Introduction

Convolutional Neural Network (CNN for short) is a deep learning model for tasks such as image processing, computer vision, and natural language processing. It mainly consists of convolutional layers, pooling layers and fully connected layers.

convolutional layer

The convolutional layer is the core layer in the convolutional neural network (CNN), which can extract useful features from the input data. The convolutional layer mainly implements feature extraction through convolution operations. The convolution operation will be introduced in detail below.

The convolution operation refers to the convolution kernel (also called filter) slides on the input data and multiplies it point by point, then sums the multiplication results, and finally generates a new feature map. The convolution kernel is usually a small square or rectangular matrix, which is automatically learned by CNN.

In the convolution operation, each element of the convolution kernel is multiplied by the element at the corresponding position in the input data, and then the results are summed to obtain a new value, which is a pixel of the new feature map. Each element of the convolution kernel will be used in the convolution operation, and they perform local feature extraction on the input data.

An important parameter of the convolution operation is the stride, which specifies the step size of the convolution kernel sliding on the input data. The size of the stride will affect the size of the output feature map. In general, a larger stride can reduce the size of the output feature map, while a smaller stride can retain more feature information.

Another important parameter of the convolution operation is padding, which specifies how many rows and columns of zeros are added around the input data. Padding can be used to control the size of the output feature map of the convolution operation and preserve more original feature information.

In addition to the conventional two-dimensional convolution operation, there are some variant convolution operations, such as one-dimensional convolution and three-dimensional convolution. 1D convolutions are usually used for sequential data such as text data and audio data, while 3D convolutions are usually used for processing video data and volumetric data.

In general, the convolutional layer is one of the core components of the convolutional neural network, which can extract useful features from the input data and provide more accurate information for subsequent layers.

pooling layer

A pooling layer is a common layer in a convolutional neural network (CNN), which usually follows a convolutional layer. The pooling layer reduces the number of parameters by reducing the spatial size of the feature map, and at the same time increases the robustness and calculation speed of the model, thereby improving the performance of the model.

The main function of the pooling layer is downsampling and feature compression. It reduces the size of the feature map and preserves the most important features in the image by downsampling the feature map. Typically, pooling layer operations can be divided into two types: max pooling and average pooling.

Maximum pooling refers to selecting the maximum value in the pooling window as the output value. This pooling method can extract the most prominent features in the feature map and will not be affected by noise. The average pooling is to calculate the average value in the pooling window as the output value. This pooling method can smooth the feature map without overemphasizing any feature.

Another important parameter of the pooling layer is the stride, which specifies the step size that the pooling window slides over the input data. The size of the stride will affect the size of the output feature map. In general, a larger stride can reduce the size of the output feature map, while a smaller stride can retain more feature information.

Another optional parameter to the pooling layer is padding, which specifies how many rows and columns of zeros to add around the input data. Padding can be used to control the size of the output feature map of the pooling operation and preserve more original feature information.

It should be noted that although the pooling layer can reduce the size and number of feature maps, it may lose some feature information. Therefore, when designing a CNN model, we need to carefully consider how and where the pooling layer is used to balance the performance and accuracy of the model.

Overall, the pooling layer is an important component in a CNN model, which can reduce the size and number of feature maps while retaining the most important features in the image, thereby improving the robustness and computational speed of the model.

fully connected layer

Fully Connected Layer (Fully Connected Layer) is a common layer in convolutional neural network (CNN). It usually appears between the convolutional layer and the output layer, and is used to convert the output of the convolutional layer into a vector suitable for tasks such as classification or regression.

The role of the fully connected layer is to connect all neurons in multiple feature maps together to form a tiled vector, and then map the vector to an output vector through matrix multiplication and nonlinear activation functions. Specifically, the input of the fully connected layer is a tensor of shape (batch_size, n_features), where batch_size specifies the number of samples used to update model parameters in one training session, and n_features is the total number of elements in the feature map. The output of a fully connected layer is a tensor of shape (batch_size, n_classes), where n_classes is the number of output classes.

