LostTech.TensorFlow : API Documentation

Type feature_column

Namespace tensorflow.compat.v2.feature_column

Methods

Properties

Public static methods

VocabularyFileCategoricalColumn categorical_column_with_vocabulary_file(IEnumerable<string> key, IEnumerable<object> vocabulary_file, Nullable<int> vocabulary_size, ImplicitContainer<T> dtype, Nullable<int> default_value, int num_oov_buckets)

VocabularyFileCategoricalColumn categorical_column_with_vocabulary_file(IEnumerable<string> key, int vocabulary_file, Nullable<int> vocabulary_size, ImplicitContainer<T> dtype, Nullable<int> default_value, int num_oov_buckets)

VocabularyFileCategoricalColumn categorical_column_with_vocabulary_file(IEnumerable<string> key, string vocabulary_file, Nullable<int> vocabulary_size, ImplicitContainer<T> dtype, Nullable<int> default_value, int num_oov_buckets)

VocabularyFileCategoricalColumn categorical_column_with_vocabulary_file(string key, IEnumerable<object> vocabulary_file, Nullable<int> vocabulary_size, ImplicitContainer<T> dtype, Nullable<int> default_value, int num_oov_buckets)

VocabularyFileCategoricalColumn categorical_column_with_vocabulary_file(string key, int vocabulary_file, Nullable<int> vocabulary_size, ImplicitContainer<T> dtype, Nullable<int> default_value, int num_oov_buckets)

VocabularyFileCategoricalColumn categorical_column_with_vocabulary_file(string key, string vocabulary_file, Nullable<int> vocabulary_size, ImplicitContainer<T> dtype, Nullable<int> default_value, int num_oov_buckets)

object categorical_column_with_vocabulary_file_dyn(object key, object vocabulary_file, object vocabulary_size, ImplicitContainer<T> dtype, object default_value, ImplicitContainer<T> num_oov_buckets)

A `CategoricalColumn` with a vocabulary file.

Use this when your inputs are in string or integer format, and you have a vocabulary file that maps each value to an integer ID. By default, out-of-vocabulary values are ignored. Use either (but not both) of `num_oov_buckets` and `default_value` to specify how to include out-of-vocabulary values.

For input dictionary `features`, `features[key]` is either `Tensor` or `SparseTensor`. If `Tensor`, missing values can be represented by `-1` for int and `''` for string, which will be dropped by this feature column.

Example with `num_oov_buckets`: File '/us/states.txt' contains 50 lines, each with a 2-character U.S. state abbreviation. All inputs with values in that file are assigned an ID 0-49, corresponding to its line number. All other values are hashed and assigned an ID 50-54. Example with `default_value`: File '/us/states.txt' contains 51 lines - the first line is 'XX', and the other 50 each have a 2-character U.S. state abbreviation. Both a literal 'XX' in input, and other values missing from the file, will be assigned ID 0. All others are assigned the corresponding line number 1-50. And to make an embedding with either:
Parameters
object key
A unique string identifying the input feature. It is used as the column name and the dictionary key for feature parsing configs, feature `Tensor` objects, and feature columns.
object vocabulary_file
The vocabulary file name.
object vocabulary_size
Number of the elements in the vocabulary. This must be no greater than length of `vocabulary_file`, if less than length, later values are ignored. If None, it is set to the length of `vocabulary_file`.
ImplicitContainer<T> dtype
The type of features. Only string and integer types are supported.
object default_value
The integer ID value to return for out-of-vocabulary feature values, defaults to `-1`. This can not be specified with a positive `num_oov_buckets`.
ImplicitContainer<T> num_oov_buckets
Non-negative integer, the number of out-of-vocabulary buckets. All out-of-vocabulary inputs will be assigned IDs in the range `[vocabulary_size, vocabulary_size+num_oov_buckets)` based on a hash of the input value. A positive `num_oov_buckets` can not be specified with `default_value`.
Returns
object
A `CategoricalColumn` with a vocabulary file.
Show Example 
states = categorical_column_with_vocabulary_file(
                key='states', vocabulary_file='/us/states.txt', vocabulary_size=50,
                num_oov_buckets=5)
            columns = [states,...]
            features = tf.io.parse_example(..., features=make_parse_example_spec(columns))
            linear_prediction = linear_model(features, columns)

IDictionary<object, FixedLenFeature> make_parse_example_spec(IEnumerable<HashedCategoricalColumn> feature_columns)

Creates parsing spec dictionary from input feature_columns.

