可以获得与浅层神经网络相当的模型性能，而不至于更糟糕。 通过在输入和输出之间添加一条直接连接的 Skip Connection 可以让神经网络具有回退 的能力。以 VGG13 深度神经网络为例，假设观察到 VGG13 模型出现梯度弥散现象，而 10 层的网络模型并没有观测到梯度弥散现象，那么可以考虑在最后的两个卷积层添加 Skip Connection，如图 10.62 中所示。通过这种方式，网络模型可以自动选择是否经由这两个 个 卷积层完成特征变换，还是直接跳过这两个卷积层而选择 Skip Connection，亦或结合两个 卷积层和 Skip Connection 的输出。 Conv2d(64, 3x3) Pooling(2x2,2) Conv2d(64, 3x3) FC(256) FC(64) FC(10) Conv2d(64, 3x3) Pooling(2x2,2) Conv2d(64, 3x3) Conv2d(64, 3x3) Pooling(2x2,2) Conv2d(64, 3x3) 图 10.62 添加了 Skip Connection 的 VGG13 网络结构 2015 年，微软亚洲研究院何凯明等人发表了基于 Skip Connection 的深度残差网络 (Residual Neural Network，简称 ResNet)算法 [10]，并提出了 18 层、34 层、50

classification  MNIST database of handwritten digits  Fashion-MNIST database of fashion articles  Boston housing price regression dataset Let us use the MNIST database of handwritten digits (or Weights. Model weights are large file so we have to download and extract the feature from ImageNet database. Some of the popular pre-trained models are listed below,  ResNet  VGG16  MobileNet

itself Example 2 T: Recognizing hand-written words P: Percentage of words correctly classified E: Database of human-labeled images of handwritten words Feng Li (SDU) Overview September 6, 2023 10 / 57 Categorize email messages as spam or legitimate P: Percentage of email messages correctly classified E: Database of emails, some with human-given labels Example 4 T: Driving on four-lane highways using vision

Model 训练模型 保存和加载 h5 模型 保存和加载 SavedModel 模型 Fashion MNIST 数据集介绍 Original MNIST dataset The MNIST database of handwritten digits, available from this page, has a training set of 60,000 examples, and

performance ▪ Stop at the highest val perf. Dropout ▪ Learning less to learn better ▪ Each connection has ? = 0, 1 to lose https://github.com/MorvanZhou/PyTorch-Tutorial Clarification ▪ torch.nn

or encoder-decoder network [Su et al, 2017] Remaining Challenges 82 Input Output conv skip connection Efficient Network Structure Multi-scale or cascaded refinement network [Nah et al, 2017] Remaining

methodology of training new machine learning models for the past decade (Refer to Figure 1-1 for the connection between deep learning and machine learning). Deep Learning models have beaten previous baselines

for the long term, you need to improve recall by repetition (i.e., increase the weight of that connection). Can we do the same with neural networks? Can we optimally prune the network connections, remove

feed‐forward network）。具体来说，在计算编码器 的自注意力时，查询、键和值都来自前一个编码器层的输出。受 7.6节中残差网络的启发，每个子层都采用 了残差连接（residual connection）。在Transformer中，对于序列中任何位置的任何输入x ∈ Rd，都要求满 足sublayer(x) ∈ Rd，以便残差连接满足x + sublayer(x) ∈ Rd。在残差连接的加法计算之后，紧接着应用层