def compile(self, optimizer, loss, metrics=None):
"""
Configure the learning process. It MUST be called before fit or evaluate.
# Arguments
optimizer: Optimization method to be used. One can alternatively pass in the corresponding
string representation, such as 'sgd'.
loss: Criterion to be used. One can alternatively pass in the corresponding string
representation, such as 'mse'.
metrics: List of validation methods to be used. Default is None if no validation is needed.
For convenience, string representations are supported: 'accuracy' (or 'acc'),
'top5accuracy' (or 'top5acc'), 'mae', 'auc', 'treennaccuracy' and 'loss'.
For example, you can either use [Accuracy()] or ['accuracy'].
"""
if isinstance(optimizer, six.string_types):
optimizer = to_bigdl_optim_method(optimizer)
criterion = loss
if isinstance(loss, six.string_types):
criterion = to_bigdl_criterion(loss)
if callable(loss):
from zoo.pipeline.api.autograd import CustomLoss
criterion = CustomLoss(loss, self.get_output_shape()[1:])
if metrics and all(isinstance(metric, six.string_types) for metric in metrics):
metrics = to_bigdl_metrics(metrics, loss)
callBigDlFunc(self.bigdl_type, "zooCompile",
self.value,
optimizer,
criterion,
metrics)
def compile(self, optimizer, loss, metrics=None):
"""
Configure the learning process. It MUST be called before fit or evaluate.
# Arguments
optimizer: Optimization method to be used. One can alternatively pass in the corresponding
string representation, such as 'sgd'.
loss: Criterion to be used. One can alternatively pass in the corresponding string
representation, such as 'mse'.
metrics: List of validation methods to be used. Default is None if no validation is needed.
One can alternatively use ['accuracy'].
"""
if isinstance(optimizer, six.string_types):
optimizer = to_bigdl_optim_method(optimizer)
if isinstance(loss, six.string_types):
loss = to_bigdl_criterion(loss)
if callable(loss):
from zoo.pipeline.api.autograd import CustomLoss
loss = CustomLoss(loss, self.get_output_shape()[1:])
if metrics and all(isinstance(metric, six.string_types) for metric in metrics):
metrics = to_bigdl_metrics(metrics)
callBigDlFunc(self.bigdl_type, "zooCompile",
self.value,
optimizer,
loss,
metrics)
