![]() So far I can't feel the power of the swish activation function. It seems that swish is not powerful as I expect, and swish need about 20% extra time to train.Īlso compare with their training history, relu seem got better training curve. If swish really better than relu? swish confusion matrix fit( x_train, y_train, batch_size = 100, epochs = 100, validation_data =( x_test, y_test) The SiLU was later rediscovered in 2017 as the Sigmoid-weighted Linear Unit. For 1, the function becomes equivalent to the Sigmoid Linear Unit 2 or SiLU, first proposed alongside the GELU in 2016. compile( optimizer = adam, loss = 'binary_crossentropy'Ĭlassifier. The swish function is a mathematical function defined as follows: where is either constant or a trainable parameter depending on the model. add( Dense( units = 1, activation = 'sigmoid', kernel_initializer = 'uniform'))Ĭlassifier. add( Dense( units = 5, activation = swish, kernel_initializer = 'uniform'))Ĭlassifier. The definition is as follows: SwiGLU ( x, W, V, b, c, ) Swish ( x W + b) ( x V + c) Source: GLU Variants Improve Transformer Read Paper See Code Papers Paper Code Results Date Stars Usage Over Time This feature is experimental we are continuously improving our matching algorithm. add( Dense( units = 10, activation = swish, kernel_initializer = 'uniform'))Ĭlassifier. Anyone want to try creating a CUDA version (. add( Dense( units = 25, activation = swish, kernel_initializer = 'uniform'))Ĭlassifier. The swish activation function is used in the excellent EfficientNet architecture - but its pretty slow right now. add( Dense( units = 50, activation = swish, kernel_initializer = 'uniform'))Ĭlassifier. add( Dense( units = 10, activation = swish, kernel_initializer = 'uniform', input_dim = 10))Ĭlassifier. utils import class_weight from keras import backend as K def swish( x): ![]() layers import Dense, Activation from keras import optimizers from sklearn. ![]()
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