-------------------------------------- First Model Parameters----------------------------------------
<bound method Module.parameters of Sequential(
(0): Conv2d(3, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(1): ReLU()
(2): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
(3): Conv2d(16, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(4): ReLU()
(5): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
(6): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(7): ReLU()
(8): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
(9): Flatten(start_dim=1, end_dim=-1)
(10): Dropout(p=0.25, inplace=False)
(11): Linear(in_features=1024, out_features=500, bias=True)
(12): ReLU()
(13): Dropout(p=0.25, inplace=False)
(14): Linear(in_features=500, out_features=10, bias=True)
)>
-------------------------------------- Second Model Parameters ----------------------------------------
<bound method Module.parameters of Net(
(conv1): Conv2d(3, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv2): Conv2d(16, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(conv3): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(pool): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
(fc1): Linear(in_features=1024, out_features=500, bias=True)
(fc2): Linear(in_features=500, out_features=10, bias=True)
(dropout): Dropout(p=0.25, inplace=False)
)>
As you see, these two model parameters are not the same