python - ValueError : Cannot feed value of shape () for Tensor 'input_example_tensor:0' , 其形状为 '(?,)'

Question

为什么会出现这个错误？

我找不到与我的案例类似的答案，并解释了为什么会这样。那里有很多关于此类错误的信息，但似乎它取决于很多原因，并且对于不同的情况，它可以有不同的解决方案。

所以我尝试加载 TensorFlow 训练模型，然后将其转换为 TensorFlow Serving 模型格式并进行预测。

iris_data.py从下面导入文件。

这就是我导出模型的方式(train 已经导出，我只是从磁盘加载它):

import tensorflow as tf
import iris_data
from tensorflow.contrib import predictor

# Fetch the data
    (train_x, train_y), (test_x, test_y) = iris_data.load_data()

    # Feature columns describe how to use the input.
    my_feature_columns = []
    for key in train_x.keys():
        my_feature_columns.append(tf.feature_column.numeric_column(key=key))

    # Build 2 hidden layer DNN with 10, 10 units respectively.
    classifier = tf.estimator.DNNClassifier(
        feature_columns=my_feature_columns,
        # Two hidden layers of 10 nodes each.
        hidden_units=[10, 10],
        # The model must choose between 3 classes.
        n_classes=3,
        model_dir='G:\AI models')

    #converting into TensorFlow Serving model format 
    path = 'G:\AI models\serve'
    feature_spec = tf.feature_column.make_parse_example_spec(my_feature_columns)
    export_input_fn = tf.estimator.export.build_parsing_serving_input_receiver_fn(feature_spec)
    servable_model_path = classifier.export_savedmodel(path, export_input_fn, as_text=True)

以下是我尝试使用 TensorFlow Serving 模型进行预测的方法:

 expected = ['Setosa', 'Versicolor', 'Virginica']
    predict_x = {
        'SepalLength': [5.1, 5.9, 6.9],
        'SepalWidth': [3.3, 3.0, 3.1],
        'PetalLength': [1.7, 4.2, 5.4],
        'PetalWidth': [0.5, 1.5, 2.1],
    }
path = 'G:\AI models\serve\\1519413839'
    predict_fn = predictor.from_saved_model(path)
    predictions = predict_fn(
        {"inputs": predict_x})

最后报错:

ValueError: Cannot feed value of shape () for Tensor 'input_example_tensor:0', which has shape '(?,)'

Answer 1

您必须使用示例 API 传递示例:

feature = {
    'SepalLength': tf.train.Feature(float_list=tf.train.FloatList(value=[5.1])),
    'SepalWidth': tf.train.Feature(float_list=tf.train.FloatList(value=[3.3])),
    'PetalLength': tf.train.Feature(float_list=tf.train.FloatList(value=[1.7])),
    'PetalWidth': tf.train.Feature(float_list=tf.train.FloatList(value=[0.5]))
}
example = tf.train.Example(
    features=tf.train.Features(
        feature=feature
    )
)
serialized_example = example.SerializeToString()

predictions = predict_fn({"inputs": [serialized_example]})

当然，如果你愿意，你可以传递很多例子。

有用的链接:http://shzhangji.com/blog/2018/05/14/serve-tensorflow-estimator-with-savedmodel/