tf.contrib.layers.create_feature_spec_for_parsing
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Helper that prepares features config from input feature_columns.
tf.contrib.layers.create_feature_spec_for_parsing(
feature_columns
)
The returned feature config can be used as arg 'features' in tf.parse_example.
Typical usage example:
# Define features and transformations
feature_a = sparse_column_with_vocabulary_file(...)
feature_b = real_valued_column(...)
feature_c_bucketized = bucketized_column(real_valued_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])
batch_examples = tf.io.parse_example(
serialized=serialized_examples,
features=create_feature_spec_for_parsing(feature_columns))
For the above example, create_feature_spec_for_parsing would return the dict:
{
"feature_a": parsing_ops.VarLenFeature(tf.string),
"feature_b": parsing_ops.FixedLenFeature([1], dtype=tf.float32),
"feature_c": parsing_ops.FixedLenFeature([1], dtype=tf.float32)
}
Args |
|---|
feature_columns
|
An iterable containing all the feature columns. All items
should be instances of classes derived from _FeatureColumn, unless
feature_columns is a dict -- in which case, this should be true of all
values in the dict.
|
Returns |
|---|
|
A dict mapping feature keys to FixedLenFeature or VarLenFeature values.
|
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Last updated 2020-10-01 UTC.
[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Missing the information I need","missingTheInformationINeed","thumb-down"],["Too complicated / too many steps","tooComplicatedTooManySteps","thumb-down"],["Out of date","outOfDate","thumb-down"],["Samples / code issue","samplesCodeIssue","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2020-10-01 UTC."],[],[],null,["# tf.contrib.layers.create_feature_spec_for_parsing\n\n\u003cbr /\u003e\n\n|------------------------------------------------------------------------------------------------------------------------------------------------------|\n| [View source on GitHub](https://github.com/tensorflow/tensorflow/blob/v1.15.0/tensorflow/contrib/layers/python/layers/feature_column.py#L2581-L2625) |\n\nHelper that prepares features config from input feature_columns. \n\n tf.contrib.layers.create_feature_spec_for_parsing(\n feature_columns\n )\n\nThe returned feature config can be used as arg 'features' in tf.parse_example.\n\n#### Typical usage example:\n\n # Define features and transformations\n feature_a = sparse_column_with_vocabulary_file(...)\n feature_b = real_valued_column(...)\n feature_c_bucketized = bucketized_column(real_valued_column(\"feature_c\"), ...)\n feature_a_x_feature_c = crossed_column(\n columns=[feature_a, feature_c_bucketized], ...)\n\n feature_columns = set(\n [feature_b, feature_c_bucketized, feature_a_x_feature_c])\n batch_examples = tf.io.parse_example(\n serialized=serialized_examples,\n features=create_feature_spec_for_parsing(feature_columns))\n\nFor the above example, create_feature_spec_for_parsing would return the dict:\n{\n\"feature_a\": parsing_ops.VarLenFeature(tf.string),\n\"feature_b\": parsing_ops.FixedLenFeature(\\[1\\], dtype=tf.float32),\n\"feature_c\": parsing_ops.FixedLenFeature(\\[1\\], dtype=tf.float32)\n}\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Args ---- ||\n|-------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| `feature_columns` | An iterable containing all the feature columns. All items should be instances of classes derived from _FeatureColumn, unless feature_columns is a dict -- in which case, this should be true of all values in the dict. |\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Returns ------- ||\n|---|---|\n| A dict mapping feature keys to FixedLenFeature or VarLenFeature values. ||\n\n\u003cbr /\u003e"]]
View source on GitHub