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Deep Learning Library featuring a higher-level API for TensorFlow

Project description

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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

# 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)
# 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):

pip install git+https://github.com/tflearn/tflearn.git

For the latest stable version:

pip install tflearn

Otherwise, you can also install from source by running (from source folder):

python setup.py install

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.

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

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