最新国产好看的视频,伊人天堂AV在线,国产Aaaaaa视频,蜜臀视频在线观看一区,人妻av色图,密臀久久久精品影片,青青视频免费观看毛片,久草在线观看视,国产三级精品色情在线

pytorch中的named_parameters()和parameters()

 更新時(shí)間：2023年09月12日 08:57:16   作者：Hanawh  
這篇文章主要介紹了pytorch中的named_parameters()和parameters()使用方式,具有很好的參考價(jià)值,希望對大家有所幫助,如有錯(cuò)誤或未考慮完全的地方,望不吝賜教

pytorch named_parameters()和parameters()

nn.Module

nn.Module里面關(guān)于參數(shù)有兩個(gè)很重要的屬性named_parameters()和parameters()，前者給出網(wǎng)絡(luò)層的名字和參數(shù)的迭代器，而后者僅僅是參數(shù)的迭代器。

import torchvision.models as models
model = models.resnet18()
for param in model.named_parameters():
    print(param[0])
'''
conv1.weight
bn1.weight
bn1.bias
layer1.0.conv1.weight
layer1.0.bn1.weight
layer1.0.bn1.bias
layer1.0.conv2.weight
layer1.0.bn2.weight
layer1.0.bn2.bias
layer1.1.conv1.weight
layer1.1.bn1.weight
layer1.1.bn1.bias
layer1.1.conv2.weight
layer1.1.bn2.weight
layer1.1.bn2.bias
layer2.0.conv1.weight
layer2.0.bn1.weight
layer2.0.bn1.bias
layer2.0.conv2.weight
layer2.0.bn2.weight
layer2.0.bn2.bias
layer2.0.downsample.0.weight
layer2.0.downsample.1.weight
layer2.0.downsample.1.bias
layer2.1.conv1.weight
layer2.1.bn1.weight
layer2.1.bn1.bias
layer2.1.conv2.weight
layer2.1.bn2.weight
layer2.1.bn2.bias
layer3.0.conv1.weight
layer3.0.bn1.weight
layer3.0.bn1.bias
layer3.0.conv2.weight
layer3.0.bn2.weight
layer3.0.bn2.bias
layer3.0.downsample.0.weight
layer3.0.downsample.1.weight
layer3.0.downsample.1.bias
layer3.1.conv1.weight
layer3.1.bn1.weight
layer3.1.bn1.bias
layer3.1.conv2.weight
layer3.1.bn2.weight
layer3.1.bn2.bias
layer4.0.conv1.weight
layer4.0.bn1.weight
layer4.0.bn1.bias
layer4.0.conv2.weight
layer4.0.bn2.weight
layer4.0.bn2.bias
layer4.0.downsample.0.weight
layer4.0.downsample.1.weight
layer4.0.downsample.1.bias
layer4.1.conv1.weight
layer4.1.bn1.weight
layer4.1.bn1.bias
layer4.1.conv2.weight
layer4.1.bn2.weight
layer4.1.bn2.bias
fc.weight
fc.bias
'''

模型參數(shù)：named_parameters()、parameters()、state_dict()區(qū)別

torch中存在3個(gè)功能極其類似的方法，它們分別是model.parameters()、model.named_parameters()、model.state_dict()，

下面就具體來說說這三個(gè)函數(shù)的差異：

一、model.parameters()和model.named_parameters()差別

  • named_parameters()返回的list中，每個(gè)元組（與list相似，只是數(shù)據(jù)不可修改）打包了2個(gè)內(nèi)容，分別是layer-name和layer-param（網(wǎng)絡(luò)層的名字和參數(shù)的迭代器）；
  • parameters()只有后者layer-param（參數(shù)的迭代器）

1、model.named_parameters()里的網(wǎng)絡(luò)層名字

import torchvision.models as models
model = models.resnet18()
for param_tuple in model.named_parameters():
    name, param = param_tuple
    print("name = ", name)
    print("-" * 100)

