Pytorch--自动求导

求导

 

 

逻辑回归

逻辑回归-Pytorch实现

import torch
import torch.nn as nn
import matplotlib.pyplot as plt
import numpy as np
torch.manual_seed(10)

# ============================ step 1 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 shape=(100, 2)
y0 = torch.zeros(sample_nums)                         # 类别0 标签 shape=(100, 1)
x1 = torch.normal(-mean_value * n_data, 1) + bias     # 类别1 数据 shape=(100, 2)
y1 = torch.ones(sample_nums)                          # 类别1 标签 shape=(100, 1)
train_x = torch.cat((x0, x1), 0)
train_y = torch.cat((y0, y1), 0)

# ============================ step 2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


# ============================ step 3 选择损失函数 ============================
loss_fn = nn.BCELoss()

# ============================ step 4 选择优化器   ============================
lr = 0.01  # 学习率
optimizer = torch.optim.SGD(lr_net.parameters(), lr=lr, momentum=0.9)

# ============================ step 5模型训练 ============================
for iteration in range(1000):

    # 前向传播
    y_pred = lr_net(train_x)

    # 计算 loss
    loss = loss_fn(y_pred.squeeze(), train_y)

    # 反向传播
    loss.backward()

    # 更新参数
    optimizer.step()

    # 清空梯度
    optimizer.zero_grad()

    # 绘图
    if iteration % 20 == 0:

        mask = y_pred.ge(0.5).float().squeeze()  # 以0.5为阈值进行分类
        correct = (mask == train_y).sum()  # 计算正确预测的样本个数
        acc = correct.item() / train_y.size(0)  # 计算分类准确率

        plt.scatter(x0.data.numpy()[:, 0], x0.data.numpy()[:, 1], c='r', label='class 0')
        plt.scatter(x1.data.numpy()[:, 0], x1.data.numpy()[:, 1], c='b', label='class 1')

        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

输出的画图结果：

Keras实现

import numpy as np

from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation, Flatten
import matplotlib.pyplot as plt
from sklearn import datasets

# 样本数据集，两个特征列，两个分类二分类不需要onehot编码，直接将类别转换为0和1，分别代表正样本的概率。
X,y=datasets.make_classification(n_samples=200, n_features=2, n_informative=2, n_redundant=0,n_repeated=0, n_classes=2, n_clusters_per_class=1)

# 构建神经网络模型
model = Sequential()
model.add(Dense(input_dim=2, units=1))
model.add(Activation('sigmoid'))

# 选定loss函数和优化器
model.compile(loss='binary_crossentropy', optimizer='sgd')

# 训练过程
print('Training -----------')
for step in range(501):
    cost = model.train_on_batch(X, y)
    if step % 50 == 0:
        print("After %d trainings, the cost: %f" % (step, cost))

# 测试过程
print('\nTesting ------------')
cost = model.evaluate(X, y, batch_size=40)
print('test cost:', cost)
W, b = model.layers[0].get_weights()
print('Weights=', W, '\nbiases=', b)

# 将训练结果绘出
Y_pred = model.predict(X)
Y_pred = (Y_pred*2).astype('int')  # 将概率转化为类标号，概率在0-0.5时，转为0，概率在0.5-1时转为1
# 绘制散点图 参数：x横轴 y纵轴
plt.subplot(2,1,1).scatter(X[:,0], X[:,1], c=Y_pred)
plt.subplot(2,1,2).scatter(X[:,0], X[:,1], c=y)
plt.show()
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原文链接：https://blog.csdn.net/luanpeng825485697/article/details/80140838