It takes a list of inputs of size 2, and the axes corresponding to each input along with the dot product is to be performed.
Let's say x and y are the two input tensors with shapes
(2, 3, 5) and (2, 10, 3). The batch dimension should be
of same size for both the inputs, and axes should correspond
to the dimensions that have the same size in the corresponding
inputs. e.g. with axes = c(1, 2), the dot product of x, and y
will result in a tensor with shape (2, 5, 10)
Arguments
- inputs
layers to combine
- ...
Standard layer keyword arguments.
- axes
Integer or list of integers, axis or axes along which to take the dot product. If a list, should be two integers corresponding to the desired axis from the first input and the desired axis from the second input, respectively. Note that the size of the two selected axes must match.
- normalize
Whether to L2-normalize samples along the dot product axis before taking the dot product. If set to
TRUE, then the output of the dot product is the cosine proximity between the two samples.
Examples
x <- op_reshape(0:9, c(1, 5, 2))
y <- op_reshape(10:19, c(1, 2, 5))
layer_dot(x, y, axes=c(2, 3))Usage in a Keras model:
x1 <- op_reshape(0:9, c(5, 2)) |> layer_dense(8)
x2 <- op_reshape(10:19, c(5, 2)) |> layer_dense(8)
shape(x1)shape(x2)See also
Other merging layers: layer_add() layer_average() layer_concatenate() 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_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_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()