tyxe/bnn.py
def predict(self, *input_data, num_predictions=1, aggregate=True):
        """Makes predictions on the input data

        :param input_data: inputs to the neural net, e.g. torch.Tensors
        :param int num_predictions: number of forward passes through the net
        :param bool aggregate: whether to aggregate the predictions depending on the likelihood, e.g. averaging them."""
        raise NotImplementedError
Makes predictions on the input data

        :param input_data: inputs to the neural net, e.g. torch.Tensors
        :param int num_predictions: number of forward passes through the net
        :param bool aggregate: whether to aggregate the predictions depending on the likelihood, e.g. averaging them.
Makes predictions on the input data
Makes predictions on the input data
def predict(self, *input_data, num_predictions=1, aggregate=True):
        raise NotImplementedError
def predict ( self , * input_data , num_predictions = 1 , aggregate = True ) : raise NotImplementedError
Makes predictions on the input data
Makes predictions on the input data
"""Makes predictions on the input data

        :param input_data: inputs to the neural net, e.g. torch.Tensors
        :param int num_predictions: number of forward passes through the net
        :param bool aggregate: whether to aggregate the predictions depending on the likelihood, e.g. averaging them."""
self
num_predictions
number of forward passes through the net
number of forward passes through the net
aggregate
whether to aggregate the predictions depending on the likelihood, e.g. averaging them.
whether to aggregate the predictions depending on the likelihood e . g . averaging them .
input_data
inputs to the neural net, e.g. torch.Tensors
inputs to the neural net e . g . torch . Tensors