78 points by deep_learning_tut 5 months ago flag hide 8 comments
johnsmith 5 months ago next
Great job on this project! I've always been fascinated by handwritten digit recognition. I'd love to know more about the specific implementation of the Convolutional Neural Network you used. Any resources or tutorials you could recommend?
original_poster 5 months ago next
Hi @johnsmith, I'm glad you found the project interesting! I used the TensorFlow library for the implementation of the CNN. I would recommend this tutorial by TensorFlow for getting started with implementing a CNN for image classification: <https://www.tensorflow.org/tutorials/keras/classification>. Thank you for your question!
notauser 5 months ago prev next
I really like how accurate this model is, but I'm curious about what kind of testing you did to determine the accuracy. Did you use a specific dataset or split the data you trained on into training and testing sets? Thank you!
original_poster 5 months ago next
@notauser Thank you for your question! I used the Modified National Institute of Standards and Technology (MNIST) dataset as the training and testing data. I split the 70,000 images contained in the dataset into a training set of 60,000 images and a testing set of 10,000 images. This allowed me to evaluate the accuracy of the model on data it had not previously seen. I used the accuracy results from the testing set to evaluate the final performance of the model.
user123 5 months ago prev next
I'm currently working on a similar project and I was wondering if you encountered any challenges when implementing the CNN and how you overcame them. I'm particularly concerned about the computational resources required for training the model. Any advice would be greatly appreciated!
original_poster 5 months ago next
@user123 I'm glad to hear you're also working on a similar project! I did encounter some difficulties when implementing the CNN, specifically related to the computational resources required for training the model. I found that using a higher level library like TensorFlow helped to ease the burden of some of the more resource-intensive components of the implementation. I would also recommend using a GPU for training the model if possible, as it can significantly reduce the amount of time required for training. Best of luck with your project!
operatorsystem 5 months ago prev next
This is really cool! I'm curious about how long it took to train the model. I've worked with CNNs before and training them can be a lengthy process. Did you use any specific techniques to speed up the training process?
original_poster 5 months ago next
@operatorsystem Thank you for your question! Yes, I did use some techniques to speed up the training process. I used a learning rate schedule to gradually reduce the learning rate during training, which helped the model converge more quickly to the optimal solution. I also used batch normalization and data augmentation to improve the generalization performance of the model. These techniques helped to reduce overfitting and improve the stability of the training process. The total time required for training the model was around 10 hours using a single GPU.