megnet.layers.graph package

Submodules

• megnet.layers.graph.base module
• megnet.layers.graph.cgcnn module
• megnet.layers.graph.megnet module
• megnet.layers.graph.schnet module

Module contents

Graph layers implementations

class CrystalGraphLayer(*args, **kwargs)

Bases: megnet.layers.graph.base.GraphNetworkLayer

The CGCNN graph implementation as described in the paper

Xie et al. PHYSICAL REVIEW LETTERS 120, 145301 (2018)

call(inputs, mask=None)

the logic of the layer, returns the final graph

compute_output_shape(input_shape)

compute static output shapes, returns list of tuple shapes

build(input_shape)

initialize the weights and biases for each function

phi_e(inputs)

update function for bonds and returns updated bond attribute e_p

rho_e_v(e_p, inputs)

aggregate updated bonds e_p to per atom attributes, b_e_p

phi_v(b_e_p, inputs)

update the atom attributes by the results from previous step b_e_p and all the inputs returns v_p.

rho_e_u(e_p, inputs)

aggregate bonds to global attribute

rho_v_u(v_p, inputs)

aggregate atom to global attributes

get_config()

part of keras interface for serialization

Parameters

• activation (str) – Default: None. The activation function used for each sub-neural network. Examples include ‘relu’, ‘softmax’, ‘tanh’, ‘sigmoid’ and etc.

• use_bias (bool) – Default: True. Whether to use the bias term in the neural network.

• kernel_initializer (str) – Default: ‘glorot_uniform’. Initialization function for the layer kernel weights,

• bias_initializer (str) – Default: ‘zeros’

• activity_regularizer (str) – Default: None. The regularization function for the output

• kernel_constraint (str) – Default: None. Keras constraint for kernel values

• bias_constraint (str) – Default: None .Keras constraint for bias values

• kwargs (dictionary) – additional keyword args

build(input_shapes)

Build the weights for the layer :param input_shapes: the shapes of all input tensors :type input_shapes: sequence of tuple

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

phi_e(inputs)

Edge update function :param inputs: :type inputs: tuple of tensor

Returns

output tensor

phi_u(b_e_p, b_v_p, inputs)

Parameters

• b_e_p (tf.Tensor) – edge/bond to global aggregated tensor

• b_v_p (tf.Tensor) – node/atom to global aggregated tensor

• inputs (Sequence) – list or tuple for the graph inputs

Returns

updated globa/state attributes

phi_v(b_ei_p, inputs)

Node update function :param b_ei_p: edge aggregated tensor :type b_ei_p: tensor :param inputs: other graph inputs :type inputs: tuple of tensors

Returns: updated node tensor

rho_e_u(e_p, inputs)

aggregate edge to state :param e_p: edge tensor :type e_p: tensor :param inputs: other graph input tensors :type inputs: tuple of tensors

Returns: edge aggregated tensor for states

rho_e_v(e_p, inputs)

Reduce edge attributes to node attribute, eqn 5 in the paper :param e_p: updated bond :param inputs: the whole input list

Returns: summed tensor

rho_v_u(v_p, inputs)

Parameters

• v_p (tf.Tensor) – updated atom/node attributes

• inputs (Sequence) – list or tuple for the graph inputs

Returns

atom/node to global/state aggregated tensor

class GraphNetworkLayer(*args, **kwargs)

Bases: keras.engine.base_layer.Layer

Implementation of a graph network layer. Current implementation is based on neural networks for each update function, and sum or mean for each aggregation function

Method:

call(inputs, mask=None): the logic of the layer, returns the final graph compute_output_shape(input_shape): compute static output shapes, returns list of tuple shapes build(input_shape): initialize the weights and biases for each function phi_e(inputs): update function for bonds and returns updated bond attribute e_p rho_e_v(e_p, inputs): aggregate updated bonds e_p to per atom attributes, b_e_p phi_v(b_e_p, inputs): update the atom attributes by the results from previous step b_e_p and all the inputs

returns v_p.

rho_e_u(e_p, inputs): aggregate bonds to global attribute rho_v_u(v_p, inputs): aggregate atom to global attributes get_config(): part of keras interface for serialization

Parameters

• activation (str) – Default: None. The activation function used for each sub-neural network. Examples include ‘relu’, ‘softmax’, ‘tanh’, ‘sigmoid’ and etc.

• use_bias (bool) – Default: True. Whether to use the bias term in the neural network.

