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This article is about how pytorch implements linear regression. The editor thinks it is very practical, so share it with you as a reference and follow the editor to have a look.

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# randomly initialize a 2D dataset Use a friend torch to train a regression model import numpy as npimport randomimport matplotlib.pyplot as pltx = np.arange (20) y = np.array ([5* x [I] + random.randint (1m 20) for i in range (len (x))]) # random.randint (parameter 1 Parameter 2) the function returns any integer print ('-'* 50) # print data set print (x) print (y) import torchx_train = torch.from_numpy (x). Float () y_train = torch.from_numpy (y). Float () # modelclass LinearRegression (torch.nn.Module): def _ init__ (self): super (LinearRegression) Self). _ _ init__ () # A new model whose inputs and outputs are both one-dimensional self.linear = torch.nn.Linear (1pm 1) def forward (self,x): return self.linear (x) # Error function Optimizer model = LinearRegression () criterion = torch.nn.MSELoss () optimizer = torch.optim.SGD (model.parameters (), 0.001) # start training num_epoch = 20for i in range (num_epoch): input_data = x_train.unsqueeze (1) target = y_train.unsqueeze (1) # unsqueeze (1) add a dimension out = model (input_data) loss = criterion (out) in the second dimension Target) optimizer.zero_grad () loss.backward () optimizer.step () print ("Eopch: [{} / {}, loss: [{: .4f}]" .format (if ()) if () 2 = = 0): predict = model (input_data) plt.plot (x_train.data.numpy (), predict.squeeze (1). Data.numpy () "r") loss = criterion (predict,target) plt.title ("Loss: {: .4f}" .format (loss.item ()) plt.xlabel ("X") plt.ylabel ("Y") plt.scatter

Experimental results:

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