Computing jacobian matrix in Tensorflow
问题 I want to calculate Jacobian matrix by Tensorflow. What I have: 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 is a loss function, vars are all trainable variables, and data_num is a number of data. But if we increase the number of data, it takes tremendous time to run the function compute_grads . Any ideas? 回答1: Assuming