def iteration_completed(self, engine):
iter = (engine.state.iteration - 1)
if iter % self.log_interval == 0:
current_state = Message.from_objects(
deepcopy(engine.state.output['state']))
current_state['iteration'] = [iter]
self.model_state = self.model_state.append(current_state)
def test_Message_from_objects():
v = vectors.copy()
t = tensors.copy()
v['c'] = np.array([1.,2.])
v['r'] = 'howdy'
t['a'] = torch.randn(5)
t['q'] = torch.randn([4,3])
combined = {**t, **v}
m = Message.from_objects(t, v)
assert (set(m.keys()) == set(['c','d','r','b','a','q']))
for key in ['c','d','b','a','q']:
assert (m[key][0] == combined[key]).all()
assert m['r'][0] == combined['r']
assert len(m) == 1
test_normalizer = Normalizer()
test_normalizer.set_state(normalizer.get_state())
test_normalizer.enable_inference()
test_normalizer.disable_updates()
test_normalizer.input = BatchingPipe(test, batch_size=20)
test_set = TensorPipe(
FunctionPipe(ShufflerPipe(test_normalizer),
function=inject_oversample_weights),
columns=['examples', 'label', 'prevalence', 'label_index'],
device=device)
# Save models
state = conv_net.get_state()
Message.from_objects(state).to('json',
path=experiment.open("conv_net.json",
string_only=True))
state = deep_net.get_state()
Message.from_objects(state).to('json',
path=experiment.open("deep_net.json",
string_only=True))
state = classifier.get_state()
Message.from_objects(state).to('json',
path=experiment.open("classifier.json",
string_only=True))
Message.from_objects(state).to('json',
path=experiment.open("deep_only.json",
string_only=True))
state = None
# Compute accuracy
if __name__ == "__main__":
model_state_metric = ModelSaverMetric()
model_state_metric.attach(trainer, 'state')
x = Message({'x': np.arange(-10, 10, .2)}).to_tensors()
# Run initial evaluation
y_initial = model(x)['y_pred'].detach().numpy()
initial_loss = loss(model(test_set[0:250]))
print("Initial loss on test set: {0}".format(initial_loss))
# Save initial state of model
file_path = experiment.open('initial_model', string_only=True)
initial_state = model.get_state()
Message.from_objects(initial_state).to('json', path=file_path)
trainer.train(max_epochs=200)
final_loss = loss(model(test_set[0:250]))
print("Final loss on test set:: {0}".format(final_loss))
# Visualize functions
true_model = NonlinearModel(
components={
'a': [params['a']],
'b': [params['b']],
'c': [params['c']],
'd': [0],
'e': [0]
})