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
For more details see TensorFlow installation instructions
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
- 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
Model Visualization
Graph
Loss & Accuracy (multiple runs)
Layers
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