The parameters of a fully connected layer consist of weights and bias terms. The shape of the weight matrix is (n_features, n_classes), where each row represents the weights of all neurons of a feature map connected to all neurons of a certain class in the output layer. The shape of offset vector is (n_classes,) Specify additional dimensions. In Python, this shape is represented as a tuple or list, such as (n_classes,) or [n_classes]] , where each element represents a bias term for a class in the output layer. During the training process, the CNN model uses the backpropagation algorithm, that is, uses the loss function to calculate the error, and backpropagates the error layer by layer to update the weights and bias items to minimize the loss function and optimize the performance of the network.

It should be noted that fully connected layers may overfit the training data. In order to avoid this situation, we usually add some regularization methods before the fully connected layer, such as Dropout and L2 regularization, to reduce the complexity and generalization error of the model.

In general, the fully connected layer is an important component in the CNN model. It converts the output of the convolutional layer into a vector suitable for tasks such as classification or regression, and maps it to an output vector through matrix multiplication and nonlinear activation functions. . It is one of the layers with the most parameters and the most computation in the CNN model, and its position and size need to be carefully considered when designing the model to balance the performance and generalization ability of the model.

Issues/Considerations in Model Construction

When building a Convolutional Neural Network (CNN) model, the following issues and considerations need to be considered:

Data preprocessing:

The CNN model has certain requirements on the format of the input data. It is usually necessary to convert the data into a tensor form and preprocess the image, such as scaling, rotating, flipping, normalizing, etc., to facilitate training and testing the model. Data preprocessing usually includes the following steps:

Data reading: read data from the dataset and store it in memory or hard disk. Usually the dataset contains training data and test data, which are used to train and evaluate the performance of the model respectively.
Data format conversion: The CNN model needs to convert data into tensor form to facilitate model processing and training. Image data is usually converted into a two-dimensional or three-dimensional tensor form, where a two-dimensional tensor represents a single-channel image, and a three-dimensional tensor represents a multi-channel image (such as an RGB color image).
Data normalization: The CNN model has certain requirements on the scale and range of the input data. Usually, the image data needs to be normalized so that its value is between 0 and 1 or between -1 and 1. Common normalization methods include dividing by 255, subtracting the mean and dividing by the standard deviation, etc.
Data enhancement: Data enhancement refers to a series of transformation operations on the original data to increase the number and diversity of training data, thereby improving the generalization ability of the model. Common data augmentation methods include image scaling, rotation, flipping, translation, cropping, brightness and contrast adjustment, etc.
Data division: Data division refers to dividing the original data set into three parts: training set, verification set and test set, usually with a ratio of 8:1:1 or 7:2:1. The training set is used to train the model, the validation set is used to adjust hyperparameters and prevent overfitting, and the test set is used to evaluate the performance and generalization ability of the model.

By preprocessing the data, the training efficiency, generalization ability and prediction accuracy of the CNN model can be improved, so as to better solve practical problems.

Model structure:

CNN models usually include components such as convolutional layers, pooling layers, fully connected layers, and activation functions. When designing the model structure, factors such as the size and complexity of the input data and the difficulty of the classification task need to be considered to select the appropriate hyperparameters such as the number of layers, convolution kernel size, stride and padding, and avoid overfitting or underfitting. fit.

1. Overfitting:

Overfitting is when a model performs well on training data but poorly on new data. The reason for overfitting is that the model is too complex or the amount of training data is too small, causing the model to "memorize" the noise and special properties in the training data too much, so that it cannot generalize to new data. In CNN models, overfitting usually manifests as a small loss function on the training set but a large loss function on the test set, or low classification accuracy on the test set.

2. Underfitting:

Underfitting is when the model performs poorly on both the training data and the test data. Underfitting is usually caused by a model that is too simple or has too little training data, preventing the model from capturing important features or patterns in the data. In the CNN model, the performance of underfitting is usually that the loss function on the training set is large, but the loss function on the test set is still large, or the classification accuracy on the test set is low.

In order to solve the problem of overfitting and underfitting, the following methods are usually taken:

Overfitting: Increase the amount of training data, reduce the complexity of the model, use regularization methods (such as L1, L2 regularization), and use methods such as Dropout.
Underfitting: Increase model complexity, adopt better feature extraction methods, increase the amount of training data, increase the number of training rounds, etc.

