tf.compat.v1.arg_min

Returns the index with the smallest value across dimensions of a tensor.

tf.compat.v1.arg_min(
    input: _atypes.TensorFuzzingAnnotation[TV_ArgMin_T],
    dimension: _atypes.TensorFuzzingAnnotation[TV_ArgMin_Tidx],
    output_type: TV_ArgMin_output_type = tf.dtypes.int64,
    name=None
) -> _atypes.TensorFuzzingAnnotation[TV_ArgMin_output_type]

Note that in case of ties the identity of the return value is not guaranteed.

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

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).

A Tensor of type output_type.