tf.math.log_sigmoid
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Computes log sigmoid of x element-wise.
tf.math.log_sigmoid(
x, name=None
)
Specifically, y = log(1 / (1 + exp(-x))). For numerical stability,
we use y = -tf.nn.softplus(-x).
Args |
|---|
x
|
A Tensor with type float32 or float64.
|
name
|
A name for the operation (optional).
|
Returns |
|---|
A Tensor with the same type as x.
|
Usage Example:
If a positive number is large, then its log_sigmoid will approach to 0 since
the formula will be y = log( <large_num> / (1 + <large_num>) ) which
approximates to log (1) which is 0.
x = tf.constant([0.0, 1.0, 50.0, 100.0])
tf.math.log_sigmoid(x)
<tf.Tensor: shape=(4,), dtype=float32, numpy=
array([-6.9314718e-01, -3.1326169e-01, -1.9287499e-22, -0.0000000e+00],
dtype=float32)>
If a negative number is large, its log_sigmoid will approach to the number
itself since the formula will be y = log( 1 / (1 + <large_num>) ) which is
log (1) - log ( (1 + <large_num>) ) which approximates to - <large_num>
that is the number itself.
x = tf.constant([-100.0, -50.0, -1.0, 0.0])
tf.math.log_sigmoid(x)
<tf.Tensor: shape=(4,), dtype=float32, numpy=
array([-100. , -50. , -1.3132616, -0.6931472],
dtype=float32)>
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Last updated 2024-04-26 UTC.
[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Missing the information I need","missingTheInformationINeed","thumb-down"],["Too complicated / too many steps","tooComplicatedTooManySteps","thumb-down"],["Out of date","outOfDate","thumb-down"],["Samples / code issue","samplesCodeIssue","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2024-04-26 UTC."],[],[],null,["# tf.math.log_sigmoid\n\n\u003cbr /\u003e\n\n|------------------------------------------------------------------------------------------------------------------------------|\n| [View source on GitHub](https://github.com/tensorflow/tensorflow/blob/v2.16.1/tensorflow/python/ops/math_ops.py#L4122-L4164) |\n\nComputes log sigmoid of `x` element-wise.\n\n#### View aliases\n\n\n**Compat aliases for migration**\n\nSee\n[Migration guide](https://www.tensorflow.org/guide/migrate) for\nmore details.\n\n[`tf.compat.v1.log_sigmoid`](https://www.tensorflow.org/api_docs/python/tf/math/log_sigmoid)\n\n\u003cbr /\u003e\n\n tf.math.log_sigmoid(\n x, name=None\n )\n\nSpecifically, `y = log(1 / (1 + exp(-x)))`. For numerical stability,\nwe use `y = -tf.nn.softplus(-x)`.\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Args ---- ||\n|--------|--------------------------------------------|\n| `x` | A Tensor with type `float32` or `float64`. |\n| `name` | A name for the operation (optional). |\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 type as `x`. ||\n\n\u003cbr /\u003e\n\n#### Usage Example:\n\nIf a positive number is large, then its log_sigmoid will approach to 0 since\nthe formula will be `y = log( \u003clarge_num\u003e / (1 + \u003clarge_num\u003e) )` which\napproximates to `log (1)` which is 0. \n\n x = tf.constant([0.0, 1.0, 50.0, 100.0])\n tf.math.log_sigmoid(x)\n \u003ctf.Tensor: shape=(4,), dtype=float32, numpy=\n array([-6.9314718e-01, -3.1326169e-01, -1.9287499e-22, -0.0000000e+00],\n dtype=float32)\u003e\n\nIf a negative number is large, its log_sigmoid will approach to the number\nitself since the formula will be `y = log( 1 / (1 + \u003clarge_num\u003e) )` which is\n`log (1) - log ( (1 + \u003clarge_num\u003e) )` which approximates to `- \u003clarge_num\u003e`\nthat is the number itself. \n\n x = tf.constant([-100.0, -50.0, -1.0, 0.0])\n tf.math.log_sigmoid(x)\n \u003ctf.Tensor: shape=(4,), dtype=float32, numpy=\n array([-100. , -50. , -1.3132616, -0.6931472],\n dtype=float32)\u003e"]]
View source on GitHub