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python - 加载已保存模型的决策森林问题

问题描述

enter code here我训练了 RandomForestModel 并保存了它。我可以用原始预测，但不能用加载的模型。如何使用加载的模型进行预测？我还没有找到任何关于加载保存的随机森林模型的例子。TFDF 库中也没有加载函数。我想我必须使用model_2添加任何操作，但我不知道它是什么。( TF 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)

有错误：

INFO:tensorflow:Assets written to: saved_model/assets

INFO:tensorflow:Assets written to: saved_model/assets

predictions:
 [[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               raise

ValueError: 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: {}

标签： pythonpython-3.xtensorflowmachine-learningkeras

解决方案

在将模型保存到磁盘或工作区之前：

import bz2
import pickle
import _pickle as cPickle
with 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)

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