theanets/feedforward.py
def predict(self, x, **kwargs):
        '''Compute a greedy classification for the given set of data.

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

        Returns
        -------
        k : ndarray (num-examples, )
            A vector of class index values, one per row of input data.
        '''
        outputs = self.feed_forward(x, **kwargs)
        return outputs[self.layers[-1].output_name].argmax(axis=-1)
def predict(self, x, **kwargs):
        '''Compute a greedy classification for the given set of data.

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

        Returns
        -------
        k : ndarray (num-examples, )
            A vector of class index values, one per row of input data.
        '''
        outputs = self.feed_forward(x, **kwargs)
        return outputs[self.layers[-1].output_name].argmax(axis=-1)
Compute a greedy classification for the given set of data.

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

        Returns
        -------
        k : ndarray (num-examples, )
            A vector of class index values, one per row of input data.