Kaggle比赛——Digit Recognizer——Part 1（Pytorch 数据集的建立）

       首先从Kaggle官网下载数据集https://www.kaggle.com/c/digit-recognizer/data里面包含三个CSV文档。train.csv是带标签的数据，用于训练和调参，test.csv是无标签的数据，在提交测试文档的时候才需要用到。

        这里，我先把train里面的数据又随机划分为两个表，一个用于训练一个用于交叉验证，代码很简单，主要是pandas的一些简单功能。

#use torch.utils.data.Dataset to build my dataset from train.csv and test.csv
import numpy as np
import pandas as pd
from sklearn.model_selection  import train_test_split
import torch
from torch.utils.data import DataLoader
from torch import nn
from torch.autograd import Variable

def train_val_split(train = 'train.csv',train_flie='train_set.csv',val_file='val_set.csv'):
    #training set "train.csv" was downloaded from kaggle.com
    train_data = pd.read_csv(train)    
    #training datas contains Feature and Label.
    #divide training datas into training set and validation set 
    train_set, val_set = train_test_split(train_data, test_size = 0.2)
    #wirte csv files
    train_set.to_csv(train_flie,index = False )
    val_set.to_csv(val_file,index = False )
    print('train_data.shape:',train_data.shape)
    print('train_set.shape:',train_set.shape)
    print('val_set.shape:',val_set.shape)

train_val_split('train.csv','train_set.csv','val_set.csv')

 

运行结果为：
(33600, 785)
(42000, 785)
(8400, 785)

        这样，训练数据和交叉验证的数据就分别存在两个表里面了。

        下一步，我们需要重写Pytorch的数据集类，构建我们的数据集。

 

#image preprocessing to Gaussian distribution from -1 to 1
def data_tf(x):
    x = np.array(x, dtype='float32') / 255
    x = (x - 0.5) / 0.5 # 标准化
    x = torch.from_numpy(x)
    return x   

#define the class of my MNIST dataset
class MyMNIST(torch.utils.data.Dataset): #创建自己的类：MyMNIST,这个类是继承的torch.utils.data.Dataset
    def __init__(self, datatxt, train=True, transform = data_tf, target_transform=None): #初始化一些需要传入的参数
        self.data = pd.read_csv(datatxt)   
        self.transform = transform
        self.train = train 
        if self.train:
            self.X = self.data.iloc[:,1:]
            self.X = np.array(self.X)
            self.y = self.data.iloc[:,0]
            self.y = np.array(self.y)
        else :
            self.X = self.data
            self.X = np.array(self.X)
                        
    def __getitem__(self, index):    #这个方法是必须要有的，用于按照索引读取每个元素的具体内容
        im = torch.tensor(self.X[index], dtype = torch.float)
        if self.transform is not None:
            im = self.transform(im)
        if self.train:
            label = torch.tensor(self.y[index], dtype = torch.long )
            return im, label
        else:
            return im
    def __len__(self): #return the length of batch
        return len(self.data)    
 
#Build trainset validset and testset from csv and perform data preprocessing
X_train = MyMNIST(datatxt = 'train_set.csv',train=True, transform = data_tf)
X_val = MyMNIST(datatxt= 'val_set.csv',train=True, transform = data_tf)
X_test = MyMNIST(datatxt = 'test.csv',train=False, transform = data_tf)
#iterator of our dataset
train_data = DataLoader(X_train, batch_size=64, shuffle=True)
val_data = DataLoader(X_val, batch_size=64, shuffle=False)
test_data = DataLoader(X_test, batch_size=1000, shuffle=False) 

以上是构建数据集的全部代码。

下一步就可以构建网络结构。