How to implement convolution neural network convolution neural network algorithm in python

Introduction to convolution neural network algorithm

Convolutional Neural Networks (CNN) is a feedforward Neural Networks (FNN) that includes convolutional computing and has deep structures. It is one of the representative algorithms of deep learning.

The following is a detailed explanation of the convolutional neural network algorithm:

1. Basic Principles

• The core idea of ​​CNN is to automatically extract features in images by simulating the working methods of human vision systems and use them for tasks such as classification, detection, and segmentation.
• It is mainly inspired by biological perception mechanisms, simulating the way perceived cells and complex cells work in human visual systems.

2. Core components

CNN mainly includes the following core components:

• Convolutional Layer: Extract the features of the input data through convolutional operations. The convolutional layer uses multiple convolution kernels (also known as filters) to slide the input image, calculates the weighted sum of each local area, and generates a feature map. Each convolution kernel represents a feature extractor that obtains reasonable weights through training learning and is used to detect specific features in the input image.
• Activation Function: After the convolution layer, the convolution result is usually nonlinearly transformed using activation functions (such as ReLU) to increase the expressive ability of the network.
• Pooling Layer: used to reduce the dimensionality of feature maps, reduce the amount of calculations and prevent overfitting. Common pooling methods include Max Pooling and Average Pooling.
• Fully Connected Layer: Flatten the output of the pooling layer and connect it to one or more fully connected neural networks for outputting classification results. Each neuron in the fully connected layer is connected to all neurons in the previous layer, receiving the output of the previous layer and converting it into a probability distribution for a given category.

3. Workflow

The workflow of CNN mainly includes the following steps:

• Input layer: Enter raw data, such as images.
• Convolutional layer: Sliding convolution of the input image through multiple convolutional kilas, extract local features of the image, and generate feature maps.
• Activation function: Perform nonlinear transformation of convolution results to increase the expressive ability of the network.
• Pooling layer: Deliver dimensionality to the feature map to reduce the amount of calculation and prevent overfitting.
• Full connection layer: Map feature maps to specific categories or labels for classification or regression tasks.
• Output layer: output results, such as classification tags.

4. Training process

• Training convolutional neural networks often requires a large amount of labeled image data to ensure that the network correctly learns its response to features.
• During the training process, the network continuously adjusts parameters through backpropagation algorithm to minimize the loss function.
• The loss function calculates the difference between the predicted value and the actual label and backpropagates the error to update the weight.

5. Application areas

• CNN has wide application in the field of computer vision
• Including image classification, object detection, image segmentation, video analysis, style transfer, image generation, etc.

6. Things to note

• With the continuous development of deep learning technology, CNN architecture and algorithms are also constantly evolving.
• When designing CNNs, challenges such as how to design more efficient network architectures to reduce computational and memory consumption, as well as how to handle large-scale datasets to improve the generalization capabilities of models are needed.
• The above is a detailed explanation of the convolutional neural network algorithm.

Please note:

• This is just a basic overview
• Specific implementations and applications may vary based on specific issues and datasets

Convolution neural network convolution neural network algorithm python implementation example

Here is a sample code for implementing a convolutional neural network (CNN) using Python:

import numpy as np

def convolve(image, kernel):
    image_height, image_width = 
    kernel_height, kernel_width = 

    output_height = image_height - kernel_height + 1
    output_width = image_width - kernel_width + 1

    output = ((output_height, output_width))

    for i in range(output_height):
        for j in range(output_width):
            output[i, j] = (image[i:i+kernel_height, j:j+kernel_width] * kernel)

    return output

def relu(x):
    return (x, 0)

def max_pool(image, pool_size):
    image_height, image_width = 

    output_height = image_height // pool_size
    output_width = image_width // pool_size

    output = ((output_height, output_width))

    for i in range(output_height):
        for j in range(output_width):
            output[i, j] = (image[i*pool_size:(i+1)*pool_size, j*pool_size:(j+1)*pool_size])

    return output

# Define the structure of a convolutional neural network# The first convolution layerkernel_1 = (3, 3)  # 3x3's convolution kernel# The second convolution layerkernel_2 = (5, 5)  #5x5 convolution kernel# Full connection layerweights = (64, 10)  # Weight matrix, input dimension is 64, output dimension is 10
def cnn(image):
    # The first convolution layer    conv1 = convolve(image, kernel_1)
    relu1 = relu(conv1)
    
    # The second convolution layer    conv2 = convolve(relu1, kernel_2)
    relu2 = relu(conv2)
    
    # Pooling layer    pool = max_pool(relu2, 2)
    
    # Expand    flatten = ()
    
    # Full connection layer    output = (weights)
    
    return output

# testimage = (28, 28)  # Enter the image, size 28x28output = cnn(image)
print(output)

This sample code implements a simple convolutional neural network structure.

First, two convolution kernels are definedkernel_1andkernel_2, and then define a weight matrix of fully connected layersweights。

Next useconvolveThe function convolutions the input image and then usesreluThe function is activated and then usedmax_poolThe function performs pooling operation.

Finally, the pooled result is expanded and dot multiply with the weight matrix of the fully connected layer to obtain the output result of the network.

Summarize

The above is personal experience. I hope you can give you a reference and I hope you can support me more.