`batch_dot`

is used to compute dot product of `x`

and `y`

when `x`

and `y`

are data in batch, i.e. in a shape of `(batch_size)`

. `batch_dot`

results in
a tensor or variable with less dimensions than the input. If the number of
dimensions is reduced to 1, we use `expand_dims`

to make sure that ndim is
at least 2.

`k_batch_dot(x, y, axes)`

- x
Keras tensor or variable with 2 more more axes.

- y
Keras tensor or variable with 2 or more axes

- axes
List of (or single) integer with target dimensions (axis indexes are 1-based). The lengths of

`axes[[1]]`

and`axes[[2]]`

should be the same.

A tensor with shape equal to the concatenation of `x`

's shape (less
the dimension that was summed over) and `y`

's shape (less the batch
dimension and the dimension that was summed over). If the final rank is 1,
we reshape it to `(batch_size, 1)`

.

This function is part of a set of Keras backend functions that enable lower level access to the core operations of the backend tensor engine (e.g. TensorFlow, CNTK, Theano, etc.).

You can see a list of all available backend functions here: https://keras.rstudio.com/articles/backend.html#backend-functions.