這個 numpy 賦值中尾隨逗號的作用是什么？

Question

我使用下面的代碼作為在 TensorFlow 中編寫數據生成器函數的模板，我想知道下面的尾隨逗號是否必要或有用：

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,]中的逗號有什么作用嗎？ 我在 Jupyter 中搜索了高低並使用類似代碼運行了一堆測試，但我找不到使用或不使用逗號之間的任何區別。

Answer 1

除了添加逗號只是使部分代碼變得多余之外，沒有太大區別。 但請注意，逗號會減慢程序的速度，見下文：

>>> 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

---

有關更多信息（盡管我確定您已經知道這一點） ，在兩個括號之間的對象之后添加逗號確實會有所不同，就像它一樣，創建了一個元組，沒有它，括號將被取消：

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

輸出：

1
(1,)

Answer 2

重要任務的時間安排：

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)

瑣碎任務的時間：

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)

有時在我的回答中我會包含尾隨冒號x[1,:,:] 。 代碼不在乎，但它可以幫助人們注意它正在訪問多個維度之一。