torch学习 (二十三)：卷积神经网络之NIN模型

卷积神经网络之NIN模型"/>

torch学习 (二十三)：卷积神经网络之NIN模型

文章目录

• 引入
• 1 NIN块
• 2 NIN模型
• 3 模型训练
• 完整代码
• 参考库
• • util.SimpleTool

引入

  NIN意为网络中的网络，提出了串联多个由卷积层和“全连接”层构成的小网络，以此构建一个深层网络 [ 1 ] \color{red}^{[1]} [1]。

1 NIN块

  NIN使用 1 × 1 1 \times 1 1×1卷积层来替代全连接层，从而使空间信息能够自然传递到后面的层中去。
  下图对比了NIN同AlexNet和VGG网络在结构上的主要区别：

  NIN块是NIN模型中的基础块，其特点如下：
  1）由一个卷积层加两个充当全连接层的 1 × 1 1 \times 1 1×1卷积层串联而成；
  2）第一个卷积层的超参数可以自行设置，余下一般固定。

"""
@author: Inki
@contact: inki.yinji@qq
@version: Created in 2020 1221, last modified in 2020 1221.
"""import time
import torch
import torch.nn as nn
from torch import optim
from torch.nn import functional
from util.SimpleTool import load_data_fashion_mnist, train, FlattenLayerdef nin_block(in_channels, out_channels, kernel_size, stride, padding):ret_block = nn.Sequential(nn.Conv2d(in_channels, out_channels, kernel_size, stride, padding),nn.ReLU(),nn.Conv2d(out_channels, out_channels, kernel_size=1),nn.ReLU(),nn.Conv2d(out_channels, out_channels, kernel_size=1),nn.ReLU())return ret_block

2 NIN模型

  模型特点：
  1）卷积窗口形状分别为 11 × 11 11 \times 11 11×11、 5 × 5 5 \times 5 5×5和 3 × 3 3 \times 3 3×3，输出通道与AlexNet一致；
  2）每个NIN块后接一个步幅为 3 3 3、窗口形状为 3 × 3 3 \times 3 3×3的最大池化层；
  3）去掉了AlexNet中最后的 3 3 3个全连接层，而用输出通道数等于标签类别数的NIN块，然后使用全局平均池化层对每个通道中所有元素求平均并直接用于分类；
  4）全局平均池化层即窗口形状等于输入空间形状的平均池化层：可显著减小模型参数尺寸，从而缓解过拟合；
  5）该设计可能导致训练时间增加。

class GlobalAvgPool2d(nn.Module):def __init__(self):super(GlobalAvgPool2d, self).__init__()def forward(self, x):"""The forward function."""return functional.avg_pool2d(x, kernel_size=x.size()[2:])def get_net():ret_net = nn.Sequential(nin_block(1, 96, kernel_size=11, stride=4, padding=0),nn.MaxPool2d(kernel_size=3, stride=2),nin_block(96, 256, kernel_size=5, stride=1, padding=2),nn.MaxPool2d(kernel_size=3, stride=2),nin_block(256, 384, kernel_size=3, stride=1, padding=1),nn.MaxPool2d(kernel_size=3, stride=2),nn.Dropout(0.5),nin_block(384, 10, kernel_size=3, stride=1, padding=1),GlobalAvgPool2d(),FlattenLayer())return ret_netdef test1():x = torch.rand(1, 1, 224, 224)temp_net = get_net()for name, block in temp_net.named_children():x = block(x)print(name, 'output shape:', x.shape)if __name__ == '__main__':test1()

  输出如下：

0 output shape: torch.Size([1, 96, 54, 54])
1 output shape: torch.Size([1, 96, 26, 26])
2 output shape: torch.Size([1, 256, 26, 26])
3 output shape: torch.Size([1, 256, 12, 12])
4 output shape: torch.Size([1, 384, 12, 12])
5 output shape: torch.Size([1, 384, 5, 5])
6 output shape: torch.Size([1, 384, 5, 5])
7 output shape: torch.Size([1, 10, 5, 5])
8 output shape: torch.Size([1, 10, 1, 1])
9 output shape: torch.Size([1, 10])

