![keras data augmentation on existing image matrix keras data augmentation on existing image matrix](https://cdn-images-1.medium.com/max/1600/1*PSLmC5odgP3-c3uQx6FS0g.png)
- Keras data augmentation on existing image matrix mac os#
- Keras data augmentation on existing image matrix windows 10#
- Keras data augmentation on existing image matrix code#
Keras data augmentation on existing image matrix code#
This is the code repository for Deep Learning with Keras, published by Packt. Used either together (e.g., Keras with Tensorflow backend), or independently - PyTorch cannot be used as a Keras backend, TensorFlow can be used on its own - they make for some of the most powerful deep learning python libraries to work natively on Windows.ĭeep-Learning-with-Keras - Code repository for Deep Learning with Keras published by Packt We also found enough misguiding/deprecated information out there to make it worthwhile putting together a step-by-step guide for the latest stable versions of Keras, Tensorflow, CNTK, MXNet, and PyTorch. Most focus on running an Ubuntu VM hosted on Windows or using Docker, unnecessary - and ultimately sub-optimal - steps.
![keras data augmentation on existing image matrix keras data augmentation on existing image matrix](https://miro.medium.com/max/665/1*Jujct_Pt-zvdWtSFpHUp3Q.png)
Keras data augmentation on existing image matrix mac os#
There are certainly a lot of guides to assist you build great deep learning (DL) setups on Linux or Mac OS (including with Tensorflow which, unfortunately, as of this posting, cannot be easily installed on Windows), but few care about building an efficient Windows 10-native setup.
![keras data augmentation on existing image matrix keras data augmentation on existing image matrix](https://machinelearningmastery.com/wp-content/uploads/2019/01/Plot-of-Augmented-Images-with-a-Vertical-Shift.png)
Keras data augmentation on existing image matrix windows 10#
Example implementations for Othello can be found in othello/OthelloGame.py and othello//NNet.py.ĭlwin - GPU-accelerated Deep Learning on Windows 10 native To use a game of your choice, subclass the classes in Game.py and NeuralNet.py and implement their functions. We also have implementations for GoBang and TicTacToe. An accompanying tutorial can be found here. A sample implementation has been provided for the game of Othello in PyTorch, Keras and TensorFlow. It is designed to be easy to adopt for any two-player turn-based adversarial game and any deep learning framework of your choice. In a nutshell: keras-rl makes it really easy to run state-of-the-art deep reinforcement learning algorithms, uses Keras and thus Theano or TensorFlow and was built with OpenAI Gym in mind.Īlpha-zero-general - A clean implementation based on AlphaZero for any game in any framework + tutorial + Othello/Gobang/TicTacToe/Connect4Ī simplified, highly flexible, commented and (hopefully) easy to understand implementation of self-play based reinforcement learning based on the AlphaGo Zero paper (Silver et al). Even more so, it is easy to implement your own environments and even algorithms by simply extending some simple abstract classes. You can use built-in Keras callbacks and metrics or define your own. Of course you can extend keras-rl according to your own needs. This means that evaluating and playing around with different algorithms is easy. Furthermore, keras-rl works with OpenAI Gym out of the box. Just like Keras, it works with either Theano or TensorFlow, which means that you can train your algorithm efficiently either on CPU or GPU. Keras-rl implements some state-of-the art deep reinforcement learning algorithms in Python and seamlessly integrates with the deep learning library Keras. Keras-rl - Deep Reinforcement Learning for Keras.