引入
NIN意为网络中的网络，提出了串联多个由卷积层和“全链接”层构成的小网络，以此构建一个深层网络 [ 1 ] \color{red}^{[1]} [1]。html
1 NIN块
NIN使用 1 × 1 1 \times 1 1×1卷积层来替代全链接层，从而使空间信息可以天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，其特色以下：
1）由一个卷积层加两个充当全链接层的 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能够自行设置，余下通常固定。python
""" @author: Inki @contact: inki.yinji@qq.com @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, FlattenLayer def 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
模型特色：
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）每一个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个全链接层，而用输出通道数等于标签类别数的NIN块，而后使用全局平均池化层对每一个通道中全部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：可显著减少模型参数尺寸，从而缓解过拟合；
5）该设计可能致使训练时间增长。web
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_net def 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()
输出以下：网络
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 = 128 temp_resize = 224 temp_le = 0.002 temp_num_epochs = 5 temp_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.com @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, FlattenLayer def 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 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_net def 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 = 128 temp_resize = 224 temp_le = 0.002 temp_num_epochs = 5 temp_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.com @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_count def get_iter(tr, tr_lab, te, te_lab): """ Get iterator. :param tr: 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_lab def is_print(para_str, para_is_print=True): """ Is print? :param para_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. :param para_file_name: The path of the given file. :return The 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_data def 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 = 0 else: 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_iter def 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. :param para_num: The length of weights list. para_type: 'linear_decrease'; 'inverse_additive', and its default setting is 'linear_decrease'. :return The 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. :param para_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. :param para_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 = 0 print("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 += 1 test_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., 0 with 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_num class Count(dict): """ The count class with dict. """ def __missing__(self, __key): return 0 class 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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