niftynet.layer.deconvolution module¶
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infer_output_dims
(input_dims, strides, kernel_sizes, padding)[source]¶ infer output dims from list, the dim can be different in different directions. Note: dilation is not considered here.
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class
DeconvLayer
(n_output_chns, kernel_size=3, stride=1, padding='SAME', with_bias=False, w_initializer=None, w_regularizer=None, b_initializer=None, b_regularizer=None, name='deconv')[source]¶ Bases:
niftynet.layer.base_layer.TrainableLayer
This class defines a simple deconvolution with an optional bias term. Please consider
DeconvolutionalLayer
if batch_norm and activation are also used.
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class
DeconvolutionalLayer
(n_output_chns, kernel_size=3, stride=1, padding='SAME', with_bias=False, feature_normalization='batch', group_size=-1, acti_func=None, w_initializer=None, w_regularizer=None, b_initializer=None, b_regularizer=None, moving_decay=0.9, eps=1e-05, name='deconv')[source]¶ Bases:
niftynet.layer.base_layer.TrainableLayer
This class defines a composite layer with optional components:
deconvolution -> batch_norm -> activation -> dropout
The b_initializer and b_regularizer are applied to the DeconvLayer The w_initializer and w_regularizer are applied to the DeconvLayer, the batch normalisation layer, and the activation layer (for ‘prelu’)