使用张量式服务的KERAS型号

Question

现在，我们成功地使用TensorFlow服务来提供模型。我们已经使用以下方法导出模型并用TensorFlow服务托管它。

     ------------
      For exporting 
     ------------------
     from tensorflow.contrib.session_bundle import exporter
     K.set_learning_phase(0)
     export_path = ... # where to save the exported graph
     export_version = ... # version number (integer)
     saver = tf.train.Saver(sharded=True)
     model_exporter = exporter.Exporter(saver)
     signature = exporter.classification_signature(input_tensor=model.input,
                                          scores_tensor=model.output)
     model_exporter.init(sess.graph.as_graph_def(),
                default_graph_signature=signature)
     model_exporter.export(export_path, tf.constant(export_version), sess)
      --------------------------------------
      For hosting
      -----------------------------------------------
      bazel-bin/tensorflow_serving/model_servers/tensorflow_model_server --port=9000 --model_name=default --model_base_path=/serving/models

但是我们的问题是 - 我们希望Keras与TensorFlow服务集成。我们想通过使用keras通过张量流提供模型。我们之所以要之所Tensorflow Keras，但是为了服务，我们只想使用一个张量式服务的可服务发动机。我们看不到任何直接的方法来实现这一目标。有评论吗？

谢谢。

Answer 1

最近的TensorFlow更改了其导出模型的方式，因此Web上可用的大多数教程已过时。老实说，我不知道Leatherning4J的工作原理，但是我经常使用keras。我设法创建了一个简单的示例，我已经在tensorflow Serving github中发布了此问题。

我不确定这是否会帮助您，但是我想分享我的表现，也许会给您一些见解。在创建自定义模型之前，我的第一次试用是使用诸如VGG19之类的Keras上的训练有素的模型。我这样做如下。

模型创建

import keras.backend as K
from keras.applications import VGG19
from keras.models import Model
# very important to do this as a first thing
K.set_learning_phase(0)
model = VGG19(include_top=True, weights='imagenet')
# The creation of a new model might be optional depending on the goal
config = model.get_config()
weights = model.get_weights()
new_model = Model.from_config(config)
new_model.set_weights(weights)

导出模型

from tensorflow.python.saved_model import builder as saved_model_builder
from tensorflow.python.saved_model import utils
from tensorflow.python.saved_model import tag_constants, signature_constants
from tensorflow.python.saved_model.signature_def_utils_impl import     build_signature_def, predict_signature_def
from tensorflow.contrib.session_bundle import exporter
export_path = 'folder_to_export'
builder = saved_model_builder.SavedModelBuilder(export_path)
signature = predict_signature_def(inputs={'images': new_model.input},
                                  outputs={'scores': new_model.output})
with K.get_session() as sess:
    builder.add_meta_graph_and_variables(sess=sess,
                                         tags=[tag_constants.SERVING],
                                         signature_def_map={'predict': signature})
    builder.save()

一些侧面注释

• 它可以根据Keras，TensorFlow和TensorFlow服务而变化版本。我使用了最新的。
• 提防签名的名称，因为它们也应在客户端中使用。
• 创建客户端时，所有的预处理步骤必须执行模型(例如PrepRocess_input(((。我没有尝试将这样的步骤添加到图形本身中，例如Inpection Client示例。

关于在同一服务器中提供不同模型的服务，我认为类似于Model_config_file的创建可能会对您有所帮助。为此，您可以创建与此类似的配置文件：

model_config_list: {
  config: {
    name: "my_model_1",
    base_path: "/tmp/model_1",
    model_platform: "tensorflow"
  },
  config: {
     name: "my_model_2",
     base_path: "/tmp/model_2",
     model_platform: "tensorflow"
  }
}

最后，您可以这样运行客户：

bazel-bin/tensorflow_serving/model_servers/tensorflow_model_server --port=9000 --config_file=model_config.conf

Answer 2

尝试我编写的此脚本，您可以将keras模型转换为张量的冷冻图，(我看到某些模型在导出而无需冻结变量的情况下导致它们会产生奇怪的行为。

import sys
from keras.models import load_model
import tensorflow as tf
from keras import backend as K
from tensorflow.python.framework import graph_util
from tensorflow.python.framework import graph_io
from tensorflow.python.saved_model import signature_constants
from tensorflow.python.saved_model import tag_constants

