高斯过程wrt特征的偏导数答案

【问题标题】：Partial derivatives of Gaussian Process wrt features高斯过程wrt特征的偏导数
【发布时间】：2020-12-24 10:18:26
【问题描述】：

给定一个具有多维特征和标量观察的高斯过程模型，我如何在 GPyTorch 或 GPFlow（或 scikit-learn）中计算输出到每个输入的导数？

【问题讨论】：

标签： python scikit-learn gpflow gaussian-process gpytorch

【解决方案1】：

如果我正确理解了您的问题，以下内容应该可以为您提供 GPflow with TensorFlow 所需的内容：

import numpy as np
import tensorflow as tf
import gpflow

### Set up toy data & model -- change as appropriate:
X = np.linspace(0, 10, 5)[:, None]
Y = np.random.randn(5, 1)
data = (X, Y)
kernel = gpflow.kernels.SquaredExponential()
model = gpflow.models.GPR(data, kernel)
Xtest = np.linspace(-1, 11, 7)[:, None]  # where you want to predict

### Compute gradient of prediction with respect to input:
# TensorFlow can only compute gradients with respect to tensor objects,
# so let's convert the inputs to a tensor:
Xtest_tensor = tf.convert_to_tensor(Xtest)  

with tf.GradientTape(
        persistent=True  # this allows us to compute different gradients below
) as tape:
    # By default, only Variables are watched. For gradients with respect to tensors,
    # we need to explicitly watch them:
    tape.watch(Xtest_tensor)

    mean, var = model.predict_f(Xtest_tensor)  # or any other predict function

grad_mean = tape.gradient(mean, Xtest_tensor)
grad_var = tape.gradient(var, Xtest_tensor)

【讨论】：