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 ]