Fix Tensorflow seed assignment

src/model.py

@@ -17,7 +17,6 @@ def build_model() -> Model:
     """
     model = Sequential(
         [
-            # Two convolutions + maxpooling blocks
             layers.Conv1D(
                 filters=16,
                 kernel_size=5,
@@ -32,9 +31,7 @@ def build_model() -> Model:
                 kernel_regularizer=l2(L2),
             ),
             layers.MaxPool1D(pool_size=3, strides=1),
-            # Flatten the input volume
             layers.Flatten(),
-            # Two fully connected layers, each followed by a dropout layer
             layers.Dense(
                 units=16,
                 activation="relu",
@@ -47,7 +44,7 @@ def build_model() -> Model:
                 kernel_regularizer=l2(L2),
             ),
             layers.Dropout(rate=0.3),
-            # Output layer with softmax activation
+            # FIXME Change output size
             layers.Dense(units=len(BASES), activation="softmax"),
         ]
     )
@@ -74,7 +71,7 @@ def run(data_file, label_file, seed_value=42) -> None:
     Create a dataset, a model and runs training and evaluation on it
     """
     seed(seed_value)
-    set_seed(seed)
+    set_seed(seed_value)
     train_data, eval_data, test_data = dataset_creation(data_file, label_file)
     tensorboard = TensorBoard(log_dir=LOG_DIR, histogram_freq=1, profile_batch=0)
     model = build_model()