layer_concatenate {keras3}

Concatenates a list of inputs.

Description

It takes as input a list of tensors, all of the same shape except for the concatenation axis, and returns a single tensor that is the concatenation of all inputs.

Usage

layer_concatenate(inputs, ..., axis = -1L)

Arguments

inputs

layers to combine

...

Standard layer keyword arguments.

axis

Axis along which to concatenate.

Value

A tensor, the concatenation of the inputs alongside axis axis.

Examples

x <- op_arange(20) |> op_reshape(c(2, 2, 5))
y <- op_arange(20, 40) |> op_reshape(c(2, 2, 5))
layer_concatenate(x, y, axis = 2)

## tf.Tensor(
## [[[ 0.  1.  2.  3.  4.]
##   [ 5.  6.  7.  8.  9.]
##   [20. 21. 22. 23. 24.]
##   [25. 26. 27. 28. 29.]]
##
##  [[10. 11. 12. 13. 14.]
##   [15. 16. 17. 18. 19.]
##   [30. 31. 32. 33. 34.]
##   [35. 36. 37. 38. 39.]]], shape=(2, 4, 5), dtype=float32)

Usage in a Keras model:

x1 <- op_arange(10)     |> op_reshape(c(5, 2)) |> layer_dense(8)
x2 <- op_arange(10, 20) |> op_reshape(c(5, 2)) |> layer_dense(8)
y <- layer_concatenate(x1, x2)

See Also

• https://keras.io/api/layers/merging_layers/concatenate#concatenate-class

Other merging layers:
layer_add()
layer_average()
layer_dot()
layer_maximum()
layer_minimum()
layer_multiply()
layer_subtract()

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_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_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()