Displaying 1 to 20 from 21 results

srgan - Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network

  •    Python

We run this script under TensorFlow 1.4 and the TensorLayer 1.8.0+. 🚀 This repo will be moved to here (please star) for life-cycle management soon. More cool Computer Vision applications such as pose estimation and style transfer can be found in this organization.

tensorlayer - Deep Learning and Reinforcement Learning Library for Developers and Scientists

  •    Python

TensorLayer is a novel TensorFlow-based deep learning and reinforcement learning library designed for researchers and engineers. It provides a large collection of customizable neural layers / functions that are key to build real-world AI applications. TensorLayer is awarded the 2017 Best Open Source Software by the ACM Multimedia Society. Simplicity : TensorLayer lifts the low-level dataflow interface of TensorFlow to high-level layers / models. It is very easy to learn through the rich example codes contributed by a wide community.




tensorlayer-tricks - How to use TensorLayer

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While research in Deep Learning continues to improve the world, we use a bunch of tricks to implement algorithms with TensorLayer day to day. Here are a summary of the tricks to use TensorLayer. If you find a trick that is particularly useful in practice, please open a Pull Request to add it to the document. If we find it to be reasonable and verified, we will merge it in.

deep-learning-book - 《Deep Learning》《深度学习》 by Ian Goodfellow, Yoshua Bengio and Aaron Courville

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This book was downloaded in HTML form and conviniently joined as a single PDF file for your enjoyment. PLEASE SUPPORT IAN GOODFELLOW and the authors if you can purchase the paper book at Amazon. It is not expensive ($72) and probably contains content that is newer and without typographic mistakes.

text-to-image - Generative Adversarial Text to Image Synthesis / Please Star -->

  •    Python

This is an experimental tensorflow implementation of synthesizing images. The images are synthesized using the GAN-CLS Algorithm from the paper Generative Adversarial Text-to-Image Synthesis. This implementation is built on top of the excellent DCGAN in Tensorflow. N.B You can downloads all data files needed manually or simply run the downloads.py and put the correct files to the right directories.


tensorflow-wrapper-compare - Comparison of TensorFlow Wrappers

  •    Python

Run Keras, TensorLayer and Tflearn with same model and data on a same GPU machine. The parameter initialization may have slightly different, but would not effect the speed.

dcgan - Deep Convolutional Generative Adversarial Networks based on TensorFlow / TensorLayer

  •    Python

TensorFlow / TensorLayer implementation of Deep Convolutional Generative Adversarial Networks which is a stabilize Generative Adversarial Networks.

pretrained-models

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Feel free to add more. The tl.models API description here, and the discussion for network architecture that can be easily use here.

im2txt2im - I2T2I: Text-to-Image Synthesis with textual data augmentation

  •    Python

This code run well under python2.7 and TensorFlow 0.11, if you use higher version of TensorFlow you may need to update the tensorlayer folder from TensorLayer Lib.

Imitation-Learning-Dagger-Torcs - A Simple Example for Imitation Learning with Dataset Aggregation (DAGGER) on Torcs Env

  •    Python

This repository implements a simple algorithm for imitation learning: DAGGER. In this example, the agent only learns to control the steer [-1, 1], the speed is computed automatically in gym_torcs.TorcsEnv. It will start a Torcs server at the beginning of every episode, and terminate the server when the car crashs or the speed is too low. Note that, the self-contained gym_torcs.py is modified from Gym-Torcs, you can try different settings (like default speed, terminated speed) by modifying it.

Spatial-Transformer-Nets - Spatial Transformer Nets in TensorFlow/ TensorLayer

  •    Python

Spatial Transformer Networks (STN) is a dynamic mechanism that produces transformations of input images (or feature maps)including scaling, cropping, rotations, as well as non-rigid deformations. This enables the network to not only select regions of an image that are most relevant (attention), but also to transform those regions to simplify recognition in the following layers. Video for different transformation click me.

u-net-brain-tumor - U-Net Brain Tumor Segmentation in TensorFlow

  •    Python

🚀:Sep 2018 the data processing implementation in this repo is not the fastest, please use TensorFlow dataset API instead. This repo show you how to train a U-Net for brain tumor segmentation. By default, you need to download the training set of BRATS 2017 dataset, which have 210 HGG and 75 LGG volumes, and put the data folder along with all scripts.