打印結(jié)果：

name =  conv1.weight
----------------------------------------------------------------------------------------------------
name =  bn1.weight
----------------------------------------------------------------------------------------------------
name =  bn1.bias
----------------------------------------------------------------------------------------------------
name =  layer1.0.conv1.weight
----------------------------------------------------------------------------------------------------
name =  layer1.0.bn1.weight
----------------------------------------------------------------------------------------------------
name =  layer1.0.bn1.bias
----------------------------------------------------------------------------------------------------
name =  layer1.0.conv2.weight
----------------------------------------------------------------------------------------------------
name =  layer1.0.bn2.weight
----------------------------------------------------------------------------------------------------
name =  layer1.0.bn2.bias
----------------------------------------------------------------------------------------------------
name =  layer1.1.conv1.weight
----------------------------------------------------------------------------------------------------
name =  layer1.1.bn1.weight
----------------------------------------------------------------------------------------------------
name =  layer1.1.bn1.bias
----------------------------------------------------------------------------------------------------
name =  layer1.1.conv2.weight
----------------------------------------------------------------------------------------------------
name =  layer1.1.bn2.weight
----------------------------------------------------------------------------------------------------
name =  layer1.1.bn2.bias
----------------------------------------------------------------------------------------------------
name =  layer2.0.conv1.weight
----------------------------------------------------------------------------------------------------
name =  layer2.0.bn1.weight
----------------------------------------------------------------------------------------------------
name =  layer2.0.bn1.bias
----------------------------------------------------------------------------------------------------
name =  layer2.0.conv2.weight
----------------------------------------------------------------------------------------------------
name =  layer2.0.bn2.weight
----------------------------------------------------------------------------------------------------
name =  layer2.0.bn2.bias
----------------------------------------------------------------------------------------------------
name =  layer2.0.downsample.0.weight
----------------------------------------------------------------------------------------------------
name =  layer2.0.downsample.1.weight
----------------------------------------------------------------------------------------------------
name =  layer2.0.downsample.1.bias
----------------------------------------------------------------------------------------------------
name =  layer2.1.conv1.weight
----------------------------------------------------------------------------------------------------
name =  layer2.1.bn1.weight
----------------------------------------------------------------------------------------------------
name =  layer2.1.bn1.bias
----------------------------------------------------------------------------------------------------
name =  layer2.1.conv2.weight
----------------------------------------------------------------------------------------------------
name =  layer2.1.bn2.weight
----------------------------------------------------------------------------------------------------
name =  layer2.1.bn2.bias
----------------------------------------------------------------------------------------------------
name =  layer3.0.conv1.weight
----------------------------------------------------------------------------------------------------
name =  layer3.0.bn1.weight
----------------------------------------------------------------------------------------------------
name =  layer3.0.bn1.bias
----------------------------------------------------------------------------------------------------
name =  layer3.0.conv2.weight
----------------------------------------------------------------------------------------------------
name =  layer3.0.bn2.weight
----------------------------------------------------------------------------------------------------
name =  layer3.0.bn2.bias
----------------------------------------------------------------------------------------------------
name =  layer3.0.downsample.0.weight
----------------------------------------------------------------------------------------------------
name =  layer3.0.downsample.1.weight
----------------------------------------------------------------------------------------------------
name =  layer3.0.downsample.1.bias
----------------------------------------------------------------------------------------------------
name =  layer3.1.conv1.weight
----------------------------------------------------------------------------------------------------
name =  layer3.1.bn1.weight
----------------------------------------------------------------------------------------------------
name =  layer3.1.bn1.bias
----------------------------------------------------------------------------------------------------
name =  layer3.1.conv2.weight
----------------------------------------------------------------------------------------------------
name =  layer3.1.bn2.weight
----------------------------------------------------------------------------------------------------
name =  layer3.1.bn2.bias
----------------------------------------------------------------------------------------------------
name =  layer4.0.conv1.weight
----------------------------------------------------------------------------------------------------
name =  layer4.0.bn1.weight
----------------------------------------------------------------------------------------------------
name =  layer4.0.bn1.bias
----------------------------------------------------------------------------------------------------
name =  layer4.0.conv2.weight
----------------------------------------------------------------------------------------------------
name =  layer4.0.bn2.weight
----------------------------------------------------------------------------------------------------
name =  layer4.0.bn2.bias
----------------------------------------------------------------------------------------------------
name =  layer4.0.downsample.0.weight
----------------------------------------------------------------------------------------------------
name =  layer4.0.downsample.1.weight
----------------------------------------------------------------------------------------------------
name =  layer4.0.downsample.1.bias
----------------------------------------------------------------------------------------------------
name =  layer4.1.conv1.weight
----------------------------------------------------------------------------------------------------
name =  layer4.1.bn1.weight
----------------------------------------------------------------------------------------------------
name =  layer4.1.bn1.bias
----------------------------------------------------------------------------------------------------
name =  layer4.1.conv2.weight
----------------------------------------------------------------------------------------------------
name =  layer4.1.bn2.weight
----------------------------------------------------------------------------------------------------
name =  layer4.1.bn2.bias
----------------------------------------------------------------------------------------------------
name =  fc.weight
----------------------------------------------------------------------------------------------------
name =  fc.bias
----------------------------------------------------------------------------------------------------

