def safe_softmax(x):
"""Return the softmax with result truly between 0 and 1."""
y = _softmax(x)
y = T.clip(y, 1e-7, 1 - 1e-7)
return y
def det_softmax(mean, var):
mean_flat = mean.reshape((mean.shape[0] * mean.shape[1], mean.shape[2]))
softmaxed = _softmax(mean_flat).reshape(mean.shape)
return T.clip(softmaxed, 1e-8, 1 - 1e-8), T.zeros_like(var) + 1e-8
