Tensorflow2.0学习教程Class2.2_神经网络复杂度、学习率
详细学习视频链接：
北京大学
在鸢尾花分类中：神经网络为4输入层，三输出层，无隐藏层
所以b=3
总参数就为4x3+3=15
import tensorflow as tf
w = tf.Variable(tf.constant(5, dtype=tf.float32))
#lr = 0.2
lr=0.001
epoch = 40
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40次迭代。
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" % (epoch, w.numpy(), loss))
# lr初始值：0.2 请自改学习率 0.001 0.999 看收敛过程
# 最终目的：找到 loss 最小 即 w = -1 的最优参数w
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)#指数衰减学习率lr
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))
result：
After 0 epoch,w is 2.600000,loss is 36.000000,lr is 0.200000
After 1 epoch,w is 1.174400,loss is 12.959999,lr is 0.198000
After 2 epoch,w is 0.321948,loss is 4.728015,lr is 0.196020
After 3 epoch,w is -0.191126,loss is 1.747547,lr is 0.194060
After 4 epoch,w is -0.501926,loss is 0.654277,lr is 0.192119
After 5 epoch,w is -0.691392,loss is 0.248077,lr is 0.190198
After 6 epoch,w is -0.807611,loss is 0.095239,lr is 0.188296
After 7 epoch,w is -0.879339,loss is 0.037014,lr is 0.186413
After 8 epoch,w is -0.923874,loss is 0.014559,lr is 0.184549
After 9 epoch,w is -0.951691,loss is 0.005795,lr is 0.182703
After 10 epoch,w is -0.969167,loss is 0.002334,lr is 0.180876
After 11 epoch,w is -0.980209,loss is 0.000951,lr is 0.179068
After 12 epoch,w is -0.987226,loss is 0.000392,lr is 0.177277
After 13 epoch,w is -0.991710,loss is 0.000163,lr is 0.175504
After 14 epoch,w is -0.994591,loss is 0.000069,lr is 0.173749
After 15 epoch,w is -0.996452,loss is 0.000029,lr is 0.172012
After 16 epoch,w is -0.997660,loss is 0.000013,lr is 0.170292
After 17 epoch,w is -0.998449,loss is 0.000005,lr is 0.168589
After 18 epoch,w is -0.998967,loss is 0.000002,lr is 0.166903
After 19 epoch,w is -0.999308,loss is 0.000001,lr is 0.165234
After 20 epoch,w is -0.999535,loss is 0.000000,lr is 0.163581
After 21 epoch,w is -0.999685,loss is 0.000000,lr is 0.161946
After 22 epoch,w is -0.999786,loss is 0.000000,lr is 0.160326
After 23 epoch,w is -0.999854,loss is 0.000000,lr is 0.158723
After 24 epoch,w is -0.999900,loss is 0.000000,lr is 0.157136
After 25 epoch,w is -0.999931,loss is 0.000000,lr is 0.155564
After 26 epoch,w is -0.999952,loss is 0.000000,lr is 0.154009
After 27 epoch,w is -0.999967,loss is 0.000000,lr is 0.152469
After 28 epoch,w is -0.999977,loss is 0.000000,lr is 0.150944
After 29 epoch,w is -0.999984,loss is 0.000000,lr is 0.149434
After 30 epoch,w is -0.999989,loss is 0.000000,lr is 0.147940
After 31 epoch,w is -0.999992,loss is 0.000000,lr is 0.146461
After 32 epoch,w is -0.999994,loss is 0.000000,lr is 0.144996
After 33 epoch,w is -0.999996,loss is 0.000000,lr is 0.143546
After 34 epoch,w is -0.999997,loss is 0.000000,lr is 0.142111
After 35 epoch,w is -0.999998,loss is 0.000000,lr is 0.140690
After 36 epoch,w is -0.999999,loss is 0.000000,lr is 0.139283
After 37 epoch,w is -0.999999,loss is 0.000000,lr is 0.137890
After 38 epoch,w is -0.999999,loss is 0.000000,lr is 0.136511
After 39 epoch,w is -0.999999,loss is 0.000000,lr is 0.135146