train

train函数

功能:训练神经网络
句法:[net,tr,Y,E,Pf,Af]=train(net,P,T,Pi,Ai)
解释:P:输入样本数据,
T:目标样本数据
Pi:初始化输入层延迟条件
Ai:初始化层延迟条件
Y:神经网络的输出数据,数据格式同输入的目标样本数据
E:神经网络的输出误差数据
Pf:训练后的网络输出层延迟条件
Af:训练后网络延迟条件
举例:假设net网络已经定义好

net.trainParam.epochs=50;
      net.trainParam.goal=0.01;
      net=train(net,p,t);%原先的神经网络已p为输入,t为目标函数的基础上训练50次得到的神经网络。
      y2=sim(net,p);%将训练完的神经网络在p的输入下进行仿真
请为我解释下列代码:(yolo11) D:\2026\ultralytics-yolo11-main>D:/anaconda3/envs/yolo11/python.exe d:/2026/ultralytics-yolo11-main/train.py Ultralytics 8.3.9 🚀 Python-3.9.23 torch-2.0.1+cu117 CUDA:0 (NVIDIA GeForce RTX 3050 Laptop GPU, 4096MiB) engine\trainer: task=detect, mode=train, model=D:\2026\ultralytics-yolo11-main\yolo11n.pt, data=D:/2026/YOLOv11/datasets/potholes/d.yaml, epochs=600, time=None, patience=100, batch=4, imgsz=640, save=True, save_period=-1, cache=disk, device=0, workers=8, project=exp10-new, name=yolov8n-c3k2-6, exist_ok=False, pretrained=True, optimizer=auto, verbose=True, seed=0, deterministic=True, single_cls=False, rect=False, cos_lr=False, close_mosaic=0, resume=D:\2026\ultralytics-yolo11-main\yolo11n.pt, amp=True, fraction=1.0, profile=False, freeze=None, multi_scale=False, overlap_mask=True, mask_ratio=4, dropout=0.0, val=True, split=val, save_json=False, save_hybrid=False, conf=None, iou=0.7, max_det=300, half=False, dnn=False, plots=True, source=None, vid_stride=1, stream_buffer=False, visualize=False, augment=False, agnostic_nms=False, classes=None, retina_masks=False, embed=None, show=False, save_frames=False, save_txt=False, save_conf=False, save_crop=False, show_labels=True, show_conf=True, show_boxes=True, line_width=None, format=torchscript, keras=False, optimize=False, int8=False, dynamic=False, simplify=False, opset=None, workspace=4, nms=False, lr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.0, box=7.5, cls=0.5, dfl=1.5, pose=12.0, kobj=1.0, label_smoothing=0.0, nbs=64, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, bgr=0.0, mosaic=0.0, mixup=0.0, copy_paste=0.0, copy_paste_mode=flip, auto_augment=randaugment, erasing=0.4, crop_fraction=1.0, cfg=None, tracker=botsort.yaml, save_dir=exp10-new\yolov8n-c3k2-6 Overriding model.yaml nc=80 with nc=1 from n params module arguments 0 -1 1 464 ultralytics.nn.modules.conv.Conv [3, 16, 3, 2] 1 -1 1 4672 ultralytics.nn.modules.conv.Conv [16, 32, 3, 2] 2 -1 1 6640 ultralytics.nn.modules.block.C3k2 [32, 64, 1, False, 0.25] 3 -1 1 36992 ultralytics.nn.modules.conv.Conv [64, 64, 3, 2] 4 -1 1 26080 ultralytics.nn.modules.block.C3k2 [64, 128, 1, False, 0.25] 5 -1 1 147712 ultralytics.nn.modules.conv.Conv [128, 128, 3, 2] 6 -1 1 87040 ultralytics.nn.modules.block.C3k2 [128, 128, 1, True] 7 -1 1 295424 ultralytics.nn.modules.conv.Conv [128, 256, 3, 2] 8 -1 1 346112 ultralytics.nn.modules.block.C3k2 [256, 256, 1, True] 9 -1 1 164608 ultralytics.nn.modules.block.SPPF [256, 256, 5] 10 -1 1 249728 ultralytics.nn.modules.block.C2PSA [256, 256, 1] 11 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] 12 [-1, 6] 1 0 ultralytics.nn.modules.conv.Concat [1] 13 -1 1 111296 ultralytics.nn.modules.block.C3k2 [384, 128, 1, False] 14 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] 15 [-1, 4] 1 0 ultralytics.nn.modules.conv.Concat [1] 16 -1 1 32096 ultralytics.nn.modules.block.C3k2 [256, 64, 1, False] 17 -1 1 36992 ultralytics.nn.modules.conv.Conv [64, 64, 3, 2] 18 [-1, 13] 1 0 ultralytics.nn.modules.conv.Concat [1] 19 -1 1 86720 ultralytics.nn.modules.block.C3k2 [192, 128, 1, False] 20 -1 1 147712 ultralytics.nn.modules.conv.Conv [128, 128, 3, 2] 21 [-1, 10] 1 0 ultralytics.nn.modules.conv.Concat [1] 22 -1 1 378880 ultralytics.nn.modules.block.C3k2 [384, 256, 1, True] 23 [16, 19, 22] 1 430867 ultralytics.nn.modules.head.Detect [1, [64, 128, 256]] YOLO11n summary: 319 layers, 2,590,035 parameters, 2,590,019 gradients, 6.4 GFLOPs Transferred 448/499 items from pretrained weights Freezing layer 'model.23.dfl.conv.weight' AMP: running Automatic Mixed Precision (AMP) checks with YOLO11n... AMP: checks passed ✅ train: Scanning D:\2026\YOLOv11\datasets\potholes\train\labels.cache... 