Predict from restored model #2
Description
Hi @lululxvi , @ShuaiMeng0601 and @jpzxshi.
Thank you for such a great work. I was looking for a version of PINN model and went through your multiple articles and decided to use MIONet since I need to use more than one 'u'. I could generate the data and train the model and save it in my local repository. I could also successfully restore the saved model.
At this stage, I am trying to predict with the test data that was generated in the step 1 using the restored model.
However, I am facing issue with using this model with new data. I looked at the FAQ of DeepXDE and issues in DeepONet and could not find the fix/solution.
Please find the code and the error below. Looking forward for your reply.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import itertools
import numpy as np
from deepxde.backend import tf
import os
from scipy.integrate import solve_ivp
import deepxde as dde
from spaces import FinitePowerSeries, FiniteChebyshev, GRF
from utils import merge_values, trim_to_65535, mean_squared_error_outlier, safe_test
from deepxde.nn.tensorflow_compat_v1.mionet import MIONetCartesianProd
from deepxde.data.quadruple import QuadrupleCartesianProd
def int_index(x, t, T):
mat = np.linspace(0, T, x)
return int(t / mat[1])
def ode(m, T, sensor_values1, sensor_values2):
"""ODE system"""
s0 = [0, 0] # initial condition
def model(t, s):
k = 1
u1 = lambda t: sensor_values1[t]
u2 = lambda t: sensor_values2[t]
return [
s[1] + u1(int_index(m, t, T)),
-k * np.sin(s[0]) + u2(int_index(m, t, T)),
]
res = solve_ivp(model, [0, T], s0, method="RK45", t_eval=np.linspace(0, T, m), vectorized = True)
return res.y[0, :], res.y[1, :]
def network(problem, m):
if problem == "ODE":
branch = [m, 200, 200]
trunk = [1, 200, 200]
elif problem == "DR":
branch = [m, 200, 200]
trunk = [2, 200, 200]
elif problem == "ADVD":
branch = [m, 300, 300, 300]
trunk = [2, 300, 300, 300]
return branch, trunk
problem = "ODE"
T = 1
m = 100
lr = 0.0002 if problem in ["ADVD"] else 0.001
epochs = 100000
activation = (
["relu", None, "relu"] if problem in ["ADVD"] else ["relu", "relu", "relu"]
)
initializer = "Glorot normal"
training_data = np.load("../data/" + problem + "_train_1.npz", allow_pickle=True)
testing_data = np.load("../data/" + problem + "_test_1.npz", allow_pickle=True)
X_train = training_data["X_train"]
y_train = training_data["y_train"]
X_test = testing_data["X_test"]
y_test = testing_data["y_test"]
branch_net, trunk_net = network(problem, m)
data = QuadrupleCartesianProd(X_train, y_train, X_test, y_test)
net = MIONetCartesianProd(
branch_net,
branch_net,
trunk_net,
{"branch1": activation[0], "branch2": activation[1], "trunk": activation[2]},
initializer,
regularization=None,
)
model = dde.Model(data, net)
model.compile("adam", lr=lr)
checker = dde.callbacks.ModelCheckpoint(
"model/mionet_model", save_better_only=True, period=1000
)
model.restore(os.path.normpath("model/mionet_model-96000.ckpt"), verbose=1)
model.predict(X_test)
I get the below error when I predict with the X_test which was the same data that was used while training the model.
