tf.keras.ops.softmax
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Softmax activation function.
tf.keras.ops.softmax(
x, axis=-1
)
The elements of the output vector lie within the range (0, 1), and their
total sum is exactly 1 (excluding the floating point rounding error).
Each vector is processed independently. The axis argument specifies the
axis along which the function is applied within the input.
It is defined as:
f(x) = exp(x) / sum(exp(x))
Args |
|---|
x
|
Input tensor.
|
axis
|
Integer, axis along which the softmax is applied.
|
Returns |
|---|
A tensor with the same shape as x.
|
Example:
x = np.array([-1., 0., 1.])
x_softmax = keras.ops.softmax(x)
print(x_softmax)
array([0.09003057, 0.24472847, 0.66524096], shape=(3,), dtype=float64)
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Last updated 2024-06-07 UTC.
[null,null,["Last updated 2024-06-07 UTC."],[],[],null,["# tf.keras.ops.softmax\n\n\u003cbr /\u003e\n\n|--------------------------------------------------------------------------------------------------------|\n| [View source on GitHub](https://github.com/keras-team/keras/tree/v3.3.3/keras/src/ops/nn.py#L513-L574) |\n\nSoftmax activation function.\n\n#### View aliases\n\n\n**Main aliases**\n\n[`tf.keras.ops.nn.softmax`](https://www.tensorflow.org/api_docs/python/tf/keras/ops/softmax)\n\n\u003cbr /\u003e\n\n tf.keras.ops.softmax(\n x, axis=-1\n )\n\nThe elements of the output vector lie within the range `(0, 1)`, and their\ntotal sum is exactly 1 (excluding the floating point rounding error).\n\nEach vector is processed independently. The `axis` argument specifies the\naxis along which the function is applied within the input.\n\n#### It is defined as:\n\n`f(x) = exp(x) / sum(exp(x))`\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Args ---- ||\n|--------|---------------------------------------------------|\n| `x` | Input tensor. |\n| `axis` | Integer, axis along which the softmax is applied. |\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Returns ------- ||\n|---|---|\n| A tensor with the same shape as `x`. ||\n\n\u003cbr /\u003e\n\n#### Example:\n\n x = np.array([-1., 0., 1.])\n x_softmax = keras.ops.softmax(x)\n print(x_softmax)\n array([0.09003057, 0.24472847, 0.66524096], shape=(3,), dtype=float64)"]]
View source on GitHub