theanets/layers/base.py
def connect(self, inputs):
        '''Create Theano variables representing the outputs of this layer.

        Parameters
        ----------
        inputs : dict of Theano expressions
            Symbolic inputs to this layer, given as a dictionary mapping string
            names to Theano expressions. Each string key should be of the form
            "{layer_name}:{output_name}" and refers to a specific output from
            a specific layer in the graph.

        Returns
        -------
        outputs : dict
            A dictionary mapping names to Theano expressions for the outputs
            from this layer.
        updates : sequence of (parameter, expression) tuples
            Updates that should be performed by a Theano function that computes
            something using this layer.
        '''
        outputs, updates = self.transform(inputs)
        # transform the outputs to be a list of ordered pairs if needed.
        if isinstance(outputs, dict):
            outputs = sorted(outputs.items())
        if isinstance(outputs, (TT.TensorVariable, SS.SparseVariable)):
            outputs = [('out', outputs)]
        outs = {self.full_name(name): expr for name, expr in outputs}
        return outs, updates
def connect(self, inputs):
        '''Create Theano variables representing the outputs of this layer.

        Parameters
        ----------
        inputs : dict of Theano expressions
            Symbolic inputs to this layer, given as a dictionary mapping string
            names to Theano expressions. Each string key should be of the form
            "{layer_name}:{output_name}" and refers to a specific output from
            a specific layer in the graph.

        Returns
        -------
        outputs : dict
            A dictionary mapping names to Theano expressions for the outputs
            from this layer.
        updates : sequence of (parameter, expression) tuples
            Updates that should be performed by a Theano function that computes
            something using this layer.
        '''
        outputs, updates = self.transform(inputs)
        # transform the outputs to be a list of ordered pairs if needed.
        if isinstance(outputs, dict):
            outputs = sorted(outputs.items())
        if isinstance(outputs, (TT.TensorVariable, SS.SparseVariable)):
            outputs = [('out', outputs)]
        outs = {self.full_name(name): expr for name, expr in outputs}
        return outs, updates
def connect ( self , inputs ) : outputs , updates = self . transform ( inputs ) # transform the outputs to be a list of ordered pairs if needed. if isinstance ( outputs , dict ) : outputs = sorted ( outputs . items ( ) ) if isinstance ( outputs , ( TT . TensorVariable , SS . SparseVariable ) ) : outputs = [ ( 'out' , outputs ) ] outs = { self . full_name ( name ) : expr for name , expr in outputs } return outs , updates
Create Theano variables representing the outputs of this layer.

        Parameters
        ----------
        inputs : dict of Theano expressions
            Symbolic inputs to this layer, given as a dictionary mapping string
            names to Theano expressions. Each string key should be of the form
            "{layer_name}:{output_name}" and refers to a specific output from
            a specific layer in the graph.

        Returns
        -------
        outputs : dict
            A dictionary mapping names to Theano expressions for the outputs
            from this layer.
        updates : sequence of (parameter, expression) tuples
            Updates that should be performed by a Theano function that computes
            something using this layer.
Create Theano variables representing the outputs of this layer .