MatConvNet：MATLAB中的卷积神经网络实战指南

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"MatConvNet是MATLAB环境下的一个卷积神经网络（CNN）工具箱，旨在简化和增强CNN的操作。该工具箱强调易用性和灵活性，提供了计算线性卷积、滤波器银行、特征池化等核心CNN操作的便捷MATLAB函数。这使得用户能够快速构建新的CNN架构原型，并在CPU和GPU上高效运行，甚至可以在大型数据集如ImageNet ILSVRC上训练复杂的模型。手册包含了CNN的基本介绍、MatConvNet的实现细节以及每个计算模块的技术详情。" MatConvNet工具箱的核心功能包括： 1. **卷积运算**：提供计算卷积层的函数，这是CNN中的基础操作，用于提取输入图像的特征。 2. **池化**：用于降低数据的空间维度，增加模型的泛化能力，包括最大池化、平均池化等。 3. **全连接层**：将卷积层后的特征映射转化为向量，为分类或回归任务做准备。 4. **激活函数**：如ReLU、Sigmoid和TanH，引入非线性，增强模型的表达能力。 5. **损失函数**：如交叉熵损失，用于评估模型预测与真实标签的差距。 6. **反向传播**：用于更新权重，通过梯度下降等优化算法进行模型训练。 7. **GPU支持**：利用GPU的并行计算能力加速模型的训练过程。在MatConvNet中，CNN的结构可以表示为序列或有向无环图（DAG）。序列结构适用于简单的前馈网络，而DAG结构则可以表示更复杂的网络结构，如残差网络（ResNets）或跳跃连接。文档和示例部分提供详细的使用指南，帮助用户快速上手。速度部分讨论了如何优化计算性能，包括CPU和GPU的使用策略。此外，MatConvNet也考虑了内存管理，以避免不必要的数据复制，提高运行效率。 MatConvNet的灵活性允许用户自定义层类型，扩展工具箱以适应特定任务的需求。这使得研究人员和开发者能够在不离开MATLAB环境的情况下，探索和实现各种先进的深度学习技术。 MatConvNet是一个强大的CNN工具箱，不仅适合初学者快速学习CNN的基本概念，也满足专业人士进行深度学习研究和应用开发的需求。通过深入理解和熟练运用MatConvNet，用户可以构建出高效且准确的计算机视觉模型。

6 CHAPTER 1. INTRODUCTION TO MATCONVNET

model batch sz. CPU GPU CuDNN

AlexNet 256 22.1 192.4 264.1

VGG-F 256 21.4 211.4 289.7

VGG-M 128 7.8 116.5 136.6

VGG-S 128 7.4 96.2 110.1

VGG-VD-16 24 1.7 18.4 20.0

VGG-VD-19 24 1.5 15.7 16.5

Table 1.1: ImageNet training speed (images/s).

1.4 Speed

Eﬃciency is very important for working with CNNs. MatConvNet supports using NVIDIA

GPUs as it includes CUDA implementations of all algorithms (or relies on MATLAB CUDA

support).

To use the GPU (provided that suitable hardware is available and the toolbox has been

compiled with GPU support), one simply converts the arguments to gpuArrays in MATLAB,

as in y = vl_nnconv(gpuArray(x), gpuArray(w), []). In this manner, switching between CPU

and GPU is fully transparent. Note that MatConvNet can also make use of the NVIDIA

CuDNN library with signiﬁcant speed and space beneﬁts.

Next we evaluate the performance of MatConvNet when training large architectures

on the ImageNet ILSVRC 2012 challenge data [2]. The test machine is a Dell server with

two Intel Xeon CPU E5-2667 v2 clocked at 3.30 GHz (each CPU has eight cores), 256 GB

of RAM, and four NVIDIA Titan Black GPUs (only one of which is used unless otherwise

noted). Experiments use MatConvNet beta12, CuDNN v2, and MATLAB R2015a. The

data is preprocessed to avoid rescaling images on the ﬂy in MATLAB and stored in a RAM

disk for faster access. The code uses the vl_imreadjpeg command to read large batches of

JPEG images from disk in a number of separate threads. The driver examples/cnn_imagenet.m

is used in all experiments.

We train the models discussed in section 1.3 on ImageNet ILSVRC. table 1.1 reports

the training speed as number of images per second processed by stochastic gradient descent.

AlexNet trains at about 264 images/s with CuDNN, which is about 40% faster than the

vanilla GPU implementation (using CuBLAS) and more than 10 times faster than using the

CPUs. Furthermore, we note that, despite MATLAB overhead, the implementation speed is

comparable to Caﬀe (they report 253 images/s with CuDNN and a Titan – a slightly slower

GPU than the Titan Black used here). Note also that, as the model grows in size, the size of

a SGD batch must be decreased (to ﬁt in the GPU memory), increasing the overhead impact

somewhat.

table 1.2 reports the speed on VGG-VD-16, a very large model, using multiple GPUs. In

this case, the batch size is set to 264 images. These are further divided in sub-batches of 22

images each to ﬁt in the GPU memory; the latter are then distributed among one to four

GPUs on the same machine. While there is a substantial communication overhead, training

speed increases from 20 images/s to 45. Addressing this overhead is one of the medium term

goals of the library.

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MatConvNet：MATLAB中的卷积神经网络实战指南

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