def test_image_block():
block = wrapper.ImageBlock(normalize=None, augment=None)
hp = kerastuner.HyperParameters()
block.build(hp, ak.ImageInput(shape=(32, 32, 3)).build())
assert utils.name_in_hps('block_type', hp)
assert utils.name_in_hps('normalize', hp)
assert utils.name_in_hps('augment', hp)
def test_dense_block():
input_shape = (32,)
block = basic.DenseBlock()
hp = kerastuner.HyperParameters()
block.build(hp, ak.Input(shape=input_shape).build())
assert utils.name_in_hps('num_layers', hp)
assert utils.name_in_hps('use_batchnorm', hp)
def test_embedding_block():
input_shape = (32,)
block = basic.Embedding()
block.max_features = 100
hp = kerastuner.HyperParameters()
block.build(hp, ak.Input(shape=input_shape).build())
assert utils.name_in_hps('pretraining', hp)
assert utils.name_in_hps('embedding_dim', hp)
def test_rnn_block():
input_shape = (32, 10)
block = basic.RNNBlock()
hp = kerastuner.HyperParameters()
block.build(hp, ak.Input(shape=input_shape).build())
assert utils.name_in_hps('bidirectional', hp)
assert utils.name_in_hps('layer_type', hp)
assert utils.name_in_hps('num_layers', hp)
def test_conv_block():
input_shape = (32, 32, 3)
block = basic.ConvBlock()
hp = kerastuner.HyperParameters()
block.build(hp, ak.Input(shape=input_shape).build())
assert utils.name_in_hps('kernel_size', hp)
assert utils.name_in_hps('num_blocks', hp)
assert utils.name_in_hps('separable', hp)
def test_imag_augmentation():
input_shape = (32, 32, 3)
block = preprocessing.ImageAugmentation()
hp = kerastuner.HyperParameters()
block = graph_module.deserialize(graph_module.serialize(block))
block.build(hp, ak.Input(shape=input_shape).build())
assert utils.name_in_hps('vertical_flip', hp)
assert utils.name_in_hps('horizontal_flip', hp)
def test_resnet_block(init, build):
input_shape = (32, 32, 3)
block = basic.ResNetBlock()
hp = kerastuner.HyperParameters()
block.build(hp, ak.Input(shape=input_shape).build())
assert utils.name_in_hps('version', hp)
assert utils.name_in_hps('pooling', hp)
assert init.called
assert build.called
def test_xception_block(init, build):
input_shape = (32, 32, 3)
block = basic.XceptionBlock()
hp = kerastuner.HyperParameters()
block.build(hp, ak.Input(shape=input_shape).build())
assert utils.name_in_hps('activation', hp)
assert utils.name_in_hps('initial_strides', hp)
assert utils.name_in_hps('num_residual_blocks', hp)
assert utils.name_in_hps('pooling', hp)
assert init.called
assert build.called
def test_text_block():
block = wrapper.TextBlock()
hp = kerastuner.HyperParameters()
block.build(hp, ak.TextInput(shape=(1, )).build())
assert utils.name_in_hps('vectorizer', hp)
def test_spatial_reduction():
input_shape = (32, 32, 3)
block = reduction.SpatialReduction()
hp = kerastuner.HyperParameters()
block.build(hp, ak.Input(shape=input_shape).build())
assert utils.name_in_hps('reduction_type', hp)
def test_temporal_reduction():
input_shape = (32, 10)
block = reduction.TemporalReduction()
hp = kerastuner.HyperParameters()
block = graph_module.deserialize(graph_module.serialize(block))
block.build(hp, ak.Input(shape=input_shape).build())
assert utils.name_in_hps('reduction_type', hp)
def test_merge():
input_shape_1 = (32, )
input_shape_2 = (4, 8)
block = reduction.Merge()
hp = kerastuner.HyperParameters()
block.build(hp, [
ak.Input(shape=input_shape_1).build(),
ak.Input(shape=input_shape_2).build()
])
assert utils.name_in_hps('merge_type', hp)
