Speed: 62.178926 samples/sec accuracy=87.535256 loss=0.487759 lr=0.000100 Epoch[086] Batch [1999]/[3760] Speed: 63.078469 samples/sec accuracy=87.524219 loss=0.488152 lr=0.000100 Epoch[086] Batch [2049]/[3760] Speed: 62.462635 samples/sec accuracy=87.553354 loss=0.487224 lr=0.000100 Epoch[086] Batch [2099]/[3760] Speed: 62.446970 samples/sec accuracy=87.561756 loss=0.486640 lr=0.000100 Epoch[086] Batch [2149]/[3760] Speed: 62.473897 samples/sec accuracy=87.566860 loss=0.486211 lr=0.000100 Epoch[086] Batch [2199]/[3760] Speed: 62.346555 samples/sec accuracy=87.564631 loss=0.486512 lr=0.000100 Epoch[086] Batch [2249]/[3760] Speed: 62.569349 samples/sec accuracy=87.590278 loss=0.485680 lr=0.000100 Epoch[086] Batch [2299]/[3760] Speed: 61.677368 samples/sec accuracy=87.598505 loss=0.485427 lr=0.000100 Epoch[086] Batch [2349]/[3760] Speed: 62.733748 samples/sec accuracy=87.604388 loss=0.485776 lr=0.000100 Epoch[086] Batch [2399]/[3760] Speed: 62.138684 samples/sec accuracy=87.602865 loss=0.486404 lr=0.000100 Epoch[086] Batch [2449]/[3760] Speed: 62.328046 samples/sec accuracy=87.607143 loss=0.486235 lr=0.000100 Epoch[086] Batch [2499]/[3760] Speed: 62.413125 samples/sec accuracy=87.595000 loss=0.486568 lr=0.000100 Epoch[086] Batch [2549]/[3760] Speed: 62.198092 samples/sec accuracy=87.583946 loss=0.487196 lr=0.000100 Epoch[086] Batch [2599]/[3760] Speed: 62.482361 samples/sec accuracy=87.575120 loss=0.487026 lr=0.000100 Epoch[086] Batch [2649]/[3760] Speed: 62.274219 samples/sec accuracy=87.583726 loss=0.487065 lr=0.000100 Epoch[086] Batch [2699]/[3760] Speed: 62.044670 samples/sec accuracy=87.583912 loss=0.486951 lr=0.000100 Epoch[086] Batch [2749]/[3760] Speed: 62.007555 samples/sec accuracy=87.586364 loss=0.486072 lr=0.000100 Epoch[086] Batch [2799]/[3760] Speed: 62.033987 samples/sec accuracy=87.604911 loss=0.485916 lr=0.000100 Epoch[086] Batch [2849]/[3760] Speed: 62.761059 samples/sec accuracy=87.600329 loss=0.485874 lr=0.000100 Epoch[086] Batch [2899]/[3760] Speed: 63.236936 samples/sec accuracy=87.576509 loss=0.486462 lr=0.000100 Epoch[086] Batch [2949]/[3760] Speed: 62.102839 samples/sec accuracy=87.561970 loss=0.486656 lr=0.000100 Epoch[086] Batch [2999]/[3760] Speed: 62.723570 samples/sec accuracy=87.564062 loss=0.486913 lr=0.000100 Epoch[086] Batch [3049]/[3760] Speed: 62.746013 samples/sec accuracy=87.570184 loss=0.486789 lr=0.000100 Epoch[086] Batch [3099]/[3760] Speed: 62.011247 samples/sec accuracy=87.574597 loss=0.486683 lr=0.000100 Epoch[086] Batch [3149]/[3760] Speed: 62.766637 samples/sec accuracy=87.572917 loss=0.486876 lr=0.000100 Epoch[086] Batch [3199]/[3760] Speed: 61.795263 samples/sec accuracy=87.574219 loss=0.487028 lr=0.000100 Epoch[086] Batch [3249]/[3760] Speed: 62.538346 samples/sec accuracy=87.553846 loss=0.487706 lr=0.000100 Epoch[086] Batch [3299]/[3760] Speed: 62.225830 samples/sec accuracy=87.572443 loss=0.486682 lr=0.000100 Epoch[086] Batch [3349]/[3760] Speed: 62.761356 samples/sec accuracy=87.574627 loss=0.486629 lr=0.000100 Epoch[086] Batch [3399]/[3760] Speed: 62.339054 samples/sec accuracy=87.566636 loss=0.486859 lr=0.000100 Epoch[086] Batch [3449]/[3760] Speed: 61.854895 samples/sec accuracy=87.568841 loss=0.486943 lr=0.000100 Epoch[086] Batch [3499]/[3760] Speed: 62.208204 samples/sec accuracy=87.575446 loss=0.486766 lr=0.000100 Epoch[086] Batch [3549]/[3760] Speed: 61.923940 samples/sec accuracy=87.563820 loss=0.487120 lr=0.000100 Epoch[086] Batch [3599]/[3760] Speed: 62.191563 samples/sec accuracy=87.563802 loss=0.487351 lr=0.000100 Epoch[086] Batch [3649]/[3760] Speed: 61.850081 samples/sec accuracy=87.558647 loss=0.487495 lr=0.000100 Epoch[086] Batch [3699]/[3760] Speed: 62.418340 samples/sec accuracy=87.557432 loss=0.487587 lr=0.000100 Epoch[086] Batch [3749]/[3760] Speed: 67.070861 samples/sec accuracy=87.567917 loss=0.487209 lr=0.000100 Batch [0049]/[0303]: acc-top1=66.375000 acc-top5=86.000000 Batch [0099]/[0303]: acc-top1=67.750000 acc-top5=86.640625 Batch [0149]/[0303]: acc-top1=67.875000 acc-top5=86.677083 Batch [0199]/[0303]: acc-top1=68.054688 acc-top5=86.742188 Batch [0249]/[0303]: acc-top1=67.981250 acc-top5=86.643750 Batch [0299]/[0303]: acc-top1=67.901042 acc-top5=86.614583 [Epoch 086] training: accuracy=87.569398 loss=0.487194 [Epoch 086] speed: 62 samples/sec time cost: 4147.027172 [Epoch 086] validation: acc-top1=67.883663 acc-top5=86.633663 loss=1.776880 Epoch[087] Batch [0049]/[3759] Speed: 42.031875 samples/sec accuracy=87.031250 loss=0.490998 lr=0.000100 Epoch[087] Batch [0099]/[3759] Speed: 60.550685 samples/sec accuracy=87.531250 loss=0.479496 lr=0.000100 Epoch[087] Batch [0149]/[3759] Speed: 62.227162 samples/sec accuracy=87.791667 loss=0.470255 lr=0.000100 Epoch[087] Batch [0199]/[3759] Speed: 61.509566 samples/sec accuracy=87.843750 loss=0.468570 lr=0.000100 Epoch[087] Batch [0249]/[3759] Speed: 62.662261 samples/sec accuracy=87.918750 loss=0.469186 lr=0.000100 Epoch[087] Batch [0299]/[3759] Speed: 61.952431 samples/sec accuracy=87.885417 loss=0.472860 lr=0.000100 Epoch[087] Batch [0349]/[3759] Speed: 62.426694 samples/sec accuracy=87.901786 loss=0.471228 lr=0.000100 Epoch[087] Batch [0399]/[3759] Speed: 62.431648 samples/sec accuracy=87.839844 loss=0.475275 lr=0.000100 Epoch[087] Batch 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accuracy=87.651042 loss=0.483218 lr=0.000100 Epoch[087] Batch [0949]/[3759] Speed: 62.884685 samples/sec accuracy=87.680921 loss=0.482025 lr=0.000100 Epoch[087] Batch [0999]/[3759] Speed: 62.228906 samples/sec accuracy=87.684375 loss=0.481334 lr=0.000100 Epoch[087] Batch [1049]/[3759] Speed: 62.154911 samples/sec accuracy=87.650298 loss=0.482510 lr=0.000100 Epoch[087] Batch [1099]/[3759] Speed: 62.279851 samples/sec accuracy=87.663352 loss=0.482347 lr=0.000100 Epoch[087] Batch [1149]/[3759] Speed: 62.603351 samples/sec accuracy=87.673913 loss=0.481238 lr=0.000100 Epoch[087] Batch [1199]/[3759] Speed: 62.487291 samples/sec accuracy=87.679688 loss=0.481205 lr=0.000100 Epoch[087] Batch [1249]/[3759] Speed: 62.144233 samples/sec accuracy=87.692500 loss=0.481499 lr=0.000100 Epoch[087] Batch [1299]/[3759] Speed: 62.720333 samples/sec accuracy=87.721154 loss=0.480973 lr=0.000100 Epoch[087] Batch [1349]/[3759] Speed: 62.745885 samples/sec accuracy=87.688657 loss=0.482131 lr=0.000100 Epoch[087] 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accuracy=87.698480 loss=0.482954 lr=0.000100 Epoch[087] Batch [1899]/[3759] Speed: 62.314510 samples/sec accuracy=87.689145 loss=0.483051 lr=0.000100 Epoch[087] Batch [1949]/[3759] Speed: 62.585443 samples/sec accuracy=87.685096 loss=0.483394 lr=0.000100 Epoch[087] Batch [1999]/[3759] Speed: 62.344950 samples/sec accuracy=87.700781 loss=0.482521 lr=0.000100 Epoch[087] Batch [2049]/[3759] Speed: 62.051525 samples/sec accuracy=87.711128 loss=0.482211 lr=0.000100 Epoch[087] Batch [2099]/[3759] Speed: 62.448810 samples/sec accuracy=87.730655 loss=0.481395 lr=0.000100 Epoch[087] Batch [2149]/[3759] Speed: 62.577135 samples/sec accuracy=87.718023 loss=0.481607 lr=0.000100 Epoch[087] Batch [2199]/[3759] Speed: 62.077934 samples/sec accuracy=87.720170 loss=0.481681 lr=0.000100 Epoch[087] Batch [2249]/[3759] Speed: 62.712481 samples/sec accuracy=87.737500 loss=0.481543 lr=0.000100 Epoch[087] Batch [2299]/[3759] Speed: 63.023386 samples/sec accuracy=87.748641 loss=0.481362 lr=0.000100 Epoch[087] 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accuracy=87.757254 loss=0.480830 lr=0.000100 Epoch[087] Batch [2849]/[3759] Speed: 62.234504 samples/sec accuracy=87.745066 loss=0.480782 lr=0.000100 Epoch[087] Batch [2899]/[3759] Speed: 62.558378 samples/sec accuracy=87.752155 loss=0.480664 lr=0.000100 Epoch[087] Batch [2949]/[3759] Speed: 62.412129 samples/sec accuracy=87.740466 loss=0.481047 lr=0.000100 Epoch[087] Batch [2999]/[3759] Speed: 62.544212 samples/sec accuracy=87.736458 loss=0.481154 lr=0.000100 Epoch[087] Batch [3049]/[3759] Speed: 62.206862 samples/sec accuracy=87.725922 loss=0.481478 lr=0.000100 Epoch[087] Batch [3099]/[3759] Speed: 62.484526 samples/sec accuracy=87.709173 loss=0.482097 lr=0.000100 Epoch[087] Batch [3149]/[3759] Speed: 62.459433 samples/sec accuracy=87.708829 loss=0.482191 lr=0.000100 Epoch[087] Batch [3199]/[3759] Speed: 62.395949 samples/sec accuracy=87.702637 loss=0.482051 lr=0.000100 Epoch[087] Batch [3249]/[3759] Speed: 62.290813 samples/sec accuracy=87.714423 loss=0.482009 lr=0.000100 Epoch[087] 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accuracy=87.669583 loss=0.483223 lr=0.000100 Batch [0049]/[0303]: acc-top1=66.781250 