Returns the index with the smallest value across dimensions of a tensor.
tf.raw_ops.ArgMin(
input,
dimension,
output_type=tf.dtypes.int64
,
name=None
)
Note that in case of ties the identity of the return value is not guaranteed.
Usage |
import tensorflow as tf
a = [1, 10, 26.9, 2.8, 166.32, 62.3]
b = tf.math.argmin(input = a)
c = tf.keras.backend.eval(b)
# c = 0
# here a[0] = 1 which is the smallest element of a across axis 0
|
Args |
input
|
A Tensor . Must be one of the following types: float32 , float64 , int32 , uint8 , int16 , int8 , int64 , bfloat16 , uint16 , half , uint32 , uint64 , qint8 , quint8 , qint32 , qint16 , quint16 , bool .
|
dimension
|
A Tensor . Must be one of the following types: int32 , int64 .
int32 or int64, must be in the range [-rank(input), rank(input)) .
Describes which dimension of the input Tensor to reduce across. For vectors,
use dimension = 0.
|
output_type
|
An optional tf.DType from: tf.int32, tf.int64 . Defaults to tf.int64 .
|
name
|
A name for the operation (optional).
|
Returns |
A Tensor of type output_type .
|