test task specific tuners (#993)

* update

* bug fix

autokeras/tuners/task_specific.py

@@ -4,29 +4,29 @@
     'image_block_1/block_type': 'vanilla',
     'image_block_1/normalize': True,
     'image_block_1/augment': False,
-    'image_block_1/vanilla_1/kernel_size': 3,
-    'image_block_1/vanilla_1/num_blocks': 1,
-    'image_block_1/vanilla_1/num_layers': 2,
-    'image_block_1/vanilla_1/max_pooling': True,
-    'image_block_1/vanilla_1/separable': False,
-    'image_block_1/vanilla_1/dropout_rate': 0.25,
-    'image_block_1/vanilla_1/filters_0_0': 32,
-    'image_block_1/vanilla_1/filters_0_1': 64,
-    'spatial_reduction_1/reduction_type': 'flatten',
+    'image_block_1/conv_block_1/kernel_size': 3,
+    'image_block_1/conv_block_1/num_blocks': 1,
+    'image_block_1/conv_block_1/num_layers': 2,
+    'image_block_1/conv_block_1/max_pooling': True,
+    'image_block_1/conv_block_1/separable': False,
+    'image_block_1/conv_block_1/dropout_rate': 0.25,
+    'image_block_1/conv_block_1/filters_0_0': 32,
+    'image_block_1/conv_block_1/filters_0_1': 64,
     'dense_block_1/num_layers': 1,
     'dense_block_1/use_batchnorm': False,
     'dense_block_1/dropout_rate': 0,
     'dense_block_1/units_0': 128,
+    'classification_head_1/spatial_reduction_1/reduction_type': 'flatten',
     'classification_head_1/dropout_rate': 0.5,
     'optimizer': 'adam'
 }, {
     'image_block_1/block_type': 'resnet',
     'image_block_1/normalize': True,
     'image_block_1/augment': True,
-    'image_block_1/resnet_1/version': 'v2',
-    'image_block_1/resnet_1/pooling': 'avg',
-    'image_block_1/resnet_1/conv3_depth': 4,
-    'image_block_1/resnet_1/conv4_depth': 6,
+    'image_block_1/res_net_block_1/version': 'v2',
+    'image_block_1/res_net_block_1/pooling': 'avg',
+    'image_block_1/res_net_block_1/conv3_depth': 4,
+    'image_block_1/res_net_block_1/conv4_depth': 6,
     'dense_block_1/num_layers': 2,
     'dense_block_1/use_batchnorm': False,
     'dense_block_1/dropout_rate': 0,

setup.py

@@ -8,17 +8,16 @@
 
 setup(
     name='autokeras',
-    version='1.0.1',
+    version='1.0.2',
     description='AutoML for deep learning',
     package_data={'': ['README.md']},
     long_description=readme.read_text(encoding='utf-8'),
     long_description_content_type='text/markdown',
     author='Data Analytics at Texas A&M (DATA) Lab, Keras Team',
     author_email='[email protected]',
     url='http://autokeras.com',
-    download_url='https://github.com/keras-team/autokeras/archive/1.0.1.tar.gz',
-    keywords=['AutoML', 'keras'],
-    # TODO: Do not install tensorflow if tensorflow-gpu is installed.
+    download_url='https://github.com/keras-team/autokeras/archive/1.0.2.tar.gz',
+    keywords=['AutoML', 'Keras'],
     install_requires=[
         'packaging',
         'keras-tuner>=1.0.1',

tests/autokeras/tuners/task_specific_test.py

@@ -0,0 +1,48 @@
+import copy
+
+import kerastuner
+import tensorflow as tf
+
+import autokeras as ak
+from autokeras import graph as graph_module
+from autokeras.tuners import task_specific
+
+
+def check_initial_hp(initial_hp, graph):
+    hp = kerastuner.HyperParameters()
+    hp.values = copy.copy(initial_hp)
+    graph.build(hp)
+    assert hp.values == initial_hp
+
+
+def test_image_classifier_tuner0():
+    tf.keras.backend.clear_session()
+    input_node = ak.ImageInput(shape=(32, 32, 3))
+    output_node = ak.ImageBlock()(input_node)
+    output_node = ak.ClassificationHead(
+        loss='categorical_crossentropy',
+        output_shape=(10,))(output_node)
+    graph = graph_module.Graph(input_node, output_node)
+    check_initial_hp(task_specific.IMAGE_CLASSIFIER[0], graph)
+
+
+def test_image_classifier_tuner1():
+    tf.keras.backend.clear_session()
+    input_node = ak.ImageInput(shape=(32, 32, 3))
+    output_node = ak.ImageBlock()(input_node)
+    output_node = ak.ClassificationHead(
+        loss='categorical_crossentropy',
+        output_shape=(10,))(output_node)
+    graph = graph_module.Graph(input_node, output_node)
+    check_initial_hp(task_specific.IMAGE_CLASSIFIER[1], graph)
+
+
+def test_text_classifier_tuner0():
+    tf.keras.backend.clear_session()
+    input_node = ak.TextInput(shape=(1,))
+    output_node = ak.TextBlock()(input_node)
+    output_node = ak.ClassificationHead(
+        loss='categorical_crossentropy',
+        output_shape=(10,))(output_node)
+    graph = graph_module.Graph(input_node, output_node)
+    check_initial_hp(task_specific.TEXT_CLASSIFIER[0], graph)