Python printing.var_descriptor函数代码示例

    def run(replay, log=None):

        if not replay:
            log = StringIO()
        else:
            log = StringIO(log)
        record = Record(replay=replay, file_object=log)

        disturb_mem.disturb_mem()

        mode = RecordMode(record=record)

        b = sharedX(np.zeros((2,)), name='b')
        channels = OrderedDict()

        disturb_mem.disturb_mem()

        v_max = b.max(axis=0)
        v_min = b.min(axis=0)
        v_range = v_max - v_min

        updates = []
        for i, val in enumerate([
                v_max.max(),
                v_max.min(),
                v_range.max(),
                ]):
            disturb_mem.disturb_mem()
            s = sharedX(0., name='s_' + str(i))
            updates.append((s, val))

        for var in theano.gof.graph.ancestors(update for _, update in updates):
            if var.name is not None and var.name is not 'b':
                if var.name[0] != 's' or len(var.name) != 2:
                    var.name = None

        for key in channels:
            updates.append((s, channels[key]))
        f = theano.function([], mode=mode, updates=updates,
                            on_unused_input='ignore', name='f')
        for output in f.maker.fgraph.outputs:
            mode.record.handle_line(var_descriptor(output) + '\n')
        disturb_mem.disturb_mem()
        f()

        mode.record.f.flush()

        if not replay:
            return log.getvalue()

def run(replay):
    disturb_mem.disturb_mem()

    mode = RecordMode(file_path= "nondeterminism_4.txt",
                      replay=replay)

    b = sharedX(np.zeros((2,)), name='b')
    channels = OrderedDict()

    disturb_mem.disturb_mem()

    v_max = b.max(axis=0)
    v_min = b.min(axis=0)
    v_range = v_max - v_min

    updates = []
    for i, val in enumerate([
            v_max.max(),
            v_max.min(),
            v_range.max(),
            ]):
        disturb_mem.disturb_mem()
        s = sharedX(0., name='s_'+str(i))
        updates.append((s, val))

    for var in theano.gof.graph.ancestors(update for var, update in updates):
        if var.name is not None and var.name is not 'b':
            if var.name[0] != 's' or len(var.name) != 2:
                var.name = None

    for key in channels:
        updates.append((s, channels[key]))
    f = theano.function([], mode=mode, updates=updates, on_unused_input='ignore', name='f')
    for output in f.maker.fgraph.outputs:
        mode.record.handle_line(var_descriptor(output)+'\n')
    disturb_mem.disturb_mem()
    f()

    mode.record.f.flush()
    mode.record.f.close()

    def add_channel(self, name, ipt, val, dataset=None, prereqs=None):
        """
        Asks the monitor to start tracking a new value.  Can be called even
        after the monitor is already in use.

        Parameters
        ----------
        name: str
            The display name in the monitor.
        ipt: tensor_like
            The symbolic tensor which should be clamped to the data.
            (or a (features,targets) list/tuple containing two symbolic tensors)
        val: tensor_like
            The value (function of `ipt`) to be tracked.
        dataset: A Dataset instance specifying which dataset to compute
            this channel on.
        prereqs: list of callables that take two numpy tensors
            (X and y, where y will be None if no labels are used)
            each prereq must be called exactly once per each new
            batch of data drawn *from dataset* before the channel
            value is computed
            if two channels provide a prereq with exactly the same
            id, that prereq will only be called once
        """

        if isinstance(val, (float, int)):
            val = np.cast[theano.config.floatX](val)

        val = T.as_tensor_variable(val)

        if not isinstance(ipt, (list, tuple)):
            tmp = [ ipt ]
        else:
            tmp = ipt
        inputs = theano.gof.graph.inputs([val])
        for elem in inputs:
            if not hasattr(elem, 'get_value') and not isinstance(elem, theano.gof.graph.Constant):
                if elem not in tmp:
                    raise ValueError("Unspecified input: "+str(elem))



        mode = self.theano_function_mode
        if mode is not None and hasattr(mode, 'record'):
            mode.record.handle_line('Adding monitor channel '+name+'\n')
            if isinstance(ipt, (list, tuple)):
                for elem in ipt:
                    mode.record.handle_line('Includes input var '+var_descriptor(elem)+'\n')
            else:
                mode.record.handle_line(name+' input var is '+var_descriptor(ipt)+'\n')
            mode.record.handle_line('channel '+name+' is '+var_descriptor(val)+'\n')

