What is TFLearn?
TFLearn is a modular and transparent deep learning library built on top of TensorFlow. It provides a higher-level API to TensorFlow to facilitate and accelerate experimentations while remaining fully transparent and compatible with it.
TFLearn offers a simplified way to implement deep neural networks and supports most recent deep learning models, such as Convolutions, LSTM, BiRNN, BatchNorm, PReLU, Residual networks, and Generative networks.
Features
- Easy-to-use API: High-level API for implementing deep neural networks.
- Fast prototyping: Modular built-in neural network layers, regularizers, optimizers, and metrics.
- TensorFlow Transparency: All functions are built over tensors and can be used independently of TFLearn.
- Graph Visualization: Detailed visualization of weights, gradients, activations, etc.
- Multiple CPU/GPU: Effortless device placement for using multiple CPU/GPU.
- Trainer: Helper functions to train any TensorFlow graph, with support of multiple inputs, outputs and optimizers.
- Model Visualization: Easy and beautiful graph visualization, with details about weights, gradients, activations and more.
Use Cases
- Deep learning model development
- Rapid prototyping of neural networks
- TensorFlow graph training
- Deep learning research and experimentation
- Sequence Generation
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