from tqdm import tqdm import time # 创建一个包含10个元素的列表 data = list(range(10)) # 使用tqdm进行循环迭代,并设置初始描述 for item in tqdm(data, desc='Processing'): # 模拟任务处理时间 time.sleep(0.5) # 循环结束后,更改描述 tqdm.set_description('Finished') 在上述代码中,我们首先导入了...
lr=LR,momentum=0.9)model.train()model,optimizer=ipex.optimize(model,optimizer=optimizer,dtype=torch.bfloat16)# 训练循环forepochinrange(epochs):tloss,vloss=0.0,0.0top1,top5=0.0,0.0pbar=tqdm(enumerate(
parameters(), lr=2e-4) for data in collector: # collect data for epoch in range(10): adv_fn(data) # compute advantage buffer.extend(data) for sample in buffer: # consume data loss_vals = loss_fn(sample) loss_val = sum( value for key, value in loss_vals.items() if key.starts...
"" class tqdm.rich.tqdm(tqdm.tqdm): """`rich.progress` version.""" class tqdm.keras.TqdmCallback(keras.callbacks.Callback): """Keras callback for epoch and batch progress.""" class tqdm.dask.TqdmCallback(dask.callbacks.Callback): """Dask callback for task progress."""...
cuda() from tqdm import tqdm for epoch in range(num_epochs): model.train() total_loss = 0 t = tqdm(train_dataloader) for step, batch in enumerate(t): for k, v in batch.items(): batch[k] = v.cuda() outputs = model( input_ids=batch["input_ids"], token_type_ids=batch["...
episode_num=N_EPISODE, episode_size=1, way_num=WAY, image_num=SHOT+QUERY_SHOT) dataloader = DataLoader(dataset, batch_sampler=sampler, num_workers=0, collate_fn=None) # 开始训练 epoch = 0 while epoch < N_EPOCH: loss = 0 acc = 0 for sample0 in tqdm(dataloader, desc="Epoch {} tr...
for epoch in range(1, config.epochs + 1): losses = 0 # 损失 accuracy = 0 # 准确率 model.train() train_bar = tqdm(train_dataloader, ncols=100) for input_ids, token_type_ids, attention_mask, label_id in train_bar: # 梯度清零 ...
parameters(), lr=lr) # scheduler scheduler = StepLR(optimizer, step_size=1, gamma=gamma) for epoch in range(epochs): epoch_loss = 0 epoch_accuracy = 0 for data, label in tqdm(train_loader): data = data.to(device) label = label.to(device) output = model(data) loss = criterion(...
acc=np.equal(pred, label_ids).sum()returnacc#训练函数deftrain(model, train_loader, optim, device, scheduler, epoch, test_dataloader): model.train() total_train_loss=0 iter_num=0 total_iter=len(train_loader)forbatchintrain_loader:#正向传播optim.zero_grad() ...
for _ in train_iterator: epoch_iterator = tqdm(train_dataloader, desc="Iteration") # 就是加了个进度条 for step, batch in enumerate(epoch_iterator): self.model.train() batch = tuple(t.to(self.device) for t in batch) # GPU or CPU inputs = {'input_ids': batch[0], 'attention_mas...