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資源簡(jiǎn)介

自 2017 年 1 月 PyTorch 推出以來(lái)，其熱度持續(xù)上升，一度有趕超 TensorFlow 的趨勢(shì)。PyTorch 能在短時(shí)間內(nèi)被眾多研究人員和工程師接受并推崇是因?yàn)槠溆兄T多優(yōu)點(diǎn)，如采用 Python 語(yǔ)言、動(dòng)態(tài)圖機(jī)制、網(wǎng)絡(luò)構(gòu)建靈活以及擁有強(qiáng)大的社群等。因此，走上學(xué)習(xí) PyTorch 的道路已刻不容緩。 本教程以實(shí)際應(yīng)用、工程開(kāi)發(fā)為目的，著重介紹模型訓(xùn)練過(guò)程中遇到的實(shí)際問(wèn)題和方法。如上圖所示，在機(jī)器學(xué)習(xí)模型開(kāi)發(fā)中，主要涉及三大部分，分別是數(shù)據(jù)、模型和損失函數(shù)及優(yōu)化器。本文也按順序的依次介紹數(shù)據(jù)、模型和損失函數(shù)及優(yōu)化器，從而給大家?guī)?lái)清晰的機(jī)器學(xué)習(xí)結(jié)構(gòu)。

資源截圖

代碼片段和文件信息 

#?coding:utf-8
“““
????將cifar10的data_batch_12345?轉(zhuǎn)換成?png格式的圖片
????每個(gè)類別單獨(dú)存放在一個(gè)文件夾，文件夾名稱為0-9
“““
from?scipy.misc?import?imsave
import?numpy?as?np
import?os
import?pickle

data_dir?=?‘../../Data/cifar-10-batches-py/‘
train_o_dir?=?‘../../Data/cifar-10-png/raw_train/‘
test_o_dir?=?‘../../Data/cifar-10-png/raw_test/‘

Train?=?False???#?不解壓訓(xùn)練集，僅解壓測(cè)試集

#?解壓縮，返回解壓后的字典
def?unpickle（file）:
????fo?=?open（file?‘rb‘）
????dict_?=?pickle.load（fo?encoding=‘bytes‘）
????fo.close（）
????return?dict_

def?my_mkdir（my_dir）:
????if?not?os.path.isdir（my_dir）:
????????os.makedirs（my_dir）


#?生成訓(xùn)練集圖片，
if?__name__?==?‘__main__‘:
????if?Train:
????????for?j?in?range（1?6）:
????????????data_path?=?data_dir?+?“data_batch_“?+?str（j）??#?data_batch_12345
????????????train_data?=?unpickle（data_path）
????????????print（data_path?+?“?is?loading...“）

????????????for?i?in?range（0?10000）:
????????????????img?=?np.reshape（train_data[b‘data‘][i]?（3?32?32））
????????????????img?=?img.transpose（1?2?0）

????????????????label_num?=?str（train_data[b‘labels‘][i]）
????????????????o_dir?=?os.path.join（train_o_dir?label_num）
????????????????my_mkdir（o_dir）

????????????????img_name?=?label_num?+?‘_‘?+?str（i?+?（j?-?1）*10000）?+?‘.png‘
????????????????img_path?=?os.path.join（o_dir?img_name）
????????????????imsave（img_path?img）
????????????print（data_path?+?“?loaded.“）

????print（“test_batch?is?loading...“）

????#?生成測(cè)試集圖片
????test_data_path?=?data_dir?+?“test_batch“
????test_data?=?unpickle（test_data_path）
????for?i?in?range（0?10000）:
????????img?=?np.reshape（test_data[b‘data‘][i]?（3?32?32））
????????img?=?img.transpose（1?2?0）

????????label_num?=?str（test_data[b‘labels‘][i]）
????????o_dir?=?os.path.join（test_o_dir?label_num）
????????my_mkdir（o_dir）

????????img_name?=?label_num?+?‘_‘?+?str（i）?+?‘.png‘
????????img_path?=?os.path.join（o_dir?img_name）
????????imsave（img_path?img）

