Zaktualizuj 'sacred.py'

sacred.py

@@ -34,33 +34,25 @@ def run_experiment():
     feature_cols = ['year', 'mileage', 'vol_engine']
     inputs = tf.keras.Input(shape=(len(feature_cols),))
 
-    # Warstwy sieci neuronowej
     x = tf.keras.layers.Dense(10, activation='relu')(inputs)
     x = tf.keras.layers.Dense(10, activation='relu')(x)
     outputs = tf.keras.layers.Dense(1, activation='linear')(x)
 
-    # Utworzenie modelu
     model = tf.keras.Model(inputs=inputs, outputs=outputs)
 
-    # Kompilacja modelu
     model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=0.001),
                   loss='mse', metrics=['mae'])
 
-    # Trenowanie modelu
     model.fit(cars_train[feature_cols], cars_train['price'], epochs=100)
 
-    # Zapis plików wejściowych
     ex.add_resource('train_data.csv')
     ex.add_resource('test_data.csv')
 
-    # Zapis kodu źródłowego
     ex.add_artifact(__file__)
 
-    # Zapis modelu do pliku
     model.save('model.h5')
     ex.add_artifact('model.h5')
 
-    # Zapisanie metryk
     metrics = model.evaluate(cars_train[feature_cols], cars_train['price'])
     ex.log_scalar('mse', metrics[0])
     ex.log_scalar('mae', metrics[1])