# Wrapper allowing a stack of RNN cells to behave as a single cell. Used to implement efficient stacked RNNs. ```r rnn_cells_stack(cells, ...) ``` ## Arguments - `cells`: List of RNN cell instances. - `...`: Unnamed arguments are treated as additional `cells`. Named arguments are passed on to the underlying layer. ## Returns A `Layer` instance, which is intended to be used with `layer_rnn()`. ## Example ``` batch_size <- 3 sentence_length <- 5 num_features <- 2 new_shape <- c(batch_size, sentence_length, num_features) x <- array(1:30, dim = new_shape) rnn_cells <- lapply(1:2, function(x) rnn_cell_lstm(units = 128)) stacked_lstm <- rnn_cells_stack(rnn_cells) lstm_layer <- layer_rnn(cell = stacked_lstm) result <- lstm_layer(x) str(result) ``` ``` ## ``` ## See Also Other rnn layers: `layer_bidirectional()` `layer_conv_lstm_1d()` `layer_conv_lstm_2d()` `layer_conv_lstm_3d()` `layer_gru()` `layer_lstm()` `layer_rnn()` `layer_simple_rnn()` `layer_time_distributed()` `rnn_cell_gru()` `rnn_cell_lstm()` `rnn_cell_simple()` 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_aug_mix()` `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_cut_mix()` `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_erasing()` `layer_random_flip()` `layer_random_gaussian_blur()` `layer_random_grayscale()` `layer_random_hue()` `layer_random_invert()` `layer_random_perspective()` `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_rms_normalization()` `layer_rnn()` `layer_separable_conv_1d()` `layer_separable_conv_2d()` `layer_simple_rnn()` `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 function