enter code here我训练了一个RandomForestModel并保存了它。我可以用原始模型进行预测，但无法用加载后的模型进行预测。我该如何用加载后的模型进行预测？我没有找到任何关于加载保存的随机森林模型的示例。TFDF库中也没有加载函数。我认为我必须对model_2进行一些操作，但不知道具体是什么操作。（TensorFlow版本2.5.0，TF-DF版本0.1.5，Python 3.8.5）
model_1.save("saved_model")model_2 = tf.keras.models.load_model("saved_model")examples = tf.data.Dataset.from_tensor_slices(sample2)predictions = model_1.predict(examples)print("predictions:\n",predictions)predictions = model_2.predict(examples)print("predictions:\n",predictions)出现错误：
INFO:tensorflow:Assets written to: saved_model/assetsINFO:tensorflow:Assets written to: saved_model/assetspredictions: [[0.99666584]]---------------------------------------------------------------------------ValueError Traceback (most recent call last)<ipython-input-36-bed461cbf29c> in <module> 206 predictions = model_1.predict(examples) 207 print("predictions:\n",predictions)--> 208 predictions = model_2.predict(examples) 209 print("predictions:\n",predictions)~/.local/lib/python3.8/site-packages/tensorflow/python/keras/engine/training.py in predict(self, x, batch_size, verbose, steps, callbacks, max_queue_size, workers, use_multiprocessing) 1725 for step in data_handler.steps(): 1726 callbacks.on_predict_batch_begin(step)-> 1727 tmp_batch_outputs = self.predict_function(iterator) 1728 if data_handler.should_sync: 1729 context.async_wait()~/.local/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in __call__(self, *args, **kwds) 887 888 with OptionalXlaContext(self._jit_compile):--> 889 result = self._call(*args, **kwds) 890 891 new_tracing_count = self.experimental_get_tracing_count()~/.local/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in _call(self, *args, **kwds) 931 # This is the first call of __call__, so we have to initialize. 932 initializers = []--> 933 self._initialize(args, kwds, add_initializers_to=initializers) 934 finally: 935 # At this point we know that the initialization is complete (or less~/.local/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in _initialize(self, args, kwds, add_initializers_to) 761 self._graph_deleter = FunctionDeleter(self._lifted_initializer_graph) 762 self._concrete_stateful_fn = (--> 763 self._stateful_fn._get_concrete_function_internal_garbage_collected( # pylint: disable=protected-access 764 *args, **kwds)) 765 ~/.local/lib/python3.8/site-packages/tensorflow/python/eager/function.py in _get_concrete_function_internal_garbage_collected(self, *args, **kwargs) 3048 args, kwargs = None, None 3049 with self._lock:-> 3050 graph_function, _ = self._maybe_define_function(args, kwargs) 3051 return graph_function 3052 ~/.local/lib/python3.8/site-packages/tensorflow/python/eager/function.py in _maybe_define_function(self, args, kwargs) 3442 3443 self._function_cache.missed.add(call_context_key)-> 3444 graph_function = self._create_graph_function(args, kwargs) 3445 self._function_cache.primary[cache_key] = graph_function 3446 ~/.local/lib/python3.8/site-packages/tensorflow/python/eager/function.py in _create_graph_function(self, args, kwargs, override_flat_arg_shapes) 3277 arg_names = base_arg_names + missing_arg_names 3278 graph_function = ConcreteFunction(-> 3279 func_graph_module.func_graph_from_py_func( 3280 self._name, 3281 self._python_function,~/.local/lib/python3.8/site-packages/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) 997 _, original_func = tf_decorator.unwrap(python_func) 998 --> 999 func_outputs = python_func(*func_args, **func_kwargs) 1000 1001 # invariant: `func_outputs` contains only Tensors, CompositeTensors,~/.local/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in wrapped_fn(*args, **kwds) 670 # the function a weak reference to itself to avoid a reference cycle. 671 with OptionalXlaContext(compile_with_xla):--> 672 out = weak_wrapped_fn().