        if dataset is None:
            if len(self._datasets) == 1:
                dataset = self._datasets[0]
            elif len(self._datasets) == 0:
                raise ValueError(_err_no_data)
            else:
                raise ValueError(_err_ambig_data)

        try:
            self._datasets.index(dataset)
        except ValueError:
            raise ValueError("The dataset specified is not " + \
                "one of the monitor's datasets")

        if name in self.channels:
            raise ValueError("Tried to create the same channel twice (%s)" %
                             name)
        if isinstance(ipt, (list, tuple)):
            if dataset is not None:
                if not dataset.has_targets():
                    raise ValueError("Tried to create a channel ("+name \
                            +") that uses targets, but monitoring dataset has no targets")
            self.require_label = True
            assert len(ipt) == 2
        self.channels[name] = MonitorChannel(ipt, val, name, dataset, prereqs)
        self._dirty = True

    def redo_theano(self):
        """
        Recompiles Theano functions used by this monitor.

        This is needed so that if new channels are added, Theano's
        optimizations make sure (to the extent that they can) that the new
        channels and old channels don't have any redundant calculations.

        It is also needed to regenerate Theano functions after pickling and
        unpickling, since Theano functions should not be pickled.
        """
        self._dirty = False

        init_names = dir(self)
        self.prereqs = OrderedDict()
        for channel in self.channels.values():
            if channel.prereqs is not None:
                dataset = channel.dataset
                if dataset not in self.prereqs:
                    self.prereqs[dataset] = []
                prereqs = self.prereqs[dataset]
                for prereq in channel.prereqs:
                    if prereq not in prereqs:
                        prereqs.append(prereq)

        updates = OrderedDict()
        for channel in self.channels.values():
            updates[channel.val_shared] = np.cast[config.floatX](0.0)
        with log_timing(log, "compiling begin_record_entry"):
            self.begin_record_entry = function(inputs=[], updates=updates, mode=self.theano_function_mode,
                    name = 'Monitor.begin_record_entry')
        updates = OrderedDict()
        givens = OrderedDict()
        #Get the appropriate kind of theano variable to represent the data the model
        #acts on
        X = self.model.get_input_space().make_theano_batch(name = "monitoring_X")
        if config.compute_test_value != 'off':
            m = self.model.get_test_batch_size()
            test_value = self.model.get_input_space().get_origin_batch(m)
            X.tag.test_value = np.cast[X.type.dtype](test_value)
        if self.require_label:
            Y = self.model.get_output_space().make_theano_batch(name = "monitoring_Y")

        log.info('Monitored channels: ')
        for key in sorted(self.channels.keys()):
            mode = self.theano_function_mode
            if mode is not None and hasattr(mode, 'record'):
                mode.record.handle_line('compiling monitor including channel '+key+'\n')
            log.info('\t%s' % key)
        it = [d.iterator(mode=i, num_batches=n, batch_size=b, topo=self.topo) \
              for d, i, n, b in safe_izip(self._datasets, self._iteration_mode,
                                    self._num_batches, self._batch_size)]
        num_examples = [np.cast[config.floatX](float(i.num_examples)) for i in it]
        givens = [OrderedDict() for d in self._datasets]
        updates = [OrderedDict() for d in self._datasets]
        for channel in self.channels.values():
            index = self._datasets.index(channel.dataset)
            d = self._datasets[index]
            g = givens[index]
            n = num_examples[index]
            u = updates[index]
            if isinstance(channel.graph_input, (list, tuple)):
                g[channel.graph_input[0]] = X
                g[channel.graph_input[1]] = Y
            else:
                g[channel.graph_input] = X
            if n == 0:
                raise ValueError("Iterating over 0 examples results in divide by 0")
            if self.topo:
                batch_index = d.get_topo_batch_axis()
            else:
                batch_index = 0
            val = channel.val * T.cast(X.shape[batch_index], config.floatX) / n
            u[channel.val_shared] = channel.val_shared + val

        with log_timing(log, "Compiling accum"):
            # Check type of update expressions
            for up in updates:
                for key in up:
                    if key.dtype != up[key].dtype:
                        raise TypeError('Monitoring channel shared variable ' \
                                + key.name + ' has dtype ' + key.dtype + \
                                ' but is driven by an expression with type ' + \
                                up[key].dtype)

