how should I deal with that the loss is well but prediction is always

[hualihus]

dear lulu
I had counter this problem，the loss is very low，but the prediction is always 0
here is my code
def pde(x, y):
y0 = y[:,0:1]
y1 = y[:,1:2]
dy0_xx = dde.grad.hessian(y, x,component=0)
dy1_xx = dde.grad.hessian(y, x,component=1)

return [-dy0_xx-k0**2*y0,
        -dy1_xx-k0**2*y1]

def pde(x, y):
y0 = y[:,0:1]
y1 = y[:,1:2]
dy0_xx = dde.grad.hessian(y, x,component=0)
dy1_xx = dde.grad.hessian(y, x,component=1)

return [-dy0_xx-k0**2*y0,
        -dy1_xx-k0**2*y1]

def boundary(x, on_boundary):
return on_boundary

def boundary_r(x, on_boundary):
return on_boundary and dde.utils.isclose(x[0], 0)

def boundary_l(x, on_boundary):
return on_boundary and dde.utils.isclose(x[0], 2.0)

def func_rigth_u0(x, y):
result = - k0 * y[:, 1:2]
return result

def boundary(x, on_boundary):
return on_boundary

def boundary_r(x, on_boundary):
return on_boundary and dde.utils.isclose(x[0], 0)

def boundary_l(x, on_boundary):
return on_boundary and dde.utils.isclose(x[0], 2.0)

def func_rigth_u0(x, y):
result = - k0 * y[:, 1:2]
return result
def create_model(config):
learning_rate, num_dense_layers, num_dense_nodes, activation, num_sigma = config
geom = dde.geometry.Interval(0, 2.0)
bc0_outer = dde.RobinBC(geom, func_rigth_u0, boundary_r, component=0)
bc1_outer = dde.RobinBC(geom, func0_rigth_u1, boundary_r, component=1)
bc0_inner = dde.NeumannBC(geom, func0_left_u0, boundary_l, component=0)
bc1_inner = dde.NeumannBC(geom, func0_left_u1, boundary_l, component=1)
bcs = [ bc0_inner,bc1_inner,bc0_outer,bc1_outer]
data = dde.data.PDE(
geom,
pde,
bcs,
1600,
2,
train_distribution="pseudo",
solution=func,
num_test=1000,
#auxiliary_var_function=ex_func
)

layer_size = [1] + [num_dense_nodes] * num_dense_layers + [2]
activation = activation
initializer = "Glorot uniform"
net = dde.nn.MsFFN(layer_size, activation, initializer, sigmas=[1, num_sigma])

model = dde.Model(data, net)
model.compile(
"adam",
lr=learning_rate,
metrics=["l2 relative error"],
decay=("inverse time", 2000, 0.9),
)