【偷偷卷死小伙伴Pytorch20天】-【day9】-【中阶API示范】

【偷偷卷死小伙伴Pytorch20天】-【day9】-【中阶API示范】

系统教程20天拿下Pytorch​ 最近和中哥、会哥进行一个小打卡活动，20天pytorch，这是第9天。欢迎一键三连。

文章目录

​​一、线性回归​​

​​1，准备数据​​​​2，定义模型​​​​3，训练模型​​​​4，结果可视化​​

​​二、DNN二分类模型​​

​​1，准备数据​​​​2, 定义模型​​​​3，训练模型​​​​4，结果可视化​​

​​总结​​

​​线性回归​​​​DNN二分类​​

下面的范例使用Pytorch的中阶API实现线性回归模型和和DNN二分类模型。

Pytorch的中阶API主要包括各种模型层，损失函数，优化器，数据管道等等。

import osimport datetime#打印时间def printbar(): nowtime = datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S') print("\n"+"=========="*8 + "%s"%nowtime)#mac系统上pytorch和matplotlib在jupyter中同时跑需要更改环境变量os.environ["KMP_DUPLICATE_LIB_OK"]="TRUE"

一、线性回归

1，准备数据

import numpy as np import pandas as pdfrom matplotlib import pyplot as plt import torchfrom torch import nnimport torch.nn.functional as Ffrom torch.utils.data import Dataset,DataLoader,TensorDataset#样本数量n = 400# 生成测试用数据集X = 10*torch.rand([n,2])-5.0 #torch.rand是均匀分布 w0 = torch.tensor([[2.0],[-3.0]])b0 = torch.tensor([[10.0]])Y = X@w0 + b0 + torch.normal( 0.0,2.0,size = [n,1]) # @表示矩阵乘法,增加正态扰动

# 数据可视化%matplotlib inline%config InlineBackend.figure_format = 'svg'plt.figure(figsize = (12,5))ax1 = plt.subplot(121)ax1.scatter(X[:,0],Y[:,0], c = "b",label = "samples")ax1.legend()plt.xlabel("x1")plt.ylabel("y",rotation = 0)ax2 = plt.subplot(122)ax2.scatter(X[:,1],Y[:,0], c = "g",label = "samples")ax2.legend()plt.xlabel("x2")plt.ylabel("y",rotation = 0)plt.show()

#构建输入数据管道ds = TensorDataset(X,Y)dl = DataLoader(ds,batch_size = 10,shuffle=True,num_workers=2)

2，定义模型

model = nn.Linear(2,1) #线性层model.loss_func = nn.MSELoss()model.optimizer = torch.optim.SGD(model.parameters(),lr = 0.01)

3，训练模型

def train_step(model, features, labels): model.optimizer.zero_grad() predictions = model(features) loss = model.loss_func(predictions,labels) loss.backward() model.optimizer.step() return loss.item()# 测试train_step效果features,labels = next(iter(dl))train_step(model,features,labels)

def train_model(model,epochs): for epoch in range(1,epochs+1): for features, labels in dl: loss = train_step(model,features,labels) if epoch%50==0: printbar() w = model.state_dict()["weight"] b = model.state_dict()["bias"] print("epoch =",epoch,"loss = ",loss) print("w =",w) print("b =",b)train_model(model,epochs = 200)

4，结果可视化

# 结果可视化%matplotlib inline%config InlineBackend.figure_format = 'svg'w,b = model.state_dict()["weight"],model.state_dict()["bias"]plt.figure(figsize = (12,5))ax1 = plt.subplot(121)ax1.scatter(X[:,0],Y[:,0], c = "b",label = "samples")ax1.plot(X[:,0],w[0,0]*X[:,0]+b[0],"-r",linewidth = 5.0,label = "model")ax1.legend()plt.xlabel("x1")plt.ylabel("y",rotation = 0)ax2 = plt.subplot(122)ax2.scatter(X[:,1],Y[:,0], c = "g",label = "samples")ax2.plot(X[:,1],w[0,1]*X[:,1]+b[0],"-r",linewidth = 5.0,label = "model")ax2.legend()plt.xlabel("x2")plt.ylabel("y",rotation = 0)plt.show()

