def _propagate(self, x, influences, **kwargs):
        """Propagate a single batch of examples through the network."""
        activation, difference_x = self.forward(x)
        update = self.backward(difference_x, influences, activation)
        # If batch size is 1 we can leave out the call to mean.
        if update.shape[0] == 1:
            self.weights += update[0]
        else:
            self.weights += update.mean(0)

        return activation
def _propagate ( self , x , influences , ** kwargs ) : activation , difference_x = self . forward ( x ) update = self . backward ( difference_x , influences , activation ) if update . shape [ 0 ] == 1 : self . weights += update [ 0 ] else : self . weights += update . mean ( 0 ) return activation
Propagate a single batch of examples through the network.