tflearn 0.3.2
Deep Learning Library featuring a higher-level API for TensorFlow
License: MIT
Keywords: TFLearn,TensorFlow,Deep Learning,Machine Learning,Neural Networks,AI

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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 helper 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: Latest TFLearn (v0.3) is only compatible with TensorFlow v1.0 and
over.

Overview
--------

.. code:: 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)

.. code:: 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')

    model = 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 (version 1.0+) to be installed.

To install TensorFlow, simply run:

::

    pip install tensorflow

or, with GPU-support:

::

    pip install tensorflow-gpu

There are many more examples available here:
http://tflearn.org/examples.

Installation
------------

**TensorFlow Installation**

TFLearn requires Tensorflow (version 1.0+) to be installed.

To install TensorFlow, simply run:

::

    pip install tensorflow

or, with GPU-support:

::

    pip install tensorflow-gpu

For more details see:
https://github.com/tensorflow/tensorflow/blob/master/tensorflow/g3doc/get\_started/os\_setup.md

**TFLearn Installation**

To install TFLearn, the easiest way is to run

For the bleeding edge version (recommended):

.. code:: python

    pip install git+https://github.com/tflearn/tflearn.git

For the latest stable version:

.. code:: python

    pip install tflearn

Otherwise, you can also install from source by running (from source
folder):

.. code:: python

    python setup.py install

-  For more details, please see the `Installation Guide`_.

Getting Started
---------------

See `Getting Started with TFLearn`_ to learn about TFLearn basic
functionalities or start browsing `TFLearn Tutorials`_.

Examples
--------

There are many neural network implementation available, see `Examples`_.

Documentation
-------------

http://tflearn.org/doc_index

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`_.

License
-------

MIT License

.. _here: http://tflearn.org/examples

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.. _Installation Guide: http://tflearn.org/installation
.. _Getting Started with TFLearn: http://tflearn.org/getting_started
.. _TFLearn Tutorials: http://tflearn.org/tutorials
.. _Examples: http://tflearn.org/examples
.. _Contribute to TFLearn: http://tflearn.org/contributions