tf.compat.v1.math.log_sigmoid | TensorFlow v2.16.1 (original) (raw)
tf.compat.v1.math.log_sigmoid
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Computes log sigmoid of x element-wise.
tf.compat.v1.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 islog (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)>