python - 找不到匹配的函数来调用从 SavedModel 加载
问题描述
我写了下面的代码,它假设加载一个模型,然后从 MNIST 数据集中预测运行一个元素。在执行开始时,代码运行良好,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
.
我的代码:
# 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))
错误信息:
-----------------------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)))
错误信息继续:
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
注意:我认为您的问题出在预测模型部分。在该部分中,您使用了与预训练模型数组维度不匹配的 x_test[0]。您必须使用 x_test 而不是 x_test[0]。在此处输入图像描述
#Use This Code TO Solve Your Problem
import tensorflow as tf # deep learning library. Tensors are just multi-dimensional arrays
mnist = tf.keras.datasets.mnist # mnist is a dataset of 28x28 images of handwritten digits and their labels
(x_train, y_train),(x_test, y_test) = mnist.load_data() # unpacks images to x_train/x_test and labels to y_train/y_test
x_train = tf.keras.utils.normalize(x_train, axis=1) # scales data between 0 and 1
x_test = tf.keras.utils.normalize(x_test, axis=1) # scales data between 0 and 1
model = tf.keras.models.Sequential() # a basic feed-forward model
model.add(tf.keras.layers.Flatten()) # takes our 28x28 and makes it 1x784
model.add(tf.keras.layers.Dense(128, activation=tf.nn.relu)) # a simple fully-connected layer, 128 units, relu activation
model.add(tf.keras.layers.Dense(128, activation=tf.nn.relu)) # a simple fully-connected layer, 128 units, relu activation
model.add(tf.keras.layers.Dense(10, activation=tf.nn.softmax)) # our output layer. 10 units for 10 classes. Softmax for probability distribution
model.compile(optimizer='adam', # Good default optimizer to start with
loss='sparse_categorical_crossentropy', # how will we calculate our "error." Neural network aims to minimize loss.
metrics=['accuracy']) # what to track
model.fit(x_train, y_train, epochs=3) # train the model
val_loss, val_acc = model.evaluate(x_test, y_test) # evaluate the out of sample data with model
print(val_loss) # model's loss (error)
print(val_acc) # model's accuracy
--------------------------Save Model----------------------------------------
model.save('epic_num_reader.model') # save the model
--------------------------Load Model----------------------------------------
new_model = tf.keras.models.load_model('epic_num_reader.model') # Load the model
--------------------------Predict Model-------------------------------------
predictions = new_model.predict(x_test)
print(predictions)
--------------------------visualize Prediction------------------------------
plt.imshow(x_test[0],cmap=plt.cm.binary)
plt.show()
-------------------------- Validated Prediction-----------------------------
import numpy as np
print(np.argmax(predictions[0]))
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