????print（“test_batch?loaded.“）

?屬性????????????大小?????日期????時(shí)間???名稱
-----------?---------??----------?-----??----
?????目錄???????????0??2018-12-20?05:05??PyTorch_Tutorial-master\
?????目錄???????????0??2018-12-20?05:05??PyTorch_Tutorial-master\Code\
?????目錄???????????0??2018-12-20?05:05??PyTorch_Tutorial-master\Code\1_data_prepare\
?????文件????????2050??2018-12-20?05:05??PyTorch_Tutorial-master\Code\1_data_prepare\1_1_cifar10_to_png.py
?????文件????????1409??2018-12-20?05:05??PyTorch_Tutorial-master\Code\1_data_prepare\1_2_split_dataset.py
?????文件????????1106??2018-12-20?05:05??PyTorch_Tutorial-master\Code\1_data_prepare\1_3_generate_txt.py
?????文件?????????991??2018-12-20?05:05??PyTorch_Tutorial-master\Code\1_data_prepare\1_3_mydataset.py
?????文件????????1148??2018-12-20?05:05??PyTorch_Tutorial-master\Code\1_data_prepare\1_5_compute_mean.py
?????目錄???????????0??2018-12-20?05:05??PyTorch_Tutorial-master\Code\2_model\
?????文件????????7118??2018-12-20?05:05??PyTorch_Tutorial-master\Code\2_model\2_finetune.py
?????文件??????249573??2018-12-20?05:05??PyTorch_Tutorial-master\Code\2_model\net_params.pkl
?????目錄???????????0??2018-12-20?05:05??PyTorch_Tutorial-master\Code\3_optimizer\
?????目錄???????????0??2018-12-20?05:05??PyTorch_Tutorial-master\Code\3_optimizer\3_1_lossFunction\
?????文件?????????737??2018-12-20?05:05??PyTorch_Tutorial-master\Code\3_optimizer\3_1_lossFunction\1_L1Loss.py
?????文件?????????744??2018-12-20?05:05??PyTorch_Tutorial-master\Code\3_optimizer\3_1_lossFunction\2_MSELoss.py
?????文件????????2698??2018-12-20?05:05??PyTorch_Tutorial-master\Code\3_optimizer\3_1_lossFunction\3_CrossEntropyLoss.py
?????文件?????????701??2018-12-20?05:05??PyTorch_Tutorial-master\Code\3_optimizer\3_1_lossFunction\4_NLLLoss.py
?????文件?????????335??2018-12-20?05:05??PyTorch_Tutorial-master\Code\3_optimizer\3_1_lossFunction\5_PoissonNLLLoss.py
?????文件?????????967??2018-12-20?05:05??PyTorch_Tutorial-master\Code\3_optimizer\3_1_lossFunction\6_KLDivLoss.py
?????目錄???????????0??2018-12-20?05:05??PyTorch_Tutorial-master\Code\3_optimizer\3_2_optimizer\
?????文件?????????600??2018-12-20?05:05??PyTorch_Tutorial-master\Code\3_optimizer\3_2_optimizer\1_param_groups.py
?????文件?????????631??2018-12-20?05:05??PyTorch_Tutorial-master\Code\3_optimizer\3_2_optimizer\2_zero_grad.py
?????文件?????????777??2018-12-20?05:05??PyTorch_Tutorial-master\Code\3_optimizer\3_2_optimizer\3_state_dict.py
?????文件????????1400??2018-12-20?05:05??PyTorch_Tutorial-master\Code\3_optimizer\3_2_optimizer\4_load_state_dict.py
?????文件?????????838??2018-12-20?05:05??PyTorch_Tutorial-master\Code\3_optimizer\3_2_optimizer\5_add_param_group.py
?????文件????????1001??2018-12-20?05:05??PyTorch_Tutorial-master\Code\3_optimizer\3_2_optimizer\net_params.pkl
?????目錄???????????0??2018-12-20?05:05??PyTorch_Tutorial-master\Code\4_viewer\
?????文件????????3901??2018-12-20?05:05??PyTorch_Tutorial-master\Code\4_viewer\1_tensorboardX_demo.py
?????文件????????2633??2018-12-20?05:05??PyTorch_Tutorial-master\Code\4_viewer\2_visual_weights.py
?????文件????????2041??2018-12-20?05:05??PyTorch_Tutorial-master\Code\4_viewer\3_visual_featuremaps.py
?????文件????????3733??2018-12-20?05:05??PyTorch_Tutorial-master\Code\4_viewer\4_hist_grad_weight.py
............此處省略14個(gè)文件信息

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