A.Going Deeper with Convolutions B.GoogleLeNet module=nn.Inception(config) This module usesn+2 parallel "columns". The original paper uses 2+2 where the first two are (but there could be more than two): 1x1 conv (reduce) -> relu -> 5x5 conv -> relu ...
This is useful when you want to output a value that is independent of the input to the neural network (see this example).SpatialUniformCropmodule = nn.SpatialUniformCrop(oheight, owidth)During training, this module will output a cropped patch of size oheight, owidth within the boundaries ...
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A. Going Deeper with Convolutions B. GoogleLeNetmodule = nn.Inception(config)This module uses n+2 parallel "columns". The original paper uses 2+2 where the first two are (but there could be more than two):1x1 conv (reduce) -> relu -> 5x5 conv -> relu 1x1 conv (reduce) -> ...
You can also use nn.Kmeans() as an auxillary layer in your network. A call to forward will generate an output containing the index of the nearest cluster for each sample in the batch. The gradInput generated by updateGradInput will be zero....
This is useful for profiling new modules you develop, and tracking down bottlenecks in the speed of a network.The timer and print statement can add a small amount of overhead to the overall speed.As an example:mlp = nn.Sequential() mlp:add(nn.Identity()) mlp:add(nn.Linear(1000,1000)...
This is useful when you want to output a value that is independent of the input to the neural network (see this example).SpatialUniformCropmodule = nn.SpatialUniformCrop(oheight, owidth)During training, this module will output a cropped patch of size oheight, owidth within the boundaries ...
This is useful when you want to output a value that is independent of the input to the neural network (see this example).SpatialUniformCropmodule = nn.SpatialUniformCrop(oheight, owidth)During training, this module will output a cropped patch of size oheight, owidth within the boundaries ...
You can also use nn.Kmeans() as an auxillary layer in your network. A call to forward will generate an output containing the index of the nearest cluster for each sample in the batch. The gradInput generated by updateGradInput will be zero....
A. Going Deeper with Convolutions B. GoogleLeNetmodule = nn.Inception(config)This module uses n+2 parallel "columns". The original paper uses 2+2 where the first two are (but there could be more than two):1x1 conv (reduce) -> relu -> 5x5 conv -> relu 1x1 conv (reduce) -> ...