acc-top5=85.750000 Batch [0099]/[0303]: acc-top1=67.859375 acc-top5=86.734375 Batch [0149]/[0303]: acc-top1=68.104167 acc-top5=86.666667 Batch [0199]/[0303]: acc-top1=68.281250 acc-top5=86.734375 Batch [0249]/[0303]: acc-top1=68.018750 acc-top5=86.681250 Batch [0299]/[0303]: acc-top1=67.963542 acc-top5=86.640625 [Epoch 087] training: accuracy=87.670424 loss=0.483184 [Epoch 087] speed: 62 samples/sec time cost: 4148.428004 [Epoch 087] validation: acc-top1=67.950701 acc-top5=86.649134 loss=1.794399 Epoch[088] Batch [0049]/[3760] Speed: 42.630401 samples/sec accuracy=86.750000 loss=0.512771 lr=0.000100 Epoch[088] Batch [0099]/[3760] Speed: 60.667520 samples/sec accuracy=87.531250 loss=0.479123 lr=0.000100 Epoch[088] Batch [0149]/[3760] Speed: 62.361614 samples/sec accuracy=87.666667 loss=0.480079 lr=0.000100 Epoch[088] Batch [0199]/[3760] Speed: 61.460940 samples/sec accuracy=87.632812 loss=0.483871 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lr=0.000100 Epoch[088] Batch [1199]/[3760] Speed: 62.468074 samples/sec accuracy=87.708333 loss=0.480232 lr=0.000100 Epoch[088] Batch [1249]/[3760] Speed: 62.540999 samples/sec accuracy=87.756250 loss=0.478645 lr=0.000100 Epoch[088] Batch [1299]/[3760] Speed: 62.612911 samples/sec accuracy=87.754808 loss=0.479151 lr=0.000100 Epoch[088] Batch [1349]/[3760] Speed: 62.258052 samples/sec accuracy=87.784722 loss=0.478218 lr=0.000100 Epoch[088] Batch [1399]/[3760] Speed: 62.530626 samples/sec accuracy=87.762277 loss=0.478820 lr=0.000100 Epoch[088] Batch [1449]/[3760] Speed: 62.245185 samples/sec accuracy=87.759698 loss=0.479019 lr=0.000100 Epoch[088] Batch [1499]/[3760] Speed: 62.755760 samples/sec accuracy=87.733333 loss=0.479707 lr=0.000100 Epoch[088] Batch [1549]/[3760] Speed: 62.413349 samples/sec accuracy=87.724798 loss=0.480104 lr=0.000100 Epoch[088] Batch [1599]/[3760] Speed: 62.635616 samples/sec accuracy=87.724609 loss=0.479739 lr=0.000100 Epoch[088] Batch [1649]/[3760] Speed: 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lr=0.000100 Epoch[088] Batch [2149]/[3760] Speed: 61.634141 samples/sec accuracy=87.770349 loss=0.477851 lr=0.000100 Epoch[088] Batch [2199]/[3760] Speed: 62.561628 samples/sec accuracy=87.757102 loss=0.478329 lr=0.000100 Epoch[088] Batch [2249]/[3760] Speed: 62.141265 samples/sec accuracy=87.747917 loss=0.478377 lr=0.000100 Epoch[088] Batch [2299]/[3760] Speed: 61.974214 samples/sec accuracy=87.718071 loss=0.479383 lr=0.000100 Epoch[088] Batch [2349]/[3760] Speed: 62.307348 samples/sec accuracy=87.729388 loss=0.478711 lr=0.000100 Epoch[088] Batch [2399]/[3760] Speed: 62.488988 samples/sec accuracy=87.758464 loss=0.478030 lr=0.000100 Epoch[088] Batch [2449]/[3760] Speed: 62.637338 samples/sec accuracy=87.761480 loss=0.477732 lr=0.000100 Epoch[088] Batch [2499]/[3760] Speed: 61.824858 samples/sec accuracy=87.766250 loss=0.477907 lr=0.000100 Epoch[088] Batch [2549]/[3760] Speed: 62.177898 samples/sec accuracy=87.749387 loss=0.478344 lr=0.000100 Epoch[088] Batch [2599]/[3760] Speed: 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lr=0.000100 Epoch[088] Batch [3099]/[3760] Speed: 62.441925 samples/sec accuracy=87.788810 loss=0.477063 lr=0.000100 Epoch[088] Batch [3149]/[3760] Speed: 62.884517 samples/sec accuracy=87.799603 loss=0.476558 lr=0.000100 Epoch[088] Batch [3199]/[3760] Speed: 62.278521 samples/sec accuracy=87.785156 loss=0.477058 lr=0.000100 Epoch[088] Batch [3249]/[3760] Speed: 62.374956 samples/sec accuracy=87.775962 loss=0.477387 lr=0.000100 Epoch[088] Batch [3299]/[3760] Speed: 62.615488 samples/sec accuracy=87.760890 loss=0.478211 lr=0.000100 Epoch[088] Batch [3349]/[3760] Speed: 62.170414 samples/sec accuracy=87.752332 loss=0.478535 lr=0.000100 Epoch[088] Batch [3399]/[3760] Speed: 62.878363 samples/sec accuracy=87.753217 loss=0.478090 lr=0.000100 Epoch[088] Batch [3449]/[3760] Speed: 62.002660 samples/sec accuracy=87.765851 loss=0.477904 lr=0.000100 Epoch[088] Batch [3499]/[3760] Speed: 62.023623 samples/sec accuracy=87.779464 loss=0.477301 lr=0.000100 Epoch[088] Batch [3549]/[3760] Speed: 62.003474 samples/sec accuracy=87.785651 loss=0.477153 lr=0.000100 Epoch[088] Batch [3599]/[3760] Speed: 62.802734 samples/sec accuracy=87.782986 loss=0.477076 lr=0.000100 Epoch[088] Batch [3649]/[3760] Speed: 61.644050 samples/sec accuracy=87.784675 loss=0.476848 lr=0.000100 Epoch[088] Batch [3699]/[3760] Speed: 62.794889 samples/sec accuracy=87.781250 loss=0.476696 lr=0.000100 Epoch[088] Batch [3749]/[3760] Speed: 67.333014 samples/sec accuracy=87.774583 loss=0.477012 lr=0.000100 Batch [0049]/[0303]: acc-top1=66.906250 acc-top5=85.781250 Batch [0099]/[0303]: acc-top1=68.109375 acc-top5=86.562500 Batch [0149]/[0303]: acc-top1=68.114583 acc-top5=86.593750 Batch [0199]/[0303]: acc-top1=68.328125 acc-top5=86.671875 Batch [0249]/[0303]: acc-top1=68.087500 acc-top5=86.618750 Batch [0299]/[0303]: acc-top1=68.020833 acc-top5=86.609375 [Epoch 088] training: accuracy=87.776762 loss=0.476862 [Epoch 088] speed: 62 samples/sec time cost: 4147.116651 [Epoch 088] validation: acc-top1=67.997112 acc-top5=86.633663 loss=1.793437 Epoch[089] Batch [0049]/[3760] Speed: 42.000009 samples/sec accuracy=87.093750 loss=0.502847 lr=0.000100 Epoch[089] Batch [0099]/[3760] Speed: 60.535580 samples/sec accuracy=87.609375 loss=0.490424 lr=0.000100 Epoch[089] Batch [0149]/[3760] Speed: 62.159572 samples/sec accuracy=87.520833 loss=0.488627 lr=0.000100 Epoch[089] Batch [0199]/[3760] Speed: 60.921428 samples/sec accuracy=87.523438 loss=0.486562 lr=0.000100 Epoch[089] Batch [0249]/[3760] Speed: 62.344109 samples/sec accuracy=87.512500 loss=0.482430 lr=0.000100 Epoch[089] Batch [0299]/[3760] Speed: 61.741075 samples/sec accuracy=87.572917 loss=0.482925 lr=0.000100 Epoch[089] Batch [0349]/[3760] Speed: 62.918353 samples/sec accuracy=87.754464 loss=0.478490 lr=0.000100 Epoch[089] Batch [0399]/[3760] Speed: 61.833029 samples/sec accuracy=87.777344 loss=0.475410 lr=0.000100 Epoch[089] Batch [0449]/[3760] Speed: 62.707551 samples/sec accuracy=87.715278 loss=0.478833 lr=0.000100 Epoch[089] Batch 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accuracy=87.769737 loss=0.476305 lr=0.000100 Epoch[089] Batch [2899]/[3760] Speed: 61.800887 samples/sec accuracy=87.765625 loss=0.476416 lr=0.000100 Epoch[089] Batch [2949]/[3760] Speed: 62.711124 samples/sec accuracy=87.751589 loss=0.476815 lr=0.000100 Epoch[089] Batch [2999]/[3760] Speed: 62.234235 samples/sec accuracy=87.744792 loss=0.476990 lr=0.000100 Epoch[089] Batch [3049]/[3760] Speed: 61.977681 samples/sec accuracy=87.734631 loss=0.477180 lr=0.000100 Epoch[089] Batch [3099]/[3760] Speed: 62.182022 samples/sec accuracy=87.739919 loss=0.477166 lr=0.000100 Epoch[089] Batch [3149]/[3760] Speed: 62.447605 samples/sec accuracy=87.732639 loss=0.477592 lr=0.000100 Epoch[089] Batch [3199]/[3760] Speed: 61.802652 samples/sec accuracy=87.753906 loss=0.476943 lr=0.000100 Epoch[089] Batch [3249]/[3760] Speed: 62.825993 samples/sec accuracy=87.770192 loss=0.476568 lr=0.000100 Epoch[089] Batch [3299]/[3760] Speed: 62.380929 samples/sec accuracy=87.763731 loss=0.477123 lr=0.000100 Epoch[089] 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[0099]/[0303]: acc-top1=68.015625 acc-top5=86.500000 Batch [0149]/[0303]: acc-top1=68.041667 acc-top5=86.489583 Batch [0199]/[0303]: acc-top1=68.195312 acc-top5=86.632812 Batch [0249]/[0303]: acc-top1=67.950000 acc-top5=86.512500 Batch [0299]/[0303]: acc-top1=67.854167 acc-top5=86.494792 [Epoch 089] training: accuracy=87.813747 loss=0.476278 [Epoch 089] speed: 61 samples/sec time cost: 4151.157243 [Epoch 089] validation: acc-top1=67.832096 acc-top5=86.499587 loss=1.812377 Epoch[090] Batch [0049]/[3759] Speed: 42.154042 samples/sec accuracy=87.750000 loss=0.491917 lr=0.000100 Epoch[090] Batch [0099]/[3759] Speed: 60.085493 samples/sec accuracy=87.890625 loss=0.488508 lr=0.000100 Epoch[090] Batch [0149]/[3759] Speed: 62.204771 samples/sec accuracy=87.770833 loss=0.484995 lr=0.000100 Epoch[090] Batch [0199]/[3759] Speed: 60.917393 samples/sec accuracy=87.703125 loss=0.486977 lr=0.000100 Epoch[090] Batch [0249]/[3759] Speed: 62.614699 samples/sec accuracy=87.600000 loss=0.489609 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lr=0.000100 Epoch[090] Batch [1249]/[3759] Speed: 62.692751 samples/sec accuracy=87.891250 loss=0.476155 lr=0.000100 Epoch[090] Batch [1299]/[3759] Speed: 61.940764 samples/sec accuracy=87.905048 loss=0.475971 lr=0.000100 Epoch[090] Batch [1349]/[3759] Speed: 62.314451 samples/sec accuracy=87.913194 loss=0.475477 lr=0.000100 Epoch[090] Batch [1399]/[3759] Speed: 62.668725 