Deep Learning Library featuring a higher-level API for Tensorflow
Project description
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: This is the first release of TFLearn. Contributions are more than welcome!
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 >= 0.7) to be installed: `See official TensorFlow installation`_.
TFLearn Installation
To install TFLearn, the easiest way is to run:
Stable version:
pip install tflearnLatest version:
pip install git+https://github.com/tflearn/tflearn.gitOtherwise, you can also install from source by running (from source folder):
python setup.py installGetting Started
To get started, see http://tflearn.org/getting_started
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
