Un module TensorFlow Hub peut-il être utilisé dans TensorFlow 2.0?
J'ai essayé d'exécuter ce code dans TensorFlow 2.0 (alpha):
import tensorflow_hub as hub
@tf.function
def elmo(texts):
elmo_module = hub.Module("https://tfhub.dev/google/elmo/2", trainable=True)
return elmo_module(texts, signature="default", as_dict=True)
embeds = elmo(tf.constant(["the cat is on the mat",
"dogs are in the fog"]))
Mais j'ai eu cette erreur:
---------------------------------------------------------------------------
RuntimeError Traceback (most recent call last)
<ipython-input-1-c7f14c7ed0e9> in <module>
9
10 elmo(tf.constant(["the cat is on the mat",
---> 11 "dogs are in the fog"]))
.../tensorflow/python/eager/def_function.py in __call__(self, *args, **kwds)
417 # This is the first call of __call__, so we have to initialize.
418 initializer_map = {}
--> 419 self._initialize(args, kwds, add_initializers_to=initializer_map)
420 if self._created_variables:
421 try:
.../tensorflow/python/eager/def_function.py in _initialize(self, args, kwds, add_initializers_to)
361 self._concrete_stateful_fn = (
362 self._stateful_fn._get_concrete_function_internal_garbage_collected( # pylint: disable=protected-access
--> 363 *args, **kwds))
364
365 def invalid_creator_scope(*unused_args, **unused_kwds):
.../tensorflow/python/eager/function.py in _get_concrete_function_internal_garbage_collected(self, *args, **kwargs)
1322 if self.input_signature:
1323 args, kwargs = None, None
-> 1324 graph_function, _, _ = self._maybe_define_function(args, kwargs)
1325 return graph_function
1326
.../tensorflow/python/eager/function.py in _maybe_define_function(self, args, kwargs)
1585 or call_context_key not in self._function_cache.missed):
1586 self._function_cache.missed.add(call_context_key)
-> 1587 graph_function = self._create_graph_function(args, kwargs)
1588 self._function_cache.primary[cache_key] = graph_function
1589 return graph_function, args, kwargs
.../tensorflow/python/eager/function.py in _create_graph_function(self, args, kwargs, override_flat_arg_shapes)
1518 arg_names=arg_names,
1519 override_flat_arg_shapes=override_flat_arg_shapes,
-> 1520 capture_by_value=self._capture_by_value),
1521 self._function_attributes)
1522
.../tensorflow/python/framework/func_graph.py in func_graph_from_py_func(name, python_func, args, kwargs, signature, func_graph, autograph, autograph_options, add_control_dependencies, arg_names, op_return_value, collections, capture_by_value, override_flat_arg_shapes)
705 converted_func)
706
--> 707 func_outputs = python_func(*func_args, **func_kwargs)
708
709 # invariant: `func_outputs` contains only Tensors, IndexedSlices,
.../tensorflow/python/eager/def_function.py in wrapped_fn(*args, **kwds)
314 # __wrapped__ allows AutoGraph to swap in a converted function. We give
315 # the function a weak reference to itself to avoid a reference cycle.
