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: This is the first release of TFLearn. Contributions are more than welcome!
# 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`_.
TFLearn requires Tensorflow (version >= 0.7) to be installed: `See official TensorFlow installation`_.
To install TFLearn, the easiest way is to run:
pip install tflearn
pip install git+https://github.com/tflearn/tflearn.git
Otherwise, you can also install from source by running (from source folder):
python setup.py install
To get started, see http://tflearn.org/getting_started
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