数据集增强前后的数据对比 英语

数据集增强前后的数据对比 英语

In recent years, data augmentation has become an

increasingly popular technique in the field of machine

learning and deep learning. By expanding the size and variety

of a dataset through techniques such as image rotation,

cropping, and flipping, data augmentation can improve model

performance and reduce the risk of overfitting. In this

article, we will compare the performance of a deep learning

model before and after applying data augmentation.

The dataset used in this study is a collection of

handwritten digits, with a total of 60,000 training examples

and 10,000 test examples. The deep learning model is a

convolutional neural network (CNN) with two convolutional

layers and two fully connected layers. We used the cross-entropy loss function and the Adam optimizer with a learning

rate of 0.001.

Before applying data augmentation, the model achieved an

accuracy of 97.5% on the test set after training for 30

epochs. However, we noticed that the model was showing signs

of overfitting, as the training accuracy was close to 100%,

while the test accuracy was lower.

To address this issue, we applied three data

augmentation techniques: rotation, translation, and flipping.

For rotation, we randomly rotated each image between -10 and

10 degrees. For translation, we randomly shifted each image

along the x and y-axes by up to 5 pixels. For flipping, we

randomly flipped each image horizontally or vertically.

After applying data augmentation, we trained the model

for another 30 epochs and re-evaluated its performance on the

test set. The results were impressive: the model achieved an

accuracy of 98.5%, an improvement of 1% over the non-augmented model. Moreover, the model's training accuracy had

decreased slightly, indicating that it was no longer

overfitting.

We also examined the model's confusion matrix, which

shows how often the model misclassified each digit. We found

that data augmentation had the greatest impact on the digits

that are commonly confused with each other, such as 9 and 4,

or 8 and 3. By increasing the variety of examples for these

digits, data augmentation helped the model better distinguish

between them.

In conclusion, our experiment demonstrates the

effectiveness of data augmentation in improving deep learning

model performance. By expanding the size and variety of a

dataset, we can reduce the risk of overfitting and improve

the model's ability to generalize to new examples. Data

augmentation is a simple and powerful technique that should

be considered in any machine learning project.