theanets/layers/base.py
def bind(self, graph, reset=True, initialize=True):
        '''Bind this layer into a computation graph.

        This method is a wrapper for performing common initialization tasks. It
        calls :func:`resolve`, :func:`setup`, and :func:`log`.

        Parameters
        ----------
        graph : :class:`Network <theanets.graph.Network>`
            A computation network in which this layer is to be bound.
        reset : bool, optional
            If ``True`` (the default), reset the resolved layers for this layer.
        initialize : bool, optional
            If ``True`` (the default), initialize the parameters for this layer
            by calling :func:`setup`.

        Raises
        ------
        theanets.util.ConfigurationError :
            If an input cannot be resolved.
        '''
        if reset:
            for k in self._input_shapes:
                self._input_shapes[k] = None
            for k in self._output_shapes:
                self._output_shapes[k] = None
        self.resolve_inputs(graph.layers)
        self.resolve_outputs()
        self.activate = activations.build(
            self.kwargs.get('activation', 'relu'), self)
        if initialize:
            self.setup()
        self.log()
def bind(self, graph, reset=True, initialize=True):
        '''Bind this layer into a computation graph.

        This method is a wrapper for performing common initialization tasks. It
        calls :func:`resolve`, :func:`setup`, and :func:`log`.

        Parameters
        ----------
        graph : :class:`Network <theanets.graph.Network>`
            A computation network in which this layer is to be bound.
        reset : bool, optional
            If ``True`` (the default), reset the resolved layers for this layer.
        initialize : bool, optional
            If ``True`` (the default), initialize the parameters for this layer
            by calling :func:`setup`.

        Raises
        ------
        theanets.util.ConfigurationError :
            If an input cannot be resolved.
        '''
        if reset:
            for k in self._input_shapes:
                self._input_shapes[k] = None
            for k in self._output_shapes:
                self._output_shapes[k] = None
        self.resolve_inputs(graph.layers)
        self.resolve_outputs()
        self.activate = activations.build(
            self.kwargs.get('activation', 'relu'), self)
        if initialize:
            self.setup()
        self.log()
def bind ( self , graph , reset = True , initialize = True ) : if reset : for k in self . _input_shapes : self . _input_shapes [ k ] = None for k in self . _output_shapes : self . _output_shapes [ k ] = None self . resolve_inputs ( graph . layers ) self . resolve_outputs ( ) self . activate = activations . build ( self . kwargs . get ( 'activation' , 'relu' ) , self ) if initialize : self . setup ( ) self . log ( )
Bind this layer into a computation graph.

        This method is a wrapper for performing common initialization tasks. It
        calls :func:`resolve`, :func:`setup`, and :func:`log`.

        Parameters
        ----------
        graph : :class:`Network <theanets.graph.Network>`
            A computation network in which this layer is to be bound.
        reset : bool, optional
            If ``True`` (the default), reset the resolved layers for this layer.
        initialize : bool, optional
            If ``True`` (the default), initialize the parameters for this layer
            by calling :func:`setup`.

        Raises
        ------
        theanets.util.ConfigurationError :
            If an input cannot be resolved.
Bind this layer into a computation graph .