src.model.deeplearn.arch.rbfnet

Classes

RBFNet(**kwargs)

class src.model.deeplearn.arch.rbfnet.RBFNet(**kwargs)

Author:

Alberto M. Esmoris Pena

The Radial Basis Function Net (RBFNet) architecture.

See https://arxiv.org/abs/1812.04302

__init__(**kwargs)

See :meth:architecture.Architecture.__init__`.

build_input()

Build the input layer of the neural network. By default, only the 3D coordinates are considered as input, i.e., input dimensionality is three.

See architecture.Architecture.build_input().

Returns:

Built layer.

Return type:

tf.Tensor

build_hidden(x, **kwargs)

Build the hidden layers of the RBFNet neural network.

Parameters:

x (tf.Tensor) – The input layer for the first hidden layer.

Returns:

The last hidden layer.

Return type:

tf.Tensor

build_FSL_block(F, fs, dim_out, name)

Assist the building of feature structuring blocks providing the common operations.

See FeaturesStructuringLayer.

Parameters:

• F – The tensor of input features.

• fs – The feature structuring specification.

• dim_out – The output dimensionality for the FSL block.

Returns:

The built FSL block

check_feature_structuring(dim_out_key)

Check whether the feature structuring specification supports the given key (True) or not (False).

Parameters:

dim_out_key – The key of the output dimensionaliy element to be checked to decide on the feature structuring availability.

Returns:

True if the feature structuring is supported for given key, false otherwise.