@wraps(Layer.get_layer_monitoring_channels)
    def get_layer_monitoring_channels(self, state_below=None,
                                      state=None, targets=None):

        rval = OrderedDict()
        if self.use_monitoring_channels:
            state = state_below
            x = state
            state_conc = None

            for layer in self.layers:
                # We don't go through all the inner layers recursively
                state_below = state
                if ((self.x_shortcut and
                    layer is not self.layers[0] and
                        layer is not self.layers[-1])):
                    state = self.create_shortcut_batch(state, x, 2, 1)
                if self.y_shortcut and layer is self.layers[-1]:
                    state = layer.fprop(state_conc)
                else:
                    state = layer.fprop(state)
                if self.y_shortcut and layer is not self.layers[-1]:
                    if layer is self.layers[0]:
                        state_conc = state
                    else:
                        state_conc = self.create_shortcut_batch(state_conc,
                                                                state, 2)
                args = [state_below, state]
                if layer is self.layers[-1] and targets is not None:
                    args.append(targets)
                ch = layer.get_layer_monitoring_channels(*args)
                if not isinstance(ch, OrderedDict):
                    raise TypeError(str((type(ch), layer.layer_name)))
                for key in ch:
                    value = ch[key]
                    doc = get_monitor_doc(value)
                    if doc is None:
                        doc = str(type(layer)) + \
                            ".get_monitoring_channels_from_state did" + \
                            " not provide any further documentation for" + \
                            " this channel."
                    doc = 'This channel came from a layer called "' + \
                        layer.layer_name + '" of an MLP.\n' + doc
                    value.__doc__ = doc
                    rval[layer.layer_name + '_' + key] = value

        return rval
Fetch image by given href to the destination path.
dataset/ETHPy150Open lisa-lab/pylearn2/pylearn2/sandbox/rnn/models/rnn.py/RNN.get_layer_monitoring_channels--crossover with--dataset/ETHPy150Open openstack/ironic/ironic/drivers/modules/image_cache.py/ImageCache.fetch_image