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]