mindspore.nn.probability.infer.SVI
- class mindspore.nn.probability.infer.SVI(net_with_loss, optimizer)[source]
Stochastic Variational Inference(SVI).
Variational inference casts the inference problem as an optimization. Some distributions over the hidden variables are indexed by a set of free parameters, which are optimized to make distributions closest to the posterior of interest. For more details, refer to Variational Inference: A Review for Statisticians.
- Parameters
net_with_loss (Cell) – Cell with loss function.
optimizer (Cell) – Optimizer for updating the weights.
- Supported Platforms:
Ascend
GPU
- run(train_dataset, epochs=10)[source]
Optimize the parameters by training the probability network, and return the trained network.
- Parameters
epochs (int) – Total number of iterations on the data. Default: 10.
train_dataset (Dataset) – A training dataset iterator.
- Outputs:
Cell, the trained probability network.