Process finished with exit code 0

2、model.named_parameters()里的網(wǎng)絡(luò)層名字、參數(shù)

import torchvision.models as models
model = models.resnet18()
for param_tuple in model.named_parameters():
    name, param = param_tuple
    print("name = ", name)
    print("-" * 100)
    print("param_tuple = ", param_tuple)
    print("*" * 200)

打印結(jié)果：

C:\Program_Files_AI\Anaconda3531\python.exe C:/Users/Admin/OneDrive/WorkSpace_AI/0-基于知識庫的智能問答系統(tǒng)-華控智加/01-意圖識別/test.py
name =  conv1.weight
----------------------------------------------------------------------------------------------------
param_tuple =  ('conv1.weight', Parameter containing:
tensor([[[[-1.4115e-05,  2.9187e-02,  2.9325e-03,  ..., -4.2247e-02,
            1.7490e-02, -4.5253e-02],
          [-2.4594e-02, -3.0836e-02,  3.8604e-02,  ...,  3.5473e-02,
           -4.7046e-03, -2.9440e-02],
          [ 2.4811e-02,  1.2679e-02,  1.0070e-02,  ..., -8.3476e-03,
            1.7960e-02, -1.7406e-02],
          ...,
          [-1.3021e-02,  2.9023e-02, -6.1800e-02,  ..., -5.2802e-02,
           -4.7817e-02, -2.2377e-02],
          [-3.8513e-03, -1.0603e-02, -3.9712e-02,  ...,  5.1941e-03,
            8.2868e-03, -8.3469e-03],
          [ 3.8993e-03,  3.2017e-02, -3.6292e-02,  ..., -2.0210e-02,
           -4.0358e-02,  1.7709e-02]],

         [[-1.0894e-03,  1.5720e-02,  7.0129e-03,  ..., -1.2024e-02,
            1.8644e-02,  1.7892e-02],
          [-2.3866e-02,  9.1136e-03,  3.5243e-02,  ..., -1.6756e-02,
            1.4441e-03,  4.7943e-02],
          [-2.0514e-03,  4.3022e-02,  2.6358e-02,  ..., -2.3662e-02,
           -7.8241e-04,  1.0167e-02],
        ...

         [[-4.6689e-02, -1.1407e-03,  1.8674e-02,  ...,  1.2649e-03,
           -2.9532e-02,  6.4535e-04],
          [ 1.4171e-03, -1.9274e-02, -8.6811e-03,  ...,  2.4428e-02,
            6.9516e-03,  4.3715e-02],
          [ 1.9982e-02,  1.3124e-02,  9.1508e-03,  ...,  2.5405e-02,
           -1.3132e-02,  4.0835e-02],
          ...,
          [-3.4174e-03,  1.8623e-02, -1.4386e-02,  ...,  1.0627e-03,
           -5.1297e-04,  2.2055e-02],
          [ 2.7333e-02,  2.4858e-02, -5.4305e-02,  ..., -1.2139e-02,
            1.7735e-03, -3.4184e-03],
          [ 1.1412e-03,  1.5794e-02, -2.0699e-02,  ..., -1.7846e-02,
            3.7425e-02, -1.6059e-02]]],


        ...,


        [[[-2.7389e-02, -3.8327e-02, -2.9043e-02,  ..., -7.6396e-03,
           -1.6519e-02,  3.9659e-02],
          [ 2.8740e-03, -1.0621e-02, -9.2430e-03,  ...,  2.2581e-02,
            5.1526e-03, -2.0006e-02],
          [ 1.3575e-02,  1.5290e-02, -1.7260e-02,  ...,  6.3830e-03,
           -1.9759e-02,  1.5501e-02],
          ...,
          [ 1.6091e-02,  2.4038e-02,  2.4507e-02,  ..., -4.5613e-02,
           -3.6233e-02,  2.1632e-02],
          [-1.1573e-02, -3.6514e-02,  4.1576e-02,  ...,  1.8090e-02,
           -2.3350e-02, -8.7074e-03],
          [-1.5837e-02, -3.1353e-02,  1.8726e-02,  ...,  9.3698e-03,
            3.0781e-02,  1.0976e-02]],

