深度神经网络训练技巧：魏秀参经验分享

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"本文介绍了魏秀参关于深度神经网络训练的一些重要技巧和建议，特别是针对卷积神经网络（CNN）。这些技巧对于提升模型性能、优化训练过程具有重要意义。" 深度神经网络（DNN）和卷积神经网络（CNN）是人工智能领域，尤其是图像识别、对象检测和文本识别等领域中的核心工具。它们通过多层处理结构学习数据的抽象表示，显著提升了相关任务的准确性和效率。随着深度学习研究的深入，大量优质论文和开源软件包相继出现，如TensorFlow、PyTorch等，为开发者提供了强大的工具。然而，尽管存在众多教程和指南，但关于如何从零开始构建并训练一个优秀的深度卷积神经网络的详细总结可能并不常见。本文将介绍魏秀参收集和总结的深度卷积神经网络（DCNN）实现细节，包括一系列的技巧和窍门，以帮助读者更好地构建和训练自己的深度网络。以下是一些关键点： 1. **初始化权重**：合适的权重初始化对于网络的收敛速度和最终性能至关重要。Xavier初始化和He初始化是两种常用的策略，它们考虑了输入和输出层神经元的数量来减小梯度消失或爆炸的问题。 2. **正则化**：L1和L2正则化可以防止过拟合，通过在损失函数中添加权重的惩罚项来约束模型复杂度。Dropout是一种有效的正则化技术，通过随机关闭一部分神经元在训练期间的激活，提高模型的泛化能力。 3. **优化器选择**：梯度下降是基础优化算法，但Adam、RMSprop和Adagrad等自适应学习率优化器通常能提供更好的性能，特别是在处理非凸损失函数时。 4. **批量归一化**：批量归一化可以在每一层内部加速训练过程，稳定网络的分布，并有助于减少对初始化的敏感性。 5. **数据增强**：通过对训练数据进行旋转、翻转、缩放等操作，可以极大地增加网络看到的样本多样性，提高模型的泛化能力。 6. **学习率调度**：动态调整学习率，如在训练初期设置较大的学习率，随着训练进行逐渐减小，可以帮助模型更快地接近局部最小值并避免陷入过深。 7. **早停法**：监控验证集的性能，一旦发现验证误差开始上升，可以提前停止训练，防止过拟合。 8. **模型集成**：结合多个模型的预测结果，通过投票或平均，可以提高整体预测的准确性。 9. **损失函数的选择**：根据任务特性选择合适的损失函数，如交叉熵用于分类问题，均方误差用于回归问题。 10. **GPU并行计算**：利用GPU的并行计算能力加速网络的训练过程，尤其是在处理大规模数据时。以上所述是深度学习和CNN训练中的一些基本技巧，但实际应用中还需要根据具体任务和数据集的特点进行调整和优化。不断学习和实践是提升网络性能的关键。

Must Know Tips/Tricks in Deep Neural Networks (by

Xiu-Shen Wei

)

Deep Neural Networks, especially

Convolutional Neural Networks

(

CNN

), allows computational models that are

composed of multiple processing layers to learn representations of data with multiple levels of abstraction. These

methods have dramatically improved the state-of-the-arts in visual object recognition, object detection, text

recognition and many other domains such as drug discovery and genomics.

In addition, many solid papers have been published in this topic, and some high quality open source CNN software

packages have been made available. There are also well-written CNN tutorials or CNN software manuals. However, it

might lack a recent and comprehensive summary about the details of how to implement an excellent deep

convolutional neural networks from scratch. Thus, we collected and concluded many implementation details for

DCNNs.

Here we will introduce these extensive implementation details, i.e.,

tricks

tips

, for building

and training your own deep networks.

Introduction

We assume you already know the basic knowledge of deep learning, and here we will present the implementation details (tricks or tips) in Deep Neural

Networks, especially CNN for image-related tasks, mainly in

eight aspects

data augmentation

;

pre-processing on images

;

initializations of

Networks

;

some tips during training

;

selections of activation functions

;

diverse regularizations

;

some insights found from figures

and

finally

methods of ensemble multiple deep networks

Additionally, the

corresponding slides

are available at

[slide]

. If there are any problems/mistakes in these materials and slides, or there are

something important/interesting you consider that should be added, just feel free to contact

Sec. 1: Data Augmentation

Since deep networks need to be trained on a huge number of training images to achieve satisfactory performance, if the original image data set contains

limited training images, it is better to do data augmentation to boost the performance. Also, data augmentation becomes the thing must to do when

training a deep network.

There are many ways to do data augmentation, such as the popular

horizontally flipping

random crops

and

color jittering

. Moreover,

you could try combinations of multiple different processing, e.g., doing the rotation and random scaling at the same time. In addition, you can try

to raise saturation and value (S and V components of the HSV color space) of all pixels to a power between 0.25 and 4 (same for all pixels within

a patch), multiply these values by a factor between 0.7 and 1.4, and add to them a value between -0.1 and 0.1. Also, you could add a value

between [-0.1, 0.1] to the hue (H component of HSV) of all pixels in the image/patch.

Krizhevsky

et al

[1]

proposed

fancy PCA

when training the famous

Alex-Net

in 2012. Fancy PCA alters the intensities of the RGB channels in

training images. In practice, you can firstly perform PCA on the set of RGB pixel values throughout your training images. And then, for each

training image, just add the following quantity to each RGB image pixel (i.e.,

where,

and

are the

-th eigenvector and eigenvalue of the

covariance matrix of RGB pixel values, respectively, and

is a random

variable drawn from a Gaussian with mean zero and standard deviation 0.1. Please note that, each

is drawn only once for all the pixels of a

particular training image until that image is used for training again. That is to say, when the model meets the same training image again, it will

randomly produce another

for data augmentation. In

[1]

, they claimed that “

fancy PCA could approximately capture an important property

of natural images, namely, that object identity is invariant to changes in the intensity and color of the illumination

”. To the classification

performance, this scheme reduced the top-1 error rate by over 1% in the competition of ImageNet 2012.

Sec. 2: Pre-Processing

Now we have obtained a large number of training samples (images/crops), but please do not hurry! Actually, it is necessary to do pre-processing on

these images/crops. In this section, we will introduce several approaches for pre-processing.

The first and simple pre-processing approach is

zero-center

the data, and then

normalize

them, which is presented as two lines Python codes as

follows:

>>> X -= np.mean(X, axis = 0)

# zero-center

>>> X /= np.std(X, axis = 0)

# normalize

where, X is the input data (NumIns×NumDim). Another form of this pre-processing normalizes each dimension so that the min and max along the

dimension is -1 and 1 respectively. It only makes sense to apply this pre-processing if you have a reason to believe that different input features have

different scales (or units), but they should be of approximately equal importance to the learning algorithm. In case of images, the relative scales of

pixels are already approximately equal (and in range from 0 to 255), so it is not strictly necessary to perform this additional pre-processing step.

Another pre-processing approach similar to the first one is

PCA Whitening

. In this process, the data is first centered as described above. Then, you

can compute the covariance matrix that tells us about the correlation structure in the data:

>>> X -= np.mean(X, axis = 0)

# zero-center

>>> cov = np.dot(X.T, X) / X.shape[0]

# compute the covariance matrix

After that, you decorrelate the data by projecting the original (but zero-centered) data into the eigenbasis:

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