tfestimators: input_layer – R documentation

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input_layer

Construct an Input Layer

Description

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.

Usage

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.

input_layer

Description

Usage

Arguments

Value

Raises

See Also

tfestimators

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