def test_minibatch_score_trainer():
X = np.random.random((100, 10))
Z = np.random.random((100, 2))
X, Z = theano_floatx(X, Z)
data = {
'train': (X, Z),
'val': (X, Z),
'test': (X, Z)
}
cut_size = 10
class MyModel(mlp.Mlp):
def score(self, X, Z):
assert X.shape[0] <= cut_size
return super(MyModel, self).score(X, Z)
m = MyModel(10, [100], 2, ['tanh'], 'identity', 'squared',
max_iter=10)
score = MinibatchScore(cut_size, [0, 0])
trainer = Trainer(
m, data, score=score, pause=lambda info: True, stop=lambda info: False)
trainer.val_key = 'val'
for _ in trainer.iter_fit(X, Z):
break
def test_minibatch_score_trainer():
X = np.random.random((100, 10))
X, = theano_floatx(X)
cut_size = 10
class MyAutoEncoder(autoencoder.AutoEncoder):
def score(self, X):
assert X.shape[0] <= cut_size
return super(MyAutoEncoder, self).score(X)
m = MyAutoEncoder(10, [100], ['tanh'],
'identity',
'squared',
tied_weights=True,
max_iter=10)
score = MinibatchScore(cut_size, [0])
trainer = Trainer(m,
score=score,
pause=lambda info: True,
stop=lambda info: False)
trainer.eval_data = {'val': (X, )}
trainer.val_key = 'val'
for _ in trainer.iter_fit(X):
break
def test_training_continuation():
if sys.platform == 'win32':
raise SkipTest()
# Make model and data for the test.
X = np.random.random((100, 10))
Z = np.random.random((100, 2))
X, Z = theano_floatx(X, Z)
data = {
'train': (X, Z),
'val': (X, Z),
'test': (X, Z)
}
optimizer = 'rmsprop', {'step_rate': 0.0001}
m = mlp.Mlp(10, [2], 2, ['tanh'], 'identity', 'squared', max_iter=10,
optimizer=optimizer)
# Train the mdoel with a trainer for 2 epochs.
stopper = climin.stops.OnSignal()
stops = climin.stops.Any([stopper, climin.stops.AfterNIterations(5)])
t = Trainer(
m,
data,
stop=stops,
pause=climin.stops.always)
t.val_key = 'val'
for info in t.iter_fit(X, Z):
os.kill(os.getpid(), stopper.sig)
assert info['n_iter'] == 1
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_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_minibatch_score_trainer():
X = np.random.random((100, 10))
Z = np.random.random((100, 2))
X, Z = theano_floatx(X, Z)
data = {'train': (X, Z), 'val': (X, Z), 'test': (X, Z)}
cut_size = 10
class MyModel(mlp.Mlp):
def score(self, X, Z):
assert X.shape[0] <= cut_size
return super(MyModel, self).score(X, Z)
m = MyModel(10, [100], 2, ['tanh'], 'identity', 'squared', max_iter=10)
score = MinibatchScore(cut_size, [0, 0])
trainer = Trainer(m,
data,
score=score,
pause=lambda info: True,
stop=lambda info: False)
trainer.val_key = 'val'
for _ in trainer.iter_fit(X, Z):
break
def test_training_continuation():
if sys.platform == 'win32':
raise SkipTest()
# Make model and data for the test.
X = np.random.random((100, 10))
Z = np.random.random((100, 2))
X, Z = theano_floatx(X, Z)
data = {'train': (X, Z), 'val': (X, Z), 'test': (X, Z)}
optimizer = 'rmsprop', {'step_rate': 0.0001}
m = mlp.Mlp(10, [2],
2, ['tanh'],
'identity',
'squared',
max_iter=10,
optimizer=optimizer)
# Train the mdoel with a trainer for 2 epochs.
stopper = climin.stops.OnSignal()
stops = climin.stops.Any([stopper, climin.stops.AfterNIterations(5)])
t = Trainer(m, data, stop=stops, pause=climin.stops.always)
t.val_key = 'val'
for info in t.iter_fit(X, Z):
os.kill(os.getpid(), stopper.sig)
assert info['n_iter'] == 1
def test_minibatch_score_trainer():
X = np.random.random((100, 10))
X, = theano_floatx(X)
cut_size = 10
class MyAutoEncoder(autoencoder.AutoEncoder):
def score(self, X):
assert X.shape[0] <= cut_size
return super(MyAutoEncoder, self).score(X)
m = MyAutoEncoder(10, [100], ['tanh'], 'identity', 'squared',
tied_weights=True, max_iter=10)
score = MinibatchScore(cut_size, [0])
trainer = Trainer(
m, score=score, pause=lambda info: True, stop=lambda info: False)
trainer.eval_data = {'val': (X,)}
trainer.val_key = 'val'
for _ in trainer.iter_fit(X):
break
