感知器没有正确学习

Question

我尝试做基本的 ML。所以这是我的 class 二进制 classificator 感知器。

class perceptron():
    def __init__(self, x, y, threshold=0.5, learning_rate=0.1, max_epochs=10):
        self.threshold = threshold
        self.learning_rate = learning_rate
        self.x = x
        self.y = y
        self.max_epochs = max_epochs
        
    def initialize(self):
        self.weights = np.random.rand(len(self.x[0]))
                
    def train(self):
        epoch = 0
        while True:
            error_count = 0
            epoch += 1
            for (x,y) in zip(self.x, self.y):
                error_count += self.train_observation(x, y, error_count)
            print('Epoch: {0} Error count: {1}'.format(epoch, error_count))
            if error_count == 0:
                print('Training successful')
                break
            if epoch >= self.max_epochs:
                print('Reached max epochs')
                break
                
    def train_observation(self, x, y, error_count):
        result = np.dot(x, self.weights) > self.threshold
        error = y - result
        if error != 0:
            error_count += 1
            for index, value in enumerate(x):
                self.weights[index] += self.learning_rate * error * value
        return error_count
        
    def predict(self, x):
        return int(np.dot(x, self.weights) > self.threshold)

我想class确定，如果列表值的总和 >=0（表示 1）或不等于（表示 0）所以我做了 50 个 len 10 的数组，每个都有随机的 int 值 [-3, 3]:

def sum01(x):
    if sum(x) >= 0:
        return 1
    else:
        return 0
x = np.random.randint(low=-3, high=3, size=(50,10))
y = [sum01(z) for z in a]

然后我初始化并训练：

p = perceptron(x, y)
p.initialize()
p.train()

然后我查了一下，很多预测都不对，我哪里做错了？

predics = [(p.predict(i), sumab(i)) for i in np.random.randint(low=-3, high=3, size=(10, 10))]
print(predics)

Answer 1

使用小错误修复重新运行您的代码，我看到损失减少到 0 并正确输出 -

p = perceptron(x, y)
p.initialize()
p.train()

Epoch: 1 Error count: 196608
Epoch: 2 Error count: 38654836736
Epoch: 3 Error count: 268437504
Epoch: 4 Error count: 0
Training successful

predics = [(p.predict(i), sum01(i)) for i in np.random.randint(low=-3, high=3, size=(10, 10))]
print(predics)

[(1, 1), (0, 0), (0, 0), (0, 0), (1, 1), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0)]

解决方案

您的代码需要进行一些快速更改 -

定义 x 和 y 时：

x = np.random.randint(low=-3, high=3, size=(50,10))
y = [sum01(z) for z in x] #CHANGE THIS TO x INSTEAD OF a

获取预测时：

#CHANGE sumab TO sum01
predics = [(p.predict(i), sum01(i)) for i in np.random.randint(low=-3, high=3, size=(10, 10))]

那应该可以了。您的完整代码变为 -

class perceptron():
    def __init__(self, x, y, threshold=0.5, learning_rate=0.1, max_epochs=10):
        self.threshold = threshold
        self.learning_rate = learning_rate
        self.x = x
        self.y = y
        self.max_epochs = max_epochs
        
    def initialize(self):
        self.weights = np.random.rand(len(self.x[0]))
                
    def train(self):
        epoch = 0
        while True:
            error_count = 0
            epoch += 1
            for (x,y) in zip(self.x, self.y):
                error_count += self.train_observation(x, y, error_count)
            print('Epoch: {0} Error count: {1}'.format(epoch, error_count))
            if error_count == 0:
                print('Training successful')
                break
            if epoch >= self.max_epochs:
                print('Reached max epochs')
                break
                
    def train_observation(self, x, y, error_count):
        result = np.dot(x, self.weights) > self.threshold
        error = y - result
        if error != 0:
            error_count += 1
            for index, value in enumerate(x):
                self.weights[index] += self.learning_rate * error * value
        return error_count
        
    def predict(self, x):
        return int(np.dot(x, self.weights) > self.threshold)
    
    
def sum01(x):
    if sum(x) >= 0:
        return 1
    else:
        return 0
    
x = np.random.randint(low=-3, high=3, size=(50,10))
y = [sum01(z) for z in x]

p = perceptron(x, y)
p.initialize()
p.train()

predics = [(p.predict(i), sum01(i)) for i in np.random.randint(low=-3, high=3, size=(10, 10))]
print(predics)

感知器没有正确学习

Perceptron does not learn correctly

machine-learning

neural-network

perceptron