mxfusion.inference.batch_loop¶
Members¶
-
class
mxfusion.inference.batch_loop.
BatchInferenceLoop
¶ Bases:
mxfusion.inference.grad_loop.GradLoop
The class for the main loop for batch gradient-based optimization.
-
run
(infr_executor, data, param_dict, ctx, optimizer='adam', learning_rate=0.001, max_iter=1000, n_prints=10, verbose=False)¶ Parameters: - infr_executor (MXNet Gluon Block) – The MXNet function that computes the training objective.
- data ([mxnet.ndarray]) – a list of observed variables
- param_dict (mxnet.gluon.ParameterDict) – The MXNet ParameterDict for Gradient-based optimization
- ctx (mxnet.cpu or mxnet.gpu) – MXNet context
- optimizer (str) – the choice of optimizer (default: ‘adam’)
- learning_rate (float) – the learning rate of the gradient optimizer (default: 0.001)
- n_prints (int) – number of messages to print
- max_iter (int) – the maximum number of iterations of gradient optimization
- verbose (boolean) – whether to print per-iteration messages.
-