Python 如何为tensorflow-2.0.0beta0中的特定型号提供服务_Python_Tensorflow_Tensorflow Serving_Tensorflow Estimator_Multilabel Classification

Python 如何为tensorflow-2.0.0beta0中的特定型号提供服务

python tensorflow

Python 如何为tensorflow-2.0.0beta0中的特定型号提供服务,python,tensorflow,tensorflow-serving,tensorflow-estimator,multilabel-classification,Python,Tensorflow,Tensorflow Serving,Tensorflow Estimator,Multilabel Classification,我想根据3列将文档分为4个类别（位置），burks是一个4位代码，lifnr是一个字典值，waers也是一个带有LinarClassifier的字典值。然后保存模型，为其服务，并向其抛出burks、lifnr和waers值以获得预测我的培训数据如下所示： bukrs;lifnr;waers;location 5280;1004008999;EUR;0 5280;1004009000;EUR;2 5280;1004003061;EUR;1 ... saved_model_cli show -

我想根据3列将文档分为4个类别（位置），burks是一个4位代码，lifnr是一个字典值，waers也是一个带有LinarClassifier的字典值。然后保存模型，为其服务，并向其抛出burks、lifnr和waers值以获得预测

我的培训数据如下所示：

bukrs;lifnr;waers;location
5280;1004008999;EUR;0
5280;1004009000;EUR;2
5280;1004003061;EUR;1
...

saved_model_cli show --dir 1561324458 --all

 saved_model_cli run --dir invoicemodel\1\1561324458 --tag_set serve --signature_def predict --input_examples 'examples=[{"bukrs": 5280, "lifnr": "1004003930", "waers": "EUR"}]'

csv_fn = "invoices_classed_npy.csv"
file = pd.read_csv(csv_fn)
np.save('invoices_classed_npy.npy', file, allow_pickle = True);

我可以成功地训练模型，并保存它，结果是已保存_model.pb和Variables文件夹

到目前为止还不错

我已经检查了模型本身是否是这样工作的：

bukrs;lifnr;waers;location
5280;1004008999;EUR;0
5280;1004009000;EUR;2
5280;1004003061;EUR;1
...

saved_model_cli show --dir 1561324458 --all

 saved_model_cli run --dir invoicemodel\1\1561324458 --tag_set serve --signature_def predict --input_examples 'examples=[{"bukrs": 5280, "lifnr": "1004003930", "waers": "EUR"}]'

csv_fn = "invoices_classed_npy.csv"
file = pd.read_csv(csv_fn)
np.save('invoices_classed_npy.npy', file, allow_pickle = True);

这给了我：

MetaGraphDef with tag-set: 'serve' contains the following SignatureDefs:

signature_def['classification']:
  The given SavedModel SignatureDef contains the following input(s):
    inputs['inputs'] tensor_info:
        dtype: DT_STRING
        shape: (-1)
        name: input_example_tensor:0
  The given SavedModel SignatureDef contains the following output(s):
    outputs['classes'] tensor_info:
        dtype: DT_STRING
        shape: (-1, 4)
        name: head/Tile:0
    outputs['scores'] tensor_info:
        dtype: DT_FLOAT
        shape: (-1, 4)
        name: head/predictions/probabilities:0
  Method name is: tensorflow/serving/classify

signature_def['predict']:
  The given SavedModel SignatureDef contains the following input(s):
    inputs['examples'] tensor_info:
        dtype: DT_STRING
        shape: (-1)
        name: input_example_tensor:0
  The given SavedModel SignatureDef contains the following output(s):
    outputs['all_class_ids'] tensor_info:
        dtype: DT_INT32
        shape: (-1, 4)
        name: head/predictions/Tile:0
    outputs['all_classes'] tensor_info:
        dtype: DT_STRING
        shape: (-1, 4)
        name: head/predictions/Tile_1:0
    outputs['class_ids'] tensor_info:
        dtype: DT_INT64
        shape: (-1, 1)
        name: head/predictions/ExpandDims:0
    outputs['classes'] tensor_info:
        dtype: DT_STRING
        shape: (-1, 1)
        name: head/predictions/str_classes:0
    outputs['logits'] tensor_info:
        dtype: DT_FLOAT
        shape: (-1, 4)
        name: linear/linear_model/linear/linear_model/linear/linear_model/weighted_sum:0
    outputs['probabilities'] tensor_info:
        dtype: DT_FLOAT
        shape: (-1, 4)
        name: head/predictions/probabilities:0
  Method name is: tensorflow/serving/predict

signature_def['serving_default']:
  The given SavedModel SignatureDef contains the following input(s):
    inputs['inputs'] tensor_info:
        dtype: DT_STRING
        shape: (-1)
        name: input_example_tensor:0
  The given SavedModel SignatureDef contains the following output(s):
    outputs['classes'] tensor_info:
        dtype: DT_STRING
        shape: (-1, 4)
        name: head/Tile:0
    outputs['scores'] tensor_info:
        dtype: DT_FLOAT
        shape: (-1, 4)
        name: head/predictions/probabilities:0
Method name is: tensorflow/serving/classify

