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AlexNet in Torch.
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| ------- AlexNet: Using my own weight initialization | |
| model = nn.Sequential() | |
| model:add(cudnn.SpatialConvolution(3,96,11,11,4,4,2,2)) | |
| model.modules[#model.modules].weight:normal(0, 0.01) | |
| model.modules[#model.modules].bias:fill(0) | |
| model:add(cudnn.ReLU()) | |
| model:add(inn.SpatialCrossResponseNormalization(5, 0.0001, 0.75, 1)) | |
| model:add(nn.SpatialMaxPooling(3,3,2,2)) | |
| model:add(cudnn.SpatialConvolution(96,256,5,5,1,1,2,2)) | |
| model.modules[#model.modules].weight:normal(0, 0.01) | |
| model.modules[#model.modules].bias:fill(0.1) | |
| model:add(cudnn.ReLU()) | |
| model:add(inn.SpatialCrossResponseNormalization(5, 0.0001, 0.75, 1)) | |
| model:add(nn.SpatialMaxPooling(3,3,2,2)) | |
| model:add(cudnn.SpatialConvolution(256,384,3,3,1,1,1,1)) | |
| model.modules[#model.modules].weight:normal(0, 0.01) | |
| model.modules[#model.modules].bias:fill(0) | |
| model:add(cudnn.ReLU()) | |
| model:add(cudnn.SpatialConvolution(384,384,3,3,1,1,1,1)) | |
| model.modules[#model.modules].weight:normal(0, 0.01) | |
| model.modules[#model.modules].bias:fill(0.1) | |
| model:add(cudnn.ReLU()) | |
| model:add(cudnn.SpatialConvolution(384,256,3,3,1,1,1,1)) | |
| model.modules[#model.modules].weight:normal(0, 0.01) | |
| model.modules[#model.modules].bias:fill(0.1) | |
| model:add(nn.ReLU()) | |
| model:add(nn.SpatialMaxPooling(3,3,2,2)) | |
| model:add(nn.View(256*6*6)) | |
| model:add(nn.Linear(256*6*6, 4096)) | |
| model.modules[#model.modules].weight:normal(0, 0.005) | |
| model.modules[#model.modules].bias:fill(0.1) | |
| model:add(cudnn.ReLU()) | |
| model:add(nn.Dropout(0.5)) | |
| model:add(nn.Linear(4096, 4096)) | |
| model.modules[#model.modules].weight:normal(0, 0.005) | |
| model.modules[#model.modules].bias:fill(0.1) | |
| model:add(cudnn.ReLU()) | |
| model:add(nn.Dropout(0.5)) | |
| model:add(nn.Linear(4096, 1000)) | |
| model.modules[#model.modules].weight:normal(0, 0.01) | |
| model.modules[#model.modules].bias:fill(0) | |
| model:add(nn.LogSoftMax()) | |
| model:cuda() |
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| ------- AlexNet: Using Torch defaults for weight initialization | |
| model = nn.Sequential() | |
| model:add(cudnn.SpatialConvolution(3,96,11,11,4,4,2,2)) | |
| -- model.modules[#model.modules].weight:normal(0, 0.01) | |
| -- model.modules[#model.modules].bias:fill(0) | |
| model:add(cudnn.ReLU()) | |
| model:add(inn.SpatialCrossResponseNormalization(5, 0.0001, 0.75, 1)) | |
| model:add(nn.SpatialMaxPooling(3,3,2,2)) | |
| model:add(cudnn.SpatialConvolution(96,256,5,5,1,1,2,2)) | |
| -- model.modules[#model.modules].weight:normal(0, 0.01) | |
| -- model.modules[#model.modules].bias:fill(0.1) | |
| model:add(cudnn.ReLU()) | |
| model:add(inn.SpatialCrossResponseNormalization(5, 0.0001, 0.75, 1)) | |
| model:add(nn.SpatialMaxPooling(3,3,2,2)) | |
| model:add(cudnn.SpatialConvolution(256,384,3,3,1,1,1,1)) | |
| -- model.modules[#model.modules].weight:normal(0, 0.01) | |
| -- model.modules[#model.modules].bias:fill(0) | |
| model:add(cudnn.ReLU()) | |
| model:add(cudnn.SpatialConvolution(384,384,3,3,1,1,1,1)) | |
| -- model.modules[#model.modules].weight:normal(0, 0.01) | |
| -- model.modules[#model.modules].bias:fill(0.1) | |
| model:add(cudnn.ReLU()) | |
| model:add(cudnn.SpatialConvolution(384,256,3,3,1,1,1,1)) | |
| -- model.modules[#model.modules].weight:normal(0, 0.01) | |
| -- model.modules[#model.modules].bias:fill(0.1) | |
| model:add(nn.ReLU()) | |
| model:add(nn.SpatialMaxPooling(3,3,2,2)) | |
| model:add(nn.View(256*6*6)) | |
| model:add(nn.Linear(256*6*6, 4096)) | |
| -- model.modules[#model.modules].weight:normal(0, 0.005) | |
| -- model.modules[#model.modules].bias:fill(0.1) | |
| model:add(cudnn.ReLU()) | |
| model:add(nn.Dropout(0.5)) | |
| model:add(nn.Linear(4096, 4096)) | |
| -- model.modules[#model.modules].weight:normal(0, 0.005) | |
| -- model.modules[#model.modules].bias:fill(0.1) | |
| model:add(cudnn.ReLU()) | |
| model:add(nn.Dropout(0.5)) | |
| model:add(nn.Linear(4096, 1000)) | |
| -- model.modules[#model.modules].weight:normal(0, 0.01) | |
| -- model.modules[#model.modules].bias:fill(0) | |
| model:add(nn.LogSoftMax()) | |
| model:cuda() |
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