K.set_learning_phase(0)
K.set_image_data_format('channels_last')
INPUT_MODEL = sys.argv[1]
NUMBER_OF_OUTPUTS = 1
OUTPUT_NODE_PREFIX = 'output_node'
OUTPUT_FOLDER= 'frozen'
OUTPUT_GRAPH = 'frozen_model.pb'
OUTPUT_SERVABLE_FOLDER = sys.argv[2]
INPUT_TENSOR = sys.argv[3]

try:
    model = load_model(INPUT_MODEL)
except ValueError as err:
    print('Please check the input saved model file')
    raise err
output = [None]*NUMBER_OF_OUTPUTS
output_node_names = [None]*NUMBER_OF_OUTPUTS
for i in range(NUMBER_OF_OUTPUTS):
    output_node_names[i] = OUTPUT_NODE_PREFIX+str(i)
    output[i] = tf.identity(model.outputs[i], name=output_node_names[i])
print('Output Tensor names: ', output_node_names)

sess = K.get_session()
try:
    frozen_graph = graph_util.convert_variables_to_constants(sess, sess.graph.as_graph_def(), output_node_names)    
    graph_io.write_graph(frozen_graph, OUTPUT_FOLDER, OUTPUT_GRAPH, as_text=False)
    print(f'Frozen graph ready for inference/serving at {OUTPUT_FOLDER}/{OUTPUT_GRAPH}')
except:
    print('Error Occured')

builder = tf.saved_model.builder.SavedModelBuilder(OUTPUT_SERVABLE_FOLDER)
with tf.gfile.GFile(f'{OUTPUT_FOLDER}/{OUTPUT_GRAPH}', "rb") as f:
    graph_def = tf.GraphDef()
    graph_def.ParseFromString(f.read())
sigs = {}
OUTPUT_TENSOR = output_node_names
with tf.Session(graph=tf.Graph()) as sess:
    tf.import_graph_def(graph_def, name="")
    g = tf.get_default_graph()
    inp = g.get_tensor_by_name(INPUT_TENSOR)
    out = g.get_tensor_by_name(OUTPUT_TENSOR[0] + ':0')
    sigs[signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY] = 
        tf.saved_model.signature_def_utils.predict_signature_def(
            {"input": inp}, {"outout": out})
    builder.add_meta_graph_and_variables(sess,
                                         [tag_constants.SERVING],
                                         signature_def_map=sigs)
    try:
        builder.save()
        print(f'Model ready for deployment at {OUTPUT_SERVABLE_FOLDER}/saved_model.pb')
        print('Prediction signature : ')
        print(sigs['serving_default'])
    except:
        print('Error Occured, please checked frozen graph')

Answer 3

我最近添加了此博客文章，该博客文章说明了如何保存keras模型并将其与TensorFlow服务一起使用。

tl; dr：保存Inception3预估计的模型：

### Load a pretrained inception_v3
inception_model = keras.applications.inception_v3.InceptionV3(weights='imagenet')
# Define a destination path for the model
MODEL_EXPORT_DIR = '/tmp/inception_v3'
MODEL_VERSION = 1
MODEL_EXPORT_PATH = os.path.join(MODEL_EXPORT_DIR, str(MODEL_VERSION))
# We'll need to create an input mapping, and name each of the input tensors.
# In the inception_v3 Keras model, there is only a single input and we'll name it 'image'
input_names = ['image']
name_to_input = {name: t_input for name, t_input in zip(input_names, inception_model.inputs)}
# Save the model to the MODEL_EXPORT_PATH
# Note using 'name_to_input' mapping, the names defined here will also be used for querying the service later
tf.saved_model.simple_save(
    keras.backend.get_session(),
    MODEL_EXPORT_PATH,
    inputs=name_to_input,
    outputs={t.name: t for t in inception_model.outputs})

然后开始使用TF服务Docker：

1. 将保存的模型复制到主机的指定目录。(source =/tmp/inception_v3在此示例中(

2. 运行Docker：

docker run -d -p 8501:8501 --name keras_inception_v3 --mount type=bind,source=/tmp/inception_v3,target=/models/inception_v3 -e MODEL_NAME=inception_v3 -t tensorflow/serving

3. 验证是否有网络访问TensorFlow服务。为了获取本地Docker IP(172。*。*。*(进行测试运行：

docker inspect -f '{{range .NetworkSettings.Networks}}{{.IPAddress}}{{end}}' keras_inception_v3