Layer that computes a dot product between samples in two tensors.

layer_dot(inputs, axes, normalize = FALSE, batch_size = NULL,
  dtype = NULL, name = NULL, trainable = NULL, weights = NULL)

Arguments

inputs	A list of input tensors (at least 2).
axes	Integer or list of integers, axis or axes along which to take the dot product.
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. **kwargs: Standard layer keyword arguments.
batch_size	Fixed batch size for layer
dtype	The data type expected by the input, as a string (`float32`, `float64`, `int32`...)
name	An optional name string for the layer. Should be unique in a model (do not reuse the same name twice). It will be autogenerated if it isn't provided.
trainable	Whether the layer weights will be updated during training.
weights	Initial weights for layer.

A tensor, the dot product of the samples from the inputs.