def fn(query=None, **kwargs):
# Feed_dict dictionary
feed_dict = dict()
for key, arg in kwargs.items():
if arg is None:
continue
elif isinstance(arg, dict):
# Support single nesting (for states, internals, actions)
for key, arg in arg.items():
feed_dict[util.join_scopes(self.name, key) + '-input:0'] = arg
else:
feed_dict[util.join_scopes(self.name, key) + '-input:0'] = arg
if not all(isinstance(x, str) and x.endswith('-input:0') for x in feed_dict):
raise TensorforceError.unexpected()
# Fetches value/tuple
fetches = util.fmap(function=(lambda x: x.name), xs=results)
if query is not None:
# If additional tensors are to be fetched
query = util.fmap(
function=(lambda x: util.join_scopes(name, x) + '-output:0'), xs=query
)
if util.is_iterable(x=fetches):
fetches = tuple(fetches) + (query,)
else:
fetches = (fetches, query)
if not util.reduce_all(
predicate=(lambda x: isinstance(x, str) and x.endswith('-output:0')), xs=fetches
):
raise TensorforceError.unexpected()
# TensorFlow session call
fetched = self.monitored_session.run(fetches=fetches, feed_dict=feed_dict)
return fetched
def assign_variable(self, *, variable, value):
if variable.startswith(self.name + '/'):
variable = variable[len(self.name) + 1:]
module = self
scope = variable.split('/')
for _ in range(len(scope) - 1):
module = module.modules[scope.pop(0)]
fetches = util.join_scopes(self.name, variable) + '-assign'
dtype = util.dtype(x=module.variables[scope[0]])
feed_dict = {util.join_scopes(self.name, 'assignment-') + dtype + '-input:0': value}
self.monitored_session.run(fetches=fetches, feed_dict=feed_dict)
def update_tensor(name, tensor):
# for n in range(len(Module.global_scope) + 1):
# partial_scope = Module.global_scope[:len(Module.global_scope) - n]
# scoped_name = util.join_scopes(*partial_scope, name)
# if scoped_name in Module.global_tensors_spec:
# break
# else:
# raise TensorforceError("Global tensor is not registered: {}.".format(name))
if name not in Module.global_tensors_spec:
raise TensorforceError(
"Global tensor is not registered: {}.".format(name))
scoped_name = name
spec = Module.global_tensors_spec[scoped_name]
if not util.is_consistent_with_value_spec(value_spec=spec, x=tensor):
raise TensorforceError(
"Invalid overwriting tensor: {}.".format(tensor))
scoped_name = util.join_scopes(*Module.global_scope, name)
previous = Module.global_tensors.get(scoped_name)
Module.global_tensors[scoped_name] = tensor
return previous
def retrieve_tensor(name):
# for n in range(len(Module.global_scope) + 1):
# partial_scope = Module.global_scope[:len(Module.global_scope) - n]
# scoped_name = util.join_scopes(*partial_scope, name)
# if scoped_name in Module.global_tensors_spec:
# break
# else:
# raise TensorforceError("Global tensor is not registered: {}.".format(name))
if name not in Module.global_tensors_spec:
raise TensorforceError(
"Global tensor is not registered: {}.".format(name))
for n in range(len(Module.global_scope) + 1):
partial_scope = Module.global_scope[:len(Module.global_scope) - n]
scoped_name = util.join_scopes(*partial_scope, name)
if scoped_name in Module.global_tensors:
break
else:
raise TensorforceError(
"Global tensor is not set: {}.".format(name))
# scoped_name = util.join_scopes(*Module.global_scope, name)
# if scoped_name not in Module.global_tensors:
# raise TensorforceError("Global tensor is not set: {}.".format(scoped_name))
return Module.global_tensors[scoped_name]
def retrieve_tensor(name):
if name not in Module.global_tensors_spec:
raise TensorforceError.value(
name='Module.retrieve_tensor', argument='name', value=name
)
for n in range(len(Module.global_scope) + 1):
partial_scope = Module.global_scope[:len(Module.global_scope) - n]
scoped_name = util.join_scopes(*partial_scope, name)
if scoped_name in Module.global_tensors:
break
else:
raise TensorforceError.value(
name='Module.retrieve_tensor', argument='name', value=name
)
return Module.global_tensors[scoped_name]
def update_tensor(name, tensor):
if name not in Module.global_tensors_spec:
raise TensorforceError("Global tensor is not registered: {}.".format(name))
scoped_name = name
spec = Module.global_tensors_spec[scoped_name]
if not util.is_consistent_with_value_spec(value_spec=spec, x=tensor):
raise TensorforceError("Invalid overwriting tensor: {}.".format(tensor))
scoped_name = util.join_scopes(*Module.global_scope, name)
previous = Module.global_tensors.get(scoped_name)
Module.global_tensors[scoped_name] = tensor
if Module.cond_counter == 0 and Module.while_counter == 0:
Module.queryable_tensors[scoped_name] = tensor
return previous
def update_tensor(name, tensor):
if name not in Module.global_tensors_spec:
raise TensorforceError.value(
name='Module.update_tensor', argument='name', value=name
)
scoped_name = name
spec = Module.global_tensors_spec[scoped_name]
if not util.is_consistent_with_value_spec(value_spec=spec, x=tensor):
raise TensorforceError.value(
name='Module.update_tensor', argument='tensor', value=tensor
)
scoped_name = util.join_scopes(*Module.global_scope, name)
previous = Module.global_tensors.get(scoped_name)
Module.global_tensors[scoped_name] = tensor
if Module.cond_counter == 0 and Module.while_counter == 0:
Module.queryable_tensors[scoped_name] = tensor
return previous
def summarize(self, *, summary, value, step=None):
fetches = util.join_scopes(self.name, summary, 'write_summary', 'Const:0')
feed_dict = {util.join_scopes(self.name, 'summarize-input:0'): value}
if step is not None:
feed_dict[util.join_scopes(self.name, 'summarize-step-input:0')] = step
self.monitored_session.run(fetches=fetches, feed_dict=feed_dict)
def get_variable(self, *, variable):
assert False, 'Not updated yet!'
if not variable.startswith(self.name):
variable = util.join_scopes(self.name, variable)
fetches = variable + '-output:0'
return self.monitored_session.run(fetches=fetches)