tf.math.log_sigmoid

TensorFlow 1 version

Returns the min of x and y (i.e. x < y ? x : y) element-wise.

View aliases

Main aliases

tf.minimum

Compat aliases for migration

See Migration guide for more details.

tf.compat.v1.math.minimum, tf.compat.v1.minimum

tf.math.minimum(
    x, y, name=None
)

Used in the notebooks

Used in the tutorials
Integrated gradients Transformer model for language understanding

Both inputs are number-type tensors (except complex). minimum expects that both tensors have the same dtype.

Examples:

x = tf.constant([0., 0., 0., 0.])
y = tf.constant([-5., -2., 0., 3.])
tf.math.minimum(x, y)
<tf.Tensor: shape=(4,), dtype=float32, numpy=array([-5., -2., 0., 0.], dtype=float32)>

Note that minimum supports broadcast semantics.

x = tf.constant([-5., 0., 0., 0.])
y = tf.constant([-3.])
tf.math.minimum(x, y)
<tf.Tensor: shape=(4,), dtype=float32, numpy=array([-5., -3., -3., -3.], dtype=float32)>

If inputs are not tensors, they will be converted to tensors. See tf.convert_to_tensor.

x = tf.constant([-3.], dtype=tf.float32)
tf.math.minimum([-5], x)
<tf.Tensor: shape=(1,), dtype=float32, numpy=array([-5.], dtype=float32)>

Args
`x`	A `Tensor`. Must be one of the following types: `bfloat16`, `half`, `float32`, `float64`, `uint8`, `int16`, `int32`, `int64`.
`y`	A `Tensor`. Must have the same type as `x`.
`name`	A name for the operation (optional).

Returns
A `Tensor`. Has the same type as `x`.

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tf.math / log_sigmoid

View aliases

Used in the notebooks

Examples:

Args

Returns