# layer_cudnn_lstm

## (Deprecated) Fast LSTM implementation backed by CuDNN.

## Description

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

## Usage

```
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
)
```

## 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. |

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. |

## Section

## References

## See Also

Other recurrent layers: `layer_cudnn_gru()`

, `layer_gru()`

, `layer_lstm()`

, `layer_rnn()`

, `layer_simple_rnn()`