def test_multitask_two_modules(self):
"""Accept a different representation for each task"""
edges = []
cards = [2, 2]
tg = TaskGraph(cards, edges)
em = MTEndModel(
layer_out_dims=[2, 8, 4],
task_graph=tg,
seed=1,
verbose=False,
input_modules=[IdentityModule(),
IdentityModule()],
task_head_layers="top",
)
Xs = []
for i, X in enumerate(self.Xs):
Xs.append([X[:, 0], X[:, 1]])
em.train_model(
(Xs[0], self.Ys[0]),
valid_data=(Xs[1], self.Ys[1]),
verbose=False,
n_epochs=10,
checkpoint=False,
)
score = em.score((Xs[2], self.Ys[2]), reduce="mean", verbose=False)
self.assertGreater(score, 0.95)
def test_multitask_custom_attachments(self):
"""Attach the task heads at user-specified layers"""
edges = [(0, 1)]
cards = [2, 2]
tg = TaskHierarchy(cards, edges)
em = MTEndModel(
layer_out_dims=[2, 8, 4],
task_graph=tg,
seed=1,
verbose=False,
task_head_layers=[1, 2],
)
self.assertEqual(em.task_map[1][0], 0)
self.assertEqual(em.task_map[2][0], 1)
em.train_model(
(self.Xs[0], self.Ys[0]),
valid_data=(self.Xs[1], self.Ys[1]),
verbose=False,
n_epochs=10,
checkpoint=False,
)
score = em.score((self.Xs[2], self.Ys[2]),
reduce="mean",
verbose=False)
self.assertGreater(score, 0.95)
def test_multitask_top(self):
"""Attach all task heads to the top layer"""
edges = []
cards = [2, 2]
tg = TaskGraph(cards, edges)
em = MTEndModel(
layer_out_dims=[2, 8, 4],
task_graph=tg,
seed=1,
verbose=False,
task_head_layers="top",
)
top_layer = len(em.config["layer_out_dims"]) - 1
self.assertEqual(len(em.task_map[top_layer]), em.t)
em.train_model(
(self.Xs[0], self.Ys[0]),
valid_data=(self.Xs[1], self.Ys[1]),
verbose=False,
n_epochs=10,
checkpoint=False,
)
score = em.score((self.Xs[2], self.Ys[2]),
reduce="mean",
verbose=False)
self.assertGreater(score, 0.95)
def test_multitask_custom_heads(self):
"""Accept a different representation for each task"""
edges = []
cards = [2, 2]
tg = TaskGraph(cards, edges)
em = MTEndModel(
layer_out_dims=[2, 8, 4],
task_graph=tg,
seed=1,
verbose=False,
dropout=0.0,
head_modules=[nn.Linear(8, 2), nn.Linear(4, 2)],
task_head_layers=[1, 2],
)
em.train_model(
(self.Xs[0], self.Ys[0]),
dev_data=(self.Xs[1], self.Ys[1]),
verbose=False,
n_epochs=10,
)
score = em.score((self.Xs[2], self.Ys[2]), reduce="mean", verbose=False)
self.assertGreater(score, 0.95)
def test_multitask_custom(self):
edges = [(0, 1)]
cards = [2, 2]
tg = TaskHierarchy(edges, cards)
em = MTEndModel(
task_graph=tg,
seed=1,
verbose=False,
dropout=0.0,
layer_output_dims=[2, 8, 4],
task_head_layers=[1, 2],
)
em.train(
self.Xs[0],
self.Ys[0],
self.Xs[1],
self.Ys[1],
verbose=False,
n_epochs=10,
)
score = em.score(self.Xs[2], self.Ys[2], reduce='mean', verbose=False)
self.assertGreater(score, 0.95)
def test_scoring(self):
edges = [(0, 1)]
cards = [2, 2]
tg = TaskHierarchy(cards, edges)
em = MTEndModel(layer_out_dims=[2, 8, 4],
task_graph=tg,
seed=1,
verbose=False)
em.train_model(
(self.Xs[0], self.Ys[0]),
valid_data=(self.Xs[1], self.Ys[1]),
verbose=False,
n_epochs=3,
checkpoint=False,
validation_task=0,
)
tasks = [0, 1]
for metric in METRICS:
all_scores = em.score((self.Xs[2], self.Ys[2]),
metric=metric,
reduce=None,
verbose=False)
task_specific_scores_score_method = [
em.score(
(self.Xs[2], self.Ys[2]),
metric=metric,
validation_task=task,
verbose=False,
) for task in tasks
]
task_specific_scores_score_task_method = [
em.score_task(self.Xs[2],
self.Ys[2],
t=task,
metric=metric,
verbose=False) for task in tasks
]
for i in range(len(tasks)):
self.assertEqual(
all_scores[i],
task_specific_scores_score_method[i],
task_specific_scores_score_task_method[i],
)
