sequential_approach.py

# create class Flatten to flatten the tensor
class Flatten(nn.Module):
    def forward(self, x):
        return x.view(x.size()[0], -1)

# use OrderedDict to give meaningful names for layers
model = nn.Sequential(OrderedDict([
                        ('conv_1', nn.Conv2d(3, 16, 3, padding=1)),
                        ('relu_1', nn.ReLU()),
                        ('max_pool_1', nn.MaxPool2d(2, 2)),
                        ('conv_2', nn.Conv2d(16, 32, 3, padding=1)),
                        ('relu_2', nn.ReLU()),
                        ('max_pool_2', nn.MaxPool2d(2, 2)),
                        ('conv_3', nn.Conv2d(32, 64, 3, padding=1)),
                        ('relu_3', nn.ReLU()),
                        ('max_pool_3', nn.MaxPool2d(2, 2)),
                        ('flatten', Flatten()),
                        ('dropout_1', nn.Dropout(0.25)),
                        ('fc_1', nn.Linear(64 * 16 * 16, 128)),
                        ('relu_4', nn.ReLU()),
                        ('dropout', nn.Dropout(0.25)),
                        ('fc_2', nn.Linear(128, 1))
]))