            self.accum = []
            for idx, packed in enumerate(safe_izip(givens, updates)):
                g, u = packed
                mode = self.theano_function_mode
                if mode is not None and hasattr(mode, 'record'):
                    for elem in g:
                        mode.record.handle_line('g key '+var_descriptor(elem)+'\n')
                        mode.record.handle_line('g val '+var_descriptor(g[elem])+'\n')
                    for elem in u:
                        mode.record.handle_line('u key '+var_descriptor(elem)+'\n')
                        mode.record.handle_line('u val '+var_descriptor(u[elem])+'\n')
                function_name = 'Monitor.accum[%d]' % idx
                if self.require_label:
                    if mode is not None and hasattr(mode, 'record'):
                        mode.record.handle_line('compiling supervised accum\n')
#.........这里部分代码省略.........

#.........这里部分代码省略.........
            cache_name = 'BatchGradientDescent.param_to_cache[%s]' % param_name
            self.param_to_cache[param] = sharedX(param.get_value(borrow=False), name=cache_name)
            cache_updates[self.param_to_cache[param]] = param
            cached = self.param_to_cache[param]
            g = self.param_to_grad_shared[param]
            if lr_scalers is not None and param in lr_scalers:
                scaled_alpha = alpha * lr_scalers[param]
            else:
                scaled_alpha = alpha
            mul = scaled_alpha * g
            diff = cached - mul
            goto_updates[param] = diff
        self._cache_values = function([], updates = cache_updates, mode=self.theano_function_mode, name='BatchGradientDescent._cache_values')
        assert isinstance(param_constrainers, (list, tuple))
        for param_constrainer in param_constrainers:
            param_constrainer(goto_updates)
        self._goto_alpha = function([alpha], updates=goto_updates,
                mode=self.theano_function_mode, name='BatchGradientDescent._goto_alpha')

        norm = T.sqrt(sum([T.sqr(elem).sum() for elem in self.param_to_grad_shared.values()]))
        norm.name = 'BatchGradientDescent.norm'
        normalize_grad_updates = OrderedDict()
        for grad_shared in self.param_to_grad_shared.values():
            normalize_grad_updates[grad_shared] = grad_shared / norm

        # useful for monitoring
        self.ave_grad_size = sharedX(0.)
        self.new_weight = sharedX(1.)
        normalize_grad_updates[self.ave_grad_size] = self.new_weight * norm + (1.-self.new_weight) * self.ave_grad_size

        self._normalize_grad = function([], norm, updates=normalize_grad_updates, mode=self.theano_function_mode,
                name='BatchGradientDescent._normalize_grad')

        if self.conjugate:
            grad_shared = self.param_to_grad_shared.values()

            grad_to_old_grad = OrderedDict()
            for elem in grad_shared:
                grad_to_old_grad[elem] = sharedX(elem.get_value(), 'old_'+elem.name)

            self._store_old_grad = function([norm], updates = OrderedDict([(grad_to_old_grad[g], g * norm)
                for g in grad_to_old_grad]), mode=self.theano_function_mode,
                name='BatchGradientDescent._store_old_grad')

            grad_ordered = list(grad_to_old_grad.keys())
            old_grad_ordered = [ grad_to_old_grad[g] for g in grad_ordered]

            def dot_product(x, y):
                return sum([ (x_elem * y_elem).sum() for x_elem, y_elem in safe_zip(x, y) ])

            beta_pr = (dot_product(grad_ordered, grad_ordered) - dot_product(grad_ordered, old_grad_ordered)) / \
                    (1e-7+dot_product(old_grad_ordered, old_grad_ordered))
            assert beta_pr.ndim == 0

            beta = T.maximum(beta_pr, 0.)

