total_time += curr_time Throughput = (repetitions*optimal_batch_size)/total_time print(‘Final Throughput:’,Throughput) 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17.
另外,我尝试优化batch size。尝试使batch size与GPU内存允许的一样大。较大的批量有助于缩短培训时间。但是,在实验中,我发现过大的batch(例如1024个样本和更多)会导致较低的验证准确度。我猜这个模型很早就开始过度配合。我最终batch size为256。 在找到一组合适的超参数后,我才切换到在更大的图像上进行更长时间...
for i in range(0, num_examples, batch_size): batch_indices = torch.tensor(indices[i:min(i + batch_size, num_examples)]) print('indices[{:},{:}]'.format(i, i + batch_size)) yield features[batch_indices], labels[batch_indices] batch_size = 10 # 定义批量数大小 for X, y in ...
loader_train = DataLoader(Dataset(data_train), batch_size=batch_size, shuffle=True, drop_last=True) loader_val = DataLoader(Dataset(data_val), batch_size=batch_size_val) # Model model = Model(transformer_path) model.train() model.to(device) # Optimizer lr = 2e-5 eps = 1e-6 betas ...
if batch_idx %10==0: print(epoch,batch_idx,loss.item()) #输出其预测loss损失函数的变化曲线 plot_curve(train_loss) #get optimal [w1,b1,w2,b2,w3,b3] total_correct=0 for x,y in test_loader: x=x.view(x.size(0),28*28)
由于梯度失效问题,异步训练虽然速度快,但是可能陷入次优解(sub-optimal training performance)。 异步训练和同步训练在TensorFlow中不同点如下图所示: 为了解决异步训练出现的梯度失效问题,微软提出了一种Asynchronous Stochastic Gradient Descent方法,主要是通过梯度补偿来提升训练效果。应该还有其他类似的研究,感兴趣的可以...
batch_size=batch_size, shuffle=True)# model initmodel = Model(input_size, output_size)# cuda devicesos.environ["CUDA_VISIBLE_DEVICES"]="0,1"device = torch.device("cuda"if torch.cuda.is_available()else"cpu") if torch.cuda.device_count() > 1: ...
batch_size =4 a = np.array([[[i,0]foriinrange(n)]forbinrange(batch_size)]) b = np.array([[[i, b +1]foriinrange(n)]forbinrange(batch_size)]) # Wrapwithtorch tensors x = torch.tensor(a, dtype=torch.float) y = torch.tensor(b, dtype=torch.float) ...
While going out of memory may necessitate reducing batch size, one can do certain check to ensure that usage of memory is optimal. Tracking Memory Usage with GPUtil One way to track GPU usage is by monitoring memory usage in a console withnvidia-smicommand. The problem with this approach is...
micro_batch_size (int)– batch size per training step. Needed for JIT Warmup, a technique where jit fused functions are warmed up before training to ensure same kernels are used for forward propogation and activation recompute phase. forward(inp: torch.Tensor, is_first_microbatch: Optional[bo...