samples/sec accuracy=87.919643 loss=0.475637 lr=0.000100 Epoch[090] Batch [1449]/[3759] Speed: 61.711456 samples/sec accuracy=87.939655 loss=0.474663 lr=0.000100 Epoch[090] Batch [1499]/[3759] Speed: 62.733526 samples/sec accuracy=87.938542 loss=0.474516 lr=0.000100 Epoch[090] Batch [1549]/[3759] Speed: 62.201952 samples/sec accuracy=87.947581 loss=0.474156 lr=0.000100 Epoch[090] Batch [1599]/[3759] Speed: 62.337862 samples/sec accuracy=87.934570 loss=0.474385 lr=0.000100 Epoch[090] Batch [1649]/[3759] Speed: 62.076335 samples/sec accuracy=87.943182 loss=0.473836 lr=0.000100 Epoch[090] Batch [1699]/[3759] Speed: 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lr=0.000100 Epoch[090] Batch [2199]/[3759] Speed: 62.631850 samples/sec accuracy=87.867188 loss=0.475786 lr=0.000100 Epoch[090] Batch [2249]/[3759] Speed: 62.425673 samples/sec accuracy=87.875000 loss=0.475582 lr=0.000100 Epoch[090] Batch [2299]/[3759] Speed: 62.699226 samples/sec accuracy=87.877038 loss=0.475802 lr=0.000100 Epoch[090] Batch [2349]/[3759] Speed: 62.161159 samples/sec accuracy=87.868351 loss=0.475849 lr=0.000100 Epoch[090] Batch [2399]/[3759] Speed: 62.335041 samples/sec accuracy=87.884115 loss=0.475015 lr=0.000100 Epoch[090] Batch [2449]/[3759] Speed: 62.507079 samples/sec accuracy=87.901786 loss=0.474547 lr=0.000100 Epoch[090] Batch [2499]/[3759] Speed: 62.487701 samples/sec accuracy=87.910000 loss=0.474249 lr=0.000100 Epoch[090] Batch [2549]/[3759] Speed: 62.657404 samples/sec accuracy=87.888480 loss=0.474444 lr=0.000100 Epoch[090] Batch [2599]/[3759] Speed: 62.016976 samples/sec accuracy=87.899639 loss=0.474129 lr=0.000100 Epoch[090] Batch [2649]/[3759] Speed: 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lr=0.000100 Epoch[090] Batch [3149]/[3759] Speed: 62.400650 samples/sec accuracy=87.902282 loss=0.473571 lr=0.000100 Epoch[090] Batch [3199]/[3759] Speed: 62.565253 samples/sec accuracy=87.904297 loss=0.473819 lr=0.000100 Epoch[090] Batch [3249]/[3759] Speed: 62.742362 samples/sec accuracy=87.892308 loss=0.473940 lr=0.000100 Epoch[090] Batch [3299]/[3759] Speed: 61.955948 samples/sec accuracy=87.894413 loss=0.473856 lr=0.000100 Epoch[090] Batch [3349]/[3759] Speed: 62.438638 samples/sec accuracy=87.896455 loss=0.473622 lr=0.000100 Epoch[090] Batch [3399]/[3759] Speed: 62.476095 samples/sec accuracy=87.887408 loss=0.473728 lr=0.000100 Epoch[090] Batch [3449]/[3759] Speed: 62.102154 samples/sec accuracy=87.880888 loss=0.473785 lr=0.000100 Epoch[090] Batch [3499]/[3759] Speed: 61.942236 samples/sec accuracy=87.893750 loss=0.473250 lr=0.000100 Epoch[090] Batch [3549]/[3759] Speed: 62.479163 samples/sec accuracy=87.898327 loss=0.473262 lr=0.000100 Epoch[090] Batch [3599]/[3759] Speed: 62.663542 samples/sec accuracy=87.902344 loss=0.473252 lr=0.000100 Epoch[090] Batch [3649]/[3759] Speed: 62.126616 samples/sec accuracy=87.898545 loss=0.473444 lr=0.000100 Epoch[090] Batch [3699]/[3759] Speed: 62.896223 samples/sec accuracy=87.907095 loss=0.472893 lr=0.000100 Epoch[090] Batch [3749]/[3759] Speed: 67.555495 samples/sec accuracy=87.902083 loss=0.473218 lr=0.000100 Batch [0049]/[0303]: acc-top1=66.562500 acc-top5=85.468750 Batch [0099]/[0303]: acc-top1=67.843750 acc-top5=86.406250 Batch [0149]/[0303]: acc-top1=67.875000 acc-top5=86.541667 Batch [0199]/[0303]: acc-top1=68.031250 acc-top5=86.609375 Batch [0249]/[0303]: acc-top1=67.781250 acc-top5=86.518750 Batch [0299]/[0303]: acc-top1=67.645833 acc-top5=86.510417 [Epoch 090] training: accuracy=87.906524 loss=0.473088 [Epoch 090] speed: 61 samples/sec time cost: 4149.658692 [Epoch 090] validation: acc-top1=67.625825 acc-top5=86.525371 loss=1.807328 Epoch[091] Batch [0049]/[3760] Speed: 42.042363 samples/sec accuracy=88.031250 loss=0.449960 lr=0.000100 Epoch[091] Batch [0099]/[3760] Speed: 61.004614 samples/sec accuracy=87.593750 loss=0.483212 lr=0.000100 Epoch[091] Batch [0149]/[3760] Speed: 61.806849 samples/sec accuracy=88.145833 loss=0.463193 lr=0.000100 Epoch[091] Batch [0199]/[3760] Speed: 60.523829 samples/sec accuracy=88.000000 loss=0.473783 lr=0.000100 Epoch[091] Batch [0249]/[3760] Speed: 62.683966 samples/sec accuracy=88.168750 loss=0.466325 lr=0.000100 Epoch[091] Batch [0299]/[3760] Speed: 61.566657 samples/sec accuracy=88.218750 loss=0.463897 lr=0.000100 Epoch[091] Batch [0349]/[3760] Speed: 62.442540 samples/sec accuracy=88.147321 loss=0.465231 lr=0.000100 Epoch[091] Batch [0399]/[3760] Speed: 62.534907 samples/sec accuracy=88.109375 loss=0.466481 lr=0.000100 Epoch[091] Batch [0449]/[3760] Speed: 61.991550 samples/sec accuracy=88.149306 loss=0.465007 lr=0.000100 Epoch[091] Batch [0499]/[3760] Speed: 62.810199 samples/sec accuracy=88.043750 loss=0.469059 lr=0.000100 Epoch[091] 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accuracy=88.002694 loss=0.471011 lr=0.000100 Epoch[091] Batch [2949]/[3760] Speed: 62.850993 samples/sec accuracy=88.003178 loss=0.470886 lr=0.000100 Epoch[091] Batch [2999]/[3760] Speed: 62.762736 samples/sec accuracy=87.997396 loss=0.471166 lr=0.000100 Epoch[091] Batch [3049]/[3760] Speed: 61.940429 samples/sec accuracy=88.007172 loss=0.470766 lr=0.000100 Epoch[091] Batch [3099]/[3760] Speed: 62.847594 samples/sec accuracy=88.008065 loss=0.470871 lr=0.000100 Epoch[091] Batch [3149]/[3760] Speed: 62.617526 samples/sec accuracy=88.013889 loss=0.470759 lr=0.000100 Epoch[091] Batch [3199]/[3760] Speed: 62.304958 samples/sec accuracy=88.006348 loss=0.470860 lr=0.000100 Epoch[091] Batch [3249]/[3760] Speed: 62.943548 samples/sec accuracy=87.993269 loss=0.471177 lr=0.000100 Epoch[091] Batch [3299]/[3760] Speed: 61.792659 samples/sec accuracy=87.986269 loss=0.471394 lr=0.000100 Epoch[091] Batch [3349]/[3760] Speed: 62.578406 samples/sec accuracy=87.991604 loss=0.471087 lr=0.000100 Epoch[091] 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acc-top5=86.322917 Batch [0199]/[0303]: acc-top1=67.968750 acc-top5=86.468750 Batch [0249]/[0303]: acc-top1=67.712500 acc-top5=86.381250 Batch [0299]/[0303]: acc-top1=67.718750 acc-top5=86.427083 [Epoch 091] training: accuracy=87.981632 loss=0.471757 [Epoch 091] speed: 61 samples/sec time cost: 4151.502601 [Epoch 091] validation: acc-top1=67.698020 acc-top5=86.432550 loss=1.814404 Epoch[092] Batch [0049]/[3760] Speed: 42.767000 samples/sec accuracy=87.781250 loss=0.475006 lr=0.000100 Epoch[092] Batch [0099]/[3760] Speed: 61.338088 samples/sec accuracy=87.921875 loss=0.470421 lr=0.000100 Epoch[092] Batch [0149]/[3760] Speed: 62.503002 samples/sec accuracy=88.104167 loss=0.470403 lr=0.000100 Epoch[092] Batch [0199]/[3760] Speed: 60.850014 samples/sec accuracy=87.968750 loss=0.474219 lr=0.000100 Epoch[092] Batch [0249]/[3760] Speed: 62.887028 samples/sec accuracy=87.987500 loss=0.471397 lr=0.000100 Epoch[092] Batch [0299]/[3760] Speed: 61.975359 samples/sec accuracy=87.963542 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accuracy=88.056250 loss=0.463678 lr=0.000100 Epoch[092] Batch [1299]/[3760] Speed: 62.353028 samples/sec accuracy=88.057692 loss=0.464485 lr=0.000100 Epoch[092] Batch [1349]/[3760] Speed: 62.108858 samples/sec accuracy=88.085648 loss=0.463280 lr=0.000100 Epoch[092] Batch [1399]/[3760] Speed: 62.399103 samples/sec accuracy=88.093750 loss=0.463069 lr=0.000100 Epoch[092] Batch [1449]/[3760] Speed: 61.962419 samples/sec accuracy=88.150862 loss=0.461883 lr=0.000100 Epoch[092] Batch [1499]/[3760] Speed: 62.367715 samples/sec accuracy=88.147917 loss=0.461860 lr=0.000100 Epoch[092] Batch [1549]/[3760] Speed: 61.806050 samples/sec accuracy=88.129032 loss=0.462433 lr=0.000100 Epoch[092] Batch [1599]/[3760] Speed: 62.670750 samples/sec accuracy=88.138672 loss=0.461887 lr=0.000100 Epoch[092] Batch [1649]/[3760] Speed: 62.354221 samples/sec accuracy=88.132576 loss=0.462263 lr=0.000100 Epoch[092] Batch [1699]/[3760] Speed: 62.444494 samples/sec accuracy=88.112132 loss=0.463390 lr=0.000100 Epoch[092] 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accuracy=88.102273 loss=0.463455 lr=0.000100 Epoch[092] Batch [2249]/[3760] Speed: 62.035206 samples/sec accuracy=88.111806 loss=0.463039 lr=0.000100 Epoch[092] Batch [2299]/[3760] Speed: 62.835335 samples/sec accuracy=88.115489 loss=0.462920 lr=0.000100 Epoch[092] Batch [2349]/[3760] Speed: 62.208173 samples/sec accuracy=88.134309 loss=0.463017 lr=0.000100 Epoch[092] Batch [2399]/[3760] Speed: 61.830910 samples/sec accuracy=88.131510 loss=0.462919 lr=0.000100 Epoch[092] Batch [2449]/[3760] Speed: 62.646782 samples/sec accuracy=88.125000 loss=0.463172 lr=0.000100 Epoch[092] Batch [2499]/[3760] Speed: 62.701054 samples/sec accuracy=88.130000 loss=0.463212 