在此 numpy 分配中尾随逗号的效果是什么？

Question

我使用下面的代码作为在 TensorFlow 中编写数据生成器函数的模板，我想知道下面的尾随逗号是否必要或有用：

def __data_generation(self, list_IDs_temp):
    'Generates data containing batch_size samples' # X : (n_samples, *dim, n_channels)
    # Initialization
    X = np.empty((self.batch_size, *self.dim, self.n_channels))
    y = np.empty((self.batch_size), dtype=int)

    # Generate data
    for i, ID in enumerate(list_IDs_temp):
        # Store sample
        X[i,] = np.load('data/' + ID + '.npy')

        # Store class
        y[i] = self.labels[ID]

    return X, keras.utils.to_categorical(y, num_classes=self.n_classes)

X[i,] 中的逗号有什么作用吗？我在 Jupyter 中搜索了高低和运行一堆具有类似代码的测试，但我找不到使用或不使用逗号之间的任何区别。

Answer 1

除了添加逗号只是使部分代码冗余之外，没有太大区别。但请注意，逗号会减慢程序速度，见下文：

>>> from timeit import timeit
>>> timeit('a[:3,]', 'import numpy as np; a = np.array([1, 2, 3, 4, 5])')
0.26200279999999765
>>> timeit('a[:3,]', 'import numpy as np; a = np.array([1, 2, 3, 4, 5])')
0.27410390000000007
>>> timeit('a[:3,]', 'import numpy as np; a = np.array([1, 2, 3, 4, 5])')
0.3642131000000006
>>> timeit('a[:3,]', 'import numpy as np; a = np.array([1, 2, 3, 4, 5])')
0.3105785999999995
>>> timeit('a[:3,]', 'import numpy as np; a = np.array([1, 2, 3, 4, 5])')
0.2766163000000006
>>> timeit('a[:3,]', 'import numpy as np; a = np.array([1, 2, 3, 4, 5])')
0.2650689999999969
>>> timeit('a[:3,]', 'import numpy as np; a = np.array([1, 2, 3, 4, 5])')
0.2776439999999951
>>> timeit('a[:3,]', 'import numpy as np; a = np.array([1, 2, 3, 4, 5])')
0.3056855999999968
>>> timeit('a[:3,]', 'import numpy as np; a = np.array([1, 2, 3, 4, 5])')
0.2718677000000014
>>> timeit('a[:3,]', 'import numpy as np; a = np.array([1, 2, 3, 4, 5])')
0.2666911999999968

>>> from timeit import timeit
>>> timeit('a[:3]', 'import numpy as np; a = np.array([1, 2, 3, 4, 5])')
0.25228500000000054
>>> timeit('a[:3]', 'import numpy as np; a = np.array([1, 2, 3, 4, 5])')
0.23471499999999423
>>> timeit('a[:3]', 'import numpy as np; a = np.array([1, 2, 3, 4, 5])')
0.3306362000000007
>>> timeit('a[:3]', 'import numpy as np; a = np.array([1, 2, 3, 4, 5])')
0.2560698000000059
>>> timeit('a[:3]', 'import numpy as np; a = np.array([1, 2, 3, 4, 5])')
0.2566029000000043
>>> timeit('a[:3]', 'import numpy as np; a = np.array([1, 2, 3, 4, 5])')
0.24175780000000202
>>> timeit('a[:3]', 'import numpy as np; a = np.array([1, 2, 3, 4, 5])')
0.23682909999999424
>>> timeit('a[:3]', 'import numpy as np; a = np.array([1, 2, 3, 4, 5])')
0.2400262999999967
>>> timeit('a[:3]', 'import numpy as np; a = np.array([1, 2, 3, 4, 5])')
0.2468849999999918
>>> timeit('a[:3]', 'import numpy as np; a = np.array([1, 2, 3, 4, 5])')
0.22863809999999773

有关更多信息（尽管我确定您已经知道这一点），在两个括号之间的对象后添加逗号确实有所不同，就像它一样，创建了一个元组，没有它，括号被取消：

a = (1)
print(a)
a = (1,)
print(a)

输出：

1
(1,)

Answer 2

重要任务的时间安排：

In [146]: x=np.zeros((100,100,100))
In [148]: y=np.arange(10000.).reshape(100,100)
In [149]: x[1]=y

In [150]: timeit x[1]=y
6.89 µs ± 64.6 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)

In [151]: timeit x[1,]=y
6.89 µs ± 70.5 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)

In [153]: timeit x[1,:,:]=y
7.12 µs ± 3.44 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)

次琐碎的任务：

In [154]: timeit x[1]
194 ns ± 3.79 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)
In [155]: timeit x[1,]
205 ns ± 10.6 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)

有时我的回答中会包含尾随冒号 x[1,:,:]。代码不关心，但它可以帮助人们注意它正在访问多个维度之一。

在此 numpy 分配中尾随逗号的效果是什么？

What is the effect of the trailing comma in this numpy assignment?

python

numpy

tensorflow2.0

comma