def test_Dropout(self):
p = 0.2
input = torch.Tensor(1000).fill_(1 - p)
module = nn.Dropout(p)
output = module.forward(input)
self.assertLess(abs(output.mean() - (1 - p)), 0.05)
gradInput = module.backward(input, input)
self.assertLess(abs(gradInput.mean() - (1 - p)), 0.05)
module = nn.Dropout(p, True)
output = module.forward(input.clone())
self.assertLess(abs(output.mean() - (1 - p)), 0.05)
gradInput = module.backward(input.clone(), input.clone())
self.assertLess(abs(gradInput.mean() - (1 - p)), 0.05)
# Check that these don't raise errors
module.__repr__()
str(module)
nn.Conv2d(32, 96, (5, 5), (1, 1), (2, 2)),
nn.ReLU(),
nn.MaxPool2d((3, 3), (1, 1), (1, 1), ceil_mode=True),
nn.Conv2d(256, 64, (1, 1)),
nn.ReLU(),
nn.MaxPool2d((3, 3), (2, 2), (0, 0), ceil_mode=True),
nn.Conv2d(480, 192, (1, 1)),
nn.ReLU(),
nn.Conv2d(480, 96, (1, 1)),
nn.ReLU(),
nn.Conv2d(96, 208, (3, 3), (1, 1), (1, 1)),
nn.ReLU(),
nn.Conv2d(480, 16, (1, 1)),
nn.ReLU(),
nn.Conv2d(16, 48, (5, 5), (1, 1), (2, 2)),
nn.ReLU(),
nn.MaxPool2d((3, 3), (1, 1), (1, 1), ceil_mode=True),
nn.Conv2d(480, 64, (1, 1)),
nn.ReLU(),
nn.AvgPool2d((5, 5), (3, 3), (0, 0), ceil_mode=True), #AvgPool2d,
nn.Conv2d(512, 128, (1, 1)),
nn.ReLU(),
Lambda(lambda x: x.view(x.size(0), -1)), # View,
nn.Sequential(Lambda(lambda x: x.view(1, -1) if 1 == len(x.size()) else x),
nn.Linear(2048, 1024)), # Linear,
nn.ReLU(),
nn.Dropout(0.7),
nn.Sequential(Lambda(lambda x: x.view(1, -1) if 1 == len(x.size()) else x),
nn.Linear(1024, 365)), # Linear,
)
