`R/layers-recurrent.R`

`layer_cudnn_lstm.Rd`

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

```
layer_cudnn_lstm(
object,
units,
kernel_initializer = "glorot_uniform",
recurrent_initializer = "orthogonal",
bias_initializer = "zeros",
unit_forget_bias = TRUE,
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
)
```

- 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.

- 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.

- unit_forget_bias
Boolean. If TRUE, add 1 to the bias of the forget gate at initialization. Setting it to true will also force

`bias_initializer="zeros"`

. This is recommended in Jozefowicz et al.- 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.

Other recurrent layers:
`layer_cudnn_gru()`

,
`layer_gru()`

,
`layer_lstm()`

,
`layer_rnn()`

,
`layer_simple_rnn()`