问题描述
我的输入数据是特征图,而不是原始图像。并具有以下形式:(4,50,1,256)
mini_batch=4 / frames=50 / channels=1 / H=1 / W= 256
TimeSformer 的参数是:
dim = 128,image_size = 256,patch_size = 16,num_frames = 50,num_classes = 2,depth = 12,heads = 8,dim_head = 32,attn_dropout = 0.,ff_dropout = 0.
)
为了检查我的网络是否正常工作,我试图通过仅使用 6 个训练数据和 2 个与 (4,256)
之前形状相同的验证数据来使其过拟合。
但是我得到的训练准确度是振荡的,永远不会达到 > 80% 的值,而且我的训练损失并没有减少,它总是在 0.6900 - 06950
我的训练函数和参数是:
epochs = 300
lr = 1e-3
device = "cuda" if torch.cuda.is_available() else "cpu"
criterion = nn.CrossEntropyLoss(weight=class_weights)
optimizer = optim.Adam(model.parameters(),lr=lr)
def accuracy(y_pred,y_test):
y_pred_softmax = torch.log_softmax(y_pred,dim = 1)
_,y_pred_tags = torch.max(y_pred_softmax,dim = 1)
correct_pred = (y_pred_tags == y_test).float()
acc = correct_pred.sum() / len(correct_pred)
acc = torch.round(acc * 100)
return acc
history = defaultdict(list)
for epoch in range(epochs):
epoch_loss = 0
epoch_accuracy = 0
model=model.train()
for data,label in tqdm(train_loader):
data = data
label = label
data=data.reshape(4,256)
output = model(data)
label=label.reshape(4,).to(torch.long)
output = output / output.sum(0).expand_as(output)
loss = criterion(output,label)
acc=accuracy(output,label)
loss.backward()
optimizer.step()
optimizer.zero_grad()
epoch_accuracy += acc / len(train_loader)
epoch_loss += loss / len(train_loader)
with torch.no_grad():
epoch_val_accuracy = 0
epoch_val_loss = 0
model=model.eval()
for data,label in val_loader:
data = data
label=label.reshape(4,).to(torch.long)
data=data.reshape(4,256)
val_output = model(data)
val_output = val_output / val_output.sum(0).expand_as(val_output)
val_loss = criterion(val_output,label)
val_acc=accuracy(val_output,label)
optimizer.zero_grad()
epoch_val_accuracy += acc / len(val_loader)
epoch_val_loss += val_loss / len(val_loader)
我将不胜感激任何建议。 谢谢
解决方法
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