def test_include_top_false():
hp = hp_module.HyperParameters()
hypermodel = xception.HyperXception(input_shape=(256, 256, 3),
classes=10,
include_top=False)
model = hypermodel.build(hp)
assert not model.optimizer
def test_input_tensor():
hp = hp_module.HyperParameters()
inputs = tf.keras.Input((256, 256, 3))
hypermodel = xception.HyperXception(
input_tensor=inputs, include_top=False)
model = hypermodel.build(hp)
assert model.inputs == [inputs]
def test_hyperparameter_override():
hp = hp_module.HyperParameters()
hp.Choice("pooling", ["flatten"])
hp.Choice("num_dense_layers", [2])
hypermodel = xception.HyperXception(input_shape=(256, 256, 3), classes=10)
hypermodel.build(hp)
assert hp.get("pooling") == "flatten"
assert hp.get("num_dense_layers") == 2
def test_hyperparameter_override():
hp = hp_module.HyperParameters()
hp.Choice('pooling', ['flatten'])
hp.Choice('num_dense_layers', [2])
hypermodel = xception.HyperXception(input_shape=(256, 256, 3), classes=10)
hypermodel.build(hp)
assert hp.get('pooling') == 'flatten'
assert hp.get('num_dense_layers') == 2
def test_model_construction(pooling):
hp = hp_module.HyperParameters()
hp.Choice("pooling", [pooling])
hypermodel = xception.HyperXception(input_shape=(128, 128, 3), classes=10)
model = hypermodel.build(hp)
assert hp.values["pooling"] == pooling
assert model.layers
assert model.name == "Xception"
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_hyperparameter_existence_and_defaults():
hp = hp_module.HyperParameters()
hypermodel = xception.HyperXception(input_shape=(256, 256, 3), classes=10)
hypermodel.build(hp)
assert hp.values == {
"activation": "relu",
"conv2d_num_filters": 64,
"kernel_size": 3,
"initial_strides": 2,
"sep_num_filters": 256,
"num_residual_blocks": 4,
"pooling": "avg",
"num_dense_layers": 1,
"dropout_rate": 0.5,
"dense_use_bn": True,
"learning_rate": 1e-3,
}
def test_hyperparameter_existence_and_defaults():
hp = hp_module.HyperParameters()
hypermodel = xception.HyperXception(input_shape=(256, 256, 3), classes=10)
hypermodel.build(hp)
assert hp.values == {
'activation': 'relu',
'conv2d_num_filters': 64,
'kernel_size': 3,
'initial_strides': 2,
'sep_num_filters': 256,
'num_residual_blocks': 4,
'pooling': 'avg',
'num_dense_layers': 1,
'dropout_rate': 0.5,
'dense_use_bn': True,
'learning_rate': 1e-3
}
