比较与tf.nn.elu的功能差异

查看源文件

tf.nn.elu

tf.nn.elu(features, name=None) -> Tensor

更多内容详见tf.nn.elu

mindspore.ops.elu

mindspore.ops.elu(input_x, alpha=1.0) -> Tensor

更多内容详见mindspore.ops.elu

差异对比

TensorFlow:计算输入features的指数线性值,返回结果为 \(\left\{\begin{array}{ll} e^{\text {feature }}-1, & \text { feature }<0 \\ \text { feature } & , \text { feature } \geq 0 \end{array}\right.\)

MindSpore:MindSpore此API实现功能与TensorFlow基本一致,不过支持数据类型有所差异。

分类

子类

TensorFlow

MindSpore

差异

参数

参数1

features

input_x

功能一致,参数名不同

参数2

name

不涉及

参数3

-

alpha

MindSpore目前只支持alpha等于1.0,与TensorFlow接口一致

代码示例1

两API实现相同功能,输出tensor的shape和数据类型与输入相同。

# TensorFlow
import tensorflow as tf
import numpy as np

x_ = np.array([[np.arange(-6,0).reshape(2, 3),np.arange(0,6).reshape(2, 3)]])
x = tf.convert_to_tensor(x_, dtype=tf.float32)
output = tf.nn.elu(x).numpy()
print(output)
# [[[[-0.9975212  -0.99326205 -0.9816844 ]
#    [-0.95021296 -0.86466473 -0.6321205 ]]
#
#   [[ 0.          1.          2.        ]
#   [ 3.          4.          5.        ]]]]

# MindSpore
import mindspore as ms
from mindspore import ops, nn
import numpy as np

x_ = np.array([[np.arange(-6,0).reshape(2, 3),np.arange(0,6).reshape(2, 3)]])
x = ms.Tensor(x_, ms.float32)
output = ops.elu(x)
print(output)
# [[[[-0.9975212  -0.99326205 -0.9816844 ]
#   [-0.95021296 -0.86466473 -0.6321205 ]]
#
#  [[ 0.          1.          2.        ]
#   [ 3.          4.          5.        ]]]]