Softmax layer

• Original Link : https://keras.io/api/layers/activation_layers/softmax/
• Last Checked at : 2024-11-25

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Softmax class

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

Softmax activation layer.

Formula:

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

Example

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

Arguments

• 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 of the same shape as inputs. The mask specifies 1 to keep and 0 to mask. Defaults to None.

Returns

Softmaxed output with the same shape as inputs.