Python – tensorflow.math.erfc()
TensorFlow 是由 Google 设计的开源Python库,用于开发机器学习模型和深度学习神经网络。
erfc()用于计算元素方式的互补高斯误差函数。
Syntax: tensorflow.math.erfc( x, name)
Parameters:
- x: It’s the input tensor. Allowed dtypes are bfloat16, half, float32, float64.
- name(optional): It defines the name for the operation.
Returns: It returns a tensor of same dtype as x.
示例 1:
Python3
# importing the library
import tensorflow as tf
# Initializing the input tensor
a = tf.constant([1, 2, 3, 4, 5], dtype = tf.float64)
# Printing the input tensor
print('Input: ', a)
# Calculating complementary Gauss error
res = tf.math.erfc(x = a)
# Printing the result
print('Result: ', res)
Python3
# importing the library
import tensorflow as tf
import matplotlib.pyplot as plt
# Initializing the input tensor
a = tf.constant([1, 2, 3, 4, 5], dtype = tf.float64)
# Calculating complementary Gauss error
res = tf.math.erfc(x = a)
# Plotting the graph
plt.plot(a, res, color ='green')
plt.title('tensorflow.math.erfc')
plt.xlabel('Input')
plt.ylabel('Result')
plt.show()
输出:
Input: tf.Tensor([1. 2. 3. 4. 5.], shape=(5, ), dtype=float64)
Result: tf.Tensor(
[1.57299207e-01 4.67773498e-03 2.20904970e-05 1.54172579e-08
1.53745979e-12], shape=(5, ), dtype=float64)
示例 2:可视化
Python3
# importing the library
import tensorflow as tf
import matplotlib.pyplot as plt
# Initializing the input tensor
a = tf.constant([1, 2, 3, 4, 5], dtype = tf.float64)
# Calculating complementary Gauss error
res = tf.math.erfc(x = a)
# Plotting the graph
plt.plot(a, res, color ='green')
plt.title('tensorflow.math.erfc')
plt.xlabel('Input')
plt.ylabel('Result')
plt.show()
输出: