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.
Artificial Neural Network Software
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.
Small (<50 employees), Medium (50 to 1000 employees), Enterprise (>1001 employees)
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. 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 and it is currently in its early development stage. TFLearn requires Tensorflow (version >= 0.9.0) to be installed. The easiest way to install TFLearn is to run but users can also install from source by running (from source folder). For users who have older versions of Tensorflow (under 0.9.0), upgrading Tensorflow may be neccessary to avoid some incompatibilities with TFLearn. And to upgrade Tensorflow, they will be required to uninstall Tensorflow and Protobuf and then re-install Tensorflow. TFLearn introduces a High-Level API that makes neural network building and training fast and easy. This API is intuitive and fully compatible with Tensorflow. Layers are a core feature of TFLearn. While completely defining a model using Tensorflow ops can be time consuming and repetitive, TFLearn brings “layers” that represent an abstract set of operations to make building neural networks more convenient. Besides the layers concept, TFLearn also provides many different ops to be used when building a neural network. These ops are first meant to be part of the ‘layers’ arguments, but they can also be used independently in any other Tensorflow graph for convenience.
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