BayesNet¶
Instantiate a deep network inside a statistical model.
This node allows one to specify a subgraph inside a statistical model that is made up of neural network layers wired into the net input of the node (see documentation for the port for more details). The weights of these layers can then be associated with priors, which then defines a fully Bayesian neural network. Note that another commonly used combination of deep networks and bayesian inference is to use the network as part of a variational approximation to some other (perhaps conventionally defined) model, in which case the weights are treated as variational parameters to optimize, but this mode is not currently exposed since custom approximations cannot yet be wired into the variational inference node. During network bring-up it can be useful to start with a Laplace approximation and a fairly loose prior (e.g., Normal(0,10)) for the weights; the BayesNet node is typically situated in a With Stacked Variables context with subsampling enabled and set to a typical batch size (e.g., 16 or 32), and the inputs wired into the BayesNet data input are subsampled accordingly using the At Subscripts node. Note also that there is direct equivalent to the number of epochs to run; instead, the number of iterations is set in the variational inference node, which multiplies with the batch size to determine the total number of samples to draw (which, when divided by the training set size, gives the equivalent number of epochs). There is also currently no direct support for early stopping, but a (computationally expensive) substitute can be to make the iterations a hyper-parameter optimized via the Parameter Optimization node and set to a p-holdout train/test split. Version 0.8.0
Ports/Properties¶
net¶
Neural network graph. This is a graph of nodes starting with at least an input placeholder whose slotname must be set to match the name here (e.g., inputs), and which is followed by some combination of neural net nodes (nodes in the deep learning package) and nodes for math operations and/or data restructuring (no stateful nodes). Random Draw or With Stacked Variables (Plate) nodes cannot be used inside the net graph (if you need these inside the net, you need to declare more than one net, e.g., before and one after the Random Draw draw). The output of the network may be a packet or a plain array.
net__signature¶
If you need to accept additional arguments in the network (e.g ., is_training), you can list those in the signature.
seed¶
Optional random seed. Only used if the net is applied in-place. If no seed is provided, one will be obtained from the context of the inference engine.
apply¶
Neural network graph that can be called with arguments after instantiation.
result¶
Result of applying the network to the arguments. Only for in-place use of the network (i.e., at the site of declaration.
netname¶
Name under which the parameters (and optionally state) of this network appear in the statistical model.
priors¶
Optional dictionary of prior distributions associated with specific named weight arrays of the network. This can either be specified textually as in the example (using the same format and distributions as available in the Random Sample node), or wired in as a dictionary with distribution nodes as the values. The dictionary keys can be wildcard strings, e.g., 'mylayer_*.b' to match specific sets of weights (be sure to review their naming in the learned variables to ensure that the wildcard matches what you expect). The weights being matched are typicalled named as in mylayer_eeg.w or mylayer_eeg.b, where mylayer is the layername, eeg is the name of the stream that the weights apply to, and w is one of the weight names of the layer for this stream. if the weight has more dimensions than the specified prior distribution, the leading dimensions are considered batch dimensions and a batch of samples will be drawn to match the dimensionality of the weight array. You can also use matrix- or tensor-variate prior distributions to draw full weight matrices. Note that you can also wire a prior distribution into the respective w_prior and b_prior ports of individual network layers, which take precedence over the dictionary-supplied priors, and which can be more explicit or less error-prone. Also note also that the prior is different from the weight initialization -- the initialization of the network weights (which needs to be delicately scaled) is unaffected by the presence of the priors. If no priors are specified for a given layer, then the parameters of that layer will be optimized as variational parameters (via variational inference), and if one is specified, then a posterior over the weights of that layer will be inferred.
help¶
Help text for the network. This can be used to annotate the purpose/meaning of the network in the context of a statistical model.
verbose_name¶
Optional verbose name for the network. Can be used for augmented human-readable output.
usage¶
Intended usage of the network. If the network is used as a model (the default), then each weight tensor in the network must have an associated prior distribution. If instead the network is used as part of a custom variational approximation, then this is not necessary, but still allowed, as long as those distributions are parameterized by variational parameters. For this reason, the priors dictionary, if used, should not use textually defined priors but wired-in distributions. Note that as of NeuroPype 2025, the variational mode is not fully supported.
arg1¶
Positional Argument 1.
arg2¶
Positional Argument 2.
arg3¶
Positional Argument 3.
arg4¶
Positional Argument 4.
arg5¶
Positional Argument 5.
arg6¶
Positional Argument 6.
arg7¶
Positional Argument 7.
arg8¶
Positional Argument 8.
arg9¶
Positional Argument 9.
argN¶
Additional positional arguments.. .
kw_args¶
Keyword arguments.
arg0¶
Positional Argument 0.
set_breakpoint¶
Set a breakpoint on this node. If this is enabled, your debugger (if one is attached) will trigger a breakpoint.
metadata¶
User-definable meta-data associated with the node. Usually reserved for technical purposes.