Tensorflow Python 转 Java

一、背景描述

最近python的tensorflow项目要弄到线上去。网络用的Tensorflow现成的包。数据用kaggle中的数据为例子。

数据地址：

https://www.kaggle.com/johnfarrell/gpu-example-from-prepared-data-try-deepfm

二、Python代码

1、Python Code

  1 # author: adrian.wu
  2 from __future__ import absolute_import
  3 from __future__ import division
  4 from __future__ import print_function
  5 
  6 import tensorflow as tf
  7 
  8 tf.logging.set_verbosity(tf.logging.INFO)
  9 # Set to INFO for tracking training, default is WARN
 10 
 11 print("Using TensorFlow version %s" % (tf.__version__))
 12 
 13 CATEGORICAL_COLUMNS = ["workclass", "education",
 14                        "marital.status", "occupation",
 15                        "relationship", "race",
 16                        "sex", "native.country"]
 17 
 18 # Columns of the input csv file
 19 COLUMNS = ["age", "workclass", "fnlwgt", "education",
 20            "education.num", "marital.status",
 21            "occupation", "relationship", "race",
 22            "sex", "capital.gain", "capital.loss",
 23            "hours.per.week", "native.country", "income"]
 24 
 25 FEATURE_COLUMNS = ["age", "workclass", "education",
 26                    "education.num", "marital.status",
 27                    "occupation", "relationship", "race",
 28                    "sex", "capital.gain", "capital.loss",
 29                    "hours.per.week", "native.country"]
 30 
 31 import pandas as pd
 32 
 33 df = pd.read_csv("/Users/adrian.wu/Desktop/learn/kaggle/adult-census-income/adult.csv")
 34 
 35 from sklearn.model_selection import train_test_split
 36 
 37 BATCH_SIZE = 40
 38 
 39 num_epochs = 1
 40 shuffle = True
 41 
 42 y = df["income"].apply(lambda x: ">50K" in x).astype(int)
 43 del df["fnlwgt"]  # Unused column
 44 del df["income"]  # Labels column, already saved to labels variable
 45 X = df
 46 
 47 print(X.describe())
 48 
 49 X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20)
 50 
 51 train_input_fn = tf.estimator.inputs.pandas_input_fn(
 52     x=X_train,
 53     y=y_train,
 54     batch_size=BATCH_SIZE,
 55     num_epochs=num_epochs,
 56     shuffle=shuffle)
 57 
 58 eval_input_fn = tf.estimator.inputs.pandas_input_fn(
 59     x=X_test,
 60     y=y_test,
 61     batch_size=BATCH_SIZE,
 62     num_epochs=num_epochs,
 63     shuffle=shuffle)
 64 
 65 
 66 def generate_input_fn(filename, num_epochs=None, shuffle=True, batch_size=BATCH_SIZE):
 67     df = pd.read_csv(filename)  # , header=None, names=COLUMNS)
 68     labels = df["income"].apply(lambda x: ">50K" in x).astype(int)
 69     del df["fnlwgt"]  # Unused column
 70     del df["income"]  # Labels column, already saved to labels variable
 71 
 72     type(df['age'].iloc[3])
 73 
 74     return tf.estimator.inputs.pandas_input_fn(
 75         x=df,
 76         y=labels,
 77         batch_size=batch_size,
 78         num_epochs=num_epochs,
 79         shuffle=shuffle)
 80 
 81 
 82 sex = tf.feature_column.categorical_column_with_vocabulary_list(
 83     key="sex",
 84     vocabulary_list=["female", "male"])
 85 race = tf.feature_column.categorical_column_with_vocabulary_list(
 86     key="race",
 87     vocabulary_list=["Amer-Indian-Eskimo",
 88                      "Asian-Pac-Islander",
 89                      "Black", "Other", "White"])
 90 
 91 # 先对categorical的列做hash
 92 education = tf.feature_column.categorical_column_with_hash_bucket(
 93     "education", hash_bucket_size=1000)
 94 marital_status = tf.feature_column.categorical_column_with_hash_bucket(
 95     "marital.status", hash_bucket_size=100)
 96 relationship = tf.feature_column.categorical_column_with_hash_bucket(
 97     "relationship", hash_bucket_size=100)
 98 workclass = tf.feature_column.categorical_column_with_hash_bucket(
 99     "workclass", hash_bucket_size=100)
100 occupation = tf.feature_column.categorical_column_with_hash_bucket(
101     "occupation", hash_bucket_size=1000)
102 native_country = tf.feature_column.categorical_column_with_hash_bucket(
103     "native.country", hash_bucket_size=1000)
104 
105 print('Categorical columns configured')
106 
107 age = tf.feature_column.numeric_column("age")
108 deep_columns = [
109     # Multi-hot indicator columns for columns with fewer possibilities
110     tf.feature_column.indicator_column(workclass),
111     tf.feature_column.indicator_column(marital_status),
112     tf.feature_column.indicator_column(sex),
113     tf.feature_column.indicator_column(relationship),
114     tf.feature_column.indicator_column(race),
115     # Embeddings for categories with more possibilities. Should have at least (possibilties)**(0.25) dims
116     tf.feature_column.embedding_column(education, dimension=8),
117     tf.feature_column.embedding_column(native_country, dimension=8),
118     tf.feature_column.embedding_column(occupation, dimension=8),
119     age
120 ]
121 
122 m2 = tf.estimator.DNNClassifier(
123     model_dir="model/dir",
124     feature_columns=deep_columns,
125     hidden_units=[100, 50])
126 
127 m2.train(input_fn=train_input_fn)
128 
129 start, end = 0, 5
130 data_predict = df.iloc[start:end]
131 predict_labels = y.iloc[start:end]
132 print(predict_labels)
133 print(data_predict.head(12))  # show this before deleting, so we know what the labels
134 predict_input_fn = tf.estimator.inputs.pandas_input_fn(
135     x=data_predict,
136     batch_size=1,
137     num_epochs=1,
138     shuffle=False)
139 
140 predictions = m2.predict(input_fn=predict_input_fn)
141 
142 for prediction in predictions:
143     print("Predictions:    {} with probabilities {}\n".format(prediction["classes"], prediction["probabilities"]))
144 
145 
146 def column_to_dtype(column):
147     if column in CATEGORICAL_COLUMNS:
148         return tf.string
149     else:
150         return tf.float32
151 
152 
153 # 什么数据要喂给输入
154 FEATURE_COLUMNS_FOR_SERVE = ["workclass", "education",
155                              "marital.status", "occupation",
156                              "relationship", "race",
157                              "sex", "native.country", "age"]
158 
159 serving_features = {column: tf.placeholder(shape=[1], dtype=column_to_dtype(column), name=column) for column in
160                     FEATURE_COLUMNS_FOR_SERVE}
161 # serving_input_receiver_fn有很多种方式
162 export_dir = m2.export_savedmodel(export_dir_base="models/export",
163                                   serving_input_receiver_fn=tf.estimator.export.build_raw_serving_input_receiver_fn(
164                                       serving_features), as_text=True)
165 export_dir = export_dir.decode("utf8")