3 模型训练

def test2():temp_batch_size = 128temp_resize = 224temp_le = 0.002temp_num_epochs = 5temp_net = get_net()temp_tr_iter, temp_te_iter = load_data_fashion_mnist(temp_batch_size, resize=temp_resize)temp_optimizer = optim.Adam(temp_net.parameters(), lr=temp_le)train(temp_net, temp_tr_iter, temp_te_iter, temp_batch_size, temp_optimizer, num_epochs=temp_num_epochs)if __name__ == '__main__':test2()

完整代码

"""
@author: Inki
@contact: inki.yinji@qq
@version: Created in 2020 1221, last modified in 2020 1221.
"""import time
import torch
import torch.nn as nn
from torch import optim
from torch.nn import functional
from util.SimpleTool import load_data_fashion_mnist, train, FlattenLayerdef nin_block(in_channels, out_channels, kernel_size, stride, padding):ret_block = nn.Sequential(nn.Conv2d(in_channels, out_channels, kernel_size, stride, padding),nn.ReLU(),nn.Conv2d(out_channels, out_channels, kernel_size=1),nn.ReLU(),nn.Conv2d(out_channels, out_channels, kernel_size=1),nn.ReLU())return ret_blockclass GlobalAvgPool2d(nn.Module):def __init__(self):super(GlobalAvgPool2d, self).__init__()def forward(self, x):"""The forward function."""return functional.avg_pool2d(x, kernel_size=x.size()[2:])def get_net():ret_net = nn.Sequential(nin_block(1, 96, kernel_size=11, stride=4, padding=0),nn.MaxPool2d(kernel_size=3, stride=2),nin_block(96, 256, kernel_size=5, stride=1, padding=2),nn.MaxPool2d(kernel_size=3, stride=2),nin_block(256, 384, kernel_size=3, stride=1, padding=1),nn.MaxPool2d(kernel_size=3, stride=2),nn.Dropout(0.5),nin_block(384, 10, kernel_size=3, stride=1, padding=1),GlobalAvgPool2d(),FlattenLayer())return ret_netdef test1():x = torch.rand(1, 1, 224, 224)temp_net = get_net()for name, block in temp_net.named_children():x = block(x)print(name, 'output shape:', x.shape)def test2():temp_batch_size = 128temp_resize = 224temp_le = 0.002temp_num_epochs = 5temp_net = get_net()temp_tr_iter, temp_te_iter = load_data_fashion_mnist(temp_batch_size, resize=temp_resize)temp_optimizer = optim.Adam(temp_net.parameters(), lr=temp_le)train(temp_net, temp_tr_iter, temp_te_iter, temp_batch_size, temp_optimizer, num_epochs=temp_num_epochs)if __name__ == '__main__':test2()

