机器学习-梯度下降法
- 不是一个机器学习算法
- 是一种基于搜索的最优化方法
- 作用：最小化一个损失函数
- 梯度上升法：最大化一个效用函数
并不是所有函数都有唯一的极值点
解决方法：
- 多次运行，随机化初始点
- 梯度下降法的初始点也是一个超参数
代码演示
import numpy as np
import matplotlib.pyplot as plt
plot_x = np.linspace(-1., 6., 141)
plot_y = (plot_x-2.5)**2 - 1.
plt.plot(plot_x, plot_y)
plt.show()
梯度下降法
epsilon = 1e-8
eta = 0.1
def J(theta):
return (theta-2.5)**2 - 1.
def dJ(theta):
return 2*(theta-2.5)
theta = 0.0
while True:
gradient = dJ(theta)
last_theta = theta
theta = theta - eta * gradient
if(abs(J(theta) - J(last_theta)) < epsilon):
break
print(theta)
print(J(theta))
可视化
theta = 0.0
theta_history = [theta]
while True:
gradient = dJ(theta)
last_theta = theta
theta = theta - eta * gradient
theta_history.append(theta)
if(abs(J(theta) - J(last_theta)) < epsilon):
break
plt.plot(plot_x, J(plot_x))
plt.plot(np.array(theta_history), J(np.array(theta_history)), color="r", marker='+')
plt.show()
封装
def gradient_descent(initial_theta, eta, epsilon=1e-8):
theta = initial_theta
theta_history.append(initial_theta)
while True:
gradient = dJ(theta)
last_theta = theta
theta = theta - eta * gradient
theta_history.append(theta)
if(abs(J(theta) - J(last_theta)) < epsilon):
break
def plot_theta_history():
plt.plot(plot_x, J(plot_x))
plt.plot(np.array(theta_history), J(np.array(theta_history)), color="r", marker='+')
plt.show()
eta = 0.01时
eta = 0.01
theta_history = []
gradient_descent(0, eta)
plot_theta_history()
eta = 0.001时
eta = 0.001
theta_history = []
gradient_descent(0, eta)
plot_theta_history()
eta = 0.8时
eta = 0.8
theta_history = []
gradient_descent(0, eta)
plot_theta_history()
优化 避免死循环
def J(theta):
try:
return (theta-2.5)**2 - 1.
except:
return float('inf')
def gradient_descent(initial_theta, eta, n_iters = 1e4, epsilon=1e-8):
theta = initial_theta
i_iter = 0
theta_history.append(initial_theta)
while i_iter < n_iters:
gradient = dJ(theta)
last_theta = theta
theta = theta - eta * gradient
theta_history.append(theta)
if(abs(J(theta) - J(last_theta)) < epsilon):
break
i_iter += 1
return
eta = 1.1时
eta = 1.1
theta_history = []
gradient_descent(0, eta, n_iters=10)
plot_theta_history()
多元线性回归中的梯度下降法
代码
生成数据
import numpy as np
import matplotlib.pyplot as plt
np.random.seed(666)
x = 2 * np.random.random(size=100)
y = x * 3. + 4. + np.random.normal(size=100)
X = x.reshape(-1, 1)
plt.scatter(x, y)
plt.show()
使用梯度下降法训练
def J(theta, X_b, y):
try:
return np.sum((y - X_b.dot(theta))**2) / len(X_b)
except:
return float('inf')
def dJ(theta, X_b, y):
res = np.empty(len(theta))
res[0] = np.sum(X_b.dot(theta) - y)
for i in range(1, len(theta)):
res[i] = (X_b.dot(theta) - y).dot(X_b[:,i])
return res * 2 / len(X_b)
def gradient_descent(X_b, y, initial_theta, eta, n_iters = 1e4, epsilon=1e-8):
theta = initial_theta
cur_iter = 0
while cur_iter < n_iters:
gradient = dJ(theta, X_b, y)
last_theta = theta
theta = theta - eta * gradient
if(abs(J(theta, X_b, y) - J(last_theta, X_b, y)) < epsilon):
break
cur_iter += 1
return theta
X_b = np.hstack([np.ones((len(x), 1)), x.reshape(-1,1)])