         [[-2.7063e-02,  8.7158e-03,  2.7193e-03,  ..., -1.6670e-03,
           -4.3033e-03,  7.2011e-04],
          [ 2.7870e-03,  1.4264e-02, -5.0581e-02,  ...,  2.5463e-02,
            7.6864e-03, -4.9655e-02],
          [ 2.6030e-03,  2.5918e-02,  2.9615e-02,  ...,  3.0676e-02,
           -2.7723e-02, -7.3628e-03],
          ...,
          [ 2.5969e-02, -1.4247e-02,  1.2516e-02,  ...,  5.9602e-03,
           -3.2843e-02,  3.5822e-02],
          [ 1.2845e-02, -2.0035e-02,  9.9398e-04,  ..., -3.1800e-02,
            5.7984e-03,  2.8756e-02],
          [ 2.3458e-02,  3.8193e-02, -2.3754e-03,  ..., -1.3867e-02,
            8.0831e-03, -3.2438e-02]],
...

         [[-9.9291e-03, -5.6023e-03, -1.7064e-02,  ...,  8.8544e-03,
           -5.8145e-03,  2.3248e-02],
          [ 1.2148e-02, -1.0730e-02, -1.2682e-02,  ...,  9.4389e-03,
            1.2149e-02,  3.8613e-03],
          [ 3.5913e-02, -5.2048e-04, -8.7133e-02,  ..., -2.0969e-03,
           -5.4117e-03,  5.4637e-05],
          ...,
          [ 4.0351e-03, -1.3189e-02,  3.1229e-02,  ...,  3.2340e-02,
           -2.8351e-02,  1.0634e-02],
          [ 2.6041e-02, -3.0633e-04, -1.2732e-02,  ...,  2.9417e-02,
           -7.3859e-03,  1.7207e-02],
          [ 6.9960e-04,  3.8486e-03,  1.0397e-02,  ...,  1.4535e-03,
           -3.6449e-02,  3.4848e-02]]]], requires_grad=True))
********************************************************************************************************************************************************************************************************
name =  bn1.weight
----------------------------------------------------------------------------------------------------
param_tuple =  ('bn1.weight', Parameter containing:
tensor([1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
        1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
        1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
        1., 1., 1., 1., 1., 1., 1., 1., 1., 1.], requires_grad=True))
********************************************************************************************************************************************************************************************************
name =  bn1.bias
----------------------------------------------------------------------------------------------------
param_tuple =  ('bn1.bias', Parameter containing:
tensor([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],
       requires_grad=True))
********************************************************************************************************************************************************************************************************

********************************************************************************************************************************************************************************************************
name =  layer1.0.conv2.weight
----------------------------------------------------------------------------------------------------
param_tuple =  ('layer1.0.conv2.weight', Parameter containing:
tensor([[[[-8.6159e-02,  1.8507e-04,  5.4006e-03],
          [-6.3063e-03,  3.9225e-03, -6.3141e-02],
          [-7.0145e-02, -3.9266e-02,  1.9724e-03]],

         [[ 4.6454e-02,  2.1519e-02,  5.3696e-02],
          [ 1.1086e-02,  1.6269e-01, -7.0579e-02],
          [-1.1220e-01, -4.9811e-02, -7.5515e-02]],

         [[ 5.5275e-02, -8.2407e-02, -8.9807e-02],
          [ 5.8418e-02,  4.4029e-02,  3.0584e-03],
          [ 5.2371e-02, -1.5983e-02,  5.1494e-02]],

         ...,

         [[ 7.3441e-02,  4.5401e-02, -1.9175e-02],
          [-6.2500e-02, -8.5905e-03, -7.4856e-02],
          [-1.6170e-02,  3.7529e-02, -5.1231e-02]],

         [[ 7.7501e-04, -5.7506e-02,  1.8422e-01],
          [ 2.4594e-02,  1.7378e-02,  4.0000e-02],
          [-8.6796e-02, -6.0548e-02,  2.6795e-02]],