lr=0.000100 Epoch[092] Batch [2549]/[3760] Speed: 61.620723 samples/sec accuracy=88.135417 loss=0.463354 lr=0.000100 Epoch[092] Batch [2599]/[3760] Speed: 62.396850 samples/sec accuracy=88.115986 loss=0.463721 lr=0.000100 Epoch[092] Batch [2649]/[3760] Speed: 62.612403 samples/sec accuracy=88.105542 loss=0.464257 lr=0.000100 Epoch[092] 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accuracy=88.119048 loss=0.465464 lr=0.000100 Epoch[092] Batch [3199]/[3760] Speed: 62.388218 samples/sec accuracy=88.115234 loss=0.465590 lr=0.000100 Epoch[092] Batch [3249]/[3760] Speed: 62.129378 samples/sec accuracy=88.096635 loss=0.465780 lr=0.000100 Epoch[092] Batch [3299]/[3760] Speed: 62.219912 samples/sec accuracy=88.102273 loss=0.465818 lr=0.000100 Epoch[092] Batch [3349]/[3760] Speed: 62.948306 samples/sec accuracy=88.100746 loss=0.465855 lr=0.000100 Epoch[092] Batch [3399]/[3760] Speed: 62.013521 samples/sec accuracy=88.096507 loss=0.465616 lr=0.000100 Epoch[092] Batch [3449]/[3760] Speed: 62.495636 samples/sec accuracy=88.093297 loss=0.465781 lr=0.000100 Epoch[092] Batch [3499]/[3760] Speed: 62.453717 samples/sec accuracy=88.073661 loss=0.466313 lr=0.000100 Epoch[092] Batch [3549]/[3760] Speed: 62.193666 samples/sec accuracy=88.078345 loss=0.466232 lr=0.000100 Epoch[092] Batch [3599]/[3760] Speed: 62.406337 samples/sec accuracy=88.086806 loss=0.466043 lr=0.000100 Epoch[092] Batch [3649]/[3760] Speed: 62.521615 samples/sec accuracy=88.090753 loss=0.465963 lr=0.000100 Epoch[092] Batch [3699]/[3760] Speed: 62.323543 samples/sec accuracy=88.087416 loss=0.465979 lr=0.000100 Epoch[092] Batch [3749]/[3760] Speed: 66.761846 samples/sec accuracy=88.083750 loss=0.466398 lr=0.000100 Batch [0049]/[0303]: acc-top1=66.531250 acc-top5=85.375000 Batch [0099]/[0303]: acc-top1=68.171875 acc-top5=86.312500 Batch [0149]/[0303]: acc-top1=68.218750 acc-top5=86.354167 Batch [0199]/[0303]: acc-top1=68.296875 acc-top5=86.453125 Batch [0249]/[0303]: acc-top1=68.000000 acc-top5=86.468750 Batch [0299]/[0303]: acc-top1=67.848958 acc-top5=86.406250 [Epoch 092] training: accuracy=88.081782 loss=0.466583 [Epoch 092] speed: 61 samples/sec time cost: 4150.783664 [Epoch 092] validation: acc-top1=67.816625 acc-top5=86.411922 loss=1.815708 Epoch[093] Batch [0049]/[3759] Speed: 42.654647 samples/sec accuracy=88.375000 loss=0.463890 lr=0.000100 Epoch[093] Batch [0099]/[3759] Speed: 60.847293 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lr=0.000100 Epoch[093] Batch [3449]/[3759] Speed: 62.097162 samples/sec accuracy=88.046196 loss=0.465144 lr=0.000100 Epoch[093] Batch [3499]/[3759] Speed: 62.247042 samples/sec accuracy=88.054464 loss=0.464882 lr=0.000100 Epoch[093] Batch [3549]/[3759] Speed: 62.570914 samples/sec accuracy=88.061620 loss=0.464700 lr=0.000100 Epoch[093] Batch [3599]/[3759] Speed: 62.099052 samples/sec accuracy=88.049045 loss=0.465222 lr=0.000100 Epoch[093] Batch [3649]/[3759] Speed: 62.443501 samples/sec accuracy=88.053938 loss=0.465008 lr=0.000100 Epoch[093] Batch [3699]/[3759] Speed: 62.454753 samples/sec accuracy=88.051520 loss=0.465253 lr=0.000100 Epoch[093] Batch [3749]/[3759] Speed: 67.604333 samples/sec accuracy=88.045833 loss=0.465842 lr=0.000100 Batch [0049]/[0303]: acc-top1=66.687500 acc-top5=85.500000 Batch [0099]/[0303]: acc-top1=67.875000 acc-top5=86.296875 Batch [0149]/[0303]: acc-top1=67.958333 acc-top5=86.364583 Batch [0199]/[0303]: acc-top1=68.101562 acc-top5=86.468750 Batch [0249]/[0303]: acc-top1=67.887500 acc-top5=86.400000 Batch [0299]/[0303]: acc-top1=67.859375 acc-top5=86.364583 [Epoch 093] training: accuracy=88.042032 loss=0.466008 [Epoch 093] speed: 61 samples/sec time cost: 4148.939021 [Epoch 093] validation: acc-top1=67.837252 acc-top5=86.380982 loss=1.816694 Epoch[094] Batch [0049]/[3760] Speed: 41.991239 samples/sec accuracy=87.968750 loss=0.467497 lr=0.000100 Epoch[094] Batch [0099]/[3760] Speed: 60.813388 samples/sec accuracy=87.718750 loss=0.485847 lr=0.000100 Epoch[094] Batch [0149]/[3760] Speed: 61.867511 samples/sec accuracy=87.812500 loss=0.479891 lr=0.000100 Epoch[094] Batch [0199]/[3760] Speed: 60.725319 samples/sec accuracy=88.078125 loss=0.466657 lr=0.000100 Epoch[094] Batch [0249]/[3760] Speed: 62.721882 samples/sec accuracy=88.218750 loss=0.460718 lr=0.000100 Epoch[094] Batch [0299]/[3760] Speed: 61.674366 samples/sec accuracy=88.421875 loss=0.451291 lr=0.000100 Epoch[094] Batch [0349]/[3760] Speed: 62.037584 samples/sec accuracy=88.424107 loss=0.453066 lr=0.000100 Epoch[094] Batch [0399]/[3760] Speed: 62.048516 samples/sec accuracy=88.476562 loss=0.450864 lr=0.000100 Epoch[094] Batch [0449]/[3760] Speed: 62.203508 samples/sec accuracy=88.548611 loss=0.450381 lr=0.000100 Epoch[094] Batch [0499]/[3760] Speed: 61.844735 samples/sec accuracy=88.550000 loss=0.449575 lr=0.000100 Epoch[094] Batch [0549]/[3760] Speed: 62.222838 samples/sec accuracy=88.559659 loss=0.449342 lr=0.000100 Epoch[094] Batch [0599]/[3760] Speed: 62.606175 samples/sec accuracy=88.536458 loss=0.451192 lr=0.000100 Epoch[094] Batch [0649]/[3760] Speed: 61.498181 samples/sec accuracy=88.504808 loss=0.451284 lr=0.000100 Epoch[094] Batch [0699]/[3760] Speed: 63.223048 samples/sec accuracy=88.462054 loss=0.452350 lr=0.000100 Epoch[094] Batch [0749]/[3760] Speed: 62.419848 samples/sec accuracy=88.387500 loss=0.453633 lr=0.000100 Epoch[094] Batch [0799]/[3760] Speed: 62.046395 samples/sec accuracy=88.382812 loss=0.454204 lr=0.000100 Epoch[094] 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accuracy=88.353365 loss=0.453846 lr=0.000100 Epoch[094] Batch [1349]/[3760] Speed: 62.245642 samples/sec accuracy=88.359954 loss=0.454729 lr=0.000100 Epoch[094] Batch [1399]/[3760] Speed: 62.037030 samples/sec accuracy=88.312500 loss=0.456568 lr=0.000100 Epoch[094] Batch [1449]/[3760] Speed: 62.705342 samples/sec accuracy=88.335129 loss=0.455111 lr=0.000100 Epoch[094] Batch [1499]/[3760] Speed: 61.892917 samples/sec accuracy=88.330208 loss=0.455445 lr=0.000100 Epoch[094] Batch [1549]/[3760] Speed: 62.776355 samples/sec accuracy=88.319556 loss=0.455593 lr=0.000100 Epoch[094] Batch [1599]/[3760] Speed: 61.926400 samples/sec accuracy=88.294922 loss=0.456374 lr=0.000100 Epoch[094] Batch [1649]/[3760] Speed: 62.740238 samples/sec accuracy=88.297348 loss=0.456306 lr=0.000100 Epoch[094] Batch [1699]/[3760] Speed: 62.182710 samples/sec accuracy=88.296875 loss=0.456812 lr=0.000100 Epoch[094] Batch [1749]/[3760] Speed: 62.230707 samples/sec accuracy=88.302679 loss=0.456988 lr=0.000100 Epoch[094] 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accuracy=88.280556 loss=0.459102 lr=0.000100 Epoch[094] Batch [2299]/[3760] Speed: 61.883902 samples/sec accuracy=88.278533 loss=0.459175 lr=0.000100 Epoch[094] Batch [2349]/[3760] Speed: 62.699370 samples/sec accuracy=88.292553 loss=0.458578 lr=0.000100 Epoch[094] Batch [2399]/[3760] Speed: 62.425061 samples/sec accuracy=88.289714 loss=0.458659 lr=0.000100 Epoch[094] Batch [2449]/[3760] Speed: 62.323702 samples/sec accuracy=88.288265 loss=0.458691 lr=0.000100 Epoch[094] Batch [2499]/[3760] Speed: 62.227930 samples/sec accuracy=88.274375 loss=0.459011 lr=0.000100 Epoch[094] Batch [2549]/[3760] Speed: 62.662525 samples/sec accuracy=88.272672 loss=0.459278 lr=0.000100 Epoch[094] Batch [2599]/[3760] Speed: 61.485423 samples/sec accuracy=88.271034 loss=0.459302 lr=0.000100 Epoch[094] Batch [2649]/[3760] Speed: 62.776889 samples/sec accuracy=88.275354 loss=0.459404 lr=0.000100 Epoch[094] Batch [2699]/[3760] Speed: 62.320845 samples/sec accuracy=88.269676 loss=0.459147 lr=0.000100 Epoch[094] 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accuracy=88.195801 loss=0.460605 lr=0.000100 Epoch[094] Batch [3249]/[3760] Speed: 62.072882 samples/sec accuracy=88.195673 loss=0.460701 lr=0.000100 Epoch[094] Batch [3299]/[3760] Speed: 62.047040 samples/sec accuracy=88.194602 loss=0.460981 lr=0.000100 Epoch[094] Batch [3349]/[3760] Speed: 62.118336 samples/sec accuracy=88.191231 loss=0.461170 lr=0.000100 Epoch[094] Batch [3399]/[3760] Speed: 62.283395 samples/sec accuracy=88.195772 loss=0.460651 lr=0.000100 Epoch[094] Batch [3449]/[3760] Speed: 62.200315 samples/sec accuracy=88.193388 loss=0.460899 lr=0.000100 Epoch[094] Batch [3499]/[3760] Speed: 62.592138 samples/sec accuracy=88.194643 loss=0.461033 lr=0.000100 Epoch[094] Batch [3549]/[3760] Speed: 62.198068 samples/sec accuracy=88.188380 loss=0.461085 lr=0.000100 Epoch[094] Batch [3599]/[3760] Speed: 62.216942 samples/sec accuracy=88.190104 loss=0.461122 lr=0.000100 Epoch[094] Batch [3649]/[3760] Speed: 62.548038 samples/sec accuracy=88.186216 loss=0.461209 