mat1 and mat2 shapes cannot be multiplied ( )的解决

问题描述
错误代码：

class Net(nn.Module):def __init__(self):super(Net, self).__init__()# nn.Conv2d(input_channel, output_channel, kernel, stride)self.conv1 = nn.Conv2d(3,64,5,1,1) # 64个5*5的filter  ->  64个124*124的matrixself.conv2 = nn.Conv2d(64,128,5,1,1)self.conv3 = nn.Conv2d(128,256,5,1,1)self.conv4 = nn.Conv2d(256,256,5,1,1)self.conv4_drop = nn.Dropout2d()self.fc1 = nn.Linear(4*4*256, 3072) # 全连接层  4*4*256=4096self.fc2 = nn.Linear(3072, 2048)self.fc3 = nn.Linear(2048, 1024)self.fc4 = nn.Linear(1024, 256)self.fc5 = nn.Linear(256, 11)def forward(self, x):# maxpooling 1x = self.conv1(x)x = F.relu(x) # 124*124*64x = F.max_pool2d(x, 2) # 62*62*20# maxpooling 2x = self.conv2(x)x = F.relu(x) # 58*58*128x = F.max_pool2d(x, 2) # 29*29*40# maxpooling 3x = self.conv3(x)x = F.relu(x) # 25*25*256x = F.max_pool2d(x, 2) # 12*12*100# maxpooling 4x = self.conv4(x)x = F.relu(x) # 8*8*256x = F.max_pool2d(x, 2) # 4*4*256# view函数将张量x变形成一维向量形式，总特征数不变，为全连接层做准备x = x.view(-1,4*4*256)x = F.relu(self.fc1(x))x = F.relu(self.fc2(x))x = F.relu(self.fc3(x))x = self.fc4(x)return F.LogSoftmax(x)

错误原因：
x = x.view(x.size()[0], -1)

改为：
x = x.view(-1,4x4x256)
self.fc1 = nn.Linear(4x4x256, 3072)

x.view的第二个参数和nn.Linear第一个参数一致

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