I am making an image classifier and I have already used CNN and Transfer Learning to classify the images. Support Vector Machine gives a very good boundary with a solid margin, so now I would like to try the SVM into my project. Now I am using PyTorch for all my models. How can I make this model now?

I got this code for making an SVM Classifier -

```
import torch
import torch.nn as nn
import torch.optim as optim
from torch.autograd import Variable
class SVM(nn.Module):
def __init__(self):
super().__init__() # Call the init function of nn.Module
self.fc1 = nn.Linear(25088, 12544)
self.fc2 = nn.Linear(12544, 6272)
self.fc3 = nn.Linear(6272, 3136)
self.fc4 = nn.Linear(3136, 1568)
self.fc5 = nn.Linear(1568, 4)
def forward(self, x):
#Flatten the Image
x = x.view(x.size(0), -1)
# add dropout layer
x = self.dropout(x)
# add 1st hidden layer, with relu activation function
x = F.leaky_relu(self.fc1(x))
# add dropout layer
x = self.dropout(x)
# add 2nd hidden layer, with relu activation function
x = F.leaky_relu(self.fc2(x))
# add dropout layer
x = self.dropout(x)
# add 3rd hidden layer, with relu activation function
x = F.leaky_relu(self.fc3(x))
# add dropout layer
x = self.dropout(x)
# add 4th hidden layer, with relu activation function
x = F.leaky_relu(self.fc4(x))
# add dropout layer
x = self.dropout(x)
# add 5th hidden layer, with relu activation function
x = self.fc5(x)
# add 2nd hidden layer, with relu activation function
#x = self.fc2(x)
return x
```

Is it the code for SVM? I am not sure about this, becasue this is only just some linear layers. Can you guys please throw some light in it? Thanks!