簡述

基于之前的框架，修改了一下CNN的結構
【Kaggle-MNIST之路】CNN+改進過的損失函數(shù)+多次的epoch（四）

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• 排名：1200+

代碼

• 和之前的一樣，會把模型生成出來，用于后續(xù)的保存等工作。
• 可以用之前的方法來改一下代碼。但是需要把生成數(shù)據(jù)的那個版本的類給替換掉才行。

import pandas as pd import torch.utils.data as data import torch import torch.nn as nnfile = './all/train.csv' LR = 0.01class MNISTCSVDataset(data.Dataset):def __init__(self, csv_file, Train=True):self.dataframe = pd.read_csv(csv_file, iterator=True)self.Train = Traindef __len__(self):if self.Train:return 42000else:return 28000def __getitem__(self, idx):data = self.dataframe.get_chunk(100)ylabel = data['label'].as_matrix().astype('float')xdata = data.ix[:, 1:].as_matrix().astype('float')return ylabel, xdataclass CNN(nn.Module):def __init__(self):super(CNN, self).__init__()self.layer1 = nn.Sequential(# (1, 28, 28)nn.Conv2d(in_channels=1,out_channels=32,kernel_size=3, # 卷積filter, 移動塊長stride=1, # filter的每次移動步長),nn.ReLU(),nn.BatchNorm2d(32),nn.Conv2d(in_channels=32,out_channels=32,kernel_size=3, # 卷積filter, 移動塊長stride=1, # filter的每次移動步長),nn.ReLU(),nn.BatchNorm2d(32),nn.Conv2d(in_channels=32,out_channels=32,kernel_size=5, # 卷積filter, 移動塊長stride=2, # filter的每次移動步長padding=2,),nn.ReLU(),nn.BatchNorm2d(32),nn.Dropout(0.4),)self.layer2 = nn.Sequential(nn.Conv2d(in_channels=32,out_channels=64,kernel_size=3, # 卷積filter, 移動塊長stride=1, # filter的每次移動步長),nn.ReLU(),nn.BatchNorm2d(64),nn.Conv2d(in_channels=64,out_channels=64,kernel_size=3, # 卷積filter, 移動塊長stride=1, # filter的每次移動步長),nn.ReLU(),nn.BatchNorm2d(64),nn.Conv2d(in_channels=64,out_channels=64,kernel_size=5, # 卷積filter, 移動塊長stride=2, # filter的每次移動步長padding=2,),nn.ReLU(),nn.BatchNorm2d(64),nn.Dropout(0.4),)self.layer3 = nn.Linear(64 * 4 * 4, 10)def forward(self, x):# print(x.shape)x = self.layer1(x)# print(x.shape)x = self.layer2(x)# print(x.shape)x = x.view(x.size(0), -1)# print(x.shape)x = self.layer3(x)# print(x.shape)return xnet = CNN() loss_function = nn.MultiMarginLoss() optimizer = torch.optim.Adam(net.parameters(), lr=LR) EPOCH = 10 for epoch in range(EPOCH):mydataset = MNISTCSVDataset(file)train_loader = torch.utils.data.DataLoader(mydataset, batch_size=1, shuffle=True)print('epoch %d' % epoch)for step, (yl, xd) in enumerate(train_loader):xd = xd.reshape(100, 1, 28, 28).float()output = net(xd)yl = yl.long()loss = loss_function(output, yl.squeeze())optimizer.zero_grad()loss.backward()optimizer.step()if step % 40 == 0:print('step %d' % step, loss)torch.save(net, 'divided-net.pkl')

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