downhill.minimize¶

downhill.
minimize
(loss, train, valid=None, params=None, inputs=None, algo='rmsprop', updates=(), monitors=(), monitor_gradients=False, batch_size=32, train_batches=None, valid_batches=None, **kwargs)¶ Minimize a loss function with respect to some symbolic parameters.
Additional keyword arguments are passed to the underlying
Optimizer
instance.Parameters: loss : Theano expression
Loss function to minimize. This must be a scalarvalued expression.
train :
Dataset
, ndarray, or callableDataset to use for computing gradient updates.
valid :
Dataset
, ndarray, or callable, optionalDataset to use for validating the minimization process. The training dataset is used if this is not provided.
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.
algo : str, optional
Name of the minimization algorithm to use. Must be one of the strings that can be passed to
build()
. Defaults to'rmsprop'
.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.
batch_size : int, optional
Size of batches provided by datasets. Defaults to 32.
train_batches : int, optional
Number of batches of training data to iterate over during one pass of optimization. Defaults to None, which uses the entire training dataset.
valid_batches : int, optional
Number of batches of validation data to iterate over during one pass of validation. Defaults to None, which uses the entire validation dataset.
Returns: train_monitors : dict
A dictionary mapping monitor names to monitor values. This dictionary will always contain the
'loss'
key, giving the value of the loss evaluated on the training dataset.valid_monitors : dict
A dictionary mapping monitor names to monitor values, evaluated on the validation dataset. This dictionary will always contain the
'loss'
key, giving the value of the loss function. Because validation is not always computed after every optimization update, these monitor values may be “stale”; however, they will always contain the most recently computed values.