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: Fast LSTM implementation backed by CuDNN.

Description

Usage

Arguments

References

See Also