lr=0.000100 Epoch[094] Batch [3699]/[3760] Speed: 62.195833 samples/sec accuracy=88.186655 loss=0.461031 lr=0.000100 Epoch[094] Batch [3749]/[3760] Speed: 67.262371 samples/sec accuracy=88.190000 loss=0.460779 lr=0.000100 Batch [0049]/[0303]: acc-top1=66.312500 acc-top5=85.468750 Batch [0099]/[0303]: acc-top1=67.750000 acc-top5=86.453125 Batch [0149]/[0303]: acc-top1=67.812500 acc-top5=86.416667 Batch [0199]/[0303]: acc-top1=68.054688 acc-top5=86.593750 Batch [0249]/[0303]: acc-top1=67.825000 acc-top5=86.487500 Batch [0299]/[0303]: acc-top1=67.770833 acc-top5=86.489583 [Epoch 094] training: accuracy=88.185672 loss=0.460864 [Epoch 094] speed: 61 samples/sec time cost: 4155.930718 [Epoch 094] validation: acc-top1=67.765058 acc-top5=86.504744 loss=1.802758 Epoch[095] Batch [0049]/[3760] Speed: 41.809434 samples/sec accuracy=88.000000 loss=0.448437 lr=0.000100 Epoch[095] Batch [0099]/[3760] Speed: 61.612433 samples/sec accuracy=88.015625 loss=0.466647 lr=0.000100 Epoch[095] Batch [0149]/[3760] Speed: 62.278950 samples/sec accuracy=88.010417 loss=0.464964 lr=0.000100 Epoch[095] Batch [0199]/[3760] Speed: 61.023472 samples/sec accuracy=88.335938 loss=0.458087 lr=0.000100 Epoch[095] Batch [0249]/[3760] Speed: 62.115464 samples/sec accuracy=88.262500 loss=0.462397 lr=0.000100 Epoch[095] Batch [0299]/[3760] Speed: 61.372671 samples/sec accuracy=88.161458 loss=0.464025 lr=0.000100 Epoch[095] Batch [0349]/[3760] Speed: 62.484132 samples/sec accuracy=88.151786 loss=0.461654 lr=0.000100 Epoch[095] Batch [0399]/[3760] Speed: 61.434240 samples/sec accuracy=88.152344 loss=0.458832 lr=0.000100 Epoch[095] Batch [0449]/[3760] Speed: 62.379305 samples/sec accuracy=88.173611 loss=0.460208 lr=0.000100 Epoch[095] Batch [0499]/[3760] Speed: 62.472532 samples/sec accuracy=88.368750 loss=0.455118 lr=0.000100 Epoch[095] Batch [0549]/[3760] Speed: 62.264980 samples/sec accuracy=88.355114 loss=0.455737 lr=0.000100 Epoch[095] Batch [0599]/[3760] Speed: 61.878803 samples/sec accuracy=88.302083 loss=0.456825 lr=0.000100 Epoch[095] Batch [0649]/[3760] Speed: 62.753423 samples/sec accuracy=88.252404 loss=0.456668 lr=0.000100 Epoch[095] Batch [0699]/[3760] Speed: 62.254115 samples/sec accuracy=88.281250 loss=0.456871 lr=0.000100 Epoch[095] Batch [0749]/[3760] Speed: 61.851558 samples/sec accuracy=88.297917 loss=0.456063 lr=0.000100 Epoch[095] Batch [0799]/[3760] Speed: 62.909460 samples/sec accuracy=88.304688 loss=0.455708 lr=0.000100 Epoch[095] Batch [0849]/[3760] Speed: 62.519281 samples/sec accuracy=88.275735 loss=0.457035 lr=0.000100 Epoch[095] Batch [0899]/[3760] Speed: 62.337964 samples/sec accuracy=88.270833 loss=0.457622 lr=0.000100 Epoch[095] Batch [0949]/[3760] Speed: 62.242692 samples/sec accuracy=88.291118 loss=0.456382 lr=0.000100 Epoch[095] Batch [0999]/[3760] Speed: 62.255096 samples/sec accuracy=88.285938 loss=0.456692 lr=0.000100 Epoch[095] Batch [1049]/[3760] Speed: 62.077839 samples/sec accuracy=88.328869 loss=0.455949 lr=0.000100 Epoch[095] Batch [1099]/[3760] Speed: 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lr=0.000100 Epoch[095] Batch [1599]/[3760] Speed: 62.088486 samples/sec accuracy=88.214844 loss=0.459276 lr=0.000100 Epoch[095] Batch [1649]/[3760] Speed: 62.529785 samples/sec accuracy=88.222538 loss=0.459073 lr=0.000100 Epoch[095] Batch [1699]/[3760] Speed: 62.269507 samples/sec accuracy=88.236213 loss=0.458964 lr=0.000100 Epoch[095] Batch [1749]/[3760] Speed: 62.625745 samples/sec accuracy=88.257143 loss=0.458123 lr=0.000100 Epoch[095] Batch [1799]/[3760] Speed: 61.920776 samples/sec accuracy=88.257812 loss=0.457874 lr=0.000100 Epoch[095] Batch [1849]/[3760] Speed: 62.484599 samples/sec accuracy=88.251689 loss=0.458157 lr=0.000100 Epoch[095] Batch [1899]/[3760] Speed: 62.060013 samples/sec accuracy=88.260691 loss=0.457871 lr=0.000100 Epoch[095] Batch [1949]/[3760] Speed: 61.846477 samples/sec accuracy=88.254006 loss=0.457973 lr=0.000100 Epoch[095] Batch [1999]/[3760] Speed: 61.858402 samples/sec accuracy=88.265625 loss=0.457580 lr=0.000100 Epoch[095] Batch [2049]/[3760] Speed: 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lr=0.000100 Epoch[095] Batch [2549]/[3760] Speed: 62.324108 samples/sec accuracy=88.285539 loss=0.458080 lr=0.000100 Epoch[095] Batch [2599]/[3760] Speed: 62.536119 samples/sec accuracy=88.289663 loss=0.458207 lr=0.000100 Epoch[095] Batch [2649]/[3760] Speed: 61.931019 samples/sec accuracy=88.311321 loss=0.457917 lr=0.000100 Epoch[095] Batch [2699]/[3760] Speed: 62.547872 samples/sec accuracy=88.292245 loss=0.458198 lr=0.000100 Epoch[095] Batch [2749]/[3760] Speed: 62.449919 samples/sec accuracy=88.293750 loss=0.458354 lr=0.000100 Epoch[095] Batch [2799]/[3760] Speed: 62.228933 samples/sec accuracy=88.287946 loss=0.458729 lr=0.000100 Epoch[095] Batch [2849]/[3760] Speed: 61.988571 samples/sec accuracy=88.281250 loss=0.458924 lr=0.000100 Epoch[095] Batch [2899]/[3760] Speed: 62.404056 samples/sec accuracy=88.283944 loss=0.458515 lr=0.000100 Epoch[095] Batch [2949]/[3760] Speed: 62.605828 samples/sec accuracy=88.286547 loss=0.458608 lr=0.000100 Epoch[095] Batch [2999]/[3760] Speed: 62.133094 samples/sec accuracy=88.293750 loss=0.458427 lr=0.000100 Epoch[095] Batch [3049]/[3760] Speed: 62.449872 samples/sec accuracy=88.297643 loss=0.458245 lr=0.000100 Epoch[095] Batch [3099]/[3760] Speed: 62.082371 samples/sec accuracy=88.289315 loss=0.458609 lr=0.000100 Epoch[095] Batch [3149]/[3760] Speed: 62.883450 samples/sec accuracy=88.301587 loss=0.458263 lr=0.000100 Epoch[095] Batch [3199]/[3760] Speed: 62.012765 samples/sec accuracy=88.300293 loss=0.458324 lr=0.000100 Epoch[095] Batch [3249]/[3760] Speed: 62.109280 samples/sec accuracy=88.299519 loss=0.458226 lr=0.000100 Epoch[095] Batch [3299]/[3760] Speed: 62.640218 samples/sec accuracy=88.291667 loss=0.458383 lr=0.000100 Epoch[095] Batch [3349]/[3760] Speed: 62.207098 samples/sec accuracy=88.283116 loss=0.458872 lr=0.000100 Epoch[095] Batch [3399]/[3760] Speed: 62.188110 samples/sec accuracy=88.272978 loss=0.459506 lr=0.000100 Epoch[095] Batch [3449]/[3760] Speed: 62.292087 samples/sec accuracy=88.272192 loss=0.459787 lr=0.000100 Epoch[095] Batch [3499]/[3760] Speed: 62.257923 samples/sec accuracy=88.286161 loss=0.459474 lr=0.000100 Epoch[095] Batch [3549]/[3760] Speed: 61.783608 samples/sec accuracy=88.292694 loss=0.459324 lr=0.000100 Epoch[095] Batch [3599]/[3760] Speed: 62.649864 samples/sec accuracy=88.289062 loss=0.459473 lr=0.000100 Epoch[095] Batch [3649]/[3760] Speed: 62.117241 samples/sec accuracy=88.294092 loss=0.459205 lr=0.000100 Epoch[095] Batch [3699]/[3760] Speed: 62.696239 samples/sec accuracy=88.290541 loss=0.459134 lr=0.000100 Epoch[095] Batch [3749]/[3760] Speed: 66.843493 samples/sec accuracy=88.287500 loss=0.459484 lr=0.000100 Batch [0049]/[0303]: acc-top1=66.218750 acc-top5=85.781250 Batch [0099]/[0303]: acc-top1=67.781250 acc-top5=86.468750 Batch [0149]/[0303]: acc-top1=67.989583 acc-top5=86.500000 Batch [0199]/[0303]: acc-top1=68.132812 acc-top5=86.554688 Batch [0249]/[0303]: acc-top1=67.868750 acc-top5=86.475000 Batch [0299]/[0303]: acc-top1=67.890625 acc-top5=86.453125 [Epoch 095] training: accuracy=88.291639 loss=0.459466 [Epoch 095] speed: 61 samples/sec time cost: 4155.451270 [Epoch 095] validation: acc-top1=67.868193 acc-top5=86.484117 loss=1.816457 Epoch[096] Batch [0049]/[3759] Speed: 42.331295 samples/sec accuracy=87.781250 loss=0.478717 lr=0.000100 Epoch[096] Batch [0099]/[3759] Speed: 60.075966 samples/sec accuracy=87.703125 loss=0.483283 lr=0.000100 Epoch[096] Batch [0149]/[3759] Speed: 62.628630 samples/sec accuracy=87.833333 loss=0.472940 lr=0.000100 Epoch[096] Batch [0199]/[3759] Speed: 60.930721 samples/sec accuracy=87.992188 loss=0.470710 lr=0.000100 Epoch[096] Batch [0249]/[3759] Speed: 62.613737 samples/sec accuracy=88.068750 loss=0.468984 lr=0.000100 Epoch[096] Batch [0299]/[3759] Speed: 61.169140 samples/sec accuracy=88.098958 loss=0.465970 lr=0.000100 Epoch[096] Batch [0349]/[3759] Speed: 62.367014 samples/sec accuracy=88.102679 loss=0.463853 lr=0.000100 Epoch[096] Batch [0399]/[3759] Speed: 61.750698 samples/sec accuracy=88.183594 loss=0.462302 lr=0.000100 Epoch[096] Batch [0449]/[3759] Speed: 62.664337 samples/sec accuracy=88.145833 loss=0.461063 lr=0.000100 Epoch[096] Batch [0499]/[3759] Speed: 61.539260 samples/sec accuracy=88.243750 loss=0.458726 lr=0.000100 Epoch[096] Batch [0549]/[3759] Speed: 62.131281 samples/sec accuracy=88.267045 loss=0.457939 lr=0.000100 Epoch[096] Batch [0599]/[3759] Speed: 62.428392 samples/sec accuracy=88.273438 loss=0.457640 lr=0.000100 Epoch[096] Batch [0649]/[3759] Speed: 61.260394 samples/sec accuracy=88.286058 loss=0.457723 lr=0.000100 Epoch[096] Batch [0699]/[3759] Speed: 63.000242 samples/sec accuracy=88.243304 loss=0.459272 lr=0.000100 Epoch[096] Batch [0749]/[3759] Speed: 62.400594 samples/sec accuracy=88.229167 loss=0.460725 lr=0.000100 Epoch[096] Batch [0799]/[3759] Speed: 62.304129 samples/sec accuracy=88.228516 loss=0.460320 lr=0.000100 Epoch[096] Batch [0849]/[3759] Speed: 62.352695 samples/sec accuracy=88.200368 loss=0.459900 lr=0.000100 Epoch[096] Batch [0899]/[3759] Speed: 62.292911 samples/sec accuracy=88.267361 loss=0.459285 lr=0.000100 Epoch[096] Batch [0949]/[3759] Speed: 62.352912 samples/sec accuracy=88.282895 loss=0.458629 lr=0.000100 Epoch[096] Batch [0999]/[3759] Speed: 62.808620 samples/sec accuracy=88.309375 loss=0.459030 lr=0.000100 Epoch[096] Batch [1049]/[3759] Speed: 62.014184 samples/sec accuracy=88.348214 loss=0.458182 lr=0.000100 Epoch[096] Batch [1099]/[3759] Speed: 62.674196 samples/sec accuracy=88.338068 loss=0.458076 lr=0.000100 Epoch[096] Batch [1149]/[3759] Speed: 62.435289 samples/sec accuracy=88.342391 loss=0.456834 lr=0.000100 Epoch[096] Batch [1199]/[3759] Speed: 61.884721 samples/sec accuracy=88.347656 loss=0.457034 lr=0.000100 Epoch[096] Batch [1249]/[3759] Speed: 62.402034 samples/sec accuracy=88.341250 loss=0.457654 lr=0.000100 Epoch[096] Batch [1299]/[3759] Speed: 62.607241 samples/sec accuracy=88.322115 loss=0.457399 lr=0.000100 Epoch[096] Batch [1349]/[3759] Speed: 62.482916 samples/sec accuracy=88.331019 loss=0.457244 lr=0.000100 Epoch[096] Batch [1399]/[3759] Speed: 62.577945 samples/sec accuracy=88.313616 loss=0.457714 lr=0.000100 Epoch[096] Batch [1449]/[3759] Speed: 62.390100 samples/sec accuracy=88.327586 loss=0.457649 lr=0.000100 Epoch[096] Batch [1499]/[3759] Speed: 62.589772 samples/sec accuracy=88.321875 loss=0.458062 lr=0.000100 Epoch[096] Batch [1549]/[3759] Speed: 61.973456 samples/sec accuracy=88.303427 loss=0.457954 lr=0.000100 Epoch[096] Batch [1599]/[3759] Speed: 62.702001 samples/sec accuracy=88.273438 loss=0.459041 lr=0.000100 Epoch[096] Batch [1649]/[3759] Speed: 62.329698 samples/sec accuracy=88.300189 loss=0.458252 lr=0.000100 Epoch[096] Batch [1699]/[3759] Speed: 62.585701 samples/sec accuracy=88.288603 loss=0.458320 lr=0.000100 Epoch[096] Batch [1749]/[3759] Speed: 61.827561 samples/sec accuracy=88.302679 loss=0.458306 lr=0.000100 Epoch[096] Batch [1799]/[3759] Speed: 62.882868 samples/sec accuracy=88.290799 loss=0.458776 lr=0.000100 Epoch[096] Batch [1849]/[3759] Speed: 62.409668 samples/sec accuracy=88.265203 loss=0.459973 lr=0.000100 Epoch[096] Batch [1899]/[3759] Speed: 62.506003 samples/sec accuracy=88.262336 loss=0.459872 lr=0.000100 Epoch[096] Batch [1949]/[3759] Speed: 61.931145 samples/sec accuracy=88.281250 loss=0.459112 lr=0.000100 Epoch[096] Batch [1999]/[3759] Speed: 62.768151 samples/sec accuracy=88.259375 loss=0.459838 lr=0.000100 Epoch[096] Batch [2049]/[3759] Speed: 62.140815 samples/sec accuracy=88.256860 loss=0.460205 lr=0.000100 Epoch[096] Batch [2099]/[3759] Speed: 62.219907 samples/sec accuracy=88.283482 loss=0.459859 lr=0.000100 Epoch[096] Batch [2149]/[3759] Speed: 62.880026 samples/sec accuracy=88.295058 loss=0.459400 lr=0.000100 Epoch[096] Batch [2199]/[3759] Speed: 62.292596 samples/sec accuracy=88.308949 loss=0.459111 lr=0.000100 Epoch[096] Batch [2249]/[3759] Speed: 62.045702 samples/sec accuracy=88.295139 loss=0.459569 lr=0.000100 Epoch[096] Batch [2299]/[3759] Speed: 62.789092 samples/sec accuracy=88.301630 loss=0.459756 lr=0.000100 Epoch[096] Batch [2349]/[3759] Speed: 62.623961 samples/sec accuracy=88.298537 loss=0.459954 lr=0.000100 Epoch[096] Batch [2399]/[3759] Speed: 61.971666 samples/sec accuracy=88.311849 loss=0.459960 lr=0.000100 Epoch[096] Batch [2449]/[3759] Speed: 62.463513 samples/sec accuracy=88.294005 loss=0.460397 lr=0.000100 Epoch[096] Batch [2499]/[3759] Speed: 61.871535 samples/sec accuracy=88.281875 loss=0.460662 lr=0.000100 Epoch[096] Batch [2549]/[3759] Speed: 62.736466 samples/sec accuracy=88.280637 loss=0.460576 lr=0.000100 Epoch[096] Batch [2599]/[3759] Speed: 62.740640 samples/sec accuracy=88.277644 loss=0.460756 lr=0.000100 Epoch[096] Batch [2649]/[3759] Speed: 62.092337 samples/sec accuracy=88.264151 loss=0.461299 lr=0.000100 Epoch[096] Batch [2699]/[3759] Speed: 62.424425 samples/sec accuracy=88.269676 loss=0.461296 lr=0.000100 Epoch[096] Batch [2749]/[3759] Speed: 62.343580 samples/sec accuracy=88.273295 loss=0.460872 lr=0.000100 Epoch[096] Batch [2799]/[3759] Speed: 62.153046 samples/sec accuracy=88.272879 loss=0.460675 lr=0.000100 Epoch[096] Batch [2849]/[3759] Speed: 61.780657 samples/sec accuracy=88.287281 loss=0.460363 lr=0.000100 Epoch[096] Batch [2899]/[3759] Speed: 62.296544 samples/sec accuracy=88.290409 loss=0.459911 lr=0.000100 Epoch[096] Batch [2949]/[3759] Speed: 61.908903 samples/sec accuracy=88.284958 loss=0.460408 lr=0.000100 Epoch[096] Batch [2999]/[3759] Speed: 62.051600 samples/sec accuracy=88.279688 loss=0.460703 lr=0.000100 Epoch[096] Batch [3049]/[3759] Speed: 62.743961 samples/sec accuracy=88.275615 loss=0.460765 lr=0.000100 Epoch[096] Batch [3099]/[3759] Speed: 62.288286 samples/sec accuracy=88.264617 loss=0.461424 lr=0.000100 Epoch[096] Batch [3149]/[3759] Speed: 62.922406 samples/sec accuracy=88.273810 loss=0.461156 lr=0.000100 Epoch[096] Batch [3199]/[3759] Speed: 61.941477 samples/sec accuracy=88.288086 loss=0.460883 lr=0.000100 Epoch[096] Batch [3249]/[3759] Speed: 62.502883 samples/sec accuracy=88.299519 loss=0.460533 lr=0.000100 Epoch[096] Batch [3299]/[3759] Speed: 62.421296 samples/sec accuracy=88.286932 loss=0.460924 lr=0.000100 Epoch[096] Batch [3349]/[3759] Speed: 62.054879 samples/sec accuracy=88.274254 loss=0.460972 lr=0.000100 Epoch[096] Batch [3399]/[3759] Speed: 62.591341 samples/sec accuracy=88.277114 loss=0.460722 lr=0.000100 Epoch[096] Batch [3449]/[3759] Speed: 62.234116 samples/sec accuracy=88.263134 loss=0.460943 lr=0.000100 Epoch[096] Batch [3499]/[3759] Speed: 62.308329 samples/sec accuracy=88.252679 loss=0.461317 lr=0.000100 Epoch[096] Batch [3549]/[3759] Speed: 61.784724 samples/sec accuracy=88.257923 loss=0.461049 lr=0.000100 Epoch[096] Batch [3599]/[3759] Speed: 62.525847 samples/sec accuracy=88.253472 loss=0.460944 lr=0.000100 Epoch[096] Batch [3649]/[3759] Speed: 62.287689 samples/sec accuracy=88.243579 loss=0.461283 lr=0.000100 Epoch[096] Batch [3699]/[3759] Speed: 63.010321 samples/sec accuracy=88.236909 loss=0.461505 lr=0.000100 Epoch[096] Batch [3749]/[3759] Speed: 67.658172 samples/sec accuracy=88.239583 loss=0.461221 lr=0.000100 Batch [0049]/[0303]: acc-top1=66.750000 acc-top5=85.593750 Batch [0099]/[0303]: acc-top1=67.812500 acc-top5=86.343750 Batch [0149]/[0303]: acc-top1=67.906250 acc-top5=86.364583 Batch [0199]/[0303]: acc-top1=68.210938 acc-top5=86.507812 Batch [0249]/[0303]: acc-top1=67.868750 acc-top5=86.500000 Batch [0299]/[0303]: acc-top1=67.859375 acc-top5=86.453125 [Epoch 096] training: accuracy=88.240307 loss=0.461173 [Epoch 096] speed: 61 samples/sec time cost: 4149.266081 [Epoch 096] validation: acc-top1=67.847566 acc-top5=86.484117 loss=1.806727 Epoch[097] Batch [0049]/[3760] Speed: 42.181720 samples/sec accuracy=87.687500 loss=0.480319 lr=0.000100 Epoch[097] Batch [0099]/[3760] Speed: 60.635233 samples/sec accuracy=88.531250 loss=0.445353 lr=0.000100 Epoch[097] Batch [0149]/[3760] Speed: 61.897041 samples/sec accuracy=88.375000 loss=0.454631 lr=0.000100 Epoch[097] Batch [0199]/[3760] Speed: 61.773240 samples/sec accuracy=88.437500 loss=0.457361 lr=0.000100 Epoch[097] Batch [0249]/[3760] Speed: 63.040170 samples/sec accuracy=88.443750 loss=0.455710 lr=0.000100 Epoch[097] Batch [0299]/[3760] Speed: 61.340825 samples/sec accuracy=88.552083 loss=0.452172 lr=0.000100 Epoch[097] Batch [0349]/[3760] Speed: 62.814416 samples/sec accuracy=88.584821 loss=0.450545 lr=0.000100 Epoch[097] Batch [0399]/[3760] Speed: 62.354544 samples/sec accuracy=88.582031 loss=0.453442 lr=0.000100 Epoch[097] Batch [0449]/[3760] Speed: 62.355264 samples/sec accuracy=88.635417 loss=0.450971 lr=0.000100 Epoch[097] Batch [0499]/[3760] Speed: 62.646115 samples/sec accuracy=88.596875 loss=0.453024 lr=0.000100 Epoch[097] Batch [0549]/[3760] Speed: 62.454764 samples/sec accuracy=88.497159 loss=0.456895 lr=0.000100 Epoch[097] Batch [0599]/[3760] Speed: 62.281347 samples/sec accuracy=88.403646 loss=0.458949 lr=0.000100 Epoch[097] Batch [0649]/[3760] Speed: 62.589709 samples/sec accuracy=88.394231 loss=0.457688 lr=0.000100 Epoch[097] Batch [0699]/[3760] Speed: 62.355100 