Anyone want to try creating a CUDA version (. However, it does not currently save the beta value in the. Usage: > layer tf.('relu') > output layer( -3.0, -1.0, 0.0, 2.0) > list(output.numpy()) 0.0, 0.0, 0.0, 2.0 > layer tf.(tf.nn.relu) > output layer( -3.0, -1.0, 0.0, 2.0) > list(output.numpy()) 0.0, 0.0, 0.0, 2.0 Input shape Arbitrary. The swish activation function is used in the excellent EfficientNet architecture - but its pretty slow right now. The Adaptive Richards Curve weighted Activation (ARiA) is also. With open(directory + 'architecture.json', 'w') as arch_file: from keras import backend as K from import getcustomobjects from keras. A flatten-T Swish considers zero function for negative inputs similar to the ReLU 28. Super(Swish, self)._init_(activation, **kwargs) Activations functions can either be used through layeractivation(), or through the activation argument supported by all forward layers. I'm trying to create an activation function in Keras that can take in a parameter beta like so: from keras import backend as Kįrom _utils import get_custom_objectsĭef _init_(self, activation, beta, **kwargs): Our experiments show that the best discovered activation function, f (x) x sigmoid(x) f ( x) x s i g m o i d ( x), which we name Swish, tends to work better than ReLU on deeper models across a number of challenging datasets.
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