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Python | tensorflow.math.argmax() method
  • Last Updated : 04 Jun, 2020

TensorFlow is open-source python library designed by Google to develop Machine Learning models  and deep learning  neural networks. argmax() is a method present in tensorflow math module. This method is used to find the maximum value across the axes.

Syntax:
tensorflow.math.argmax(
    input,axes,output_type,name
)

Arguments:
1. input: It is a tensor. Allowed dtypes for this tensor are float32,
          float64, int32, uint8, int16, int8, complex64, int64, qint8,
          quint8, qint32, bfloat16, uint16, complex128, half, uint32, uint64. 
2. axes: It is also a vector. It describes the axes to reduce the tensor.
         Allowed dtype are int32 and int64. Also [-rank(input),rank(input)) is the range allowed.
         axes=0 is used for vector.
3. output_type: It defines the dtype in which returned result should be.
                Allowed values are int32, int64 and the default value is int64.
4. name: It is an optional argument which defines name for the operation.
 
Return:
A tensor of output_type which contains the indices of the maximum value along the axes. 

Example 1:

Python3




# importing the library
import tensorflow as tf
  
# initializing the constat tensor
a = tf.constant([5,10,5.6,7.9,1,50]) # 50 is the maximum value at index 5
  
# getting the maximum value index tensor
b = tf.math.argmax(input = a)
  
# printing the tensor
print('tensor: ',b)
  
# Evaluating the value of tensor
c = tf.keras.backend.eval(b)
  
#printing the value
print('value: ',c)

Output:

tensor:  tf.Tensor(5, shape=(), dtype=int64)
value: 5

Example 2: 

This example uses a tensor of shape(3,3).



Python3




# importing the library
import tensorflow as tf
  
# initializing the constat tensor
a = tf.constant(value = [9,8,7,3,5,4,6,2,1],shape = (3,3))
  
# printing the initialized tensor
print(a)
  
# getting the maximum value indices tensor
b = tf.math.argmax(input = a)
  
# printing the tensor
print('Indices Tensor: ',b)
  
# Evaluating the tesor value
c = tf.keras.backend.eval(b)
  
# printing the value
print('Indices: ',c)

Output:

tf.Tensor(
[[9 8 7]
 [3 5 4]
 [6 2 1]], shape=(3, 3), dtype=int32)
 Indices tensor: tf.Tensor([0 0 0], shape=(3,), dtype=int64)
 Indices: [0 0 0]
 # maximum value along the axes are 9,8,7 at indices 0,0,0 respectively.

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