their giant counterparts. In the first chapter, we briefly introduced architectures like depthwise separable convolution, attention mechanism and the hashing trick. In this chapter, we will deepdive into their corresponding animal in the embedding table. ● Train the model: As we saw earlier the points are linearly separable. We can train a model with a single fully connected layer followed by a softmax activation, since provided a breakthrough for efficiently learning from sequential data, depthwise separable convolution extended the reach of convolution models to mobile and other devices with limited compute and memory resources

the training process: performance and convergence. Hyperparameters like number of filters in a convolution network or 1 Note that this search space is just choosing if we are applying the techniques. The manipulate the structure of a network. The number of dense units, number of convolution channels or the size of convolution kernels can sometimes be 4 Jaderberg, Max, et al. "Population based training a simple convolution network. Each timestep outputs a convolution layer parameter such as number of filters, filter height, filter width and other parameters required to describe a convolution layer. It

depth_multiplier=1, activation=None, use_bias=True, depthwise_initializer='glorot_uniform', pointwise_initializer='glorot_uniform', bias_initializer='zeros', depthwise_regularizer=None, pointwise_regularizer=None one, bias_regularizer=None, activity_regularizer=None, depthwise_constraint=None, pointwise_constraint=None, bias_constraint=None) 深度方向的可分离 2D 卷积。 可分离的卷积的操作包括，首先执行深度方向的空间卷积（分别作用于每个输入通道），紧 接一个将所得输 布尔值，该层是否使用偏置向量。 • depthwise_initializer: 运用到深度方向的核矩阵的初始化器 (详见 initializers)。 • pointwise_initializer: 运用到逐点核矩阵的初始化器 (详见 initializers)。 • bias_initializer: 偏置向量的初始化器 (详见 initializers)。 • depthwise_regularizer:

accuracy. This model script is available on GitHub and NGC. ‣ Mask R-CNN model: Mask R-CNN is a convolution-based neural network that is used for object instance segmentation. PyTorch Release 23.07 PyTorch accuracy. This model script is available on GitHub and NGC. ‣ Mask R-CNN model: Mask R-CNN is a convolution-based neural network that is used for object instance segmentation. The paper describing the model accuracy. This model script is available on GitHub and NGC. ‣ Mask R-CNN model: Mask R-CNN is a convolution-based neural network that is used for object instance segmentation. The paper describing the model

络、通信网络、蛋白质分子结构等一系列的不规则空间拓扑结构的数据，它们往往显得力 不从心。2016 年，Thomas Kipf 等人基于前人在一阶近似的谱卷积算法上提出了图卷积网 络(Graph Convolution Network，GCN)模型。GCN 算法实现简单，从空间一阶邻居信息聚 合的角度也能直观地理解，在半监督任务上取得了不错效果。随后，一系列的网络模型相 继被提出，如 GAT、EdgeConv、DeepGCN 和3 × 3感受 野大小。小卷积核使得网络提取特征时的感受野区域有限，但是增大感受野的区域又会增 加网络的参数量和计算代价，因此需要权衡设计。 空洞卷积(Dilated/Atrous Convolution)的提出较好地解决这个问题，空洞卷积在普通卷 积的感受野上增加一个 Dilation Rate 参数，用于控制感受野区域的采样步长，如下图 10.51 所示：当感受野的采样步长 Dilation 时，使用普通卷积方式进行运算；当 dilation 参数大于 1 时，采样空洞卷积方式进行计算。 10.11.2 转置卷积 转置卷积(Transposed Convolution，或 Fractionally Strided Convolution，部分资料也称 之为反卷积/Deconvolution，实际上反卷积在数学上定义为卷积的逆过程，但转置卷积并不 能恢复出原卷积的输入，因此称为反卷积并不妥当)通过在输入之间填充大量的

........................................................................................ 45 Convolution Layers ....................................................................................... ............................................................................ 71 12. Keras ― Convolution Neural Network ............................................................................... Keras neural networks are written in Python which makes things simpler.  Keras supports both convolution and recurrent networks. 1. Keras ― Introduction Keras 2  Deep learning

Forward Block Forward Block deconvolution deconvolution convolution convolution 检测 Forward Block Forward Block convolution convolution 识别 Forward Block Forward Block SACC2017 视觉感知模型-融合 分割 Forward Block Forward Block deconvolution deconvolution convolution convolution 检测 Forward Block Forward Block convolution convolution 识别 Forward Block Forward Block Forward Block Forward Forward Block deconvolution deconvolution 分割 convolution convolution 检测 识别 Single Frame Predictor SACC2017 视觉感知模型-融合 检测 识别 分割 跟踪 核 心 深度学习 •完全基于深度学习 •统一分类，检测，分割，跟踪 ü通过共享计算提高算法效率 ü通过多个相关任务共同学习提高算法性能

given a set of m training data {(x(i), y(i))}i=1,··· ,m, we first assume that they are linearly separable. Specifically, there exists a hyperplane (parameterized by ω and b) such that ωT x(i) + b ≥ 0 for features (“derived” from the old representation). As shown in Fig. 4 (b), data become linearly separable in the new higher-dimensional feature space (a) (b) Figure 4: Feature mapping for 1-dimensional apply the mapping x = {x1, x2} → z = {x2 1, √ 2x1x2, x2 2}, such that the data become linearly separable in the resulting 3-dimensional feature space. We now consider a general quadratic feature mapping

example now has two features (“derived” from the old representa- tion) Data now becomes linearly separable in the new representation Feng Li (SDU) SVM December 28, 2021 42 / 82 Feature Mapping (Contd.) example now has three features (“derived” from the old represen- tation) Data now becomes linearly separable in the new representation Feng Li (SDU) SVM December 28, 2021 44 / 82 Feature Mapping (Contd.) Soft-Margin SVM (Contd.) Recall that, for the separable case (training loss = 0), the constraints were y(i)(ωTx(i) + b) ≥ 1 for ∀i For the non-separable case, we relax the above constraints as: y(i)(ωTx(i)

matrix of size [5, 6]. This is because we have simply removed the first neuron. Now, consider a convolution layer with 3x3 sized filters and 3 input channels. At 1-D granularity, a vector of weights is pruned filters project consisted of thirteen convolution blocks and five deconvolution blocks. Our model achieved an accuracy of 85.11%. Here, we will prune the convolution blocks from block two (zero indexed) model for pruning. The prunable_blocks variable is the list of names of prunable convolution blocks. We prune all convolution blocks from second (zero indexed) onwards. The model variable refers to the pet