            """

            beta_pr is the Polak-Ribiere formula for beta.
            According to wikipedia, the beta to use for NCG is "a matter of heuristics or taste"
            but max(0, beta_pr) is "a popular choice... which provides direction reset automatically."
            (ie, it is meant to revert to steepest descent when you have traveled far enough that
            the objective function is behaving non-quadratically enough that the conjugate gradient
            formulas aren't working anymore)

            http://en.wikipedia.org/wiki/Nonlinear_conjugate_gradient_method

            """

            assert grad not in grad_to_old_grad

            make_conjugate_updates = [(g, g + beta * grad_to_old_grad[g]) for g in grad_ordered]

            mode = self.theano_function_mode
            if mode is not None and hasattr(mode, 'record'):
                for v, u in make_conjugate_updates:
                    mode.record.handle_line('BatchGradientDescent._make_conjugate var ' \
                            + var_descriptor(v) + '\n')
                    mode.record.handle_line('BatchGradientDescent._make_conjugate update ' \
                            + var_descriptor(u) + '\n')

            self._make_conjugate = function([], updates=make_conjugate_updates,
                    mode=self.theano_function_mode, name='BatchGradientDescent._make_conjugate')

            if mode is not None and hasattr(mode, 'record'):
                for output in self._make_conjugate.maker.fgraph.outputs:
                    mode.record.handle_line('BatchGradientDescent._make_conjugate output ' \
                            + var_descriptor(output) + '\n')


        if tol is None:
            if objective.dtype == "float32":
                self.tol = 1e-6
            else:
                self.tol = 3e-7
        else:
            self.tol = tol

        self.ave_step_size = sharedX(0.)
        self.ave_grad_mult = sharedX(0.)

#.........这里部分代码省略.........
        self._goto_alpha = function(
            [alpha],
            updates=goto_updates,
            mode=self.theano_function_mode,
            name='BatchGradientDescent._goto_alpha')

        norm = T.sqrt(sum([T.sqr(elem).sum() for elem in
                           self.param_to_grad_shared.values()]))
        norm.name = 'BatchGradientDescent.norm'
        normalize_grad_updates = OrderedDict()
        for grad_shared in self.param_to_grad_shared.values():
            normalize_grad_updates[grad_shared] = grad_shared / norm

        # useful for monitoring
        self.ave_grad_size = sharedX(0.)
        self.new_weight = sharedX(1.)
        normalize_grad_updates[self.ave_grad_size] = \
            self.new_weight * norm + (1.-self.new_weight) * self.ave_grad_size

        self._normalize_grad = \
            function([],
                     norm,
                     updates=normalize_grad_updates,
                     mode=self.theano_function_mode,
                     name='BatchGradientDescent._normalize_grad')

        if self.conjugate:
            grad_shared = self.param_to_grad_shared.values()

            grad_to_old_grad = OrderedDict()
            for elem in grad_shared:
                grad_to_old_grad[elem] = \
                    sharedX(elem.get_value(), 'old_'+elem.name)

            self._store_old_grad = \
                function([norm],
                         updates=OrderedDict([(grad_to_old_grad[g_], g_ * norm)
                                             for g_ in grad_to_old_grad]),
                         mode=self.theano_function_mode,
                         name='BatchGradientDescent._store_old_grad')

            grad_ordered = list(grad_to_old_grad.keys())
            old_grad_ordered = [grad_to_old_grad[g_] for g_ in grad_ordered]

            def dot_product(x, y):
                return sum([(x_elem * y_elem).sum()
                           for x_elem, y_elem in safe_zip(x, y)])

            beta_pr = (dot_product(grad_ordered, grad_ordered) - dot_product(grad_ordered, old_grad_ordered)) / \
                (1e-7+dot_product(old_grad_ordered, old_grad_ordered))
            assert beta_pr.ndim == 0

            beta = T.maximum(beta_pr, 0.)

            # beta_pr is the Polak-Ribiere formula for beta.
            # According to wikipedia, the beta to use for NCG is "a matter of
            # heuristics or taste" but max(0, beta_pr) is "a popular choice...
            # which provides direction reset automatically." (ie, it is meant
            # to revert to steepest descent when you have traveled far enough
            # that the objective function is behaving non-quadratically enough
            # that the conjugate gradient formulas aren't working anymore)

            # http://en.wikipedia.org/wiki/Nonlinear_conjugate_gradient_method

            assert grad not in grad_to_old_grad

            make_conjugate_updates = \
                [(g_, g_ + beta * grad_to_old_grad[g_]) for g_ in grad_ordered]

            mode = self.theano_function_mode
            if mode is not None and hasattr(mode, 'record'):
                for v, u in make_conjugate_updates:
                    mode.record.handle_line(
                        'BatchGradientDescent._make_conjugate var '
                        + var_descriptor(v) + '\n')
                    mode.record.handle_line(
                        'BatchGradientDescent._make_conjugate update '
                        + var_descriptor(u) + '\n')

