[MRG] Created MlpModule (#301)

* Created MlpModule

* Changed generators to a list

* Changed name of subclass while calling super

* Fix for failed test cases

autokeras/image/image_supervised.py

@@ -7,7 +7,7 @@
 import torch
 from sklearn.model_selection import train_test_split
 
-from autokeras.cnn_module import CnnModule
+from autokeras.net_module import CnnModule
 from autokeras.constant import Constant
 from autokeras.nn.loss_function import classification_loss, regression_loss
 from autokeras.nn.metric import Accuracy, MSE

autokeras/cnn_module.py → autokeras/net_module.py

@@ -4,16 +4,18 @@
 from autokeras.constant import Constant
 from autokeras.search import Searcher, train
 from autokeras.utils import pickle_to_file
+from autokeras.nn.generator import CnnGenerator, MlpGenerator
 
 
-class CnnModule(object):
+class NetworkModule:
     def __init__(self, loss, metric, searcher_args, path, verbose=False):
         self.searcher_args = searcher_args
         self.searcher = None
         self.path = path
         self.verbose = verbose
         self.loss = loss
         self.metric = metric
+        self.generators = []
 
     def fit(self, n_output_node, input_shape, train_data, test_data, time_limit=24 * 60 * 60):
         """ Search the best CnnModule.
@@ -34,6 +36,7 @@ def fit(self, n_output_node, input_shape, train_data, test_data, time_limit=24 *
             self.searcher_args['path'] = self.path
             self.searcher_args['metric'] = self.metric
             self.searcher_args['loss'] = self.loss
+            self.searcher_args['generators'] = self.generators
             self.searcher_args['verbose'] = self.verbose
             self.searcher = Searcher(**self.searcher_args)
             pickle_to_file(self, os.path.join(self.path, 'module'))
@@ -85,3 +88,15 @@ def final_fit(self, train_data, test_data, trainer_args=None, retrain=False):
     @property
     def best_model(self):
         return self.searcher.load_best_model()
+
+
+class CnnModule(NetworkModule):
+    def __init__(self, loss, metric, searcher_args, path, verbose=False):
+        super(CnnModule, self).__init__(loss, metric, searcher_args, path, verbose)
+        self.generators.append(CnnGenerator)
+
+
+class MlpModule(NetworkModule):
+    def __init__(self, loss, metric, searcher_args, path, verbose=False):
+        super(MlpModule, self).__init__(loss, metric, searcher_args, path, verbose)
+        self.generators.append(MlpGenerator)

autokeras/search.py

@@ -7,8 +7,8 @@
 
 from autokeras.bayesian import edit_distance, BayesianOptimizer
 from autokeras.constant import Constant
+from autokeras.nn.generator import CnnGenerator, MlpGenerator
 from autokeras.net_transformer import default_transform
-from autokeras.nn.generator import CnnGenerator
 from autokeras.nn.model_trainer import ModelTrainer
 from autokeras.utils import pickle_to_file, pickle_from_file, verbose_print, get_system
 
@@ -42,7 +42,7 @@ class Searcher:
         t_min: A float. The minimum temperature during simulated annealing.
     """
 
-    def __init__(self, n_output_node, input_shape, path, metric, loss, verbose,
+    def __init__(self, n_output_node, input_shape, path, metric, loss, generators, verbose,
                  trainer_args=None,
                  default_model_len=Constant.MODEL_LEN,
                  default_model_width=Constant.MODEL_WIDTH,
@@ -72,6 +72,7 @@ def __init__(self, n_output_node, input_shape, path, metric, loss, verbose,
         self.metric = metric
         self.loss = loss
         self.path = path
+        self.generators = generators
         self.model_count = 0
         self.descriptors = []
         self.trainer_args = trainer_args
@@ -145,18 +146,20 @@ def add_model(self, metric_value, loss, graph, model_id):
     def init_search(self):
         if self.verbose:
             print('\nInitializing search.')
-        graph = CnnGenerator(self.n_classes,
-                             self.input_shape).generate(self.default_model_len,
-                                                        self.default_model_width)
-        model_id = self.model_count
-        self.model_count += 1
-        self.training_queue.append((graph, -1, model_id))
-        self.descriptors.append(graph.extract_descriptor())
-        for child_graph in default_transform(graph):
-            child_id = self.model_count
+        graph, model_id = None, None
+        for generator in self.generators:
+            graph = generator(self.n_classes, self.input_shape).\
+                generate(self.default_model_len, self.default_model_width)
+            model_id = self.model_count
             self.model_count += 1
-            self.training_queue.append((child_graph, model_id, child_id))
-            self.descriptors.append(child_graph.extract_descriptor())
+            self.training_queue.append((graph, -1, model_id))
+            self.descriptors.append(graph.extract_descriptor())
+        if graph is not None and model_id is not None:
+            for child_graph in default_transform(graph):
+                child_id = self.model_count
+                self.model_count += 1
+                self.training_queue.append((child_graph, model_id, child_id))
+                self.descriptors.append(child_graph.extract_descriptor())
         if self.verbose:
             print('Initialization finished.')
 

