Dot
classkeras.layers.Dot(axes, normalize=False, **kwargs)
Computes element-wise dot product of two tensors.
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=(1, 2)
, the dot product of x
, and y
will result in a tensor with shape (2, 5, 10)
Example
>>> x = np.arange(10).reshape(1, 5, 2)
>>> y = np.arange(10, 20).reshape(1, 2, 5)
>>> keras.layers.Dot(axes=(1, 2))([x, y])
Usage in a Keras model:
>>> x1 = keras.layers.Dense(8)(np.arange(10).reshape(5, 2))
>>> x2 = keras.layers.Dense(8)(np.arange(10, 20).reshape(5, 2))
>>> y = keras.layers.Dot(axes=1)([x1, x2])
Arguments
True
, then
the output of the dot product is the cosine proximity
between the two samples.Returns
A tensor, the dot product of the samples from the inputs.