layer_dense_features: Constructs a DenseFeatures.

Description Usage Arguments See Also

View source: R/layers-features.R

Description

A layer that produces a dense Tensor based on given feature_columns.

Usage

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layer_dense_features(object, feature_columns, name = NULL,
  trainable = NULL, input_shape = NULL, batch_input_shape = NULL,
  batch_size = NULL, dtype = NULL, weights = NULL)

Arguments

object

Model or layer object

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. See tfestimators::feature_columns().

name

An optional name string for the layer. Should be unique in a model (do not reuse the same name twice). It will be autogenerated if it isn't provided.

trainable

Whether the layer weights will be updated during training.

input_shape

Dimensionality of the input (integer) not including the samples axis. This argument is required when using this layer as the first layer in a model.

batch_input_shape

Shapes, including the batch size. For instance, batch_input_shape=c(10, 32) indicates that the expected input will be batches of 10 32-dimensional vectors. batch_input_shape=list(NULL, 32) indicates batches of an arbitrary number of 32-dimensional vectors.

batch_size

Fixed batch size for layer

dtype

The data type expected by the input, as a string (float32, float64, int32...)

weights

Initial weights for layer.

See Also

Other core layers: layer_activation, layer_activity_regularization, layer_dense, layer_dropout, layer_flatten, layer_input, layer_lambda, layer_masking, layer_permute, layer_repeat_vector, layer_reshape


keras documentation built on Oct. 9, 2019, 1:04 a.m.