tensorflow2.0学习笔记第二章第一节

2.1预备知识

# 条件判断tf.where(条件语句，真返回A,假返回B)
import tensorflow as tf
a = tf.constant([1,2,3,1,1])
b = tf.constant([0,1,2,4,5])
c = tf.where(tf.greater(a,b),a,b)  # 返回张量中比较大的元素
print(c)

tf.Tensor([1 2 3 4 5], shape=(5,), dtype=int32)

# 返回[0,1)之间的随机数
import numpy as np
rdm = np.random.RandomState(seed=1) # seed=常数，每次生成的随机数相同
a = rdm.rand() # 返回一个随即标量
b = rdm.rand(2,3) # 返回维度为2行3列随机数矩阵

print("a:",a)
print("b:",b)

a: 0.417022004702574
b: [[7.20324493e-01 1.14374817e-04 3.02332573e-01]
 [1.46755891e-01 9.23385948e-02 1.86260211e-01]]

# np.stack((数组一,数组二))将两个数组按垂直方向叠加
a = np.array([1,2,3])
b = np.array([4,5,6])
c = np.vstack((a,b))
print(c)

[[1 2 3]
 [4 5 6]]

# np.mgrid[起始值：结束值：步长，起始值：结束值：步长]输出一个两行四列的张量
# x.ravel() 将x展平为一维数组
# np.c_[数组1，数组2，。。。] 是返回的间隔数值点配对
x,y = np.mgrid[-3:3:1,-3:3:1]
grid = np.c_[x.ravel(),y.ravel()]
print("x:",x)
print("y:",y)

print("grid:",grid)

x: [[-3 -3 -3 -3 -3 -3]
 [-2 -2 -2 -2 -2 -2]
 [-1 -1 -1 -1 -1 -1]
 [ 0  0  0  0  0  0]
 [ 1  1  1  1  1  1]
 [ 2  2  2  2  2  2]]
y: [[-3 -2 -1  0  1  2]
 [-3 -2 -1  0  1  2]
 [-3 -2 -1  0  1  2]
 [-3 -2 -1  0  1  2]
 [-3 -2 -1  0  1  2]
 [-3 -2 -1  0  1  2]]
grid: [[-3 -3]
 [-3 -2]
 [-3 -1]
 [-3  0]
 [-3  1]
 [-3  2]
 [-2 -3]
 [-2 -2]
 [-2 -1]
 [-2  0]
 [-2  1]
 [-2  2]
 [-1 -3]
 [-1 -2]
 [-1 -1]
 [-1  0]
 [-1  1]
 [-1  2]
 [ 0 -3]
 [ 0 -2]
 [ 0 -1]
 [ 0  0]
 [ 0  1]
 [ 0  2]
 [ 1 -3]
 [ 1 -2]
 [ 1 -1]
 [ 1  0]
 [ 1  1]
 [ 1  2]
 [ 2 -3]
 [ 2 -2]
 [ 2 -1]
 [ 2  0]
 [ 2  1]
 [ 2  2]]