__wrapped__(*args, **kwds) 673 return out 674 ~/.local/lib/python3.8/site-packages/tensorflow/python/framework/func_graph.py in wrapper(*args, **kwargs) 984 except Exception as e: # pylint:disable=broad-except 985 if hasattr(e, "ag_error_metadata"):--> 986 raise e.ag_error_metadata.to_exception(e) 987 else: 988 raiseValueError: in user code: /home/shiba/.local/lib/python3.8/site-packages/tensorflow/python/keras/engine/training.py:1569 predict_function * return step_function(self, iterator) /home/shiba/.local/lib/python3.8/site-packages/tensorflow/python/keras/engine/training.py:1559 step_function ** outputs = model.distribute_strategy.run(run_step, args=(data,)) /home/shiba/.local/lib/python3.8/site-packages/tensorflow/python/distribute/distribute_lib.py:1285 run return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs) /home/shiba/.local/lib/python3.8/site-packages/tensorflow/python/distribute/distribute_lib.py:2833 call_for_each_replica return self._call_for_each_replica(fn, args, kwargs) /home/shiba/.local/lib/python3.8/site-packages/tensorflow/python/distribute/distribute_lib.py:3608 _call_for_each_replica return fn(*args, **kwargs) /home/shiba/.local/lib/python3.8/site-packages/tensorflow/python/keras/engine/training.py:1552 run_step ** outputs = model.predict_step(data) /home/shiba/.local/lib/python3.8/site-packages/tensorflow/python/keras/engine/training.py:1525 predict_step return self(x, training=False) /home/shiba/.local/lib/python3.8/site-packages/tensorflow/python/keras/engine/base_layer.py:1030 __call__ outputs = call_fn(inputs, *args, **kwargs) /home/shiba/.local/lib/python3.8/site-packages/tensorflow/python/keras/saving/saved_model/utils.py:69 return_outputs_and_add_losses outputs, losses = fn(*args, **kwargs) /home/shiba/.local/lib/python3.8/site-packages/tensorflow/python/keras/saving/saved_model/utils.py:165 wrap_with_training_arg return control_flow_util.smart_cond( /home/shiba/.local/lib/python3.8/site-packages/tensorflow/python/keras/utils/control_flow_util.py:109 smart_cond return smart_module.smart_cond( /home/shiba/.local/lib/python3.8/site-packages/tensorflow/python/framework/smart_cond.py:56 smart_cond return false_fn() /home/shiba/.local/lib/python3.8/site-packages/tensorflow/python/keras/saving/saved_model/utils.py:167 <lambda> lambda: replace_training_and_call(False)) /home/shiba/.local/lib/python3.8/site-packages/tensorflow/python/keras/saving/saved_model/utils.py:163 replace_training_and_call return wrapped_call(*args, **kwargs) /home/shiba/.local/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py:889 __call__ result = self._call(*args, **kwds) /home/shiba/.local/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py:933 _call self._initialize(args, kwds, add_initializers_to=initializers) /home/shiba/.local/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py:763 _initialize self._stateful_fn._get_concrete_function_internal_garbage_collected( # pylint: disable=protected-access /home/shiba/.local/lib/python3.8/site-packages/tensorflow/python/eager/function.py:3050 _get_concrete_function_internal_garbage_collected graph_function, _ = self._maybe_define_function(args, kwargs) /home/shiba/.local/lib/python3.8/site-packages/tensorflow/python/eager/function.py:3444 _maybe_define_function graph_function = self._create_graph_function(args, kwargs) /home/shiba/.local/lib/python3.8/site-packages/tensorflow/python/eager/function.py:3279 _create_graph_function func_graph_module.func_graph_from_py_func( /home/shiba/.local/lib/python3.8/site-packages/tensorflow/python/framework/func_graph.py:999 func_graph_from_py_func func_outputs = python_func(*func_args, **func_kwargs) /home/shiba/.local/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py:672 wrapped_fn out = weak_wrapped_fn().__wrapped__(*args, **kwds) /home/shiba/.local/lib/python3.8/site-packages/tensorflow/python/saved_model/function_deserialization.py:285 restored_function_body raise ValueError( ValueError: Could not find matching function to call loaded from the SavedModel. Got: Positional arguments (2 total)...* False Keyword arguments: {} Expected these arguments to match one of the following 4 option(s): Option 1: Positional arguments (2 total): ...* False Keyword arguments: {} Option 2: Positional arguments (2 total): ... * True Keyword arguments: {} Option 3: Positional arguments (2 total): ...* False Keyword arguments: {} Option 4: Positional arguments (2 total): ... * True Keyword arguments: {}回答：
在你保存模型到磁盘或工作区之前：
import bz2import pickleimport _pickle as cPicklewith bz2.BZ2File('.../randfmodel' + '.pbz2', 'wb') as f: cPickle.dump(model, f)然后加载保存的模型：
model_directory='YOUR_DIRECTORY_PATH' pkl_file = open(r'{}/randfmodel.pbz2'.format(model_directory), 'rb') model = cPickle.load(pkl_file)