关于重塑numpy数组 [英] About reshaping numpy array

问题描述

trainX.size == 43120000
trainX = trainX.reshape([-1, 28, 28, 1])

(1)重塑是否接受列表作为argment而不是元组?

(1)Does reshape accept a list as an argment instead of a tuple?

(2)以下两个语句是否等效?

(2)Are the following two statements equivalent?

trainX = trainX.reshape([-1, 28, 28, 1])
trainX = trainX.reshape((55000, 28, 28, 1))

推荐答案

尝试各种版本:

In [1]: np.arange(12).reshape(3,4)
Out[1]: 
array([[ 0,  1,  2,  3],
       [ 4,  5,  6,  7],
       [ 8,  9, 10, 11]])
In [2]: np.arange(12).reshape([3,4])
Out[2]: 
array([[ 0,  1,  2,  3],
       [ 4,  5,  6,  7],
       [ 8,  9, 10, 11]])
In [3]: np.arange(12).reshape((3,4))
Out[3]: 
array([[ 0,  1,  2,  3],
       [ 4,  5,  6,  7],
       [ 8,  9, 10, 11]])

使用reshape方法，形状可以是参数，元组或列表. reshape函数必须位于列表或元组中，以将它们与第一个数组参数分开

With the reshape method, the shape can be arguments, a tuple or a list. In the reshape function is has to be in a list or tuple, to separate them from the first array argument

In [4]: np.reshape(np.arange(12), (3,4))
Out[4]: 
array([[ 0,  1,  2,  3],
       [ 4,  5,  6,  7],
       [ 8,  9, 10, 11]])

是的，可以使用一个-1.整形的总大小是固定的，因此可以从其他值中推导出一个值.

and yes, one -1 can be used. The total size of the reshape is fixed, so one value can be deduced from the others.

In [5]: np.arange(12).reshape(-1,4)
Out[5]: 
array([[ 0,  1,  2,  3],
       [ 4,  5,  6,  7],
       [ 8,  9, 10, 11]])

方法文档中有以下注释:

The method documentation has this note:

与自由功能numpy.reshape不同，ndarray上的此方法允许 要作为单独参数传递的shape参数的元素. 例如，a.reshape(10, 11)等效于 a.reshape((10, 11)).

Unlike the free function numpy.reshape, this method on ndarray allows the elements of the shape parameter to be passed in as separate arguments. For example, a.reshape(10, 11) is equivalent to a.reshape((10, 11)).

这是一个内置函数，但签名看起来像x.reshape(*shape)，只要值有意义，它就会尝试保持灵活性.

It's a builtin function, but the signature looks like x.reshape(*shape), and it tries to be flexible as long as the values make sense.

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