在Tensorflow中计算jacobian矩阵 [英] Computing jacobian matrix in Tensorflow

问题描述

我想通过Tensorflow计算Jacobian矩阵.

I want to calculate Jacobian matrix by Tensorflow.

我所拥有的:

def compute_grads(fn, vars, data_num):
    grads = []
    for n in range(0, data_num):
        for v in vars:
            grads.append(tf.gradients(tf.slice(fn, [n, 0], [1, 1]), v)[0])
    return tf.reshape(tf.stack(grads), shape=[data_num, -1])

fn是损失函数，vars都是可训练变量，data_num是许多数据.

fn is a loss function, vars are all trainable variables, and data_num is a number of data.

但是，如果我们增加数据数量，则需要花费大量时间来运行函数compute_grads. 有什么想法吗?

But if we increase the number of data, it takes tremendous time to run the function compute_grads. Any ideas?

推荐答案

假定X和Y是Tensorflow张量，并且Y取决于X:

Assuming that X and Y are Tensorflow tensors and that Y depends on X:

from tensorflow.python.ops.parallel_for.gradients import jacobian
J=jacobian(Y,X)

结果的形状为Y.shape + X.shape，并且提供了Y的每个元素相对于X的每个元素的偏导数.

The result has the shape Y.shape + X.shape and provides the partial derivative of each element of Y with respect to each element of X.

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