Не удалось найти подходящую функцию для вызова, загруженную из SavedModel

Question

Я написал код ниже, и он предполагает загрузку модели с последующим прогностическим прогоном элемента из набора данных MNIST. В начале выполнения код работает нормально, и я получаю желаемое предсказание, но затем неожиданно я получаю следующую ошибку после ошибки, и я не уверен, может ли это быть связано с .predict arguments.

Мой код:

# 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))

Сообщение об ошибке:

    -----------------------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)))

Сообщение об ошибке продолжено:

    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: {}

Answer 1

Примечание. Я думаю, что ваша проблема связана с моделью Predict. В этой части вы использовали x_test [0], который не совпадает с предварительно обученным размером массива модели. Вы должны использовать x_test вместо x_test [0]. введите описание изображения здесь

   #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]))