In CNN models, methods such as data augmentation, early stopping, and regularization are usually used to avoid overfitting and underfitting problems.

The choice of convolution kernel:

The convolution kernel is one of the most important components in the CNN model, and its selection will directly affect the performance and generalization ability of the model. When choosing a convolution kernel, you need to consider factors such as the characteristics, size, and shape of the image, and try different convolution kernel sizes, numbers, and combinations to obtain better feature representation and higher classification accuracy.

Regularization method:

CNN models are prone to overfitting, so some regularization methods are needed to reduce the complexity and generalization error of the model. Common regularization methods include Dropout, L1 regularization, and L2 regularization.

1、Dropout：

Dropout is a method to randomly delete a part of neurons during training, thereby reducing the overfitting phenomenon of the model. Dropout randomly selects some neurons in each iteration and sets their output values to 0, thus forcing the model not to rely on certain specific neurons to make decisions, thus making the model more robust. During the test, the output of all neurons will be retained, but in order to maintain the consistency of the model, it is usually necessary to multiply the output value of each neuron by a retention probability (such as 0.5) to keep the expected value of the total output unchanged .

2. L1 regularization:

L1 regularization increases the L1 norm penalty term in the loss function of the model to limit the sum of the absolute values of the model parameters, so that some parameters are 0, thereby achieving the effect of sparsity.

3. L2 regularization:

L2 regularization increases the L2 norm penalty term in the loss function of the model, limits the sum of squares of the model parameters, and makes the model parameters as small as possible, thereby reducing the risk of overfitting.

Choice of loss function:

CNN models usually use cross-entropy as a loss function to measure the difference between the predicted result and the true label. When choosing a loss function, it is necessary to consider the nature of the classification task and the optimization goal of the model, such as multi-classification, binary classification or regression.

1. Loss function:

In CNN, the loss function is usually related to the task type. The following are several common CNN tasks and their corresponding loss functions:

1. Classification tasks: For multi-classification problems, the Cross-Entropy Loss function (Cross-Entropy Loss) is usually used, which can help the model better distinguish the differences between different categories, and can handle unbalanced data sets well. For binary classification problems, you can use the logarithmic loss function (Log Loss).

2. Target detection task: For target detection problems, commonly used loss functions include Mean Average Precision (mAP) and cross-entropy loss functions. Among them, mAP is usually used to evaluate the accuracy of the detection algorithm, and the cross-entropy loss function is used to measure the error of the position and category of the prediction frame.

3. Segmentation tasks: For segmentation problems, commonly used loss functions are Pixel-Wise Cross-Entropy Loss (Pixel-Wise Cross-Entropy Loss) or Dice Loss (Dice Loss), which can measure pixel-level predictions and real values. Between difference, thus helping the model to better segment out the target.

Choice of optimization algorithm:

CNN models usually use the backpropagation algorithm to update the model parameters to minimize the loss function. When choosing an optimization algorithm, factors such as the convergence speed, memory consumption, and complexity of the model need to be considered, such as stochastic gradient descent (SGD), Adam, and Adagrad.

1. Back propagation algorithm:

The backpropagation algorithm is an algorithm for calculating the gradient of each parameter in a neural network, calculating the derivative of each parameter by backpropagating the error signal, and then using an optimization algorithm such as gradient descent to update the parameters to minimize the loss function .

2. Stochastic Gradient Descent (SGD):

SGD is a gradient-based optimization algorithm for training deep neural networks. It works by computing the gradient of each parameter and updating the parameters to minimize the loss function. Unlike batch gradient descent (Batch Gradient Descent), SGD calculates the gradient of the loss function on each training sample and updates the parameters, so it has a faster convergence speed, but for noisy data, SGD may cause instability of convergence.

3、Adam：

Adam is an adaptive learning rate optimization algorithm that combines first and second moment estimates of gradients to update model parameters. Adam has advantages such as adaptive learning rate, adaptive matrix scaling, and momentum adjustment based on gradient history, so it is more efficient than traditional gradient descent algorithms such as SGD in most cases.

4、Adagrad：

Adagrad is an adaptive learning rate optimization algorithm that adapts to different learning rates for each parameter by adjusting the gradient size of each parameter. Adagrad automatically learns the learning rate of each parameter during the training process, so that the learning rate can be adaptively changed over time. This algorithm is very effective for sparse data and noisy data, but it may also cause the learning rate to gradually decrease, causing the model to fail to continue to optimize.

