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ref： https://blog.csdn.net/zhning12l/article/details/78765391（题目齐全）
https://blog.csdn.net/qq_31735049/article/details/54813993（代码齐全）
练习1 创建感知机my work： # TODO: return 0 or 1 based on the threshold
if strength <= self.threshold :
self.result = 0
else:
self.result = 1
# ----------
#
# In this exercise, you will add in code that decides whether a perceptron will fire based
# on the threshold. Your code will go in lines 32 and 34.
#
# ----------
import numpy as np
class Perceptron:
"""
This class models an artificial neuron with step activation function.
"""
def __init__(self, weights = np.array([1]), threshold = 0):
"""
Initialize weights and threshold based on input arguments. Note that no
type-checking is being performed here for simplicity.
"""
self.weights = weights
self.threshold = threshold
def activate(self,inputs):
"""
Takes in @param inputs, a list of numbers equal to length of weights.
@return the output of a threshold perceptron with given inputs based on
perceptron weights and threshold.
"""
# The strength with which the perceptron fires.
strength = np.dot(self.weights, inputs)
# TODO: return 0 or 1 based on the threshold
if strength <= self.threshold :
self.result = 0
else:
self.result = 1
return self.result
def test():
"""
A few tests to make sure that the perceptron class performs as expected.
Nothing should show up in the output if all the assertions pass.
"""
p1 = Perceptron(np.array([1, 2]), 0.)
assert p1.activate(np.array([ 1,-1])) == 0 # < threshold --> 0
assert p1.activate(np.array([-1, 1])) == 1 # > threshold --> 1
assert p1.activate(np.array([ 2,-1])) == 0 # on threshold --> 0
if __name__ == "__main__":
test()
练习3 在哪儿训练感知机
答案： 
练习4 感知输入人工神经网络是由感知机单元构成的，人工神经网络的输入应该是什么格式的呢？- □ 带有标签的数值型矩阵
- □ 有向图
- □ 无标签的数值型矩阵
- □ 带有标签的集合
- □ 每行带有标签的数值型矩阵
答案： 
练习5 神经网络输出我们能从神经网络的输出中得到什么信息？- □ 一个有向图（神经网络本身）
- □ 一个标量
- □ 用向量表示的分类信息
- □ 每个输入向量都对应一个输出向量
答案： 
练习6 感知机更新规则my work： # For each data point:
for data_point in xrange(len(values)):
# TODO: Obtain the neuron's prediction for the data_point --> values[data_point]
prediction = self.activate(values[data_point])
# Get the prediction accuracy calculated as (expected value - predicted value)
# expected value = train[data_point], predicted value = prediction
error = train[data_point] - prediction
# TODO: update self.weights based on the multiplication of:
# - prediction accuracy(error)
# - learning rate(eta)
# - input value(values[data_point])
weight_update = eta * values[data_point] * error
self.weights += weight_update # ----------
#
# In this exercise, you will update the perceptron class so that it can update
# its weights.
#
# Finish writing the update() method so that it updates the weights according
# to the perceptron update rule. Updates should be performed online, revising
# the weights after each data point.
#
# YOUR CODE WILL GO IN LINES 51 AND 59.
# ----------import numpy as np
class Perceptron:
"""
This class models an artificial neuron with step activation function.
"""
def __init__(self, weights = np.array([1]), threshold = 0):
"""
Initialize weights and threshold based on input arguments. Note that no
type-checking is being performed here for simplicity.
"""
self.weights = weights.astype(float)
self.threshold = threshold
def activate(self, values):
"""
Takes in @param values, a list of numbers equal to length of weights.
@return the output of a threshold perceptron with given inputs based on
perceptron weights and threshold.
"""
# First calculate the strength with which the perceptron fires
strength = np.dot(values,self.weights)
# Then return 0 or 1 depending on strength compared to threshold
return int(strength > self.threshold)
def update(self, values, train, eta=.1):
"""
Takes in a 2D array @param values consisting of a LIST of inputs and a
1D array @param train, consisting of a corresponding list of expected
outputs. Updates internal weights according to the perceptron training
rule using these values and an optional learning rate, @param eta.
