https://github.com/gabrieleangeletti/Deep-Learning-TensorFlow

Ready to use implementations of various Deep Learning algorithms using TensorFlow.
https://github.com/gabrieleangeletti/Deep-Learning-TensorFlow

deep-learning tensorflow

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Ready to use implementations of various Deep Learning algorithms using TensorFlow.

• Host: GitHub
• URL: https://github.com/gabrieleangeletti/Deep-Learning-TensorFlow
• Owner: gabrieleangeletti
• License: mit
• Created: 2015-08-17T07:08:07.000Z (about 9 years ago)
• Default Branch: master
• Last Pushed: 2017-09-24T15:52:26.000Z (about 7 years ago)
• Last Synced: 2024-06-08T02:44:42.365Z (5 months ago)
• Topics: deep-learning, tensorflow
• Language: Python
• Homepage: http://blackecho.github.io
• Size: 17.6 MB
• Stars: 965
• Watchers: 91
• Forks: 380
• Open Issues: 25
• Metadata Files:
  • Readme: README.md
  • License: LICENSE.txt

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README

        
# Deep Learning algorithms with TensorFlow

This repository is a collection of various Deep Learning algorithms implemented using the

[TensorFlow](http://www.tensorflow.org) library. This package is intended as a command line utility you can use to quickly train and

evaluate popular Deep Learning models and maybe use them as benchmark/baseline in comparison to your custom models/datasets.

If you want to use the package from ipython or maybe integrate it in your code, I published a pip package named `yadlt`: Yet Another Deep Learning Tool.

### Requirements:

* tensorflow >= 1.0

### List of available models:

* Convolutional Network

* Restricted Boltzmann Machine

* Deep Belief Network

* Deep Autoencoder as stack of RBMs

* Denoising Autoencoder

* Stacked Denoising Autoencoder

* Deep Autoencoder as stack of Denoising Autoencoders

* MultiLayer Perceptron

* Logistic Regression

### Installation

#### Through pip:

    pip install yadlt

You can learn the basic usage of the models by looking at the ``command_line/`` directory. Or you can take a look at the [documentation](http://deep-learning-tensorflow.readthedocs.io/en/latest/).

**Note**: the documentation is still a work in progress for the pip package, but the package usage is very simple. The classes have a sklearn-like interface, so basically you just have to create the object

(e.g. `sdae = StackedDenoisingAutoencoder()`) and call the fit/predict methods, and the pretrain() method if the model supports it

(e.g. `sdae.pretrain(X_train, y_train)`, `sdae.fit(X_train, y_train)` and `predictions = sdae.predict(X_test)`)

#### Through github:

* cd in a directory where you want to store the project, e.g. ``/home/me``

* clone the repository: ``git clone https://github.com/blackecho/Deep-Learning-TensorFlow.git``

* ``cd Deep-Learning-TensorFlow``

* now you can configure the software and run the models (see the [documentation](http://deep-learning-tensorflow.readthedocs.io/en/latest/))!

### Documentation:

You can find the documentation for this project at this [link](http://deep-learning-tensorflow.readthedocs.io/en/latest/).

### Models TODO list

* Recurrent Networks (LSTMs)

* Variational Autoencoders

* Deep Q Reinforcement Learning