# encoding: utf-8
# 案例一
import tensorflow as tf
import numpy as np
seed =
23455
cost =
1#成本
profit =
99#利潤
rdm = np.random.randomstate(seed)
x = rdm.rand(32,
2)y_ =
[[x1 + x2 +
(rdm.rand()/
10.0
-0.05)]
for(x1, x2)
in x]
# 生成雜訊[0,1)/10=[0,0.1); [0,0.1)-0.05=[-0.05,0.05)
x = tf.cast(x, dtype=tf.float32)
w1 = tf.variable(tf.random.normal([2
,1], stddev=
1, seed=1)
)epoch =
10000
lr =
0.002
for epoch in
range
(epoch)
:with tf.gradienttape(
)as tape:
y = tf.matmul(x, w1)
loss = tf.reduce_sum(tf.where(tf.greater(y, y_)
,(y - y_)
* cost,
(y_ - y)
* profit)
) grads = tape.gradient(loss, w1)
w1.assign_sub(lr * grads)
if epoch %
500==0:
print
("after %d training steps,w1 is "
%(epoch)
)print
(w1.numpy(),
"\n"
)print
("final w1 is: "
, w1.numpy())
# 自定義損失函式
# 酸奶成本1元， 酸奶利潤99元
# 成本很低，利潤很高，人們希望多**些，生成模型係數大於1，往多了**
after 0 training steps,w1 is
[[2.8786578]
[3.2517848]]
after 500 training steps,w1 is
[[1.1460369]
[1.0672572]]
after 1000 training steps,w1 is
[[1.1364173]
[1.0985414]]
after 1500 training steps,w1 is
[[1.1267972]
[1.1298251]]
after 2000 training steps,w1 is
[[1.1758107]
[1.1724023]]
after 2500 training steps,w1 is
[[1.1453722]
[1.0272155]]
after 3000 training steps,w1 is
[[1.1357522]
[1.0584993]]
after 3500 training steps,w1 is
[[1.1261321]
[1.0897831]]
after 4000 training steps,w1 is
[[1.1751455]
[1.1323601]]
after 4500 training steps,w1 is
[[1.1655253]
[1.1636437]]
after 5000 training steps,w1 is
[[1.1350871]
[1.0184573]]
after 5500 training steps,w1 is
[[1.1254673]
[1.0497413]]
after 6000 training steps,w1 is
[[1.1158477]
[1.0810255]]
after 6500 training steps,w1 is
[[1.1062276]
[1.1123092]]
after 7000 training steps,w1 is
[[1.1552413]
[1.1548865]]
after 7500 training steps,w1 is
[[1.1248026]
[1.0096996]]
after 8000 training steps,w1 is
[[1.1151826]
[1.0409834]]
after 8500 training steps,w1 is
[[1.1055626]
[1.0722672]]
after 9000 training steps,w1 is
[[1.1545763]
[1.1148446]]
after 9500 training steps,w1 is
[[1.144956]
[1.146128]]
final w1 is: [[1.1255957]
[1.0237043]]
#案例二
# 交叉熵損失函式，更小更接近
loss_ce1 = tf.losses.categorical_crossentropy([1
,0],
[0.6
,0.4])
loss_ce2 = tf.losses.categorical_crossentropy([1
,0],
[0.8
,0.2])
print
("loss_ce1:"
, loss_ce1)
print
("loss_ce2:"
, loss_ce2)
loss_ce1: tf.tensor(0.5108256, shape=(), dtype=float32)
loss_ce2: tf.tensor(0.22314353, shape=(), dtype=float32)
# 案例三
# softmax與交叉熵損失函式的結合，softmax轉換成符合概率分布
y_ = np.array([[
1,0,
0],[
0,1,
0],[
0,0,
1],[
1,0,
0],[
0,1,
0]])
y = np.array([[
12,3,
2],[
3,10,
1],[
1,2,
5],[
4,6.5,
1.2],[
3,6,
1]])
y_pro = tf.nn.softmax(y)
loss_ce1 = tf.losses.categorical_crossentropy(y_,y_pro)
loss_ce2 = tf.nn.softmax_cross_entropy_with_logits(y_, y)
print
('分步計算的結果:\n'
, loss_ce1)
print
('結合計算的結果:\n'
, loss_ce2)
分步計算的結果:
tf.tensor(
[1.68795487e-04 1.03475622e-03 6.58839038e-02 2.58349207e+00
5.49852354e-02], shape=(5,), dtype=float64)
結合計算的結果:
tf.tensor(
[1.68795487e-04 1.03475622e-03 6.58839038e-02 2.58349207e+00
5.49852354e-02], shape=(5,), dtype=float64)
Tensorflow 啟用函式
一些常見啟用函式 維基百科 建立輸入資料 x np.linspace 7,7,180 7,7 之間等間隔的 180 個點 啟用函式的原始實現 defsigmoid inputs y 1 float 1 np.exp x for x in inputs return ydef relu inputs ...
tensorflow 損失函式與啟用函式
損失函式用於評價模型的準確度。無論什麼樣的網路結構，如果損失函式不正確，都難以訓練出正確的模型。損失值用於描述 值與真實值之間的差距大小。常用的損失函式是 均方差函式和交叉熵函式。均方差mse tf.reduce mean tf.pow tf.sub logits,outputs 2.0 tenso...
Tensorflow2 0 啟用函式
常用啟用函式及對應特點 神經網路結構的輸出為所有輸入的加權和，這導致整個神經網路是乙個線性模型。而線性模型不能解決異或問題，且面對多分類問題，也顯得束手無策。所以為了解決非線性的分類或回歸問題，啟用函式必須是非線性函式。神經網路中啟用函式的主要作用是提供網路的非線性建模能力。這是因為反向傳播演算法就...