# theanets.losses.Loss¶

class theanets.losses.Loss(target, weight=1.0, weighted=False, output_name='out')

A loss function base class.

Parameters: target : int or Theano variable If this is an integer, it specifies the number of dimensions required to store the target values for computing the loss. If it is a Theano variable, this variable will be used directly to access target values. weight : float, optional The importance of this loss for the model being trained. Defaults to 1. weighted : bool, optional If True, a floating-point array of weights with the same dimensions as target will be required to compute the “weighted” loss. Defaults to False. output_name : str, optional Name of the network output to tap for computing the loss. Defaults to ‘out:out’, the name of the default output of the last layer in a linear network.

Attributes

 weight (float) The importance of this loss for the model being trained. output_name (str) Name of the network output to tap for computing the loss.
__init__(target, weight=1.0, weighted=False, output_name='out')

Methods

 __init__(target[, weight, weighted, output_name]) log() Log some diagnostic info about this loss.

Attributes

 variables A list of Theano variables used in this loss.
log()

variables