Question

我在编码后写道,它假定装上模型,然后从多国专家名册数据集中预测出一个元件。在执行之初,该守则进行细微的工作,我得到我所期望的预测,但随后,我突然发现以下错误,我无法确定这是否与<编码>有关。

我的法典:

# 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:UserslotfiDesktopDigitsDetectormain1.py", line 24, in <module>
        predictions = new_model.predict(x_test[0])
      File "C:UserslotfiAnaconda3envs	flibsite-packages	ensorflow_corepythonkerasengine	raining.py", line 909, in predict
        use_multiprocessing=use_multiprocessing)
      File "C:UserslotfiAnaconda3envs	flibsite-packages	ensorflow_corepythonkerasengine	raining_v2.py", line 462, in predict
        steps=steps, callbacks=callbacks, **kwargs)
      File "C:UserslotfiAnaconda3envs	flibsite-packages	ensorflow_corepythonkerasengine	raining_v2.py", line 444, in _model_iteration
        total_epochs=1)
      File "C:UserslotfiAnaconda3envs	flibsite-packages	ensorflow_corepythonkerasengine	raining_v2.py", line 123, in run_one_epoch
        batch_outs = execution_function(iterator)
      File "C:UserslotfiAnaconda3envs	flibsite-packages	ensorflow_corepythonkerasengine	raining_v2_utils.py", line 86, in execution_function
        distributed_function(input_fn))
      File "C:UserslotfiAnaconda3envs	flibsite-packages	ensorflow_corepythoneagerdef_function.py", line 457, in __call__
        result = self._call(*args, **kwds)
      File "C:UserslotfiAnaconda3envs	flibsite-packages	ensorflow_corepythoneagerdef_function.py", line 503, in _call
        self._initialize(args, kwds, add_initializers_to=initializer_map)
      File "C:UserslotfiAnaconda3envs	flibsite-packages	ensorflow_corepythoneagerdef_function.py", line 408, in _initialize
        *args, **kwds))
      File "C:UserslotfiAnaconda3envs	flibsite-packages	ensorflow_corepythoneagerfunction.py", line 1848, in _get_concrete_function_internal_garbage_collected
        graph_function, _, _ = self._maybe_define_function(args, kwargs)
      File "C:UserslotfiAnaconda3envs	flibsite-packages	ensorflow_corepythoneagerfunction.py", line 2150, in _maybe_define_function
        graph_function = self._create_graph_function(args, kwargs)
      File "C:UserslotfiAnaconda3envs	flibsite-packages	ensorflow_corepythoneagerfunction.py", line 2041, in _create_graph_function
        capture_by_value=self._capture_by_value),
      File "C:UserslotfiAnaconda3envs	flibsite-packages	ensorflow_corepythonframeworkfunc_graph.py", line 915, in func_graph_from_py_func
        func_outputs = python_func(*func_args, **func_kwargs)
      File "C:UserslotfiAnaconda3envs	flibsite-packages	ensorflow_corepythoneagerdef_function.py", line 358, in wrapped_fn
        return weak_wrapped_fn().__wrapped__(*args, **kwds)
      File "C:UserslotfiAnaconda3envs	flibsite-packages	ensorflow_corepythonkerasengine	raining_v2_utils.py", line 73, in distributed_function
        per_replica_function, args=(model, x, y, sample_weights))
      File "C:UserslotfiAnaconda3envs	flibsite-packages	ensorflow_corepythondistributedistribute_lib.py", line 760, in experimental_run_v2
        return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs)
      File "C:UserslotfiAnaconda3envs	flibsite-packages	ensorflow_corepythondistributedistribute_lib.py", line 1787, in call_for_each_replica
        return self._call_for_each_replica(fn, args, kwargs)
      File "C:UserslotfiAnaconda3envs	flibsite-packages	ensorflow_corepythondistributedistribute_lib.py", line 2132, in _call_for_each_replica
        return fn(*args, **kwargs)
      File "C:UserslotfiAnaconda3envs	flibsite-packages	ensorflow_corepythonautographimplapi.py", line 292, in wrapper
        return func(*args, **kwargs)
      File "C:UserslotfiAnaconda3envs	flibsite-packages	ensorflow_corepythonkerasengine	raining_v2_utils.py", line 162, in _predict_on_batch
        return predict_on_batch(model, x)
      File "C:UserslotfiAnaconda3envs	flibsite-packages	ensorflow_corepythonkerasengine	raining_v2_utils.py", line 370, in predict_on_batch
        return model(inputs)  # pylint: disable=not-callable
      File "C:UserslotfiAnaconda3envs	flibsite-packages	ensorflow_corepythonkerasenginease_layer.py", line 847, in __call__
        outputs = call_fn(cast_inputs, *args, **kwargs)
      File "C:UserslotfiAnaconda3envs	flibsite-packages	ensorflow_corepythonkerasenginesequential.py", line 270, in call
        outputs = layer(inputs, **kwargs)
      File "C:UserslotfiAnaconda3envs	flibsite-packages	ensorflow_corepythonkerasenginease_layer.py", line 847, in __call__
        outputs = call_fn(cast_inputs, *args, **kwargs)
      File "C:UserslotfiAnaconda3envs	flibsite-packages	ensorflow_corepythonkerassavingsaved_modelutils.py", line 57, in return_outputs_and_add_losses
        outputs, losses = fn(inputs, *args, **kwargs)
      File "C:UserslotfiAnaconda3envs	flibsite-packages	ensorflow_corepythoneagerdef_function.py", line 457, in __call__
        result = self._call(*args, **kwds)
      File "C:UserslotfiAnaconda3envs	flibsite-packages	ensorflow_corepythoneagerdef_function.py", line 494, in _call
        results = self._stateful_fn(*args, **kwds)
      File "C:UserslotfiAnaconda3envs	flibsite-packages	ensorflow_corepythoneagerfunction.py", line 1822, in __call__
        graph_function, args, kwargs = self._maybe_define_function(args, kwargs)
      File "C:UserslotfiAnaconda3envs	flibsite-packages	ensorflow_corepythoneagerfunction.py", line 2150, in _maybe_define_function
        graph_function = self._create_graph_function(args, kwargs)
      File "C:UserslotfiAnaconda3envs	flibsite-packages	ensorflow_corepythoneagerfunction.py", line 2041, in _create_graph_function
        capture_by_value=self._capture_by_value),
      File "C:UserslotfiAnaconda3envs	flibsite-packages	ensorflow_corepythonframeworkfunc_graph.py", line 915, in func_graph_from_py_func
        func_outputs = python_func(*func_args, **func_kwargs)
      File "C:UserslotfiAnaconda3envs	flibsite-packages	ensorflow_corepythoneagerdef_function.py", line 358, in wrapped_fn
        return weak_wrapped_fn().__wrapped__(*args, **kwds)
      File "C:UserslotfiAnaconda3envs	flibsite-packages	ensorflow_corepythonsaved_modelfunction_deserialization.py", line 262, in restored_function_body
        "

".join(signature_descriptions)))

3. 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: {}

Answer 1

注:我认为你的问题是《规则范本》的一部分。在该部分,你使用了x_test[0],这与预先培训的模式阵容不相配。你们必须使用X-测试而不是X-测试[0]。 “entergraph

   #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]))

Answer 2

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/tensor板/issues/22<26/a> - 从2019年6月29日,毫米号发表评论。

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