机器学习-线性回归-小批量-梯度下降法-04

1. 随机梯度下降法

梯度计算的时候 随机抽取一条

import numpy as np

X = 2 * np.random.rand(100, 1)
y = 4 + 3 * X + np.random.randn(100, 1)

X_b = np.c_[np.ones((100, 1)), X]

n_epochs = 10000
learn_rate = 0.001
m = 100

theta = np.random.randn(2, 1)

for epoch in range(n_epochs):
    random_index = np.random.randint(m)

    Xi = X_b[random_index: random_index + 1]  # 切片 切出一条样本
    yi = y[random_index: random_index + 1]  # 切片 切出一条样本

    gradient = Xi.T.dot(Xi.dot(theta) - yi)  # 梯度公式Xi.T * (Xi*theta-yi)  Xi yi 可以是全部样本 也可以是部分样本
    theta = theta - learn_rate * gradient

print(theta)

2. 销批量梯度下降法

梯度计算的时候 抽取一部分

import numpy as np

X = 2 * np.random.rand(100, 1)
y = 4 + 3 * X + np.random.randn(100, 1)

X_b = np.c_[np.ones((100, 1)), X]

n_epochs = 10000
learn_rate = 0.001
batch_size = 10
m = 100
num_batch = int(m/10)


theta = np.random.randn(2, 1)

for epoch in range(n_epochs):

    for i in range(num_batch):
        random_index = np.random.randint(m)
        Xi = X_b[random_index: random_index + batch_size]  # 切片 切出batch_size样本
        yi = y[random_index: random_index + batch_size]  # 切片 切出batch_size样本

        gradient = Xi.T.dot(Xi.dot(theta) - yi)  # 梯度公式Xi.T * (Xi*theta-yi)  Xi yi 可以是全部样本 也可以是部分样本
        theta = theta - learn_rate * gradient

print(theta)

3. 优化1

双层 for循环之间将样本打乱
arr = np.arange(len(X_b)) # 生成索引序列
np.random.shuffle(arr) # 将索引打乱
# 相同的打乱的搜索引序列arr去 取值 一一对应
X_b = X_b[arr]
y = y[arr]

import numpy as np

X = 2 * np.random.rand(100, 1)
y = 4 + 3 * X + np.random.randn(100, 1)

X_b = np.c_[np.ones((100, 1)), X]

n_epochs = 10000
learn_rate = 0.001
batch_size = 10
m = 100
num_batch = int(m/10)


theta = np.random.randn(2, 1)

for epoch in range(n_epochs):

    arr = np.arange(len(X_b))  # 生成索引序列
    np.random.shuffle(arr)  # 将索引打乱
    # 相同的打乱的搜索引序列arr去 取值 一一对应
    X_b = X_b[arr]
    y = y[arr]

    for i in range(num_batch):
        random_index = np.random.randint(m)
        Xi = X_b[random_index: random_index + batch_size]  # 切片 切出batch_size样本
        yi = y[random_index: random_index + batch_size]  # 切片 切出batch_size样本

        gradient = Xi.T.dot(Xi.dot(theta) - yi)  # 梯度公式Xi.T * (Xi*theta-yi)  Xi yi 可以是全部样本 也可以是部分样本
        theta = theta - learn_rate * gradient

print(theta)

4. 优化2

随着迭代的次数增加 学习率 减小
def schedule_learn_rate(t):
return t0/(t+t1)

import numpy as np

X = 2 * np.random.rand(100, 1)
y = 4 + 3 * X + np.random.randn(100, 1)

X_b = np.c_[np.ones((100, 1)), X]

n_epochs = 10000
learn_rate = 0.001
batch_size = 10
m = 100
num_batch = int(m/10)


theta = np.random.randn(2, 1)


t0, t1 = 5, 500


def schedule_learn_rate(t):
    return t0/(t+t1)


for epoch in range(n_epochs):

    arr = np.arange(len(X_b))  # 生成索引序列
    np.random.shuffle(arr)  # 将索引打乱
    # 相同的打乱的搜索引序列arr去 取值 一一对应
    X_b = X_b[arr]
    y = y[arr]

    for i in range(num_batch):
        random_index = np.random.randint(m)
        Xi = X_b[random_index: random_index + batch_size]  # 切片 切出batch_size样本
        yi = y[random_index: random_index + batch_size]  # 切片 切出batch_size样本

        gradient = Xi.T.dot(Xi.dot(theta) - yi)  # 梯度公式Xi.T * (Xi*theta-yi)  Xi yi 可以是全部样本 也可以是部分样本
        learn_rate = schedule_learn_rate(epoch * m+i)
        theta = theta - learn_rate * gradient

print(theta)