运行时错误:mat1 和 mat2 形状不能相乘(5376x28 和 784x512) [英] RuntimeError: mat1 and mat2 shapes cannot be multiplied (5376x28 and 784x512)
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问题描述
基本网络
class Baseline(nn.Module):
def __init__(self):
super().__init__()
# 5 Hidden Layer Network
self.fc1 = nn.Linear(28 * 28, 512)
self.fc2 = nn.Linear(512, 256)
self.fc3 = nn.Linear(256, 128)
self.fc4 = nn.Linear(128, 64)
self.fc5 = nn.Linear(64, 3)
# Dropout module with 0.2 probbability
self.dropout = nn.Dropout(p=0.2)
# Add softmax on output layer
self.log_softmax = F.log_softmax
def forward(self, x):
x = self.dropout(F.relu(self.fc1(x)))
x = self.dropout(F.relu(self.fc2(x)))
x = self.dropout(F.relu(self.fc3(x)))
x = self.dropout(F.relu(self.fc4(x)))
x = self.log_softmax(self.fc5(x), dim=1)
return x
错误:
---------------------------------------------------------------------------
RuntimeError Traceback (most recent call last)
<ipython-input-48-0030d9c3852c> in <module>
18 optimizer.zero_grad()
19 # Make predictions
---> 20 log_ps = model(images)
21 loss = criterion(log_ps, labels)
22 #backprop
~/anaconda3/envs/torch/lib/python3.8/site-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
725 result = self._slow_forward(*input, **kwargs)
726 else:
--> 727 result = self.forward(*input, **kwargs)
728 for hook in itertools.chain(
729 _global_forward_hooks.values(),
<ipython-input-46-09dd06cd0a72> in forward(self, x)
15
16 def forward(self, x):
---> 17 x = self.dropout(F.relu(self.fc1(x)))
18 x = self.dropout(F.relu(self.fc2(x)))
19 x = self.dropout(F.relu(self.fc3(x)))
~/anaconda3/envs/torch/lib/python3.8/site-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
725 result = self._slow_forward(*input, **kwargs)
726 else:
--> 727 result = self.forward(*input, **kwargs)
728 for hook in itertools.chain(
729 _global_forward_hooks.values(),
~/anaconda3/envs/torch/lib/python3.8/site-packages/torch/nn/modules/linear.py in forward(self, input)
91
92 def forward(self, input: Tensor) -> Tensor:
---> 93 return F.linear(input, self.weight, self.bias)
94
95 def extra_repr(self) -> str:
~/anaconda3/envs/torch/lib/python3.8/site-packages/torch/nn/functional.py in linear(input, weight, bias)
1690 ret = torch.addmm(bias, input, weight.t())
1691 else:
-> 1692 output = input.matmul(weight.t())
1693 if bias is not None:
1694 output += bias
RuntimeError: mat1 and mat2 shapes cannot be multiplied (5376x28 and 784x512)
我尝试将 fc1 更改为 self.fc1 = nn.Linear(5376, 512) 但我仍然得到 RuntimeError: mat1 和 mat2 形状不能相乘(5376x28 和 5376x512).
I tried changing fc1 to self.fc1 = nn.Linear(5376, 512) but I still get RuntimeError: mat1 and mat2 shapes cannot be multiplied (5376x28 and 5376x512).
然后我将整个架构调整如下:
I then adjusted the whole architecture as follows:
class Baseline(nn.Module):
def __init__(self):
super().__init__()
# 5 Hidden Layer Network
self.fc1 = nn.Linear(5376, 28)
self.fc2 = nn.Linear(28, 256)
self.fc3 = nn.Linear(256, 128)
self.fc4 = nn.Linear(128, 64)
self.fc5 = nn.Linear(64, 3)
# Dropout module with 0.2 probbability
self.dropout = nn.Dropout(p=0.2)
# Add softmax on output layer
self.log_softmax = F.log_softmax
def forward(self, x):
x = self.dropout(F.relu(self.fc1(x)))
x = self.dropout(F.relu(self.fc2(x)))
x = self.dropout(F.relu(self.fc3(x)))
x = self.dropout(F.relu(self.fc4(x)))
x = self.log_softmax(self.fc5(x), dim=1)
return x
我仍然收到以下错误:
RuntimeError Traceback (most recent call last)
<ipython-input-54-0030d9c3852c> in <module>
18 optimizer.zero_grad()
19 # Make predictions
---> 20 log_ps = model(images)
21 loss = criterion(log_ps, labels)
22 #backprop
~/anaconda3/envs/torch/lib/python3.8/site-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
725 result = self._slow_forward(*input, **kwargs)
726 else:
--> 727 result = self.forward(*input, **kwargs)
728 for hook in itertools.chain(
729 _global_forward_hooks.values(),
<ipython-input-52-f98f89e15885> in forward(self, x)
15
16 def forward(self, x):
---> 17 x = self.dropout(F.relu(self.fc1(x)))
18 x = self.dropout(F.relu(self.fc2(x)))
19 x = self.dropout(F.relu(self.fc3(x)))
~/anaconda3/envs/torch/lib/python3.8/site-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
725 result = self._slow_forward(*input, **kwargs)
726 else:
--> 727 result = self.forward(*input, **kwargs)
728 for hook in itertools.chain(
729 _global_forward_hooks.values(),
~/anaconda3/envs/torch/lib/python3.8/site-packages/torch/nn/modules/linear.py in forward(self, input)
91
92 def forward(self, input: Tensor) -> Tensor:
---> 93 return F.linear(input, self.weight, self.bias)
94
95 def extra_repr(self) -> str:
~/anaconda3/envs/torch/lib/python3.8/site-packages/torch/nn/functional.py in linear(input, weight, bias)
1690 ret = torch.addmm(bias, input, weight.t())
1691 else:
-> 1692 output = input.matmul(weight.t())
1693 if bias is not None:
1694 output += bias
RuntimeError: mat1 and mat2 shapes cannot be multiplied (5376x28 and 5376x28)
注意:输入数据的形状为 torch.Size([64, 3, 28, 28]).
NB: The input data is of shape torch.Size([64, 3, 28, 28]).
为了复制:
model = Baseline()
X = torch.rand(64, 3, 28, 28)
model(X)
推荐答案
我在代码中看到一个问题.线性层不接受您传递给模型的具有 4d 形状的矩阵.
I see one issue in the code. Linear layers do not accept matrices with a 4d shape that you passed into the model.
为了使用 torch.Size([64, 3, 28, 28]) 通过 nn.Linear() 层传递数据，就像你在你的模型.您需要在前向函数中展平张量，例如:
In order to pass data with torch.Size([64, 3, 28, 28]) through a nn.Linear() layers like you have in your model. You need to flatten the tensor in your forward function like:
# New code
x = x.view(x.size(0), -1)
#Your code
x = self.dropout(F.relu(self.fc1(x)))
...
这可能有助于解决您得到的权重矩阵误差.
This will probably help solve the weight matrix error you are getting.
萨萨克耆那教
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