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# tf.compat.v2.argmax

Returns the index with the largest value across axes of a tensor.

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

#### For example:

``````A=tf.constant([2,20,30,3,6]) # Constant 1-D Tensor
tf.math.argmax(A) # output 2 as index 2 (A[2]) is maximum in tensor A
B=tf.constant([[2,20,30,3,6],[3,11,16,1,8],[14,45,23,5,27]])
tf.math.argmax(B,0) # [2, 2, 0, 2, 2]
tf.math.argmax(B,1) # [2, 2, 1]
``````

Args: input: A `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `uint8`, `int16`, `int8`, `complex64`, `int64`, `qint8`, `quint8`, `qint32`, `bfloat16`, `uint16`, `complex128`, `half`, `uint32`, `uint64`. axis: 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 axis of the input Tensor to reduce across. For vectors, use axis = 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`.

#### Usage:

``````import tensorflow as tf
a = [1, 10, 26.9, 2.8, 166.32, 62.3]
b = tf.math.argmax(input = a)
c = tf.keras.backend.eval(b)
# c = 4
# here a[4] = 166.32 which is the largest element of a across axis 0
``````
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