深度学习

前言

2012年，AlexNet横空出世。它首次证明了学习到的特征可以超越手工设计的特征。它一举打破了计算机视觉研究的现状。 AlexNet以很大的优势赢得了当年的ImageNet图像识别挑战赛。

AlexNet与VGG的对比：

AlexNet

VGG模型代码：

import torch.nn as nn

def vgg_block(num_convs, in_channels, out_channels):
    layers = []
    for _ in range(num_convs):
        layers.append(nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1))
        layers.append(nn.ReLU())
        in_channels = out_channels
    layers.append(nn.MaxPool2d(kernel_size=2, stride=2))
    return nn.Sequential(*layers)

def vgg(conv_arch):
    conv_blks = []
    in_channels = 1
    # 卷积层部分
    for (num_convs, out_channels) in conv_arch:
        conv_blks.append(vgg_block(num_convs, in_channels, out_channels))
        in_channels = out_channels

    return nn.Sequential(
        *conv_blks, nn.Flatten(),
        # 全连接层部分
        nn.Linear(out_channels * 7 * 7, 4096), nn.ReLU(), nn.Dropout(0.5),
        nn.Linear(4096, 4096), nn.ReLU(), nn.Dropout(0.5),
        nn.Linear(4096, 10))

note：开始了更深，更大的竞赛！

Ref：李沐

VGG神经网络

前言

VGG模型代码：

CATALOG

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