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图像深度学习

从头开始训练卷积神经网络或使用预训练的网络快速学习新任务

您可以通过定义网络架构并从头开始训练网络，来创建新的用于图像分类和回归任务的深度网络。您还可以使用迁移学习以利用预训练网络所提供的知识来学习新数据中的新模式。通常来说，使用迁移学习对预训练的图像分类网络进行微调比从头开始训练更快更容易。使用预训练的深度网络，您可以快速学习新任务，而无需定义和训练新网络，也不需要使用数百万个图像或强大的 GPU。

定义网络架构后，必须使用 trainingOptions 函数定义训练参数。然后，您可以使用 trainNetwork 训练网络。使用经过训练的网络预测类标签或数值响应。

您可以在一个 CPU、一个 GPU、多个 CPU 或 GPU 上训练卷积神经网络，或者在群集中并行训练或在云中训练。在 GPU 上训练或并行训练需要 Parallel Computing Toolbox™。使用 GPU 训练需要具有 3.0 或更高计算能力的支持 CUDA^® 的 NVIDIA^® GPU。使用 trainingOptions 函数指定执行环境。

App

Deep Network Designer

Design, visualize, and train deep learning networks

函数

全部展开

训练网络

`trainingOptions`	Options for training deep learning neural network
`trainNetwork`	Train neural network for deep learning
`analyzeNetwork`	Analyze deep learning network architecture

预训练网络

`alexnet`	AlexNet convolutional neural network
`vgg16`	VGG-16 convolutional neural network
`vgg19`	VGG-19 convolutional neural network
`squeezenet`	SqueezeNet convolutional neural network
`googlenet`	GoogLeNet convolutional neural network
`inceptionv3`	Inception-v3 convolutional neural network
`densenet201`	DenseNet-201 convolutional neural network
`mobilenetv2`	MobileNet-v2 convolutional neural network
`resnet18`	ResNet-18 convolutional neural network
`resnet50`	ResNet-50 convolutional neural network
`resnet101`	ResNet-101 convolutional neural network
`xception`	Xception convolutional neural network
`inceptionresnetv2`	Pretrained Inception-ResNet-v2 convolutional neural network
`nasnetlarge`	Pretrained NASNet-Large convolutional neural network
`nasnetmobile`	Pretrained NASNet-Mobile convolutional neural network
`shufflenet`	Pretrained ShuffleNet convolutional neural network

网络层

输入层

`imageInputLayer`	Image input layer
`image3dInputLayer`	3-D image input layer

卷积和全连接层

`convolution2dLayer`	2-D convolutional layer
`convolution3dLayer`	3-D convolutional layer
`groupedConvolution2dLayer`	2-D grouped convolutional layer
`transposedConv2dLayer`	Transposed 2-D convolution layer
`transposedConv3dLayer`	Transposed 3-D convolution layer
`fullyConnectedLayer`	Fully connected layer

激活层

`reluLayer`	Rectified Linear Unit (ReLU) layer
`leakyReluLayer`	Leaky Rectified Linear Unit (ReLU) layer
`clippedReluLayer`	Clipped Rectified Linear Unit (ReLU) layer
`eluLayer`	Exponential linear unit (ELU) layer
`tanhLayer`	Hyperbolic tangent (tanh) layer

归一化、丢弃和裁剪层

`batchNormalizationLayer`	Batch normalization layer
`crossChannelNormalizationLayer`	Channel-wise local response normalization layer
`dropoutLayer`	Dropout layer
`crop2dLayer`	2-D crop layer
`crop3dLayer`	3-D crop layer

池化和去池化层

`averagePooling2dLayer`	Average pooling layer
`averagePooling3dLayer`	3-D average pooling layer
`globalAveragePooling2dLayer`	Global average pooling layer
`globalAveragePooling3dLayer`	3-D global average pooling layer
`maxPooling2dLayer`	Max pooling layer
`maxPooling3dLayer`	3-D max pooling layer
`maxUnpooling2dLayer`	Max unpooling layer

组合层

`additionLayer`	Addition layer
`concatenationLayer`	Concatenation layer
`depthConcatenationLayer`	Depth concatenation layer

输出层

`softmaxLayer`	Softmax layer
`classificationLayer`	Classification output layer
`regressionLayer`	Create a regression output layer

预处理图像

`augmentedImageDatastore`	Transform batches to augment image data
`imageDataAugmenter`	Configure image data augmentation
`augment`	Apply identical random transformations to multiple images

