Training convolutional neural network for self-driving - Python plays GTA p.11

TL;DR

This content discusses the process of building and training the neural network for a self-driving car scooter using the AlexNet model.

Transcript

what's going on everybody and welcome to part 11 of our self driving car scooter thing so what we're going to do now is like we built the train data we balanced the training data so our neural network more accurately predicts what we should do correctly if we're at least if we're going to draw an arc max on it and stuff anyway we've we've we've bui... Read More

Key Insights

• 🏛️ The author has built the training data and is ready to train the neural network using the AlexNet model.
• 🔠 The structure of the AlexNet model includes input data, convolutional layers, and fully connected layers.
• 🆘 Adjusting layer sizes can help prevent memory errors during training.
• ❓ TensorFlow and TF Learn are used in conjunction with the AlexNet model to enhance its capabilities.

Questions & Answers

Q: Why did the author choose to use the AlexNet model for training the neural network?

The author chose the AlexNet model because it is a well-known CNN model and has been proven to be effective in image classification tasks. Additionally, the author has already covered tutorials on TensorFlow and TF Learn, so they did not want to repeat the same content.

Q: How should the layers be adjusted in the AlexNet model to prevent memory errors?

If memory errors occur, it is recommended to reduce the size of the fully connected layers in the model. The author suggests trying smaller layer sizes, such as 256 or 512, to see if that resolves the issue.

Q: What is the purpose of using TensorFlow and TF Learn in addition to the AlexNet model?

TensorFlow and TF Learn are used to enhance the capabilities of the AlexNet model. TensorFlow provides a powerful framework for building and training neural networks, while TF Learn offers additional modules and functions for simplifying the process of training the model.

Q: How can the progress of the model during training be monitored?

The author mentions the use of TensorBoard to monitor the progress of the model during training. By specifying a log directory and running TensorBoard, the user can view visualizations of the model's performance, such as loss and accuracy graphs.

Summary & Key Takeaways

• The author begins by explaining that they have built the training data and are ready to start training the neural network using a convolutional neural network (CNN) model.

• They decide to use the AlexNet model and provide a link to find it on GitHub.

• The author then discusses the structure of the AlexNet model, including the input data, convolutional layers, and fully connected layers.