我觉得这很好

以下是我的整个python培训脚本：

from __future__ import absolute_import, division, print_function, unicode_literals

import tensorflow as tf
import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
import re
import seaborn as sns
from tensorflow import feature_column
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report
import itertools
from itertools import islice
#read data
dataframe = pd.read_csv('invoices_classed2.csv', sep=';',header=0)
dataframe.head()
#cut in sets
train, test = train_test_split(dataframe, test_size=0.3)
train, val = train_test_split(train, test_size=0.3)
#print metrics
print(len(train), 'train examples')
print(len(val), 'validation examples')
print(len(test), 'test examples')
# A utility method to create a tf.data dataset from a Pandas Dataframe
labels = pd.Series();
def df_to_dataset(dataframe, shuffle=False, batch_size=32):
  dataframe = dataframe.copy()
  labels = dataframe.pop('location')
  ds = tf.data.Dataset.from_tensor_slices((dict(dataframe), labels))
  if shuffle:
    ds = ds.shuffle(buffer_size=len(dataframe))
  ds = ds.batch(batch_size)
  return ds

# A utility method to create a tf.data dataset from a Pandas Dataframe and use it as functional variable
def make_input_fn(dataframe=None, n_epochs=None, shuffle=False, batch_size=32):
  def input_fn():
    internal_dataframe = dataframe.copy()
    labels = internal_dataframe.pop('location')
    ds = tf.data.Dataset.from_tensor_slices((dict(internal_dataframe), labels))
    if shuffle:
      ds = ds.shuffle(buffer_size=len(internal_dataframe))
    ds = ds.repeat(n_epochs)
    ds = ds.batch(batch_size)
    return ds
  return input_fn

#building feature columns
bukrs = feature_column.numeric_column("bukrs")
lifnr = feature_column.categorical_column_with_vocabulary_list(
    'lifnr',['1004000409','1004003061','1004008999','1004009001','1004009000','1004003768','1004009002'])
lifnr_one_hot = feature_column.indicator_column(lifnr)
waers = feature_column.categorical_column_with_vocabulary_list(
      'waers', ['EUR', 'GBP', 'USD','JPY','CZK','HUF'])
waers_one_hot = feature_column.indicator_column(waers)
actual_feature_columns = []
actual_feature_columns.append(bukrs)
actual_feature_columns.append(lifnr_one_hot)
actual_feature_columns.append(waers_one_hot)
#making datasets
train_ds = make_input_fn(train)
val_ds = make_input_fn(val)
test_ds = make_input_fn(test)
print ('####################creating model####################')
linear_est = tf.estimator.LinearClassifier(feature_columns=actual_feature_columns,n_classes=4,model_dir="C:\\Users\\70D4867\\Desktop\\invoicemodel")
print ('####################Train model####################')
#Train model.
linear_est.train(train_ds,max_steps=10000)
print ('####################Evaluation####################')
# Evaluation.
result = linear_est.evaluate(val_ds, steps=1000)
print ('####################printing result####################')
print(result)
print ('####################Done evaluating####################')
for key in sorted(result):
    print (key, result[key])
print ('####################predictions####################')
y_generator = linear_est.predict(test_ds)
print ('####################slice predictions####################')
predictions = list(itertools.islice(y_generator,len(test)))
print ('####################predictions output####################')
final_preds = []
template = ('\nPrediction is "{}" ({:.1f}%)')
i = 0;
for pred in (predictions):
    final_preds.append(pred['class_ids'][0])
    class_id = pred['class_ids'][0]
    probability = pred['probabilities'][class_id]
    i = i +1
expected = []
for index, row in test.iterrows():
    expected.append(row['location'])

print ('####################Test Results####################')
print(classification_report(expected,final_preds))
print ('####################Saving Model####################')
feature_spec = tf.feature_column.make_parse_example_spec(actual_feature_columns)
print(feature_spec)
my_serving_input_receiver_fn = tf.estimator.export.build_parsing_serving_input_receiver_fn(feature_spec)
linear_est.export_saved_model(export_dir_base="invoicemodel\\1",serving_input_receiver_fn=my_serving_input_receiver_fn)

但是当我想从这个模型中得到一个预测时，就像这样：

bukrs;lifnr;waers;location
5280;1004008999;EUR;0
5280;1004009000;EUR;2
5280;1004003061;EUR;1
...

saved_model_cli show --dir 1561324458 --all

 saved_model_cli run --dir invoicemodel\1\1561324458 --tag_set serve --signature_def predict --input_examples 'examples=[{"bukrs": 5280, "lifnr": "1004003930", "waers": "EUR"}]'

csv_fn = "invoices_classed_npy.csv"
file = pd.read_csv(csv_fn)
np.save('invoices_classed_npy.npy', file, allow_pickle = True);

我希望有这样的情况：

[0]

我得到一个错误：

NameError: name 'bukrs' is not defined

我还尝试向它提供一个.npy文件。我用我的训练数据中的一些药剂创建了这个文件：

bukrs;lifnr;waers
5280;1004008999;EUR
5280;1004009000;EUR
5280;1004003061;EUR
...

