We’ll use SGD with epochs steps, dividing the learning rate by 10 each time. MXNet contains an image classification script which lets us train with a variety of network architectures and data sets ( incubator-mxnet/example/gluon/image_classification.py). If you’re not comfortable with this, no worries: just wait for an official 1.2 installation package :) Training on CIFAR-10
Swish activation how to#
Of course, you’ll need to build and install: you should know how to do this by now ) If you’ve already built the master branch, you can get away with just installing the Python API again.
Swish activation full#
Here’s the full diff if you’re interested. Then, we plug this new set of models into incubator-mxnet/python/mxnet/gluon/model_zoo/_init_.py and voila! This is pretty straightforward: starting from the master branch, we simply create a vggswish.py file and replace ReLU by Swish, e.g.: (nn.Dense(4096, activation='relu', weight_initializer='normal', bias_initializer='zeros')) -> (nn.Dense(4096, weight_initializer='normal', bias_initializer='zeros')) (nn.Swish()) The same network modified to use Swish for the convolution layers and the fully connected layers.using ReLU ( incubator-mxnet/python/mxnet/gluon/model_zoo/vision/vgg.py) VGG16 with batch normalization as implemented in the Gluon model zoo, i.e.In order to evaluate its performance, we’re going to train two different versions of the VGG16 convolution neural network on CIFAR-10:
Swish activation code#
It’s defined in incubator-mxnet/python/mxnet/gluon/nn/activations.py and using it in our Gluon code is as easy as: nn.Swish(). Swish is available for the Gluon API in MXNet 1.2. This sounds like an easy improvement, doesn’t it? Let’s test it on MXNet! Swish in MXNet The sweet spot for β seems to be between 1 and 2: it creates a non-monotonic “bump” for negative values which seems to have interesting properties (more details in the research paper).Īs highlighted by the authors: “ simply replacing ReLUs with Swish units improves top-1 classification accuracy on ImageNet by 0.9% for Mobile NASNet-A and 0.6% for Inception-ResNet-v2”. One of them, which they named Swish, turned out to be better than others.į( x)= x⋅sigmoid( βx) - Source: research paper.Īs you can see, if the β parameter is small, Swish is close to the linear when the β parameter is small and close to ReLU when it’s large. The Swish functionīy automatically combining different mathematical operators, Prajit Ramachandran, Barret Zoph and Quoc V evaluated the performance of a large number of candidate activation functions (“Searching for Activation Functions”, research paper). In late 2017, a new function was discovered: Swish. Of course, the race for better activation function never stopped. For example, the popular Rectified Linear Unit function (aka ReLU) improved on the Sigmoid function by solving the vanishing gradient problem. Over time, a number of activation functions have been designed, each new one trying to overcome the shortcomings of its predecessors. In a way, we’re trying to mimic - in a simplistic way, no doubt - the behavior of biological neurons which either fire or not. to enforce a non-linear decision threshold on neuron outputs. In deep neural networks, the purpose of the activation function is to introduce non-linearity, i.e. As hinted at by the change log, this looks like a major release, but in this post, we’ll focus on a new activation function: Swish. A quick look at the Swish activation function in Apache MXNet 1.2Īpache MXNet 1.2 is right around the corner.
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