            self._make_conjugate = \
                function([], updates=make_conjugate_updates,
                         mode=self.theano_function_mode,
                         name='BatchGradientDescent._make_conjugate')

            if mode is not None and hasattr(mode, 'record'):
                for output in self._make_conjugate.maker.fgraph.outputs:
                    mode.record.handle_line(
                        'BatchGradientDescent._make_conjugate output '
                        + var_descriptor(output) + '\n')

        if tol is None:
            if objective.dtype == "float32":
                self.tol = 1e-6
            else:
                self.tol = 3e-7
        else:
            self.tol = tol

        self.ave_step_size = sharedX(0.)
        self.ave_grad_mult = sharedX(0.)

    def add_channel(self, name, ipt, val, dataset=None, prereqs=None,
                    data_specs=None):
        """
        Asks the monitor to start tracking a new value.  Can be called
        even after the monitor is already in use.

        Parameters
        ----------
        name : str
            The display name in the monitor.
        ipt : tensor_like
            The symbolic tensor which should be clamped to the data.
            (or a list/tuple containing symbolic tensors, following the
            data_specs)
        val : tensor_like
            The value (function of `ipt`) to be tracked.
        dataset : pylearn2.datasets.Dataset
            Which dataset to compute this channel on
        prereqs : list of callables that take a list of numpy tensors
            Each prereq must be called exactly once per each new batch
            of data drawn *from dataset* before the channel value is
            computed if two channels provide a prereq with exactly the
            same id, that prereq will only be called once
        data_specs : (space, source) pair
            Identifies the order, format and semantics of ipt
        """
        if isinstance(val, (float, int, long)):
            val = np.cast[theano.config.floatX](val)

        val = T.as_tensor_variable(val)

        if data_specs is None:
            warnings.warn("parameter 'data_specs' should be provided when " +
                          "calling add_channel. We will build a default one.",
                          stacklevel=2)
            if isinstance(ipt, list):
                ipt = tuple(ipt)
            if ipt is not None and not isinstance(ipt, tuple):
                ipt = (ipt,)

            if ipt is None:
                data_specs = (NullSpace(), '')
            elif len(ipt) == 0:
                data_specs = (CompositeSpace([]), ())
            elif hasattr(dataset, 'get_data_specs'):
                dataset_space, dataset_source = dataset.get_data_specs()
                if (len(ipt) == 1 and
                        dataset_source is not None and
                        (not isinstance(dataset_source, tuple) or
                            len(dataset_source) == 1) and
                        'features' in dataset_source):
                    data_specs = (dataset_space, dataset_source)
                elif (len(ipt) == 2 and
                        dataset_source == ('features', 'targets')):
                    data_specs = (dataset_space, dataset_source)
                else:
                    raise ValueError("Cannot infer default data_specs for " +
                                     "the following input points and " +
                                     "dataset: ipt = %s, dataset = %s"
                                     % (ipt, dataset))

        data_specs[0].validate(ipt)

        mapping = DataSpecsMapping(data_specs)
        flat_ipt = mapping.flatten(ipt)
        if not isinstance(flat_ipt, tuple):
            flat_ipt = (flat_ipt,)
        inputs = theano.gof.graph.inputs([val])
        for elem in inputs:
            if not hasattr(elem, 'get_value') and \
               not isinstance(elem, theano.gof.graph.Constant):
                if elem not in flat_ipt:
                    raise ValueError("Unspecified input: " + str(elem) +
                                     ". This may be due to an incorrect " +
                                     "implementation of a cost's " +
                                     "get_data_specs() method, or of a " +
                                     "model's get_monitoring_data_specs() " +
                                     "method.")

        mode = self.theano_function_mode
        if mode is not None and hasattr(mode, 'record'):
            mode.record.handle_line('Adding monitor channel '+name+'\n')
            assert isinstance(flat_ipt, tuple)
            if len(flat_ipt) != 1:
                for elem in flat_ipt:
                    mode.record.handle_line('Includes input var ' +
                                            var_descriptor(elem) + '\n')
            else:
                mode.record.handle_line(name + ' input var is ' +
                                        var_descriptor(flat_ipt[0]) + '\n')
            mode.record.handle_line('channel ' + name + ' is ' +
                                    var_descriptor(val) + '\n')

        if dataset is None:
            if len(self._datasets) == 1:
                dataset = self._datasets[0]
            elif len(self._datasets) == 0:
                raise ValueError(_err_no_data)
            else:
                raise ValueError(_err_ambig_data)
#.........这里部分代码省略.........