像这样：

bukrs;lifnr;waers;location
5280;1004008999;EUR;0
5280;1004009000;EUR;2
5280;1004003061;EUR;1
...

saved_model_cli show --dir 1561324458 --all

 saved_model_cli run --dir invoicemodel\1\1561324458 --tag_set serve --signature_def predict --input_examples 'examples=[{"bukrs": 5280, "lifnr": "1004003930", "waers": "EUR"}]'

csv_fn = "invoices_classed_npy.csv"
file = pd.read_csv(csv_fn)
np.save('invoices_classed_npy.npy', file, allow_pickle = True);

但当我尝试时：

saved_model_cli run --dir .\invoicemodel\1\1561324458 --tag_set serve --signature_def classification --inputs 'inputs="invoices_classed_npy.npy"'

我预料

[1],[2],[0]

我得到：

ValueError: Cannot feed value of shape (55276, 1) for Tensor 
'input_example_tensor:0', which has shape '(None,)'

好吧，我也试着把它装在一个容器里：

 docker run -t --rm -p 8501:8501 \
>     -v "/data/container/tensorflow/model:/models/saved_model" \
>     -e MODEL_NAME=saved_model \
>     tensorflow/serving

我得到：

/usr/bin/tf_serving_entrypoint.sh: line 3:     6 Illegal instruction     (core dumped) tensorflow_model_server --port=8500 --rest_api_port=8501 --model_name=${MODEL_NAME} --model_base_path=${MODEL_BASE_PATH}/${MODEL_NAME} "$@"

我做错了什么？

如何正确地从模型中获得预测？

我发现了问题所在

在训练模型之前，您必须指定服务输入接收器fn，并且从要素列中提取它是不起作用的

这是在我的脚本结束时不起作用的部分：

feature_spec = tf.feature_column.make_parse_example_spec(actual_feature_columns)
my_serving_input_receiver_fn = tf.estimator.export.build_parsing_serving_input_receiver_fn(feature_spec)
linear_est.export_saved_model(export_dir_base="invoicemodel\\1",serving_input_receiver_fn=my_serving_input_receiver_fn)

这导致了predict的签名，看起来像：

signature_def['predict']:
  The given SavedModel SignatureDef contains the following input(s):
    inputs['examples'] tensor_info:
        dtype: DT_STRING
        shape: (-1)
        name: input_example_tensor:0

对于错误：

NameError: name 'bukrs' is not defined

我改变了服务输入接收器的定义如下

feature_placeholder = {'bukrs': tf.placeholder(tf.int32, [1], 
name='bukrs_placeholder'),'lifnr': tf.placeholder('string', [1],  
name='lifnr_placeholder'),'waers': tf.placeholder('string', [1], 
name='waers_placeholder')}
my_serving_input_receiver_fn = 
tf.estimator.export.build_raw_serving_input_receiver_fn(feature_placeholder)

####now train the model####
linear_est.train(train_ds,max_steps=10000)
#evaluation ... 
#test ....
#### then save at the end ###
linear_est.export_saved_model(export_dir_base="invoicemodel\\1",serving_input_receiver_fn=my_serving_input_receiver_fn)

这导致了模型的正确签名：

signature_def['predict']:
  The given SavedModel SignatureDef contains the following input(s):
    inputs['bukrs'] tensor_info:
        dtype: DT_INT32
        shape: (-1)
        name: bukrs_placeholder:0
    inputs['lifnr'] tensor_info:
        dtype: DT_STRING
        shape: (-1)
        name: lifnr_placeholder:0
    inputs['waers'] tensor_info:
        dtype: DT_STRING
        shape: (-1)
        name: waers_placeholder:0

saved_model_cli run --dir \1\1561727347 --tag_set serve --signature_def predict --input_exprs="bukrs=[5280];lifnr=['1004002578'];waers=['EUR']"

现在我可以从模型中得到预测：

signature_def['predict']:
  The given SavedModel SignatureDef contains the following input(s):
    inputs['bukrs'] tensor_info:
        dtype: DT_INT32
        shape: (-1)
        name: bukrs_placeholder:0
    inputs['lifnr'] tensor_info:
        dtype: DT_STRING
        shape: (-1)
        name: lifnr_placeholder:0
    inputs['waers'] tensor_info:
        dtype: DT_STRING
        shape: (-1)
        name: waers_placeholder:0

saved_model_cli run --dir \1\1561727347 --tag_set serve --signature_def predict --input_exprs="bukrs=[5280];lifnr=['1004002578'];waers=['EUR']"

这导致了一个预测：

Result for output key all_class_ids:
[[0 1 2 3]]
Result for output key all_classes:
[[b'0' b'1' b'2' b'3']]
Result for output key class_ids:
[[0]]
Result for output key classes:
[[b'0']]
Result for output key logits:
[[493.99664 475.6605  482.86667 483.922  ]]
Result for output key probabilities:
[[9.99943256e-01 1.08814335e-08 1.46652310e-05 4.21320110e-05]]

我希望这能帮助别人。

我发现了我的问题所在