深度学习之pytorch 第三课

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最新推荐文章于 2021-07-27 20:18:53 发布

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分类专栏： pycharm tensorflow 安装出错解决办法文章标签： pytorch learning

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本教程使用PyTorch实现线性回归模型，通过训练数据集调整模型参数，最终绘制出拟合曲线。涉及PyTorch基础操作、变量定义、损失函数计算及优化器应用。

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pytorch 第三课

	import torch
	import numpy as np
	from torch.autograd import Variable
	from torch import nn
	from torch import optim
	import matplotlib.pyplot as plt
	
	x_train = np.array([[3.3], [4.4], [5.5], [6.71], [6.93]], dtype=np.float32)
	y_train = np.array([[1.7], [2.76], [2.09], [3.19], [1.694]], dtype=np.float32)
	
	x_train = torch.from_numpy(x_train)
	y_train = torch.from_numpy(y_train)
	
	class LinearRegression(nn.Module):
	    def __init__(self):
	        super(LinearRegression, self).__init__()
	        self.linear = nn.Linear(1, 1)
	    def forward(self, x):
	        out = self.linear(x)
	        return out
	
	if torch.cuda.is_available():
	    model = LinearRegression().cuda()
	else:
	    model = LinearRegression()
	
	criterion = nn.MSELoss()
	optimizer = optim.SGD(model.parameters(), lr = 1e-3)
	
	num_epochs = 1000
	for epoch in range(num_epochs):
	    if torch.cuda.is_available():
	        inputs = Variable(x_train).cuda()
	        target = Variable(y_train).cuda()
	    else:
	        inputs = Variable(x_train)
	        target = Variable(y_train)
	
	# forward
	out = model(inputs)
	loss = criterion(out, target)
	# backward
	optimizer.zero_grad()
	loss.backward()
	optimizer.step()
	
	if(epoch+1) % 20 == 0:
	    print('Epoch[{}/{}], loss: {:.6f}'
	          .format(epoch+1, num_epochs, loss.data[0]))
	
	
	model.eval() # 测试模型
	predict = model(Variable(x_train))
	predict = predict.data.numpy()
	plt.plot(x_train.numpy(), y_train.numpy(), 'ro', label='Original data')
	plt.plot(x_train.numpy(), predict, label='Fitting Line')
	plt.show()