theanets/feedforward.py
def decode(self, z, layer=None, **kwargs):
        '''Decode an encoded dataset by computing the output layer activation.

        Parameters
        ----------
        z : ndarray
            A matrix containing encoded data from this autoencoder.
        layer : int or str or :class:`Layer <layers.Layer>`, optional
            The index or name of the hidden layer that was used to encode `z`.

        Returns
        -------
        decoded : ndarray
            The decoded dataset.
        '''
        key = self._find_output(layer)
        if key not in self._functions:
            regs = regularizers.from_kwargs(self, **kwargs)
            outputs, updates = self.build_graph(regs)
            self._functions[key] = theano.function(
                [outputs[key]],
                [outputs[self.layers[-1].output_name]],
                updates=updates)
        return self._functions[key](z)[0]
def decode(self, z, layer=None, **kwargs):
        '''Decode an encoded dataset by computing the output layer activation.

        Parameters
        ----------
        z : ndarray
            A matrix containing encoded data from this autoencoder.
        layer : int or str or :class:`Layer <layers.Layer>`, optional
            The index or name of the hidden layer that was used to encode `z`.

        Returns
        -------
        decoded : ndarray
            The decoded dataset.
        '''
        key = self._find_output(layer)
        if key not in self._functions:
            regs = regularizers.from_kwargs(self, **kwargs)
            outputs, updates = self.build_graph(regs)
            self._functions[key] = theano.function(
                [outputs[key]],
                [outputs[self.layers[-1].output_name]],
                updates=updates)
        return self._functions[key](z)[0]
def decode ( self , z , layer = None , * * kwargs ) : key = self . _find_output ( layer ) if key not in self . _functions : regs = regularizers . from_kwargs ( self , * * kwargs ) outputs , updates = self . build_graph ( regs ) self . _functions [ key ] = theano . function ( [ outputs [ key ] ] , [ outputs [ self . layers [ - 1 ] . output_name ] ] , updates = updates ) return self . _functions [ key ] ( z ) [ 0 ]
Decode an encoded dataset by computing the output layer activation.

        Parameters
        ----------
        z : ndarray
            A matrix containing encoded data from this autoencoder.
        layer : int or str or :class:`Layer <layers.Layer>`, optional
            The index or name of the hidden layer that was used to encode `z`.

        Returns
        -------
        decoded : ndarray
            The decoded dataset.
Decode an encoded dataset by computing the output layer activation .