def clone_get_equiv(self, check_integrity=True, attach_feature=True):
"""Clone the graph and get a memo( a dict )that map old node to new node
----------------------------
Parameters:
check_integrity - { bool } Whether to check integrity.
Default is True.
attach_feature - { bool } Whether to attach feature of origin graph to
cloned graph. Default is True.
----------------------------
Returns:
e - { FunctionGraph } Cloned fgraph. Every node in cloned graph is cloned.
equiv - { dict } A dict that map old node to new node.
"""
equiv = graph.clone_get_equiv(self.inputs, self.outputs)
if check_integrity:
self.check_integrity()
e = FunctionGraph([equiv[i] for i in self.inputs],
[equiv[o] for o in self.outputs],
clone=False)
if check_integrity:
e.check_integrity()
if attach_feature:
for feature in self._features:
e.attach_feature(feature)
return e, equiv
def clone_get_equiv(self, check_integrity=True, attach_feature=True):
"""Clone the graph and get a dict that maps old nodes to new ones
Parameters:
check_integrity: bool
Whether to check integrity. Default is True.
attach_feature: bool
Whether to attach feature of origin graph to cloned graph.
Default is True.
Returns:
e: FunctionGraph
Cloned fgraph. Every node in cloned graph is cloned.
equiv: dict
A dict that map old node to new node.
"""
equiv = clone_get_equiv(self.inputs, self.outputs)
if check_integrity:
self.check_integrity()
e = FunctionGraph(
[equiv[i] for i in self.inputs],
[equiv[o] for o in self.outputs],
clone=False,
)
if check_integrity:
e.check_integrity()
if attach_feature:
for feature in self._features:
e.attach_feature(feature)
return e, equiv
def clone_get_equiv(self, check_integrity=True, attach_feature=True):
"""Clone the graph and get a memo( a dict )that map old node to new node
----------------------------
Parameters:
check_integrity - { bool } Whether to check integrity.
Default is True.
attach_feature - { bool } Whether to attach feature of origin graph to
cloned graph. Default is True.
----------------------------
Returns:
e - { FunctionGraph } Cloned fgraph. Every node in cloned graph is cloned.
equiv - { dict } A dict that map old node to new node.
"""
equiv = graph.clone_get_equiv(self.inputs, self.outputs)
if check_integrity:
self.check_integrity()
e = FunctionGraph([equiv[i] for i in self.inputs],
[equiv[o] for o in self.outputs],
clone=False)
if check_integrity:
e.check_integrity()
if attach_feature:
for feature in self._features:
e.attach_feature(feature)
return e, equiv
def clone_get_equiv(self):
"""WRITEME"""
equiv = graph.clone_get_equiv(self.inputs, self.outputs)
self.check_integrity()
e = FunctionGraph([equiv[i] for i in self.inputs], [equiv[o] for o in self.outputs])
e.check_integrity()
for feature in self._features:
e.attach_feature(feature)
return e, equiv
def clone_get_equiv(self):
"""WRITEME"""
equiv = graph.clone_get_equiv(self.inputs, self.outputs)
self.check_integrity()
e = FunctionGraph([equiv[i] for i in self.inputs],
[equiv[o] for o in self.outputs])
e.check_integrity()
for feature in self._features:
e.attach_feature(feature)
return e, equiv
def clone_get_equiv(self):
"""WRITEME"""
equiv = graph.clone_get_equiv(self.inputs, self.outputs)
self.check_integrity()
e = self.__class__([equiv[i] for i in self.inputs],
[equiv[o] for o in self.outputs])
e.check_integrity()
for feature in self._features:
e.extend(feature)
return e, equiv
def std_interactive_env(inputs, outputs, clone_inputs_and_orphans=True):
features = []
features.append(toolbox.ReplaceValidate())
features.append(Newest())
features.append(destroyhandler.DestroyHandler())
features.append(toolbox.PreserveNames())
equiv = graph.clone_get_equiv(inputs, outputs,
copy_inputs_and_orphans=clone_inputs_and_orphans)
rval = InteractiveEnv(equiv, features)
rval.add_inputs([equiv[v] for v in inputs])
if not clone_inputs_and_orphans:
for v in inputs:
assert equiv[v] is v
assert rval.inputs == inputs
rval.add_outputs([equiv[v] for v in outputs])
for k, v in equiv.iteritems():
assert v.env is rval
return rval
def optimize_graph(x, optimization, return_graph=None, in_place=False):
"""Easily optimize Theano graphs.
Apply an optimization to either the graph formed by a Theano variable or an
existing graph and return the resulting optimized graph.
When given an existing `FunctionGraph`, the optimization is
performed without side-effects (i.e. won't change the given graph).
"""
if not isinstance(x, tt_FunctionGraph):
inputs = tt_inputs([x])
outputs = [x]
model_memo = clone_get_equiv(inputs, outputs, copy_orphans=False)
cloned_inputs = [
model_memo[i] for i in inputs if not isinstance(i, tt.Constant)
]
cloned_outputs = [model_memo[i] for i in outputs]
x_graph = FunctionGraph(cloned_inputs, cloned_outputs, clone=False)
x_graph.memo = model_memo
if return_graph is None:
return_graph = False
else:
x_graph = x
if return_graph is None:
return_graph = True
x_graph_opt = x_graph if in_place else x_graph.clone()
_ = optimization.optimize(x_graph_opt)
if return_graph:
res = x_graph_opt
else:
res = x_graph_opt.outputs
x_graph_opt.disown()
if len(res) == 1:
(res, ) = res
return res
