NodeLayer Class Reference

Inheritance diagram for NodeLayer:

Collaboration diagram for NodeLayer:

Public Member Functions
def	__init__ (self, nfeat_in_dim, efeat_in_dim, gfeat_in_dim, nfeat_hid_dim, nfeat_out_dim, num_hid_layers, dropout, normalize=True)
def	forward (self, x, edge_index, edge_attr, u, batch)

Public Attributes
	nonlin_function
	Non-linear activation.
	num_hid_layers
	Number of hidden layers.
	dropout_prob
	Dropout probability.
	normalize
	Normalize.
	lin_in
	Input linear layer.
	lins_hid
	Intermediate linear layers.
	lin_out
	Output linear layer.
	norm
	Batch normalization.

Detailed Description

Updates node features in MetaLayer:

.. math::
    v_{i}^{'} = \\phi^{v}(v_{i}, \\rho^{e \\to v}(v_{i}), u)

with

.. math::
    \\rho^{e \\to v}(v_{i}) = \\frac{\\sum_{j=1,\\ j \\neq i}^{N} (e_{ji} + e _{ij})}{2 \\cdot (N-1)},

where :math:`\\phi^{v}` is a neural network of the form

.. figure:: figs/MLP_structure.png
    :width: 42em
    :align: center

Args:
    nfeat_in_dim (int): Node features input dimension (number of node features in input).
    efeat_in_dim (int): Edge features input dimension (number of edge features in input).
    gfeat_in_dim (int): Gloabl features input dimension (number of global features in input).
    nfeat_hid_dim (int): Node features dimension in hidden layers.
    nfeat_out_dim (int): Node features output dimension.
    num_hid_layers (int): Number of hidden layers.
    dropout (float): Dropout rate :math:`r \\in [0,1]`.
    normalize (str): Type of normalization (batch/layer).

:return: Updated node features tensor.
:rtype: `Tensor <https://pytorch.org/docs/stable/tensors.html#torch.Tensor>`_

Definition at line 137 of file geometric_layers.py.

Constructor & Destructor Documentation

__init__()

def __init__	(		self,
			nfeat_in_dim,
			efeat_in_dim,
			gfeat_in_dim,
			nfeat_hid_dim,
			nfeat_out_dim,
			num_hid_layers,
			dropout,
			normalize = True
	)

Initialization.

Definition at line 169 of file geometric_layers.py.

Member Function Documentation

forward()

def forward	(		self,
			x,
			edge_index,
			edge_attr,
			u,
			batch
	)

Called internally by PyTorch to propagate the input through the network.
 - x: [N, F_x], where N is the number of nodes.
 - edge_index: [2, E] with max entry N - 1.
 - edge_attr: [E, F_e]
 - u: [B, F_u]
 - batch: [N] with max entry B - 1.

Edge labels are averaged (dim_size = N: number of nodes in the graph)

Definition at line 214 of file geometric_layers.py.

---

The documentation for this class was generated from the following file:

• analysis/scripts/grafei/model/geometric_layers.py