autokeras/text/text_supervised.py

@@ -6,7 +6,7 @@
 import torch
 from sklearn.model_selection import train_test_split
 
-from autokeras.cnn_module import CnnModule
+from autokeras.net_module import CnnModule
 from autokeras.constant import Constant
 from autokeras.nn.loss_function import classification_loss, regression_loss
 from autokeras.nn.metric import Accuracy, MSE

tests/test_preprocessor.py

@@ -1,6 +1,6 @@
 from unittest.mock import patch
 
-from autokeras.cnn_module import CnnModule
+from autokeras.net_module import CnnModule
 from autokeras.nn.loss_function import classification_loss
 from autokeras.nn.metric import Accuracy
 from autokeras.preprocessor import *

tests/test_search.py

@@ -3,6 +3,7 @@
 from autokeras.nn.loss_function import classification_loss
 from autokeras.nn.metric import Accuracy
 from autokeras.search import *
+from autokeras.nn.generator import CnnGenerator
 
 from tests.common import clean_dir, MockProcess, get_classification_data_loaders, get_add_skip_model, \
     get_concat_skip_model, simple_transform, MockMemoryOutProcess, TEST_TEMP_DIR
@@ -19,7 +20,7 @@ def test_bayesian_searcher(_, _1, _2):
     train_data, test_data = get_classification_data_loaders()
     clean_dir(TEST_TEMP_DIR)
     generator = Searcher(3, (28, 28, 3), verbose=False, path=TEST_TEMP_DIR, metric=Accuracy,
-                         loss=classification_loss)
+                         loss=classification_loss, generators=[CnnGenerator])
     Constant.N_NEIGHBOURS = 1
     Constant.T_MIN = 0.8
     for _ in range(2):
@@ -44,7 +45,7 @@ def test_export_json(_, _1, _2):
 
     clean_dir(TEST_TEMP_DIR)
     generator = Searcher(3, (28, 28, 3), verbose=False, path=TEST_TEMP_DIR, metric=Accuracy,
-                         loss=classification_loss)
+                         loss=classification_loss, generators=[CnnGenerator])
     Constant.N_NEIGHBOURS = 1
     Constant.T_MIN = 0.8
     for _ in range(3):
@@ -75,7 +76,7 @@ def test_max_acq(_, _1, _2):
     Constant.T_MIN = 0.8
     Constant.BETA = 1
     generator = Searcher(3, (28, 28, 3), verbose=False, path=TEST_TEMP_DIR, metric=Accuracy,
-                         loss=classification_loss)
+                         loss=classification_loss, generators=[CnnGenerator])
     for _ in range(3):
         generator.search(train_data, test_data)
     for index1, descriptor1 in enumerate(generator.descriptors):
@@ -92,7 +93,7 @@ def test_out_of_memory(_, _1, _2):
     train_data, test_data = get_classification_data_loaders()
     clean_dir(TEST_TEMP_DIR)
     searcher = Searcher(3, (28, 28, 3), verbose=False, path=TEST_TEMP_DIR, metric=Accuracy,
-                        loss=classification_loss)
+                        loss=classification_loss, generators=[CnnGenerator])
     Constant.N_NEIGHBOURS = 1
     Constant.T_MIN = 0.8
     for _ in range(4):