# Python | tensorflow.math.argmin() method

• Last Updated : 20 Oct, 2021

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

```Syntax:
tensorflow.math.argmin(
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 minimum value along the axes. ```

Example 1:

## Python3

 `# importing the library``import` `tensorflow as tf` `# initializing the constant tensor``a ``=` `tf.constant([``5``,``10``,``5.6``,``7.9``,``1``,``50``]) ``# 1 is the minimum value at index 4` `# getting the minimum value index tensor``b ``=` `tf.math.argmin(``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(4, shape=(), dtype=int64)
value: 4```

Example 2:

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

## Python3

 `# importing the library``import` `tensorflow as tf` `# initializing the constant 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 minimum value indices tensor``b ``=` `tf.math.argmin(``input` `=` `a)` `# printing the tensor``print``(``'Indices Tensor: '``,b)` `# Evaluating the tensor 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([1 2 2], shape=(3,), dtype=int64)
Indices: [1 2 2]```

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