pytorch深度学习自动求导训练模型

[python]代码库

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

[代码运行效果截图]