Basic steps to build a convolutional neural network using PyTorch

Convolutional neural network (CNN) is a deep learning model widely used in computer vision tasks. Compared with fully connected neural networks, CNN has fewer parameters and more powerful feature extraction capabilities, and performs well in tasks such as image classification, target detection, and image segmentation. Below we will introduce how to build a basic CNN model.

Convolutional Neural Network (CNN) is a deep learning model with multiple convolutional layers, pooling layers, activation functions and fully connected layers. The convolutional layer is the core component of CNN and is used to extract features of the input image. The pooling layer can reduce the size of the feature map and preserve the main features of the image. The activation function introduces nonlinear transformation to increase the expressive ability of the model. The fully connected layer converts the feature map into an output result. By combining these components, we can build a basic convolutional neural network. CNN performs well in tasks such as image classification, target detection, and image generation, and is widely used in the field of computer vision.

Secondly, for the structure of CNN, the parameters of each convolution layer and pooling layer need to be determined. These parameters include the size of the convolution kernel, the number of convolution kernels, and the size of the pooling kernel. At the same time, it is also necessary to determine the dimensions of the input data and the dimensions of the output data. The selection of these parameters usually needs to be determined experimentally. A common approach is to first build a simple CNN model and then gradually adjust the parameters until optimal performance is achieved.

When training a CNN model, we need to set the loss function and optimizer. Typically, the cross-entropy loss function is widely used, while the stochastic gradient descent optimizer is also a common choice. During the training process, we input the training data into the CNN model in batches and calculate the loss value based on the loss function. Then, use the optimizer to update the model parameters to reduce the loss value. Typically, multiple iterations are required to complete training, with each iteration batching training data into the model until a predetermined number of training epochs is reached or certain performance criteria are met.

The following is a code example for building a basic convolutional neural network (CNN) using PyTorch:

import torch
import torch.nn as nn

class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(3, 6, 5) # 3个输入通道，6个输出通道，5x5的卷积核
        self.pool = nn.MaxPool2d(2, 2) # 2x2的最大池化层
        self.conv2 = nn.Conv2d(6, 16, 5) # 6个输入通道，16个输出通道，5x5的卷积核
        self.fc1 = nn.Linear(16 * 5 * 5, 120) # 全连接层1，输入大小为16x5x5，输出大小为120
        self.fc2 = nn.Linear(120, 84) # 全连接层2，输入大小为120，输出大小为84
        self.fc3 = nn.Linear(84, 10) # 全连接层3，输入大小为84，输出大小为10（10个类别）

    def forward(self, x):
        x = self.pool(torch.relu(self.conv1(x))) # 第一层卷积+激活函数+池化
        x = self.pool(torch.relu(self.conv2(x))) # 第二层卷积+激活函数+池化
        x = x.view(-1, 16 * 5 * 5) # 将特征图展开成一维向量
        x = torch.relu(self.fc1(x)) # 第一层全连接+激活函数
        x = torch.relu(self.fc2(x)) # 第二层全连接+激活函数
        x = self.fc3(x) # 第三层全连接
        return x

The above code defines a class named Net, inherited from nn.Module. This class contains convolutional layers, pooling layers and fully connected layers, as well as the forward method, which is used to define the forward propagation process of the model. In the __init__ method, we define two convolutional layers, three fully connected layers and a pooling layer. In the forward method, we call these layers in sequence and use the ReLU activation function to perform nonlinear transformation on the outputs of the convolutional layer and the fully connected layer. Finally, we return the output of the last fully connected layer as the model’s prediction. To add, the input of this CNN model should be a four-dimensional tensor with the shape of (batch_size, channels, height, width). Where batch_size is the batch size of the input data, channels is the number of channels of the input data, height and width are the height and width of the input data respectively. In this example, the input data should be an RGB color image with a channel count of 3.

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