niftynet.layer.convolution module¶
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class
niftynet.layer.convolution.
ConvLayer
(n_output_chns, kernel_size=3, stride=1, dilation=1, padding='SAME', with_bias=False, w_initializer=None, w_regularizer=None, b_initializer=None, b_regularizer=None, name='conv')¶ Bases:
niftynet.layer.base_layer.TrainableLayer
This class defines a simple convolution with an optional bias term. Please consider ConvolutionalLayer if batch_norm and activation are also used.
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layer_op
(input_tensor)¶
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class
niftynet.layer.convolution.
ConvolutionalLayer
(n_output_chns, kernel_size=3, stride=1, dilation=1, padding='SAME', with_bias=False, with_bn=True, acti_func=None, w_initializer=None, w_regularizer=None, b_initializer=None, b_regularizer=None, moving_decay=0.9, eps=1e-05, name='conv')¶ Bases:
niftynet.layer.base_layer.TrainableLayer
- This class defines a composite layer with optional components:
- convolution -> batch_norm -> activation -> dropout
The b_initializer and b_regularizer are applied to the ConvLayer The w_initializer and w_regularizer are applied to the ConvLayer, the batch normalisation layer, and the activation layer (for ‘prelu’)
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layer_op
(input_tensor, is_training=None, keep_prob=None)¶
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niftynet.layer.convolution.
default_b_initializer
()¶
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niftynet.layer.convolution.
default_w_initializer
()¶