Create a validation set

src/constants.py

@@ -1,7 +1,9 @@
 BASES = "ACGT"
 TRAIN_DATASET = "data/train_data.tfrecords"
 TEST_DATASET = "data/test_data.tfrecords"
+EVAL_DATASET = "data/eval_data.tfrecords"
 EPOCHS = 1000
 BATCH_SIZE = 256
 LEARNING_RATE = 0.004
 L2 = 0.001
+LOG_DIR = "logs"

src/preprocessing.py

@@ -59,11 +59,15 @@ def create_dataset(data_file, label_file) -> None:
     Create a training and test dataset with a 70/30 split respectively
     """
     data = read_fastq(data_file, label_file)
-    train_test_split = 0.7
-    with TFRecordWriter(TRAIN_DATASET) as train, TFRecordWriter(TEST_DATASET) as test:
+    train_eval_test_split = [0.8, 0.1, 0.1]
+    with TFRecordWriter(TRAIN_DATASET) as training, TFRecordWriter(
+        TEST_DATASET
+    ) as test, TFRecordWriter(EVAL_DATASET) as evaluation:
         for element in data:
-            if random() < train_test_split:
-                train.write(element)
+            if random() < train_eval_test_split[0]:
+                training.write(element)
+            elif random() < train_eval_test_split[0] + train_eval_test_split[1]:
+                evaluation.write(element)
             else:
                 test.write(element)
 
@@ -94,8 +98,11 @@ def read_dataset(filepath) -> TFRecordDataset:
     return batched_dataset
 
 
-def dataset_creation(data_file, label_file) -> Tuple[TFRecordDataset, TFRecordDataset]:
+def dataset_creation(
+    data_file, label_file
+) -> Tuple[TFRecordDataset, TFRecordDataset, TFRecordDataset]:
     create_dataset(data_file, label_file)
     train_data = read_dataset(TRAIN_DATASET)
+    eval_data = read_dataset(EVAL_DATASET)
     test_data = read_dataset(TEST_DATASET)
-    return train_data, test_data
+    return train_data, eval_data, test_data