lr_decay_epoch = 40
时间: 2023-05-20 16:05:18 浏览: 43
这是一个超参数,用于控制学习率的衰减。在训练神经网络时,学习率的设置非常重要,如果学习率过高,可能会导致模型无法收敛;如果学习率过低,可能会导致模型训练缓慢。lr_decay_epoch = 40 表示在训练过程中,每经过40个epoch,学习率会按照一定的规则进行衰减。具体的衰减方式可以根据具体的模型和任务进行调整。
相关问题
lr_scheduler_func = get_lr_scheduler(lr_decay_type, Init_lr_fit, Min_lr_fit, UnFreeze_Epoch) model.Unfreeze_backbone() epoch_step = num_train // batch_size epoch_step_val = num_val // batch_size if epoch_step == 0 or epoch_step_val == 0: raise ValueError("数据集过小,无法继续进行训练,请扩充数据集。") if distributed: batch_size = batch_size // ngpus_per_node gen = DataLoader(train_dataset, shuffle=shuffle, batch_size=batch_size, num_workers=num_workers, pin_memory=True, drop_last=True, collate_fn=detection_collate, sampler=train_sampler) gen_val = DataLoader(val_dataset, shuffle=shuffle, batch_size=batch_size, num_workers=num_workers, pin_memory=True, drop_last=True, collate_fn=detection_collate, sampler=val_sampler) UnFreeze_flag = True if distributed: train_sampler.set_epoch(epoch) set_optimizer_lr(optimizer, lr_scheduler_func, epoch) fit_one_epoch(model_train, model, loss_history, optimizer, epoch, epoch_step, epoch_step_val, gen, gen_val, UnFreeze_Epoch, Cuda, fp16, scaler, save_period, save_dir, local_rank) if local_rank == 0: loss_history.writer.close() 转为伪代码
lr_scheduler_func = get_lr_scheduler(lr_decay_type, Init_lr_fit, Min_lr_fit, UnFreeze_Epoch)
model.Unfreeze_backbone()
epoch_step = num_train // batch_size
epoch_step_val = num_val // batch_size
if epoch_step == 0 or epoch_step_val == 0:
raise ValueError("数据集过小,无法继续进行训练,请扩充数据集。")
if distributed:
batch_size = batch_size // ngpus_per_node
gen = DataLoader(train_dataset, shuffle=shuffle, batch_size=batch_size, num_workers=num_workers, pin_memory=True, drop_last=True, collate_fn=detection_collate, sampler=train_sampler)
gen_val = DataLoader(val_dataset, shuffle=shuffle, batch_size=batch_size, num_workers=num_workers, pin_memory=True, drop_last=True, collate_fn=detection_collate, sampler=val_sampler)
UnFreeze_flag = True
if distributed:
train_sampler.set_epoch(epoch)
set_optimizer_lr(optimizer, lr_scheduler_func, epoch)
fit_one_epoch(model_train, model, loss_history, optimizer, epoch, epoch_step, epoch_step_val, gen, gen_val, UnFreeze_Epoch, Cuda, fp16, scaler, save_period, save_dir, local_rank)
if local_rank == 0:
loss_history.writer.close()
伪代码并不是一种具体的编程语言,而是一种算法描述语言,因此将上述代码转换为伪代码就是将其转换为类似于自然语言的算法描述。在这个过程中,可以将代码中的特定语法和语言结构替换为通用的算法表达方式,以便更清晰地表达算法的逻辑和流程。
用伪代码书写以下代码 r_scheduler_func = get_lr_scheduler(lr_decay_type, Init_lr_fit, Min_lr_fit, UnFreeze_Epoch) model.Unfreeze_backbone() epoch_step = num_train // batch_size epoch_step_val = num_val // batch_size if epoch_step == 0 or epoch_step_val == 0: raise ValueError("数据集过小,无法继续进行训练,请扩充数据集。") if distributed: batch_size = batch_size // ngpus_per_node gen = DataLoader(train_dataset, shuffle=shuffle, batch_size=batch_size, num_workers=num_workers, pin_memory=True, drop_last=True, collate_fn=detection_collate, sampler=train_sampler) gen_val = DataLoader(val_dataset, shuffle=shuffle, batch_size=batch_size, num_workers=num_workers, pin_memory=True, drop_last=True, collate_fn=detection_collate, sampler=val_sampler) UnFreeze_flag = True if distributed: train_sampler.set_epoch(epoch) set_optimizer_lr(optimizer, lr_scheduler_func, epoch) fit_one_epoch(model_train, model, loss_history, optimizer, epoch, epoch_step, epoch_step_val, gen, gen_val, UnFreeze_Epoch, Cuda, fp16, scaler, save_period, save_dir, local_rank) if local_rank == 0: loss_history.writer.close()
r_scheduler_func = get_lr_scheduler(lr_decay_type, Init_lr_fit, Min_lr_fit, UnFreeze_Epoch)
model.Unfreeze_backbone()
epoch_step = num_train // batch_size
epoch_step_val = num_val // batch_size
if epoch_step equals to 0 or epoch_step_val equals to 0:
raise ValueError("数据集过小,无法继续进行训练,请扩充数据集。")
if distributed equals to true:
batch_size = batch_size // ngpus_per_node
gen = DataLoader(train_dataset, shuffle=shuffle, batch_size=batch_size, num_workers=num_workers, pin_memory=True, drop_last=True, collate_fn=detection_collate, sampler=train_sampler)
gen_val = DataLoader(val_dataset, shuffle=shuffle, batch_size=batch_size, num_workers=num_workers, pin_memory=True, drop_last=True, collate_fn=detection_collate, sampler=val_sampler)
UnFreeze_flag = True
if distributed equals to true:
train_sampler.set_epoch(epoch)
set_optimizer_lr(optimizer, lr_scheduler_func, epoch)
fit_one_epoch(model_train, model, loss_history, optimizer, epoch, epoch_step, epoch_step_val, gen, gen_val, UnFreeze_Epoch, Cuda, fp16, scaler, save_period, save_dir, local_rank)
if local_rank equals to 0:
loss_history.writer.close()
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