initial_theta = np.zeros(X_b.shape[1])
eta = 0.01
theta = gradient_descent(X_b, y, initial_theta, eta)
封装
def fit_gd(self, X_train, y_train, eta=0.01, n_iters=1e4):
"""根据训练数据集X_train, y_train, 使用梯度下降法训练Linear Regression模型"""
assert X_train.shape[0] == y_train.shape[0], \
"the size of X_train must be equal to the size of y_train"
def J(theta, X_b, y):
try:
return np.sum((y - X_b.dot(theta)) ** 2) / len(y)
except:
return float('inf')
def dJ(theta, X_b, y):
res = np.empty(len(theta))
res[0] = np.sum(X_b.dot(theta) - y)
for i in range(1, len(theta)):
res[i] = (X_b.dot(theta) - y).dot(X_b[:, i])
return res * 2 / len(X_b)
def gradient_descent(X_b, y, initial_theta, eta, n_iters=1e4, epsilon=1e-8):
theta = initial_theta
cur_iter = 0
while cur_iter < n_iters:
gradient = dJ(theta, X_b, y)
last_theta = theta
theta = theta - eta * gradient
if (abs(J(theta, X_b, y) - J(last_theta, X_b, y)) < epsilon):
break
cur_iter += 1
return theta
X_b = np.hstack([np.ones((len(X_train), 1)), X_train])
initial_theta = np.zeros(X_b.shape[1])
self._theta = gradient_descent(X_b, y_train, initial_theta, eta, n_iters)
self.intercept_ = self._theta[0]
self.coef_ = self._theta[1:]
return self
全：
import numpy as np
from .metrics import r2_score
class LinearRegression:
def __init__(self):
"""初始化Linear Regression模型"""
self.coef_ = None
self.intercept_ = None
self._theta = None
def fit_normal(self, X_train, y_train):
"""根据训练数据集X_train, y_train训练Linear Regression模型"""
assert X_train.shape[0] == y_train.shape[0], \
"the size of X_train must be equal to the size of y_train"
X_b = np.hstack([np.ones((len(X_train), 1)), X_train])
self._theta = np.linalg.inv(X_b.T.dot(X_b)).dot(X_b.T).dot(y_train)
self.intercept_ = self._theta[0]
self.coef_ = self._theta[1:]
return self
def fit_gd(self, X_train, y_train, eta=0.01, n_iters=1e4):
"""根据训练数据集X_train, y_train, 使用梯度下降法训练Linear Regression模型"""
assert X_train.shape[0] == y_train.shape[0], \
"the size of X_train must be equal to the size of y_train"
def J(theta, X_b, y):
try:
return np.sum((y - X_b.dot(theta)) ** 2) / len(y)
except:
return float('inf')
def dJ(theta, X_b, y):
res = np.empty(len(theta))
res[0] = np.sum(X_b.dot(theta) - y)
for i in range(1, len(theta)):
res[i] = (X_b.dot(theta) - y).dot(X_b[:, i])
return res * 2 / len(X_b)
def gradient_descent(X_b, y, initial_theta, eta, n_iters=1e4, epsilon=1e-8):
theta = initial_theta
cur_iter = 0
while cur_iter < n_iters:
gradient = dJ(theta, X_b, y)
last_theta = theta
theta = theta - eta * gradient
if (abs(J(theta, X_b, y) - J(last_theta, X_b, y)) < epsilon):
break
cur_iter += 1
return theta
X_b = np.hstack([np.ones((len(X_train), 1)), X_train])
initial_theta = np.zeros(X_b.shape[1])
self._theta = gradient_descent(X_b, y_train, initial_theta, eta, n_iters)
self.intercept_ = self._theta[0]
self.coef_ = self._theta[1:]
return self
def predict(self, X_predict):
"""给定待预测数据集X_predict，返回表示X_predict的结果向量"""
assert self.intercept_ is not None and self.coef_ is not None, \
"must fit before predict!"