         [[ 5.3264e-02, -8.6190e-02,  4.2443e-02],
          [-6.8029e-03, -1.6581e-02,  7.8568e-02],
          [ 3.2037e-02, -7.3002e-02,  4.9353e-02]]]], requires_grad=True))
...，
********************************************************************************************************************************************************************************************************
name =  layer4.1.bn2.bias
----------------------------------------------------------------------------------------------------
param_tuple =  ('layer4.1.bn2.bias', Parameter containing:
tensor([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
...,
        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
        0., 0., 0., 0., 0., 0., 0., 0.], requires_grad=True))
********************************************************************************************************************************************************************************************************
name =  fc.weight
----------------------------------------------------------------------------------------------------
param_tuple =  ('fc.weight', Parameter containing:
tensor([[-0.0125,  0.0437, -0.0014,  ..., -0.0230,  0.0280,  0.0249],
        [-0.0105,  0.0242,  0.0291,  ...,  0.0153,  0.0366, -0.0236],
        [-0.0315,  0.0306, -0.0216,  ...,  0.0387,  0.0403,  0.0056],
        ...,
        [-0.0068, -0.0222, -0.0027,  ..., -0.0243,  0.0260,  0.0065],
        [ 0.0213,  0.0167, -0.0379,  ..., -0.0140,  0.0037, -0.0372],
        [ 0.0180,  0.0101, -0.0341,  ..., -0.0295, -0.0146,  0.0416]],
       requires_grad=True))
********************************************************************************************************************************************************************************************************
name =  fc.bias
----------------------------------------------------------------------------------------------------
param_tuple =  ('fc.bias', Parameter containing:
tensor([ 3.5711e-02,  3.2682e-02,  7.5932e-03, -3.1623e-02, -9.6316e-03,
        -2.4051e-02, -1.0393e-02,  2.3210e-02, -3.6044e-02,  2.3099e-02,
        -3.5723e-02, -3.9482e-02,  4.8526e-03, -3.2688e-02,  3.7720e-03,
        -2.2014e-02, -4.0935e-02,  4.0533e-02, -4.1172e-02,  3.9513e-02,
        -3.0332e-02,  3.2777e-02,  1.3342e-02,  2.3394e-02,  8.2328e-03,
         1.3757e-02, -1.7578e-02, -2.7165e-02,  3.8495e-03, -3.2116e-02,
         7.9903e-03,  9.9640e-04, -8.3106e-03,  2.5033e-02, -3.0446e-02,
        -1.8282e-02, -3.8420e-03, -8.6129e-03, -4.2712e-03,  1.7169e-02,
       ...,
        -5.3570e-05, -3.7353e-02, -9.8633e-03, -9.1069e-03,  3.2688e-02,
         2.2457e-02,  7.6379e-03, -3.6287e-02, -1.0444e-02,  2.1669e-02,
         2.5270e-02, -4.3881e-02,  2.1960e-02,  2.6293e-02, -3.5049e-02,
        -2.0074e-02, -9.7686e-03, -2.3766e-02, -5.0265e-03, -2.1095e-02,
         2.0981e-02, -3.5132e-02,  8.6407e-03,  1.8453e-02,  2.4282e-02,
         3.8392e-02, -1.7470e-02,  3.6958e-02, -3.7590e-02, -4.1951e-02,
        -1.8246e-02,  9.0818e-03,  3.8774e-02,  7.3408e-03,  1.7728e-02,
         3.5547e-02, -7.2857e-03, -2.7015e-02, -8.6983e-03, -2.3785e-02],
       requires_grad=True))
********************************************************************************************************************************************************************************************************

Process finished with exit code 0

二、model.named_parameters()和model.state_dict()差別

它們的差異主要體現(xiàn)在3方面：

  • 返回值類型不同
  • 存儲的模型參數(shù)的種類不同
  • 返回的值的require_grad屬性不同
named_parameters()state_dict()
將layer_name : layer_param的鍵值信息打包成一個(gè)元祖然后再存到list當(dāng)中將layer_name : layer_param的鍵值信息存儲為dict形式
只保存可學(xué)習(xí)、可被更新的參數(shù)，model.buffer()中的參數(shù)不包含在model.named_parameters()中存儲的是該model中包含的所有l(wèi)ayer中的所有參數(shù)
require_grad屬性都是True存儲的模型參數(shù)tensor的require_grad屬性都是False

為何model.parameters()迭代出來的所有參數(shù)的require_grad屬性都是True，因?yàn)樗鼈冊诒粍?chuàng)建時(shí)，默認(rèn)的require_grad就是True。

這也符合邏輯，即，使用nn.Parameter()創(chuàng)建的變量是模型參數(shù)，本就是要參與學(xué)習(xí)和更新的

總結(jié)

以上為個(gè)人經(jīng)驗(yàn)，希望能給大家一個(gè)參考，也希望大家多多支持腳本之家。

相關(guān)文章

  • python實(shí)現(xiàn)車牌識別的示例代碼

    python實(shí)現(xiàn)車牌識別的示例代碼

    這篇文章主要介紹了python實(shí)現(xiàn)車牌識別的示例代碼，文中通過示例代碼介紹的非常詳細(xì)，對大家的學(xué)習(xí)或者工作具有一定的參考學(xué)習(xí)價(jià)值，需要的朋友們下面隨著小編來一起學(xué)習(xí)學(xué)習(xí)吧
    2019-08-08
  • Python工程師面試必備25條知識點(diǎn)

