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Author: typhoonzero

tests/base.py

@@ -6,10 +6,10 @@ def train_input_fn(features, labels, batch_size=32):
     dataset = dataset.shuffle(1000).repeat().batch(batch_size)
     return dataset
 
-
-def prepare_dataset():
-    pass
-
+def eval_input_fn(features, labels, batch_size=32):
+    dataset = tf.data.Dataset.from_tensor_slices((dict(features), labels))
+    dataset = dataset.batch(batch_size)
+    return dataset
 
 class BaseTestCases:
     class BaseTest(object):
@@ -18,8 +18,14 @@ def setUp(self):
 
         def test_train_and_predict(self):
             self.setUp()
-            train_input_fn(self.features, self.label)
 
-            print('Calling BaseTest:testCommon')
-            value = 5
-            assert(value == 5)
+            self.model.compile(optimizer=self.model.default_optimizer(),
+                loss=self.model.default_loss(),
+                metrics=["accuracy"])
+            self.model.fit(train_input_fn(self.features, self.label),
+                epochs=self.model.default_training_epochs(),
+                steps_per_epoch=100, verbose=0)
+            loss, acc = self.model.evaluate(eval_input_fn(self.features, self.label))
+            print(loss, acc)
+            assert(loss < 10)
+            assert(acc > 0.3)

tests/test_dnnclassifier.py

@@ -11,7 +11,7 @@ def setUp(self):
                          "c2": [float(x) for x in range(100)],
                          "c3": [float(x) for x in range(100)],
                          "c4": [float(x) for x in range(100)]}
-        self.label = {"label": [0 for _ in range(50)] + [1 for _ in range(50)]}
+        self.label = [0 for _ in range(50)] + [1 for _ in range(50)]
         feature_columns = [tf.feature_column.numeric_column(key) for key in
                            self.features]
         self.model = sqlflow_models.DNNClassifier(feature_columns=feature_columns)