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- import torch
- import numpy as np
- from torch import nn
- from torch.autograd import Variable
- import torch.nn.functional as F
- import matplotlib.pyplot as plt
-
- plt.rcParams['font.sans-serif']=['SimHei']
- plt.rcParams['axes.unicode_minus'] = False
-
- #%matplotlib inline
- np.random.seed(1)
- m = 400 # 样本数量
- N = int(m/2) # 每一类的点的个数
- D = 2 # 维度
- x = np.zeros((m, D))
- y = np.zeros((m, 1), dtype='uint8') # label 向量， 0 表示红色， 1 表示蓝色
- a = 4
-
- # 生成两类数据
- for j in range(2):
- ix = range(N*j,N*(j+1))
- t = np.linspace(j*3.12,(j+1)*3.12,N) + np.random.randn(N)*0.2 # theta
- r = a*np.sin(4*t) + np.random.randn(N)*0.2 # radius
- x[ix] = np.c_[r*np.sin(t), r*np.cos(t)]
- y[ix] = j
-
- #尝试用逻辑回归解决
- x = torch.from_numpy(x).float()
- y = torch.from_numpy(y).float()
-
- w = nn.Parameter(torch.randn(2, 1))
- b = nn.Parameter(torch.zeros(1))
-
- # [w,b]是模型的参数； 1e-1是学习速率
- optimizer = torch.optim.SGD([w, b], 1e-1)
- criterion = nn.BCEWithLogitsLoss()
- def logistic_regression(x):
- return torch.mm(x, w) + b
-
-
- for e in range(100):
- # 模型正向计算
- out = logistic_regression(Variable(x))
- # 计算误差
- loss = criterion(out, Variable(y))
- # 误差反传和参数更新
- optimizer.zero_grad()
- loss.backward()
- optimizer.step()
- if (e + 1) % 20 == 0:
- print('epoch:{}, loss:{}'.format(e+1, loss.item()))
-
-
- def plot_decision_boundary(model, x, y):
- # Set min and max values and give it some padding
- x_min, x_max = x[:, 0].min() - 1, x[:, 0].max() + 1
- y_min, y_max = x[:, 1].min() - 1, x[:, 1].max() + 1
- h = 0.01
- # Generate a grid of points with distance h between them
- xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min,y_max, h))
-
- # Predict the function value for the whole grid .c_ 按行连接两个矩阵，左右相加。
- Z = model(np.c_[xx.ravel(), yy.ravel()])
- Z = Z.reshape(xx.shape)
- # Plot the contour and training examples
- plt.contourf(xx, yy, Z, cmap=plt.cm.Spectral)
- plt.ylabel("x2")
- plt.xlabel("x1")
- for i in range(m):
- if y[i] == 0:
- plt.scatter(x[i, 0], x[i, 1], marker='8',c=0, s=40, cmap=plt.cm.Spectral)
- else:
- plt.scatter(x[i, 0], x[i, 1], marker='^',c=1, s=40)
-
-
- def plot_logistic(x):
- x = Variable(torch.from_numpy(x).float())
- out = F.sigmoid(logistic_regression(x))
- out = (out > 0.5) * 1
- return out.data.numpy()
-
- plot_decision_boundary(lambda x: plot_logistic(x), x.numpy(), y.numpy())
- plt.title('逻辑回归')
- plt.savefig('fig-res-8.3.pdf')
- plt.show()
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