Description Usage Arguments Details Value

LSTM cell with layer normalization and recurrent dropout.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 | ```
layer_norm_lstm_cell(
object,
units,
activation = "tanh",
recurrent_activation = "sigmoid",
use_bias = TRUE,
kernel_initializer = "glorot_uniform",
recurrent_initializer = "orthogonal",
bias_initializer = "zeros",
unit_forget_bias = TRUE,
kernel_regularizer = NULL,
recurrent_regularizer = NULL,
bias_regularizer = NULL,
kernel_constraint = NULL,
recurrent_constraint = NULL,
bias_constraint = NULL,
dropout = 0,
recurrent_dropout = 0,
norm_gamma_initializer = "ones",
norm_beta_initializer = "zeros",
norm_epsilon = 0.001,
...
)
``` |

`object` |
Model or layer object |

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

`activation` |
Activation function to use. Default: hyperbolic tangent ('tanh'). If you pass 'NULL', no activation is applied (ie. "linear" activation: 'a(x) = x'). |

`recurrent_activation` |
Activation function to use for the recurrent step. Default: sigmoid ('sigmoid'). If you pass 'NULL', no activation is applied (ie. "linear" activation: 'a(x) = x'). |

`use_bias` |
Boolean, whether the layer uses a bias vector. |

`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.](http://www.jmlr.org/proceedings/papers/v37/jozefowicz15.pdf) |

`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. |

`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. |

`dropout` |
Float between 0 and 1. Fraction of the units to drop for the linear transformation of the inputs. |

`recurrent_dropout` |
Float between 0 and 1. Fraction of the units to drop for the linear transformation of the recurrent state. |

`norm_gamma_initializer` |
Initializer for the layer normalization gain initial value. |

`norm_beta_initializer` |
Initializer for the layer normalization shift initial value. |

`norm_epsilon` |
Float, the epsilon value for normalization layers. |

`...` |
List, the other keyword arguments for layer creation. |

This class adds layer normalization and recurrent dropout to a LSTM unit. Layer normalization implementation is based on: https://arxiv.org/abs/1607.06450. "Layer Normalization" Jimmy Lei Ba, Jamie Ryan Kiros, Geoffrey E. Hinton and is applied before the internal nonlinearities. Recurrent dropout is based on: https://arxiv.org/abs/1603.05118 "Recurrent Dropout without Memory Loss" Stanislau Semeniuta, Aliaksei Severyn, Erhardt Barth.

A tensor

Embedding an R snippet on your website

Add the following code to your website.

For more information on customizing the embed code, read Embedding Snippets.