import tensorflow as tfw = tf.variable(tf.constant(
5, dtype=tf.float32)
)epoch =
40lr_base =
0.2# 最初學習率
lr_decay =
0.99
# 學習率衰減率
lr_step =
1# 餵入多少輪batch_size後，更新一次學習率
for epoch in
range
(epoch)
:# for epoch 定義頂層迴圈，表示對資料集迴圈epoch次，此例資料集資料僅有1個w,初始化時候constant賦值為5，迴圈100次迭代。
lr = lr_base * lr_decay **
(epoch / lr_step)
with tf.gradienttape(
)as tape:
# with結構到grads框起了梯度的計算過程。
loss = tf.square(w +1)
grads = tape.gradient(loss, w)
# .gradient函式告知誰對誰求導
w.assign_sub(lr * grads)
# .assign_sub 對變數做自減 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)
)
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
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