def test_edit_distance2():
descriptor1 = get_conv_dense_model().extract_descriptor()
graph = get_conv_dense_model()
graph.to_wider_model(4, 6)
graph.to_wider_model(9, 3)
descriptor2 = graph.extract_descriptor()
assert edit_distance(descriptor1, descriptor2) == 2.0 / 9
def test_edit_distance2():
descriptor1 = get_conv_dense_model().extract_descriptor()
graph = get_conv_dense_model()
graph.to_wider_model(4, 6)
graph.to_wider_model(9, 3)
descriptor2 = graph.extract_descriptor()
assert edit_distance(descriptor1, descriptor2) == 2.0 / 9
def test_edit_distance2():
descriptor1 = get_conv_dense_model().extract_descriptor()
graph = get_conv_dense_model()
graph.to_conv_deeper_model(1, 3)
graph.to_wider_model(5, 6)
graph.to_wider_model(17, 3)
descriptor2 = graph.extract_descriptor()
assert edit_distance(descriptor1, descriptor2, 1.0) == 1.5
def test_dense_deeper_stub():
graph = get_conv_dense_model()
graph.weighted = False
layer_num = graph.n_layers
graph.to_dense_deeper_model(9)
assert graph.n_layers == layer_num + 2
def test_produce_keras_model():
for graph in [get_conv_dense_model(),
get_add_skip_model(),
get_pooling_model(),
get_concat_skip_model()]:
model = graph.produce_keras_model()
assert isinstance(model, keras.models.Model)
def test_dense_deeper_stub():
model = get_conv_dense_model()
graph = Graph(model, False)
layer_num = graph.n_layers
graph.to_dense_deeper_model(5)
assert graph.n_layers == layer_num + 2
def test_dense_deeper_stub():
model = to_stub_model(get_conv_dense_model())
graph = Graph(model)
layer_num = graph.n_layers
graph.to_dense_deeper_model(graph.layer_to_id[model.layers[5]])
assert graph.n_layers == layer_num + 1
def test_dense_deeper_stub():
graph = get_conv_dense_model()
graph.weighted = False
layer_num = graph.n_layers
graph.to_dense_deeper_model(10)
assert graph.n_layers == layer_num + 3
def test_keras_model():
for graph in [get_conv_dense_model(),
get_add_skip_model(),
get_pooling_model(),
get_concat_skip_model()]:
keras_model = KerasModel(graph)
keras_model.set_weight_to_graph()
assert isinstance(keras_model, KerasModel)
def test_produce_keras_model():
for graph in [
get_conv_dense_model(),
get_add_skip_model(),
get_pooling_model(),
get_concat_skip_model()
]:
model = graph.produce_keras_model()
assert isinstance(model, keras.models.Model)
def test_dense_deeper():
model = get_conv_dense_model()
graph = Graph(model)
new_model = graph.to_dense_deeper_model(model.layers[5])
input_data = get_conv_data()
output1 = model.predict_on_batch(input_data).flatten()
output2 = new_model.predict_on_batch(input_data).flatten()
assert np.sum(output1 - output2) == 0
def test_keras_model():
for graph in [
get_conv_dense_model(),
get_add_skip_model(),
get_pooling_model(),
get_concat_skip_model()
]:
keras_model = KerasModel(graph)
keras_model.set_weight_to_graph()
assert isinstance(keras_model, KerasModel)
def test_dense_deeper():
model = get_conv_dense_model()
graph = NetworkMorphismGraph(model)
graph.to_dense_deeper_model(graph.layer_to_id[model.layers[5]])
new_model = graph.produce_model()
input_data = get_conv_data()
output1 = model.predict_on_batch(input_data).flatten()
output2 = new_model.predict_on_batch(input_data).flatten()
assert np.sum(np.abs(output1 - output2)) < 1e-4
def test_dense_deeper():
model = get_conv_dense_model()
graph = Graph(model, True)
graph.to_dense_deeper_model(5)
new_model = graph.produce_model()
input_data = get_conv_data()
output1 = model.predict_on_batch(input_data).flatten()
