A layer that produces a dense Tensor based on given feature_columns.
layer_dense_features(
object,
feature_columns,
name = NULL,
trainable = NULL,
input_shape = NULL,
batch_input_shape = NULL,
batch_size = NULL,
dtype = NULL,
weights = NULL
)What to compose the new Layer instance with. Typically a
Sequential model or a Tensor (e.g., as returned by layer_input()).
The return value depends on object. If object is:
missing or NULL, the Layer instance is returned.
a Sequential model, the model with an additional layer is returned.
a Tensor, the output tensor from layer_instance(object) is returned.
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().
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.
Whether the layer weights will be updated during training.
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.
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.
Fixed batch size for layer
The data type expected by the input, as a string (float32,
float64, int32...)
Initial weights for layer.
Other core layers:
layer_activation(),
layer_activity_regularization(),
layer_attention(),
layer_dense(),
layer_dropout(),
layer_flatten(),
layer_input(),
layer_lambda(),
layer_masking(),
layer_permute(),
layer_repeat_vector(),
layer_reshape()