Converting TensorFlow Wide and Deep Family Models

Danger

The code described here has been deprecated! Do not use it to avoid working with a legacy solution. It will be kept for some time to ensure backwards compatibility, but you should not use it in contemporary applications.

This guide describes a deprecated conversion method. The guide on the new and recommended method can be found in the Python tutorials.

The Wide and Deep models is a combination of wide and deep parts for memorization and generalization of object features respectively. These models can contain different types of object features such as numerical, categorical, sparse and sequential features. These feature types are specified through Tensorflow tf.feature_column API. Table below presents what feature types are supported by the OpenVINO toolkit.

numeric	(weighted) categorical	categorical with hash	bucketized	sequential	crossed
yes	yes	no	yes	yes	no

Note

The categorical with hash and crossed features are currently unsupported since OpenVINO does not cover tensors of the string type and operations with them.

Preparing an Example of Wide and Deep Model

Step 1. Clone the GitHub repository with TensorFlow models and move to the directory with an example of Wide and Deep model:

git clone https://github.com/tensorflow/models.git --branch r2.2.0;
cd official/r1/wide_deep

The Wide and Deep model is no longer in the master branch of the repository but is still available in the r2.2.0 branch.

Step 2. Train the model

As the OpenVINO™ toolkit does not support the categorical with hash and crossed features, such feature types must be switched off in the model by changing the build_model_columns() function in census_dataset.py as follows:

 def build_model_columns():
 """Builds a set of wide and deep feature columns."""
 # Continuous variable columns
 age = tf.feature_column.numeric_column('age')
 education_num = tf.feature_column.numeric_column('education_num')
 capital_gain = tf.feature_column.numeric_column('capital_gain')
 capital_loss = tf.feature_column.numeric_column('capital_loss')
 hours_per_week = tf.feature_column.numeric_column('hours_per_week')
 education = tf.feature_column.categorical_column_with_vocabulary_list(
     'education', [
         'Bachelors', 'HS-grad', '11th', 'Masters', '9th', 'Some-college',
         'Assoc-acdm', 'Assoc-voc', '7th-8th', 'Doctorate', 'Prof-school',
         '5th-6th', '10th', '1st-4th', 'Preschool', '12th'])
 marital_status = tf.feature_column.categorical_column_with_vocabulary_list(
     'marital_status', [
         'Married-civ-spouse', 'Divorced', 'Married-spouse-absent',
         'Never-married', 'Separated', 'Married-AF-spouse', 'Widowed'])
 relationship = tf.feature_column.categorical_column_with_vocabulary_list(
     'relationship', [
         'Husband', 'Not-in-family', 'Wife', 'Own-child', 'Unmarried',
         'Other-relative'])
 workclass = tf.feature_column.categorical_column_with_vocabulary_list(
     'workclass', [
         'Self-emp-not-inc', 'Private', 'State-gov', 'Federal-gov',
         'Local-gov', '?', 'Self-emp-inc', 'Without-pay', 'Never-worked'])
 # To show an example of hashing:
 #occupation = tf.feature_column.categorical_column_with_hash_bucket(
 #    'occupation', hash_bucket_size=_HASH_BUCKET_SIZE)
 # Transformations.
 age_buckets = tf.feature_column.bucketized_column(
     age, boundaries=[18, 25, 30, 35, 40, 45, 50, 55, 60, 65])
 # Wide columns and deep columns.
 base_columns = [
     education, marital_status, relationship, workclass,
     age_buckets,
 ]
 crossed_columns = []
 wide_columns = base_columns + crossed_columns
 deep_columns = [
     age,
     education_num,
     capital_gain,
     capital_loss,
     hours_per_week,
     tf.feature_column.indicator_column(workclass),
     tf.feature_column.indicator_column(education),
     tf.feature_column.indicator_column(marital_status),
     tf.feature_column.indicator_column(relationship),
     # To show an example of embedding
 ]
 return wide_columns, deep_columns

