Regression using Images and Tabular data

I am Trying to create an effeicient neural net and the best opzimizer + learning rate to take image and tabular data and get a rank (the target) that is a float, the model and optimizer are as following:


class MyModel7(nn.Module):
    def __init__(self):
        super(MyModel7, self).__init__()
        self.cnn = GoogleNet()# any conv net for image processing
        self.dropout = nn.Dropout(0.5)
        self.norm = nn.BatchNorm1d(250)
        # 12 tabular features
        self.fc1 = nn.Linear(12 + 1, 250)
        self.fc2 = nn.Linear(250,1)
    def forward(self, image, data):
        x1 = self.cnn(image)
        x2 = data
        x =, x2), dim=1)
        x = F.relu(self.fc1(x))
        #apply dropout
        x = self.dropout(x)
        # apply batchnorm
        x = self.norm(x)
        x = self.fc2(x)
        return x

optimizer = optim.Adam(model.parameters(), lr = 1e-3, weight_decay=1e-3)

the model is trained with an early stopping mechanism to ensure the best epoch parameters are saved

This model is performing ok but i want it to be as best as possible, what is the best way to approach this regression problem with the given data to minimize the MSE?