brainpy.initialize.calculate_gain

brainpy.initialize.calculate_gain(nonlinearity, param=None)

Return the recommended gain value for the given nonlinearity function. The values are as follows:

nonlinearity	gain
Linear / Identity	\(1\)
Conv{1,2,3}D	\(1\)
Sigmoid	\(1\)
Tanh	\(\frac{5}{3}\)
ReLU	\(\sqrt{2}\)
Leaky Relu	\(\sqrt{\frac{2}{1 + \text{negative\_slope}^2}}\)
SELU	\(\frac{3}{4}\)

Warning

In order to implement Self-Normalizing Neural Networks , you should use nonlinearity='linear' instead of nonlinearity='selu'. This gives the initial weights a variance of 1 / N, which is necessary to induce a stable fixed point in the forward pass. In contrast, the default gain for SELU sacrifices the normalisation effect for more stable gradient flow in rectangular layers.

Parameters:

• nonlinearity – the non-linear function (nn.functional name)

• param – optional parameter for the non-linear function