theanets/feedforward.py
def predict_logit(self, x, **kwargs):
        '''Compute the logit values that underlie the softmax output.

        Parameters
        ----------
        x : ndarray (num-examples, num-variables)
            An array containing examples to classify. Examples are given as the
            rows in this array.

        Returns
        -------
        l : ndarray (num-examples, num-classes)
            An array of posterior class logit values, one row of logit values
            per row of input data.
        '''
        return self.feed_forward(x, **kwargs)[self.layers[-1].full_name('pre')]
def predict_logit(self, x, **kwargs):
        '''Compute the logit values that underlie the softmax output.

        Parameters
        ----------
        x : ndarray (num-examples, num-variables)
            An array containing examples to classify. Examples are given as the
            rows in this array.

        Returns
        -------
        l : ndarray (num-examples, num-classes)
            An array of posterior class logit values, one row of logit values
            per row of input data.
        '''
        return self.feed_forward(x, **kwargs)[self.layers[-1].full_name('pre')]
def predict_logit ( self , x , * * kwargs ) : return self . feed_forward ( x , * * kwargs ) [ self . layers [ - 1 ] . full_name ( 'pre' ) ]
Compute the logit values that underlie the softmax output.

        Parameters
        ----------
        x : ndarray (num-examples, num-variables)
            An array containing examples to classify. Examples are given as the
            rows in this array.

        Returns
        -------
        l : ndarray (num-examples, num-classes)
            An array of posterior class logit values, one row of logit values
            per row of input data.
Compute the logit values that underlie the softmax output .