"""
# For each data point:
for data_point in xrange(len(values)):
# TODO: Obtain the neuron's prediction for the data_point --> values[data_point]
prediction = self.activate(values[data_point])
# Get the prediction accuracy calculated as (expected value - predicted value)
# expected value = train[data_point], predicted value = prediction
error = train[data_point] - prediction
# TODO: update self.weights based on the multiplication of:
# - prediction accuracy(error)
# - learning rate(eta)
# - input value(values[data_point])
weight_update = eta * values[data_point] * error
self.weights += weight_update
def test():
"""
A few tests to make sure that the perceptron class performs as expected.
Nothing should show up in the output if all the assertions pass.
"""
def sum_almost_equal(array1, array2, tol = 1e-6):
return sum(abs(array1 - array2)) < tol
p1 = Perceptron(np.array([1,1,1]),0)
p1.update(np.array([[2,0,-3]]), np.array([1]))
assert sum_almost_equal(p1.weights, np.array([1.2, 1, 0.7]))
p2 = Perceptron(np.array([1,2,3]),0)
p2.update(np.array([[3,2,1],[4,0,-1]]),np.array([0,0]))
assert sum_almost_equal(p2.weights, np.array([0.7, 1.8, 2.9]))
p3 = Perceptron(np.array([3,0,2]),0)
p3.update(np.array([[2,-2,4],[-1,-3,2],[0,2,1]]),np.array([0,1,0]))
assert sum_almost_equal(p3.weights, np.array([2.7, -0.3, 1.7]))
if __name__ == "__main__":
test()
练习7 多层网络示例
练习8 线性表征能力 答案： 
练习9 创建XOR网络my work： # Part 1: Set up the perceptron network
Network = [
# input layer, declare input layer perceptrons here
[Perceptron([1, -1], 0.5), Perceptron([-1, 1], 0.5), Perceptron([0, 1], 0)],
# output node, declare output layer perceptron here
[Perceptron([1, -2, 1], 0)]
]
# Part 2: Define a procedure to compute the output of the network, given inputs
def EvalNetwork(inputValues, Network):
"""
Takes in @param inputValues, a list of input values, and @param Network
that specifies a perceptron network. @return the output of the Network for
the given set of inputs.
"""
# YOUR CODE HERE
OutputValue=Network[1][0].activate([h.activate(inputValues) for h in Network[0]])
# Be sure your output value is a single number
return OutputValue
#
# In this exercise, you will create a network of perceptrons that can represent
# the XOR function, using a network structure like those shown in the previous
# quizzes.
#
# You will need to do two things:
# First, create a network of perceptrons with the correct weights
# Second, define a procedure EvalNetwork() which takes in a list of inputs and
# outputs the value of this network.
#
# ----------
import numpy as np
class Perceptron:
"""
This class models an artificial neuron with step activation function.
"""
def __init__(self, weights = np.array([1]), threshold = 0):
"""
Initialize weights and threshold based on input arguments. Note that no
type-checking is being performed here for simplicity.
"""
self.weights = weights
self.threshold = threshold
def activate(self, values):
"""
Takes in @param values, a list of numbers equal to length of weights.
@return the output of a threshold perceptron with given inputs based on
perceptron weights and threshold.
"""
# First calculate the strength with which the perceptron fires
strength = np.dot(values,self.weights)
# Then return 0 or 1 depending on strength compared to threshold
return int(strength > self.threshold)
# Part 1: Set up the perceptron network
Network = [
# input layer, declare input layer perceptrons here
[Perceptron([1, -1], 0.5), Perceptron([-1, 1], 0.5), Perceptron([0, 1], 0)],
# output node, declare output layer perceptron here
[Perceptron([1, -2, 1], 0)]
]
# Part 2: Define a procedure to compute the output of the network, given inputs
def EvalNetwork(inputValues, Network):
"""
Takes in @param inputValues, a list of input values, and @param Network
that specifies a perceptron network. @return the output of the Network for
the given set of inputs.
"""
# YOUR CODE HERE
OutputValue=Network[1][0].activate([h.activate(inputValues) for h in Network[0]])
# Be sure your output value is a single number
return OutputValue
def test():
"""
A few tests to make sure that the perceptron class performs as expected.
"""
print "0 XOR 0 = 0?:", EvalNetwork(np.array([0,0]), Network)
print "0 XOR 1 = 1?:", EvalNetwork(np.array([0,1]), Network)
print "1 XOR 0 = 1?:", EvalNetwork(np.array([1,0]), Network)
print "1 XOR 1 = 0?:", EvalNetwork(np.array([1,1]), Network)
if __name__ == "__main__":
test()
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