layer_simple_rnn: 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,
  ...
)

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.

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 the 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 an 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.

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)

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_auto_contrast()
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_equalization()
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_num_bounding_boxes()
layer_max_pooling_1d()
layer_max_pooling_2d()
layer_max_pooling_3d()
layer_maximum()
layer_mel_spectrogram()
layer_minimum()
layer_mix_up()
layer_multi_head_attention()
layer_multiply()
layer_normalization()
layer_permute()
layer_rand_augment()
layer_random_brightness()
layer_random_color_degeneration()
layer_random_color_jitter()
layer_random_contrast()
layer_random_crop()
layer_random_flip()
layer_random_grayscale()
layer_random_hue()
layer_random_posterization()
layer_random_rotation()
layer_random_saturation()
layer_random_sharpness()
layer_random_shear()
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_solarization()
layer_spatial_dropout_1d()
layer_spatial_dropout_2d()
layer_spatial_dropout_3d()
layer_spectral_normalization()
layer_stft_spectrogram()
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()

Description

Usage

Value

Arguments

Call Arguments

Examples

See Also