• kernel_initializer (str) – Default: ‘glorot_uniform’. Initialization function for the layer kernel weights,

• bias_initializer (str) – Default: ‘zeros’

• activity_regularizer (str) – Default: None. The regularization function for the output

• kernel_constraint (str) – Default: None. Keras constraint for kernel values

• bias_constraint (str) – Default: None .Keras constraint for bias values

• **kwargs –

call(inputs: Sequence, mask=None) → Sequence

Core logic of graph network :param inputs: input tensors :type inputs: Sequence :param mask: mask tensor :type mask: tensor

Returns: output tensor

get_config() → Dict

Part of keras layer interface, where the signature is converted into a dict :returns: configurational dictionary

phi_e(inputs: Sequence) → tensorflow.python.framework.ops.Tensor

This is for updating the edge attributes ek’ = phi_e(ek, vrk, vsk, u)

Parameters

inputs (Sequence) – list or tuple for the graph inputs

Returns

updated edge/bond attributes

phi_u(b_e_p: tensorflow.python.framework.ops.Tensor, b_v_p: tensorflow.python.framework.ops.Tensor, inputs: Sequence) → tensorflow.python.framework.ops.Tensor

u’ = phi_u(bar e’, bar v’, u) :param b_e_p: edge/bond to global aggregated tensor :type b_e_p: tf.Tensor :param b_v_p: node/atom to global aggregated tensor :type b_v_p: tf.Tensor :param inputs: list or tuple for the graph inputs :type inputs: Sequence

Returns

updated globa/state attributes

phi_v(b_ei_p: tensorflow.python.framework.ops.Tensor, inputs: Sequence)

Step 3. Compute updated node attributes v_i’ = phi_v(bar e_i, vi, u)

Parameters

• b_ei_p (tf.Tensor) – edge-to-node aggregated tensor

• inputs (Sequence) – list or tuple for the graph inputs

Returns

updated node/atom attributes

rho_e_u(e_p: tensorflow.python.framework.ops.Tensor, inputs: Sequence) → tensorflow.python.framework.ops.Tensor

let V’ = {v’} i = 1:Nv let E’ = {(e_k’, rk, sk)} k = 1:Ne bar e’ = rho_e_u(E’)

Parameters

• e_p (tf.Tensor) – updated edge/bond attributes

• inputs (Sequence) – list or tuple for the graph inputs

Returns

edge/bond to global/state aggregated tensor

rho_e_v(e_p: tensorflow.python.framework.ops.Tensor, inputs: Sequence) → tensorflow.python.framework.ops.Tensor

This is for step 2, aggregate edge attributes per node Ei’ = {(ek’, rk, sk)} with rk =i, k=1:Ne

Parameters

• e_p (tf.Tensor) – the updated edge attributes

• inputs (Sequence) – list or tuple for the graph inputs

Returns

edge/bond to node/atom aggregated tensor

rho_v_u(v_p: tensorflow.python.framework.ops.Tensor, inputs: Sequence) → tensorflow.python.framework.ops.Tensor

bar v’ = rho_v_u(V’)

Parameters

• v_p (tf.Tensor) – updated atom/node attributes

• inputs (Sequence) – list or tuple for the graph inputs

Returns

atom/node to global/state aggregated tensor

class InteractionLayer(*args, **kwargs)

Bases: megnet.layers.graph.base.GraphNetworkLayer

The Continuous filter InteractionLayer in Schnet

Schütt et al. SchNet: A continuous-filter convolutional neural network for modeling quantum interactions

call(inputs, mask=None)

the logic of the layer, returns the final graph

compute_output_shape(input_shape)

compute static output shapes, returns list of tuple shapes

build(input_shape)

initialize the weights and biases for each function

phi_e(inputs)

update function for bonds and returns updated bond attribute e_p

rho_e_v(e_p, inputs)

aggregate updated bonds e_p to per atom attributes, b_e_p

phi_v(b_e_p, inputs)

update the atom attributes by the results from previous step b_e_p and all the inputs returns v_p.

rho_e_u(e_p, inputs)

aggregate bonds to global attribute

rho_v_u(v_p, inputs)

aggregate atom to global attributes

get_config()

part of keras interface for serialization

Parameters

• activation (str) – Default: None. The activation function used for each sub-neural network. Examples include ‘relu’, ‘softmax’, ‘tanh’, ‘sigmoid’ and etc.

• use_bias (bool) – Default: True. Whether to use the bias term in the neural network.

