Displaying 1 to 17 from 17 results

tensorflow-101 - learn code with tensorflow

  •    Python

In folder finetuning, we use tf.slim to finetuning the pretrain model (I use the same method in my porn detection) and use flask to buid a very simple inference system. I deploy a image classification in demo page. It is based on Tensorflow and Flask. Feel free to try.

raccoon_dataset - The dataset is used to train my own raccoon detector and I blogged about it on Medium

  •    Jupyter

This is a dataset that I collected to train my own Raccoon detector with TensorFlow's Object Detection API. Images are from Google and Pixabay. In total, there are 200 images (160 are used for training and 40 for validation). See LICENSE for details. Copyright (c) 2017 Dat Tran.

tensorlayer-tricks - How to use TensorLayer


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.

gin-config - Gin provides a lightweight configuration framework for Python

  •    Python

Gin provides a lightweight configuration framework for Python, based on dependency injection. Functions or classes can be decorated with @gin.configurable, allowing default parameter values to be supplied from a config file (or passed via the command line) using a simple but powerful syntax. This removes the need to define and maintain configuration objects (e.g. protos), or write boilerplate parameter plumbing and factory code, while often dramatically expanding a project's flexibility and configurability. Gin is particularly well suited for machine learning experiments (e.g. using TensorFlow), which tend to have many parameters, often nested in complex ways.

polyaxon-lib - Deep Learning and Reinforcement learning library for TensorFlow for building end to end models and experiments

  •    Python

Deep Learning and Reinforcement learning library for TensorFlow for building end to end models and experiments. Modularity: The creation of a computation graph based on modular and understandable modules, with the possibility to reuse and share the module in subsequent usage.

LSTM-Text-Generation - Tons of fun with text and recurrent neural networks! Let your computer read a book and tell you its own story

  •    Hy

During the time that I was writing my bachelor's thesis Sequence-to-Sequence Learning of Financial Time Series in Algorithmic Trading (in which I used LSTM-based RNNs for modeling the thesis problem), I became interested in natural language processing. After reading Andrej Karpathy's blog post titled The Unreasonable Effectiveness of Recurrent Neural Networks, I decided to give text generation using LSTMs for NLP a go. Although slightly trivial, the project still comprises an interesting program and demo, and gives really interesting (and sometimes very funny) results. I implemented the program over the course of a weekend in Hy (a LISP built on top of Python) using Keras and TensorFlow. You can train the model on any text sources you like. Remember to give it enough time to go over at least fifty epochs, otherwise the generated text will not be very interesting, rather seemingly random garbage.

Deep-Steganography - Hiding Images within other images using Deep Learning

  •    Jupyter

Steganography is the science of Hiding a message in another message. In this case, a Picture is hidden inside another picture using Deep Learning. This basically reinstalls the gpu version of tensorflow for your system.

image-classifier-service - Sorting my messed folder where I put my holidays pictures was not fun

  •    Python

I have a folder where I put all my cambridge holidays pictures... I would love to sort by topics my pictures of my holidays in Cambridge. The installation procedure has been tested on Ubuntu 16.04.

ImageRecognition - An Image Recognition project using Inception-v3 (for training) and cv2 (for visualizing)

  •    Python

An Image Recognition project using Inception-v3 (for training) and cv2 (for visualizing)

dreamcanvas - DeepDream experiment

  •    TypeScript

Rest of the dependencies are listed in package.json and will be installed automatically. Run these in separate terminal windows for best results.

a-nice-mc - Code for "A-NICE-MC: Adversarial Training for MCMC"

  •    Jupyter

Tensorflow implementation for the paper A-NICE-MC: Adversarial Training for MCMC, NIPS 2017. A-NICE-MC is a framework that trains a parametric Markov Chain Monte Carlo proposal. It achieves higher performance than traditional nonparametric proposals, such as Hamiltonian Monte Carlo (HMC). This repository provides code to replicate the experiments, as well as providing grounds for further research.

PSO_in_TensorFlow - PSO algorithm written in TensorFlow

  •    Jupyter

A generic implementation of the PSO algorithm.

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