I have a few problems implementing the suggestions I’ve read through.
My Network has the Form:
def __init__(self):
    super(Hybrid_Net, self).__init__()
    self.conv1 = nn.Conv2d(in_channels=1,out_channels=50, kernel_size=5, stride=1, padding=0)
    self.drop1=nn.Dropout2d(p=0.5)
    self.conv2 = nn.Conv2d(in_channels=50,out_channels=20,kernel_size=5, stride=1,padding=0)
    self.drop2=nn.Dropout2d(p=0.5)
    self.fc1 = nn.Linear(20 * 9 * 9, 200)   # End of Image Net1
    
    self.conv3 = nn.Conv2d(in_channels=1,out_channels=50, kernel_size=5, stride=1, padding=0)
    self.drop3=nn.Dropout2d(p=0.5)
    self.conv4 = nn.Conv2d(in_channels=50,out_channels=20,kernel_size=5, stride=1,padding=0)
    self.drop4=nn.Dropout2d(p=0.5)
    self.fc2 = nn.Linear(20 * 9 * 9, 200)   # End of Image Net2
    
    self.fc3 = nn.Linear(5, 5000)   
    self.fc4 = nn.Linear(5000, 500)
    self.fc5 = nn.Linear(500, 100)   
    self.drop5 =nn.Dropout(p=0.5)
            
    self.fc6 = nn.Linear(400 + 100, 100)
    self.drop6 =nn.Dropout(p=0.5)
    self.fc7 = nn.Linear(100, 2)   # output layer
   
def forward(self, image, data):
    x1 = F.relu(self.conv1(image))
    x1 = self.drop1(x1)
    x1 = F.max_pool2d(x1, 2, 2)
    x1 = F.relu(self.conv2(x1))
    x1 = self.drop2(x1)
    x1 = F.max_pool2d(x1, 2, 2)
    x1 = x1.view(-1, 20 * 9 * 9)
    x1 = F.relu(self.fc1(x1))
    
    x2 = F.relu(self.conv3(image))
    x2 = self.drop3(x2)
    x2 = F.avg_pool2d(x2, 2, 2)
    x2 = F.relu(self.conv4(x2))
    x2 = self.drop4(x2)
    x2 = F.avg_pool2d(x2, 2, 2)
    x2 = x2.view(-1, 20 * 9 * 9)
    x2 = F.relu(self.fc2(x2))
    
    x3 = torch.cat((x1, x2), dim=1)
    
    x4 = F.relu(self.fc3(data))
    x4 = F.relu(self.fc4(x4))
    x4 = F.relu(self.fc5(x4))
    x4 = self.drop5(x4)
    
    x = torch.cat((x3, x4), dim=1)
    x = F.relu(self.fc6(x))
    x = self.drop6(x)
    x = self.fc7(x)
    return x

I have two kinds of Inputs (Image and Data) and i want to get rid of the Image.
To start a training without the image, I delete the convolutional layers and reduce the nodes in fc5 from 500 to 100. I have read that with
modelB = TheModelBClass(*args, **kwargs)
modelB.load_state_dict(torch.load(PATH), strict=False)

you can still use the trained network, but I just can’t get it loaded. Do you have any Idea?