在this tutorial的训练过程之后，我尝试使用以下代码保存tf模型：
tf.keras.models.save_model(decoder, 'path', save_format='tf')
但出现错误消息：
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-33-5a5dd79f753f> in <module>()
----> 1 tf.keras.models.save_model(decoder, './drive/My Drive/DeepLearning/decoder/kerasencoder', save_format='tf')
20 frames
/tensorflow-2.1.0/python3.6/tensorflow_core/python/keras/saving/save.py in save_model(model, filepath, overwrite, include_optimizer, save_format, signatures, options)
113 else:
114 saved_model_save.save(model, filepath, overwrite, include_optimizer,
--> 115 signatures, options)
116
117
/tensorflow-2.1.0/python3.6/tensorflow_core/python/keras/saving/saved_model/save.py in save(model, filepath, overwrite, include_optimizer, signatures, options)
76 # we use the default replica context here.
77 with distribution_strategy_context._get_default_replica_context(): # pylint: disable=protected-access
---> 78 save_lib.save(model, filepath, signatures, options)
79
80 if not include_optimizer:
/tensorflow-2.1.0/python3.6/tensorflow_core/python/saved_model/save.py in save(obj, export_dir, signatures, options)
884 if signatures is None:
885 signatures = signature_serialization.find_function_to_export(
--> 886 checkpoint_graph_view)
887
888 signatures = signature_serialization.canonicalize_signatures(signatures)
/tensorflow-2.1.0/python3.6/tensorflow_core/python/saved_model/signature_serialization.py in find_function_to_export(saveable_view)
72 # If the user did not specify signatures, check the root object for a function
73 # that can be made into a signature.
---> 74 functions = saveable_view.list_functions(saveable_view.root)
75 signature = functions.get(DEFAULT_SIGNATURE_ATTR, None)
76 if signature is not None:
/tensorflow-2.1.0/python3.6/tensorflow_core/python/saved_model/save.py in list_functions(self, obj)
140 if obj_functions is None:
141 obj_functions = obj._list_functions_for_serialization( # pylint: disable=protected-access
--> 142 self._serialization_cache)
143 self._functions[obj] = obj_functions
144 return obj_functions
/tensorflow-2.1.0/python3.6/tensorflow_core/python/keras/engine/base_layer.py in _list_functions_for_serialization(self, serialization_cache)
2418 def _list_functions_for_serialization(self, serialization_cache):
2419 return (self._trackable_saved_model_saver
-> 2420 .list_functions_for_serialization(serialization_cache))
2421
2422 def __getstate__(self):
/tensorflow-2.1.0/python3.6/tensorflow_core/python/keras/saving/saved_model/base_serialization.py in list_functions_for_serialization(self, serialization_cache)
89 `ConcreteFunction`.
90 """
---> 91 fns = self.functions_to_serialize(serialization_cache)
92
93 # The parent AutoTrackable class saves all user-defined tf.functions, and
/tensorflow-2.1.0/python3.6/tensorflow_core/python/keras/saving/saved_model/layer_serialization.py in functions_to_serialize(self, serialization_cache)
78 def functions_to_serialize(self, serialization_cache):
79 return (self._get_serialized_attributes(
---> 80 serialization_cache).functions_to_serialize)
81
82 def _get_serialized_attributes(self, serialization_cache):
/tensorflow-2.1.0/python3.6/tensorflow_core/python/keras/saving/saved_model/layer_serialization.py in _get_serialized_attributes(self, serialization_cache)
93
94 object_dict, function_dict = self._get_serialized_attributes_internal(
---> 95 serialization_cache)
96
97 serialized_attr.set_and_validate_objects(object_dict)
/tensorflow-2.1.0/python3.6/tensorflow_core/python/keras/saving/saved_model/model_serialization.py in _get_serialized_attributes_internal(self, serialization_cache)
45 # cache (i.e. this is the root level object).
