Training Model - Training a neural network to play a game with TensorFlow and Open AI p.3

TL;DR

This tutorial teaches how to create a neural network model using TensorFlow in Python for game playing.

Transcript

what's going on everybody welcome to part three of playing games with open a Python tensorflow and all that where we left off we created our training data and now what we want to do is actually create a neural network model and train a model based on that training data to do this we're going to be using tensorflow if you're not sure yeah we're goin... Read More

Key Insights

• 😒 The tutorial emphasizes the use of TensorFlow and TF Learn for creating neural network models in Python.
• 👻 The author highlights the importance of separating model definition, training, and usage to allow for easy modification and reusability.
• 👾 The tutorial provides guidance on adjusting the model for different games and suggests ways to handle more complex games.
• 🥳 The tutorial mentions the possibility of training models for days and the need for additional data to retrain the models.

Questions & Answers

Q: What tools are used to create the neural network model?

The tutorial uses TensorFlow and TF Learn libraries to create the neural network model in Python.

Q: Why is it important to separate the model definition from the training process?

Separating the model definition allows for loading and using pre-trained models, making it easier to reuse and modify models for different scenarios.

Q: Can the model be trained for different games?

Yes, the model can be trained for different games by adjusting the input size and the number of actions in the output layer.

Q: How does the tutorial suggest adjusting the model for games with more complexity?

The author suggests tweaking the model by changing the number of layers, nodes, and activation functions to handle more complex games.

Summary & Key Takeaways

• The tutorial focuses on creating a neural network model using TensorFlow and TF Learn for game playing.

• The author explains the process of defining the model, including creating input and output layers, fully connected layers, activation functions, and optimizer.

• The tutorial also covers training the model using training data and provides insights on adjusting the model for different games.