2592 images, 0 backgrounds, 0 corrupt: 100%|██████████| 2592/2592 [00:00<?, ?it/s] train: D:\2026\YOLOv11\datasets\potholes\train\images\00001.jpg: corrupt JPEG restored and saved train: D:\2026\YOLOv11\datasets\potholes\train\images\00002.jpg: corrupt JPEG restored and saved train: D:\2026\YOLOv11\datasets\potholes\train\images\00003.jpg: corrupt JPEG restored and saved train: D:\2026\YOLOv11\datasets\potholes\train\images\00004.jpg: corrupt JPEG restored and saved train: D:\2026\YOLOv11\datasets\potholes\train\images\00005.jpg: corrupt JPEG restored and saved train: D:\2026\YOLOv11\datasets\potholes\train\images\00006.jpg: corrupt JPEG restored and saved train: D:\2026\YOLOv11\datasets\potholes\train\images\00007.jpg: corrupt JPEG restored and saved train: D:\2026\YOLOv11\datasets\potholes\train\images\00008.jpg: corrupt JPEG restored and saved train: 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D:\2026\YOLOv11\datasets\potholes\train\images\00097.jpg: corrupt JPEG restored and saved train: D:\2026\YOLOv11\datasets\potholes\train\images\00098.jpg: corrupt JPEG restored and saved train: D:\2026\YOLOv11\datasets\potholes\train\images\00099.jpg: corrupt JPEG restored and saved train: D:\2026\YOLOv11\datasets\potholes\train\images\00098.jpg: corrupt JPEG restored and saved train: D:\2026\YOLOv11\datasets\potholes\train\images\00099.jpg: corrupt JPEG restored and saved train: D:\2026\YOLOv11\datasets\potholes\train\images\00099.jpg: corrupt JPEG restored and saved train: D:\2026\YOLOv11\datasets\potholes\train\images\00100.jpg: corrupt JPEG restored and saved train: D:\2026\YOLOv11\datasets\potholes\train\images\00100.jpg: corrupt JPEG restored and saved train: D:\2026\YOLOv11\datasets\potholes\train\images\00101.jpg: corrupt JPEG restored and saved train: D:\2026\YOLOv11\datasets\potholes\train\images\00101.jpg: corrupt JPEG restored and saved train: D:\2026\YOLOv11\datasets\potholes\train\images\00102.jpg: corrupt JPEG restored and saved train: D:\2026\YOLOv11\datasets\potholes\train\images\00103.jpg: corrupt JPEG restored and saved train: D:\2026\YOLOv11\datasets\potholes\train\images\00104.jpg: corrupt JPEG restored and saved train: D:\2026\YOLOv11\datasets\potholes\train\images\00105.jpg: corrupt JPEG restored and saved train: D:\2026\YOLOv11\datasets\potholes\train\images\00106.jpg: corrupt JPEG restored and saved train: D:\2026\YOLOv11\datasets\potholes\train\images\00107.jpg: corrupt JPEG restored and saved train: Caching images (35.5GB disk): 100%|██████████| 2592/2592 [03:35<00:00, 12.05it/s] val: Scanning D:\2026\YOLOv11\datasets\potholes\val\labels.cache... 740 images, 1 backgrounds, 0 corrupt: 100%|██████████| 741/741 [00:00<?, ?it/s] val: D:\2026\YOLOv11\datasets\potholes\val\images\03116.jpg: 1 duplicate labels removed val: D:\2026\YOLOv11\datasets\potholes\val\images\03176.jpg: 1 duplicate labels removed val: D:\2026\YOLOv11\datasets\potholes\val\images\03189.jpg: 1 duplicate labels removed val: Caching images (3.6GB disk): 100%|██████████| 741/741 [01:19<00:00, 9.31it/s] Plotting labels to exp10-new\yolov8n-c3k2-6\labels.jpg... optimizer: 'optimizer=auto' found, ignoring 'lr0=0.01' and 'momentum=0.937' and determining best 'optimizer', 'lr0' and 'momentum' automatically... optimizer: SGD(lr=0.01, momentum=0.9) with parameter groups 81 weight(decay=0.0), 88 weight(decay=0.0005), 87 bias(decay=0.0) Traceback (most recent call last): File "d:\2026\ultralytics-yolo11-main\train.py", line 32, in <module> model.train(data='D:/2026/YOLOv11/datasets/potholes/d.yaml', File "d:\2026\ultralytics-yolo11-main\ultralytics\engine\model.py", line 802, in train self.trainer.train() File "d:\2026\ultralytics-yolo11-main\ultralytics\engine\trainer.py", line 208, in train self._do_train(world_size) File "d:\2026\ultralytics-yolo11-main\ultralytics\engine\trainer.py", line 328, in _do_train self._setup_train(world_size) File "d:\2026\ultralytics-yolo11-main\ultralytics\engine\trainer.py", line 320, in _setup_train self.resume_training(ckpt) File "d:\2026\ultralytics-yolo11-main\ultralytics\engine\trainer.py", line 758, in resume_training assert start_epoch > 0, ( AssertionError: D:\2026\ultralytics-yolo11-main\yolo11n.pt training to 600 epochs is finished, nothing to resume. Start a new training without resuming, i.e. 'yolo train model=D:\2026\ultralytics-yolo11-main\yolo11n.pt'
最新发布
07-20
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