46 if len(serialization_cache[constants.KERAS_CACHE_KEY]) == 1:
---> 47 default_signature = save_impl.default_save_signature(self.obj)
48
49 # Other than the default signature function, all other attributes match with
/tensorflow-2.1.0/python3.6/tensorflow_core/python/keras/saving/saved_model/save_impl.py in default_save_signature(layer)
210 original_losses = _reset_layer_losses(layer)
211 fn = saving_utils.trace_model_call(layer)
--> 212 fn.get_concrete_function()
213 _restore_layer_losses(original_losses)
214 return fn
/tensorflow-2.1.0/python3.6/tensorflow_core/python/eager/def_function.py in get_concrete_function(self, *args, **kwargs)
907 if self._stateful_fn is None:
908 initializers = []
--> 909 self._initialize(args, kwargs, add_initializers_to=initializers)
910 self._initialize_uninitialized_variables(initializers)
911
/tensorflow-2.1.0/python3.6/tensorflow_core/python/eager/def_function.py in _initialize(self, args, kwds, add_initializers_to)
495 self._concrete_stateful_fn = (
496 self._stateful_fn._get_concrete_function_internal_garbage_collected( # pylint: disable=protected-access
--> 497 *args, **kwds))
498
499 def invalid_creator_scope(*unused_args, **unused_kwds):
/tensorflow-2.1.0/python3.6/tensorflow_core/python/eager/function.py in _get_concrete_function_internal_garbage_collected(self, *args, **kwargs)
2387 args, kwargs = None, None
2388 with self._lock:
-> 2389 graph_function, _, _ = self._maybe_define_function(args, kwargs)
2390 return graph_function
2391
/tensorflow-2.1.0/python3.6/tensorflow_core/python/eager/function.py in _maybe_define_function(self, args, kwargs)
2701
2702 self._function_cache.missed.add(call_context_key)
-> 2703 graph_function = self._create_graph_function(args, kwargs)
2704 self._function_cache.primary[cache_key] = graph_function
2705 return graph_function, args, kwargs
/tensorflow-2.1.0/python3.6/tensorflow_core/python/eager/function.py in _create_graph_function(self, args, kwargs, override_flat_arg_shapes)
2591 arg_names=arg_names,
2592 override_flat_arg_shapes=override_flat_arg_shapes,
-> 2593 capture_by_value=self._capture_by_value),
2594 self._function_attributes,
2595 # Tell the ConcreteFunction to clean up its graph once it goes out of
/tensorflow-2.1.0/python3.6/tensorflow_core/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)
976 converted_func)
977
--> 978 func_outputs = python_func(*func_args, **func_kwargs)
979
980 # invariant: `func_outputs` contains only Tensors, CompositeTensors,
/tensorflow-2.1.0/python3.6/tensorflow_core/python/eager/def_function.py in wrapped_fn(*args, **kwds)
437 # __wrapped__ allows AutoGraph to swap in a converted function. We give
438 # the function a weak reference to itself to avoid a reference cycle.
--> 439 return weak_wrapped_fn().__wrapped__(*args, **kwds)
440 weak_wrapped_fn = weakref.ref(wrapped_fn)
441
/tensorflow-2.1.0/python3.6/tensorflow_core/python/keras/saving/saving_utils.py in _wrapped_model(*args)
148 with base_layer_utils.call_context().enter(
149 model, inputs=inputs, build_graph=False, training=False, saving=True):
--> 150 outputs_list = nest.flatten(model(inputs=inputs, training=False))
151
152 try:
/tensorflow-2.1.0/python3.6/tensorflow_core/python/keras/engine/base_layer.py in __call__(self, inputs, *args, **kwargs)
776 outputs = base_layer_utils.mark_as_return(outputs, acd)
777 else:
--> 778 outputs = call_fn(cast_inputs, *args, **kwargs)
779
780 except errors.OperatorNotAllowedInGraphError as e:
/tensorflow-2.1.0/python3.6/tensorflow_core/python/autograph/impl/api.py in wrapper(*args, **kwargs)
290 def wrapper(*args, **kwargs):
291 with ag_ctx.ControlStatusCtx(status=ag_ctx.Status.DISABLED):
--> 292 return func(*args, **kwargs)
293
294 if inspect.isfunction(func) or inspect.ismethod(func):
TypeError: call() missing 2 required positional arguments: 'hidden' and 'enc_output'
在链接中，解码器定义为：
class Decoder(tf.keras.Model):
def __init__(self, vocab_size, embedding_dim, dec_units, batch_sz):
super(Decoder, self).__init__()
self.batch_sz = batch_sz
self.dec_units = dec_units
self.embedding = tf.keras.layers.Embedding(vocab_size, embedding_dim)
self.gru = tf.keras.layers.GRU(self.dec_units,
return_sequences=True,
return_state=True,
recurrent_initializer='glorot_uniform')
self.fc = tf.keras.layers.Dense(vocab_size)
# used for attention
self.attention = BahdanauAttention(self.dec_units)
def call(self, x, hidden, enc_output):
# enc_output shape == (batch_size, max_length, hidden_size)
context_vector, attention_weights = self.attention(hidden, enc_output)
# x shape after passing through embedding == (batch_size, 1, embedding_dim)
x = self.embedding(x)
# x shape after concatenation == (batch_size, 1, embedding_dim + hidden_size)
x = tf.concat([tf.expand_dims(context_vector, 1), x], axis=-1)
# passing the concatenated vector to the GRU
output, state = self.gru(x)
# output shape == (batch_size * 1, hidden_size)
output = tf.reshape(output, (-1, output.shape[2]))
# output shape == (batch_size, vocab)
x = self.fc(output)
return x, state, attention_weights
如何解决此错误？
答案 0 :(得分：2)
由于序列化问题，可能会发生错误。
有多种方法可以保存Tensorflow模型。在本教程中，您提供了他们使用tf.Checkpoint的情况，主要是因为无法通过tf.keras.Model或tf.keras.model.save_model安全地序列化子类model.save。
看看docs（尤其是自定义对象部分）和this issue。
使用子类化API时，通过model.save_weights仅保存模型权重更为安全。
希望这会有所帮助。