def get_variational_regularization(self, X):
mean = self.activation(K.dot(X, self.W_mean) + self.b_mean)
logsigma = self.activation(K.dot(X, self.W_logsigma) + self.b_logsigma)
return GaussianKL(mean, logsigma,
regularizer_scale=self.regularizer_scale,
prior_mean=self.prior_mean,
prior_logsigma=self.prior_logsigma)
def get_variational_regularization(self, X):
X = K.reshape(X, (-1, self.input_shape[-1]))
mean = self.activation(K.dot(X, self.W_mean) + self.b_mean)
logsigma = self.activation(K.dot(X, self.W_logsigma) + self.b_logsigma)
return GaussianKL(mean, logsigma,
regularizer_scale=self.regularizer_scale,
prior_mean=self.prior_mean,
prior_logsigma=self.prior_logsigma)