output2 = new_model.predict_on_batch(input_data).flatten()
assert np.sum(np.abs(output1 - output2)) < 1e-4
def test_dense_deeper():
graph = get_conv_dense_model()
model = graph.produce_model()
graph = deepcopy(graph)
graph.to_dense_deeper_model(10)
new_model = graph.produce_model()
input_data = torch.Tensor(get_conv_data())
model.eval()
new_model.eval()
output1 = model(input_data)
output2 = new_model(input_data)
assert (output1 - output2).abs().sum() < 1e-3
def test_dense_deeper():
graph = get_conv_dense_model()
model = graph.produce_model()
graph = deepcopy(graph)
graph.to_dense_deeper_model(9)
new_model = graph.produce_model()
input_data = torch.Tensor(get_conv_data())
model.eval()
new_model.eval()
output1 = model(input_data)
output2 = new_model(input_data)
assert (output1 - output2).abs().sum() < 1e-3
def test_deeper(_, _1):
model = to_deeper_model(get_conv_dense_model())
assert model is None
def test_conv_deeper_stub():
graph = get_conv_dense_model()
layer_num = graph.n_layers
graph.to_conv_deeper_model(4, 3)
assert graph.n_layers == layer_num + 3
def test_skip_connection_layer_ids():
graph = get_conv_dense_model()
assert len(graph.skip_connection_layer_ids()) == 1
def test_skip(_, _1):
model = to_skip_connection_model(get_conv_dense_model())
assert model is None
def test_deep_layer_ids():
graph = get_conv_dense_model()
assert len(graph.deep_layer_ids()) == 3
def test_wide_layer_ids():
graph = get_conv_dense_model()
assert len(graph.wide_layer_ids()) == 2
def test_transform(_, _1, _2, _3, _4):
models = transform(get_conv_dense_model())
assert len(models) == constant.N_NEIGHBORS
def test_wider_next_dense():
real_layer = get_conv_dense_model().layer_list[9]
layer = StubDense(real_layer.input_units, real_layer.units)
layer.set_weights(real_layer.get_weights())
new_layer = wider_next_dense(layer, 3, 3, 3)
assert new_layer.get_weights()[0].shape == (5, 6144)
def test_skip_connection_layer_ids():
model = get_conv_dense_model()
graph = Graph(model, True)
assert len(graph.skip_connection_layer_ids()) == 0
def test_wider(_):
model = to_wider_model(get_conv_dense_model())
assert model is None
def test_wide_layer_ids():
model = get_conv_dense_model()
graph = Graph(model, True)
assert len(graph.wide_layer_ids()) == 1
def test_wider_next_dense():
layer = get_conv_dense_model().layers[5]
new_layer = wider_next_dense(layer, 3, 3, 3)
assert new_layer.get_weights()[0].shape == (150, 5)
def test_deep_layer_ids():
graph = get_conv_dense_model()
assert len(graph.deep_layer_ids()) == 3
def test_wide_layer_ids():
graph = get_conv_dense_model()
assert len(graph.wide_layer_ids()) == 2
def test_skip_connection_layer_ids():
graph = get_conv_dense_model()
assert len(graph.skip_connection_layer_ids()) == 1
def test_conv_deeper_stub():
graph = get_conv_dense_model()
layer_num = graph.n_layers
graph.to_conv_deeper_model(5, 3)
assert graph.n_layers == layer_num + 4
def test_wider_next_dense():
real_layer = get_conv_dense_model().layer_list[9]
layer = StubDense(real_layer.input_units, real_layer.units)
layer.set_weights(real_layer.get_weights())
new_layer = wider_next_dense(layer, 3, 3, 3)
assert new_layer.get_weights()[0].shape == (5, 6144)
def test_deeper():
model = to_deeper_graph(get_conv_dense_model())
assert isinstance(model, Graph)
def test_wider_next_dense():
real_layer = get_conv_dense_model().layers[6]
layer = StubDense(real_layer.units, 'relu')
layer.set_weights(real_layer.get_weights())
new_layer = wider_next_dense(layer, 3, 3, 3)
assert new_layer.get_weights()[0].shape == (6, 5)