tf.compat.v1.feature_column.categorical_column_with_vocabulary

Returns a dense Tensor as input layer based on given feature_columns.

tf.compat.v1.feature_column.input_layer(
    features, feature_columns, weight_collections=None, trainable=True,
    cols_to_vars=None, cols_to_output_tensors=None
)

Generally a single example in training data is described with FeatureColumns. At the first layer of the model, this column oriented data should be converted to a single Tensor.

Example:

price = numeric_column('price')
keywords_embedded = embedding_column(
    categorical_column_with_hash_bucket("keywords", 10K), dimensions=16)
columns = [price, keywords_embedded, ...]
features = tf.io.parse_example(..., features=make_parse_example_spec(columns))
dense_tensor = input_layer(features, columns)
for units in [128, 64, 32]:
  dense_tensor = tf.compat.v1.layers.dense(dense_tensor, units, tf.nn.relu)
prediction = tf.compat.v1.layers.dense(dense_tensor, 1)

Args
`features`	A mapping from key to tensors. `_FeatureColumn`s look up via these keys. For example `numeric_column('price')` will look at 'price' key in this dict. Values can be a `SparseTensor` or a `Tensor` depends on corresponding `_FeatureColumn`.
`feature_columns`	An iterable containing the FeatureColumns to use as inputs to your model. All items should be instances of classes derived from `_DenseColumn` such as `numeric_column`, `embedding_column`, `bucketized_column`, `indicator_column`. If you have categorical features, you can wrap them with an `embedding_column` or `indicator_column`.
`weight_collections`	A list of collection names to which the Variable will be added. Note that variables will also be added to collections `tf.GraphKeys.GLOBAL_VARIABLES` and `ops.GraphKeys.MODEL_VARIABLES`.
`trainable`	If `True` also add the variable to the graph collection `GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`).
`cols_to_vars`	If not `None`, must be a dictionary that will be filled with a mapping from `_FeatureColumn` to list of `Variable`s. For example, after the call, we might have cols_to_vars = {_EmbeddingColumn( categorical_column=_HashedCategoricalColumn( key='sparse_feature', hash_bucket_size=5, dtype=tf.string), dimension=10): [
`cols_to_output_tensors`	If not `None`, must be a dictionary that will be filled with a mapping from '_FeatureColumn' to the associated output `Tensor`s.

Returns
A `Tensor` which represents input layer of a model. Its shape is (batch_size, first_layer_dimension) and its dtype is `float32`. first_layer_dimension is determined based on given `feature_columns`.

Raises
`ValueError`	if an item in `feature_columns` is not a `_DenseColumn`.

TensorFlow

tf.compat / v1 / v1.feature_column.categorical_column_with_vocabulary_file

Example:

Args

Returns

Raises

TensorFlow

tf

tf.audio

tf.autograph

tf.bitwise

tf.compat

tf.config

tf.data

tf.debugging

tf.distribute

tf.dtypes

tf.errors

tf.estimator

tf.experimental

tf.feature_column

tf.graph_util

tf.image

tf.initializers

tf.io

tf.keras

tf.linalg

tf.lite

tf.lookup

tf.losses

tf.math

tf.metrics

tf.nest

tf.nn

tf.optimizers

tf.quantization

tf.queue

tf.ragged

tf.random

tf.raw_ops

tf.saved_model

tf.sets

tf.signal

tf.sparse

tf.strings

tf.summary

tf.sysconfig

tf.test

tf.tpu

tf.train

tf.version

tf.xla

tf.compat / v1 / v1.feature_column.categorical_column_with_vocabulary_file

Example:

Args

Returns

Raises