问如何使用自微分功能计算向量相对于向量的导数

EN

tf.gradients(
    ys,
    xs,
    ...
)
ys: A Tensor or list of tensors to be differentiated.
xs: A Tensor or list of tensors to be used for differentiation.

x = tf.get_variable("x", 
                    [2, 5],
                    initializer=tf.truncated_normal_initializer(stddev=0.02))
x_0 = x[0]
x_1 = x[1]

y_0 = x_0 + x_1
y_1 = x_0 - 2*x_1

y = tf.concat([[y_0], [y_1]], axis=0)

grad = tf.gradients(ys=y, xs=[x_0, x_1], unconnected_gradients='zero')

with tf.Session() as s:
  s.run(tf.initialize_all_variables())

  x0, x1, x, y, grad = s.run([x_0, x_1, x, y, grad])
  print 'x0=\n', x0, '\nx1=\n', x1
  print 'x=\n', x
  print 'y=\n', y
  print 'grad=\n', grad

x0=
[-0.02764007  0.01410516  0.02441488  0.02322472  0.03130293] 
x1=
[-0.00922771  0.0021055  -0.00121181  0.00638576  0.01953333]
x=
[[-0.02764007  0.01410516  0.02441488  0.02322472  0.03130293]
 [-0.00922771  0.0021055  -0.00121181  0.00638576  0.01953333]]
y=
[[-0.03686778  0.01621066  0.02320307  0.02961049  0.05083626]
 [-0.00918464  0.00989417  0.0268385   0.0104532  -0.00776374]]
grad=
  [array([ 2.,  2.,  2.,  2.,  2.], dtype=float32), 
   array([-1., -1., -1., -1., -1.], dtype=float32)]