megnet.layers.readout package

Submodules

• megnet.layers.readout.linear module
• megnet.layers.readout.set2set module

Module contents

readout layers

class LinearWithIndex(*args, **kwargs)

Bases: keras.engine.base_layer.Layer

Sum or average the node/edge attributes to get a structure-level vector

Parameters

• mode – (str) ‘mean’, ‘sum’, ‘max’, ‘mean’ or ‘prod’

• **kwargs –

build(input_shape)

Build tensors :param input_shape: input shapes :type input_shape: sequence of tuple

call(inputs, mask=None)

Main logic :param inputs: input tensors :type inputs: tuple of tensor :param mask: mask tensor :type mask: tensor

Returns: output tensor

compute_output_shape(input_shape)

Compute output shapes from input shapes :param input_shape: input shapes :type input_shape: sequence of tuple

Returns: sequence of tuples output shapes

get_config()

Part of keras layer interface, where the signature is converted into a dict

Returns

configurational dictionary

class Set2Set(*args, **kwargs)

Bases: keras.engine.base_layer.Layer

For a set of vectors, the set2set neural network maps it to a single vector. The order invariance is acheived by a attention mechanism. See Vinyals, Oriol, Samy Bengio, and Manjunath Kudlur. “Order matters: Sequence to sequence for sets.” arXiv preprint arXiv:1511.06391 (2015).

Parameters

• T – (int) recurrent step

• n_hidden – (int) number of hidden units

• activation – (str or object) activation function

• activation_lstm – (str or object) activation function for lstm

• recurrent_activation – (str or object) activation function for recurrent step

• kernel_initializer – (str or object) initializer for kernel weights

• recurrent_initializer – (str or object) initializer for recurrent weights

• bias_initializer – (str or object) initializer for biases

• use_bias – (bool) whether to use biases

• unit_forget_bias – (bool) whether to use basis in forget gate

• kernel_regularizer – (str or object) regularizer for kernel weights

• recurrent_regularizer – (str or object) regularizer for recurrent weights

• bias_regularizer – (str or object) regularizer for biases

• kernel_constraint – (str or object) constraint for kernel weights

• recurrent_constraint – (str or object) constraint for recurrent weights

• bias_constraint – (str or object) constraint for biases

• kwargs – other inputs for keras Layer class

build(input_shape)

Build tensors :param input_shape: input shapes :type input_shape: sequence of tuple

call(inputs, mask=None)

Main logic :param inputs: input tensors :type inputs: tuple of tensor :param mask: mask tensor :type mask: tensor

Returns: output tensor

compute_output_shape(input_shape)

Compute output shapes from input shapes :param input_shape: input shapes :type input_shape: sequence of tuple

Returns: sequence of tuples output shapes

get_config()

Part of keras layer interface, where the signature is converted into a dict

Returns

configurational dictionary