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evaluate.py

+import numpy as np
+import h5py
+import tensorflow as tf
+from tensorflow.keras.models import Sequential
+from tensorflow.keras.layers import Dense
+from tensorflow.keras.optimizers import Adam
+from tensorflow.keras import metrics
+from tensorflow.keras.layers import Dropout
+from tensorflow.keras.layers import BatchNormalization
+from data_loader import *
+
+n_in = 5
+n_out = 1
+
+infilename = "/m100_work/IscrC_CD-DLS/simulations/"
+rho, T, vx, vy, vz, Bx, By, Bz, B2 = load_data(infilename, [500, 500, 500], [1000, 1000, 501])
+
+n_cells = rho.size
+n_train = n_cells
+
+Xtrain = np.zeros((n_train, n_in),dtype=np.float64)
+Ytrain = np.zeros((n_train, n_out),dtype=np.float64)
+
+xxx = rho.flatten()
+Xtrain[0:n_train,0] = xxx[0:n_train]
+xxx = T.flatten()
+Xtrain[0:n_train,1] = xxx[0:n_train]
+xxx = vx.flatten()
+Xtrain[0:n_train,2] = xxx[0:n_train]
+xxx = vy.flatten()
+Xtrain[0:n_train,3] = xxx[0:n_train]
+xxx = vz.flatten()
+Xtrain[0:n_train,4] = xxx[0:n_train]
+
+Bxx = Bx.flatten()
+Bxy = By.flatten()
+Bxz = Bz.flatten()
+Bx2 = B2.flatten()
+
+# save the trained network
+ckptfile = 'models/trained_networ.ckpt'
+model = tf.keras.models.load_model(ckptfile)
+model.summary()
+
+Ytrain = model.predict(Xtrain)
+print(Ytrain.shape)
+
+Bx2.tofile('B.bin')
+Xtrain[:,0].tofile('rho.bin')
+Xtrain[:,1].tofile('T.bin')
+#B2 = Ytrain[:,0]
+#B2.tofile('eval.bin')
+Ytrain[:,0].tofile('eval.bin')
+
+