Python日記（三）：numpy矩陣以及Torch張量騷操作

Python?記（三）：numpy矩陣以及Torch張量騷操作

?錄

Numpy

(array, pad_width, mode, **kwargs)

給?個(gè)n維矩陣最外圍補(bǔ)?圈任意數(shù)，類似于CNN中的padding。

array：需要padding的array；pad_width：元組形式的數(shù)據(jù)，表明了不同的axis的padding位置，before_1表?在axis=0的最開(kāi)始

補(bǔ)，after_2表?在axis=1的末尾補(bǔ)；mode：padding的模式，可以是常量甚?也可以是函數(shù)。

下?是源代碼中給出的參數(shù)描述，那么?長(zhǎng)。

'''

Pads an array.

Parameters

----------

array : array_like of rank N

Input array

pad_width : {quence, array_like, int}

Number of values padded to the edges of each axis.

((before_1, after_1), ... (before_N, after_N)) unique pad widths

for each axis.

((before, after),) yields same before and after pad for each axis.

(pad,) or int is a shortcut for before = after = pad width for all

axes.

mode : str or function

One of the following string values or a ur supplied function.

'constant'

Pads with a constant value.

'edge'

Pads with the edge values of array.

'linear_ramp'

Pads with the linear ramp between end_value and the

array edge value.

'maximum'

Pads with the maximum value of all or part of the

vector along each axis.

'mean'

Pads with the mean value of all or part of the

vector along each axis.

'median'

Pads with the median value of all or part of the

vector along each axis.

'minimum'

Pads with the minimum value of all or part of the

vector along each axis.

'reflect'

Pads with the reflection of the vector mirrored on

the first and last values of the vector along each

axis.

'symmetric'

Pads with the reflection of the vector mirrored

along the edge of the array.

'wrap'

Pads with the wrap of the vector along the axis.

The first values are ud to pad the end and the

end values are ud to pad the beginning.

end values are ud to pad the beginning.

<function>

Padding function, e Notes.

stat_length : quence or int, optional

Ud in 'maximum', 'mean', 'median', and 'minimum'. Number of

values at edge of each axis ud to calculate the statistic value.

((before_1, after_1), ... (before_N, after_N)) unique statistic

lengths for each axis.

((before, after),) yields same before and after statistic lengths

for each axis.

(stat_length,) or int is a shortcut for before = after = statistic

length for all axes.

Default is ``None``, to u the entire axis.

constant_values : quence or int, optional

Ud in 'constant'. The values to t the padded values for each

axis.

((before_1, after_1), ... (before_N, after_N)) unique pad constants

for each axis.

((before, after),) yields same before and after constants for each

axis.

(constant,) or int is a shortcut for before = after = constant for

all axes.

Default is 0.

end_values : quence or int, optional

Ud in 'linear_ramp'. The values ud for the ending value of the

linear_ramp and that will form the edge of the padded array.

((before_1, after_1), ... (before_N, after_N)) unique end values

for each axis.

((before, after),) yields same before and after end values for each

axis.

(constant,) or int is a shortcut for before = after = end value for

all axes.

Default is 0.

reflect_type : {'even', 'odd'}, optional

Ud in 'reflect', and 'symmetric'. The 'even' style is the

default with an unaltered reflection around the edge value. For

the 'odd' style, the extended part of the array is created by

subtracting the reflected values from two times the edge value.

Returns

-------

pad : ndarray

Padded array of rank equal to `array` with shape incread

according to `pad_width`.

import numpy as np

ones3_3 = np.ones([3,3], int)

print(ones3_3)

>>>[[1 1 1]

[1 1 1]

[1 1 1]]

在值為1的3×3的矩陣的?的開(kāi)始補(bǔ)1?2，在?的末尾補(bǔ)2?3；在列的開(kāi)始補(bǔ)兩?4，列的末尾補(bǔ)3?5。這操作超好玩。(￣▽￣)~*

X = np.pad(ones3_3, ((1, 2), (2, 3)), 'constant',

constant_values=((2,3), (4,5)))

print(X)

>>>

[[4 4 2 2 2 5 5 5]

[4 4 1 1 1 5 5 5]

[4 4 1 1 1 5 5 5]

[4 4 1 1 1 5 5 5]

[4 4 3 3 3 5 5 5]

[4 4 3 3 3 5 5 5]]

Torch

(x, dim=0)

移除某?維度并返回?個(gè)和移除維度長(zhǎng)度相同的元組，每個(gè)元組中存放剩余維度的張量。

假設(shè)() = [30, 128, 100]，unbind之后返回：

30 torch.Size([128, 100])的張量元組。