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|
Computes element-wise dot product of two tensors.
Inherits From: Layer, Operation
tf.keras.layers.Dot(
axes, normalize=False, **kwargs
)
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])
Returns | |
|---|---|
| A tensor, the dot product of the samples from the inputs. |
Methods
from_config
@classmethodfrom_config( config )
Creates an operation from its config.
This method is the reverse of get_config, capable of instantiating the
same operation from the config dictionary.
if "dtype" in config and isinstance(config["dtype"], dict):
policy = dtype_policies.deserialize(config["dtype"])
| Args | |
|---|---|
config
|
A Python dictionary, typically the output of get_config.
|
| Returns | |
|---|---|
| An operation instance. |
View source on GitHub