layer_embedding {keras} | R Documentation

## Turns positive integers (indexes) into dense vectors of fixed size.

### Description

For example, `list(4L, 20L) -> list(c(0.25, 0.1), c(0.6, -0.2))` This layer can only be used as the first layer in a model.

### Usage

    layer_embedding(object, input_dim, output_dim,
      embeddings_initializer = "uniform", embeddings_regularizer = NULL,
      activity_regularizer = NULL, embeddings_constraint = NULL,
      mask_zero = FALSE, input_length = NULL, batch_size = NULL,
      name = NULL, trainable = NULL, weights = NULL)

### Arguments

`object` | Model or layer object
`input_dim` | int > 0. Size of the vocabulary, i.e. maximum integer index + 1.
`output_dim` | int >= 0. Dimension of the dense embedding.
`embeddings_initializer` | Initializer for the `embeddings` matrix.
`embeddings_regularizer` | Regularizer function applied to the `embeddings` matrix.
`activity_regularizer` | activity_regularizer
`embeddings_constraint` | Constraint function applied to the `embeddings` matrix.
`mask_zero` | Whether or not the input value 0 is a special "padding" value that should be masked out. This is useful when using recurrent layers, which may take variable length inputs. If this is `TRUE` then all subsequent layers in the model need to support masking or an exception will be raised. If mask_zero is set to TRUE, as a consequence, index 0 cannot be used in the vocabulary (input_dim should equal size of vocabulary + 1).
`input_length` | Length of input sequences, when it is constant. This argument is required if you are going to connect `Flatten` then `Dense` layers upstream (without it, the shape of the dense outputs cannot be computed).
`batch_size` | Fixed batch size for layer
`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.
`weights` | Initial weights for layer.

### Input shape

2D tensor with shape: `(batch_size, sequence_length)`.

### Output shape

3D tensor with shape: `(batch_size, sequence_length, output_dim)`.

### References

- A Theoretically Grounded Application of Dropout in Recurrent Neural Networks

[Package keras version 2.2.4 Index]