#.........这里部分代码省略.........
        log.info('Monitored channels: ')
        for key in sorted(self.channels.keys()):
            mode = self.theano_function_mode
            if mode is not None and hasattr(mode, 'record'):
                mode.record.handle_line('compiling monitor including ' +
                                        'channel ' + key + '\n')
            log.info('\t%s' % key)
        it = [d.iterator(mode=i, num_batches=n, batch_size=b,
                         data_specs=self._flat_data_specs,
                         return_tuple=True)
              for d, i, n, b in safe_izip(self._datasets, self._iteration_mode,
                                          self._num_batches, self._batch_size)]
        self.num_examples = [np.cast[config.floatX](float(i.num_examples))
                             for i in it]
        self.num_examples = [float(i.num_examples) for i in it]

        givens = [OrderedDict() for d in self._datasets]
        updates = [OrderedDict() for d in self._datasets]
        for i, channel in enumerate(self.channels.values()):
            index = self._datasets.index(channel.dataset)
            d = self._datasets[index]
            g = givens[index]
            inv_cur_num_examples = as_floatX(1./self.num_examples[index])
            u = updates[index]

            # Flatten channel.graph_input and the appropriate part of
            # nested_theano_args, to iterate jointly over them.
            c_mapping = DataSpecsMapping(channel.data_specs)
            channel_inputs = c_mapping.flatten(channel.graph_input,
                                               return_tuple=True)
            inputs = c_mapping.flatten(nested_theano_args[i + 1],
                                       return_tuple=True)

            for (channel_X, X) in safe_izip(channel_inputs, inputs):
                assert channel_X not in g or g[channel_X] is X
                assert channel_X.type == X.type, (channel_X.type, X.type)
                g[channel_X] = X

            if batch_size == 0:
                # No channel does need any data, so there is not need to
                # average results, and we will call the accum functions only
                # once.
                # TODO: better handling of channels not needing data when
                # some other channels need data.
                assert len(self._flat_data_specs[1]) == 0
                val = channel.val
            else:
                if n == 0:
                    raise ValueError("Iterating over 0 examples results in " +
                                     "divide by 0")
                val = (channel.val * T.cast(batch_size, config.floatX)
                       * inv_cur_num_examples)
            u[channel.val_shared] = channel.val_shared + val

        with log_timing(log, "Compiling accum"):
            # Check type of update expressions
            for up in updates:
                for key in up:
                    if key.dtype != up[key].dtype:
                        raise TypeError('Monitoring channel shared variable ' +
                                        key.name + ' has dtype ' + key.dtype +
                                        ' but is driven by an expression ' +
                                        'with type ' + up[key].dtype)

            self.accum = []
            for idx, packed in enumerate(safe_izip(givens, updates)):
                g, u = packed
                mode = self.theano_function_mode
                if mode is not None and hasattr(mode, 'record'):
                    for elem in g:
                        mode.record.handle_line('g key ' +
                                                var_descriptor(elem) + '\n')
                        mode.record.handle_line('g val ' +
                                                var_descriptor(g[elem]) + '\n')
                    for elem in u:
                        mode.record.handle_line('u key ' +
                                                var_descriptor(elem) + '\n')
                        mode.record.handle_line('u val ' +
                                                var_descriptor(u[elem]) + '\n')
                function_name = 'Monitor.accum[%d]' % idx
                if mode is not None and hasattr(mode, 'record'):
                    mode.record.handle_line('compiling supervised accum\n')
                # Some channels may not depend on the data, ie, they might just
                # monitor the model parameters, or some shared variable updated
                # by the training algorithm, so we need to ignore the unused
                # input error
                self.accum.append(function(theano_args,
                                           givens=g,
                                           updates=u,
                                           mode=self.theano_function_mode,
                                           name=function_name))
            for a in self.accum:
                if mode is not None and hasattr(mode, 'record'):
                    for elem in a.maker.fgraph.outputs:
                        mode.record.handle_line('accum output ' +
                                                var_descriptor(elem) + '\n')
                log.info("graph size: %d" % len(a.maker.fgraph.toposort()))
        final_names = dir(self)
        self.register_names_to_del([name for name in final_names
                                    if name not in init_names])