我早些时候在 Jupiter 笔记本中使用此代码它没有显示错误但准确性非常低然后我在 google colab 中尝试了相同的代码它显示错误，请提出一些提高准确性的方法。我正在尝试执行多级 CNN 来检测具有图像下采样的叶子
import torch
import torch.nn as nn
import torch.nn.functional as F
class Net(nn.Module):
def __init__(self):
super().__init__()
self.conv1 = nn.Conv2d(1,32,2)
self.conv2 = nn.Conv2d(32,64,2)
self.conv2_bn = nn.BatchNorm2d(64)
x= torch.randn(256,256).view(-1,1,256,256)
self._to_linear = None
self.convs(x)
self.fc1= nn.Linear(self._to_linear, 512)
self.fc2 = nn.Linear(512,6)
def convs(self,x):
y=torch.nn.functional.interpolate(x, size=([128,128]), scale_factor=None, mode='nearest', align_corners=None)
z=torch.nn.functional.interpolate(x, size=([64,64]), scale_factor=None, mode='nearest', align_corners=None)
w=torch.nn.functional.interpolate(x, size=([32,32]), scale_factor=None, mode='nearest', align_corners=None)
# print(x[0].shape)
x= F.relu(self.conv1(x))
m = nn.ConstantPad2d(1,0)
x=m(x)
x = F.relu(F.max_pool2d(self.conv2_bn(self.conv2(x)), 2))
# print(x[0].shape)
y= F.relu(self.conv1(y))
m = nn.ConstantPad2d(1,0)
y=m(y)
y = F.relu(self.conv2_bn(self.conv2(y)), 2)
# print(y[0].shape)
CAT_1=torch.cat((x,y),1)
CAT_1=F.max_pool2d(CAT_1,(2,2))
# print(CAT_1[0].shape)
z= F.relu(self.conv1(z))
m = nn.ConstantPad2d(1,0)
z=m(z)
z= F.relu(self.conv2_bn(self.conv2(z)))
# print(z[0].shape)
CAT_2=torch.cat((CAT_1,z),1)
CAT_2=F.max_pool2d(CAT_2,(2,2))
# print(CAT_2[0].shape)
w= F.relu(self.conv1(w))
m = nn.ConstantPad2d(1,0)
w=m(w)
w = F.relu((self.conv2_bn(self.conv2(w))))
# print(w[0].shape)
x=torch.cat((CAT_2,w),1)
x=F.max_pool2d(x,(2,2))
# print("i lov pp")
# print(x[0].shape)
x=torch.nn.functional.avg_pool2d(x, (2,2))
# print("i lov pp")
# print(x[0].shape)
if self._to_linear is None:
self._to_linear = x[0].shape[0]*x[0].shape[1]*x[0].shape[2]
return x
def forward(self, x):
# print("i lov pp")
x=self.convs(x)
x=x.view(-1, self._to_linear)
x= F.relu(self.fc1(x))
x= self.fc2(x)
return F.softmax(x, dim=1)
# print(x[0].shape)
net=Net()
import torch.optim as optim
optimizer = optim.Adam(net.parameters(), lr=0.001)
loss_function = nn.MSELoss()
X = torch.Tensor([i[0] for i in training_data]).view(-1,256,256)
X=X/255.0
y = torch.Tensor([i[1] for i in training_data])
VAL_PCT = 0.1
val_size=int (len(X)*VAL_PCT)
print(val_size)
train_X= X[:-val_size]
train_y= y[:-val_size]
test_X=X[-val_size:]
test_y = y[-val_size:]
print(len(train_X))
print(len(test_X))
BATCH_SIZE =10
EPOCHS = 1
for epoch in range(EPOCHS):
for i in (range(0, len(train_X), BATCH_SIZE)):
#print(i, i+BATCH_SIZE)
batch_X = train_X[i:i+BATCH_SIZE].view(-1,1,256,256)
# print(batch_X.shape)
batch_y = train_y[i:i+BATCH_SIZE]
#print(batch_y.shape)
net.zero_grad()
outputs = net(batch_X)
#print (outputs.shape)
loss = loss_function(outputs, batch_y)
loss.backward()
optimizer.step()
#print(loss)
#print(f"Epoch: {epoch}. Loss: {loss}")
/usr/local/lib/python3.6/dist-packages/torch/nn/modules/loss.py:432: UserWarning: Using a target size (torch.Size([10, 256, 256, 3])) that is different to the input size (torch.Size([10, 6])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.
return F.mse_loss(input, target, reduction=self.reduction)
---------------------------------------------------------------------------
RuntimeError Traceback (most recent call last)
<ipython-input-38-a154b102127f> in <module>()
15 outputs = net(batch_X)
16 #print (outputs.shape)
---> 17 loss = loss_function(outputs, batch_y)
18 loss.backward()
19 optimizer.step()
3 frames
/usr/local/lib/python3.6/dist-packages/torch/functional.py in broadcast_tensors(*tensors)
60 if any(type(t) is not Tensor for t in tensors) and has_torch_function(tensors):
61 return handle_torch_function(broadcast_tensors, tensors, *tensors)
---> 62 return _VF.broadcast_tensors(tensors)
63
64
RuntimeError: The size of tensor a (10) must match the size of tensor b (9) at non-singleton dimension 3