曲阜市住房和城乡建设局网站,自己怎么创建免费网站,网站收录查询站长工具,dede网站架设教程文章目录1.简单回归实战#xff1a;2.手写数据识别1.简单回归实战#xff1a;
用 线性回归拟合二维平面中的100个点 公式#xff1a;ywxbywxbywxb 损失函数#xff1a;∑(yreally−y)2\sum(y_{really}-y)^2∑(yreally−y)2 迭代方法#xff1a;梯度下降法#xff0c;…
文章目录1.简单回归实战2.手写数据识别1.简单回归实战
用 线性回归拟合二维平面中的100个点 公式ywxbywxbywxb 损失函数∑(yreally−y)2\sum(y_{really}-y)^2∑(yreally−y)2 迭代方法梯度下降法其中www,bbb更新公式如下 wN1wN−η∗∂loss∂wbN1bN−η∗∂loss∂bw_{N1}w_N-\eta*\frac{\partial loss}{\partial w}\\ b_{N1}b_{N}-\eta*\frac{\partial loss}{\partial b}wN1wN−η∗∂w∂lossbN1bN−η∗∂b∂loss 其中η\etaη表示学习率∂\partial∂表示微分 ∂loss∂w2(wxb−y)x/n∂loss∂b2(wxb−y)/n\frac{\partial loss}{\partial w}2(wxb-y)x/n\\ \frac{\partial loss}{\partial b}2(wxb-y)/n ∂w∂loss2(wxb−y)x/n∂b∂loss2(wxb−y)/n 项目文件 计算损失函数
def compute_loss(b,w,points):total 0for i in range(0,len(points)):x points[i,0]y points[i,1]total (y-(w*xb)) ** 2return total / float(len(points))梯度下降迭代更新
def gradient(b,w,points,leanrningRate):b_gradient 0w_gradient 0N float(len(points))for i in range(0,len(points)):x points[i,0]y points[i,1]b_gradient (2/N) * (((w * x)b)-y)w_gradient (2/N) * (((w * x)b)-y) * xnew_b b - (leanrningRate * b_gradient)new_w w - (leanrningRate * w_gradient)return [new_b , new_w]def graient_descent_runner(points, b, w, learning_rate, num_iterations):new_b bnew_w wfor i in range(num_iterations):new_b, new_w gradient(new_b, new_w, np.array(points), learning_rate)return [new_b, new_w]主函数运行以及绘图结果
def run():points np.genfromtxt(data.csv,delimiter,)learning_rate 0.0001initial_b 0initial_w 0num_iteractions 1000print(Starting gradient descent at b {0}, w {1}, error {2}.format(initial_b,initial_w,compute_loss(initial_b,initial_w,points)))print(Runing...)[b, w] graient_descent_runner(points,initial_b,initial_w,learning_rate,num_iteractions)print(After {0} iterations b {1}, w {2}, error {3}.format(num_iteractions,b,w,compute_loss(b,w,points)))x np.linspace(20, 80, 5)y w * x bpyplot.plot(x, y)pyplot.scatter(points[:, 0], points[:, 1])pyplot.show()if __name__ __main__:run()2.手写数据识别
工具函数库
import torch
from matplotlib import pyplot as pltdef plot_curve(data):fig plt.figure()plt.plot(range(len(data)), data, colorblue)plt.legend([value], locupper right)plt.xlabel(step)plt.ylabel(value)plt.show()def plot_image(img, label, name):fig plt.figure()for i in range(6):plt.subplot(2, 3, i 1)plt.tight_layout()plt.imshow(img[i][0]*0.30810.1307, cmapgray, interpolationnone)plt.title({}: {}.format(name, label[i].item()))plt.xticks([])plt.yticks([])plt.show()def one_hot(label, depth10):out torch.zeros(label.size(0), depth)idx torch.LongTensor(label).view(-1, 1)out.scatter_(dim1, indexidx, value1)return out第一步导入库和图像数据
import torch
from torch import nn #构建神经网络
from torch.nn import functional as F
from torch import optim #最优化工具
import torchvision #视觉工具
from utils import plot_image, plot_curve, one_hotbatch_size 512
train_loader torch.utils.data.DataLoader(torchvision.datasets.MNIST(mnist_data, trainTrue, downloadTrue,transformtorchvision.transforms.Compose([torchvision.transforms.ToTensor(),torchvision.transforms.Normalize((0.1307,), (0.3081,))])),batch_sizebatch_size, shuffleTrue)test_loader torch.utils.data.DataLoader(torchvision.datasets.MNIST(mnist_data/, trainFalse, downloadTrue,transformtorchvision.transforms.Compose([torchvision.transforms.ToTensor(),torchvision.transforms.Normalize((0.1307,), (0.3081,))])),batch_sizebatch_size, shuffleFalse)x, y next(iter(train_loader))