samples/sec accuracy=88.337054 loss=0.459428 lr=0.000100 Epoch[097] Batch [0749]/[3760] Speed: 62.353551 samples/sec accuracy=88.316667 loss=0.459604 lr=0.000100 Epoch[097] Batch [0799]/[3760] Speed: 61.918374 samples/sec accuracy=88.347656 loss=0.458710 lr=0.000100 Epoch[097] Batch [0849]/[3760] Speed: 62.456575 samples/sec accuracy=88.380515 loss=0.457403 lr=0.000100 Epoch[097] Batch [0899]/[3760] Speed: 62.786181 samples/sec accuracy=88.373264 loss=0.456289 lr=0.000100 Epoch[097] Batch [0949]/[3760] Speed: 62.452068 samples/sec accuracy=88.355263 loss=0.457088 lr=0.000100 Epoch[097] Batch [0999]/[3760] Speed: 61.965383 samples/sec accuracy=88.325000 loss=0.458001 lr=0.000100 Epoch[097] Batch [1049]/[3760] Speed: 62.469547 samples/sec accuracy=88.333333 loss=0.457487 lr=0.000100 Epoch[097] Batch [1099]/[3760] Speed: 62.189474 samples/sec accuracy=88.326705 loss=0.456620 lr=0.000100 Epoch[097] Batch [1149]/[3760] Speed: 61.789135 samples/sec accuracy=88.278533 loss=0.458395 lr=0.000100 Epoch[097] Batch [1199]/[3760] Speed: 62.436720 samples/sec accuracy=88.266927 loss=0.458732 lr=0.000100 Epoch[097] Batch [1249]/[3760] Speed: 62.532244 samples/sec accuracy=88.282500 loss=0.457555 lr=0.000100 Epoch[097] Batch [1299]/[3760] Speed: 62.334467 samples/sec accuracy=88.288462 loss=0.457387 lr=0.000100 Epoch[097] Batch [1349]/[3760] Speed: 62.569112 samples/sec accuracy=88.304398 loss=0.457480 lr=0.000100 Epoch[097] Batch [1399]/[3760] Speed: 62.324040 samples/sec accuracy=88.329241 loss=0.457312 lr=0.000100 Epoch[097] Batch [1449]/[3760] Speed: 62.280376 samples/sec accuracy=88.318966 loss=0.457448 lr=0.000100 Epoch[097] Batch [1499]/[3760] Speed: 62.885291 samples/sec accuracy=88.290625 loss=0.458414 lr=0.000100 Epoch[097] Batch [1549]/[3760] Speed: 61.902675 samples/sec accuracy=88.310484 loss=0.457699 lr=0.000100 Epoch[097] Batch [1599]/[3760] Speed: 62.569290 samples/sec accuracy=88.316406 loss=0.456925 lr=0.000100 Epoch[097] Batch [1649]/[3760] Speed: 62.411680 samples/sec accuracy=88.323864 loss=0.456965 lr=0.000100 Epoch[097] Batch [1699]/[3760] Speed: 61.821050 samples/sec accuracy=88.261029 loss=0.459305 lr=0.000100 Epoch[097] Batch [1749]/[3760] Speed: 62.573317 samples/sec accuracy=88.240179 loss=0.460262 lr=0.000100 Epoch[097] Batch [1799]/[3760] Speed: 62.802815 samples/sec accuracy=88.248264 loss=0.460130 lr=0.000100 Epoch[097] Batch [1849]/[3760] Speed: 62.619060 samples/sec accuracy=88.249155 loss=0.460081 lr=0.000100 Epoch[097] Batch [1899]/[3760] Speed: 62.148901 samples/sec accuracy=88.226974 loss=0.460792 lr=0.000100 Epoch[097] Batch [1949]/[3760] Speed: 62.408112 samples/sec accuracy=88.238782 loss=0.460658 lr=0.000100 Epoch[097] Batch [1999]/[3760] Speed: 62.871208 samples/sec accuracy=88.222656 loss=0.461137 lr=0.000100 Epoch[097] Batch [2049]/[3760] Speed: 61.518130 samples/sec accuracy=88.211890 loss=0.461240 lr=0.000100 Epoch[097] Batch [2099]/[3760] Speed: 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lr=0.000100 Epoch[097] Batch [2599]/[3760] Speed: 62.621170 samples/sec accuracy=88.248798 loss=0.460785 lr=0.000100 Epoch[097] Batch [2649]/[3760] Speed: 62.347894 samples/sec accuracy=88.240566 loss=0.460811 lr=0.000100 Epoch[097] Batch [2699]/[3760] Speed: 62.576206 samples/sec accuracy=88.258681 loss=0.460553 lr=0.000100 Epoch[097] Batch [2749]/[3760] Speed: 61.770548 samples/sec accuracy=88.257386 loss=0.460747 lr=0.000100 Epoch[097] Batch [2799]/[3760] Speed: 62.572717 samples/sec accuracy=88.244420 loss=0.461176 lr=0.000100 Epoch[097] Batch [2849]/[3760] Speed: 63.093036 samples/sec accuracy=88.251645 loss=0.461227 lr=0.000100 Epoch[097] Batch [2899]/[3760] Speed: 62.377348 samples/sec accuracy=88.249461 loss=0.460983 lr=0.000100 Epoch[097] Batch [2949]/[3760] Speed: 62.611833 samples/sec accuracy=88.231462 loss=0.461494 lr=0.000100 Epoch[097] Batch [2999]/[3760] Speed: 62.408815 samples/sec accuracy=88.231771 loss=0.461608 lr=0.000100 Epoch[097] Batch [3049]/[3760] Speed: 62.771065 samples/sec accuracy=88.236680 loss=0.461289 lr=0.000100 Epoch[097] Batch [3099]/[3760] Speed: 62.254855 samples/sec accuracy=88.247480 loss=0.460960 lr=0.000100 Epoch[097] Batch [3149]/[3760] Speed: 62.161957 samples/sec accuracy=88.267361 loss=0.459971 lr=0.000100 Epoch[097] Batch [3199]/[3760] Speed: 62.246645 samples/sec accuracy=88.266602 loss=0.459935 lr=0.000100 Epoch[097] Batch [3249]/[3760] Speed: 62.298536 samples/sec accuracy=88.270192 loss=0.460001 lr=0.000100 Epoch[097] Batch [3299]/[3760] Speed: 62.122505 samples/sec accuracy=88.261364 loss=0.460419 lr=0.000100 Epoch[097] Batch [3349]/[3760] Speed: 62.270868 samples/sec accuracy=88.257463 loss=0.460487 lr=0.000100 Epoch[097] Batch [3399]/[3760] Speed: 63.037989 samples/sec accuracy=88.247243 loss=0.460911 lr=0.000100 Epoch[097] Batch [3449]/[3760] Speed: 62.159785 samples/sec accuracy=88.230978 loss=0.461312 lr=0.000100 Epoch[097] Batch [3499]/[3760] Speed: 62.372406 samples/sec accuracy=88.239286 loss=0.460788 lr=0.000100 Epoch[097] Batch [3549]/[3760] Speed: 62.270193 samples/sec accuracy=88.242958 loss=0.460898 lr=0.000100 Epoch[097] Batch [3599]/[3760] Speed: 62.741171 samples/sec accuracy=88.242188 loss=0.460872 lr=0.000100 Epoch[097] Batch [3649]/[3760] Speed: 61.995889 samples/sec accuracy=88.241866 loss=0.461055 lr=0.000100 Epoch[097] Batch [3699]/[3760] Speed: 62.237328 samples/sec accuracy=88.234375 loss=0.461217 lr=0.000100 Epoch[097] Batch [3749]/[3760] Speed: 67.703632 samples/sec accuracy=88.232083 loss=0.461343 lr=0.000100 Batch [0049]/[0303]: acc-top1=66.750000 acc-top5=85.562500 Batch [0099]/[0303]: acc-top1=67.890625 acc-top5=86.375000 Batch [0149]/[0303]: acc-top1=68.104167 acc-top5=86.395833 Batch [0199]/[0303]: acc-top1=68.187500 acc-top5=86.523438 Batch [0249]/[0303]: acc-top1=67.806250 acc-top5=86.487500 Batch [0299]/[0303]: acc-top1=67.875000 acc-top5=86.484375 [Epoch 097] training: accuracy=88.233045 loss=0.461425 [Epoch 097] speed: 62 samples/sec time cost: 4144.784023 [Epoch 097] validation: acc-top1=67.863036 acc-top5=86.494431 loss=1.813667 Epoch[098] Batch [0049]/[3759] Speed: 43.248357 samples/sec accuracy=88.937500 loss=0.425423 lr=0.000100 Epoch[098] Batch [0099]/[3759] Speed: 60.287970 samples/sec accuracy=88.515625 loss=0.448097 lr=0.000100 Epoch[098] Batch [0149]/[3759] Speed: 62.204713 samples/sec accuracy=88.666667 loss=0.450733 lr=0.000100 Epoch[098] Batch [0199]/[3759] Speed: 61.301808 samples/sec accuracy=88.500000 loss=0.457310 lr=0.000100 Epoch[098] Batch [0249]/[3759] Speed: 62.166680 samples/sec accuracy=88.462500 loss=0.458861 lr=0.000100 Epoch[098] Batch [0299]/[3759] Speed: 62.000426 samples/sec accuracy=88.447917 loss=0.457993 lr=0.000100 Epoch[098] Batch [0349]/[3759] Speed: 62.957187 samples/sec accuracy=88.486607 loss=0.454910 lr=0.000100 Epoch[098] Batch [0399]/[3759] Speed: 62.531340 samples/sec accuracy=88.472656 loss=0.455341 lr=0.000100 Epoch[098] Batch [0449]/[3759] Speed: 62.191049 samples/sec accuracy=88.468750 loss=0.454730 lr=0.000100 Epoch[098] Batch [0499]/[3759] Speed: 62.263198 samples/sec accuracy=88.431250 loss=0.456481 lr=0.000100 Epoch[098] Batch [0549]/[3759] Speed: 62.470411 samples/sec accuracy=88.400568 loss=0.456471 lr=0.000100 Epoch[098] Batch [0599]/[3759] Speed: 62.830738 samples/sec accuracy=88.440104 loss=0.454571 lr=0.000100 Epoch[098] Batch [0649]/[3759] Speed: 61.406517 samples/sec accuracy=88.487981 loss=0.451978 lr=0.000100 Epoch[098] Batch [0699]/[3759] Speed: 62.715280 samples/sec accuracy=88.439732 loss=0.452930 lr=0.000100 Epoch[098] Batch [0749]/[3759] Speed: 62.285840 samples/sec accuracy=88.441667 loss=0.452405 lr=0.000100 Epoch[098] Batch [0799]/[3759] Speed: 62.663226 samples/sec accuracy=88.513672 loss=0.450939 lr=0.000100 Epoch[098] Batch [0849]/[3759] Speed: 62.175486 samples/sec accuracy=88.511029 loss=0.450418 lr=0.000100 Epoch[098] Batch [0899]/[3759] Speed: 62.216107 samples/sec accuracy=88.501736 loss=0.451274 lr=0.000100 Epoch[098] 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accuracy=88.542411 loss=0.448147 lr=0.000100 Epoch[098] Batch [1449]/[3759] Speed: 62.491622 samples/sec accuracy=88.558190 loss=0.448092 lr=0.000100 Epoch[098] Batch [1499]/[3759] Speed: 62.532236 samples/sec accuracy=88.531250 loss=0.449471 lr=0.000100 Epoch[098] Batch [1549]/[3759] Speed: 62.026636 samples/sec accuracy=88.540323 loss=0.449795 lr=0.000100 Epoch[098] Batch [1599]/[3759] Speed: 62.849102 samples/sec accuracy=88.530273 loss=0.449866 lr=0.000100 Epoch[098] Batch [1649]/[3759] Speed: 62.101555 samples/sec accuracy=88.552083 loss=0.448747 