Implement the training loop and metrics evaluation

src/model.py

@@ -1,28 +1,35 @@
+from random import seed
+
 from tensorflow.keras import Model, Sequential, layers
+from tensorflow.keras.callbacks import TensorBoard
+from tensorflow.keras.losses import sparse_categorical_crossentropy
+from tensorflow.keras.optimizers import Adam
 from tensorflow.keras.regularizers import l2
+from tensorflow.random import set_seed
 
-from constants import BASES
+from constants import *
+from preprocessing import dataset_creation
 
 
-def build_model(hyper_parameters) -> Model:
+def build_model() -> Model:
     """
-    Builds the CNN model
+    Build the CNN model
     """
-    return Sequential(
+    model = Sequential(
         [
             # Two convolutions + maxpooling blocks
             layers.Conv1D(
                 filters=16,
                 kernel_size=5,
                 activation="relu",
-                kernel_regularizer=l2(hyper_parameters.l2),
+                kernel_regularizer=l2(L2),
             ),
             layers.MaxPool1D(pool_size=3, strides=1),
             layers.Conv1D(
                 filters=16,
                 kernel_size=3,
                 activation="relu",
-                kernel_regularizer=l2(hyper_parameters.l2),
+                kernel_regularizer=l2(L2),
             ),
             layers.MaxPool1D(pool_size=3, strides=1),
             # Flatten the input volume
@@ -31,16 +38,53 @@ def build_model(hyper_parameters) -> Model:
             layers.Dense(
                 units=16,
                 activation="relu",
-                kernel_regularizer=l2(hyper_parameters.l2),
+                kernel_regularizer=l2(L2),
             ),
             layers.Dropout(rate=0.3),
             layers.Dense(
                 units=16,
                 activation="relu",
-                kernel_regularizer=l2(hyper_parameters.l2),
+                kernel_regularizer=l2(L2),
             ),
             layers.Dropout(rate=0.3),
             # Output layer with softmax activation
             layers.Dense(units=len(BASES), activation="softmax"),
         ]
     )
+    model.compile(
+        optimizer=Adam(LEARNING_RATE),
+        loss=sparse_categorical_crossentropy,
+        metrics=["accuracy"],
+    )
+    return model
+
+
+def show_metrics(model, eval_dataset, test_dataset) -> None:
+    """
+    Show the model metrics
+    """
+    eval_metrics = model.evaluate(eval_dataset, verbose=0)
+    test_metrics = model.evaluate(test_dataset, verbose=0)
+    print(f"Final eval metrics - loss: {eval_metrics[0]} - accuracy: {eval_metrics[1]}")
+    print(f"Final test metrics - loss: {test_metrics[0]} - accuracy: {test_metrics[1]}")
+
+
+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)
+    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()
+    print("Training the model")
+    model.fit(
+        train_data,
+        epochs=EPOCHS,
+        validation_data=eval_data,
+        callbacks=[tensorboard],
+        verbose=0,
+    )
+    print("Training complete. Obtaining final metrics...")
+    show_metrics(model, eval_data, test_data)