二、DNN二分类模型

1，准备数据

import numpy as np import pandas as pd from matplotlib import pyplot as pltimport torchfrom torch import nnimport torch.nn.functional as Ffrom torch.utils.data import Dataset,DataLoader,TensorDataset%matplotlib inline%config InlineBackend.figure_format = 'svg'#正负样本数量n_positive,n_negative = 2000,2000#生成正样本, 小圆环分布r_p = 5.0 + torch.normal(0.0,1.0,size = [n_positive,1]) theta_p = 2*np.pi*torch.rand([n_positive,1])Xp = torch.cat([r_p*torch.cos(theta_p),r_p*torch.sin(theta_p)],axis = 1)Yp = torch.ones_like(r_p)#生成负样本, 大圆环分布r_n = 8.0 + torch.normal(0.0,1.0,size = [n_negative,1]) theta_n = 2*np.pi*torch.rand([n_negative,1])Xn = torch.cat([r_n*torch.cos(theta_n),r_n*torch.sin(theta_n)],axis = 1)Yn = torch.zeros_like(r_n)#汇总样本X = torch.cat([Xp,Xn],axis = 0)Y = torch.cat([Yp,Yn],axis = 0)#可视化plt.figure(figsize = (6,6))plt.scatter(Xp[:,0],Xp[:,1],c = "r")plt.scatter(Xn[:,0],Xn[:,1],c = "g")plt.legend(["positive","negative"]);

#构建输入数据管道ds = TensorDataset(X,Y)dl = DataLoader(ds,batch_size = 10,shuffle=True,num_workers=2)

2, 定义模型

class DNNModel(nn.Module): def __init__(self): super(DNNModel, self).__init__() self.fc1 = nn.Linear(2,4) self.fc2 = nn.Linear(4,8) self.fc3 = nn.Linear(8,1) # 正向传播 def forward(self,x): x = F.relu(self.fc1(x)) x = F.relu(self.fc2(x)) y = nn.Sigmoid()(self.fc3(x)) return y # 损失函数 def loss_func(self,y_pred,y_true): return nn.BCELoss()(y_pred,y_true) # 评估函数(准确率) def metric_func(self,y_pred,y_true): y_pred = torch.where(y_pred>0.5,torch.ones_like(y_pred,dtype = torch.float32), torch.zeros_like(y_pred,dtype = torch.float32)) acc = torch.mean(1-torch.abs(y_true-y_pred)) return acc # 优化器 @property def optimizer(self): return torch.optim.Adam(self.parameters(),lr = 0.001) model = DNNModel()

# 测试模型结构(features,labels) = next(iter(dl))predictions = model(features)loss = model.loss_func(predictions,labels)metric = model.metric_func(predictions,labels)print("init loss:",loss.item())print("init metric:",metric.item())

3，训练模型

def train_step(model, features, labels): # 正向传播求损失 predictions = model(features) loss = model.loss_func(predictions,labels) metric = model.metric_func(predictions,labels) model.optimizer.zero_grad() # 反向传播求梯度 loss.backward() # 更新模型参数 model.optimizer.step() return loss.item(),metric.item()# 测试train_step效果features,labels = next(iter(dl))train_step(model,features,labels)

def train_model(model,epochs): for epoch in range(1,epochs+1): loss_list,metric_list = [],[] for features, labels in dl: lossi,metrici = train_step(model,features,labels) loss_list.append(lossi) metric_list.append(metrici) loss = np.mean(loss_list) metric = np.mean(metric_list) if epoch%100==0: printbar() print("epoch =",epoch,"loss = ",loss,"metric = ",metric) train_model(model,epochs = 300)

4，结果可视化

# 结果可视化fig, (ax1,ax2) = plt.subplots(nrows=1,ncols=2,figsize = (12,5))ax1.scatter(Xp[:,0],Xp[:,1], c="r")ax1.scatter(Xn[:,0],Xn[:,1],c = "g")ax1.legend(["positive","negative"]);ax1.set_title("y_true");Xp_pred = X[torch.squeeze(model.forward(X)>=0.5)]Xn_pred = X[torch.squeeze(model.forward(X)<0.5)]ax2.scatter(Xp_pred[:,0],Xp_pred[:,1],c = "r")ax2.scatter(Xn_pred[:,0],Xn_pred[:,1],c = "g")ax2.legend(["positive","negative"]);ax2.set_title("y_pred");

总结

线性回归

1.TensorDataset(X,Y) 2.对于高纬数据可以分维度可视化结果

DNN二分类

版权声明：本文内容由网络用户投稿，版权归原作者所有，本站不拥有其著作权，亦不承担相应法律责任。如果您发现本站中有涉嫌抄袭或描述失实的内容，请联系我们jiasou666@gmail.com 处理，核实后本网站将在24小时内删除侵权内容。