CURRENNT is a machine learning library for Recurrent Neural Networks (RNNs) which uses NVIDIA graphics cards to accelerate the computations.
The library implements uni- and bidirectional Long Short-Term Memory (LSTM) architectures and supports deep networks as well as very large data sets that do not fit into main memory.
- Uni- and bidirectional Long Short-Term Memory (LSTM) layers with forget gates and peepholes
- Feedforward layers with tanh, logistic sigmoid and softmax activation functions
- Deep neural network architectures supported
- Cached on-line learning from large data sets (training data does not need to fit in main memory)
- Reads training data from NetCDF files
- Gradient descent with momentum
- Supports on-line, batch and hybrid on-line/batch learning
- Minimization of cross-entropy and squared error objectives
- Supports regression and binary/multiclass classification tasks
- Training with input activation noise for improved generalization
- Autosave after each training epoch
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