assert X_predict.shape[1] == len(self.coef_), \
"the feature number of X_predict must be equal to X_train"
X_b = np.hstack([np.ones((len(X_predict), 1)), X_predict])
return X_b.dot(self._theta)
def score(self, X_test, y_test):
"""根据测试数据集 X_test 和 y_test 确定当前模型的准确度"""
y_predict = self.predict(X_test)
return r2_score(y_test, y_predict)
def __repr__(self):
return "LinearRegression()"
线性回归中使用梯度下降法
优化代码
def dJ(theta, X_b, y):
return X_b.T.dot(X_b.dot(theta) - y) * 2. / len(y)
import numpy as np
from .metrics import r2_score
class LinearRegression:
def __init__(self):
"""初始化Linear Regression模型"""
self.coef_ = None
self.intercept_ = None
self._theta = None
def fit_normal(self, X_train, y_train):
"""根据训练数据集X_train, y_train训练Linear Regression模型"""
assert X_train.shape[0] == y_train.shape[0], \
"the size of X_train must be equal to the size of y_train"
X_b = np.hstack([np.ones((len(X_train), 1)), X_train])
self._theta = np.linalg.inv(X_b.T.dot(X_b)).dot(X_b.T).dot(y_train)
self.intercept_ = self._theta[0]
self.coef_ = self._theta[1:]
return self
def fit_gd(self, X_train, y_train, eta=0.01, n_iters=1e4):
"""根据训练数据集X_train, y_train, 使用梯度下降法训练Linear Regression模型"""
assert X_train.shape[0] == y_train.shape[0], \
"the size of X_train must be equal to the size of y_train"
def J(theta, X_b, y):
try:
return np.sum((y - X_b.dot(theta)) ** 2) / len(y)
except:
return float('inf')
def dJ(theta, X_b, y):
return X_b.T.dot(X_b.dot(theta) - y) * 2. / len(y)
def gradient_descent(X_b, y, initial_theta, eta, n_iters=1e4, epsilon=1e-8):
theta = initial_theta
cur_iter = 0
while cur_iter < n_iters:
gradient = dJ(theta, X_b, y)
last_theta = theta
theta = theta - eta * gradient
if (abs(J(theta, X_b, y) - J(last_theta, X_b, y)) < epsilon):
break
cur_iter += 1
return theta
X_b = np.hstack([np.ones((len(X_train), 1)), X_train])
initial_theta = np.zeros(X_b.shape[1])
self._theta = gradient_descent(X_b, y_train, initial_theta, eta, n_iters)
self.intercept_ = self._theta[0]
self.coef_ = self._theta[1:]
return self
def predict(self, X_predict):
"""给定待预测数据集X_predict，返回表示X_predict的结果向量"""
assert self.intercept_ is not None and self.coef_ is not None, \
"must fit before predict!"
assert X_predict.shape[1] == len(self.coef_), \
"the feature number of X_predict must be equal to X_train"
X_b = np.hstack([np.ones((len(X_predict), 1)), X_predict])
return X_b.dot(self._theta)
def score(self, X_test, y_test):
"""根据测试数据集 X_test 和 y_test 确定当前模型的准确度"""
y_predict = self.predict(X_test)
return r2_score(y_test, y_predict)
def __repr__(self):
return "LinearRegression()"
代码测试
import numpy as np
from sklearn import datasets
boston = datasets.load_boston()
X = boston.data
y = boston.target
X = X[y < 50.0]
y = y[y < 50.0]
from playML.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, seed=666)
from playML.LinearRegression import LinearRegression
lin_reg1 = LinearRegression()
%time lin_reg1.fit_normal(X_train, y_train)
lin_reg1.score(X_test, y_test)
使用梯度下降法
lin_reg2 = LinearRegression()
lin_reg2.fit_gd(X_train, y_train)
更改eta值
lin_reg2.fit_gd(X_train, y_train, eta=0.000001)
lin_reg2.score(X_test, y_test)
再优化
%time lin_reg2.fit_gd(X_train, y_train, eta=0.000001, n_iters=1e6)
lin_reg2.score(X_test, y_test)
归一化
from sklearn.preprocessing import StandardScaler
standardScaler = StandardScaler()
standardScaler.fit(X_train)
X_train_standard = standardScaler.transform(X_train)
lin_reg3 = LinearRegression()
%time lin_reg3.fit_gd(X_train_standard, y_train)
X_test_standard = standardScaler.transform(X_test)
lin_reg3.score(X_test_standard, y_test)
随机梯度下降法 Stochastic Gradient Descent
模拟退化的思想
代码
批量梯度下降法
import numpy as np
import matplotlib.pyplot as plt
m = 100000
x = np.random.normal(size=m)
X = x.reshape(-1,1)
y = 4.*x + 3. + np.random.normal(0, 3, size=m)
