tf.keras.layers.SimpleRNN

TensorFlow 1 version

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Softmax activation function.

Inherits From: Layer, Module

View aliases

Compat aliases for migration

See Migration guide for more details.

tf.compat.v1.keras.layers.Softmax

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 TensorFlow Federated for Image Classification Overview Federated Learning for Image Classification

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.

Arguments
`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 as inputs. Defaults to None.

Returns
softmaxed output with the same shape as `inputs`.

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tf.keras / layers / layers.SimpleRNN