Save (#967)

* save

* save

autokeras/engine/tuner.py

@@ -7,6 +7,9 @@
 import tensorflow as tf
 from tensorflow.python.util import nest
 
+from autokeras import graph as graph_module
+from autokeras import utils
+
 
 class AutoTuner(kerastuner.engine.multi_execution_tuner.MultiExecutionTuner):
     """A Tuner class based on KerasTuner for AutoKeras.
@@ -28,6 +31,9 @@ class AutoTuner(kerastuner.engine.multi_execution_tuner.MultiExecutionTuner):
     def __init__(self, **kwargs):
         super().__init__(**kwargs)
         self._finished = False
+        # Save or load the HyperModel.
+        utils.save_json(os.path.join(self.directory, 'graph'),
+                        graph_module.serialize(self.hypermodel.hypermodel))
 
     # Override the function to prevent building the model during initialization.
     def _populate_initial_space(self):

autokeras/hypermodels/preprocessing.py

@@ -51,8 +51,8 @@ def __init__(self,
     def get_config(self):
         config = super().get_config()
         config.update({
-            'max_len': self.max_len,
-            'max_tokens': self.vocab_size,
+            'output_sequence_length': self.output_sequence_length,
+            'max_tokens': self.max_tokens,
         })
         return config
 

autokeras/utils.py

@@ -1,4 +1,4 @@
-import pickle
+import json
 import re
 
 import numpy as np
@@ -99,16 +99,6 @@ def is_label(y):
     return len(y.flatten()) == len(y)
 
 
-def pickle_from_file(path):
-    """Load the pickle file from the provided path and returns the object."""
-    return pickle.load(open(path, 'rb'))
-
-
-def pickle_to_file(obj, path):
-    """Save the pickle file to the specified path."""
-    pickle.dump(obj, open(path, 'wb'))
-
-
 def to_snake_case(name):
     intermediate = re.sub('(.)([A-Z][a-z0-9]+)', r'\1_\2', name)
     insecure = re.sub('([a-z])([A-Z])', r'\1_\2', intermediate).lower()
@@ -142,3 +132,15 @@ def check_tf_version():
             'You can use `pip freeze` to check afterwards that everything is '
             'ok.'.format(version=tf.__version__)
         )
+
+
+def save_json(path, obj):
+    obj = json.dumps(obj)
+    with tf.io.gfile.GFile(path, 'w') as f:
+        f.write(obj)
+
+
+def load_json(path):
+    with tf.io.gfile.GFile(path, 'r') as f:
+        obj = f.read()
+    return json.loads(obj)

tests/autokeras/engine/tuner_test.py

@@ -4,19 +4,18 @@
 import tensorflow as tf
 
 from autokeras.engine import tuner as tuner_module
+from autokeras.tuners import greedy
 from tests import utils
 
 
-@mock.patch('kerastuner.engine.base_tuner.BaseTuner.__init__')
 @mock.patch('kerastuner.engine.base_tuner.BaseTuner.search')
 @mock.patch('tensorflow.keras.Model.fit')
-def test_add_early_stopping(fit_fn, base_tuner_search, init, tmp_path):
+def test_add_early_stopping(fit_fn, base_tuner_search, tmp_path):
     graph = utils.build_graph()
-    tuner = tuner_module.AutoTuner(oracle=mock.Mock(), hypermodel=graph)
-    tuner.hypermodel = graph
-    tuner.oracle = mock.Mock()
-    tuner.directory = tmp_path
-    tuner.project_name = ''
+    tuner = tuner_module.AutoTuner(
+        oracle=greedy.GreedyOracle(graph, objective='val_loss'),
+        hypermodel=graph,
+        directory=tmp_path)
     hp = kerastuner.HyperParameters()
     trial = mock.Mock()
     trial.hyperparameters = hp
@@ -31,26 +30,14 @@ def test_add_early_stopping(fit_fn, base_tuner_search, init, tmp_path):
                 for callback in callbacks])
 
 
-@mock.patch('kerastuner.engine.base_tuner.BaseTuner.__init__')
-def test_overwrite_init(base_tuner_init, tmp_path):
-    tuner_module.AutoTuner(
-        oracle=mock.Mock(),
-        hypermodel=lambda hp: None,
-        directory=tmp_path)
-
-    assert not base_tuner_init.call_args_list[0][1]['overwrite']
-
-
-@mock.patch('kerastuner.engine.base_tuner.BaseTuner.__init__')
 @mock.patch('kerastuner.engine.base_tuner.BaseTuner.search')
 @mock.patch('tensorflow.keras.Model.fit')
-def test_overwrite_search(fit_fn, base_tuner_search, init, tmp_path):
+def test_overwrite_search(fit_fn, base_tuner_search, tmp_path):
     graph = utils.build_graph()
-    tuner = tuner_module.AutoTuner(oracle=mock.Mock(), hypermodel=graph)
-    tuner.hypermodel = graph
-    tuner.oracle = mock.Mock()
-    tuner.directory = tmp_path
-    tuner.project_name = ''
+    tuner = tuner_module.AutoTuner(
+        oracle=greedy.GreedyOracle(graph, objective='val_loss'),
+        hypermodel=graph,
+        directory=tmp_path)
     hp = kerastuner.HyperParameters()
     trial = mock.Mock()
     trial.hyperparameters = hp