TensorFlow2.1入门学习笔记(6)——激活函数

1
2
3
4
5
6

import tensorflow as tf
a = tf.constant([1, 2, 3, 1, 1])
b = tf.constant([0, 1, 3, 4, 5])
# 若a>b，返回a对应位置的元素，否则返回b对应位置的元素
c = tf.where(tf.greater(a, b), a, b)  
print("c：", c)		# c： tf.Tensor([1 2 3 4 5], shape=(5,), dtype=int32)

1
2
3
4
5
6

import numpy as np
rdm = np.random.RandomState
a = rdm.rand()		# 返回一个随机标量
b = rdm.rand(2,3)	# 返回一个维度为2行3列的随机数矩阵
print("a:",a)		# a: 0.417022004702574
print("b:",b)		# b: [[7.20324493e-01 1.14374817e-04 3.02332573e-01][1.46755891e-01 9.23385948e-02 1.86260211e-01]]

1
2
3
4
5

import numpy as np
a = np.array([1,2,3])
b = np.array([4,5,6])
c = np.vstack(a,b)
print("c:",c)		# c:[[1 2 3][4 5 6]]

1
2
3
4
5
6

import numpy as np
x, y = np.mgrid[1:3:1,2:4:0.5]
grid = np.c_[x.ravel(),y.ravel()]
print("x:",x)
print("y:",y)
print("grid:\n",grid)

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17

import tensorflow as tf

w = tf.Variable(tf.constant(5, dtype=tf.float32))

epoch = 40
LR_BASE = 0.2  		# 最初学习率
LR_DECAY = 0.99  	# 学习率衰减率
LR_STEP = 1			# 喂入多少轮BATCH_SIZE后，更新一次学习率

for epoch in range(epoch):  # for epoch 定义顶层循环，表示对数据集循环epoch次，此例数据集数据仅有1个w,初始化时候constant赋值为5，循环100次迭代。
    lr = LR_BASE * LR_DECAY ** (epoch / LR_STEP)
    with tf.GradientTape() as tape:  	# with结构到grads框起了梯度的计算过程。
        loss = tf.square(w + 1)
    grads = tape.gradient(loss, w)  	# .gradient函数告知谁对谁求导

    w.assign_sub(lr * grads) 			# .assign_sub 对变量做自减 即：w -= lr*grads 即 w = w - lr*grads
    print("After %s epoch,w is %f,loss is %f,lr is %f" % (epoch, w.numpy(), loss, lr))