Deep learning for NeuroImaging in Python.

Note

This page is a reference documentation. It only explains the class signature, and not how to use it. Please refer to the gallery for the big picture.

class surfify.models.vgg.SphericalGVGG16BN(input_channels, n_classes, input_dim=194, hidden_dim=4096, fusion_level=1, init_weights=True)¶

Init class.

Parameters:

input_channels : int

the number of input channels.

cfg : list

the definition of layers where ‘M’ stands for max pooling.

n_classes : int

the number of class in the classification problem.

input_dim : int, default 192

the size of the converted 3-D surface to the 2-D grid.

hidden_dim : int, default 4096

the 2-layer classification MLP number of hidden dims.

batch_norm : bool, default False

wether or not to use batch normalization after a convolution layer.

fusion_level : int, default 1

at which max pooling level left and right hemisphere data are concatenated.

init_weights : bool, default True

initialize network weights.