Choice of hyperparameters:

There are many hyperparameters in the CNN model, such as learning rate, batch size, convolution kernel size and step size, etc. When selecting hyperparameters, experiments and tuning are required to find the optimal combination of hyperparameters.

Hyperparameters:

In convolutional neural networks, hyperparameters refer to parameters that need to be manually adjusted during training, and these parameters cannot be optimized by the backpropagation algorithm. The following are some common hyperparameters in convolutional neural networks:

Kernel size: The size of the convolution kernel is a hyperparameter that specifies the size of the filter. The choice of convolution kernel size depends on the size of the input image and the application scenario.
Number of convolutional kernels: The number of convolutional kernels is a hyperparameter that specifies the number of filters in a convolutional layer. Increasing the number of convolution kernels can improve the expressiveness of the model, but it also increases the computational burden.
Stride: Stride is a hyperparameter that specifies how long the filter moves during convolution. A larger stride can reduce the size of the feature map, which reduces computation, but also leads to information loss.
Padding: Padding is a hyperparameter that specifies whether the edges of the input image need to be filled in the convolution operation. Padding can increase the size of the feature map and enable the edge information of the input image to be better preserved.
Pooling size: Pooling size is a hyperparameter that specifies the pooling size of the pooling layer. The choice of pooling size depends on the size of the input image and the application scenario.
Dropout: Dropout is a hyperparameter that specifies the proportion of neurons that are randomly disconnected during training. This can effectively reduce overfitting phenomenon.
Learning Rate: The learning rate is a hyperparameter that controls the parameter update step size in an optimization algorithm. An appropriate learning rate can speed up model training, but if the learning rate is too large, it may cause the model to fail to converge, and if the learning rate is too small, it may take longer to train.

The choice of these hyperparameters often requires experimentation and tuning to find the optimal combination of hyperparameters to achieve the best model performance.

Summary：

Convolutional neural network is a neural network model specially used for image recognition. It uses multiple layers such as convolutional layer, pooling layer, and fully connected layer, which can extract higher-level features from input image data. Representation, and used in image classification, object detection, face recognition, automatic driving and other fields.

The basic process of convolutional neural network image recognition includes the following steps:

Data preparation: including image preprocessing, data set division, etc.
Network design: Select the appropriate network structure according to the specific problem and data set, you can use the existing pre-training model or design your own model.
Network training: Use the training set to train the network, optimize the network parameters through the backpropagation algorithm, and select the appropriate optimization algorithm and hyperparameters.
Model evaluation: Use the test set to evaluate the trained model, and calculate the accuracy of the model on the test set and other indicators.

In convolutional neural network image recognition, the following issues need to be paid attention to:

Dataset quality: Factors such as the size, quality, and sample distribution of the dataset will affect the performance of the model, and adequate analysis and preprocessing of the dataset is required.
Rationality of network design: It is necessary to select the appropriate network structure and hyperparameters according to the characteristics of specific problems and data sets, and make appropriate adjustments and optimizations.
Overfitting and underfitting problems: Certain measures need to be taken, such as regularization, dropout, etc., to avoid overfitting and underfitting problems.
Optimization algorithm and learning rate: It is necessary to select an appropriate optimization algorithm and learning rate to ensure that the model can converge to the optimal solution during training.
Data augmentation: Data augmentation is an important means to improve the generalization ability of the model. Data sets can be expanded by rotating, flipping, zooming, etc. to increase the robustness of the model.
- Migration learning: If there is not enough data to train a complete model, transfer learning can be used to use the parameters of the pre-trained model as initial parameters to fine-tune specific problems.
- Hardware and software support: The training of convolutional neural networks requires a large amount of computing resources and storage resources, and requires corresponding hardware and software support, such as GPU, distributed training, etc.

In short, convolutional neural network image recognition is a complex task that requires comprehensive consideration of multiple factors, data preparation, network design, training optimization, model evaluation and other aspects of work in order to obtain high-quality recognition results.

Convolutional neural network for image recognition learning (1)