layer_simple_rnn {keras3}

Fully-connected RNN where the output is to be fed back as the new input.

Description

Fully-connected RNN where the output is to be fed back as the new input.

Usage

layer_simple_rnn(
  object,
  units,
  activation = "tanh",
  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,
  return_sequences = FALSE,
  return_state = FALSE,
  go_backwards = FALSE,
  stateful = FALSE,
  unroll = FALSE,
  seed = NULL,
  ...
)

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).

use_bias

Boolean, (default TRUE), whether the layer uses 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.

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.

seed

Initial seed for the random number generator

...

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

• sequence: A 3D tensor, with shape ⁠[batch, timesteps, feature]⁠.

• mask: Binary tensor of shape ⁠[batch, timesteps]⁠ indicating whether a given timestep should be masked. An individual TRUE entry indicates that the corresponding timestep should be utilized, while a FALSE entry indicates that the corresponding timestep should be ignored.

• 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.

• initial_state: List of initial state tensors to be passed to the first call of the cell.

Examples

inputs <- random_uniform(c(32, 10, 8))
simple_rnn <- layer_simple_rnn(units = 4)
output <- simple_rnn(inputs)  # The output has shape `(32, 4)`.
simple_rnn <- layer_simple_rnn(
    units = 4, return_sequences=TRUE, return_state=TRUE
)
# whole_sequence_output has shape `(32, 10, 4)`.
# final_state has shape `(32, 4)`.
c(whole_sequence_output, final_state) %<-% simple_rnn(inputs)

See Also

• https://keras.io/api/layers/recurrent_layers/simple_rnn#simplernn-class

Other simple rnn layers:
rnn_cell_simple()

Other rnn layers:
layer_bidirectional()
layer_conv_lstm_1d()
layer_conv_lstm_2d()
layer_conv_lstm_3d()
layer_gru()
layer_lstm()
layer_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_gru()
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_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()