 

2、通过 export_savedmodel这个函数生成了variables变量和pbtxt文件。如图所示：

 

3、先打开saved_model.pbtxt文件浏览一下，会发现这是对tensorflow 的一个个描述。包含了node name， operation name，dtype等信息。在套用java时需要明确node的name。

node {
      name: "dnn/head/predictions/probabilities"
      op: "Softmax"
      input: "dnn/head/predictions/two_class_logits"
      attr {
        key: "T"
        value {
          type: DT_FLOAT
        }
      }
      attr {
        key: "_output_shapes"
        value {
          list {
            shape {
              dim {
                size: -1
              }
              dim {
                size: 2
              }
            }
          }
        }

 


三、Java代码

1、先将variable和pbtxt文件放到resources下面。

 

2、Java代码

 1 import org.tensorflow.SavedModelBundle;
 2 import org.tensorflow.Session;
 3 import org.tensorflow.Tensor;
 4 
 5 /**
 6  * Created by adrian.wu on 2019/3/14.
 7  */
 8 public class TestAdultIncome {
 9 
10     public static void main(String[] args) throws Exception {
11 
12         SavedModelBundle model = SavedModelBundle.load("/Users/adrian.wu/Desktop/sc/adrian_test/src/main/resources/adultincomemodel", "serve");
13         Session sess = model.session();
14 
15         String sex = "Female";
16         String workclass = "?";
17         String education = "HS-grad";
18         String ms = "Widowed";
19         String occupation = "?";
20         String relationship = "Not-in-family";
21         String race = "White";
22         String nc = "United-States";
23 
24         //不能将string直接喂给create()接口
25         Tensor sexTensor = Tensor.create(new byte[][]{sex.getBytes()});
26         Tensor workclassTensor = Tensor.create(new byte[][]{workclass.getBytes()});
27         Tensor eduTensor = Tensor.create(new byte[][]{education.getBytes()});
28         Tensor msTensor = Tensor.create(new byte[][]{ms.getBytes()});
29         Tensor occuTensor = Tensor.create(new byte[][]{occupation.getBytes()});
30         Tensor ralaTensor = Tensor.create(new byte[][]{relationship.getBytes()});
31         Tensor raceTensor = Tensor.create(new byte[][]{race.getBytes()});
32         Tensor ncTesnsor = Tensor.create(new byte[][]{nc.getBytes()});
33 
34         float[][] age = {{90f}};
35 
36         Tensor ageTensor = Tensor.create(age);
37         
38         //根据pbtxt文件，查看operation name。
39         Tensor result = sess.runner()
40                 .feed("workclass", workclassTensor)
41                 .feed("education", eduTensor)
42                 .feed("marital.status", msTensor)
43                 .feed("relationship", ralaTensor)
44                 .feed("race", raceTensor)
45                 .feed("sex", sexTensor)
46                 .feed("native.country", ncTesnsor)
47                 .feed("occupation",occuTensor)
48                 .feed("age", ageTensor)
49                 .fetch("dnn/head/predictions/probabilities")
50                 .run()
51                 .get(0);
52 
53         float[][] buffer = new float[1][2];
54         result.copyTo(buffer);
55         System.out.println("" + String.valueOf(buffer[0][0]));
56     }
57 
58 }

 

四、结果对比

python和java结果：

1 java: 0.9432887
2 python: 0.9432887

 

　　

posted @ 2019-03-21 14:55  ylxn  阅读(3652)  评论(0)  编辑  收藏  举报