# Construct an Input Layer

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)

## Arguments

 features 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. feature_columns 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(). weight_collections 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. trainable If TRUE also add the variable to the graph collection graph_keys()$TRAINABLE_VARIABLES (see tf$Variable).

## Value

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 dense column.

## See also

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