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Deep Learning using Rectified Linear Units

Abstract

We present a simple comparison of using the rectified linear units (ReLU) activation function, and a number of its variations, in a deep neural network. We present empirical results comparing ReLU functions with Logistic and Hyperbolic Tangent functions in image classification, text classification, and image reconstruction.

License

Copyright 2018-2020 Abien Fred Agarap

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

   http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.