如果您想实施 dropout 方法来测量不确定性，您应该执行以下操作:

If you want to implement dropout approach to measure uncertainty you should do the following:

1. 实现在测试期间也应用dropout的函数:

import keras.backend as K
f = K.function([model.layers[0].input, K.learning_phase()],
               [model.layers[-1].output])

def predict_with_uncertainty(f, x, n_iter=10):
    result = numpy.zeros((n_iter,) + x.shape)

    for iter in range(n_iter):
        result[iter] = f(x, 1)

    prediction = result.mean(axis=0)
    uncertainty = result.var(axis=0)
    return prediction, uncertainty

当然，您可以使用任何不同的函数来计算不确定性.

Of course you may use any different function to compute uncertainty.