Function Differences with tf.nn.softmax

tf.nn.softmax

tf.nn.softmax(logits, axis=None, name=None) -> Tensor

For more information, see tf.nn.softmax.

mindspore.nn.Softmax

class mindspore.nn.Softmax(axis=-1)(x) -> Tensor

For more information, see mindspore.nn.Softmax.

Differences

TensorFlow: a generalization of the binary classification function on multiclassification, which aims to present the results of multiclassification in the form of probabilities.

MindSpore: MindSpore API implements the same function as TensorFlow, and only the parameter names are different.

Categories	Subcategories	TensorFlow	MindSpore	Differences
Parameters	parameter 1	logits	x	Same function, different parameter names
	parameter 2	axis	axis	-
	parameter 3	name	-	Not involved

Code Example

The two APIs achieve the same function and have the same usage.

# TensorFlow
import numpy as np
import tensorflow as tf

x = tf.constant([-1, -2, 0, 2, 1], dtype=tf.float16)
output = tf.nn.softmax(x)
print(output.numpy())
# [0.03168 0.01165 0.0861  0.636   0.2341 ]

# MindSpore
import mindspore
import numpy as np
from mindspore import Tensor

x = Tensor(np.array([-1, -2, 0, 2, 1]), mindspore.float16)
softmax = mindspore.nn.Softmax()
output = softmax(x)
print(output)
# [0.03168 0.01165 0.0861  0.636   0.2341 ]