找不到匹配的函数来调用从 SavedModel 加载 [英] Could not find matching function to call loaded from the SavedModel
本文介绍了找不到匹配的函数来调用从 SavedModel 加载的处理方法，对大家解决问题具有一定的参考价值，需要的朋友们下面随着小编来一起学习吧！
问题描述
我写了下面的代码，它假设加载一个模型，然后对来自 MNIST 数据集的元素进行预测运行.在执行开始时，代码工作正常，我得到了我想要的预测，但突然间我确实收到了以下错误，我不确定这是否与 .predict arguments 有关.
我的代码:
# 导入库将 tensorflow 导入为 tf # 深度学习库.张量只是多维数组import gzip,sys,pickle # 数据集操作库# 导入 MNIST 数据集f = gzip.open('mnist.pkl.gz', 'rb')如果 sys.version_info <(3,):数据 = pickle.load(f)别的:data = pickle.load(f, encoding='bytes')f.close()(x_train, _), (x_test, _) = 数据打印(--------------数据集准备好---------------")# 使用 x_test/中的一个示例稍后删除# 预处理x_test = tf.keras.utils.normalize(x_test,axis=1) # 在 0 和 1 之间缩放数据# 导入模型new_model = tf.keras.models.load_model('epic_num_reader.model')打印(----------------------模型准备好了-----------------------")# 获取预测预测 = new_model.predict(x_test[0])将 numpy 导入为 np打印(----------------------预测准备好了-----------------------")打印(np.argmax(预测))错误信息:
<块引用> ---------------数据集准备好-----------------------2019-10-27 00:36:58.767359: I tensorflow/core/platform/cpu_feature_guard.cc:142] 你的 CPU 支持这个 TensorFlow 二进制文件没有被编译使用的指令:AVX2-----------------------模型准备好----------------------回溯(最近一次调用最后一次):文件c:\Users\lotfi\Desktop\DigitsDetector\main1.py"，第 24 行，在 <module> 中预测 = new_model.predict(x_test[0])文件C:\Users\lotfi\Anaconda3\envs\tf\lib\site-packages\tensorflow_core\python\keras\engine\training.py"，第 909 行，在预测中use_multiprocessing=use_multiprocessing)文件C:\Users\lotfi\Anaconda3\envs\tf\lib\site-packages\tensorflow_core\python\keras\engine\training_v2.py"，第 462 行，在预测中步骤=步骤，回调=回调，**kwargs)文件C:\Users\lotfi\Anaconda3\envs\tf\lib\site-packages\tensorflow_core\python\keras\engine\training_v2.py"，第 444 行，在 _model_iterationtotal_epochs=1)文件C:\Users\lotfi\Anaconda3\envs\tf\lib\site-packages\tensorflow_core\python\keras\engine\training_v2.py"，第 123 行，在 run_one_epochbatch_outs = execution_function(迭代器)文件C:\Users\lotfi\Anaconda3\envs\tf\lib\site-packages\tensorflow_core\python\keras\engine\training_v2_utils.py"，第86行，在execution_function中分布式函数(input_fn))文件C:\Users\lotfi\Anaconda3\envs\tf\lib\site-packages\tensorflow_core\python\eager\def_function.py"，第 457 行，在 __call__ 中结果 = self._call(*args, **kwds)文件C:\Users\lotfi\Anaconda3\envs\tf\lib\site-packages\tensorflow_core\python\eager\def_function.py"，第 503 行，在 _callself._initialize(args, kwds, add_initializers_to=initializer_map)文件C:\Users\lotfi\Anaconda3\envs\tf\lib\site-packages\tensorflow_core\python\eager\def_function.py"，第 408 行，在 _initialize*参数，**kwds))文件C:\Users\lotfi\Anaconda3\envs\tf\lib\site-packages\tensorflow_core\python\eager\function.py"，第 1848 行，在 _get_concrete_function_internal_garbage_collected图函数，_，_ = self._maybe_define_function(args, kwargs)文件C:\Users\lotfi\Anaconda3\envs\tf\lib\site-packages\tensorflow_core\python\eager\function.py"，第 2150 行，在 _maybe_define_functiongraph_function = self._create_graph_function(args, kwargs)文件C:\Users\lotfi\Anaconda3\envs\tf\lib\site-packages\tensorflow_core\python\eager\function.py"，第 2041 行，在 _create_graph_function 中capture_by_value=self._capture_by_value),文件C:\Users\lotfi\Anaconda3\envs\tf\lib\site-packages\tensorflow_core\python\framework\func_graph.py"，第 915 行，在 func_graph_from_py_funcfunc_outputs = python_func(*func_args, **func_kwargs)文件C:\Users\lotfi\Anaconda3\envs\tf\lib\site-packages\tensorflow_core\python\eager\def_function.py"，第358行，在wrapped_fn返回weak_wrapped_fn().