PEN-Method: Predictor model and Validation Data

Published: 20-11-2020| Version 1 | DOI: 10.17632/459f33wxf6.1
Contributor:
Alex Halle

Description

This Data contains the PEN-Predictor-Keras-Model as well as the 100 validation data sets.

Files

Steps to reproduce

With the following steps, the prediction of data (using the predictor and the validation data sets as input) can be reproduced. ```python # Using python 3.7 import tensorflow as tf import numpy as np # loading the model model_predictor = tf.keras.models.load_model('model_predictor_32x32.h5') # loading input validation datasets d_inp = 8 inputrows = np.load("Validation Data/inputrows_{0}x{0}.npz".format(d_inp))['arr_0'] R_k_val = inputrows[:, :2 * (d_inp + 1)**2] R_s_val = inputrows[:, 2 * (d_inp + 1)**2:4 * (d_inp + 1)**2] M_tar_val = inputrows[:, 4 * (d_inp + 1)**2] # loading output validation dataset (index 0) d = 32 index = 0 np_load = np.load("Validation Data/outputrows_{0}_{1}x{1}.npz".format(index, d)) X_TO_val = np.reshape(np_load['min_Material'], [d, d]) c_TO_val = np.asscalar(np_load['Nachgiebigkeit']) M_TO_val = np.asscalar(np_load['Materialabweichung']) F_TO_val = np.asscalar(np_load['Filter']) t_TO_val = np.asscalar(np_load['time_fe']) # predicting output for all 100 validation input data sets X_p_val = model_predictor.predict([R_k_val, R_s_val, M_tar_val]) ```