pytorch實現線性回歸以及多元回歸
本文實例為大傢分享瞭pytorch實現線性回歸以及多元回歸的具體代碼,供大傢參考,具體內容如下
最近在學習pytorch,現在把學習的代碼放在這裡,下面是github鏈接
直接附上github代碼
# 實現一個線性回歸 # 所有的層結構和損失函數都來自於 torch.nn # torch.optim 是一個實現各種優化算法的包,調用的時候必須是需要優化的參數傳入,這些參數都必須是Variable x_train = np.array([[3.3],[4.4],[5.5],[6.71],[6.93],[4.168],[9.779],[6.182],[7.59],[2.167],[7.042],[10.791],[5.313],[7.997],[3.1]],dtype=np.float32) y_train = np.array([[1.7],[2.76],[2.09],[3.19],[1.694],[1.573],[3.366],[2.596],[2.53],[1.221],[2.827],[3.465],[1.65],[2.904],[1.3]],dtype=np.float32) # 首先我們需要將array轉化成tensor,因為pytorch處理的單元是Tensor x_train = torch.from_numpy(x_train) y_train = torch.from_numpy(y_train) # def a simple network class LinearRegression(nn.Module): def __init__(self): super(LinearRegression,self).__init__() self.linear = nn.Linear(1, 1) # input and output is 2_dimension def forward(self, x): out = self.linear(x) return out if torch.cuda.is_available(): model = LinearRegression().cuda() #model = model.cuda() else: model = LinearRegression() #model = model.cuda() # 定義loss function 和 optimize func criterion = nn.MSELoss() # 均方誤差作為優化函數 optimizer = torch.optim.SGD(model.parameters(),lr=1e-3) num_epochs = 30000 for epoch in range(num_epochs): if torch.cuda.is_available(): inputs = Variable(x_train).cuda() outputs = Variable(y_train).cuda() else: inputs = Variable(x_train) outputs = Variable(y_train) # forward out = model(inputs) loss = criterion(out,outputs) # backword optimizer.zero_grad() # 每次做反向傳播之前都要進行歸零梯度。不然梯度會累加在一起,造成不收斂的結果 loss.backward() optimizer.step() if (epoch +1)%20==0: print('Epoch[{}/{}], loss: {:.6f}'.format(epoch+1,num_epochs,loss.data)) model.eval() # 將模型變成測試模式 predict = model(Variable(x_train).cuda()) predict = predict.data.cpu().numpy() plt.plot(x_train.numpy(),y_train.numpy(),'ro',label = 'original data') plt.plot(x_train.numpy(),predict,label = 'Fitting line') plt.show()
結果如圖所示:
多元回歸:
# _*_encoding=utf-8_*_ # pytorch 裡面最基本的操作對象是Tensor,pytorch 的tensor可以和numpy的ndarray相互轉化。 # 實現一個線性回歸 # 所有的層結構和損失函數都來自於 torch.nn # torch.optim 是一個實現各種優化算法的包,調用的時候必須是需要優化的參數傳入,這些參數都必須是Variable # 實現 y = b + w1 *x + w2 *x**2 +w3*x**3 import os os.environ['CUDA_DEVICE_ORDER']="PCI_BUS_ID" os.environ['CUDA_VISIBLE_DEVICES']='0' import torch import numpy as np from torch.autograd import Variable import matplotlib.pyplot as plt from torch import nn # pre_processing def make_feature(x): x = x.unsqueeze(1) # unsquenze 是為瞭添加維度1的,0表示第一維度,1表示第二維度,將tensor大小由3變為(3,1) return torch.cat([x ** i for i in range(1, 4)], 1) # 定義好真實的數據 def f(x): W_output = torch.Tensor([0.5, 3, 2.4]).unsqueeze(1) b_output = torch.Tensor([0.9]) return x.mm(W_output)+b_output[0] # 外積,矩陣乘法 # 批量處理數據 def get_batch(batch_size =32): random = torch.randn(batch_size) x = make_feature(random) y = f(x) if torch.cuda.is_available(): return Variable(x).cuda(),Variable(y).cuda() else: return Variable(x),Variable(y) # def model class poly_model(nn.Module): def __init__(self): super(poly_model,self).__init__() self.poly = nn.Linear(3,1) def forward(self,input): output = self.poly(input) return output if torch.cuda.is_available(): print("sdf") model = poly_model().cuda() else: model = poly_model() # 定義損失函數和優化器 criterion = nn.MSELoss() optimizer = torch.optim.SGD(model.parameters(), lr=1e-3) epoch = 0 while True: batch_x, batch_y = get_batch() #print(batch_x) output = model(batch_x) loss = criterion(output,batch_y) print_loss = loss.data optimizer.zero_grad() loss.backward() optimizer.step() epoch = epoch +1 if print_loss < 1e-3: print(print_loss) break model.eval() print("Epoch = {}".format(epoch)) batch_x, batch_y = get_batch() predict = model(batch_x) a = predict - batch_y y = torch.sum(a) print('y = ',y) predict = predict.data.cpu().numpy() plt.plot(batch_x.cpu().numpy(),batch_y.cpu().numpy(),'ro',label = 'Original data') plt.plot(batch_x.cpu().numpy(),predict,'b', ls='--',label = 'Fitting line') plt.show()
以上就是本文的全部內容,希望對大傢的學習有所幫助,也希望大傢多多支持WalkonNet。
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