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.