参考库

util.SimpleTool

"""
@author: Inki
@contact: inki.yinji@qq
@version: Created in 2020 0903, last modified in 2020 1221.
@note: Some common function, and all given vector data's type must be numpy.array.
"""import time
import numpy as np
import sys
import scipy.io as scio
import torch
import torchvision.transforms as transforms
import torchvision
from torch import nn
from multiprocessing import cpu_countdef get_iter(tr, tr_lab, te, te_lab):"""Get iterator.:paramtr:The training set.tr_lab:The training set's label.te:The test set.te_lab:The test set's label."""yield tr, tr_lab, te, te_labdef is_print(para_str, para_is_print=True):"""Is print?:parampara_str:The print string.para_is_print:True print else not."""if para_is_print:print(para_str)def load_file(para_path):"""Load file.:parampara_file_name:The path of the given file.:returnThe data."""temp_type = para_path.split('.')[-1]if temp_type == 'mat':ret_data = scio.loadmat(para_path)return ret_data['data']else:with open(para_path) as temp_fd:ret_data = temp_fd.readlines()return ret_datadef load_data_fashion_mnist(batch_size=10, root='D:/Data/Datasets/FashionMNIST', resize=None):"""Download the fashion mnist dataset and then load into memory."""trans = []if resize:trans.append(transforms.Resize(size=resize))trans.append(transforms.ToTensor())transform = transforms.Compose(trans)mnist_train = torchvision.datasets.FashionMNIST(root=root, train=True, download=True, transform=transform)mnist_test = torchvision.datasets.FashionMNIST(root=root, train=False, download=True, transform=transform)if sys.platform.startswith('win'):num_workers = 0else:num_workers = cpu_count()train_iter = torch.utils.data.DataLoader(mnist_train, batch_size=batch_size, shuffle=True, num_workers=num_workers)test_iter = torch.utils.data.DataLoader(mnist_test, batch_size=batch_size, shuffle=False, num_workers=num_workers)return train_iter, test_iterdef owa_weight(para_num, para_type='linear_decrease'):"""The ordered weighted averaging operators (OWA) can replace the maximum or minimum operators.And the purpose of this function is to generate the owa weights.And the more refer is:R. R. Yager, J. Kacprzyk, The ordered weighted averaging operators: Theory and applications, Springer Science &Business Media, 2012.:parampara_num:The length of weights list.para_type:'linear_decrease';'inverse_additive',and its default setting is 'linear_decrease'.:returnThe owa weights."""if para_num == 1:return np.array([1])else:if para_type == 'linear_decrease':temp_num = 2 / para_num / (para_num + 1)return np.array([(para_num - i) * temp_num for i in range(para_num)])elif para_type == 'inverse_additive':temp_num = np.sum([1 / i for i in range(1, para_num + 1)])return np.array([1 / i / temp_num for i in range(1, para_num + 1)])else:return owa_weight(para_num)def print_go_round(para_idx, para_str='Program processing'):"""Print the round.:parampara_idx:The current index.para_str:The print words."""round_list = ["\\", "|", "/", "-"]print('\r' + para_str + ': ' + round_list[para_idx % 4], end="")def print_progress_bar(para_idx, para_len):"""Print the progress bar.:parampara_idx:The current index.para_len:The loop length."""print('\r' + '▇' * int(para_idx // (para_len / 50)) + str(np.ceil((para_idx + 1) * 100 / para_len)) + '%', end='')def train(net, tr_iter, te_iter, batch_size, optimizer,loss=nn.CrossEntropyLoss(),device=torch.device('cuda' if torch.cuda.is_available() else 'cpu'),num_epochs=100):"""The train function."""net = net.to(device)temp_batch_count = 0print("Training on", device)for epoch in range(num_epochs):temp_tr_loss_sum, temp_tr_acc_sum, temp_num, temp_start_time = 0., 0., 0, time.time()for x, y in tr_iter:x = x.to(device)y = y.to(device)temp_y_pred = net(x)temp_loss = loss(temp_y_pred, y)optimizer.zero_grad()temp_loss.backward()optimizer.step()temp_tr_loss_sum += temp_loss.cpu().item()temp_tr_acc_sum += (temp_y_pred.argmax(dim=1) == y).sum().cpu().item()temp_num += y.shape[0]temp_batch_count += 1test_acc = evaluate_accuracy(te_iter, net)print("Epoch %d, loss %.4f, training acc %.3f, test ass %.3f, time %.1f s" %(epoch + 1, temp_tr_loss_sum / temp_batch_count, temp_tr_acc_sum / temp_num, test_acc,time.time() - temp_start_time))def evaluate_accuracy(data_iter, net, device=torch.device('cuda' if torch.cuda.is_available() else 'cpu')):"""The evaluate function, and the performance measure is accuracy."""ret_acc, temp_num = 0., 0with torch.no_grad():for x, y in data_iter:net.eval() # The evaluate mode, and the dropout is closed.ret_acc += (net(x.to(device)).argmax(dim=1) == y.to(device)).float().sum().cpu().item()net.train()temp_num += y.shape[0]return ret_acc / temp_numclass Count(dict):"""The count class with dict."""def __missing__(self, __key):return 0class FlattenLayer(torch.nn.Module):def __init__(self):super(FlattenLayer, self).__init__()def forward(self, x):return x.view(x.shape[0], -1)if __name__ == '__main__':load_data_fashion_mnist()

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