Bug fix for compatibility issue with kt 1.1.0

autokeras/auto_model.py

@@ -195,6 +195,9 @@ def _build_graph(self):
         elif all([isinstance(output, head_module.Head) for output in self.outputs]):
             # Clear session to reset get_uid(). The names of the blocks will
             # start to count from 1 for new blocks in a new AutoModel afterwards.
+            # When initializing multiple AutoModel with Task API, if not
+            # counting from 1 for each of the AutoModel, the predefined hp
+            # values in task specifiec tuners would not match the names.
             tf.keras.backend.clear_session()
             graph = self._assemble()
             self.outputs = graph.outputs

autokeras/engine/tuner.py

@@ -88,7 +88,7 @@ def _prepare_model_build(self, hp, **kwargs):
         return pipeline, dataset, validation_data
 
     def _build_and_fit_model(self, trial, *args, **kwargs):
-        model = self.hypermodel.build(trial.hyperparameters)
+        model = self._try_build(trial.hyperparameters)
         (
             pipeline,
             kwargs["x"],
@@ -188,7 +188,7 @@ def search(
         # Populate initial search space.
         hp = self.oracle.get_space()
         self._prepare_model_build(hp, **fit_kwargs)
-        self.hypermodel.build(hp)
+        self._try_build(hp)
         self.oracle.update_space(hp)
         super().search(
             epochs=epochs, callbacks=new_callbacks, verbose=verbose, **fit_kwargs
@@ -255,7 +255,7 @@ def _get_best_trial_epochs(self):
     def _build_best_model(self):
         best_trial = self.oracle.get_best_trials(1)[0]
         best_hp = best_trial.hyperparameters
-        return self.hypermodel.build(best_hp)
+        return self._try_build(best_hp)
 
     def final_fit(self, **kwargs):
         best_trial = self.oracle.get_best_trials(1)[0]

autokeras/engine/tuner_test.py

@@ -18,6 +18,7 @@
 import tensorflow as tf
 from tensorflow.keras.layers.experimental import preprocessing
 
+import autokeras as ak
 from autokeras import keras_layers
 from autokeras import test_utils
 from autokeras.engine import tuner as tuner_module
@@ -188,3 +189,28 @@ def test_adapt_with_model_with_preprocessing_layer_only():
             (np.random.rand(100, 10), np.random.rand(100, 10))
         ).batch(32),
     )
+
+
+def test_build_block_in_blocks_with_same_name(tmp_path):
+    class Block1(ak.Block):
+        def build(self, hp, inputs):
+            hp.Boolean("a")
+            return tf.keras.layers.Dense(3)(tf.nest.flatten(inputs)[0])
+
+    class Block2(ak.Block):
+        def build(self, hp, inputs):
+            hp.Boolean("b")
+            return Block1().build(hp, inputs)
+
+    inputs = ak.Input()
+    outputs = Block2()(inputs)
+    outputs = ak.RegressionHead()(outputs)
+    auto_model = ak.AutoModel(inputs, outputs, max_trials=5, directory=tmp_path)
+    auto_model.fit(np.random.rand(100, 5), np.random.rand(100, 1), epochs=1)
+
+    trials = [trial for trial_id, trial in auto_model.tuner.oracle.trials.items()]
+    for trial in trials:
+        print(trial.hyperparameters.values)
+        assert len(trial.hyperparameters.values) == len(
+            trials[0].hyperparameters.values
+        )