After that, start training with the following command:

python census_main.py

Converting the Wide and Deep Model to IR

Use the following command line to convert the saved model file with the checkpoint:

mo
--input_checkpoint checkpoint --input_meta_graph model.ckpt.meta
--input "IteratorGetNext:0[2],
        IteratorGetNext:1[2],
        IteratorGetNext:2[2],
        IteratorGetNext:4[2],
        IteratorGetNext:7[2],
        linear/linear_model/linear_model/linear_model/education/to_sparse_input/indices:0[10,2]{i64},
        linear/linear_model/linear_model/linear_model/education/hash_table_Lookup/LookupTableFindV2:0[10]{i64},
        linear/linear_model/linear_model/linear_model/education/to_sparse_input/dense_shape:0[2]{i64}->[2,50],
        linear/linear_model/linear_model/linear_model/marital_status/to_sparse_input/indices:0[10,2]{i64},
        linear/linear_model/linear_model/linear_model/marital_status/hash_table_Lookup/LookupTableFindV2:0[10]{i64},
        linear/linear_model/linear_model/linear_model/marital_status/to_sparse_input/dense_shape:0[2]{i64}->[2,50],
        linear/linear_model/linear_model/linear_model/relationship/to_sparse_input/indices:0[10,2]{i64},
        linear/linear_model/linear_model/linear_model/relationship/hash_table_Lookup/LookupTableFindV2:0[10]{i64},
        linear/linear_model/linear_model/linear_model/relationship/to_sparse_input/dense_shape:0[2]{i64}->[2,50],
        linear/linear_model/linear_model/linear_model/workclass/to_sparse_input/indices:0[10,2]{i64},
        linear/linear_model/linear_model/linear_model/workclass/hash_table_Lookup/LookupTableFindV2:0[10]{i64},
        linear/linear_model/linear_model/linear_model/workclass/to_sparse_input/dense_shape:0[2]{i64}->[2,50],
        dnn/input_from_feature_columns/input_layer/education_indicator/to_sparse_input/indices:0[10,2]{i64},
        dnn/input_from_feature_columns/input_layer/education_indicator/hash_table_Lookup/LookupTableFindV2:0[10]{i64},
        dnn/input_from_feature_columns/input_layer/education_indicator/to_sparse_input/dense_shape:0[2]{i64}->[2,50],
        dnn/input_from_feature_columns/input_layer/marital_status_indicator/to_sparse_input/indices:0[10,2]{i64},
        dnn/input_from_feature_columns/input_layer/marital_status_indicator/hash_table_Lookup/LookupTableFindV2:0[10]{i64},
        dnn/input_from_feature_columns/input_layer/marital_status_indicator/to_sparse_input/dense_shape:0[2]{i64}->[2,50],
        dnn/input_from_feature_columns/input_layer/relationship_indicator/to_sparse_input/indices:0[10,2]{i64},
        dnn/input_from_feature_columns/input_layer/relationship_indicator/hash_table_Lookup/LookupTableFindV2:0[10]{i64},
        dnn/input_from_feature_columns/input_layer/relationship_indicator/to_sparse_input/dense_shape:0[2]{i64}->[2,50],
        dnn/input_from_feature_columns/input_layer/workclass_indicator/to_sparse_input/indices:0[10,2]{i64},
        dnn/input_from_feature_columns/input_layer/workclass_indicator/hash_table_Lookup/LookupTableFindV2:0[10]{i64},
        dnn/input_from_feature_columns/input_layer/workclass_indicator/to_sparse_input/dense_shape:0[2]{i64}->[2,50]"
--output head/predictions/probabilities

The model contains operations unsupported by the OpenVINO™ toolkit such as IteratorGetNext and LookupTableFindV2, so the Model Optimizer must prune these nodes. The pruning is specified through –input option. The prunings for IteratorGetNext:* nodes correspond to numeric features. The pruning for each categorical feature consists of three prunings for the following nodes: */to_sparse_input/indices:0, */hash_table_Lookup/LookupTableFindV2:0, and */to_sparse_input/dense_shape:0.

The above command line generates an OpenVINO model for a batch of two objects, with the total number of actual categorical feature values equal to 10 and maximum size of a sparse categorical feature for one object equal to 50.