以下代码的运行结果是( )。import numpy as np a=np.arange(8).reshape(2,4) s=a.sum(axis=0) print(s)
A: [6 22]
B: 28
C: 36
D: [4 6 8 10]
A: [6 22]
B: 28
C: 36
D: [4 6 8 10]
D
举一反三
- 如下代码的输出是( )import numpy as npa = np.arange(6).reshape(3, 2)b = a.sum(axis=0)c = b.sum(axis=0)
- 如下代码的输出结果是( )import numpy as npa = np.arange(12).reshape(3, 4)print(np.sum(a[[0, 2], 2:]))
- 如下代码的输出结果是( )import numpy as npa = np.arange(12).reshape(3, 4)print(np.sum(a[1:, 2:]))
- 下面程序段的执行结果为_______。 import numpy as np t = np.arange(120).reshape(3,4,5,2) t0=np.sum(t,axis=1) print(t0.shape)
- 以下代码的输出结果是()for i in range(0,10,2): print(i,end="") A: 0 2 4 6 8 B: 2 4 6 8 C: 0 2 4 6 8 10 D: 2 4 6 8 10
内容
- 0
set1 = {x for x in range(10) if x%2==0} print(set1) 以上代码的运行结果为? A: {0, 2, 4, 6} B: {2, 4, 6, 8} C: {0, 2, 4, 6, 8} D: {4, 6, 8}
- 1
import numpy as np np.arange(0, 1, 0.1)的结果是?
- 2
以下代码()能够创建一个值范围在1到10的数组 A: np.arange(1,11) B: import numpy as np np.arrange(1,11) C: import numpy as np np.arrange(1,10) D: import numpy as np np(1,11)
- 3
ndarray对象实例a,代码如下:import numpy as np a = np.array([[0, 1, 2, 3, 4], [9, 8, 7, 6, 5]])a.itemsize的执行结果是什么? A: 32 B: 2 C: 4 D: 10
- 4
执行下列程序段后,得到的结果是 。import tensorflow as tfimport numpy as npa = tf.constant(np.arange(48).reshape(3,2,4,2))b =tf.random.shuffle(a)c = tf.constant(np.arange(8).reshape(2,4))d =[email protected](d.sh A: (3, 2, 2, 2) B: (3, 2, 4, 4) C: (3, 4, 4, 2) D: (3, 4, 4)