keras: layer_cudnn_lstm – R documentation

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keras

layer_cudnn_lstm

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

`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.
`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.

layer_cudnn_lstm

Description

Usage

Arguments

References

See Also

keras

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