initial release

CONTRIBUTING.md

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+# Contributions
+
+## Report a bug
+
+TFLearn is actually at its early stage, there are probably many bugs around... We will be grateful if you would help us to find them. To report a bug, simply open an issue in the GitHub 'issues' section.
+
+## Pull request
+
+If you made improvements to TFLearn or fixed a bug, feel free to send us a pull-request. Please give a brief introduction of the new feature or the bug. When adding new class or functions, make sure that you are following TFLearn docstring syntax.
+
+## Request a new feature
+
+If you think about a new feature to improve TFLearn, let us know by opening an issue in GitHub.
+
+## Questions
+
+To get help on how to use TFLearn or its functionalities, you can as well open an issue in GitHub.

LICENSE

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+MIT License
+
+Copyright (c) 2016 TFLearn Contributors.
+Each contributor holds copyright over his own contributions. The project
+versioning keep tracks of such information.
+
+By contributing to the TFLearn repository, the contributor releases their
+content to the license and copyright terms herein.
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

README.md

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+# TFLearn: Deep learning library featuring a higher-level API for TensorFlow.
+
+TFlearn is a modular and transparent deep learning library built on top of Tensorflow.  It was designed to provide a higher-level API to TensorFlow in order to facilitate and speed-up experimentations, while remaining fully transparent and compatible with it.
+
+TFLearn features include:
+
+- Easy-to-use and understand high-level API for implementing deep neural networks, with tutorial and examples.
+- Fast prototyping through highly modular built-in neural network layers, regularizers, optimizers, metrics...
+- Full transparency over Tensorflow. All functions are built over tensors and can be used independently of TFLearn.
+- Powerful helpers functions to train any TensorFlow graph, with support of multiple inputs, outputs and optimizers.
+- Easy and beautiful graph visualization, with details about weights, gradients, activations and more...
+- Effortless device placement for using multiple CPU/GPU.
+
+The high-level API currently supports most of recent deep learning models, such as Convolutions, LSTM, BiRNN, BatchNorm, PReLU, Residual networks, Generative networks... In the future, TFLearn is also intended to stay up-to-date with latest deep learning techniques.
+
+Note: This is the first release of TFLearn. Contributions are more than welcome!
+
+## Overview
+```python
+# Classification
+tflearn.init_graph(num_cores=8, gpu_memory_fraction=0.5)
+
+net = tflearn.input_data(shape=[None, 784])
+net = tflearn.fully_connected(net, 64)
+net = tflearn.dropout(net, 0.5)
+net = tflearn.fully_connected(net, 10, activation='softmax')
+net = tflearn.regression(net, optimizer='adam', loss='categorical_crossentropy')
+
+model = tflearn.DNN(net)
+model.fit(X, Y)
+```
+
+```python
+# Sequence Generation
+net = tflearn.input_data(shape=[None, 100, 5000])
+net = tflearn.lstm(net, 64)
+net = tflearn.dropout(net, 0.5)
+net = tflearn.fully_connected(net, 5000, activation='softmax')
+net = tflearn.regression(net, optimizer='adam', loss='categorical_crossentropy')
+
+mmodel = tflearn.SequenceGenerator(net, dictionary=idx, seq_maxlen=100)
+model.fit(X, Y)
+model.generate(50, temperature=1.0)
+```
+
+There are many more examples available [here]().
+
+## Installation
+
+**TensorFlow Installation**
+
+TFLearn requires Tensorflow to be installed: *[Tensorflow installation instructions](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/g3doc/get_started/os_setup.md)*.
+
+**TFLearn Installation**
+
+To install TFLearn stable version, you can run:
+```python
+pip install tflearn
+```
+
+To install the latest version of TFLearn, you can run:
+```python
+pip install git+https://github.com/tflearn/tflearn.git
+```
+Otherwise, you can also install from source by running (from source folder):
+```python
+python setup.py install
+```
+
+- For more details about installation process, please see the [Installation Guide](http://tflearn.org/installation).
+
+## Getting Started
+
+See *[Getting Started with TFLearn](http://tflearn.org/getting_started)* for a
+ tutorial to learn more about TFLearn functionalities.
+
+## Examples
+
+There are many neural network implementation available, see *[Examples](http://tflearn.org/examples)*.
+
+## Documentation
+
+[http://tflearn.org/documentation](http://tflearn.org/documentation).
+
+## Model Visualization
+
+**Graph**
+
+![Graph Visualization](docs/templates/img/graph.png)
+
+**Loss & Accuracy (multiple runs)**
+
+![Loss Visualization](docs/templates/img/loss_acc.png)
+
+**Layers**
+
+![Layers Visualization](docs/templates/img/layer_visualization.png)
+
+## Contributions
+
+This is the first release of TFLearn, if you find any bug, please report it in the GitHub issues section.
+
+Improvements and requests for new features are more than welcome! Do not hesitate to twist and tweak TFLearn, and send pull-requests.
+
+For more info: *[Contribute to TFLearn](http://tflearn.org/contributions)*.
+
+## License
+
+MIT License

RELEASE.md

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+# Release 0.1.0
+
+Initial release of TFLearn