登录
TensorFlow 1 version
|
View source on GitHub
|
Computes sigmoid of x element-wise.
tf.math.sigmoid(
x, name=None
)
Used in the notebooks
| Used in the guide | Used in the tutorials |
|---|---|
Formula for calculating $\mathrm{sigmoid}(x) = y = 1 / (1 + \exp(-x))$.
For $x \in (-\infty, \infty)$, $\mathrm{sigmoid}(x) \in (0, 1)$.
Example Usage:
If a positive number is large, then its sigmoid will approach to 1 since the
formula will be y = <large_num> / (1 + <large_num>)
x = tf.constant([0.0, 1.0, 50.0, 100.0])tf.math.sigmoid(x)<tf.Tensor: shape=(4,), dtype=float32,numpy=array([0.5 , 0.7310586, 1. , 1. ], dtype=float32)>
If a negative number is large, its sigmoid will approach to 0 since the
formula will be y = 1 / (1 + <large_num>)
x = tf.constant([-100.0, -50.0, -1.0, 0.0])tf.math.sigmoid(x)<tf.Tensor: shape=(4,), dtype=float32, numpy=array([0.0000000e+00, 1.9287499e-22, 2.6894143e-01, 0.5],dtype=float32)>
Args | |
|---|---|
x
|
A Tensor with type float16, float32, float64, complex64, or
complex128.
|
name
|
A name for the operation (optional). |
Returns | |
|---|---|
A Tensor with the same type as x.
|
Usage Example:
x = tf.constant([-128.0, 0.0, 128.0], dtype=tf.float32)tf.sigmoid(x)<tf.Tensor: shape=(3,), dtype=float32,numpy=array([0. , 0.5, 1. ], dtype=float32)>
Scipy Compatibility
Equivalent to scipy.special.expit