def test_image_block():
block = hyperblock_module.ImageBlock(normalize=None, augment=None)
block.set_state(block.get_state())
hp = kerastuner.HyperParameters()
block.build(hp, ak.Input())
assert common.name_in_hps('block_type', hp)
assert common.name_in_hps('normalize', hp)
assert common.name_in_hps('augment', hp)
def test_dense_block():
input_shape = (32,)
block = block_module.DenseBlock()
block.set_state(block.get_state())
hp = kerastuner.HyperParameters()
block.build(hp, ak.Input(shape=input_shape).build())
assert common.name_in_hps('num_layers', hp)
assert common.name_in_hps('use_batchnorm', hp)
def test_rnn_block():
input_shape = (32, 10)
block = block_module.RNNBlock()
block.set_state(block.get_state())
hp = kerastuner.HyperParameters()
block.build(hp, ak.Input(shape=input_shape).build())
assert common.name_in_hps('bidirectional', hp)
assert common.name_in_hps('layer_type', hp)
assert common.name_in_hps('num_layers', hp)
def test_conv_block():
input_shape = (32, 32, 3)
block = block_module.ConvBlock()
block.set_state(block.get_state())
hp = kerastuner.HyperParameters()
block.build(hp, ak.Input(shape=input_shape).build())
assert common.name_in_hps('kernel_size', hp)
assert common.name_in_hps('num_blocks', hp)
assert common.name_in_hps('separable', hp)
def test_embedding_block():
input_shape = (32,)
block = block_module.EmbeddingBlock()
block.max_features = 100
block.set_state(block.get_state())
hp = kerastuner.HyperParameters()
block.build(hp, ak.Input(shape=input_shape).build())
assert common.name_in_hps('pretraining', hp)
assert common.name_in_hps('embedding_dim', hp)
def test_resnet_block(init, build):
input_shape = (32, 32, 3)
block = block_module.ResNetBlock()
block.set_state(block.get_state())
hp = kerastuner.HyperParameters()
block.build(hp, ak.Input(shape=input_shape).build())
assert common.name_in_hps('version', hp)
assert common.name_in_hps('pooling', hp)
assert init.called
assert build.called
def test_xception_block(init, build):
input_shape = (32, 32, 3)
block = block_module.XceptionBlock()
block.set_state(block.get_state())
hp = kerastuner.HyperParameters()
block.build(hp, ak.Input(shape=input_shape).build())
assert common.name_in_hps('activation', hp)
assert common.name_in_hps('initial_strides', hp)
assert common.name_in_hps('num_residual_blocks', hp)
assert common.name_in_hps('pooling', hp)
assert init.called
assert build.called
def test_embedding_block_with_pretraining(get_file, load_embedding_index):
load_embedding_index.return_value = {'test': np.ones((100, ))}
get_file.return_value = ''
input_shape = (32, )
block = block_module.EmbeddingBlock(pretraining='glove')
block.max_features = 2
block.word_index = {'test': 1}
block.set_state(block.get_state())
hp = kerastuner.HyperParameters()
block.build(hp, ak.Input(shape=input_shape).build())
embedding_matrix = block._build_embedding_matrix('glove')
assert np.array_equal(embedding_matrix[1], np.ones((100, )))
assert not common.name_in_hps('pretraining', hp)
assert not common.name_in_hps('embedding_dim', hp)
def test_text_block():
block = hyperblock_module.TextBlock()
block.set_state(block.get_state())
hp = kerastuner.HyperParameters()
block.build(hp, ak.TextInput())
assert common.name_in_hps('vectorizer', hp)
def test_spatial_reduction():
input_shape = (32, 32, 3)
block = block_module.SpatialReduction()
block.set_state(block.get_state())
hp = kerastuner.HyperParameters()
block.build(hp, ak.Input(shape=input_shape).build())
assert common.name_in_hps('reduction_type', hp)
def test_structured_data_block():
block = hyperblock_module.StructuredDataBlock()
block.num_heads = 1
block.set_state(block.get_state())
hp = kerastuner.HyperParameters()
block.build(hp, ak.Input())
assert common.name_in_hps('block_type', hp)
def test_structured_data_block():
block = hyperblock_module.StructuredDataBlock()
block.heads = [ak.ClassificationHead()]
block.set_state(block.get_state())
hp = kerastuner.HyperParameters()
block.build(hp, ak.Input())
assert common.name_in_hps('module_type', hp)
def test_temporal_reduction():
input_shape = (32, 10)
block = block_module.TemporalReduction()
block.set_config(block.get_config())
hp = kerastuner.HyperParameters()
block.build(hp, ak.Input(shape=input_shape).build())
assert common.name_in_hps('reduction_type', hp)
def test_merge():
input_shape_1 = (32,)
input_shape_2 = (4, 8)
block = block_module.Merge()
block.set_state(block.get_state())
hp = kerastuner.HyperParameters()
block.build(hp, [ak.Input(shape=input_shape_1).build(),
ak.Input(shape=input_shape_2).build()])
assert common.name_in_hps('merge_type', hp)