The returned dictionary can be used as arg 'features' in tf.io.parse_example.

Typical usage example: For the above example, make_parse_example_spec would return the dict:
Parameters
IEnumerable<HashedCategoricalColumn> feature_columns
An iterable containing all feature columns. All items should be instances of classes derived from `_FeatureColumn`.
Returns
IDictionary<object, FixedLenFeature>
A dict mapping each feature key to a `FixedLenFeature` or `VarLenFeature` value.
Show Example 
# Define features and transformations
            feature_a = categorical_column_with_vocabulary_file(...)
            feature_b = numeric_column(...)
            feature_c_bucketized = bucketized_column(numeric_column("feature_c"),...)
            feature_a_x_feature_c = crossed_column(
                columns=["feature_a", feature_c_bucketized],...) 
 feature_columns = set(
    [feature_b, feature_c_bucketized, feature_a_x_feature_c])
features = tf.io.parse_example(
    serialized=serialized_examples,
    features=make_parse_example_spec(feature_columns))

IDictionary<object, FixedLenFeature> make_parse_example_spec(ValueTuple<object, string> feature_columns)

Creates parsing spec dictionary from input feature_columns.

The returned dictionary can be used as arg 'features' in tf.io.parse_example.

Typical usage example: For the above example, make_parse_example_spec would return the dict:
Parameters
ValueTuple<object, string> feature_columns
An iterable containing all feature columns. All items should be instances of classes derived from `_FeatureColumn`.
Returns
IDictionary<object, FixedLenFeature>
A dict mapping each feature key to a `FixedLenFeature` or `VarLenFeature` value.
Show Example 
# Define features and transformations
            feature_a = categorical_column_with_vocabulary_file(...)
            feature_b = numeric_column(...)
            feature_c_bucketized = bucketized_column(numeric_column("feature_c"),...)
            feature_a_x_feature_c = crossed_column(
                columns=["feature_a", feature_c_bucketized],...) 
 feature_columns = set(
    [feature_b, feature_c_bucketized, feature_a_x_feature_c])
features = tf.io.parse_example(
    serialized=serialized_examples,
    features=make_parse_example_spec(feature_columns))

object make_parse_example_spec_dyn(object feature_columns)

Creates parsing spec dictionary from input feature_columns.

The returned dictionary can be used as arg 'features' in tf.io.parse_example.

Typical usage example: For the above example, make_parse_example_spec would return the dict:
Parameters
object feature_columns
An iterable containing all feature columns. All items should be instances of classes derived from `_FeatureColumn`.
Returns
object
A dict mapping each feature key to a `FixedLenFeature` or `VarLenFeature` value.
Show Example 
# Define features and transformations
            feature_a = categorical_column_with_vocabulary_file(...)
            feature_b = numeric_column(...)
            feature_c_bucketized = bucketized_column(numeric_column("feature_c"),...)
            feature_a_x_feature_c = crossed_column(
                columns=["feature_a", feature_c_bucketized],...) 
 feature_columns = set(
    [feature_b, feature_c_bucketized, feature_a_x_feature_c])
features = tf.io.parse_example(
    serialized=serialized_examples,
    features=make_parse_example_spec(feature_columns))

Public properties

PythonFunctionContainer categorical_column_with_vocabulary_file_fn_ get;

PythonFunctionContainer make_parse_example_spec_fn get;