tf.keras.layers.Dot

Computes element-wise dot product of two tensors.

Inherits From: Layer, Operation

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

Used in the notebooks

Used in the tutorials

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])

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.

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

input Retrieves the input tensor(s) of a symbolic operation.

Only returns the tensor(s) corresponding to the first time the operation was called.

output Retrieves the output tensor(s) of a layer.

Only returns the tensor(s) corresponding to the first time the operation was called.

Methods

from_config

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@classmethod
from_config(
    config
)

Creates a layer from its config.

This method is the reverse of get_config, capable of instantiating the same layer from the config dictionary. It does not handle layer connectivity (handled by Network), nor weights (handled by set_weights).

Args
config A Python dictionary, typically the output of get_config.

Returns
A layer instance.

symbolic_call

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symbolic_call(
    *args, **kwargs
)