![]() Read through the paper and check in what cases did swish perform better.ģ. For example, we can use swish and ReLU to train different models to increase the variety for ensembling.Įxperiments with SWISH activation function on MNIST dataset (Medium)Ģ. Overall, I think swish is still a good choice considering activation function selection is task dependent. import tensorflow as tf Define the Swish activation function def swish(x): return x tf.sigmoid(x) Define a simple neural network with Swish activation function in the hidden layer model tf. Instead, popular CNNs, like ResNet, DenseNet and Mobilenet, perform better when using swish according to the paper. It should be mentioned that I used only shallow networks in toy experiments, which are not representative. Defining the swish activation function in Tensorflow: 1 2 def swish(x): return x tf.nn.sigmoid(x) 2. Activations that are more complex than a simple TensorFlow function (eg. However, swish usually had lower training accuracy/loss. Swish Activation Function Image Source With ReLU, the consistent problem is that its derivative is 0 for half of the values of the input x in ramp Function, i.e. The Swish (or Silu) activation function is a smooth, non-monotonic function. I tried several configurations, e.g., w/ and w/o batch norm, ReLU always outperformed swish in terms of validation accuracy. (Ioffe & Szegedy, 2015) should be set when training with the Swish activation function Hendrycks. Unfortunately, ReLU beat swish on both validation and training data. Overview All Symbols Python v2.14.0 tf tf.audio tf.autodiff tf.autograph tf.bitwise tf.compat tf.config tf.data tf.debugging tf.distribute tf.dtypes tf.errors tf.estimator tf.experimental tf.featurecolumn tf.graphutil tf.image tf.io tf.keras Swish activation function, swish (x) x sigmoid (x). (2018) warned that the scale parameter in batch normalization. In this experiment, we used SGD optimizer for all models and trained longer to see if swish can make a comeback.
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