[MRG] Evaluation criteria (#568)

* added evaluation function

* improved code quality

* changed condition of assertion

autokeras/net_module.py

@@ -11,6 +11,7 @@
 
 from autokeras.utils import pickle_to_file, rand_temp_folder_generator, ensure_dir
 from autokeras.nn.generator import CnnGenerator, MlpGenerator, ResNetGenerator, DenseNetGenerator
+from autokeras.utils import get_device
 
 
 class NetworkModule:
@@ -123,6 +124,24 @@ def predict(self, test_loader):
         output = reduce(lambda x, y: np.concatenate((x, y)), outputs)
         return output
 
+    def evaluate(self, test_data):
+        """Evaluate the performance of the best architecture in terms of the loss.
+
+        Args:
+            test_data: A DataLoader instance representing the testing data.
+        """
+        model = self.best_model.produce_model()
+        model.eval()
+        device = get_device()
+        target, prediction = [], []
+
+        with torch.no_grad():
+            for _, (x, y) in enumerate(test_data):
+                x, y = x.to(device), y.to(device)
+                prediction.append(model(x))
+                target.append(y)
+        return self.metric().compute(prediction, target)
+
 
 class CnnModule(NetworkModule):
     """ Class to create a CNN module."""

tests/test_preprocessor.py

@@ -24,4 +24,7 @@ def test_batch_dataset(_, _1):
     test_dataloader = DataLoader(test_dataset, batch_size=1, shuffle=False)
     cnn = CnnModule(classification_loss, Accuracy, {}, TEST_TEMP_DIR, True)
     cnn.fit(2, (4, 250, 250, 3), train_dataloader, test_dataloader, 12 * 60 * 60)
+    score = cnn.evaluate(test_dataloader)
+    if score < 0 or score > 1.0:
+        raise AssertionError()
     clean_dir(TEST_TEMP_DIR)