structure into the process of pruning. One way to do this is through pruning blocks of weights together (block sparsity). The blocks could be 1-D, 2-D or 3-D, and so on. Let’s start with a simple example of a neuron in the first layer to the neurons in the second layer as shown in figure 5-3. Figure 5-3: 1-D block pruning between two dense layers. The network on the right is the pruned version of the network on blocks. Our model achieved an accuracy of 85.11%. Here, we will prune the convolution blocks from block two (zero indexed) onwards. We will leave the deconvolution blocks untouched. We define a create_model_for_pruning()

广告库 日志收集 展示日志 点击日志 深度学习在搜狗搜索广告的一些应用 无需分词：基于字符粒度表达的问答系统设计 L.X Meng, Y.Li, M.Y Liu, P Shu. Skipping Word: A Character-Sequential Representation based Framework for Question Answering. CIKM2016, pages

for mobile vision applications." arXiv preprint arXiv:1704.04861 (2017). On the other hand, a DSC block (figure 4-21) performs two step convolution. In the first step, the input is convolved with m (dk define a get_conv_builder() function that chooses between a regular convolution block and a depthwise convolution block. LEARNING_RATE = 0.001 N_CLASSES = 3 layer_id = -1 def get_layer_id(): global get_regular_conv_block(filters, strides): return tf.keras.Sequential( [ layers.Conv2D(filters, 3, padding='same', strides=strides), layers.BatchNormalization(), layers.ReLU(), ], name='regular_conv_block_' +

transferable architectures for scalable image recognition. NASNet cells are composed of blocks. A single block corresponds to two hidden inputs, two primitive operations for the hidden states, and a combination operation as shown in figure 7-8 (left). NASNet predicts these five inputs and operations for every block. Each cell contains such blocks. Hence, for each cell, NASNet predicts parameters. Since we predict paper, the value for is chosen to be 5. Figure 7-8 (right) shows a predicted block. Figure 7-8: The structure of a block used to compose normal and reduction cells. The image on the left shows the timesteps

视觉感知模型 分割 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 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

paper. It is based on the regular ResNet model, which substitutes 3x3 convolutions in the bottleneck block for 3x3 grouped convolutions. This model script is available on GitHub. ‣ SE-ResNext model: This paper. It is based on the regular ResNet model, which substitutes 3x3 convolutions in the bottleneck block for 3x3 grouped convolutions. This model script is available on GitHub. ‣ SE-ResNext model: This paper. It is based on the regular ResNet model, which substitutes 3x3 convolutions in the bottleneck block for 3x3 grouped convolutions. This model script is available on GitHub. ‣ SE-ResNext model: This

illustration). Let the output of the -th residual block be denoted by . For the -th residual block, we start with the input from the previous residual block, . This input is branched into two, in one branch through a non-linearity (ReLU). Figure 6-16: A residual block in ResNet. Formally, it is computed as follows: . Note how in the residual block, the output of the previous layer ( ) skips the layers further by probabilistically dropping a residual block with a probability . The goal is to keep this probability high (less likelihood of dropping the given block) in the earlier layers, and low for later layers

提供 越来越粗糙的抽象。就像半导体设计师从指定晶体管到逻辑电路再到编写代码一样，神经网络研究人员已经 从考虑单个人工神经元的行为转变为从层的角度构思网络，通常在设计架构时考虑的是更粗糙的块（block）。 之前我们已经介绍了一些基本的机器学习概念，并慢慢介绍了功能齐全的深度学习模型。在上一章中，我们 从零开始实现了多层感知机的每个组件，然后展示了如何利用高级API轻松地实现相同的模型。为了易于学 2016)。目前ResNet架构仍然是许多 视觉任务的首选架构。在其他的领域，如自然语言处理和语音，层组以各种重复模式排列的类似架构现在也 是普遍存在。 为了实现这些复杂的网络，我们引入了神经网络块的概念。块（block）可以描述单个层、由多个层组成的组 件或整个模型本身。使用块进行抽象的一个好处是可以将一些块组合成更大的组件，这一过程通常是递归的， 如 图5.1.1所示。通过定义代码来按需生成任意复杂度的块 (continues on next page) 194 5. 深度学习计算 (continued from previous page) for block in self._modules.values(): X = block(X) return X __init__函数将每个模块逐个添加到有序字典_modules中。读者可能会好奇为什么每个Module都有一 个_modul

least latency during inference. Figure 1-14: Standard Transformer Encoder block (left), and an Evolved Transformer Encoder block (right). While the former was designed manually, the latter was learnt through

MACs in a model is another metric to measure its complexity. And since they are such a fundamental block of models, they have been optimized both in hardware and software. Let’s take a look at how we can convolutional layer has 32 filters, and the second one has 64 filters. The output of the second convolution block is flattened to a rank-2 tensor (rank-1 excluding the batch dimension). A dropout layer follows right