File size: 1,150 Bytes
68ea1b0 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 | import torch
from torch import nn
class MiniVisionV3(nn.Module):
def __init__(self):
super().__init__()
self.model = nn.Sequential(
nn.Conv2d(1, 32, 3, padding=1),
nn.BatchNorm2d(32),
nn.ReLU(),
nn.MaxPool2d(2),
nn.Conv2d(32, 64, 3, padding=1),
nn.BatchNorm2d(64),
nn.ReLU(),
nn.MaxPool2d(2),
nn.Conv2d(64, 128, 3, padding=1),
nn.BatchNorm2d(128),
nn.ReLU(),
nn.MaxPool2d(2),
nn.Flatten(),
nn.Linear(1152, 256),
nn.ReLU(),
nn.Dropout(0.3),
nn.Linear(256, 47),
)
def forward(self, x):
x = self.model(x)
return x
if __name__ == '__main__':
minivisionv3 = MiniVisionV3()
total_params = sum(param.numel() for param in minivisionv3.parameters())
print(f"Total params: {total_params / 1000000: .2f}M")
# with torch.no_grad():
# input = torch.randn(256, 1, 28, 28)
# output = minivisionv3(input)
# print(output)
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