python - 来自保存模型的 TF 估计器 : Can't Load *. pb

Question

我使用 TF Estimator 创建一个简单模型，并使用 export_savedmodel 函数保存模型。我使用一个简单的 Iris 数据集，它有 4 个特征。

num_epoch = 50
num_train = 120
num_test = 30

# 1 Define input function
def input_function(x, y, is_train):
    dict_x = {
        "thisisinput" : x,
    }

    dataset = tf.data.Dataset.from_tensor_slices((
        dict_x, y
    ))

    if is_train:
        dataset = dataset.shuffle(num_train).repeat(num_epoch).batch(num_train)
    else:   
        dataset = dataset.batch(num_test)

    return dataset

def my_serving_input_fn():
    input_data = {
        "thisisinput" : tf.placeholder(tf.float32, [None, 4], name='inputtensors')
    }
    return tf.estimator.export.ServingInputReceiver(input_data, input_data)

def main(argv):
    tf.set_random_seed(1103) # avoiding different result of random

    # 2 Define feature columns
    feature_columns = [
        tf.feature_column.numeric_column(key="thisisinput",shape=4),
    ]

    # 3 Define an estimator
    classifier = tf.estimator.DNNClassifier(
        feature_columns=feature_columns,
        hidden_units=[10],
        n_classes=3,
        optimizer=tf.train.GradientDescentOptimizer(0.001),
        activation_fn=tf.nn.relu,
        model_dir = 'modeliris2/'
    )

    # Train the model
    classifier.train(
        input_fn=lambda:input_function(xtrain, ytrain, True)
    )

    # Evaluate the model
    eval_result = classifier.evaluate(
        input_fn=lambda:input_function(xtest, ytest, False)
    )

    print('\nTest set accuracy: {accuracy:0.3f}\n'.format(**eval_result))
    print('\nSaving models...')
    classifier.export_savedmodel("modeliris2pb", my_serving_input_fn)

if __name__ == "__main__":
    os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3' 
    tf.app.run(main)

运行程序后，它会生成一个包含saved_model.pb的文件夹。我看到很多教程建议使用 contrib.predictor 加载 saved_model.pb 但我不能。我使用 contrib.predictor 函数来加载模型:

def main(a):
    with tf.Session() as sess:
        PB_PATH= "modeliris2pb/1536219836/"
        predict_fn = predictor.from_saved_model(PB_PATH)

if __name__=="__main__":
    main()

但它会产生错误:

ValueError: Got signature_def_key "serving_default". Available signatures are ['predict']. Original error: No SignatureDef with key 'serving_default' found in MetaGraphDef.

还有其他更好的方法来加载 *.pb 文件吗？为什么会出现这个错误？我怀疑这是因为 my_serving_input_fn() 函数，但我不知道为什么

Answer 1

我遇到了同样的问题，我尝试在网络上搜索，但没有对此的解释，所以我尝试了不同的方法:

节省:

首先，您需要以字典格式定义特征长度，如下所示:

feature_spec = {'x': tf.FixedLenFeature([4],tf.float32)}

然后您必须构建一个具有相同形状特征的占位符的函数，并使用 tf.estimator.export.ServingInputReceiver 返回

def serving_input_receiver_fn():
    serialized_tf_example = tf.placeholder(dtype=tf.string,
                                         shape=[None],
                                         name='input_tensors')
    receiver_tensors = {'inputs': serialized_tf_example}

    features = tf.parse_example(serialized_tf_example, feature_spec)
    return tf.estimator.export.ServingInputReceiver(features, receiver_tensors)

然后只需使用export_savedmodel保存:

classifier.export_savedmodel(dir_path, serving_input_receiver_fn)

完整示例代码:

import os
from six.moves.urllib.request import urlopen

import numpy as np
import tensorflow as tf


dir_path = os.path.dirname('.')

IRIS_TRAINING = os.path.join(dir_path,  "iris_training.csv")
IRIS_TEST = os.path.join(dir_path,   "iris_test.csv") 

feature_spec = {'x': tf.FixedLenFeature([4],tf.float32)}

def serving_input_receiver_fn():
    serialized_tf_example = tf.placeholder(dtype=tf.string,
                                         shape=[None],
                                         name='input_tensors')
    receiver_tensors = {'inputs': serialized_tf_example}

    features = tf.parse_example(serialized_tf_example, feature_spec)
    return tf.estimator.export.ServingInputReceiver(features, receiver_tensors)




def main():
    training_set = tf.contrib.learn.datasets.base.load_csv_with_header(
        filename=IRIS_TRAINING,
        target_dtype=np.int,
        features_dtype=np.float32)
    test_set = tf.contrib.learn.datasets.base.load_csv_with_header(
        filename=IRIS_TEST,
        target_dtype=np.int,
        features_dtype=np.float32)

    feature_columns = [tf.feature_column.numeric_column("x", shape=[4])]


    classifier = tf.estimator.DNNClassifier(feature_columns=feature_columns,
                                          hidden_units=[10, 20, 10],
                                          n_classes=3,
                                          model_dir=dir_path)
  # Define the training inputs
    train_input_fn = tf.estimator.inputs.numpy_input_fn(
      x={"x": np.array(training_set.data)},
      y=np.array(training_set.target),
      num_epochs=None,
      shuffle=True)

  # Train model.
    classifier.train(input_fn=train_input_fn, steps=200)


    classifier.export_savedmodel(dir_path, serving_input_receiver_fn)


if __name__ == "__main__":
    main()

正在恢复

现在让我们恢复模型:

import tensorflow as tf 
import os

dir_path = os.path.dirname('.') #current directory
exported_path= os.path.join(dir_path,  "1536315752")

def main():
    with tf.Session() as sess:

        tf.saved_model.loader.load(sess, [tf.saved_model.tag_constants.SERVING], exported_path)

        model_input= tf.train.Example(features=tf.train.Features(feature={
                'x': tf.train.Feature(float_list=tf.train.FloatList(value=[6.4, 3.2, 4.5, 1.5]))        
                })) 

        predictor= tf.contrib.predictor.from_saved_model(exported_path)

        input_tensor=tf.get_default_graph().get_tensor_by_name("input_tensors:0")

        model_input=model_input.SerializeToString()

        output_dict= predictor({"inputs":[model_input]})

        print(" prediction is " , output_dict['scores'])


if __name__ == "__main__":
    main()

这里是Ipython notebook demo带有数据和解释的示例: