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Gated Recurrent Unit - Cho et al. 2014.

Source: R/layers-rnn.R
layer_gru.Rd

Based on available runtime hardware and constraints, this layer will choose different implementations (cuDNN-based or backend-native) to maximize the performance. If a GPU is available and all the arguments to the layer meet the requirement of the cuDNN kernel (see below for details), the layer will use a fast cuDNN implementation when using the TensorFlow backend.

The requirements to use the cuDNN implementation are:

  1. activation == tanh

  2. recurrent_activation == sigmoid

  3. dropout == 0 and recurrent_dropout == 0

  4. unroll is FALSE

  5. use_bias is TRUE

  6. reset_after is TRUE

  7. Inputs, if use masking, are strictly right-padded.

  8. Eager execution is enabled in the outermost context.

There are two variants of the GRU implementation. The default one is based on v3 and has reset gate applied to hidden state before matrix multiplication. The other one is based on original and has the order reversed.

The second variant is compatible with CuDNNGRU (GPU-only) and allows inference on CPU. Thus it has separate biases for kernel and recurrent_kernel. To use this variant, set reset_after=TRUE and recurrent_activation='sigmoid'.

For example:

inputs <- random_uniform(c(32, 10, 8))
outputs <- inputs |> layer_gru(4)
shape(outputs)

## shape(32, 4)


# (32, 4)
gru <- layer_gru(, 4, return_sequences = TRUE, return_state = TRUE)
c(whole_sequence_output, final_state) %<-% gru(inputs)
shape(whole_sequence_output)

## shape(32, 10, 4)


shape(final_state)

## shape(32, 4)

Usage

layer_gru(
  object,
  units,
  activation = "tanh",
  recurrent_activation = "sigmoid",
  use_bias = TRUE,
  kernel_initializer = "glorot_uniform",
  recurrent_initializer = "orthogonal",
  bias_initializer = "zeros",
  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,
  seed = NULL,
  return_sequences = FALSE,
  return_state = FALSE,
  go_backwards = FALSE,
  stateful = FALSE,
  unroll = FALSE,
  reset_after = TRUE,
  use_cudnn = "auto",
  ...
)

Arguments

object

Object to compose the layer with. A tensor, array, or sequential model.

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, (default TRUE), whether the layer should use a bias vector.

kernel_initializer

Initializer for the kernel weights matrix, used for the linear transformation of the inputs. Default: "glorot_uniform".

recurrent_initializer

Initializer for the recurrent_kernel weights matrix, used for the linear transformation of the recurrent state. Default: "orthogonal".

bias_initializer

Initializer for the bias vector. Default: "zeros".

kernel_regularizer

Regularizer function applied to the kernel weights matrix. Default: NULL.

recurrent_regularizer

Regularizer function applied to the recurrent_kernel weights matrix. Default: NULL.

bias_regularizer

Regularizer function applied to the bias vector. Default: NULL.

activity_regularizer

Regularizer function applied to the output of the layer (its "activation"). Default: NULL.

kernel_constraint

Constraint function applied to the kernel weights matrix. Default: NULL.

recurrent_constraint

Constraint function applied to the recurrent_kernel weights matrix. Default: NULL.

bias_constraint

Constraint function applied to the bias vector. Default: NULL.

dropout

Float between 0 and 1. Fraction of the units to drop for the linear transformation of the inputs. Default: 0.

recurrent_dropout

Float between 0 and 1. Fraction of the units to drop for the linear transformation of the recurrent state. Default: 0.

seed

Random seed for dropout.

return_sequences

Boolean. Whether to return the last output in the output sequence, or the full sequence. Default: FALSE.

return_state

Boolean. Whether to return the last state in addition to the output. Default: FALSE.

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.

reset_after

GRU convention (whether to apply reset gate after or before matrix multiplication). FALSE is "before", TRUE is "after" (default and cuDNN compatible).

use_cudnn

Whether to use a cuDNN-backed implementation. "auto" will attempt to use cuDNN when feasible, and will fallback to the default implementation if not.

...

For forward/backward compatability.

Value

The return value depends on the value provided for the first argument. If object is:

  • a keras_model_sequential(), then the layer is added to the sequential model (which is modified in place). To enable piping, the sequential model is also returned, invisibly.

  • a keras_input(), then the output tensor from calling layer(input) is returned.

  • NULL or missing, then a Layer instance is returned.

Call Arguments

  • inputs: A 3D tensor, with shape (batch, timesteps, feature).

  • mask: Binary tensor of shape (samples, timesteps) indicating whether a given timestep should be masked (optional). An individual TRUE entry indicates that the corresponding timestep should be utilized, while a FALSE entry indicates that the corresponding timestep should be ignored. Defaults to NULL.

  • training: Python boolean indicating whether the layer should behave in training mode or in inference mode. This argument is passed to the cell when calling it. This is only relevant if dropout or recurrent_dropout is used (optional). Defaults to NULL.

  • initial_state: List of initial state tensors to be passed to the first call of the cell (optional, NULL causes creation of zero-filled initial state tensors). Defaults to NULL.

See also

Other gru rnn layers:
rnn_cell_gru()

Other rnn layers:
layer_bidirectional()
layer_conv_lstm_1d()
layer_conv_lstm_2d()
layer_conv_lstm_3d()
layer_lstm()
layer_rnn()
layer_simple_rnn()
layer_time_distributed()
rnn_cell_gru()
rnn_cell_lstm()
rnn_cell_simple()
rnn_cells_stack()

Other layers:
Layer()
layer_activation()
layer_activation_elu()
layer_activation_leaky_relu()
layer_activation_parametric_relu()
layer_activation_relu()
layer_activation_softmax()
layer_activity_regularization()
layer_add()
layer_additive_attention()
layer_alpha_dropout()
layer_attention()
layer_average()
layer_average_pooling_1d()
layer_average_pooling_2d()
layer_average_pooling_3d()
layer_batch_normalization()
layer_bidirectional()
layer_category_encoding()
layer_center_crop()
layer_concatenate()
layer_conv_1d()
layer_conv_1d_transpose()
layer_conv_2d()
layer_conv_2d_transpose()
layer_conv_3d()
layer_conv_3d_transpose()
layer_conv_lstm_1d()
layer_conv_lstm_2d()
layer_conv_lstm_3d()
layer_cropping_1d()
layer_cropping_2d()
layer_cropping_3d()
layer_dense()
layer_depthwise_conv_1d()
layer_depthwise_conv_2d()
layer_discretization()
layer_dot()
layer_dropout()
layer_einsum_dense()
layer_embedding()
layer_feature_space()
layer_flatten()
layer_flax_module_wrapper()
layer_gaussian_dropout()
layer_gaussian_noise()
layer_global_average_pooling_1d()
layer_global_average_pooling_2d()
layer_global_average_pooling_3d()
layer_global_max_pooling_1d()
layer_global_max_pooling_2d()
layer_global_max_pooling_3d()
layer_group_normalization()
layer_group_query_attention()
layer_hashed_crossing()
layer_hashing()
layer_identity()
layer_integer_lookup()
layer_jax_model_wrapper()
layer_lambda()
layer_layer_normalization()
layer_lstm()
layer_masking()
layer_max_pooling_1d()
layer_max_pooling_2d()
layer_max_pooling_3d()
layer_maximum()
layer_mel_spectrogram()
layer_minimum()
layer_multi_head_attention()
layer_multiply()
layer_normalization()
layer_permute()
layer_random_brightness()
layer_random_contrast()
layer_random_crop()
layer_random_flip()
layer_random_rotation()
layer_random_translation()
layer_random_zoom()
layer_repeat_vector()
layer_rescaling()
layer_reshape()
layer_resizing()
layer_rnn()
layer_separable_conv_1d()
layer_separable_conv_2d()
layer_simple_rnn()
layer_spatial_dropout_1d()
layer_spatial_dropout_2d()
layer_spatial_dropout_3d()
layer_spectral_normalization()
layer_string_lookup()
layer_subtract()
layer_text_vectorization()
layer_tfsm()
layer_time_distributed()
layer_torch_module_wrapper()
layer_unit_normalization()
layer_upsampling_1d()
layer_upsampling_2d()
layer_upsampling_3d()
layer_zero_padding_1d()
layer_zero_padding_2d()
layer_zero_padding_3d()
rnn_cell_gru()
rnn_cell_lstm()
rnn_cell_simple()
rnn_cells_stack()