Which of the following statements are true? Check all that apply.
Suppose you have a multi-class classification problem with three classes, trained with a 3 layer network. Let a^{(3)}1 = (h\Theta(x))1a1(3)=(hΘ(x))1 be the activation of the first output unit, and similarly a^{(3)}_2 = (h\Theta(x))2a2(3)=(hΘ(x))2 and a^{(3)}_3 = (h\Theta(x))_3a3(3)=(hΘ(x))3. Then for any input xx, it must be the case that a^{(3)}_1 + a^{(3)}_2 + a^{(3)}_3 = 1a1(3)+a2(3)+a3(3)=1.
two layer (one input layer, one output layer; no hidden layer) neural network can represent the XOR function.
Any logical function over binary-valued (0 or 1) inputs x_1x1 and x_2x2 can be (approximately) represented using some neural network.
The activation values of the hidden units in a neural network, with the sigmoid activation function applied at every layer, are always in the range (0, 1).
答案：CD
解析：
A選項：每一層的節點都是通過前一層計算而來，同層節點互不影響，加上softmax纔會影響。
B選項：XOR異或，需要通過疊加產生。
2.Consider the following neural network which takes two binary-valued inputs x1,x2∈{0,1} and outputs hΘ(x). Which of the following logical functions does it (approximately) compute? -30 ，20，20
AND
NAND (meaning “NOT AND”)
OR
XOR (exclusive OR）
答案A
解析：
將-30，20，20，帶入sigmoid函數，得到只有x1=1，x2=1時輸出爲g(10)=1，即表示邏輯AND.
3.Consider the neural network given below. Which of the following equations correctly computes the activation a(3)1? Note: g(z) is the sigmoid activation function.
答案：A
4.You have the following neural network:You’d like to compute the activations of the hidden layer a(2)∈R3. One way to do so is the following Octave code:You want to have a vectorized implementation of this (i.e., one that does not use for loops). Which of the following implementations correctly compute a(2)? Check all that apply
a2 = sigmoid (Theta1 * x);
a2 = sigmoid (x * Theta1);
a2 = sigmoid (Theta2 * x);
z = sigmoid(x); a2 = Theta1 * z;
答案：A
解析：（Theta1是個3×的矩陣，x是個列向量）
答案：A
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原文：https://blog.csdn.net/yyxyuxueYang/article/details/81563663
