def test_training(self, simple_model, loss, optimizer, simple_data):
""" Tests that a single training epoch can succeed.
"""
simple_model.parse(None)
simple_model.build()
trainer = Executor(model=simple_model, loss=loss, optimizer=optimizer)
trainer.train(provider=simple_data, stop_when={'epochs': 1})
def test_ctc_train(self, ctc_model, ctc_data, ctc_loss, optimizer):
""" Tests that we can compile and train a model using the CTC loss
function.
"""
ctc_model.parse(None)
ctc_model.register_provider(ctc_data)
ctc_model.build()
trainer = Executor(model=ctc_model, loss=ctc_loss, optimizer=optimizer)
trainer.train(provider=ctc_data, stop_when={'epochs': 1})
def test_uber_train(self, uber_model, uber_data, jinja_engine, loss,
optimizer):
""" Tests that we can compile and train a diverse model.
"""
uber_model.parse(jinja_engine)
uber_model.register_provider(uber_data)
uber_model.build()
trainer = Executor(model=uber_model, loss=loss, optimizer=optimizer)
trainer.compile()
trainer.train(provider=uber_data, stop_when={'epochs': 1})
def test_embedding_train(self, embedding_model, embedding_data, loss,
optimizer):
""" Tests that we can compile and train a model which has an Embedding.
"""
embedding_model.parse(None)
embedding_model.register_provider(embedding_data)
embedding_model.build()
trainer = Executor(model=embedding_model,
loss=loss,
optimizer=optimizer)
trainer.train(provider=embedding_data, stop_when={'epochs': 1})
def test_training(self, simple_model, loss, optimizer, simple_data):
""" Tests that a single training epoch can succeed.
"""
simple_model.parse(None)
simple_model.build()
trainer = Executor(
model=simple_model,
loss=loss,
optimizer=optimizer
)
trainer.train(provider=simple_data, stop_when={'epochs' : 1})
def test_embedding_train(self, embedding_model, embedding_data, loss,
optimizer):
""" Tests that we can compile and train a model which has an Embedding.
"""
embedding_model.parse(None)
embedding_model.register_provider(embedding_data)
embedding_model.build()
trainer = Executor(
model=embedding_model,
loss=loss,
optimizer=optimizer
)
trainer.train(provider=embedding_data, stop_when={'epochs' : 1})
def test_ctc_train(self, ctc_model, ctc_data, ctc_loss, optimizer):
""" Tests that we can compile and train a model using the CTC loss
function.
"""
ctc_model.parse(None)
ctc_model.register_provider(ctc_data)
ctc_model.build()
trainer = Executor(
model=ctc_model,
loss=ctc_loss,
optimizer=optimizer
)
trainer.train(provider=ctc_data, stop_when={'epochs' : 1})
def test_uber_train(self, uber_model, uber_data, jinja_engine, loss,
optimizer):
""" Tests that we can compile and train a diverse model.
"""
if uber_model.get_backend().get_name() == 'keras' and \
uber_model.get_backend().keras_version() == 2 and \
uber_model.get_backend().get_toolchain() == 'tensorflow' and \
sys.version_info < (3, 5):
pytest.skip('Occassional SIGSEGV')
uber_model.parse(jinja_engine)
uber_model.register_provider(uber_data)
uber_model.build()
trainer = Executor(model=uber_model, loss=loss, optimizer=optimizer)
trainer.compile()
trainer.train(provider=uber_data, stop_when={'epochs': 1})
def test_uber_train(self, uber_model, uber_data, jinja_engine, loss,
optimizer):
""" Tests that we can compile and train a diverse model.
"""
if uber_model.get_backend().get_name() == 'keras' and \
uber_model.get_backend().keras_version() == 2 and \
uber_model.get_backend().get_toolchain() == 'tensorflow' and \
sys.version_info < (3, 5):
pytest.skip('Occassional SIGSEGV')
uber_model.parse(jinja_engine)
uber_model.register_provider(uber_data)
uber_model.build()
trainer = Executor(
model=uber_model,
loss=loss,
optimizer=optimizer
)
trainer.compile()
trainer.train(provider=uber_data, stop_when={'epochs' : 1})