小编Ada*_*ula的帖子

我正在使用Neuralnet包来训练分类器.训练数据如下所示:

> head(train_data)
   mvar_12      mvar_40 v10       mvar_1   mvar_2  Labels
1 136.51551310       6   0   656.78784220      0      0
2 145.10739860      87   0    14.21413596      0      0
3 194.74940330       4   0   196.62888080      0      0
4 202.38663480       2   0   702.27307720      0      1
5  60.14319809       9   0    -1.00000000     -1      0
6  95.46539380       6   0   539.09479640      0      0

代码如下:

n <- names(train_data)
f <- as.formula(paste("Labels ~", paste(n[!n %in% "Labels"], collapse = " + ")))
library(neuralnet)
nn <- neuralnet(f, tr_nn, hidden = 4, threshold = 0.01,        
                stepmax = …

对于两个字典d1，d2定义为

d1 = {'foo':123, 'bar':789}
d2 = {'bar':789, 'foo':123}

键的顺序在Python 3.6+中保留。当我们遍历字典并打印项目时，这一点很明显。

>>> for x in d1.items():
...     print(x)
...
('foo', 123)
('bar', 789)


>>> for x in d2.items():
...     print(x)
...
('bar', 789)
('foo', 123)

为什么Python仍然考虑d1并d2保持平等？

>>> d1 == d2
True