layer_cudnn_gru {keras} | R Documentation

## Fast GRU implementation backed by CuDNN.

### Description

Can only be run on GPU, with the TensorFlow backend.

### Usage

    layer_cudnn_gru(object, units, kernel_initializer = "glorot_uniform",
      recurrent_initializer = "orthogonal", bias_initializer = "zeros",
      kernel_regularizer = NULL, recurrent_regularizer = NULL,
      bias_regularizer = NULL, activity_regularizer = NULL,
      kernel_constraint = NULL, recurrent_constraint = NULL,
      bias_constraint = NULL, return_sequences = FALSE,
      return_state = FALSE, stateful = FALSE, input_shape = NULL,
      batch_input_shape = NULL, batch_size = NULL, dtype = NULL,
      name = NULL, trainable = NULL, weights = NULL)

### Arguments

`object` | Model or layer object
`units` | Positive integer, dimensionality of the output space.
`kernel_initializer` | Initializer for the `kernel` weights matrix, used for the linear transformation of the inputs.
`recurrent_initializer` | Initializer for the `recurrent_kernel` weights matrix, used for the linear transformation of the recurrent state.
`bias_initializer` | Initializer for the bias vector.
`kernel_regularizer` | Regularizer function applied to the `kernel` weights matrix.
`recurrent_regularizer` | Regularizer function applied to the `recurrent_kernel` weights matrix.
`bias_regularizer` | Regularizer function applied to the bias vector.
`activity_regularizer` | Regularizer function applied to the output of the layer (its "activation")..
`kernel_constraint` | Constraint function applied to the `kernel` weights matrix.
`recurrent_constraint` | Constraint function applied to the `recurrent_kernel` weights matrix.
`bias_constraint` | Constraint function applied to the bias vector.
`return_sequences` | Boolean. Whether to return the last output in the output sequence, or the full sequence.
`return_state` | Boolean (default FALSE). Whether to return the last state in addition to the output.
`stateful` | Boolean (default FALSE). If TRUE, the last state for each sample at index i in a batch will be used as initial state for the sample of index i in the following batch.
`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`...)
`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.

### References

- On the Properties of Neural Machine Translation: Encoder-Decoder Approaches

- Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling

- A Theoretically Grounded Application of Dropout in Recurrent Neural Networks

### See Also

Other recurrent layers: `layer_cudnn_lstm`, `layer_gru`, `layer_lstm`, `layer_simple_rnn`

[Package keras version 2.2.4 Index]