for e=1:n_epoch perm = randperm(n_train); for i = 1:n_train ni = perm(i); best_j = -1; picked_y = labels(ni); while(picked_y==labels(ni)) picked_y = randi(n_class); end [max_score, best_j] = argmaxOverMatrices(X(ni,:), Y(:,picked_y), W); [best_score_yi, best_j_yi] = argmaxOverMatrices(X(ni,:), Y(:,labels(ni)), W); if(max_score + 1 > best_score_yi) if(best_j==best_j_yi) W{best_j} = W{best_j} - eta * X(ni,:)' * (Y(:,picked_y) - Y(:,labels(ni)))'; else W{best_j} = W{best_j} - eta * X(ni,:)' * Y(:,picked_y)'; W{best_j_yi} = W{best_j_yi} + eta * X(ni,:)' * Y(:,labels(ni))'; end end end end
时间: 2024-04-13 13:27:26 浏览: 20
这段代码是一个训练循环,用于训练一个分类器。让我来解释一下这段代码的逻辑:
首先,代码使用一个循环来迭代训练多个epoch(训练轮数)。在每个epoch中,代码通过随机排列训练样本的索引来创建一个perm数组。
然后,代码使用另一个循环遍历训练样本。对于每个样本,代码首先选择一个与当前样本标签不同的随机标签picked_y。
接下来,代码调用argmaxOverMatrices函数来计算当前样本与picked_y标签对应的最高分数及其索引best_j。同时,代码还计算了当前样本与其真实标签labels(ni)对应的最高分数及其索引best_j_yi。
然后,代码通过比较max_score + 1和best_score_yi的大小来检查当前预测的标签是否比真实标签更好。如果是,则进入条件块。
在条件块内部,代码首先检查best_j是否等于best_j_yi。如果是,则更新W{best_j}的权重以减小与picked_y标签的差异,并增加与labels(ni)标签的相似性。
如果best_j和best_j_yi不相等,则分别更新W{best_j}和W{best_j_yi}的权重,以减小与picked_y标签的差异和增加与labels(ni)标签的相似性。
最后,代码继续迭代下一个训练样本,直到完成所有训练样本的遍历。
整个过程将重复执行n_epoch次,以便进行多轮训练。
这段代码实现了一个简单的多类别分类器的训练过程,通过更新权重W来最小化标签预测的错误。
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()