layer_dense_features
Constructs a DenseFeatures.
Description
A layer that produces a dense Tensor based on given feature_columns.
Usage
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
Arguments | Description |
---|---|
object | 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. |
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_attention()
, layer_dense()
, layer_dropout()
, layer_flatten()
, layer_input()
, layer_lambda()
, layer_masking()
, layer_permute()
, layer_repeat_vector()
, layer_reshape()