偏差計算

目錄

• Outline
• MSE
• Entropy
• Cross Entropy
• Binary Classification
  • Single output
• Classification
• Why not MSE？
• logits-->CrossEntropy

Outline

• MSE

• Cross Entropy Loss

• Hinge Loss

MSE

• \(loss = \frac{1}{N}\sum(y-out)^2\)

• \(L_{2-norm} = \sqrt{\sum(y-out)}\)

import tensorflow as tf

y = tf.constant([1, 2, 3, 0, 2])
y = tf.one_hot(y, depth=4)  # max_label=3種
y = tf.cast(y, dtype=tf.float32)

out = tf.random.normal([5, 4])
out

<tf.Tensor: id=117, shape=(5, 4), dtype=float32, numpy=
array([[ 0.8138832 , -1.1521571 ,  0.05197939,  2.3684442 ],
       [ 0.28827545, -0.35568208, -0.3952962 , -1.2576817 ],
       [-0.4354525 , -1.9914867 ,  0.37045303, -0.38287213],
       [-0.7680094 , -0.98293644,  0.62572837, -0.5673917 ],
       [ 1.5299634 ,  0.38036177, -0.28049606, -0.708137  ]],
      dtype=float32)>

loss1 = tf.reduce_mean(tf.square(y - out))
loss1

<tf.Tensor: id=122, shape=(), dtype=float32, numpy=1.5140966>

loss2 = tf.square(tf.norm(y - out)) / (5 * 4)
loss2

<tf.Tensor: id=99, shape=(), dtype=float32, numpy=1.3962512>

loss3 = tf.reduce_mean(tf.losses.MSE(y, out))
loss3

<tf.Tensor: id=105, shape=(), dtype=float32, numpy=1.3962513>

Entropy

• Uncertainty

• measure of surprise

• lower entropy --> more info.

\[ \text{Entropy} = -\sum_{i}P(i)log\,P(i) \]

a = tf.fill([4], 0.25)
a * tf.math.log(a) / tf.math.log(2.)

<tf.Tensor: id=134, shape=(4,), dtype=float32, numpy=array([-0.5, -0.5, -0.5, -0.5], dtype=float32)>

-tf.reduce_sum(a * tf.math.log(a) / tf.math.log(2.))

<tf.Tensor: id=143, shape=(), dtype=float32, numpy=2.0>

a = tf.constant([0.1, 0.1, 0.1, 0.7])
-tf.reduce_sum(a * tf.math.log(a) / tf.math.log(2.))

<tf.Tensor: id=157, shape=(), dtype=float32, numpy=1.3567797>

a = tf.constant([0.01, 0.01, 0.01, 0.97])
-tf.reduce_sum(a * tf.math.log(a) / tf.math.log(2.))

<tf.Tensor: id=167, shape=(), dtype=float32, numpy=0.24194068>

Cross Entropy

\[ H(p,q) = -\sum{p(x)log\,q(x)} \\ H(p,q) = H(p) + D_{KL}(p|q) \]

• for p = q
  • Minima: H(p,q) = H(p)
• for P: one-hot encodint
  • \(h(p:[0,1,0]) = -1log\,1=0\)
  • \(H([0,1,0],[p_0,p_1,p_2]) = 0 + D_{KL}(p|q) = -1log\,q_1\) # p,q即真實值和預測值相等的話交叉熵爲0

Binary Classification

• Two cases（第二種格式只須要輸出一種狀況，節省計算，無心義）

Single output

\[ H(P,Q) = -P(cat)log\,Q(cat) - (1-P(cat))log\,(1-Q(cat)) \\ P(dog) = (1-P(cat)) \\ \]

\[ \begin{aligned} H(P,Q) & = -\sum_{i=(cat,dog)}P(i)log\,Q(i)\\ & = -P(cat)log\,Q(cat) - P(dog)log\,Q(dog)-(ylog(p)+(1-y)log\,(1-p)) \end{aligned} \]

Classification

• \(H([0,1,0],[p_0,p_1,p_2])=0+D_{KL}(p|q) = -1log\,q_1\)

\[ \begin{aligned} & P_1 = [1,0,0,0,0]\\ & Q_1=[0.4,0.3,0.05,0.05,0.2] \end{aligned} \]

\[ \begin{aligned} H(P_1,Q_1) & = -\sum{P_1(i)}log\,Q_1(i) \\ & = -(1log\,0.4+0log\,0.3+0log\,0.05+0log\,0.05+0log\,0.2) \\ & =-log\,0.4 \\ & \approx{0.916} \end{aligned} \]

\[ \begin{aligned} & P_1 = [1,0,0,0,0]\\ & Q_1=[0.98,0.01,0,0,0.01] \end{aligned} \]

\[ \begin{aligned} H(P_1,Q_1) & = -\sum{P_1(i)}log\,Q_1(i) \\ & =-log\,0.98 \\ & \approx{0.02} \end{aligned} \]

tf.losses.categorical_crossentropy([0, 1, 0, 0], [0.25, 0.25, 0.25, 0.25])

<tf.Tensor: id=186, shape=(), dtype=float32, numpy=1.3862944>

tf.losses.categorical_crossentropy([0, 1, 0, 0], [0.1, 0.1, 0.8, 0.1])

<tf.Tensor: id=205, shape=(), dtype=float32, numpy=2.3978953>

tf.losses.categorical_crossentropy([0, 1, 0, 0], [0.1, 0.7, 0.1, 0.1])

<tf.Tensor: id=243, shape=(), dtype=float32, numpy=0.35667497>

tf.losses.categorical_crossentropy([0, 1, 0, 0], [0.01, 0.97, 0.01, 0.01])

<tf.Tensor: id=262, shape=(), dtype=float32, numpy=0.030459179>

tf.losses.BinaryCrossentropy()([1],[0.1])

<tf.Tensor: id=306, shape=(), dtype=float32, numpy=2.3025842>

tf.losses.binary_crossentropy([1],[0.1])

<tf.Tensor: id=333, shape=(), dtype=float32, numpy=2.3025842>

Why not MSE？

• sigmoid + MSE
  • gradient vanish

• converge slower

• However
  • e.g. meta-learning

logits-->CrossEntropy