__wrapped__(*args, **kwds)文件C:\Users\lotfi\Anaconda3\envs\tf\lib\site-packages\tensorflow_core\python\keras\engine\training_v2_utils.py"，第 73 行，在分布式函数中per_replica_function, args=(model, x, y, sample_weights))文件C:\Users\lotfi\Anaconda3\envs\tf\lib\site-packages\tensorflow_core\python\distribute\distribute_lib.py"，第760行，experimental_run_v2返回 self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs)文件C:\Users\lotfi\Anaconda3\envs\tf\lib\site-packages\tensorflow_core\python\distribute\distribute_lib.py"，第 1787 行，在 call_for_each_replica返回 self._call_for_each_replica(fn, args, kwargs)文件C:\Users\lotfi\Anaconda3\envs\tf\lib\site-packages\tensorflow_core\python\distribute\distribute_lib.py"，第 2132 行，在 _call_for_each_replica返回 fn(*args, **kwargs)文件C:\Users\lotfi\Anaconda3\envs\tf\lib\site-packages\tensorflow_core\python\autograph\impl\api.py"，第 292 行，在包装器中返回 func(*args, **kwargs)文件C:\Users\lotfi\Anaconda3\envs\tf\lib\site-packages\tensorflow_core\python\keras\engine\training_v2_utils.py"，第 162 行，在 _predict_on_batch返回 predict_on_batch(model, x)文件C:\Users\lotfi\Anaconda3\envs\tf\lib\site-packages\tensorflow_core\python\keras\engine\training_v2_utils.py"，第370行，在predict_on_batch中返回模型(输入)#pylint:禁用=不可调用文件C:\Users\lotfi\Anaconda3\envs\tf\lib\site-packages\tensorflow_core\python\keras\engine\base_layer.py"，第 847 行，在 __call__输出 = call_fn(cast_inputs, *args, **kwargs)文件C:\Users\lotfi\Anaconda3\envs\tf\lib\site-packages\tensorflow_core\python\keras\engine\sequential.py"，第270行，调用输出 = 层(输入，**kwargs)文件C:\Users\lotfi\Anaconda3\envs\tf\lib\site-packages\tensorflow_core\python\keras\engine\base_layer.py"，第 847 行，在 __call__输出 = call_fn(cast_inputs, *args, **kwargs)文件C:\Users\lotfi\Anaconda3\envs\tf\lib\site-packages\tensorflow_core\python\keras\saving\saved_model\utils.py"，第 57 行，在 return_outputs_and_add_losses输出，损失 = fn(输入，*args，**kwargs)文件C:\Users\lotfi\Anaconda3\envs\tf\lib\site-packages\tensorflow_core\python\eager\def_function.py"，第 457 行，在 __call__ 中结果 = self._call(*args, **kwds)文件C:\Users\lotfi\Anaconda3\envs\tf\lib\site-packages\tensorflow_core\python\eager\def_function.py"，第 494 行，在 _call结果 = self._stateful_fn(*args, **kwds)文件C:\Users\lotfi\Anaconda3\envs\tf\lib\site-packages\tensorflow_core\python\eager\function.py"，第 1822 行，在 __call__ 中graph_function, args, kwargs = self._maybe_define_function(args, kwargs)文件C:\Users\lotfi\Anaconda3\envs\tf\lib\site-packages\tensorflow_core\python\eager\function.py"，第 2150 行，在 _maybe_define_functiongraph_function = self._create_graph_function(args, kwargs)文件C:\Users\lotfi\Anaconda3\envs\tf\lib\site-packages\tensorflow_core\python\eager\function.py"，第 2041 行，在 _create_graph_function 中capture_by_value=self._capture_by_value),文件C:\Users\lotfi\Anaconda3\envs\tf\lib\site-packages\tensorflow_core\python\framework\func_graph.py"，第 915 行，在 func_graph_from_py_funcfunc_outputs = python_func(*func_args, **func_kwargs)文件C:\Users\lotfi\Anaconda3\envs\tf\lib\site-packages\tensorflow_core\python\eager\def_function.py"，第358行，在wrapped_fn返回weak_wrapped_fn().__wrapped__(*args, **kwds)文件C:\Users\lotfi\Anaconda3\envs\tf\lib\site-packages\tensorflow_core\python\saved_model\function_deserialization.py"，第262行，在restored_function_body"\n\n".join(signature_descriptions)))错误消息继续:
