Returns a dense tensor as input layer based on given feature_columns.
At the first layer of the model, this column oriented data should be converted
to a single tensor.
input_layer(features, feature_columns, weight_collections = NULL,
trainable = TRUE)A mapping from key to tensors. Feature columns look up via
these keys. For example column_numeric('price') will look at 'price' key
in this dict. Values can be a sparse tensor or tensor depends on
corresponding feature column.
An iterable containing the FeatureColumns to use as
inputs to your model. All items should be instances of classes derived from
a dense column such as column_numeric(), column_embedding(),
column_bucketized(), column_indicator(). If you have categorical features,
you can wrap them with an column_embedding() or column_indicator().
A list of collection names to which the Variable
will be added. Note that, variables will also be added to collections
graph_keys()$GLOBAL_VARIABLES and graph_keys()$MODEL_VARIABLES.
If TRUE also add the variable to the graph collection
graph_keys()$TRAINABLE_VARIABLES (see tf$Variable).
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.
ValueError: if an item in feature_columns is not a dense column.
Other feature column constructors: column_bucketized,
column_categorical_weighted,
column_categorical_with_hash_bucket,
column_categorical_with_identity,
column_categorical_with_vocabulary_file,
column_categorical_with_vocabulary_list,
column_crossed,
column_embedding,
column_numeric