Function Differences with tf.nn.leaky_relu
tf.nn.leaky_relu
tf.nn.leaky_relu(features, alpha=0.2, name=None) -> Tensor
For more information, see tf.nn.leaky_relu.
mindspore.nn.LeakyReLU
class mindspore.nn.LeakyReLU(alpha=0.2)(x) -> Tensor
For more information, see mindspore.nn.LeakyReLU.
Differences
TensorFlow: Apply the Leaky ReLU activation function, where the parameter alpha
is used to control the slope of the activation function.
MindSpore: MindSpore API basically implements the same function as TensorFlow.
Categories |
Subcategories |
TensorFlow |
MindSpore |
Differences |
---|---|---|---|---|
Parameters |
Parameter 1 |
features |
x |
Same function, different parameter names |
Parameter 2 |
alpha |
alpha |
- |
|
Parameter 3 |
name |
- |
Not involved |
Code Example
The two APIs achieve the same function and have the same usage.
# TensorFlow
import tensorflow as tf
features = tf.constant([[-1.0, 4.0, -8.0], [2.0, -5.0, 9.0]], dtype=tf.float32)
output = tf.nn.leaky_relu(features).numpy()
print(output)
# [[-0.2 4. -1.6]
# [ 2. -1. 9. ]]
# MindSpore
import mindspore
from mindspore import Tensor
import mindspore.nn as nn
x = Tensor([[-1.0, 4.0, -8.0], [2.0, -5.0, 9.0]]).astype('float32')
m = nn.LeakyReLU()
output = m(x)
print(output)
# [[-0.2 4. -1.6]
# [ 2. -1. 9. ]]