For a step-by-step description of the algorithm, see thistutorial.

layer_lstm(object, units, activation = "tanh",
  recurrent_activation = "hard_sigmoid", use_bias = TRUE,
  return_sequences = FALSE, return_state = FALSE, go_backwards = FALSE,
  stateful = FALSE, unroll = FALSE, implementation = 0L,
  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, dropout = 0,
  recurrent_dropout = 0, 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.

activation

Activation function to use. If you pass NULL, no activation is applied (ie. "linear" activation: a(x) = x).

recurrent_activation

Activation function to use for the recurrent step.

use_bias

Boolean, whether the layer uses a 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.

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.

unroll

Boolean (default FALSE). If TRUE, the network will be unrolled, else a symbolic loop will be used. Unrolling can speed-up a RNN, although it tends to be more memory-intensive. Unrolling is only suitable for short sequences.

implementation

one of 0, 1, or 2. If set to 0, the RNN will use an implementation that uses fewer, larger matrix products, thus running faster on CPU but consuming more memory. If set to 1, the RNN will use more matrix products, but smaller ones, thus running slower (may actually be faster on GPU) while consuming less memory. If set to 2 (LSTM/GRU only), the RNN will combine the input gate, the forget gate and the output gate into a single matrix, enabling more time-efficient parallelization on the GPU.

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 etal.

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.

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.

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.

Input shapes

3D tensor with shape (batch_size, timesteps, input_dim), (Optional) 2D tensors with shape (batch_size, output_dim).

Output shape

  • if return_state: a list of tensors. The first tensor is the output. The remaining tensors are the last states, each with shape (batch_size, units).

  • if return_sequences: 3D tensor with shape (batch_size, timesteps, units).

  • else, 2D tensor with shape (batch_size, units).

Masking

This layer supports masking for input data with a variable number of timesteps. To introduce masks to your data, use an embedding layer with the mask_zero parameter set to TRUE.

Statefulness in RNNs

You can set RNN layers to be 'stateful', which means that the states computed for the samples in one batch will be reused as initial states for the samples in the next batch. This assumes a one-to-one mapping between samples in different successive batches.

To enable statefulness:

  • Specify stateful=TRUE in the layer constructor.

  • Specify a fixed batch size for your model. For sequential models, pass batch_input_shape = c(...) to the first layer in your model. For functional models with 1 or more Input layers, pass batch_shape = c(...) to all the first layers in your model. This is the expected shape of your inputs including the batch size. It should be a vector of integers, e.g. c(32, 10, 100).

  • Specify shuffle = FALSE when calling fit().

To reset the states of your model, call reset_states() on either a specific layer, or on your entire model.

Initial State of RNNs

You can specify the initial state of RNN layers symbolically by calling them with the keyword argument initial_state. The value of initial_state should be a tensor or list of tensors representing the initial state of the RNN layer.

You can specify the initial state of RNN layers numerically by calling reset_states with the keyword argument states. The value of states should be a numpy array or list of numpy arrays representing the initial state of the RNN layer.

References

See also

Other recurrent layers: layer_gru, layer_simple_rnn

Other recurrent layers: layer_gru, layer_simple_rnn