Layer that computes a dot product between samples in two tensors.
Inherits From: Layer, Module
tf.keras.layers.Dot(
axes, normalize=False, **kwargs
)
Used in the notebooks
E.g. if applied to a list of two tensors a and b of shape
(batch_size, n), the output will be a tensor of shape (batch_size, 1)
where each entry i will be the dot product between
a[i] and b[i].
x = np.arange(10).reshape(1, 5, 2)
print(x)
[[[0 1]
[2 3]
[4 5]
[6 7]
[8 9]]]
y = np.arange(10, 20).reshape(1, 2, 5)
print(y)
[[[10 11 12 13 14]
[15 16 17 18 19]]]
tf.keras.layers.Dot(axes=(1, 2))([x, y])
<tf.Tensor: shape=(1, 2, 2), dtype=int64, numpy=
array([[[260, 360],
[320, 445]]])>
x1 = tf.keras.layers.Dense(8)(np.arange(10).reshape(5, 2))
x2 = tf.keras.layers.Dense(8)(np.arange(10, 20).reshape(5, 2))
dotted = tf.keras.layers.Dot(axes=1)([x1, x2])
dotted.shape
TensorShape([5, 1])
Arguments |
|---|
axes
|
Integer or tuple of integers,
axis or axes along which to take the dot product. If a tuple, 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.
|
**kwargs
|
Standard layer keyword arguments.
|
登录
TensorFlow 1 version
View source on GitHub