为什么会出现这个错误?
我找不到类似于我的情况并且有解释原因的答案。对于这种错误,有很多相关信息,但似乎它取决于很多原因,并且对于不同的情况,可能会有不同的解决方案。
所以我正在尝试加载 TensorFlow
训练模型,然后将其转换为 TensorFlow Serving
模型格式并进行预测。
从下面的导入中导入 iris_data.py 文件。
这是我如何导出我的模型(已经完成训练,我只需从磁盘加载):
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)
这是我使用 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})
最后出现错误:
ValueError: Cannot feed value of shape () for Tensor 'input_example_tensor:0', which has shape '(?,)'