--> 316 return weak_wrapped_fn().__wrapped__(*args, **kwds)
317 weak_wrapped_fn = weakref.ref(wrapped_fn)
318
.../tensorflow/python/framework/func_graph.py in wrapper(*args, **kwargs)
697 optional_features=autograph_options,
698 force_conversion=True,
--> 699 ), args, kwargs)
700
701 # Wrapping around a decorator allows checks like tf_inspect.getargspec
.../tensorflow/python/autograph/impl/api.py in converted_call(f, owner, options, args, kwargs)
355
356 if kwargs is not None:
--> 357 result = converted_f(*effective_args, **kwargs)
358 else:
359 result = converted_f(*effective_args)
/var/folders/wy/h39t6kb11pnbb0pzhksd_fqh0000gn/T/tmp4v3g2d_1.py in tf__elmo(texts)
11 retval_ = None
12 print('Eager:', ag__.converted_call('executing_eagerly', tf, ag__.ConversionOptions(recursive=True, force_conversion=False, optional_features=(), internal_convert_user_code=True), (), None))
---> 13 elmo_module = ag__.converted_call('Module', hub, ag__.ConversionOptions(recursive=True, force_conversion=False, optional_features=(), internal_convert_user_code=True), ('https://tfhub.dev/google/elmo/2',), {'trainable': True})
14 do_return = True
15 retval_ = ag__.converted_call(elmo_module, None, ag__.ConversionOptions(recursive=True, force_conversion=False, optional_features=(), internal_convert_user_code=True), (texts,), {'signature': 'default', 'as_dict': True})
.../tensorflow/python/autograph/impl/api.py in converted_call(f, owner, options, args, kwargs)
252 if tf_inspect.isclass(f):
253 logging.log(2, 'Permanently whitelisted: %s: constructor', f)
--> 254 return _call_unconverted(f, args, kwargs)
255
256 # Other built-in modules are permanently whitelisted.
.../tensorflow/python/autograph/impl/api.py in _call_unconverted(f, args, kwargs)
174
175 if kwargs is not None:
--> 176 return f(*args, **kwargs)
177 else:
178 return f(*args)
.../tensorflow_hub/module.py in __init__(self, spec, trainable, name, tags)
167 name=self._name,
168 trainable=self._trainable,
--> 169 tags=self._tags)
170 # pylint: enable=protected-access
171
.../tensorflow_hub/native_module.py in _create_impl(self, name, trainable, tags)
338 trainable=trainable,
339 checkpoint_path=self._checkpoint_variables_path,
--> 340 name=name)
341
342 def _export(self, path, variables_saver):
.../tensorflow_hub/native_module.py in __init__(self, spec, meta_graph, trainable, checkpoint_path, name)
389 # TPU training code.
390 with tf.init_scope():
--> 391 self._init_state(name)
392
393 def _init_state(self, name):
.../tensorflow_hub/native_module.py in _init_state(self, name)
392
393 def _init_state(self, name):
--> 394 variable_tensor_map, self._state_map = self._create_state_graph(name)
395 self._variable_map = recover_partitioned_variable_map(
396 get_node_map_from_tensor_map(variable_tensor_map))
.../tensorflow_hub/native_module.py in _create_state_graph(self, name)
449 meta_graph,
450 input_map={},
--> 451 import_scope=relative_scope_name)
452
453 # Build a list from the variable name in the module definition to the actual
.../tensorflow/python/training/saver.py in import_meta_graph(meta_graph_or_file, clear_devices, import_scope, **kwargs)
1443 """ # pylint: disable=g-doc-exception
1444 return _import_meta_graph_with_return_elements(
-> 1445 meta_graph_or_file, clear_devices, import_scope, **kwargs)[0]
1446
1447
.../tensorflow/python/training/saver.py in _import_meta_graph_with_return_elements(meta_graph_or_file, clear_devices, import_scope, return_elements, **kwargs)
1451 """Import MetaGraph, and return both a saver and returned elements."""
1452 if context.executing_eagerly():
-> 1453 raise RuntimeError("Exporting/importing meta graphs is not supported when "
1454 "eager execution is enabled. No graph exists when eager "
1455 "execution is enabled.")
RuntimeError: Exporting/importing meta graphs is not supported when eager execution is enabled. No graph exists when eager execution is enabled.
cette charge de fonction fonctionnera avec tensorflow 2
embed = hub.load("https://tfhub.dev/google/universal-sentence-encoder-large/3")
au lieu de
embed = hub.Module("https://tfhub.dev/google/universal-sentence-encoder-large/3")
[ceci n'est pas accepté dans tf2] utilisez hub.load ()