A tf.contrib.layers style input layer builder based on FeatureColumns.
tf.contrib.layers.input_from_feature_columns(
columns_to_tensors, feature_columns, weight_collections=None, trainable=True,
scope=None, cols_to_outs=None
)
Generally a single example in training data is described with feature columns.
At the first layer of the model, this column oriented data should be converted
to a single tensor. Each feature column needs a different kind of operation
during this conversion. For example sparse features need a totally different
handling than continuous features.
Example:
# Building model for training
columns_to_tensor = tf.io.parse_example(...)
first_layer = input_from_feature_columns(
columns_to_tensors=columns_to_tensor,
feature_columns=feature_columns)
second_layer = fully_connected(inputs=first_layer, ...)
...
where feature_columns can be defined as follows:
sparse_feature = sparse_column_with_hash_bucket(
column_name="sparse_col", ...)
sparse_feature_emb = embedding_column(sparse_id_column=sparse_feature, ...)
real_valued_feature = real_valued_column(...)
real_valued_buckets = bucketized_column(
source_column=real_valued_feature, ...)
feature_columns=[sparse_feature_emb, real_valued_buckets]
Args |
columns_to_tensors
|
A mapping from feature column to tensors. 'string' key
means a base feature (not-transformed). It can have FeatureColumn as a
key too. That means that FeatureColumn is already transformed by input
pipeline.
|
feature_columns
|
A set containing all the feature columns. All items in the
set should be instances of classes derived by FeatureColumn.
|
weight_collections
|
List of graph collections to which weights are added.
|
trainable
|
If True also add variables to the graph collection
GraphKeys.TRAINABLE_VARIABLES (see tf.Variable).
|
scope
|
Optional scope for variable_scope.
|
cols_to_outs
|
Optional dict from feature column to output tensor,
which is concatenated into the returned tensor.
|
Returns |
A Tensor which can be consumed by hidden layers in the neural network.
|
Raises |
ValueError
|
if FeatureColumn cannot be consumed by a neural network.
|