Merge remote-tracking branch 'origin/master'

src/loaders.py

 import tensorflow as tf
 
 
-def load_dataset(filename, preproc, label):
+def load_dataset(filename, label):
     # Loading the dataset file
     dataset = tf.data.TextLineDataset(filename, num_parallel_reads=5)
 
@@ -26,12 +26,13 @@ def load_dataset(filename, preproc, label):
 def combine_datasets(datasets):
     # Combining the datasets
 
-    combined_dataset = datasets[0].concatenate(datasets[1])
+    c_dataset = datasets[0].concatenate(datasets[1])
 
     for dataset in datasets[2:]:
-        combined_dataset = combined_dataset.concatenate(dataset)
-
+        c_dataset = c_dataset.concatenate(dataset)
 
+    return c_dataset
+    
 def split_train_test_validation(dataset, train_size, test_size, val_size):
     train_dataset = dataset.take(train_size)
     test_dataset = dataset.take(test_size)

src/main.py

@@ -18,21 +18,26 @@ def main(argv):
     dtype = args.datatype
     adj = args.adjacent
     norm = args.normalised
-    net = args.normalised
+    net = args.network
     epochs = args.epochs
     knots = set_constants(prob)
+    Nbeads = 200
+
+    if dtype == "XYZ":
+        in_layer = (Nbeads, 3)
+    else:
+        in_layer = (Nbeads,1)
 
     datasets = []
     for knot in knots:
-        datasets.append(load_dataset(knot))
+        datasets.append(load_dataset("/storage/cmstore02/groups/TAPLab/knots_database/" + str(knot) + "/N200/lp10/SIGWRITHE/3DSignedWrithe_" + knot + ".dat.lp10.dat.nos", knot))
 
     dataset = combine_datasets(datasets)
-    Nbeads = 200
 
-    if dtype == "XYZ":
-        in_layer = (Nbeads, 3)
-    else:
-        in_layer = (Nbeads,)
+    for element in dataset.as_numpy_iterator():
+        print(element)
+        break
+
     if net == "FFNN":
         model = setup_NN(
             [320, 320, 320, 320],
@@ -87,14 +92,14 @@ def main(argv):
     cb_list = [es, mc]
     bs = 256
 
-    # model.fit(
-    #     dataset,
-    #     epochs=epochs,
-    #     batch_size=bs,
-    #     verbose=1,
-    #     validation_split=1 / 5,
-    #     callbacks=cb_list,
-    # )
+    model.fit(
+        dataset,
+        epochs=1,
+        batch_size=bs,
+        verbose=0,
+        # validation_split=1 / 5,
+        callbacks=cb_list,
+    )
 
 
 if __name__ == "__main__":