This layer can perform einsum calculations of arbitrary dimensionality.
Usage
layer_einsum_dense(
object,
equation,
output_shape,
activation = NULL,
bias_axes = NULL,
kernel_initializer = "glorot_uniform",
bias_initializer = "zeros",
kernel_regularizer = NULL,
bias_regularizer = NULL,
kernel_constraint = NULL,
bias_constraint = NULL,
lora_rank = NULL,
...
)
Arguments
 object
Object to compose the layer with. A tensor, array, or sequential model.
 equation
An equation describing the einsum to perform. This equation must be a valid einsum string of the form
ab,bc>ac
,...ab,bc>...ac
, orab...,bc>ac...
where 'ab', 'bc', and 'ac' can be any valid einsum axis expression sequence. output_shape
The expected shape of the output tensor (excluding the batch dimension and any dimensions represented by ellipses). You can specify
NA
orNULL
for any dimension that is unknown or can be inferred from the input shape. activation
Activation function to use. If you don't specify anything, no activation is applied (that is, a "linear" activation:
a(x) = x
). bias_axes
A string containing the output dimension(s) to apply a bias to. Each character in the
bias_axes
string should correspond to a character in the output portion of theequation
string. kernel_initializer
Initializer for the
kernel
weights matrix. bias_initializer
Initializer for the bias vector.
 kernel_regularizer
Regularizer function applied to the
kernel
weights matrix. bias_regularizer
Regularizer function applied to the bias vector.
 kernel_constraint
Constraint function applied to the
kernel
weights matrix. bias_constraint
Constraint function applied to the bias vector.
 lora_rank
Optional integer. If set, the layer's forward pass will implement LoRA (LowRank Adaptation) with the provided rank. LoRA sets the layer's kernel to nontrainable and replaces it with a delta over the original kernel, obtained via multiplying two lowerrank trainable matrices (the factorization happens on the last dimension). This can be useful to reduce the computation cost of finetuning large dense layers. You can also enable LoRA on an existing
EinsumDense
layer by callinglayer$enable_lora(rank)
. ...
Base layer keyword arguments, such as
name
anddtype
.
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 callinglayer(input)
is returned.NULL
or missing, then aLayer
instance is returned.
Examples
Biased dense layer with einsums
This example shows how to instantiate a standard Keras dense layer using
einsum operations. This example is equivalent to
layer_Dense(64, use_bias=TRUE)
.
input < layer_input(shape = c(32))
output < input >
layer_einsum_dense("ab,bc>ac",
output_shape = 64,
bias_axes = "c")
output # shape(NA, 64)
Applying a dense layer to a sequence
This example shows how to instantiate a layer that applies the same dense
operation to every element in a sequence. Here, the output_shape
has two
values (since there are two nonbatch dimensions in the output); the first
dimension in the output_shape
is NA
, because the sequence dimension
b
has an unknown shape.
input < layer_input(shape = c(32, 128))
output < input >
layer_einsum_dense("abc,cd>abd",
output_shape = c(NA, 64),
bias_axes = "d")
output # shape(NA, 32, 64)
Applying a dense layer to a sequence using ellipses
This example shows how to instantiate a layer that applies the same dense operation to every element in a sequence, but uses the ellipsis notation instead of specifying the batch and sequence dimensions.
Because we are using ellipsis notation and have specified only one axis, the
output_shape
arg is a single value. When instantiated in this way, the
layer can handle any number of sequence dimensions  including the case
where no sequence dimension exists.
input < layer_input(shape = c(32, 128))
output < input >
layer_einsum_dense("...x,xy>...y",
output_shape = 64,
bias_axes = "y")
output # shape(NA, 32, 64)
Methods

enable_lora( rank, a_initializer = 'he_uniform', b_initializer = 'zeros' )

quantize(mode, type_check = TRUE)
See also
Other core layers: layer_dense()
layer_embedding()
layer_identity()
layer_lambda()
layer_masking()
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_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()