def backward(self, input, target):
"""
NB: It's for debug only, please use optimizer.optimize() in production.
Performs a back-propagation step through the criterion, with respect to the given input.
:param input: ndarray or list of ndarray
:param target: ndarray or list of ndarray
:return: ndarray
"""
output = callBigDlFunc(self.bigdl_type, "criterionBackward",
self.value, Model.check_input(input),
Model.check_input(target))
return Model.convert_output(output)
def forward(self, input, target):
"""
NB: It's for debug only, please use optimizer.optimize() in production.
Takes an input object, and computes the corresponding loss of the criterion,
compared with `target`
:param input: ndarray or list of ndarray
:param target: ndarray or list of ndarray
:return: value of loss
"""
output = callBigDlFunc(self.bigdl_type, "criterionForward", self.value,
Model.check_input(input),
Model.check_input(target))
return output
def optimize(self):
"""
Do an optimization.
"""
jmodel = callJavaFunc(SparkContext.getOrCreate(), self.value.optimize)
from nn.layer import Model
return Model.of(jmodel)
def optimize(self):
"""
Do an optimization.
"""
jmodel = callJavaFunc(SparkContext.getOrCreate(), self.value.optimize)
from nn.layer import Model
return Model.of(jmodel)
def optimize(self):
"""
Do an optimization.
"""
jmodel = callJavaFunc(get_spark_context(), self.value.optimize)
from nn.layer import Model
return Model.of(jmodel)