Learn Convolutional Neural Networks From Scratch: Understand CNN Core and Practice

Learn Convolutional Neural Networks From Scratch: Understand CNN Core and Practice

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Introduction

Have you ever felt overwhelmed by the complexity of deep learning? Have you heard of the powerful tool known as Convolutional Neural Networks (CNN) but found it confusing? Don’t worry! This article will guide you step by step through the core concepts of CNN and teach you how to easily build and train a simple CNN model using Python. This article is suitable for those who have a basic understanding of machine learning but are new to the world of deep learning. Let’s learn and explore together!

What is CNN?

Convolutional Neural Network (CNN) is a type of neural network specifically designed to process structured data such as images. It automatically analyzes features like edges and textures through “convolutional layers” and ultimately classifies or predicts the data. CNNs are widely used in the field of computer vision, such as in face recognition, image classification, object detection, etc.

Core Concepts

  1. Convolutional Layer: Extracts features using filters, such as edges and textures.
  2. Pooling Layer: Downsamples the feature map, reducing computation and improving model robustness.
  3. Activation Function: Increases the model’s non-linearity, commonly using the ReLU function.
  4. Fully Connected Layer: Integrates the extracted features and generates prediction results.

Why Choose CNN?

  • Local Connectivity: Reduces the parameters found in fully connected neural networks.
  • Weight Sharing: Allows the same filter to detect similar features at different positions.
  • Superior Performance: Excels in image classification and object detection tasks.

Building Your First CNN Model

Environment Setup

First, make sure you have the following libraries installed:

pip install tensorflow keras matplotlib

Data Preparation

We will use the classic MNIST dataset, which is an introductory dataset for handwritten digit classification.

import tensorflow as tf
from tensorflow.keras import layers, models
import matplotlib.pyplot as plt

# Load dataset
(train_images, train_labels), (test_images, test_labels) = tf.keras.datasets.mnist.load_data()

# Data preprocessing
train_images = train_images.reshape((60000, 28, 28, 1)).astype('float32') / 255
test_images = test_images.reshape((10000, 28, 28, 1)).astype('float32') / 255

Building the CNN Model

Here is a simple CNN model:

model = models.Sequential()

# First Convolution + Pooling Layer
model.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1)))
model.add(layers.MaxPooling2D((2, 2)))

# Second Convolution + Pooling Layer
model.add(layers.Conv2D(64, (3, 3), activation='relu'))
model.add(layers.MaxPooling2D((2, 2)))

# Flatten + Fully Connected Layer
model.add(layers.Flatten())
model.add(layers.Dense(64, activation='relu'))
model.add(layers.Dense(10, activation='softmax'))  # Output 10 classes

# Print model structure
model.summary()

Model Training and Evaluation

Next, compile and train the model:

model.compile(optimizer='adam',
              loss='sparse_categorical_crossentropy',
              metrics=['accuracy'])

# Train the model
history = model.fit(train_images, train_labels, epochs=5, batch_size=64, validation_split=0.1)

# Evaluate the model
test_loss, test_acc = model.evaluate(test_images, test_labels)
print(f"Test set accuracy: {test_acc:.2f}")

Visualizing Training Results

plt.plot(history.history['accuracy'], label='Training Accuracy')
plt.plot(history.history['val_accuracy'], label='Validation Accuracy')
plt.xlabel('Epochs')
plt.ylabel('Accuracy')
plt.legend()
plt.show()

Things to Note When Learning CNN

  1. Tuning Hyperparameters: Such as filter size, learning rate, etc.
  2. Adding Regularization: To prevent overfitting, you can use Dropout layers.
  3. Data Augmentation: Improve model generalization through data augmentation.

Powerful Functions and Application Scenarios

  • Image Classification: Classic ImageNet classification tasks.
  • Object Detection: Such as object recognition in autonomous driving.
  • Semantic Segmentation: Assigning a category to each pixel in an image.

Bonus: More Complex CNN Models

In practice, pre-trained models like VGG and ResNet can significantly enhance performance. It is recommended for beginners to try transfer learning by applying these models to their own tasks.

Conclusion

Convolutional Neural Networks are the cornerstone of deep learning. They extract features and classify data by simulating the human visual system, making them powerful tools in the field of image processing. This article helps you quickly get started with CNN, from basic concepts to practical code. If you have any questions, feel free to leave a message to contact me, and let’s improve together!

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