Function Differences with tf.nn.elu

tf.nn.elu

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

For more information, see tf.nn.elu.

mindspore.ops.elu

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

For more information, see mindspore.ops.elu.

Differences

TensorFlow: Compute the exponential linear value of the input features and return the result as \(\left\{\begin{array}{ll} e^{\text {feature }}-1, & \text { feature }<0 \\ \text { feature } & , \text { feature } \geq 0 \end{array}\right.\)

MindSpore: MindSpore API basically implements the same function as TensorFlow, but the supported data types are different.

Categories	Subcategories	TensorFlow	MindSpore	Differences
Parameters	Parameter 1	features	input_x	Same function, different parameter names
	Parameter 2	name		Not involved
	Parameter 3	-	alpha	MindSpore currently only supports alpha equal to 1.0, consistent with the TensorFlow interface

Code Example 1

Both APIs implement the same function, and the output tensor has the same shape and data type as the input.

# 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.        ]]]]