Replace with new training points

[shydyhlei]

冒昧求教，我现在想迭代若干次后，重新采样。在原始代码中添加了等号之间的部分：
geom = dde.geometry.Interval(-1, 1)
timedomain = dde.geometry.TimeDomain(0, 0.99)
geomtime = dde.geometry.GeometryXTime(geom, timedomain)

bc = dde.icbc.DirichletBC(geomtime, lambda x: 0, lambda _, on_boundary: on_boundary)
ic = dde.icbc.IC(geomtime, lambda x: -np.sin(np.pi * x[:, 0:1]), lambda _, on_initial: on_initial)

data = dde.data.TimePDE(geomtime, pde, [bc, ic], num_domain=2500, num_boundary=100, num_initial=160)
net = dde.nn.FNN([2] + [20] * 3 + [1], "tanh", "Glorot normal")
model = dde.Model(data, net)

model.compile("adam", lr=1.0e-3)
np.savetxt("training_points.dat", data.train_x_all)
model.train(iterations=1000)
#===============================================================================
new_resample_points = rad_resampler(model, geomtime, num_resample=2500, k=1, c=1)
data.replace_with_anchors(new_resample_points)
np.savetxt("training_points.dat", data.train_x_all)
#================================================================================
model.compile("L-BFGS")
losshistory, train_state = model.train()

其中rad_resampler的定义如下：

def rad_resampler(model, geomtime, num_resample, k=1, c=1):
"""
Resample residual points based on the Residual-based Adaptive Distribution (RAD) method.
"""
# Generate a dense set of candidate points
candidate_points = geomtime.random_points(10 * num_resample)

# Evaluate the PDE residual at candidate points
candidate_points_tensor = paddle.to_tensor(candidate_points, dtype='float32')
y_pred = model.predict(candidate_points_tensor)
residuals = pde(candidate_points_tensor, y_pred)

# Compute the probability density function (PDF) based on residuals

residuals = np.abs(residuals)
pdf = residuals ** k / (np.mean(residuals ** k) + c)
pdf /= np.sum(pdf)

# Sample new residual points based on the PDF
indices = np.random.choice(len(candidate_points), size=num_resample, p=pdf)
new_rps = candidate_points[indices]

return new_rps

#-------------------------------------------------------------------------------------------------
运行报错如下信息

Backend tkagg is interactive backend. Turning interactive mode on.
Traceback (most recent call last):
File "C:\Program Files\JetBrains\PyCharm Community Edition 2023.1\plugins\python-ce\helpers\pydev\pydevd.py", line 1570, in _exec
pydev_imports.execfile(file, globals, locals) # execute the script
File "C:\Program Files\JetBrains\PyCharm Community Edition 2023.1\plugins\python-ce\helpers\pydev_pydev_imps_pydev_execfile.py", line 18, in execfile
exec(compile(contents+"\n", file, 'exec'), glob, loc)
File "E:\deepxde-study\1dproblems\1dxdeburgers-rad.py", line 88, in
new_resample_points = rad_resampler(model, geomtime, num_resample=2500, k=1, c=1)
File "E:\deepxde-study\1dproblems\1dxdeburgers-rad.py", line 33, in rad_resampler
residuals = pde(candidate_points_tensor, y_pred)
File "E:\deepxde-study\1dproblems\1dxdeburgers-rad.py", line 16, in pde
dy_x = dde.grad.jacobian(y, x, i=0, j=0)
File "E:\deepxde-study\1dproblems\venv\lib\site-packages\deepxde\gradients\gradients.py", line 34, in jacobian
return gradients_reverse.jacobian(ys, xs, i=i, j=j)
File "E:\deepxde-study\1dproblems\venv\lib\site-packages\deepxde\gradients\gradients_reverse.py", line 73, in jacobian
return jacobian._Jacobians(ys, xs, i=i, j=j)
File "E:\deepxde-study\1dproblems\venv\lib\site-packages\deepxde\gradients\jacobian.py", line 126, in call
if key not in self.Js:
TypeError: unhashable type: 'numpy.ndarray'

冒昧求教，问题出在哪里？谢谢！