def test_include_top_false():
hp = hp_module.HyperParameters()
hypermodel = resnet.HyperResNet(
input_shape=(256, 256, 3), classes=10, include_top=False)
model = hypermodel.build(hp)
# Check that model wasn't compiled.
assert not model.optimizer
def test_hyperparameter_override():
hp = hp_module.HyperParameters()
hp.Choice('version', ['v1'])
hypermodel = resnet.HyperResNet(input_shape=(256, 256, 3), classes=10)
model = hypermodel.build(hp)
assert hp.get('version') == 'v1'
assert hp.get('v1/conv3_depth') == 4
assert hp.get('v1/conv4_depth') == 6
def test_hyperparameter_existence_and_defaults():
hp = hp_module.HyperParameters()
hypermodel = resnet.HyperResNet(input_shape=(256, 256, 3), classes=10)
model = hypermodel.build(hp)
assert hp.get('version') == 'v2'
assert hp.get('v2/conv3_depth') == 4
assert hp.get('v2/conv4_depth') == 6
assert hp.get('learning_rate') == 0.01
assert hp.get('pooling') == 'avg'
def test_hyperparameter_override():
hp = hp_module.HyperParameters()
hp.Choice("version", ["v1"])
hp.Fixed("conv3_depth", 10)
hypermodel = resnet.HyperResNet(input_shape=(256, 256, 3), classes=10)
hypermodel.build(hp)
assert hp.get("version") == "v1"
assert hp.get("conv3_depth") == 10
assert hp.get("conv4_depth") == 6
def test_hyperparameter_existence_and_defaults():
hp = hp_module.HyperParameters()
hypermodel = resnet.HyperResNet(input_shape=(256, 256, 3), classes=10)
hypermodel.build(hp)
assert hp.get("version") == "v2"
assert hp.get("conv3_depth") == 4
assert hp.get("conv4_depth") == 6
assert hp.get("learning_rate") == 0.01
assert hp.get("pooling") == "avg"
def test_model_construction(version):
hp = hp_module.HyperParameters()
hp.Choice('version', [version])
hypermodel = resnet.HyperResNet(input_shape=(128, 128, 3), classes=10)
model = hypermodel.build(hp)
assert hp.values['version'] == version
assert model.layers
assert model.name == 'ResNet'
assert model.output_shape == (None, 10)
model.train_on_batch(np.ones((1, 128, 128, 3)), np.ones((1, 10)))
out = model.predict(np.ones((1, 128, 128, 3)))
assert out.shape == (1, 10)
def test_input_tensor():
hp = hp_module.HyperParameters()
inputs = tf.keras.Input(shape=(256, 256, 3))
hypermodel = resnet.HyperResNet(input_tensor=inputs, include_top=False)
model = hypermodel.build(hp)
assert model.inputs == [inputs]