• kernel_initializer (str) – Default: ‘glorot_uniform’. Initialization function for the layer kernel weights,

• bias_initializer (str) – Default: ‘zeros’

• activity_regularizer (str) – Default: None. The regularization function for the output

• kernel_constraint (str) – Default: None. Keras constraint for kernel values

• bias_constraint (str) – Default: None .Keras constraint for bias values

build(input_shapes)

Build the weights for the layer :param input_shapes: the shapes of all input tensors :type input_shapes: sequence of tuple

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

phi_e(inputs)

Edge update function :param inputs: :type inputs: tuple of tensor

Returns

output tensor

phi_u(b_e_p, b_v_p, inputs)

Parameters

• b_e_p (tf.Tensor) – edge/bond to global aggregated tensor

• b_v_p (tf.Tensor) – node/atom to global aggregated tensor

• inputs (Sequence) – list or tuple for the graph inputs

Returns

updated globa/state attributes

phi_v(b_ei_p, inputs)

Node update function :param b_ei_p: edge aggregated tensor :type b_ei_p: tensor :param inputs: other graph inputs :type inputs: tuple of tensors

Returns: updated node tensor

rho_e_u(e_p, inputs)

aggregate edge to state :param e_p: edge tensor :type e_p: tensor :param inputs: other graph input tensors :type inputs: tuple of tensors

Returns: edge aggregated tensor for states

rho_e_v(e_p, inputs)

Reduce edge attributes to node attribute, eqn 5 in the paper :param e_p: updated bond :param inputs: the whole input list

Returns: summed tensor

rho_v_u(v_p, inputs)

Parameters

• v_p (tf.Tensor) – updated atom/node attributes

• inputs (Sequence) – list or tuple for the graph inputs

Returns

atom/node to global/state aggregated tensor

class MEGNetLayer(*args, **kwargs)

Bases: megnet.layers.graph.base.GraphNetworkLayer

The MEGNet graph implementation as described in the paper

Chen, Chi; Ye, Weike Ye; Zuo, Yunxing; Zheng, Chen; Ong, Shyue Ping. Graph Networks as a Universal Machine Learning Framework for Molecules and Crystals, 2018, arXiv preprint. [arXiv:1812.05055](https://arxiv.org/abs/1812.05055) .. method:: call(inputs, mask=None)

the logic of the layer, returns the final graph

compute_output_shape(input_shape)

compute static output shapes, returns list of tuple shapes

build(input_shape)

initialize the weights and biases for each function

phi_e(inputs)

update function for bonds and returns updated bond attribute e_p

rho_e_v(e_p, inputs)

aggregate updated bonds e_p to per atom attributes, b_e_p

phi_v(b_e_p, inputs)

update the atom attributes by the results from previous step b_e_p and all the inputs returns v_p.

rho_e_u(e_p, inputs)

aggregate bonds to global attribute

rho_v_u(v_p, inputs)

aggregate atom to global attributes

get_config()

part of keras interface for serialization

Parameters

• units_v (list of integers) – the hidden layer sizes for node update neural network

• units_e (list of integers) – the hidden layer sizes for edge update neural network

• units_u (list of integers) – the hidden layer sizes for state update neural network

• pool_method (str) – ‘mean’ or ‘sum’, determines how information is gathered to nodes from neighboring edges

• activation (str) – Default: None. The activation function used for each sub-neural network. Examples include ‘relu’, ‘softmax’, ‘tanh’, ‘sigmoid’ and etc.

• use_bias (bool) – Default: True. Whether to use the bias term in the neural network.

• kernel_initializer (str) – Default: ‘glorot_uniform’. Initialization function for the layer kernel weights,

• bias_initializer (str) – Default: ‘zeros’

• activity_regularizer (str) – Default: None. The regularization function for the output

• kernel_constraint (str) – Default: None. Keras constraint for kernel values

• bias_constraint (str) – Default: None .Keras constraint for bias values

build(input_shapes)

Build the weights for the layer :param input_shapes: the shapes of all input tensors :type input_shapes: sequence of tuple

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

phi_e(inputs)

Edge update function :param inputs: :type inputs: tuple of tensor

Returns

output tensor

phi_u(b_e_p, b_v_p, inputs)

Parameters

• b_e_p (tf.Tensor) – edge/bond to global aggregated tensor

• b_v_p (tf.Tensor) – node/atom to global aggregated tensor

• inputs (Sequence) – list or tuple for the graph inputs

Returns

updated globa/state attributes

phi_v(b_ei_p, inputs)

Node update function :param b_ei_p: edge aggregated tensor :type b_ei_p: tensor :param inputs: other graph inputs :type inputs: tuple of tensors

Returns: updated node tensor

rho_e_u(e_p, inputs)

aggregate edge to state :param e_p: edge tensor :type e_p: tensor :param inputs: other graph input tensors :type inputs: tuple of tensors

Returns: edge aggregated tensor for states

rho_e_v(e_p, inputs)

Reduce edge attributes to node attribute, eqn 5 in the paper :param e_p: updated bond :param inputs: the whole input list

Returns: summed tensor

rho_v_u(v_p, inputs)

Parameters

• v_p (tf.Tensor) – updated atom/node attributes

• inputs (Sequence) – list or tuple for the graph inputs

Returns

atom/node to global/state aggregated tensor