def testModel(self):
# HParams
hparams = trainer_lib.create_hparams(
"transformer_tiny",
data_dir=algorithmic.TinyAlgo.data_dir,
problem_name="tiny_algo")
# Dataset
problem = hparams.problem
dataset = problem.dataset(tf.estimator.ModeKeys.TRAIN,
algorithmic.TinyAlgo.data_dir)
dataset = dataset.repeat(None).padded_batch(10, dataset.output_shapes)
features = dataset.make_one_shot_iterator().get_next()
features = problem_lib.standardize_shapes(features)
# Model
model = registry.model("transformer")(hparams,
tf.estimator.ModeKeys.TRAIN)
logits, losses = model(features)
self.assertTrue("training" in losses)
loss = losses["training"]
with self.test_session() as sess:
sess.run(tf.global_variables_initializer())
logits_val, loss_val = sess.run([logits, loss])
logits_shape = list(logits_val.shape)
logits_shape[1] = None
self.assertAllEqual(logits_shape, [10, None, 1, 1, 4])
self.assertEqual(loss_val.shape, tuple())
def testModel(self):
# HParams
hparams = trainer_lib.create_hparams(
"transformer_tiny", data_dir=self.data_dir, problem_name="tiny_algo")
# Dataset
problem = hparams.problem
dataset = problem.dataset(tf.estimator.ModeKeys.TRAIN, self.data_dir)
dataset = dataset.repeat(None).padded_batch(10, dataset.output_shapes)
features = dataset.make_one_shot_iterator().get_next()
features = problem_lib.standardize_shapes(features)
# Model
model = registry.model("transformer")(hparams, tf.estimator.ModeKeys.TRAIN)
logits, losses = model(features)
self.assertTrue("training" in losses)
loss = losses["training"]
with self.test_session() as sess:
sess.run(tf.global_variables_initializer())
logits_val, loss_val = sess.run([logits, loss])
logits_shape = list(logits_val.shape)
logits_shape[1] = None
self.assertAllEqual(logits_shape, [10, None, 1, 1, 4])
self.assertEqual(loss_val.shape, tuple())
def testMultipleTargetModalities(self):
# Use existing hparams and override target modality.
hparams = trainer_lib.create_hparams(
"transformer_tiny",
data_dir=algorithmic.TinyAlgo.data_dir,
problem_name="tiny_algo")
# Manually turn off sharing. It is not currently supported for multitargets.
hparams.shared_embedding_and_softmax_weights = 0 # pylint: disable=line-too-long
hparams.problem_hparams.target_modality = {
"targets": hparams.problem_hparams.target_modality,
"A": hparams.problem_hparams.target_modality,
"B": hparams.problem_hparams.target_modality,
}
hparams.problem._hparams = hparams.problem_hparams
# Dataset
problem = hparams.problem
dataset = problem.dataset(tf.estimator.ModeKeys.TRAIN,
algorithmic.TinyAlgo.data_dir)
dataset = dataset.repeat(None).padded_batch(10, dataset.output_shapes)
features = dataset.make_one_shot_iterator().get_next()
features = problem_lib.standardize_shapes(features)
features["A"] = features["B"] = features["targets"]
# Model
model = registry.model("transformer")(hparams,
tf.estimator.ModeKeys.TRAIN)
def body(args, mb=model.body):
out = mb(args)
return {"targets": out, "A": out, "B": out}
model.body = body
logits, losses = model(features)
self.assertTrue("training" in losses)
loss = losses["training"]
with self.test_session() as sess:
sess.run(tf.global_variables_initializer())
sess.run([logits, loss])
def testMultipleTargetModalities(self):
# Use existing hparams and override target modality.
hparams = trainer_lib.create_hparams(
"transformer_tiny", data_dir=algorithmic.TinyAlgo.data_dir,
problem_name="tiny_algo")
# Manually turn off sharing. It is not currently supported for multitargets.
hparams.shared_embedding_and_softmax_weights = 0 # pylint: disable=line-too-long
hparams.problem_hparams.modality = {
"targets": hparams.problem_hparams.modality["targets"],
"targets_A": hparams.problem_hparams.modality["targets"],
"targets_B": hparams.problem_hparams.modality["targets"],
}
hparams.problem._hparams = hparams.problem_hparams
# Dataset
problem = hparams.problem
dataset = problem.dataset(tf.estimator.ModeKeys.TRAIN,
algorithmic.TinyAlgo.data_dir)
dataset = dataset.repeat(None).padded_batch(10, dataset.output_shapes)
features = dataset.make_one_shot_iterator().get_next()
features = problem_lib.standardize_shapes(features)
features["targets_A"] = features["targets_B"] = features["targets"]
# Model
model = registry.model("transformer")(hparams, tf.estimator.ModeKeys.TRAIN)
def body(args, mb=model.body):
out = mb(args)
return {"targets": out, "targets_A": out, "targets_B": out}
model.body = body
logits, losses = model(features)
self.assertTrue("training" in losses)
loss = losses["training"]
with self.test_session() as sess:
sess.run(tf.global_variables_initializer())
sess.run([logits, loss])
def testMultipleTargetModalities(self):
# HParams
hparams = trainer_lib.create_hparams(
"transformer_tiny",
data_dir=algorithmic.TinyAlgo.data_dir,
problem_name="tiny_algo")
tm = hparams.problem.get_hparams().target_modality
hparams.problem.get_hparams().target_modality = {
"targets": tm,
"A": tm,
"B": tm
}
# Dataset
problem = hparams.problem
dataset = problem.dataset(tf.estimator.ModeKeys.TRAIN,
algorithmic.TinyAlgo.data_dir)
dataset = dataset.repeat(None).padded_batch(10, dataset.output_shapes)
features = dataset.make_one_shot_iterator().get_next()
features = problem_lib.standardize_shapes(features)
features["A"] = features["B"] = features["targets"]
# Model
model = registry.model("transformer")(hparams,
tf.estimator.ModeKeys.TRAIN)
def body(args, mb=model.body):
out = mb(args)
return {"targets": out, "A": out, "B": out}
model.body = body
logits, losses = model(features)
self.assertTrue("training" in losses)
loss = losses["training"]
with self.test_session() as sess:
sess.run(tf.global_variables_initializer())
sess.run([logits, loss])
def testMultipleTargetModalities(self):
# HParams
hparams = trainer_lib.create_hparams(
"transformer_tiny", data_dir=self.data_dir, problem_name="tiny_algo")
tm = hparams.problem.get_hparams().target_modality
hparams.problem.get_hparams().target_modality = {
"targets": tm,
"A": tm,
"B": tm
}
# Dataset
problem = hparams.problem
dataset = problem.dataset(tf.estimator.ModeKeys.TRAIN, self.data_dir)
dataset = dataset.repeat(None).padded_batch(10, dataset.output_shapes)
features = dataset.make_one_shot_iterator().get_next()
features = problem_lib.standardize_shapes(features)
features["A"] = features["B"] = features["targets"]
# Model
model = registry.model("transformer")(hparams, tf.estimator.ModeKeys.TRAIN)
def body(args, mb=model.body):
out = mb(args)
return {"targets": out, "A": out, "B": out}
model.body = body
logits, losses = model(features)
self.assertTrue("training" in losses)
loss = losses["training"]
with self.test_session() as sess:
sess.run(tf.global_variables_initializer())
sess.run([logits, loss])