tidy up

code/input/exampleMN.cfg

@@ -29,10 +29,10 @@ CFL_NUMBER = 0.7
 % Final time for simulation
 TIME_FINAL = 0.3
 % Maximal Moment degree
-MAX_MOMENT_SOLVER = 1
+MAX_MOMENT_SOLVER = 0
 %
 %% Entropy settings
-ENTROPY_FUNCTIONAL = MAXWELL_BOLZMANN
+ENTROPY_FUNCTIONAL = MAXWELL_BOLTZMANN
 ENTROPY_OPTIMIZER = NEWTON
 %
 % ----- Newton Solver Specifications ----

code/python/callNN.py

-# imports
-import tensorflow as tf
-import numpy as np
-import math
-
-# Custom Loss
-def custom_loss1dMBPrime(): # (label,prediciton)
-    def loss(u_input, alpha_pred):
-        return 0.5*tf.square(4*math.pi*np.sqrt(1/(4*np.pi))*tf.math.exp(alpha_pred*np.sqrt(1/(4*np.pi))) - u_input)
-    return loss
-
-
-def initialize_network():
-    # Load model
-    model = tf.keras.models.load_model('neural_network_model/_EntropyLoss_1_300_M_0', custom_objects={ 'loss':custom_loss1dMBPrime })
-
-    # Check its architecture
-    model.summary()
-
-    return model
-
-# make the network a gobal variable here
-model = initialize_network()
-
-def call_network(input):
-    inputNP = np.asarray([input])
-
-    predictions = model.predict(inputNP)
-
-    return predictions[0]
-
-
-def call_networkBatchwise(input):
-
-    #print(input)
-    inputNP = np.asarray(input)
-    #print(inputNP.shape)
-    #print(inputNP)
-
-    predictions = model.predict(inputNP)
-
-    #print(predictions)
-
-    size = predictions.shape[0]*predictions.shape[1]
-    test = np.zeros(size)
-    for i in  range(0,size):
-        test[i] = predictions.flatten(order='C')[i]
-    return test
-
-    #return predictions.flatten(order='C')[0]
-
-
-def main():
-    input = [[1], [2], [3], [2], [3], [4], [5]]
-
-    # initialize_network()
-    print(call_network(1))
-    print("-----")
-    print(call_networkBatchwise(input))
-
-    return 0
-
-
-if __name__ == '__main__':
-    main()