Function Differences with tf.arg_min

tf.arg_min

tf.arg_min(input, dimension, output_type=tf.dtypes.int64, name=None)

For more information, see tf.arg_min.

mindspore.Tensor.argmin

mindspore.Tensor.argmin(axis=None)

For more information, see mindspore.Tensor.argmin.

Usage

Same function. Two interfaces of MindSpore and TensorFlow decide on which dimension to return the index of the minimum value through the parameters axis and dimension, respectively.

The difference is that in the default state, axis=None of MindSpore returns the global index of the minimum value; TensorFlow dimension returns the minimum index of dimension=0 by default when no value is passed in.

Code Example

import mindspore as ms

a = ms.Tensor([[1, 10, 166.32, 62.3], [1, -5, 2, 200]], ms.float32)
print(a.argmin())
print(a.argmin(axis=0))
print(a.argmin(axis=1))
# output:
# 5
# [0 1 1 0]
# [0 1]

import tensorflow as tf
tf.enable_eager_execution()

b = tf.constant([[1, 10, 166.32, 62.3], [1, -5, 2, 200]])
print(tf.argmin(b).numpy())
print(tf.argmin(b, dimension=0).numpy())
print(tf.argmin(b, dimension=1).numpy())
# output:
# [0 1 1 0]
# [0 1 1 0]
# [0 1]