<块引用> ValueError: 找不到匹配的函数来调用从 SavedModel 加载.得到了:位置参数(共 1 个):*张量(输入:0"，形状=(无，28)，dtype=float32)关键字参数:{}预期这些参数与以下 1 个选项之一匹配:选项1:位置参数(共 1 个):* TensorSpec(shape=(None, 28, 28), dtype=tf.float32, name='inputs')关键字参数:{} 解决方案
遇到了同样的问题.试试:
pip install tf-nightly解决方案来自这里:https://github.com/tensorflow/tensorflow/issues/35446 - 评论来自奥努什.
但是如果你有张量板，这可能会中断对张量板的调用.对此的分步解决方案如下:
pip卸载tensorflowpip卸载张量板pip install -q tf-nightlypip install --ignore-installed tf-nightly如果从这里得到:https://github.com/tensorflow/tensorboard/issues/2226 - 来自 mmehedin 的评论，2019 年 6 月 29 日.
I wrote below code and it suppose to load a model followed by a predictive run of an element from MNIST dataset. At the beginning of the execution the code works fine and I get my desired prediction, but then suddenly I did get the below error following error and I'm not sure if this could be related to .predict arguments.
My code:
# importing libraries
import tensorflow as tf # deep learning library. Tensors are just multi-dimensional arrays
import gzip,sys,pickle # dataset manipulation library
# importing MNIST dataset
f = gzip.open('mnist.pkl.gz', 'rb')
if sys.version_info < (3,):
data = pickle.load(f)
else:
data = pickle.load(f, encoding='bytes')
f.close()
(x_train, _), (x_test, _) = data
print("-----------------------dataset ready-----------------------")
# using an expample from x_test / to remove later
# preprocessing
x_test = tf.keras.utils.normalize(x_test, axis=1) # scales data between 0 and 1
# importing model
new_model = tf.keras.models.load_model('epic_num_reader.model')
print("-----------------------model ready-----------------------")
# getting prediction
predictions = new_model.predict(x_test[0])
import numpy as np
print("-----------------------predection ready-----------------------")
print(np.argmax(predictions))
The error message:
-----------------------dataset ready----------------------- 2019-10-27 00:36:58.767359: I tensorflow/core/platform/cpu_feature_guard.cc:142] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 -----------------------model ready----------------------- Traceback (most recent call last): File "c:\Users\lotfi\Desktop\DigitsDetector\main1.py", line 24, in <module> predictions = new_model.predict(x_test[0]) File "C:\Users\lotfi\Anaconda3\envs\tf\lib\site-packages\tensorflow_core\python\keras\engine\training.py", line 909, in predict use_multiprocessing=use_multiprocessing) File "C:\Users\lotfi\Anaconda3\envs\tf\lib\site-packages\tensorflow_core\python\keras\engine\training_v2.py", line 462, in predict steps=steps, callbacks=callbacks, **kwargs) File "C:\Users\lotfi\Anaconda3\envs\tf\lib\site-packages\tensorflow_core\python\keras\engine\training_v2.py", line 444, in _model_iteration total_epochs=1) File "C:\Users\lotfi\Anaconda3\envs\tf\lib\site-packages\tensorflow_core\python\keras\engine\training_v2.py", line 123, in run_one_epoch batch_outs = execution_function(iterator) File "C:\Users\lotfi\Anaconda3\envs\tf\lib\site-packages\tensorflow_core\python\keras\engine\training_v2_utils.py", line 86, in execution_function distributed_function(input_fn)) File "C:\Users\lotfi\Anaconda3\envs\tf\lib\site-packages\tensorflow_core\python\eager\def_function.py", line 457, in __call__ result = self._call(*args, **kwds) File "C:\Users\lotfi\Anaconda3\envs\tf\lib\site-packages\tensorflow_core\python\eager\def_function.py", line 503, in _call self._initialize(args, kwds, add_initializers_to=initializer_map) File "C:\Users\lotfi\Anaconda3\envs\tf\lib\site-packages\tensorflow_core\python\eager\def_function.py", line 408, in _initialize *args, **kwds)) File "C:\Users\lotfi\Anaconda3\envs\tf\lib\site-packages\tensorflow_core\python\eager\function.py", line 1848, in _get_concrete_function_internal_garbage_collected graph_function, _, _ = self._maybe_define_function(args, kwargs) File "C:\Users\lotfi\Anaconda3\envs\tf\lib\site-packages\tensorflow_core\python\eager\function.py", line 2150, in _maybe_define_function graph_function = self._create_graph_function(args, kwargs) File "C:\Users\lotfi\Anaconda3\envs\tf\lib\site-packages\tensorflow_core\python\eager\function.py", line 2041, in _create_graph_function capture_by_value=self._capture_by_value), File "C:\Users\lotfi\Anaconda3\envs\tf\lib\site-packages\tensorflow_core\python\framework\func_graph.py", line 915, in func_graph_from_py_func func_outputs = python_func(*func_args, **func_kwargs) File "C:\Users\lotfi\Anaconda3\envs\tf\lib\site-packages\tensorflow_core\python\eager\def_function.py", line 358, in wrapped_fn return weak_wrapped_fn().__wrapped__(*args, **kwds) File "C:\Users\lotfi\Anaconda3\envs\tf\lib\site-packages\tensorflow_core\python\keras\engine\training_v2_utils.py", line 73, in distributed_function per_replica_function, args=(model, x, y, sample_weights)) File "C:\Users\lotfi\Anaconda3\envs\tf\lib\site-packages\tensorflow_core\python\distribute\distribute_lib.py", line 760, in experimental_run_v2 return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs) File "C:\Users\lotfi\Anaconda3\envs\tf\lib\site-packages\tensorflow_core\python\distribute\distribute_lib.py", line 1787, in call_for_each_replica return self._call_for_each_replica(fn, args, kwargs) File "C:\Users\lotfi\Anaconda3\envs\tf\lib\site-packages\tensorflow_core\python\distribute\distribute_lib.py", line 2132, in _call_for_each_replica return fn(*args, **kwargs) File "C:\Users\lotfi\Anaconda3\envs\tf\lib\site-packages\tensorflow_core\python\autograph\impl\api.py", line 292, in wrapper return func(*args, **kwargs) File "C:\Users\lotfi\Anaconda3\envs\tf\lib\site-packages\tensorflow_core\python\keras\engine\training_v2_utils.py", line 162, in _predict_on_batch return predict_on_batch(model, x) File "C:\Users\lotfi\Anaconda3\envs\tf\lib\site-packages\tensorflow_core\python\keras\engine\training_v2_utils.py", line 370, in predict_on_batch return model(inputs) # pylint: disable=not-callable File "C:\Users\lotfi\Anaconda3\envs\tf\lib\site-packages\tensorflow_core\python\keras\engine\base_layer.py", line 847, in __call__ outputs = call_fn(cast_inputs, *args, **kwargs) File "C:\Users\lotfi\Anaconda3\envs\tf\lib\site-packages\tensorflow_core\python\keras\engine\sequential.py", line 270, in call outputs = layer(inputs, **kwargs) File "C:\Users\lotfi\Anaconda3\envs\tf\lib\site-packages\tensorflow_core\python\keras\engine\base_layer.py", line 847, in __call__ outputs = call_fn(cast_inputs, *args, **kwargs) File "C:\Users\lotfi\Anaconda3\envs\tf\lib\site-packages\tensorflow_core\python\keras\saving\saved_model\utils.py", line 57, in return_outputs_and_add_losses outputs, losses = fn(inputs, *args, **kwargs) File "C:\Users\lotfi\Anaconda3\envs\tf\lib\site-packages\tensorflow_core\python\eager\def_function.py", line 457, in __call__ result = self._call(*args, **kwds) File "C:\Users\lotfi\Anaconda3\envs\tf\lib\site-packages\tensorflow_core\python\eager\def_function.py", line 494, in _call results = self._stateful_fn(*args, **kwds) File "C:\Users\lotfi\Anaconda3\envs\tf\lib\site-packages\tensorflow_core\python\eager\function.py", line 1822, in __call__ graph_function, args, kwargs = self._maybe_define_function(args, kwargs) File "C:\Users\lotfi\Anaconda3\envs\tf\lib\site-packages\tensorflow_core\python\eager\function.py", line 2150, in _maybe_define_function graph_function = self._create_graph_function(args, kwargs) File "C:\Users\lotfi\Anaconda3\envs\tf\lib\site-packages\tensorflow_core\python\eager\function.py", line 2041, in _create_graph_function capture_by_value=self._capture_by_value), File "C:\Users\lotfi\Anaconda3\envs\tf\lib\site-packages\tensorflow_core\python\framework\func_graph.py", line 915, in func_graph_from_py_func func_outputs = python_func(*func_args, **func_kwargs) File "C:\Users\lotfi\Anaconda3\envs\tf\lib\site-packages\tensorflow_core\python\eager\def_function.py", line 358, in wrapped_fn return weak_wrapped_fn().__wrapped__(*args, **kwds) File "C:\Users\lotfi\Anaconda3\envs\tf\lib\site-packages\tensorflow_core\python\saved_model\function_deserialization.py", line 262, in restored_function_body "\n\n".join(signature_descriptions)))
Error message continued:
ValueError: Could not find matching function to call loaded from the SavedModel. Got: Positional arguments (1 total): * Tensor("inputs:0", shape=(None, 28), dtype=float32) Keyword arguments: {} Expected these arguments to match one of the following 1 option(s): Option 1: Positional arguments (1 total): * TensorSpec(shape=(None, 28, 28), dtype=tf.float32, name='inputs') Keyword arguments: {}
解决方案
Had the same issue. Try:
pip install tf-nightly
Solution is from here: https://github.com/tensorflow/tensorflow/issues/35446 - comment from oanush.
But this may break calling of tensorboards if you have one. Step by step solution for this is below:
pip uninstall tensorflow
pip uninstall tensorboard
pip install -q tf-nightly
pip install --ignore-installed tf-nightly
Got if from here: https://github.com/tensorflow/tensorboard/issues/2226 - comment from mmehedin, 29 Jun 2019.
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