def J(theta, X_b, y):
try:
return np.sum((y - X_b.dot(theta)) ** 2) / len(y)
except:
return float('inf')
def dJ(theta, X_b, y):
return X_b.T.dot(X_b.dot(theta) - y) * 2. / len(y)
def gradient_descent(X_b, y, initial_theta, eta, n_iters=1e4, epsilon=1e-8):
theta = initial_theta
cur_iter = 0
while cur_iter < n_iters:
gradient = dJ(theta, X_b, y)
last_theta = theta
theta = theta - eta * gradient
if (abs(J(theta, X_b, y) - J(last_theta, X_b, y)) < epsilon):
break
cur_iter += 1
return theta
X_b = np.hstack([np.ones((len(X), 1)), X])
initial_theta = np.zeros(X_b.shape[1])
eta = 0.01
theta = gradient_descent(X_b, y, initial_theta, eta)
随机梯度下降法
def dJ_sgd(theta, X_b_i, y_i):
return 2 * X_b_i.T.dot(X_b_i.dot(theta) - y_i)
def sgd(X_b, y, initial_theta, n_iters):
t0, t1 = 5, 50
def learning_rate(t):
return t0 / (t + t1)
theta = initial_theta
for cur_iter in range(n_iters):
rand_i = np.random.randint(len(X_b))
gradient = dJ_sgd(theta, X_b[rand_i], y[rand_i])
theta = theta - learning_rate(cur_iter) * gradient
return theta
X_b = np.hstack([np.ones((len(X), 1)), X])
initial_theta = np.zeros(X_b.shape[1])
theta = sgd(X_b, y, initial_theta, n_iters=m//3)
封装
def fit_sgd(self, X_train, y_train, n_iters=50, t0=5, t1=50):
"""根据训练数据集X_train, y_train, 使用梯度下降法训练Linear Regression模型"""
assert X_train.shape[0] == y_train.shape[0], \
"the size of X_train must be equal to the size of y_train"
assert n_iters >= 1
def dJ_sgd(theta, X_b_i, y_i):
return X_b_i * (X_b_i.dot(theta) - y_i) * 2.
def sgd(X_b, y, initial_theta, n_iters=5, t0=5, t1=50):
def learning_rate(t):
return t0 / (t + t1)
theta = initial_theta
m = len(X_b)
for i_iter in range(n_iters):
indexes = np.random.permutation(m)
X_b_new = X_b[indexes,:]
y_new = y[indexes]
for i in range(m):
gradient = dJ_sgd(theta, X_b_new[i], y_new[i])
theta = theta - learning_rate(i_iter * m + i) * gradient
return theta
import numpy as np
from .metrics import r2_score
class LinearRegression:
def __init__(self):
"""初始化Linear Regression模型"""
self.coef_ = None
self.intercept_ = None
self._theta = None
def fit_normal(self, X_train, y_train):
"""根据训练数据集X_train, y_train训练Linear Regression模型"""
assert X_train.shape[0] == y_train.shape[0], \
"the size of X_train must be equal to the size of y_train"
X_b = np.hstack([np.ones((len(X_train), 1)), X_train])
self._theta = np.linalg.inv(X_b.T.dot(X_b)).dot(X_b.T).dot(y_train)
self.intercept_ = self._theta[0]
self.coef_ = self._theta[1:]
return self
def fit_bgd(self, X_train, y_train, eta=0.01, n_iters=1e4):
"""根据训练数据集X_train, y_train, 使用梯度下降法训练Linear Regression模型"""
assert X_train.shape[0] == y_train.shape[0], \
"the size of X_train must be equal to the size of y_train"
def J(theta, X_b, y):
try:
return np.sum((y - X_b.dot(theta)) ** 2) / len(y)
except:
return float('inf')
def dJ(theta, X_b, y):
return X_b.T.dot(X_b.dot(theta) - y) * 2. / len(y)
def gradient_descent(X_b, y, initial_theta, eta, n_iters=1e4, epsilon=1e-8):
theta = initial_theta
cur_iter = 0
while cur_iter < n_iters:
gradient = dJ(theta, X_b, y)
last_theta = theta
theta = theta - eta * gradient
if (abs(J(theta, X_b, y) - J(last_theta, X_b, y)) < epsilon):
break
cur_iter += 1
return theta
X_b = np.hstack([np.ones((len(X_train), 1)), X_train])
initial_theta = np.zeros(X_b.shape[1])
self._theta = gradient_descent(X_b, y_train, initial_theta, eta, n_iters)
self.intercept_ = self._theta[0]
self.coef_ = self._theta[1:]
return self
def fit_sgd(self, X_train, y_train, n_iters=50, t0=5, t1=50):
"""根据训练数据集X_train, y_train, 使用梯度下降法训练Linear Regression模型"""
assert X_train.shape[0] == y_train.shape[0], \
"the size of X_train must be equal to the size of y_train"
assert n_iters >= 1
def dJ_sgd(theta, X_b_i, y_i):
return X_b_i * (X_b_i.dot(theta) - y_i) * 2.
