Perform one hot encoding on the sequences

src/preprocessing.py

@@ -3,8 +3,7 @@ from typing import Dict, List, Tuple
 from Bio.pairwise2 import align
 from Bio.SeqIO import parse
 from numpy.random import random
-from tensorflow import Tensor, int64
+from tensorflow import Tensor, int64, one_hot
-from tensorflow.data import TFRecordDataset
 from tensorflow.data import AUTOTUNE, TFRecordDataset
 from tensorflow.io import TFRecordWriter, VarLenFeature, parse_single_example
 from tensorflow.sparse import to_dense
@@ -84,6 +83,7 @@ def transform_features(parsed_features) -> Dict[str, Tensor]:
     sparse_features = ["sequence", "label"]
     for element in sparse_features:
         features[element] = to_dense(parsed_features[element])
+        features[element] = one_hot(features[element], depth=len(BASES))
     return features