def test_two_classes():
y = np.array(['a', 'a', 'a', 'b'])
head = head_module.ClassificationHead(name='a')
adapter = head.get_adapter()
adapter.fit_transform(y)
head.config_from_adapter(adapter)
head.output_shape = (1,)
head.build(kerastuner.HyperParameters(), input_module.Input(shape=(32,)).build())
assert head.loss.name == 'binary_crossentropy'
def test_three_classes_infer_categorical_crossentropy():
dataset = np.array(["a", "a", "c", "b"])
head = head_module.ClassificationHead(name="a", shape=(1,))
adapter = head.get_adapter()
dataset = adapter.adapt(dataset, batch_size=32)
analyser = head.get_analyser()
for data in dataset:
analyser.update(data)
analyser.finalize()
head.config_from_analyser(analyser)
head.build(
keras_tuner.HyperParameters(),
input_module.Input(shape=(32,)).build_node(keras_tuner.HyperParameters()),
)
assert head.loss.name == "categorical_crossentropy"
def test_input_get_block_return_general_block():
input_node = nodes.Input()
assert isinstance(input_node.get_block(), blocks.GeneralBlock)