def sgd(X_b, y, initial_theta, n_iters=5, t0=5, t1=50):
def learning_rate(t):
return t0 / (t + t1)
theta = initial_theta
m = len(X_b)
for i_iter in range(n_iters):
indexes = np.random.permutation(m)
X_b_new = X_b[indexes,:]
y_new = y[indexes]
for i in range(m):
gradient = dJ_sgd(theta, X_b_new[i], y_new[i])
theta = theta - learning_rate(i_iter * m + i) * gradient
return theta
X_b = np.hstack([np.ones((len(X_train), 1)), X_train])
initial_theta = np.random.randn(X_b.shape[1])
self._theta = sgd(X_b, y_train, initial_theta, n_iters, t0, t1)
self.intercept_ = self._theta[0]
self.coef_ = self._theta[1:]
return self
def predict(self, X_predict):
"""给定待预测数据集X_predict，返回表示X_predict的结果向量"""
assert self.intercept_ is not None and self.coef_ is not None, \
"must fit before predict!"
assert X_predict.shape[1] == len(self.coef_), \
"the feature number of X_predict must be equal to X_train"
X_b = np.hstack([np.ones((len(X_predict), 1)), X_predict])
return X_b.dot(self._theta)
def score(self, X_test, y_test):
"""根据测试数据集 X_test 和 y_test 确定当前模型的准确度"""
y_predict = self.predict(X_test)
return r2_score(y_test, y_predict)
def __repr__(self):
return "LinearRegression()"
真实使用我们自己的SGD
from sklearn import datasets
boston = datasets.load_boston()
X = boston.data
y = boston.target
X = X[y < 50.0]
y = y[y < 50.0]
from playML.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, seed=666)
from sklearn.preprocessing import StandardScaler
standardScaler = StandardScaler()
standardScaler.fit(X_train)
X_train_standard = standardScaler.transform(X_train)
X_test_standard = standardScaler.transform(X_test)
from playML.LinearRegression import LinearRegression
lin_reg = LinearRegression()
lin_reg.fit_sgd(X_train_standard, y_train, n_iters=100)
lin_reg.score(X_test_standard, y_test)
scikit-learn中的SGD
from sklearn.linear_model import SGDRegressor
sgd_reg = SGDRegressor()
sgd_reg.fit(X_train_standard, y_train)
sgd_reg.score(X_test_standard, y_test)
sgd_reg = SGDRegressor(n_iter=50)
sgd_reg.fit(X_train_standard, y_train)
sgd_reg.score(X_test_standard, y_test)
关于梯度的调试
生成数据
import numpy as np
import matplotlib.pyplot as plt
np.random.seed(666)
X = np.random.random(size=(1000, 10))
true_theta = np.arange(1, 12, dtype=float)
X_b = np.hstack([np.ones((len(X), 1)), X])
y = X_b.dot(true_theta) + np.random.normal(size=1000)
def J(theta, X_b, y):
try:
return np.sum((y - X_b.dot(theta))**2) / len(X_b)
except:
return float('inf')
def dJ_math(theta, X_b, y):
return X_b.T.dot(X_b.dot(theta) - y) * 2. / len(y)
def dJ_debug(theta, X_b, y, epsilon=0.01):
res = np.empty(len(theta))
for i in range(len(theta)):
theta_1 = theta.copy()
theta_1[i] += epsilon
theta_2 = theta.copy()
theta_2[i] -= epsilon
res[i] = (J(theta_1, X_b, y) - J(theta_2, X_b, y)) / (2 * epsilon)
return res
def gradient_descent(dJ, X_b, y, initial_theta, eta, n_iters = 1e4, epsilon=1e-8):
theta = initial_theta
cur_iter = 0
while cur_iter < n_iters:
gradient = dJ(theta, X_b, y)
last_theta = theta
theta = theta - eta * gradient
if(abs(J(theta, X_b, y) - J(last_theta, X_b, y)) < epsilon):
break
cur_iter += 1
return theta
X_b = np.hstack([np.ones((len(X), 1)), X])
initial_theta = np.zeros(X_b.shape[1])
eta = 0.01
%time theta = gradient_descent(dJ_debug, X_b, y, initial_theta, eta)
theta
%time theta = gradient_descent(dJ_math, X_b, y, initial_theta, eta)
theta
