tf.keras.layers.Dot

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

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Compat aliases for migration

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tf.compat.v1.keras.layers.Dot

tf.keras.layers.Dot(
    axes, normalize=False, **kwargs
)

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.