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860天前import torch |
import torch.nn as nn |
import matplotlib.pyplot as plt |
import numpy as np |
#import os |
#os.environ["KMP_DUPLICATE_LIB_OK"] = "TRUE" |
torch.manual_seed(10) |
# ========生成数据============= |
sample_nums = 100 |
mean_value = 1.7 |
bias = 1 |
n_data = torch.ones(sample_nums, 2) |
x0 = torch.normal(mean_value * n_data, 1) + bias # 类别0数据 |
y0 = torch.zeros(sample_nums) # 类别0标签 |
x1 = torch.normal(-mean_value * n_data, 1) + bias # 类别1数据 |
y1 = torch.ones(sample_nums) # 类别1标签 |
train_x = torch.cat((x0, x1), 0) |
train_y = torch.cat((y0, y1), 0) |
# ==========选择模型=========== |
class LR(nn.Module): |
def __init__(self): |
super(LR, self).__init__() |
self.features = nn.Linear(2, 1) |
self.sigmoid = nn.Sigmoid() |
def forward(self, x): |
x = self.features(x) |
x = self.sigmoid(x) |
return x |
lr_net = LR() # 实例化逻辑回归模型 |
# ==============选择损失函数=============== |
loss_fn = nn.BCELoss() |
# ==============选择优化器================= |
lr = 0.01 |
optimizer = torch.optim.SGD(lr_net.parameters(), lr=lr, momentum=0.9) |
# ===============模型训练================== |
for iteration in range(1000): |
# 前向传播 |
y_pred = lr_net(train_x) # 模型的输出 |
# 计算loss |
loss = loss_fn(y_pred.squeeze(), train_y) |
# 反向传播 |
loss.backward() |
# 更新参数 |
optimizer.step() |
# 绘图 |
if iteration % 20 == 0: |
mask = y_pred.ge(0.5).float().squeeze() # 以0.5分类 |
correct = (mask == train_y).sum() # 正确预测样本数 |
acc = correct.item() / train_y.size(0) # 分类准确率 |
plt.scatter(x0.data.numpy()[:, 0], x0.data.numpy()[:, 1], c='r', label='class0') |
plt.scatter(x1.data.numpy()[:, 0], x1.data.numpy()[:, 1], c='b', label='class1') |
w0, w1 = lr_net.features.weight[0] |
w0, w1 = float(w0.item()), float(w1.item()) |
plot_b = float(lr_net.features.bias[0].item()) |
plot_x = np.arange(-6, 6, 0.1) |
plot_y = (-w0 * plot_x - plot_b) / w1 |
plt.xlim(-5, 7) |
plt.ylim(-7, 7) |
plt.plot(plot_x, plot_y) |
plt.text(-5, 5, 'Loss=%.4f' % loss.data.numpy(), fontdict={'size': 20, 'color': 'red'}) |
plt.title('Iteration:{}\nw0:{:.2f} w1:{:.2f} b{:.2f} accuracy:{:2%}'.format(iteration, w0, w1, plot_b, acc)) |
plt.legend() |
plt.show() |
plt.pause(0.5) |
if acc > 0.99: |
break |