DAG 网络

`layerGraph`	Graph of network layers for deep learning
`plot`	Plot neural network layer graph
`addLayers`	Add layers to layer graph
`removeLayers`	Remove layers from layer graph
`replaceLayer`	Replace layer in layer graph
`connectLayers`	Connect layers in layer graph
`disconnectLayers`	Disconnect layers in layer graph
`DAGNetwork`	Directed acyclic graph (DAG) network for deep learning

预测

`classify`	Classify data using a trained deep learning neural network
`activations`	Compute deep learning network layer activations
`predict`	Predict responses using a trained deep learning neural network
`confusionchart`	Create confusion matrix chart for classification problem
ConfusionMatrixChart Properties	Confusion matrix chart appearance and behavior
`sortClasses`	Sort classes of confusion matrix chart

示例和操作指南

使用预训练网络

使用 GoogLeNet 对图像进行分类

此示例说明如何使用预训练的深度卷积神经网络 GoogLeNet 对图像进行分类。

使用深度学习对网络摄像头图像进行分类

此示例说明如何使用预训练的深度卷积神经网络 GoogLeNet 实时对来自网络摄像头的图像进行分类。

Transfer Learning with Deep Network Designer

Interactively fine-tune a pretrained deep learning network to learn a new image classification task.

训练深度学习网络以对新图像进行分类

此示例说明如何使用迁移学习来重新训练卷积神经网络以对新图像集进行分类。

使用预训练网络提取图像特征

此示例说明如何从预训练的卷积神经网络中提取已学习的图像特征，并使用这些特征来训练图像分类器。特征提取是使用预训练深度网络的表征能力的最简单最快捷的方式。例如，您可以使用 fitcecoc(Statistics and Machine Learning Toolbox™) 基于提取的特征来训练支持向量机 (SVM)。由于特征提取只需要遍历一次数据，因此如果没有 GPU 来加速网络训练，则不妨从特征提取开始。

使用 AlexNet 进行迁移学习

此示例说明如何微调预训练的 AlexNet 卷积神经网络以对新的图像集合执行分类。

Pretrained Deep Neural Networks

Learn how to download and use pretrained convolutional neural networks for classification, transfer learning and feature extraction.

创建新的深度网络

创建简单的深度学习网络以用于分类

此示例说明如何创建和训练简单的卷积神经网络来进行深度学习分类。卷积神经网络是深度学习的基本工具，尤其适用于图像识别。

Build Networks with Deep Network Designer

Interactively build and edit deep learning networks.

针对回归训练卷积神经网络

此示例说明如何使用卷积神经网络拟合回归模型来预测手写数字的旋转角度。

List of Deep Learning Layers

Discover all the deep learning layers in MATLAB^®.

Specify Layers of Convolutional Neural Network

Learn about the layers of a convolutional neural network (ConvNet), and the order they appear in a ConvNet.

Generate MATLAB Code from Deep Network Designer

Generate MATLAB code to recreate designing and training a network in Deep Network Designer.

训练残差网络进行图像分类

此示例说明如何创建包含残差连接的深度学习神经网络，并针对 CIFAR-10 数据对其进行训练。残差连接是卷积神经网络架构中的常见元素。使用残差连接可以改善网络中的梯度流，从而可以训练更深的网络。

Train Generative Adversarial Network (GAN)

This example shows how to train a generative adversarial network (GAN) to generate images.

Train Network Using Custom Training Loop

This example shows how to train a network that classifies handwritten digits with a custom learning rate schedule.

Train Network with Multiple Outputs

This example shows how to train a deep learning network with multiple outputs that predict both labels and angles of rotations of handwritten digits.

Train a Siamese Network to Compare Images

This example shows how to train a Siamese network to identify similar images of handwritten characters.

概念

Deep Learning in MATLAB

Discover deep learning capabilities in MATLAB using convolutional neural networks for classification and regression, including pretrained networks and transfer learning, and training on GPUs, CPUs, clusters, and clouds.

Set Up Parameters and Train Convolutional Neural Network

Learn how to set up training parameters for a convolutional neural network.

Preprocess Images for Deep Learning

Learn how to resize images for training, prediction, and classification, and how to preprocess images using data augmentation, transformations, and specialized datastores.

Preprocess Volumes for Deep Learning

Read and preprocess volumetric image and label data for 3-D deep learning.

Datastores for Deep Learning

Learn how to use datastores in deep learning applications.

将分类网络转换为回归网络

此示例说明如何将经过训练的分类网络转换为回归网络。

Deep Learning Tips and Tricks

Learn how to improve the accuracy of deep learning networks.