如何解决PyTorch遍历历元,然后输出所有历元的最终值
下面的代码计算MSE和MAE值,但我遇到一个问题,即在每个时期结束后,MAE和MSE的值不会得到store_MAE并不会存储MSE。它似乎仅使用最后一个纪元的值。任何想法我都需要在代码中执行以保存每个纪元的值,我希望这是有道理的。谢谢您的帮助
global_step = 0
best_test_error = 10000
MAE_for_all_epochs = []
MSE_for_all_epochs = []
for epoch in range(4):
print("Epoch %d" % epoch)
model.train()
for images,paths in tqdm(loader_train):
images = images.to(device)
targets = torch.tensor([metadata['count'][os.path.split(path)[-1]] for path in paths]) # B
targets = targets.float().to(device)
# forward pass:
output = model(images) # B x 1 x 9 x 9 (analogous to a heatmap)
preds = output.sum(dim=[1,2,3]) # predicted cell counts (vector of length B)
# backward pass:
loss = torch.mean((preds - targets)**2)
optimizer.zero_grad()
loss.backward()
optimizer.step()
# logging:
count_error = torch.abs(preds - targets).mean()
writer.add_scalar('train_loss',loss.item(),global_step=global_step)
writer.add_scalar('train_count_error',count_error.item(),global_step=global_step)
print("Step %d,loss=%f,count error=%f" % (global_step,count_error.item()))
global_step += 1
mean_test_error = 0
model.eval()
for images,paths in tqdm(loader_test):
images = images.to(device)
targets = torch.tensor([metadata['count'][os.path.split(path)[-1]] for path in paths]) # B
targets = targets.float().to(device)
# forward pass:
output = model(images) # B x 1 x 9 x 9 (analogous to a heatmap)
preds = output.sum(dim=[1,3]) # predicted cell counts (vector of length B)
# logging:
#error = torch.abs(preds - targets).sum().data
#squared_error = ((preds - targets)*(preds - targets)).sum().data
#runnning_mae += error
#runnning_mse += squared_error
loss = torch.mean((preds - targets)**2)
count_error = torch.abs(preds - targets).mean()
mean_test_error += count_error
writer.add_scalar('test_loss',global_step=global_step)
writer.add_scalar('test_count_error',global_step=global_step)
global_step += 1
#store_MAE = 0
#store_MSE = 0
mean_test_error = mean_test_error / len(loader_test)
#store_MAE += mean_test_error
MAE_for_all_epochs = np.append(MAE_for_all_epochs,mean_test_error)
mse = math.sqrt(loss / len(loader_test))
#store_MSE +=mse
MSE_for_all_epochs = np.append(MSE_for_all_epochs,mse)
print("Test count error: %f" % mean_test_error)
print("MSE: %f" % mse)
if mean_test_error < best_test_error:
best_test_error = mean_test_error
torch.save({'state_dict':model.state_dict(),'optimizer_state_dict':optimizer.state_dict(),'globalStep':global_step,'train_paths':dataset_train.files,'test_paths':dataset_test.files},checkpoint_path)
print("MAE Total: %f" % store_MAE)
print("MSE Total: %f" % store_MSE)
model_mae= MAE_for_all_epochs / epoch
model_mse= MSE_for_all_epochs / epoch
print("Model MAE: %f" % model_mae)
print("Model MSE: %f" % model_mse)
解决方法
np.append()
将适用于您的情况,
#outside epochs loop
MAE_for_all_epochs = []
#inside loop
#replace this store_MAE with relevant variable
MAE_for_all_epochs = np.append(MAE_for_all_epochs,store_MAE)
编辑:玩具示例:根据用法
import numpy as np
all_var = []
for e in range(1,10):
var1 = np.random.random(1)
all_var = np.append(all_var,var1)
print(all_var)
# output : [0.07660848 0.46824825 0.09432051 0.79462902 0.97798061 0.67299183 0.50996432 0.13084029 0.95100381]
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