VGAE

class VGAE

Bases: VGAE, EmbeddingMixin

__init__(dim, hidden_dim, num_features, dist=False)

initialise a Variational Graph Auto-Encoder model

Parameters:

• dim – output dimension

• hidden_dim – inner hidden dimension

• num_features – number of input features

• dist – if True use distance decoder, otherwise use inner product decoder (default: False)

Returns:

initialised tg.nn.VGAE model

Methods

__init__	initialise a Variational Graph Auto-Encoder model
add_module	Add a child module to the current module.
apply	Apply fn recursively to every submodule (as returned by .children()) as well as self.
bfloat16	Casts all floating point parameters and buffers to bfloat16 datatype.
buffers	Return an iterator over module buffers.
children	Return an iterator over immediate children modules.
compile	Compile this Module's forward using torch.compile().
cpu	Move all model parameters and buffers to the CPU.
cuda	Move all model parameters and buffers to the GPU.
decode	Runs the decoder and computes edge probabilities.
double	Casts all floating point parameters and buffers to double datatype.
embed	Compute embedding for model and data
encode
eval	Set the module in evaluation mode.
extra_repr	Set the extra representation of the module.
float	Casts all floating point parameters and buffers to float datatype.
forward	Alias for encode().
get_buffer	Return the buffer given by target if it exists, otherwise throw an error.
get_extra_state	Return any extra state to include in the module's state_dict.
get_parameter	Return the parameter given by target if it exists, otherwise throw an error.
get_submodule	Return the submodule given by target if it exists, otherwise throw an error.
half	Casts all floating point parameters and buffers to half datatype.
ipu	Move all model parameters and buffers to the IPU.
kl_loss	Computes the KL loss, either for the passed arguments mu and logstd, or based on latent variables from last encoding.
load_state_dict	Copy parameters and buffers from state_dict into this module and its descendants.
modules	Return an iterator over all modules in the network.
named_buffers	Return an iterator over module buffers, yielding both the name of the buffer as well as the buffer itself.
named_children	Return an iterator over immediate children modules, yielding both the name of the module as well as the module itself.
named_modules	Return an iterator over all modules in the network, yielding both the name of the module as well as the module itself.
named_parameters	Return an iterator over module parameters, yielding both the name of the parameter as well as the parameter itself.
parameters	Return an iterator over module parameters.
recon_loss	Given latent variables z, computes the binary cross entropy loss for positive edges pos_edge_index and negative sampled edges.
register_backward_hook	Register a backward hook on the module.
register_buffer	Add a buffer to the module.
register_forward_hook	Register a forward hook on the module.
register_forward_pre_hook	Register a forward pre-hook on the module.
register_full_backward_hook	Register a backward hook on the module.
register_full_backward_pre_hook	Register a backward pre-hook on the module.
register_load_state_dict_post_hook	Register a post hook to be run after module's load_state_dict is called.
register_module	Alias for add_module().
register_parameter	Add a parameter to the module.
register_state_dict_pre_hook	Register a pre-hook for the load_state_dict() method.
reparametrize
requires_grad_	Change if autograd should record operations on parameters in this module.
reset_parameters	Resets all learnable parameters of the module.
set_extra_state	Set extra state contained in the loaded state_dict.
share_memory	See torch.Tensor.share_memory_().
state_dict	Return a dictionary containing references to the whole state of the module.
test	Given latent variables z, positive edges pos_edge_index and negative edges neg_edge_index, computes area under the ROC curve (AUC) and average precision (AP) scores.
to	Move and/or cast the parameters and buffers.
to_empty	Move the parameters and buffers to the specified device without copying storage.
train	Set the module in training mode.
type	Casts all parameters and buffers to dst_type.
xpu	Move all model parameters and buffers to the XPU.
zero_grad	Reset gradients of all model parameters.

Attributes

T_destination
call_super_init
dump_patches
training