Hi Guys,
I need to create Inception network in pytorch for one of my project from scratch so I am digging some tutorials most of the tutorials advice to use pre trained weight. But with some research I build small block of inception model on MNIST dataset.
Just need to know is it correct or not.
**Input size 128,1,28,28 **
(128 is batch size)

Screenshot from 2020-04-05 18-31-03759×369 41.5 KB

class InceptionA(nn.Module):
    def __init__(self):
        super().__init__()
        ##Branch 1
        self.conv1x1   =  nn.Conv2d(in_channels=1,out_channels= 32 , kernel_size = 1)

       
        ##Branch 2
        self.conv1x1_1 =  nn.Conv2d(in_channels=1,out_channels= 32 , kernel_size = 1)        
        self.conv5x5   =  nn.Conv2d(in_channels=32,out_channels= 64 , kernel_size = 5,padding =2)
        
        ##Branch 3
        self.conv1x1_2 =  nn.Conv2d(in_channels=1,out_channels= 64 , kernel_size = 1)
        self.conv3x3   =  nn.Conv2d(in_channels=64,out_channels= 128 , kernel_size = 3)
        self.conv3x3_1 =  nn.Conv2d(in_channels=128,out_channels= 196 , kernel_size = 3,padding=2)
        
        ##Branch 4        
        self.pooling   =  nn.MaxPool2d(kernel_size=3, stride=1, padding=1)
        self.conv1x1_3 =  nn.Conv2d(in_channels=1,out_channels= 64 , kernel_size = 1 ,padding = 0)
        
        ## Linear Layer
        self.fc1 = nn.Linear(356*28*28,32)
        self.fc2 = nn.Linear(32,10)
        
        
        
    
    def forward(self,x):
        ## 1st branch
        conv1x1   = torch.relu(self.conv1x1(x))
        
        ## 2nd Branch
        conv1x1_1 = torch.relu(self.conv1x1_1(x))
        conv5x5 = torch.relu(self.conv5x5(conv1x1_1))
        
        ## 3rd Branch
        conv1x1_2 = torch.relu(self.conv1x1_2(x))
        conv3x3   = torch.relu(self.conv3x3(conv1x1_2))
        conv3x3_1 = torch.relu(self.conv3x3_1(conv3x3))
        
        ## 4th Branch
        pooling = torch.relu(self.pooling(x))
        conv1x1_3  = torch.relu(self.conv1x1_3(pooling))
        
        output = [conv1x1,conv5x5,conv3x3_1,conv1x1_3]
        out = torch.cat(output,1)
        
        out = out.view(-1,356*28*28)
        
        x = self.fc1(out)
        x = self.fc2(x)
        
        return x

Please let me know if anyone  need more info.
Stay safe.!!!