vanilla_CNN.rb

class CNN(nn.Module):
    def __init__(self, im_size, hidden_dim,hidden_dim2,hidden_dim3, kernel_size, n_classes):
        super(CNN, self).__init__()
        self.conv1 = nn.Conv2d(3,16,3,padding=1)
        self.bn1 = nn.BatchNorm2d(16)
        self.conv2 = nn.Conv2d(16,32,3,padding=1)
        self.bn2 = nn.BatchNorm2d(32)
        self.conv3 = nn.Conv2d(32,64,3,padding=1)
        self.bn3 = nn.BatchNorm2d(64)
        self.conv4 = nn.Conv2d(64,128,3,padding=1)
        self.bn4 = nn.BatchNorm2d(128)        
        self.pool = nn.MaxPool2d(2,2)
        (C,N,H) = im_size
        hout_size = H/(2**4)
        self.fc1  = nn.Linear(hout_size*hout_size*128,hidden_dim)
        self.fc2  = nn.Linear(hidden_dim,hidden_dim2)
        self.fc3  = nn.Linear(hidden_dim2,n_classes)
        self.dropout1 = nn.Dropout(p=0.25)
        self.dropout2 = nn.Dropout(p=0.2)

    def forward(self, images):
        x = self.pool(F.relu(self.bn1(self.conv1(images))))
        x = self.pool(F.relu(self.bn2(self.conv2(x))))
        x = self.pool(F.relu(self.bn3(self.conv3(x))))
        x = self.pool(F.relu(self.bn4(self.conv4(x))))
        
        #Flatten the output to a vector
        x = x.view(x.shape[0],-1)
        x = self.dropout1(x)
        x = F.relu(self.fc1(x))
        x = self.dropout1(x)
        x = F.relu(self.fc2(x))
        x = self.dropout2(x)
        x = self.fc3(x)
        scores = x
        return scores