print(x.shape, y.shape, x.min(), y.min())
plot_image(x, y, image sample)第二步新建一个三层的非线性的网层
class Net(nn.Module):def __init__(self):super(Net, self).__init__()#第一层28*28是图片256根据经验随机决定self.fc1 nn.Linear(28 * 28, 256)self.fc2 nn.Linear(256, 64)#第三层(十分类输出一定是10)self.fc3 nn.Linear(64, 10)def forward(self, x):# x: [b, 1, 28, 28]# h1 relu(xw1b1) h2 relu(h1w2b2) h3 h2w3b3x F.relu(self.fc1(x))x F.relu(self.fc2(x))x self.fc3(x)return x第三步train训练
net Net()
# [w1, b1, w2, b2, w3, b3]
optimizer optim.SGD(net.parameters(), lr0.01, momentum0.9)
train_loss []for epoch in range(3):for batch_idx, (x, y) in enumerate(train_loader):# x: [b, 1, 28, 28], y: [512]# [b, 1, 28, 28] [b, 784]将整个图片看做特征向量x x.view(x.size(0), 28*28)# [b, 10]out net(x)# [b, 10]y_onehot one_hot(y)# loss mse(out, y_onehot)loss F.mse_loss(out, y_onehot)optimizer.zero_grad()loss.backward()#梯度下降# w w - lr*gradoptimizer.step()train_loss.append(loss.item())if batch_idx % 100:print(epoch, batch_idx, loss.item())plot_curve(train_loss)第四步准确度测试
total_correct 0
for x,y in test_loader:x x.view(x.size(0), 28*28)out net(x)# out: [b, 10] pred: [b]pred out.argmax(dim1)correct pred.eq(y).sum().float().item()total_correct correcttotal_num len(test_loader.dataset)
acc total_correct / total_num
print(test acc:, acc)x, y next(iter(test_loader))
out net(x.view(x.size(0), 28*28))
pred out.argmax(dim1)
plot_image(x, pred, test)无注释代码
import torch
from torch import nn
from torch.nn import functional as F
from torch import optim
import torchvision
from utils import plot_image, plot_curve, one_hotbatch_size 512
train_loader torch.utils.data.DataLoader(torchvision.datasets.MNIST(mnist_data, trainTrue, downloadTrue,transformtorchvision.transforms.Compose([torchvision.transforms.ToTensor(),torchvision.transforms.Normalize((0.1307,), (0.3081,))])),batch_sizebatch_size, shuffleTrue)test_loader torch.utils.data.DataLoader(torchvision.datasets.MNIST(mnist_data/, trainFalse, downloadTrue,transformtorchvision.transforms.Compose([torchvision.transforms.ToTensor(),torchvision.transforms.Normalize((0.1307,), (0.3081,))])),batch_sizebatch_size, shuffleFalse)x, y next(iter(train_loader))
print(x.shape, y.shape, x.min(), y.min())
plot_image(x, y, image sample)class Net(nn.Module):def __init__(self):super(Net, self).__init__()self.fc1 nn.Linear(28 * 28, 256)self.fc2 nn.Linear(256, 64)self.fc3 nn.Linear(64, 10)def forward(self, x):x F.relu(self.fc1(x))x F.relu(self.fc2(x))x self.fc3(x)return xnet Net()
# [w1, b1, w2, b2, w3, b3]
optimizer optim.SGD(net.parameters(), lr0.01, momentum0.9)
train_loss []for epoch in range(3):for batch_idx, (x, y) in enumerate(train_loader):x x.view(x.size(0), 28*28)out net(x)y_onehot one_hot(y)loss F.mse_loss(out, y_onehot)optimizer.zero_grad()loss.backward()optimizer.step()train_loss.append(loss.item())if batch_idx % 100:print(epoch, batch_idx, loss.item())plot_curve(train_loss)total_correct 0
for x,y in test_loader:x x.view(x.size(0), 28*28)out net(x)pred out.argmax(dim1)correct pred.eq(y).sum().float().item()total_correct correcttotal_num len(test_loader.dataset)
acc total_correct / total_num
print(test acc:, acc)x, y next(iter(test_loader))
out net(x.view(x.size(0), 28*28))
pred out.argmax(dim1)
plot_image(x, pred, test)
