tyxe/bnn.py
def evaluate(self, input_data, y, num_predictions=1, aggregate=True, reduction="sum"):
        """"Utility method for evaluation. Calculates a likelihood-dependent errors measure, e.g. squared errors or
        mis-classifications and

        :param input_data: Inputs to the neural net. Must be a tuple of more than one.
        :param y: observations, e.g. class labels.
        :param int num_predictions: number of forward passes.
        :param bool aggregate: whether to aggregate the outputs of the forward passes before evaluating.
        :param str reduction: "sum", "mean" or "none". How to process the tensor of errors. "sum" adds them up,
            "mean" averages them and "none" simply returns the tensor."""
        predictions = self.predict(*_as_tuple(input_data), num_predictions=num_predictions, aggregate=aggregate)
        error = self.likelihood.error(predictions, y, reduction=reduction)
        ll = self.likelihood.log_likelihood(predictions, y, reduction=reduction)
        return error, ll
Utility method for evaluation. Calculates a likelihood-dependent errors measure, e.g. squared errors or
        mis-classifications and

        :param input_data: Inputs to the neural net. Must be a tuple of more than one.
        :param y: observations, e.g. class labels.
        :param int num_predictions: number of forward passes.
        :param bool aggregate: whether to aggregate the outputs of the forward passes before evaluating.
        :param str reduction: "sum", "mean" or "none". How to process the tensor of errors. "sum" adds them up,
            "mean" averages them and "none" simply returns the tensor.
Utility method for evaluation. Calculates a likelihood-dependent errors measure, e.g. squared errors or
mis-classifications and
Utility method for evaluation . Calculates a likelihood - dependent errors measure e . g . squared errors or mis - classifications and
def evaluate(self, input_data, y, num_predictions=1, aggregate=True, reduction="sum"):
        predictions = self.predict(*_as_tuple(input_data), num_predictions=num_predictions, aggregate=aggregate)
        error = self.likelihood.error(predictions, y, reduction=reduction)
        ll = self.likelihood.log_likelihood(predictions, y, reduction=reduction)
        return error, ll
def evaluate ( self , input_data , y , num_predictions = 1 , aggregate = True , reduction = "sum" ) : predictions = self . predict ( * _as_tuple ( input_data ) , num_predictions = num_predictions , aggregate = aggregate ) error = self . likelihood . error ( predictions , y , reduction = reduction ) ll = self . likelihood . log_likelihood ( predictions , y , reduction = reduction ) return error , ll
Utility method for evaluation.
Utility method for evaluation .
""""Utility method for evaluation. Calculates a likelihood-dependent errors measure, e.g. squared errors or
        mis-classifications and

        :param input_data: Inputs to the neural net. Must be a tuple of more than one.
        :param y: observations, e.g. class labels.
        :param int num_predictions: number of forward passes.
        :param bool aggregate: whether to aggregate the outputs of the forward passes before evaluating.
        :param str reduction: "sum", "mean" or "none". How to process the tensor of errors. "sum" adds them up,
            "mean" averages them and "none" simply returns the tensor."""
self
input_data
Inputs to the neural net. Must be a tuple of more than one.
Inputs to the neural net . Must be a tuple of more than one .
y
observations, e.g. class labels.
observations e . g . class labels .
num_predictions
number of forward passes.
number of forward passes .
aggregate
whether to aggregate the outputs of the forward passes before evaluating.
whether to aggregate the outputs of the forward passes before evaluating .
reduction
"sum", "mean" or "none". How to process the tensor of errors. "sum" adds them up,
"mean" averages them and "none" simply returns the tensor.
" sum " " mean " or " none " . How to process the tensor of errors . " sum " adds them up " mean " averages them and " none " simply returns the tensor .