def test_training_continuation():
# Make model and data for the test.
X = np.random.random((10, 2))
X, = theano_floatx(X)
optimizer = 'gd'
m = autoencoder.AutoEncoder(2, [2], ['tanh'],
'identity',
'squared',
tied_weights=True,
max_iter=10,
optimizer=optimizer)
# Train the mdoel with a trainer for 2 epochs.
stopper = climin.stops.OnSignal()
print stopper.sig
stops = climin.stops.Any([stopper, climin.stops.AfterNIterations(5)])
t = Trainer(m, stop=stops, pause=climin.stops.always)
t.val_key = 'val'
t.eval_data = {'val': (X, )}
killed = False
for info in t.iter_fit(X):
os.kill(os.getpid(), stopper.sig)
assert info['n_iter'] == 1
def test_autoencoder():
X = np.random.random((100, 10))
X, = theano_floatx(X)
m = autoencoder.AutoEncoder(10, [100], ['tanh'],
'identity',
'squared',
tied_weights=True,
max_iter=10)
m.fit(X)
m.score(X)
m.transform(X)
def test_checkpoint_trainer():
# Make model and data for the test.
X = np.random.random((10, 2))
X, = theano_floatx(X)
optimizer = 'rmsprop', {'step_rate': 0.0001}
m = autoencoder.AutoEncoder(2, [2], ['tanh'],
'identity',
'squared',
tied_weights=True,
max_iter=10,
optimizer=optimizer)
# Train the mdoel with a trainer for 2 epochs.
t = Trainer(m,
stop=climin.stops.AfterNIterations(2),
pause=climin.stops.always)
t.val_key = 'val'
t.eval_data = {'val': (X, )}
t.fit(X)
# Make a copy of the trainer.
t2 = copy.deepcopy(t)
intermediate_pars = t2.model.parameters.data.copy()
intermediate_info = t2.current_info.copy()
# Train original for 2 more epochs.
t.stop = climin.stops.AfterNIterations(4)
t.fit(X)
# Check that the snapshot has not changed
assert np.all(t2.model.parameters.data == intermediate_pars)
final_pars = t.model.parameters.data.copy()
final_info = t.current_info.copy()
check_infos(intermediate_info, t2.current_info)
t2.stop = climin.stops.AfterNIterations(4)
t2.fit(X)
check_infos(final_info, t2.current_info)
assert np.allclose(final_pars, t2.model.parameters.data)
t_pickled = cPickle.dumps(t2)
t_unpickled = cPickle.loads(t_pickled)
t.stop = climin.stops.AfterNIterations(4)
t_unpickled.fit(X)
assert np.allclose(final_pars,
t_unpickled.model.parameters.data,
atol=5.e-3)