    Python工程師面試必備25條知識點(diǎn)

    這篇文章主要為大家詳細(xì)介紹了Python工程師面試必備25條知識點(diǎn)，具有一定的參考價(jià)值，感興趣的小伙伴們可以參考一下
    2018-01-01
  • Python中hash()函數(shù)之哈希值的奧秘詳解

    Python中hash()函數(shù)之哈希值的奧秘詳解

    hash()是Python 中的一個(gè)內(nèi)置函數(shù),用于計(jì)算對象的哈希值,哈希值是一個(gè)整數(shù),用于唯一標(biāo)識對象,下面這篇文章主要給大家介紹了關(guān)于Python中hash()函數(shù)之哈希值奧秘的相關(guān)資料,需要的朋友可以參考下
    2024-06-06
  • 詳解python-opencv 常用函數(shù)

    詳解python-opencv 常用函數(shù)

    這篇文章主要介紹了python-opencv 常用函數(shù),主要包括讀取圖像保存圖像和縮放圖像的相關(guān)知識，需要的朋友可以參考下
    2022-08-08
  • python之線程通過信號pyqtSignal刷新ui的方法

    python之線程通過信號pyqtSignal刷新ui的方法

    今天小編就為大家分享一篇python之線程通過信號pyqtSignal刷新ui的方法，具有很好的參考價(jià)值，希望對大家有所幫助。一起跟隨小編過來看看吧
    2019-01-01
  • 簡單了解Django ContentType內(nèi)置組件

    簡單了解Django ContentType內(nèi)置組件

    這篇文章主要介紹了簡單了解Django ContentType內(nèi)置組件,文中通過示例代碼介紹的非常詳細(xì)，對大家的學(xué)習(xí)或者工作具有一定的參考學(xué)習(xí)價(jià)值,需要的朋友可以參考下
    2019-07-07
  • Python基本文件操作實(shí)用指南

    Python基本文件操作實(shí)用指南

    文件操作是我們開發(fā)中必不可少的一項(xiàng)需求,下面這篇文章主要給大家介紹了關(guān)于Python基本文件操作的相關(guān)資料,文中通過實(shí)例代碼介紹的非常詳細(xì),需要的朋友可以參考下
    2022-06-06
  • Python動(dòng)態(tài)創(chuàng)建類實(shí)例詳解

    Python動(dòng)態(tài)創(chuàng)建類實(shí)例詳解

    這篇文章主要為大家介紹了Python動(dòng)態(tài)創(chuàng)建類實(shí)例詳解,有需要的朋友可以借鑒參考下,希望能夠有所幫助,祝大家多多進(jìn)步,早日升職加薪
    2023-12-12
  • 關(guān)于numpy兩個(gè)array疊加操作詳解

    關(guān)于numpy兩個(gè)array疊加操作詳解

    numpy是Python用來科學(xué)計(jì)算的一個(gè)非常重要的庫,numpy主要用來處理一些矩陣對象,可以說numpy讓Python有了Matlab的味道,下面這篇文章主要給大家介紹了關(guān)于numpy兩個(gè)array疊加操作的相關(guān)資料,需要的朋友可以參考下
    2022-08-08
  • python實(shí)現(xiàn)感知器算法詳解

    python實(shí)現(xiàn)感知器算法詳解

    這篇文章主要為大家詳細(xì)介紹了python實(shí)現(xiàn)感知器算法，文中示例代碼介紹的非常詳細(xì)，具有一定的參考價(jià)值，感興趣的小伙伴們可以參考一下
    2017-12-12

最新評論

安泽县| 邵东县| 瑞安市| 玛纳斯县| 江达县| 定安县| 界首市| 灯塔市| 略阳县| 梁河县| 宜阳县| 拉孜县| 铜梁县| 新晃| 文昌市| 兴文县| 衡水市| 景谷| 招远市| 巴彦县| 自治县| 怀安县| 和静县| 深水埗区| 准格尔旗| 余姚市| 嘉峪关市| 建湖县| 东宁县| 南安市| 仁化县| 沁阳市| 诏安县| 微博| 武平县| 女性| 山阴县| 陇西县| 涿州市| 韩城市| 团风县|