megnet.layers.readout package¶
Submodules¶
Module contents¶
readout layers
- class LinearWithIndex(*args, **kwargs)[source]¶
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)[source]¶
Build tensors :param input_shape: input shapes :type input_shape: sequence of tuple
- call(inputs, mask=None)[source]¶
Main logic :param inputs: input tensors :type inputs: tuple of tensor :param mask: mask tensor :type mask: tensor
Returns: output tensor
- class Set2Set(*args, **kwargs)[source]¶
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)[source]¶
Build tensors :param input_shape: input shapes :type input_shape: sequence of tuple
- call(inputs, mask=None)[source]¶
Main logic :param inputs: input tensors :type inputs: tuple of tensor :param mask: mask tensor :type mask: tensor
Returns: output tensor