TensorFlow 1 version | View source on GitHub |
Softmax activation function.
tf.keras.layers.Softmax(
axis=-1, **kwargs
)
Example without mask:
inp = np.asarray([1., 2., 1.])
layer = tf.keras.layers.Softmax()
layer(inp).numpy()
array([0.21194157, 0.5761169 , 0.21194157], dtype=float32)
mask = np.asarray([True, False, True], dtype=bool)
layer(inp, mask).numpy()
array([0.5, 0. , 0.5], dtype=float32)
Input shape:
Arbitrary. Use the keyword argument input_shape
(tuple of integers, does not include the samples axis)
when using this layer as the first layer in a model.
Output shape:
Same shape as the input.
Args | |
---|---|
axis
|
Integer, or list of Integers, axis along which the softmax normalization is applied. |
Call arguments:
inputs
: The inputs, or logits to the softmax layer.mask
: A boolean mask of the same shape asinputs
. Defaults toNone
. The mask specifies 1 to keep and 0 to mask.
Returns | |
---|---|
softmaxed output with the same shape as inputs .
|