# downhill.base.build¶

downhill.base.build(algo, loss, params=None, inputs=None, updates=(), monitors=(), monitor_gradients=False)

Construct an optimizer by name.

Parameters: algo : str The name of the optimization algorithm to build. loss : Theano expression Loss function to minimize. This must be a scalar-valued expression. params : list of Theano variables, optional Symbolic variables to adjust to minimize the loss. If not given, these will be computed automatically by walking the computation graph. inputs : list of Theano variables, optional Symbolic variables required to compute the loss. If not given, these will be computed automatically by walking the computation graph. updates : list of update pairs, optional A list of pairs providing updates for the internal of the loss computation. Normally this is empty, but it can be provided if the loss, for example, requires an update to an internal random number generator. monitors : dict or sequence of (str, Theano expression) tuples, optional Additional values to monitor during optimization. These must be provided as either a sequence of (name, expression) tuples, or as a dictionary mapping string names to Theano expressions. monitor_gradients : bool, optional If True, add monitors to log the norms of the parameter gradients during optimization. Defaults to False. optimizer : Optimizer An optimizer instance.