Fast LSTM implementation backed by cuDNN.

tf.keras.layers.CuDNNLSTM( units, kernel_initializer='glorot_uniform', recurrent_initializer='orthogonal', bias_initializer='zeros', unit_forget_bias=True, kernel_regularizer=None, recurrent_regularizer=None, bias_regularizer=None, activity_regularizer=None, kernel_constraint=None, recurrent_constraint=None, bias_constraint=None, return_sequences=False, return_state=False, go_backwards=False, stateful=False, **kwargs )

More information about cuDNN can be found on the NVIDIA developer website. Can only be run on GPU.

Arguments | |
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`units` | Positive integer, dimensionality of the output space. |

`kernel_initializer` | Initializer for the `kernel` weights matrix, used for the linear transformation of the inputs. |

`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. |

`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. Whether to return the last state in addition to the output. |

`go_backwards` | Boolean (default False). If True, process the input sequence backwards and return the reversed sequence. |

`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. |

Attributes | |
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`cell` | |

`states` |

`get_initial_state`

get_initial_state( inputs )

`reset_states`

reset_states( states=None )

© 2020 The TensorFlow Authors. All rights reserved.

Licensed under the Creative Commons Attribution License 3.0.

Code samples licensed under the Apache 2.0 License.

https://www.tensorflow.org/versions/r1.15/api_docs/python/tf/keras/layers/CuDNNLSTM