--- a
+++ b/predict.py
@@ -0,0 +1,37 @@
+import numpy as np
+import warnings
+import argparse
+warnings.filterwarnings("ignore")
+from tensorflow.keras.models import load_model
+from tensorflow.keras.optimizers import Adam
+from datasets import ECGSequence
+
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser(description='Get performance on test set from hdf5')
+    parser.add_argument('path_to_hdf5', type=str,
+                        help='path to hdf5 file containing tracings')
+    parser.add_argument('path_to_model',  # or model_date_order.hdf5
+                        help='file containing training model.')
+    parser.add_argument('--dataset_name', type=str, default='tracings',
+                        help='name of the hdf5 dataset containing tracings')
+    parser.add_argument('--output_file', default="./dnn_output.npy",  # or predictions_date_order.csv
+                        help='output csv file.')
+    parser.add_argument('-bs', type=int, default=32,
+                        help='Batch size.')
+
+    args, unk = parser.parse_known_args()
+    if unk:
+        warnings.warn("Unknown arguments:" + str(unk) + ".")
+
+    # Import data
+    seq = ECGSequence(args.path_to_hdf5, args.dataset_name, batch_size=args.bs)
+    # Import model
+    model = load_model(args.path_to_model, compile=False)
+    model.compile(loss='binary_crossentropy', optimizer=Adam())
+    y_score = model.predict(seq,  verbose=1)
+
+    # Generate dataframe
+    np.save(args.output_file, y_score)
+
+    print("Output predictions saved")