Displaying 1 to 6 from 6 results

Bender - Easily craft fast Neural Networks on iOS! Use TensorFlow models. Metal under the hood.

  •    Swift

Bender is an abstraction layer over MetalPerformanceShaders useful for working with neural networks. Bender is an abstraction layer over MetalPerformanceShaders which is used to work with neural networks. It is of growing interest in the AI environment to execute neural networks on mobile devices even if the training process has been done previously. We want to make it easier for everyone to execute pretrained networks on iOS.

Residual-of-Residual-Networks - Residual Network of Residual Networks in Keras

  •    Python

Ordinarily, Residual networks have hundreds or even thousands of layers to accurately classify images in major image recognition tasks, but building a network by simply stacking residual blocks inevitably limits its optimization ability. This paper attempts to improve the optimization ability of Residual Networks by adding level-wise shortcut connections upon original residual networks, to promote the learning capability of residual networks.

Wide-Residual-Networks - Wide Residual Networks in Keras

  •    Python

Implementation of Wide Residual Networks from the paper Wide Residual Networks in Keras. It can be used by importing the wide_residial_network script and using the create_wide_residual_network() method. There are several parameters which can be changed to increase the depth or width of the network.

Keras-Classification-Models - Collection of Keras models used for classification

  •    Python

A set of models which allow easy creation of Keras models to be used for classification purposes. Also contains modules which offer implementations of recent papers. An implementation of "SparseNets" from the paper Sparsely Connected Convolutional Networks in Keras 2.0+.

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