2021-09-25 关于启动代码出现炸内核的解决方法

notebook关于启动代码出现炸内核的解决方法

在安装配置pycharm的后，运行import torch as t 就出现了炸内核的情况，后来知道是因为安装notebook的时候多点了一个东西，导致内核便空。后来我重新安装了notebook，配置了pytorch环境，但是还是出现

以下是原代码：
import torch
import torch.nn as nn
import numpy as np
import matplotlib.pyplot as plt
from torch.autograd import Variable

Hyper Parameters
input_size = 1
output_size = 1
num_epochs = 1000
learning_rate = 0.001

x_train = np.array([[2.3], [4.4], [3.7], [6.1], [7.3], [2.1],[5.6], [7.7], [7.7], [4.1],

                [6.7], [6.1], [7.5], [2.1], [7.2],

                [5.6], [5.7], [7.7], [3.1]], dtype=np.float32)

#xtrain生成矩阵数据

y_train = np.array([[2.7], [4.76], [4.1], [7.1], [7.6], [3.5],[5.4], [7.6], [7.9], [5.3],

                [7.3], [7.5], [7.5], [3.2], [7.7],

                [6.4], [6.6], [7.9], [4.9]], dtype=np.float32)

plt.figure()
#画图散点图
plt.scatter(x_train,y_train)
plt.xlabel(‘x_train’)
#x轴名称
plt.ylabel(‘y_train’)
#y轴名称
#显示图片
plt.show()

#Linear Regression Model
class LinearRegression(nn.Module):
def init(self, input_size, output_size):
super(LinearRegression, self).init()
self.linear = nn.Linear(input_size, output_size)

def forward(self, x):
    out = self.linear(x)
    return out

model = LinearRegression(input_size, output_size)

Loss and Optimizer
criterion = nn.MSELoss()
optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate)

#Train the Model
for epoch in range(num_epochs):
# Convert numpy array to torch Variable
inputs = Variable(torch.from_numpy(x_train))
targets = Variable(torch.from_numpy(y_train))

# Forward + Backward + Optimize
optimizer.zero_grad()  
outputs = model(inputs)
loss = criterion(outputs, targets)
loss.backward()
optimizer.step()

if (epoch+1) % 50 == 0:
    print ('Epoch [%d/%d], Loss: %.4f' 
           %(epoch+1, num_epochs, loss.item()))

Plot the graph

model.eval()
predicted = model(Variable(torch.from_numpy(x_train))).data.numpy()
plt.plot(x_train, y_train, ‘ro’)
plt.plot(x_train, predicted, label=‘predict’)
plt.legend()
plt.show()

执行这个代码会出现炸内核的情况，后来发现是因为是由于在每次打开jupyter notebook后没有实际上关掉，所以占据了大量的内存
只要执行：

import os
os.environ[“KMP_DUPLICATE_LIB_OK”] = “TRUE”
就可以了

最终代码如下：