theanets/feedforward.py
def monitors(self, **kwargs):
        '''Return expressions that should be computed to monitor training.

        Returns
        -------
        monitors : list of (name, expression) pairs
            A list of named monitor expressions to compute for this network.
        '''
        monitors = super(Classifier, self).monitors(**kwargs)
        regs = regularizers.from_kwargs(self, **kwargs)
        outputs, _ = self.build_graph(regs)
        return monitors + [('acc', self.losses[0].accuracy(outputs))]
def monitors(self, **kwargs):
        '''Return expressions that should be computed to monitor training.

        Returns
        -------
        monitors : list of (name, expression) pairs
            A list of named monitor expressions to compute for this network.
        '''
        monitors = super(Classifier, self).monitors(**kwargs)
        regs = regularizers.from_kwargs(self, **kwargs)
        outputs, _ = self.build_graph(regs)
        return monitors + [('acc', self.losses[0].accuracy(outputs))]
def monitors ( self , * * kwargs ) : monitors = super ( Classifier , self ) . monitors ( * * kwargs ) regs = regularizers . from_kwargs ( self , * * kwargs ) outputs , _ = self . build_graph ( regs ) return monitors + [ ( 'acc' , self . losses [ 0 ] . accuracy ( outputs ) ) ]
Return expressions that should be computed to monitor training.

        Returns
        -------
        monitors : list of (name, expression) pairs
            A list of named monitor expressions to compute for this network.
Return expressions that should be computed to monitor training .