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)