tf.keras.layers.Softmax

Softmax activation layer.

Inherits From: Layer, Operation

tf.keras.layers.Softmax(
    axis=-1, **kwargs
)

Used in the notebooks

Used in the tutorials
Basic classification: Classify images of clothing TensorFlow 2 quickstart for beginners Building Your Own Federated Learning Algorithm Composing Learning Algorithms Federated Learning for Image Classification

Formula:

exp_x = exp(x - max(x))
f(x) = exp_x / sum(exp_x)

Example:

softmax_layer = keras.layers.Softmax()
input = np.array([1.0, 2.0, 1.0])
result = softmax_layer(input)
result
[0.21194157, 0.5761169, 0.21194157]

Args
axis	Integer, or list of Integers, axis along which the softmax normalization is applied.
**kwargs	Base layer keyword arguments, such as name and dtype.

Call arguments
inputs	The inputs (logits) to the softmax layer.
mask	A boolean mask that is broadcastable to inputs. The mask specifies 1 to keep and 0 to mask. Each dimension of the mask must either be 1 or match the corresponding dimension of inputs; it must not be larger. Defaults to None.

Returns
Softmaxed output with the same shape as inputs.

Attributes
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

View source

@classmethod
from_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.

Note: If you override this method, you might receive a serialized dtype config, which is a dict. You can deserialize it as follows:

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.