lr=0.000100 Epoch[098] Batch [1699]/[3759] Speed: 62.613980 samples/sec accuracy=88.533088 loss=0.448799 lr=0.000100 Epoch[098] Batch [1749]/[3759] Speed: 62.378472 samples/sec accuracy=88.515179 loss=0.449590 lr=0.000100 Epoch[098] Batch [1799]/[3759] Speed: 62.766164 samples/sec accuracy=88.532986 loss=0.449650 lr=0.000100 Epoch[098] Batch [1849]/[3759] Speed: 61.795585 samples/sec accuracy=88.526182 loss=0.449517 lr=0.000100 Epoch[098] 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accuracy=88.523271 loss=0.450461 lr=0.000100 Epoch[098] Batch [2399]/[3759] Speed: 63.180068 samples/sec accuracy=88.520182 loss=0.450815 lr=0.000100 Epoch[098] Batch [2449]/[3759] Speed: 61.658801 samples/sec accuracy=88.532526 loss=0.450459 lr=0.000100 Epoch[098] Batch [2499]/[3759] Speed: 62.662654 samples/sec accuracy=88.514375 loss=0.450679 lr=0.000100 Epoch[098] Batch [2549]/[3759] Speed: 62.666262 samples/sec accuracy=88.525735 loss=0.450517 lr=0.000100 Epoch[098] Batch [2599]/[3759] Speed: 62.421588 samples/sec accuracy=88.512019 loss=0.450584 lr=0.000100 Epoch[098] Batch [2649]/[3759] Speed: 62.348360 samples/sec accuracy=88.516509 loss=0.450415 lr=0.000100 Epoch[098] Batch [2699]/[3759] Speed: 62.388718 samples/sec accuracy=88.501736 loss=0.450975 lr=0.000100 Epoch[098] Batch [2749]/[3759] Speed: 62.185219 samples/sec accuracy=88.493182 loss=0.451356 lr=0.000100 Epoch[098] Batch [2799]/[3759] Speed: 62.085243 samples/sec accuracy=88.516183 loss=0.450766 lr=0.000100 Epoch[098] 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accuracy=88.483902 loss=0.451915 lr=0.000100 Epoch[098] Batch [3349]/[3759] Speed: 62.470447 samples/sec accuracy=88.481343 loss=0.452100 lr=0.000100 Epoch[098] Batch [3399]/[3759] Speed: 62.415555 samples/sec accuracy=88.486673 loss=0.452079 lr=0.000100 Epoch[098] Batch [3449]/[3759] Speed: 62.655716 samples/sec accuracy=88.474185 loss=0.452585 lr=0.000100 Epoch[098] Batch [3499]/[3759] Speed: 62.704596 samples/sec accuracy=88.460714 loss=0.453087 lr=0.000100 Epoch[098] Batch [3549]/[3759] Speed: 61.986599 samples/sec accuracy=88.462588 loss=0.453020 lr=0.000100 Epoch[098] Batch [3599]/[3759] Speed: 62.647379 samples/sec accuracy=88.458333 loss=0.453238 lr=0.000100 Epoch[098] Batch [3649]/[3759] Speed: 62.454282 samples/sec accuracy=88.452483 loss=0.453290 lr=0.000100 Epoch[098] Batch [3699]/[3759] Speed: 61.771114 samples/sec accuracy=88.451858 loss=0.453453 lr=0.000100 Epoch[098] Batch [3749]/[3759] Speed: 67.950131 samples/sec accuracy=88.442500 loss=0.453390 lr=0.000100 Batch [0049]/[0303]: acc-top1=66.593750 acc-top5=85.656250 Batch [0099]/[0303]: acc-top1=67.578125 acc-top5=86.609375 Batch [0149]/[0303]: acc-top1=67.760417 acc-top5=86.427083 Batch [0199]/[0303]: acc-top1=68.015625 acc-top5=86.507812 Batch [0249]/[0303]: acc-top1=67.793750 acc-top5=86.400000 Batch [0299]/[0303]: acc-top1=67.734375 acc-top5=86.375000 [Epoch 098] training: accuracy=88.437334 loss=0.453481 [Epoch 098] speed: 62 samples/sec time cost: 4141.934276 [Epoch 098] validation: acc-top1=67.713490 acc-top5=86.396452 loss=1.823668 Epoch[099] Batch [0049]/[3760] Speed: 42.372848 samples/sec accuracy=88.531250 loss=0.446176 lr=0.000100 Epoch[099] Batch [0099]/[3760] Speed: 60.457935 samples/sec accuracy=89.140625 loss=0.421248 lr=0.000100 Epoch[099] Batch [0149]/[3760] Speed: 62.406561 samples/sec accuracy=89.166667 loss=0.430956 lr=0.000100 Epoch[099] Batch [0199]/[3760] Speed: 61.287346 samples/sec accuracy=88.945312 loss=0.435404 lr=0.000100 Epoch[099] Batch [0249]/[3760] Speed: 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lr=0.000100 Epoch[099] Batch [1699]/[3760] Speed: 62.642222 samples/sec accuracy=88.672794 loss=0.444394 lr=0.000100 Epoch[099] Batch [1749]/[3760] Speed: 62.632145 samples/sec accuracy=88.692857 loss=0.443549 lr=0.000100 Epoch[099] Batch [1799]/[3760] Speed: 62.186430 samples/sec accuracy=88.690972 loss=0.444152 lr=0.000100 Epoch[099] Batch [1849]/[3760] Speed: 62.655851 samples/sec accuracy=88.646115 loss=0.445572 lr=0.000100 Epoch[099] Batch [1899]/[3760] Speed: 62.783794 samples/sec accuracy=88.616776 loss=0.446778 lr=0.000100 Epoch[099] Batch [1949]/[3760] Speed: 61.735191 samples/sec accuracy=88.592147 loss=0.447786 lr=0.000100 Epoch[099] Batch [1999]/[3760] Speed: 63.163795 samples/sec accuracy=88.591406 loss=0.448374 lr=0.000100 Epoch[099] Batch [2049]/[3760] Speed: 62.421927 samples/sec accuracy=88.607470 loss=0.447765 lr=0.000100 Epoch[099] Batch [2099]/[3760] Speed: 62.258900 samples/sec accuracy=88.602679 loss=0.448246 lr=0.000100 Epoch[099] Batch [2149]/[3760] Speed: 62.632400 samples/sec accuracy=88.612645 loss=0.447812 lr=0.000100 Epoch[099] Batch [2199]/[3760] Speed: 62.718952 samples/sec accuracy=88.610795 loss=0.448203 lr=0.000100 Epoch[099] Batch [2249]/[3760] Speed: 62.448212 samples/sec accuracy=88.602778 loss=0.448387 lr=0.000100 Epoch[099] Batch [2299]/[3760] Speed: 62.122403 samples/sec accuracy=88.601902 loss=0.448584 lr=0.000100 Epoch[099] Batch [2349]/[3760] Speed: 62.595043 samples/sec accuracy=88.583112 loss=0.448942 lr=0.000100 Epoch[099] Batch [2399]/[3760] Speed: 62.286265 samples/sec accuracy=88.561198 loss=0.449879 lr=0.000100 Epoch[099] Batch [2449]/[3760] Speed: 62.022753 samples/sec accuracy=88.573980 loss=0.449568 lr=0.000100 Epoch[099] Batch [2499]/[3760] Speed: 62.763764 samples/sec accuracy=88.566250 loss=0.449731 lr=0.000100 Epoch[099] Batch [2549]/[3760] Speed: 62.435558 samples/sec accuracy=88.580882 loss=0.448850 lr=0.000100 Epoch[099] Batch [2599]/[3760] Speed: 62.304618 samples/sec accuracy=88.585337 loss=0.448557 lr=0.000100 Epoch[099] Batch [2649]/[3760] Speed: 62.258927 samples/sec accuracy=88.589033 loss=0.448142 lr=0.000100 Epoch[099] Batch [2699]/[3760] Speed: 62.405393 samples/sec accuracy=88.600694 loss=0.447864 lr=0.000100 Epoch[099] Batch [2749]/[3760] Speed: 62.696597 samples/sec accuracy=88.587500 loss=0.448469 lr=0.000100 Epoch[099] Batch [2799]/[3760] Speed: 62.424523 samples/sec accuracy=88.575335 loss=0.448818 lr=0.000100 Epoch[099] Batch [2849]/[3760] Speed: 62.112535 samples/sec accuracy=88.566338 loss=0.448872 lr=0.000100 Epoch[099] Batch [2899]/[3760] Speed: 62.638397 samples/sec accuracy=88.554418 loss=0.449089 lr=0.000100 Epoch[099] Batch [2949]/[3760] Speed: 62.033308 samples/sec accuracy=88.540784 loss=0.449632 lr=0.000100 Epoch[099] Batch [2999]/[3760] Speed: 62.769537 samples/sec accuracy=88.539583 loss=0.449794 lr=0.000100 Epoch[099] Batch [3049]/[3760] Speed: 61.748107 samples/sec accuracy=88.534836 loss=0.449532 lr=0.000100 Epoch[099] Batch [3099]/[3760] Speed: 62.869259 samples/sec accuracy=88.531754 loss=0.449737 lr=0.000100 Epoch[099] Batch [3149]/[3760] Speed: 62.230630 samples/sec accuracy=88.520337 loss=0.449955 lr=0.000100 Epoch[099] Batch [3199]/[3760] Speed: 61.836293 samples/sec accuracy=88.505859 loss=0.450478 lr=0.000100 Epoch[099] Batch [3249]/[3760] Speed: 62.318624 samples/sec accuracy=88.494231 loss=0.450668 lr=0.000100 Epoch[099] Batch [3299]/[3760] Speed: 62.585518 samples/sec accuracy=88.489583 loss=0.450813 lr=0.000100 Epoch[099] Batch [3349]/[3760] Speed: 62.091565 samples/sec accuracy=88.489272 loss=0.450876 lr=0.000100 Epoch[099] Batch [3399]/[3760] Speed: 62.248430 samples/sec accuracy=88.475643 loss=0.451240 lr=0.000100 Epoch[099] Batch [3449]/[3760] Speed: 62.493258 samples/sec accuracy=88.484149 loss=0.451177 lr=0.000100 Epoch[099] Batch [3499]/[3760] Speed: 62.430909 samples/sec accuracy=88.500893 loss=0.450603 lr=0.000100 Epoch[099] Batch [3549]/[3760] Speed: 62.506543 samples/sec accuracy=88.506162 loss=0.450492 lr=0.000100 Epoch[099] Batch [3599]/[3760] Speed: 62.096632 samples/sec accuracy=88.494358 loss=0.450702 lr=0.000100 Epoch[099] Batch [3649]/[3760] Speed: 62.546260 samples/sec accuracy=88.483733 loss=0.451315 lr=0.000100 Epoch[099] Batch [3699]/[3760] Speed: 62.607633 samples/sec accuracy=88.465794 loss=0.451859 lr=0.000100 Epoch[099] Batch [3749]/[3760] Speed: 66.583666 samples/sec accuracy=88.466250 loss=0.451812 lr=0.000100 Batch [0049]/[0303]: acc-top1=66.843750 acc-top5=85.531250 Batch [0099]/[0303]: acc-top1=67.734375 acc-top5=86.437500 Batch [0149]/[0303]: acc-top1=67.895833 acc-top5=86.614583 Batch [0199]/[0303]: acc-top1=68.156250 acc-top5=86.625000 Batch [0249]/[0303]: acc-top1=67.850000 acc-top5=86.512500 Batch [0299]/[0303]: acc-top1=67.796875 acc-top5=86.468750 [Epoch 099] training: accuracy=88.464096 loss=0.451832 [Epoch 099] speed: 62 samples/sec time cost: 4145.792525 [Epoch 099] validation: acc-top1=67.790842 acc-top5=86.473804 loss=1.826618